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google-data-analytics-capstone-track-1.r
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{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/vigneshnokanaidu/google-data-analytics-capstone-track-1?scriptVersionId=116756254\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"code","execution_count":1,"id":"e1f71cee","metadata":{"_execution_state":"idle","_uuid":"051d70d956493feee0c6d64651c6a088724dca2a","execution":{"iopub.execute_input":"2023-01-19T07:54:24.21106Z","iopub.status.busy":"2023-01-19T07:54:24.209078Z","iopub.status.idle":"2023-01-19T07:54:24.378921Z","shell.execute_reply":"2023-01-19T07:54:24.377093Z"},"papermill":{"duration":0.194689,"end_time":"2023-01-19T07:54:24.383385","exception":false,"start_time":"2023-01-19T07:54:24.188696","status":"completed"},"tags":[]},"outputs":[],"source":["# This R environment comes with many helpful analytics packages installed\n","# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n","# For example, here's a helpful package to load"]},{"cell_type":"markdown","id":"828bd8d7","metadata":{"papermill":{"duration":0.017238,"end_time":"2023-01-19T07:54:24.41908","exception":false,"start_time":"2023-01-19T07:54:24.401842","status":"completed"},"tags":[]},"source":["# Context"]},{"cell_type":"markdown","id":"22f1198d","metadata":{"papermill":{"duration":0.016396,"end_time":"2023-01-19T07:54:24.454449","exception":false,"start_time":"2023-01-19T07:54:24.438053","status":"completed"},"tags":[]},"source":["This is a case study, where I would conduct the 6 phases of data analytics - Ask, Prepare, Process, Analyze, Share, Act. I'll perform real-world tasks of a junior data analyst and in this case, I'm working for a fictional company, Cyclistic a Bike-share company in Chicago."]},{"cell_type":"markdown","id":"6676f3f5","metadata":{"papermill":{"duration":0.017451,"end_time":"2023-01-19T07:54:24.488243","exception":false,"start_time":"2023-01-19T07:54:24.470792","status":"completed"},"tags":[]},"source":["Director of marketing believes the company’s future success depends on maximizing the number of annual memberships. But, before we need to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, our team will design a new marketing strategy to convert casual riders into annual members. We will develop this tasks and a the end we will present compelling data insights and share a professional data visualization."]},{"cell_type":"markdown","id":"b78cc9a7","metadata":{"papermill":{"duration":0.016551,"end_time":"2023-01-19T07:54:24.521188","exception":false,"start_time":"2023-01-19T07:54:24.504637","status":"completed"},"tags":[]},"source":["In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as **casual riders.** Customers who purchase annual memberships are **Cyclistic members. **"]},{"cell_type":"markdown","id":"fc4d5776","metadata":{"papermill":{"duration":0.016807,"end_time":"2023-01-19T07:54:24.554423","exception":false,"start_time":"2023-01-19T07:54:24.537616","status":"completed"},"tags":[]},"source":["This is a Capestone Project, as a part of a course: Google Data Analytic Professional Certificate. All tools and language learned comes from this course."]},{"cell_type":"markdown","id":"c3a858bb","metadata":{"papermill":{"duration":0.01603,"end_time":"2023-01-19T07:54:24.586594","exception":false,"start_time":"2023-01-19T07:54:24.570564","status":"completed"},"tags":[]},"source":["# Ask"]},{"cell_type":"markdown","id":"5f571692","metadata":{"papermill":{"duration":0.017143,"end_time":"2023-01-19T07:54:24.620598","exception":false,"start_time":"2023-01-19T07:54:24.603455","status":"completed"},"tags":[]},"source":["Director of Marketing set a clear goal: strategies aimed at converting casual riders into annual members. In order to do that the marketing analyst team needs to better understand three questions: \n","**1. how annual members and casual riders use differ \n","2. why casual riders would buy a membership,and \n","3. and how digital media could affect their marketing tactics.**"]},{"cell_type":"markdown","id":"e3a22154","metadata":{"papermill":{"duration":0.017788,"end_time":"2023-01-19T07:54:24.65492","exception":false,"start_time":"2023-01-19T07:54:24.637132","status":"completed"},"tags":[]},"source":["# Prepare\n","## Dataset"]},{"cell_type":"markdown","id":"6762f298","metadata":{"papermill":{"duration":0.016683,"end_time":"2023-01-19T07:54:24.688903","exception":false,"start_time":"2023-01-19T07:54:24.67222","status":"completed"},"tags":[]},"source":["I will use R language, R Markdown to document and share insights, Tableau to create attractive visualiations and Google Slides to present insigths at the stakeholders."]},{"cell_type":"markdown","id":"3003fa81","metadata":{"papermill":{"duration":0.016066,"end_time":"2023-01-19T07:54:24.720847","exception":false,"start_time":"2023-01-19T07:54:24.704781","status":"completed"},"tags":[]},"source":["We will use Cyclistic’s historical trip data to elaborate metrics, analyze it ,and if is possible identify patterns and trends. \n"," \n","First, I downloaded the latest 12 months of data from Dec 2021 - Nov 2022, and upload it to Kaggle. Data was downloaded from Motivate international Inc to my desktop. \n"," \n","Our date was verifiable as Reliable (data is umbiased), Original(data comes from original source), Comprehensive (contains neccesary data to answer the questions), Current (data is generated continuosly,on January 5th, will be ready to download total 2022), and Cited data was obtaining from Motivate International Inc under license. dowload from divvy-tripdata, and credible source of information."]},{"cell_type":"markdown","id":"800204cd","metadata":{"papermill":{"duration":0.016452,"end_time":"2023-01-19T07:54:24.754051","exception":false,"start_time":"2023-01-19T07:54:24.737599","status":"completed"},"tags":[]},"source":["### First we will install the necessary library and packages"]},{"cell_type":"code","execution_count":2,"id":"99ffbfda","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:54:24.827087Z","iopub.status.busy":"2023-01-19T07:54:24.789836Z","iopub.status.idle":"2023-01-19T07:54:26.585242Z","shell.execute_reply":"2023-01-19T07:54:26.582813Z"},"papermill":{"duration":1.818389,"end_time":"2023-01-19T07:54:26.588565","exception":false,"start_time":"2023-01-19T07:54:24.770176","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n","\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.5 \n","\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.10\n","\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.2.1 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.1 \n","\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.3 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n","── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n","\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n","\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n","\n","Attaching package: ‘janitor’\n","\n","\n","The following objects are masked from ‘package:stats’:\n","\n"," chisq.test, fisher.test\n","\n","\n","\n","Attaching package: ‘lubridate’\n","\n","\n","The following objects are masked from ‘package:base’:\n","\n"," date, intersect, setdiff, union\n","\n","\n"]}],"source":["library(tidyverse)\n","library(skimr)\n","library(janitor)\n","library(lubridate)"]},{"cell_type":"markdown","id":"9e077093","metadata":{"papermill":{"duration":0.018579,"end_time":"2023-01-19T07:54:26.625383","exception":false,"start_time":"2023-01-19T07:54:26.606804","status":"completed"},"tags":[]},"source":["Then I will create dataframes to create our datasets \n","The tables are read, one for each month, and the directory could be copied from the input section."]},{"cell_type":"code","execution_count":3,"id":"50ab0218","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:54:26.669439Z","iopub.status.busy":"2023-01-19T07:54:26.667207Z","iopub.status.idle":"2023-01-19T07:55:02.311748Z","shell.execute_reply":"2023-01-19T07:55:02.3096Z"},"papermill":{"duration":35.670107,"end_time":"2023-01-19T07:55:02.315719","exception":false,"start_time":"2023-01-19T07:54:26.645612","status":"completed"},"tags":[]},"outputs":[],"source":["df_1 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202112-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_2 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202201-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_3 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202202-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_4 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202203-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_5 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202204-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_6 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202205-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_7 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202206-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_8 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202207-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_9 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202208-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_10 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202209-divvy-publictripdata.csv\",show_col_types=FALSE)\n","df_11 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202210-divvy-tripdata.csv\",show_col_types=FALSE)\n","df_12 <- read_csv(\"/kaggle/input/dec-21-nov-22-riders-data/202211-divvy-tripdata.csv\",show_col_types=FALSE)\n"]},{"cell_type":"markdown","id":"3604455d","metadata":{"papermill":{"duration":0.017413,"end_time":"2023-01-19T07:55:02.351264","exception":false,"start_time":"2023-01-19T07:55:02.333851","status":"completed"},"tags":[]},"source":["All the individual data frames will be merged into a single data frame with 12 months of merged data: called df_merged"]},{"cell_type":"code","execution_count":4,"id":"1879f403","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:02.389208Z","iopub.status.busy":"2023-01-19T07:55:02.387541Z","iopub.status.idle":"2023-01-19T07:55:22.211587Z","shell.execute_reply":"2023-01-19T07:55:22.209671Z"},"papermill":{"duration":19.847727,"end_time":"2023-01-19T07:55:22.216086","exception":false,"start_time":"2023-01-19T07:55:02.368359","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged <- rbind(df_1,df_2,df_3,df_4,df_5,df_6,df_7,df_8,df_9,df_10,df_11,df_12)"]},{"cell_type":"markdown","id":"d9534a00","metadata":{"papermill":{"duration":0.07002,"end_time":"2023-01-19T07:55:22.303396","exception":false,"start_time":"2023-01-19T07:55:22.233376","status":"completed"},"tags":[]},"source":["## Cleaning data"]},{"cell_type":"markdown","id":"a0c63655","metadata":{"papermill":{"duration":0.017061,"end_time":"2023-01-19T07:55:22.337235","exception":false,"start_time":"2023-01-19T07:55:22.320174","status":"completed"},"tags":[]},"source":["Now, I have my dataset, I'll begin to understand it, first verify number of variables, datatypes, number of rows, and consistency of data from our dataset with 13 variables. \n","If there's nothing unusual, I'll continue proccesing our data."]},{"cell_type":"markdown","id":"b806773e","metadata":{"papermill":{"duration":0.016469,"end_time":"2023-01-19T07:55:22.370161","exception":false,"start_time":"2023-01-19T07:55:22.353692","status":"completed"},"tags":[]},"source":["All the files has de same variables (13), same columns, types of data, and same right names, "]},{"cell_type":"code","execution_count":5,"id":"4a66f5f0","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:22.407664Z","iopub.status.busy":"2023-01-19T07:55:22.406054Z","iopub.status.idle":"2023-01-19T07:55:22.461691Z","shell.execute_reply":"2023-01-19T07:55:22.458919Z"},"papermill":{"duration":0.078034,"end_time":"2023-01-19T07:55:22.464818","exception":false,"start_time":"2023-01-19T07:55:22.386784","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A data.frame: 13 × 13</caption>\n","<thead>\n","\t<tr><th scope=col>column_name</th><th scope=col>df_1</th><th scope=col>df_2</th><th scope=col>df_3</th><th scope=col>df_4</th><th scope=col>df_5</th><th scope=col>df_6</th><th scope=col>df_7</th><th scope=col>df_8</th><th scope=col>df_9</th><th scope=col>df_10</th><th scope=col>df_11</th><th scope=col>df_12</th></tr>\n","\t<tr><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th><th scope=col><chr></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>end_lat </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td></tr>\n","\t<tr><td>end_lng </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td></tr>\n","\t<tr><td>end_station_id </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>end_station_name </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>ended_at </td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td></tr>\n","\t<tr><td>member_casual </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>ride_id </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>rideable_type </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>start_lat </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td></tr>\n","\t<tr><td>start_lng </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td><td>numeric </td></tr>\n","\t<tr><td>start_station_id </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>start_station_name</td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td><td>character </td></tr>\n","\t<tr><td>started_at </td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td><td>POSIXct, POSIXt</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A data.frame: 13 × 13\n","\\begin{tabular}{lllllllllllll}\n"," column\\_name & df\\_1 & df\\_2 & df\\_3 & df\\_4 & df\\_5 & df\\_6 & df\\_7 & df\\_8 & df\\_9 & df\\_10 & df\\_11 & df\\_12\\\\\n"," <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n","\\hline\n","\t end\\_lat & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric \\\\\n","\t end\\_lng & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric \\\\\n","\t end\\_station\\_id & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t end\\_station\\_name & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t ended\\_at & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt\\\\\n","\t member\\_casual & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t ride\\_id & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t rideable\\_type & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t start\\_lat & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric \\\\\n","\t start\\_lng & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric & numeric \\\\\n","\t start\\_station\\_id & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t start\\_station\\_name & character & character & character & character & character & character & character & character & character & character & character & character \\\\\n","\t started\\_at & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt & POSIXct, POSIXt\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A data.frame: 13 × 13\n","\n","| column_name <chr> | df_1 <chr> | df_2 <chr> | df_3 <chr> | df_4 <chr> | df_5 <chr> | df_6 <chr> | df_7 <chr> | df_8 <chr> | df_9 <chr> | df_10 <chr> | df_11 <chr> | df_12 <chr> |\n","|---|---|---|---|---|---|---|---|---|---|---|---|---|\n","| end_lat | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric |\n","| end_lng | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric |\n","| end_station_id | character | character | character | character | character | character | character | character | character | character | character | character |\n","| end_station_name | character | character | character | character | character | character | character | character | character | character | character | character |\n","| ended_at | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt |\n","| member_casual | character | character | character | character | character | character | character | character | character | character | character | character |\n","| ride_id | character | character | character | character | character | character | character | character | character | character | character | character |\n","| rideable_type | character | character | character | character | character | character | character | character | character | character | character | character |\n","| start_lat | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric |\n","| start_lng | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric |\n","| start_station_id | character | character | character | character | character | character | character | character | character | character | character | character |\n","| start_station_name | character | character | character | character | character | character | character | character | character | character | character | character |\n","| started_at | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt | POSIXct, POSIXt |\n","\n"],"text/plain":[" column_name df_1 df_2 df_3 \n","1 end_lat numeric numeric numeric \n","2 end_lng numeric numeric numeric \n","3 end_station_id character character character \n","4 end_station_name character character character \n","5 ended_at POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n","6 member_casual character character character \n","7 ride_id character character character \n","8 rideable_type character character character \n","9 start_lat numeric numeric numeric \n","10 start_lng numeric numeric numeric \n","11 start_station_id character character character \n","12 start_station_name character character character \n","13 started_at POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n"," df_4 df_5 df_6 df_7 \n","1 numeric numeric numeric numeric \n","2 numeric numeric numeric numeric \n","3 character character character character \n","4 character character character character \n","5 POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n","6 character character character character \n","7 character character character character \n","8 character character character character \n","9 numeric numeric numeric numeric \n","10 numeric numeric numeric numeric \n","11 character character character character \n","12 character character character character \n","13 POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n"," df_8 df_9 df_10 df_11 \n","1 numeric numeric numeric numeric \n","2 numeric numeric numeric numeric \n","3 character character character character \n","4 character character character character \n","5 POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n","6 character character character character \n","7 character character character character \n","8 character character character character \n","9 numeric numeric numeric numeric \n","10 numeric numeric numeric numeric \n","11 character character character character \n","12 character character character character \n","13 POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt POSIXct, POSIXt\n"," df_12 \n","1 numeric \n","2 numeric \n","3 character \n","4 character \n","5 POSIXct, POSIXt\n","6 character \n","7 character \n","8 character \n","9 numeric \n","10 numeric \n","11 character \n","12 character \n","13 POSIXct, POSIXt"]},"metadata":{},"output_type":"display_data"}],"source":["compare_df_cols(df_1,df_2,df_3,df_4,df_5,df_6,df_7,df_8,df_9,df_10,df_11,df_12)"]},{"cell_type":"markdown","id":"5a7b199b","metadata":{"papermill":{"duration":0.017406,"end_time":"2023-01-19T07:55:22.499485","exception":false,"start_time":"2023-01-19T07:55:22.482079","status":"completed"},"tags":[]},"source":["Identify the structure of data, number of rows, number and type of columns"]},{"cell_type":"code","execution_count":6,"id":"90a874a9","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:22.537857Z","iopub.status.busy":"2023-01-19T07:55:22.53623Z","iopub.status.idle":"2023-01-19T07:55:22.591498Z","shell.execute_reply":"2023-01-19T07:55:22.588737Z"},"papermill":{"duration":0.077989,"end_time":"2023-01-19T07:55:22.59464","exception":false,"start_time":"2023-01-19T07:55:22.516651","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["spec_tbl_df [5,733,451 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)\n"," $ ride_id : chr [1:5733451] \"46F8167220E4431F\" \"73A77762838B32FD\" \"4CF42452054F59C5\" \"3278BA87BF698339\" ...\n"," $ rideable_type : chr [1:5733451] \"electric_bike\" \"electric_bike\" \"electric_bike\" \"classic_bike\" ...\n"," $ started_at : POSIXct[1:5733451], format: \"2021-12-07 15:06:07\" \"2021-12-11 03:43:29\" ...\n"," $ ended_at : POSIXct[1:5733451], format: \"2021-12-07 15:13:42\" \"2021-12-11 04:10:23\" ...\n"," $ start_station_name: chr [1:5733451] \"Laflin St & Cullerton St\" \"LaSalle Dr & Huron St\" \"Halsted St & North Branch St\" \"Halsted St & North Branch St\" ...\n"," $ start_station_id : chr [1:5733451] \"13307\" \"KP1705001026\" \"KA1504000117\" \"KA1504000117\" ...\n"," $ end_station_name : chr [1:5733451] \"Morgan St & Polk St\" \"Clarendon Ave & Leland Ave\" \"Broadway & Barry Ave\" \"LaSalle Dr & Huron St\" ...\n"," $ end_station_id : chr [1:5733451] \"TA1307000130\" \"TA1307000119\" \"13137\" \"KP1705001026\" ...\n"," $ start_lat : num [1:5733451] 41.9 41.9 41.9 41.9 41.9 ...\n"," $ start_lng : num [1:5733451] -87.7 -87.6 -87.6 -87.6 -87.7 ...\n"," $ end_lat : num [1:5733451] 41.9 42 41.9 41.9 41.9 ...\n"," $ end_lng : num [1:5733451] -87.7 -87.7 -87.6 -87.6 -87.6 ...\n"," $ member_casual : chr [1:5733451] \"member\" \"casual\" \"member\" \"member\" ...\n"," - attr(*, \"spec\")=\n"," .. cols(\n"," .. ride_id = \u001b[31mcol_character()\u001b[39m,\n"," .. rideable_type = \u001b[31mcol_character()\u001b[39m,\n"," .. started_at = \u001b[34mcol_datetime(format = \"\")\u001b[39m,\n"," .. ended_at = \u001b[34mcol_datetime(format = \"\")\u001b[39m,\n"," .. start_station_name = \u001b[31mcol_character()\u001b[39m,\n"," .. start_station_id = \u001b[31mcol_character()\u001b[39m,\n"," .. end_station_name = \u001b[31mcol_character()\u001b[39m,\n"," .. end_station_id = \u001b[31mcol_character()\u001b[39m,\n"," .. start_lat = \u001b[32mcol_double()\u001b[39m,\n"," .. start_lng = \u001b[32mcol_double()\u001b[39m,\n"," .. end_lat = \u001b[32mcol_double()\u001b[39m,\n"," .. end_lng = \u001b[32mcol_double()\u001b[39m,\n"," .. member_casual = \u001b[31mcol_character()\u001b[39m\n"," .. )\n"," - attr(*, \"problems\")=<externalptr> \n"]}],"source":["str(df_merged)"]},{"cell_type":"code","execution_count":7,"id":"cd246165","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:22.633483Z","iopub.status.busy":"2023-01-19T07:55:22.631819Z","iopub.status.idle":"2023-01-19T07:55:22.667221Z","shell.execute_reply":"2023-01-19T07:55:22.66526Z"},"papermill":{"duration":0.058761,"end_time":"2023-01-19T07:55:22.67067","exception":false,"start_time":"2023-01-19T07:55:22.611909","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Rows: 5,733,451\n","Columns: 13\n","$ ride_id \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"46F8167220E4431F\", \"73A77762838B32FD\", \"4CF4245205…\n","$ rideable_type \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n","$ started_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-07 15:06:07, 2021-12-11 03:43:29, 2021-12-…\n","$ ended_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-07 15:13:42, 2021-12-11 04:10:23, 2021-12-…\n","$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Laflin St & Cullerton St\", \"LaSalle Dr & Huron St\"…\n","$ start_station_id \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"13307\", \"KP1705001026\", \"KA1504000117\", \"KA1504000…\n","$ end_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Morgan St & Polk St\", \"Clarendon Ave & Leland Ave\"…\n","$ end_station_id \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"TA1307000130\", \"TA1307000119\", \"13137\", \"KP1705001…\n","$ start_lat \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.85483, 41.89441, 41.89936, 41.89939, 41.89558, 4…\n","$ start_lng \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.66366, -87.63233, -87.64852, -87.64854, -87.682…\n","$ end_lat \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 41.87197, 41.96797, 41.93758, 41.89488, 41.93125, 4…\n","$ end_lng \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m -87.65097, -87.65000, -87.64410, -87.63233, -87.644…\n","$ member_casual \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"casual\", \"member\", \"member\", \"member\", \"…\n"]}],"source":["glimpse(df_merged)"]},{"cell_type":"code","execution_count":8,"id":"01fdfbe3","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:22.7095Z","iopub.status.busy":"2023-01-19T07:55:22.7077Z","iopub.status.idle":"2023-01-19T07:55:27.096948Z","shell.execute_reply":"2023-01-19T07:55:27.095137Z"},"papermill":{"duration":4.411317,"end_time":"2023-01-19T07:55:27.099677","exception":false,"start_time":"2023-01-19T07:55:22.68836","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":[" ride_id rideable_type started_at \n"," Length:5733451 Length:5733451 Min. :2021-12-01 00:00:01 \n"," Class :character Class :character 1st Qu.:2022-05-17 12:04:44 \n"," Mode :character Mode :character Median :2022-07-13 22:04:44 \n"," Mean :2022-07-06 05:55:33 \n"," 3rd Qu.:2022-09-07 17:55:40 \n"," Max. :2022-11-30 23:56:11 \n"," \n"," ended_at start_station_name start_station_id \n"," Min. :2021-12-01 00:02:40 Length:5733451 Length:5733451 \n"," 1st Qu.:2022-05-17 12:27:04 Class :character Class :character \n"," Median :2022-07-13 22:22:06 Mode :character Mode :character \n"," Mean :2022-07-06 06:14:59 \n"," 3rd Qu.:2022-09-07 18:11:41 \n"," Max. :2022-12-01 11:45:53 \n"," \n"," end_station_name end_station_id start_lat start_lng \n"," Length:5733451 Length:5733451 Min. :41.64 Min. :-87.84 \n"," Class :character Class :character 1st Qu.:41.88 1st Qu.:-87.66 \n"," Mode :character Mode :character Median :41.90 Median :-87.64 \n"," Mean :41.90 Mean :-87.65 \n"," 3rd Qu.:41.93 3rd Qu.:-87.63 \n"," Max. :45.64 Max. :-73.80 \n"," \n"," end_lat end_lng member_casual \n"," Min. : 0.00 Min. :-88.14 Length:5733451 \n"," 1st Qu.:41.88 1st Qu.:-87.66 Class :character \n"," Median :41.90 Median :-87.64 Mode :character \n"," Mean :41.90 Mean :-87.65 \n"," 3rd Qu.:41.93 3rd Qu.:-87.63 \n"," Max. :42.37 Max. : 0.00 \n"," NA's :5874 NA's :5874 "]},"metadata":{},"output_type":"display_data"}],"source":["summary(df_merged)"]},{"cell_type":"markdown","id":"e018b5d3","metadata":{"papermill":{"duration":0.017759,"end_time":"2023-01-19T07:55:27.135248","exception":false,"start_time":"2023-01-19T07:55:27.117489","status":"completed"},"tags":[]},"source":["From the above summary, there are some NA's found in the **end_lat and end_lng**"]},{"cell_type":"markdown","id":"0aec960b","metadata":{"papermill":{"duration":0.017958,"end_time":"2023-01-19T07:55:27.170449","exception":false,"start_time":"2023-01-19T07:55:27.152491","status":"completed"},"tags":[]},"source":["# Process"]},{"cell_type":"markdown","id":"69559110","metadata":{"papermill":{"duration":0.017687,"end_time":"2023-01-19T07:55:27.205851","exception":false,"start_time":"2023-01-19T07:55:27.188164","status":"completed"},"tags":[]},"source":["## Trying - Metrics \n","#### trying with a small file df_11\n","#### ride_length - duration trip mins"]},{"cell_type":"markdown","id":"f54887e9","metadata":{"papermill":{"duration":0.018042,"end_time":"2023-01-19T07:55:27.241475","exception":false,"start_time":"2023-01-19T07:55:27.223433","status":"completed"},"tags":[]},"source":["We will create our first metrics, begining the analysis of our dataset in order to get insight and answer at our stakeholder. To be more efficient, we take a small table df_11.\n","\n","These is a very important metric, how long does a cassual rider take a bike?, how many minutes does a annual member take a bike on every labourable day?\n","\n","Note: We can see there are ride_length more than 24 hours and others negative ride_length, this is a problem that we will filter out."]},{"cell_type":"code","execution_count":9,"id":"b3a44f37","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:27.280358Z","iopub.status.busy":"2023-01-19T07:55:27.278775Z","iopub.status.idle":"2023-01-19T07:55:30.173107Z","shell.execute_reply":"2023-01-19T07:55:30.171262Z"},"papermill":{"duration":2.917074,"end_time":"2023-01-19T07:55:30.175889","exception":false,"start_time":"2023-01-19T07:55:27.258815","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 558685 × 3</caption>\n","<thead>\n","\t<tr><th scope=col>started_at</th><th scope=col>ended_at</th><th scope=col>ride_length</th></tr>\n","\t<tr><th scope=col><dttm></th><th scope=col><dttm></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>2022-10-01 15:04:38</td><td>2022-10-30 08:51:53</td><td>41387.25</td></tr>\n","\t<tr><td>2022-10-09 11:24:11</td><td>2022-10-31 04:33:40</td><td>31269.48</td></tr>\n","\t<tr><td>2022-10-01 14:33:51</td><td>2022-10-23 04:47:04</td><td>31093.22</td></tr>\n","\t<tr><td>2022-10-08 19:46:31</td><td>2022-10-29 20:52:50</td><td>30306.32</td></tr>\n","\t<tr><td>2022-10-08 19:11:54</td><td>2022-10-29 20:04:45</td><td>30292.85</td></tr>\n","\t<tr><td>2022-10-09 02:19:39</td><td>2022-10-29 20:59:34</td><td>29919.92</td></tr>\n","\t<tr><td>2022-10-09 11:34:45</td><td>2022-10-29 21:00:58</td><td>29366.22</td></tr>\n","\t<tr><td>2022-10-09 13:02:55</td><td>2022-10-29 21:02:05</td><td>29279.17</td></tr>\n","\t<tr><td>2022-10-09 14:57:49</td><td>2022-10-29 21:03:44</td><td>29165.92</td></tr>\n","\t<tr><td>2022-10-01 10:42:04</td><td>2022-10-20 10:10:06</td><td>27328.03</td></tr>\n","\t<tr><td>2022-10-01 10:41:54</td><td>2022-10-20 10:09:20</td><td>27327.43</td></tr>\n","\t<tr><td>2022-10-15 20:30:04</td><td>2022-11-03 04:58:47</td><td>26428.72</td></tr>\n","\t<tr><td>2022-10-09 18:52:58</td><td>2022-10-27 04:42:40</td><td>25069.70</td></tr>\n","\t<tr><td>2022-10-07 06:35:02</td><td>2022-10-22 19:17:55</td><td>22362.88</td></tr>\n","\t<tr><td>2022-10-07 16:01:41</td><td>2022-10-22 20:31:48</td><td>21870.12</td></tr>\n","\t<tr><td>2022-10-07 16:02:16</td><td>2022-10-22 20:31:53</td><td>21869.62</td></tr>\n","\t<tr><td>2022-10-15 05:50:06</td><td>2022-10-29 20:54:16</td><td>21064.17</td></tr>\n","\t<tr><td>2022-10-15 09:59:36</td><td>2022-10-29 20:56:14</td><td>20816.63</td></tr>\n","\t<tr><td>2022-10-07 18:45:32</td><td>2022-10-22 04:45:53</td><td>20760.35</td></tr>\n","\t<tr><td>2022-10-02 06:53:14</td><td>2022-10-16 09:34:17</td><td>20321.05</td></tr>\n","\t<tr><td>2022-10-02 18:42:44</td><td>2022-10-16 09:34:57</td><td>19612.22</td></tr>\n","\t<tr><td>2022-10-05 08:16:12</td><td>2022-10-18 07:24:09</td><td>18667.95</td></tr>\n","\t<tr><td>2022-10-12 12:07:32</td><td>2022-10-24 09:01:36</td><td>17094.07</td></tr>\n","\t<tr><td>2022-10-11 14:29:26</td><td>2022-10-23 02:01:03</td><td>16531.62</td></tr>\n","\t<tr><td>2022-10-09 19:00:23</td><td>2022-10-21 06:12:10</td><td>16511.78</td></tr>\n","\t<tr><td>2022-10-11 16:07:20</td><td>2022-10-23 02:05:01</td><td>16437.68</td></tr>\n","\t<tr><td>2022-10-14 16:00:52</td><td>2022-10-25 18:05:12</td><td>15964.33</td></tr>\n","\t<tr><td>2022-10-12 10:02:19</td><td>2022-10-23 09:22:58</td><td>15800.65</td></tr>\n","\t<tr><td>2022-10-06 15:49:00</td><td>2022-10-17 06:40:32</td><td>15291.53</td></tr>\n","\t<tr><td>2022-10-06 16:00:23</td><td>2022-10-17 06:46:43</td><td>15286.33</td></tr>\n","\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n","\t<tr><td>2022-10-25 12:29:35</td><td>2022-10-25 12:29:35</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-06 18:12:59</td><td>2022-10-06 18:12:59</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-20 15:37:49</td><td>2022-10-20 15:37:49</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-01 18:44:24</td><td>2022-10-01 18:44:24</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-29 09:34:50</td><td>2022-10-29 09:34:50</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-03 17:08:54</td><td>2022-10-03 17:08:54</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-09 17:49:34</td><td>2022-10-09 17:49:34</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-28 20:42:34</td><td>2022-10-28 20:42:34</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-01 17:01:15</td><td>2022-10-01 17:01:15</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-14 07:13:37</td><td>2022-10-14 07:13:37</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-16 10:34:34</td><td>2022-10-16 10:34:34</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-09 16:40:23</td><td>2022-10-09 16:40:23</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-12 17:28:05</td><td>2022-10-12 17:28:05</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-21 11:26:32</td><td>2022-10-21 11:26:32</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-12 08:05:13</td><td>2022-10-12 08:05:13</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-07 15:40:18</td><td>2022-10-07 15:40:18</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-07 15:19:21</td><td>2022-10-07 15:19:21</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-23 17:01:13</td><td>2022-10-23 17:01:13</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-13 11:17:28</td><td>2022-10-13 11:17:28</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-18 15:40:17</td><td>2022-10-18 15:40:17</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-17 17:03:25</td><td>2022-10-17 17:03:25</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-09 07:59:30</td><td>2022-10-09 07:59:30</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-11 11:27:34</td><td>2022-10-11 11:27:34</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-05 18:25:33</td><td>2022-10-05 18:25:33</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-30 15:13:23</td><td>2022-10-30 15:13:23</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-26 16:04:39</td><td>2022-10-26 16:04:39</td><td> 0.00000000</td></tr>\n","\t<tr><td>2022-10-21 19:29:00</td><td>2022-10-21 19:28:59</td><td> -0.01666667</td></tr>\n","\t<tr><td>2022-10-24 17:03:29</td><td>2022-10-24 17:03:28</td><td> -0.01666667</td></tr>\n","\t<tr><td>2022-10-03 08:55:01</td><td>2022-10-03 08:54:45</td><td> -0.26666667</td></tr>\n","\t<tr><td>2022-10-13 14:42:10</td><td>2022-10-13 11:53:28</td><td>-168.70000000</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 558685 × 3\n","\\begin{tabular}{lll}\n"," started\\_at & ended\\_at & ride\\_length\\\\\n"," <dttm> & <dttm> & <dbl>\\\\\n","\\hline\n","\t 2022-10-01 15:04:38 & 2022-10-30 08:51:53 & 41387.25\\\\\n","\t 2022-10-09 11:24:11 & 2022-10-31 04:33:40 & 31269.48\\\\\n","\t 2022-10-01 14:33:51 & 2022-10-23 04:47:04 & 31093.22\\\\\n","\t 2022-10-08 19:46:31 & 2022-10-29 20:52:50 & 30306.32\\\\\n","\t 2022-10-08 19:11:54 & 2022-10-29 20:04:45 & 30292.85\\\\\n","\t 2022-10-09 02:19:39 & 2022-10-29 20:59:34 & 29919.92\\\\\n","\t 2022-10-09 11:34:45 & 2022-10-29 21:00:58 & 29366.22\\\\\n","\t 2022-10-09 13:02:55 & 2022-10-29 21:02:05 & 29279.17\\\\\n","\t 2022-10-09 14:57:49 & 2022-10-29 21:03:44 & 29165.92\\\\\n","\t 2022-10-01 10:42:04 & 2022-10-20 10:10:06 & 27328.03\\\\\n","\t 2022-10-01 10:41:54 & 2022-10-20 10:09:20 & 27327.43\\\\\n","\t 2022-10-15 20:30:04 & 2022-11-03 04:58:47 & 26428.72\\\\\n","\t 2022-10-09 18:52:58 & 2022-10-27 04:42:40 & 25069.70\\\\\n","\t 2022-10-07 06:35:02 & 2022-10-22 19:17:55 & 22362.88\\\\\n","\t 2022-10-07 16:01:41 & 2022-10-22 20:31:48 & 21870.12\\\\\n","\t 2022-10-07 16:02:16 & 2022-10-22 20:31:53 & 21869.62\\\\\n","\t 2022-10-15 05:50:06 & 2022-10-29 20:54:16 & 21064.17\\\\\n","\t 2022-10-15 09:59:36 & 2022-10-29 20:56:14 & 20816.63\\\\\n","\t 2022-10-07 18:45:32 & 2022-10-22 04:45:53 & 20760.35\\\\\n","\t 2022-10-02 06:53:14 & 2022-10-16 09:34:17 & 20321.05\\\\\n","\t 2022-10-02 18:42:44 & 2022-10-16 09:34:57 & 19612.22\\\\\n","\t 2022-10-05 08:16:12 & 2022-10-18 07:24:09 & 18667.95\\\\\n","\t 2022-10-12 12:07:32 & 2022-10-24 09:01:36 & 17094.07\\\\\n","\t 2022-10-11 14:29:26 & 2022-10-23 02:01:03 & 16531.62\\\\\n","\t 2022-10-09 19:00:23 & 2022-10-21 06:12:10 & 16511.78\\\\\n","\t 2022-10-11 16:07:20 & 2022-10-23 02:05:01 & 16437.68\\\\\n","\t 2022-10-14 16:00:52 & 2022-10-25 18:05:12 & 15964.33\\\\\n","\t 2022-10-12 10:02:19 & 2022-10-23 09:22:58 & 15800.65\\\\\n","\t 2022-10-06 15:49:00 & 2022-10-17 06:40:32 & 15291.53\\\\\n","\t 2022-10-06 16:00:23 & 2022-10-17 06:46:43 & 15286.33\\\\\n","\t ⋮ & ⋮ & ⋮\\\\\n","\t 2022-10-25 12:29:35 & 2022-10-25 12:29:35 & 0.00000000\\\\\n","\t 2022-10-06 18:12:59 & 2022-10-06 18:12:59 & 0.00000000\\\\\n","\t 2022-10-20 15:37:49 & 2022-10-20 15:37:49 & 0.00000000\\\\\n","\t 2022-10-01 18:44:24 & 2022-10-01 18:44:24 & 0.00000000\\\\\n","\t 2022-10-29 09:34:50 & 2022-10-29 09:34:50 & 0.00000000\\\\\n","\t 2022-10-03 17:08:54 & 2022-10-03 17:08:54 & 0.00000000\\\\\n","\t 2022-10-09 17:49:34 & 2022-10-09 17:49:34 & 0.00000000\\\\\n","\t 2022-10-28 20:42:34 & 2022-10-28 20:42:34 & 0.00000000\\\\\n","\t 2022-10-01 17:01:15 & 2022-10-01 17:01:15 & 0.00000000\\\\\n","\t 2022-10-14 07:13:37 & 2022-10-14 07:13:37 & 0.00000000\\\\\n","\t 2022-10-16 10:34:34 & 2022-10-16 10:34:34 & 0.00000000\\\\\n","\t 2022-10-09 16:40:23 & 2022-10-09 16:40:23 & 0.00000000\\\\\n","\t 2022-10-12 17:28:05 & 2022-10-12 17:28:05 & 0.00000000\\\\\n","\t 2022-10-21 11:26:32 & 2022-10-21 11:26:32 & 0.00000000\\\\\n","\t 2022-10-12 08:05:13 & 2022-10-12 08:05:13 & 0.00000000\\\\\n","\t 2022-10-07 15:40:18 & 2022-10-07 15:40:18 & 0.00000000\\\\\n","\t 2022-10-07 15:19:21 & 2022-10-07 15:19:21 & 0.00000000\\\\\n","\t 2022-10-23 17:01:13 & 2022-10-23 17:01:13 & 0.00000000\\\\\n","\t 2022-10-13 11:17:28 & 2022-10-13 11:17:28 & 0.00000000\\\\\n","\t 2022-10-18 15:40:17 & 2022-10-18 15:40:17 & 0.00000000\\\\\n","\t 2022-10-17 17:03:25 & 2022-10-17 17:03:25 & 0.00000000\\\\\n","\t 2022-10-09 07:59:30 & 2022-10-09 07:59:30 & 0.00000000\\\\\n","\t 2022-10-11 11:27:34 & 2022-10-11 11:27:34 & 0.00000000\\\\\n","\t 2022-10-05 18:25:33 & 2022-10-05 18:25:33 & 0.00000000\\\\\n","\t 2022-10-30 15:13:23 & 2022-10-30 15:13:23 & 0.00000000\\\\\n","\t 2022-10-26 16:04:39 & 2022-10-26 16:04:39 & 0.00000000\\\\\n","\t 2022-10-21 19:29:00 & 2022-10-21 19:28:59 & -0.01666667\\\\\n","\t 2022-10-24 17:03:29 & 2022-10-24 17:03:28 & -0.01666667\\\\\n","\t 2022-10-03 08:55:01 & 2022-10-03 08:54:45 & -0.26666667\\\\\n","\t 2022-10-13 14:42:10 & 2022-10-13 11:53:28 & -168.70000000\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 558685 × 3\n","\n","| started_at <dttm> | ended_at <dttm> | ride_length <dbl> |\n","|---|---|---|\n","| 2022-10-01 15:04:38 | 2022-10-30 08:51:53 | 41387.25 |\n","| 2022-10-09 11:24:11 | 2022-10-31 04:33:40 | 31269.48 |\n","| 2022-10-01 14:33:51 | 2022-10-23 04:47:04 | 31093.22 |\n","| 2022-10-08 19:46:31 | 2022-10-29 20:52:50 | 30306.32 |\n","| 2022-10-08 19:11:54 | 2022-10-29 20:04:45 | 30292.85 |\n","| 2022-10-09 02:19:39 | 2022-10-29 20:59:34 | 29919.92 |\n","| 2022-10-09 11:34:45 | 2022-10-29 21:00:58 | 29366.22 |\n","| 2022-10-09 13:02:55 | 2022-10-29 21:02:05 | 29279.17 |\n","| 2022-10-09 14:57:49 | 2022-10-29 21:03:44 | 29165.92 |\n","| 2022-10-01 10:42:04 | 2022-10-20 10:10:06 | 27328.03 |\n","| 2022-10-01 10:41:54 | 2022-10-20 10:09:20 | 27327.43 |\n","| 2022-10-15 20:30:04 | 2022-11-03 04:58:47 | 26428.72 |\n","| 2022-10-09 18:52:58 | 2022-10-27 04:42:40 | 25069.70 |\n","| 2022-10-07 06:35:02 | 2022-10-22 19:17:55 | 22362.88 |\n","| 2022-10-07 16:01:41 | 2022-10-22 20:31:48 | 21870.12 |\n","| 2022-10-07 16:02:16 | 2022-10-22 20:31:53 | 21869.62 |\n","| 2022-10-15 05:50:06 | 2022-10-29 20:54:16 | 21064.17 |\n","| 2022-10-15 09:59:36 | 2022-10-29 20:56:14 | 20816.63 |\n","| 2022-10-07 18:45:32 | 2022-10-22 04:45:53 | 20760.35 |\n","| 2022-10-02 06:53:14 | 2022-10-16 09:34:17 | 20321.05 |\n","| 2022-10-02 18:42:44 | 2022-10-16 09:34:57 | 19612.22 |\n","| 2022-10-05 08:16:12 | 2022-10-18 07:24:09 | 18667.95 |\n","| 2022-10-12 12:07:32 | 2022-10-24 09:01:36 | 17094.07 |\n","| 2022-10-11 14:29:26 | 2022-10-23 02:01:03 | 16531.62 |\n","| 2022-10-09 19:00:23 | 2022-10-21 06:12:10 | 16511.78 |\n","| 2022-10-11 16:07:20 | 2022-10-23 02:05:01 | 16437.68 |\n","| 2022-10-14 16:00:52 | 2022-10-25 18:05:12 | 15964.33 |\n","| 2022-10-12 10:02:19 | 2022-10-23 09:22:58 | 15800.65 |\n","| 2022-10-06 15:49:00 | 2022-10-17 06:40:32 | 15291.53 |\n","| 2022-10-06 16:00:23 | 2022-10-17 06:46:43 | 15286.33 |\n","| ⋮ | ⋮ | ⋮ |\n","| 2022-10-25 12:29:35 | 2022-10-25 12:29:35 | 0.00000000 |\n","| 2022-10-06 18:12:59 | 2022-10-06 18:12:59 | 0.00000000 |\n","| 2022-10-20 15:37:49 | 2022-10-20 15:37:49 | 0.00000000 |\n","| 2022-10-01 18:44:24 | 2022-10-01 18:44:24 | 0.00000000 |\n","| 2022-10-29 09:34:50 | 2022-10-29 09:34:50 | 0.00000000 |\n","| 2022-10-03 17:08:54 | 2022-10-03 17:08:54 | 0.00000000 |\n","| 2022-10-09 17:49:34 | 2022-10-09 17:49:34 | 0.00000000 |\n","| 2022-10-28 20:42:34 | 2022-10-28 20:42:34 | 0.00000000 |\n","| 2022-10-01 17:01:15 | 2022-10-01 17:01:15 | 0.00000000 |\n","| 2022-10-14 07:13:37 | 2022-10-14 07:13:37 | 0.00000000 |\n","| 2022-10-16 10:34:34 | 2022-10-16 10:34:34 | 0.00000000 |\n","| 2022-10-09 16:40:23 | 2022-10-09 16:40:23 | 0.00000000 |\n","| 2022-10-12 17:28:05 | 2022-10-12 17:28:05 | 0.00000000 |\n","| 2022-10-21 11:26:32 | 2022-10-21 11:26:32 | 0.00000000 |\n","| 2022-10-12 08:05:13 | 2022-10-12 08:05:13 | 0.00000000 |\n","| 2022-10-07 15:40:18 | 2022-10-07 15:40:18 | 0.00000000 |\n","| 2022-10-07 15:19:21 | 2022-10-07 15:19:21 | 0.00000000 |\n","| 2022-10-23 17:01:13 | 2022-10-23 17:01:13 | 0.00000000 |\n","| 2022-10-13 11:17:28 | 2022-10-13 11:17:28 | 0.00000000 |\n","| 2022-10-18 15:40:17 | 2022-10-18 15:40:17 | 0.00000000 |\n","| 2022-10-17 17:03:25 | 2022-10-17 17:03:25 | 0.00000000 |\n","| 2022-10-09 07:59:30 | 2022-10-09 07:59:30 | 0.00000000 |\n","| 2022-10-11 11:27:34 | 2022-10-11 11:27:34 | 0.00000000 |\n","| 2022-10-05 18:25:33 | 2022-10-05 18:25:33 | 0.00000000 |\n","| 2022-10-30 15:13:23 | 2022-10-30 15:13:23 | 0.00000000 |\n","| 2022-10-26 16:04:39 | 2022-10-26 16:04:39 | 0.00000000 |\n","| 2022-10-21 19:29:00 | 2022-10-21 19:28:59 | -0.01666667 |\n","| 2022-10-24 17:03:29 | 2022-10-24 17:03:28 | -0.01666667 |\n","| 2022-10-03 08:55:01 | 2022-10-03 08:54:45 | -0.26666667 |\n","| 2022-10-13 14:42:10 | 2022-10-13 11:53:28 | -168.70000000 |\n","\n"],"text/plain":[" started_at ended_at ride_length \n","1 2022-10-01 15:04:38 2022-10-30 08:51:53 41387.25 \n","2 2022-10-09 11:24:11 2022-10-31 04:33:40 31269.48 \n","3 2022-10-01 14:33:51 2022-10-23 04:47:04 31093.22 \n","4 2022-10-08 19:46:31 2022-10-29 20:52:50 30306.32 \n","5 2022-10-08 19:11:54 2022-10-29 20:04:45 30292.85 \n","6 2022-10-09 02:19:39 2022-10-29 20:59:34 29919.92 \n","7 2022-10-09 11:34:45 2022-10-29 21:00:58 29366.22 \n","8 2022-10-09 13:02:55 2022-10-29 21:02:05 29279.17 \n","9 2022-10-09 14:57:49 2022-10-29 21:03:44 29165.92 \n","10 2022-10-01 10:42:04 2022-10-20 10:10:06 27328.03 \n","11 2022-10-01 10:41:54 2022-10-20 10:09:20 27327.43 \n","12 2022-10-15 20:30:04 2022-11-03 04:58:47 26428.72 \n","13 2022-10-09 18:52:58 2022-10-27 04:42:40 25069.70 \n","14 2022-10-07 06:35:02 2022-10-22 19:17:55 22362.88 \n","15 2022-10-07 16:01:41 2022-10-22 20:31:48 21870.12 \n","16 2022-10-07 16:02:16 2022-10-22 20:31:53 21869.62 \n","17 2022-10-15 05:50:06 2022-10-29 20:54:16 21064.17 \n","18 2022-10-15 09:59:36 2022-10-29 20:56:14 20816.63 \n","19 2022-10-07 18:45:32 2022-10-22 04:45:53 20760.35 \n","20 2022-10-02 06:53:14 2022-10-16 09:34:17 20321.05 \n","21 2022-10-02 18:42:44 2022-10-16 09:34:57 19612.22 \n","22 2022-10-05 08:16:12 2022-10-18 07:24:09 18667.95 \n","23 2022-10-12 12:07:32 2022-10-24 09:01:36 17094.07 \n","24 2022-10-11 14:29:26 2022-10-23 02:01:03 16531.62 \n","25 2022-10-09 19:00:23 2022-10-21 06:12:10 16511.78 \n","26 2022-10-11 16:07:20 2022-10-23 02:05:01 16437.68 \n","27 2022-10-14 16:00:52 2022-10-25 18:05:12 15964.33 \n","28 2022-10-12 10:02:19 2022-10-23 09:22:58 15800.65 \n","29 2022-10-06 15:49:00 2022-10-17 06:40:32 15291.53 \n","30 2022-10-06 16:00:23 2022-10-17 06:46:43 15286.33 \n","⋮ ⋮ ⋮ ⋮ \n","558656 2022-10-25 12:29:35 2022-10-25 12:29:35 0.00000000\n","558657 2022-10-06 18:12:59 2022-10-06 18:12:59 0.00000000\n","558658 2022-10-20 15:37:49 2022-10-20 15:37:49 0.00000000\n","558659 2022-10-01 18:44:24 2022-10-01 18:44:24 0.00000000\n","558660 2022-10-29 09:34:50 2022-10-29 09:34:50 0.00000000\n","558661 2022-10-03 17:08:54 2022-10-03 17:08:54 0.00000000\n","558662 2022-10-09 17:49:34 2022-10-09 17:49:34 0.00000000\n","558663 2022-10-28 20:42:34 2022-10-28 20:42:34 0.00000000\n","558664 2022-10-01 17:01:15 2022-10-01 17:01:15 0.00000000\n","558665 2022-10-14 07:13:37 2022-10-14 07:13:37 0.00000000\n","558666 2022-10-16 10:34:34 2022-10-16 10:34:34 0.00000000\n","558667 2022-10-09 16:40:23 2022-10-09 16:40:23 0.00000000\n","558668 2022-10-12 17:28:05 2022-10-12 17:28:05 0.00000000\n","558669 2022-10-21 11:26:32 2022-10-21 11:26:32 0.00000000\n","558670 2022-10-12 08:05:13 2022-10-12 08:05:13 0.00000000\n","558671 2022-10-07 15:40:18 2022-10-07 15:40:18 0.00000000\n","558672 2022-10-07 15:19:21 2022-10-07 15:19:21 0.00000000\n","558673 2022-10-23 17:01:13 2022-10-23 17:01:13 0.00000000\n","558674 2022-10-13 11:17:28 2022-10-13 11:17:28 0.00000000\n","558675 2022-10-18 15:40:17 2022-10-18 15:40:17 0.00000000\n","558676 2022-10-17 17:03:25 2022-10-17 17:03:25 0.00000000\n","558677 2022-10-09 07:59:30 2022-10-09 07:59:30 0.00000000\n","558678 2022-10-11 11:27:34 2022-10-11 11:27:34 0.00000000\n","558679 2022-10-05 18:25:33 2022-10-05 18:25:33 0.00000000\n","558680 2022-10-30 15:13:23 2022-10-30 15:13:23 0.00000000\n","558681 2022-10-26 16:04:39 2022-10-26 16:04:39 0.00000000\n","558682 2022-10-21 19:29:00 2022-10-21 19:28:59 -0.01666667\n","558683 2022-10-24 17:03:29 2022-10-24 17:03:28 -0.01666667\n","558684 2022-10-03 08:55:01 2022-10-03 08:54:45 -0.26666667\n","558685 2022-10-13 14:42:10 2022-10-13 11:53:28 -168.70000000"]},"metadata":{},"output_type":"display_data"}],"source":["df_11 %>% \n"," select(started_at,ended_at) %>% \n"," mutate(ride_length=interval(started_at,ended_at)/minutes()) %>% \n","arrange(desc(ride_length))"]},{"cell_type":"markdown","id":"437d0853","metadata":{"papermill":{"duration":0.020127,"end_time":"2023-01-19T07:55:30.214513","exception":false,"start_time":"2023-01-19T07:55:30.194386","status":"completed"},"tags":[]},"source":["### ride_length in secs"]},{"cell_type":"markdown","id":"0eb63a4e","metadata":{"papermill":{"duration":0.018534,"end_time":"2023-01-19T07:55:30.251197","exception":false,"start_time":"2023-01-19T07:55:30.232663","status":"completed"},"tags":[]},"source":["to verify our data, we get same results from other way to calculate."]},{"cell_type":"code","execution_count":10,"id":"29d5e049","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:30.291932Z","iopub.status.busy":"2023-01-19T07:55:30.290268Z","iopub.status.idle":"2023-01-19T07:55:30.455524Z","shell.execute_reply":"2023-01-19T07:55:30.453639Z"},"papermill":{"duration":0.188651,"end_time":"2023-01-19T07:55:30.458134","exception":false,"start_time":"2023-01-19T07:55:30.269483","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 558685 × 3</caption>\n","<thead>\n","\t<tr><th scope=col>started_at</th><th scope=col>ended_at</th><th scope=col>ride_length</th></tr>\n","\t<tr><th scope=col><dttm></th><th scope=col><dttm></th><th scope=col><drtn></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>2022-10-01 15:04:38</td><td>2022-10-30 08:51:53</td><td>2483235 secs</td></tr>\n","\t<tr><td>2022-10-09 11:24:11</td><td>2022-10-31 04:33:40</td><td>1876169 secs</td></tr>\n","\t<tr><td>2022-10-01 14:33:51</td><td>2022-10-23 04:47:04</td><td>1865593 secs</td></tr>\n","\t<tr><td>2022-10-08 19:46:31</td><td>2022-10-29 20:52:50</td><td>1818379 secs</td></tr>\n","\t<tr><td>2022-10-08 19:11:54</td><td>2022-10-29 20:04:45</td><td>1817571 secs</td></tr>\n","\t<tr><td>2022-10-09 02:19:39</td><td>2022-10-29 20:59:34</td><td>1795195 secs</td></tr>\n","\t<tr><td>2022-10-09 11:34:45</td><td>2022-10-29 21:00:58</td><td>1761973 secs</td></tr>\n","\t<tr><td>2022-10-09 13:02:55</td><td>2022-10-29 21:02:05</td><td>1756750 secs</td></tr>\n","\t<tr><td>2022-10-09 14:57:49</td><td>2022-10-29 21:03:44</td><td>1749955 secs</td></tr>\n","\t<tr><td>2022-10-01 10:42:04</td><td>2022-10-20 10:10:06</td><td>1639682 secs</td></tr>\n","\t<tr><td>2022-10-01 10:41:54</td><td>2022-10-20 10:09:20</td><td>1639646 secs</td></tr>\n","\t<tr><td>2022-10-15 20:30:04</td><td>2022-11-03 04:58:47</td><td>1585723 secs</td></tr>\n","\t<tr><td>2022-10-09 18:52:58</td><td>2022-10-27 04:42:40</td><td>1504182 secs</td></tr>\n","\t<tr><td>2022-10-07 06:35:02</td><td>2022-10-22 19:17:55</td><td>1341773 secs</td></tr>\n","\t<tr><td>2022-10-07 16:01:41</td><td>2022-10-22 20:31:48</td><td>1312207 secs</td></tr>\n","\t<tr><td>2022-10-07 16:02:16</td><td>2022-10-22 20:31:53</td><td>1312177 secs</td></tr>\n","\t<tr><td>2022-10-15 05:50:06</td><td>2022-10-29 20:54:16</td><td>1263850 secs</td></tr>\n","\t<tr><td>2022-10-15 09:59:36</td><td>2022-10-29 20:56:14</td><td>1248998 secs</td></tr>\n","\t<tr><td>2022-10-07 18:45:32</td><td>2022-10-22 04:45:53</td><td>1245621 secs</td></tr>\n","\t<tr><td>2022-10-02 06:53:14</td><td>2022-10-16 09:34:17</td><td>1219263 secs</td></tr>\n","\t<tr><td>2022-10-02 18:42:44</td><td>2022-10-16 09:34:57</td><td>1176733 secs</td></tr>\n","\t<tr><td>2022-10-05 08:16:12</td><td>2022-10-18 07:24:09</td><td>1120077 secs</td></tr>\n","\t<tr><td>2022-10-12 12:07:32</td><td>2022-10-24 09:01:36</td><td>1025644 secs</td></tr>\n","\t<tr><td>2022-10-11 14:29:26</td><td>2022-10-23 02:01:03</td><td> 991897 secs</td></tr>\n","\t<tr><td>2022-10-09 19:00:23</td><td>2022-10-21 06:12:10</td><td> 990707 secs</td></tr>\n","\t<tr><td>2022-10-11 16:07:20</td><td>2022-10-23 02:05:01</td><td> 986261 secs</td></tr>\n","\t<tr><td>2022-10-14 16:00:52</td><td>2022-10-25 18:05:12</td><td> 957860 secs</td></tr>\n","\t<tr><td>2022-10-12 10:02:19</td><td>2022-10-23 09:22:58</td><td> 948039 secs</td></tr>\n","\t<tr><td>2022-10-06 15:49:00</td><td>2022-10-17 06:40:32</td><td> 917492 secs</td></tr>\n","\t<tr><td>2022-10-06 16:00:23</td><td>2022-10-17 06:46:43</td><td> 917180 secs</td></tr>\n","\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n","\t<tr><td>2022-10-25 12:29:35</td><td>2022-10-25 12:29:35</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-06 18:12:59</td><td>2022-10-06 18:12:59</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-20 15:37:49</td><td>2022-10-20 15:37:49</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-01 18:44:24</td><td>2022-10-01 18:44:24</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-29 09:34:50</td><td>2022-10-29 09:34:50</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-03 17:08:54</td><td>2022-10-03 17:08:54</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-09 17:49:34</td><td>2022-10-09 17:49:34</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-28 20:42:34</td><td>2022-10-28 20:42:34</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-01 17:01:15</td><td>2022-10-01 17:01:15</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-14 07:13:37</td><td>2022-10-14 07:13:37</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-16 10:34:34</td><td>2022-10-16 10:34:34</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-09 16:40:23</td><td>2022-10-09 16:40:23</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-12 17:28:05</td><td>2022-10-12 17:28:05</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-21 11:26:32</td><td>2022-10-21 11:26:32</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-12 08:05:13</td><td>2022-10-12 08:05:13</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-07 15:40:18</td><td>2022-10-07 15:40:18</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-07 15:19:21</td><td>2022-10-07 15:19:21</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-23 17:01:13</td><td>2022-10-23 17:01:13</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-13 11:17:28</td><td>2022-10-13 11:17:28</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-18 15:40:17</td><td>2022-10-18 15:40:17</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-17 17:03:25</td><td>2022-10-17 17:03:25</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-09 07:59:30</td><td>2022-10-09 07:59:30</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-11 11:27:34</td><td>2022-10-11 11:27:34</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-05 18:25:33</td><td>2022-10-05 18:25:33</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-30 15:13:23</td><td>2022-10-30 15:13:23</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-26 16:04:39</td><td>2022-10-26 16:04:39</td><td> 0 secs</td></tr>\n","\t<tr><td>2022-10-21 19:29:00</td><td>2022-10-21 19:28:59</td><td> -1 secs</td></tr>\n","\t<tr><td>2022-10-24 17:03:29</td><td>2022-10-24 17:03:28</td><td> -1 secs</td></tr>\n","\t<tr><td>2022-10-03 08:55:01</td><td>2022-10-03 08:54:45</td><td> -16 secs</td></tr>\n","\t<tr><td>2022-10-13 14:42:10</td><td>2022-10-13 11:53:28</td><td>-10122 secs</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 558685 × 3\n","\\begin{tabular}{lll}\n"," started\\_at & ended\\_at & ride\\_length\\\\\n"," <dttm> & <dttm> & <drtn>\\\\\n","\\hline\n","\t 2022-10-01 15:04:38 & 2022-10-30 08:51:53 & 2483235 secs\\\\\n","\t 2022-10-09 11:24:11 & 2022-10-31 04:33:40 & 1876169 secs\\\\\n","\t 2022-10-01 14:33:51 & 2022-10-23 04:47:04 & 1865593 secs\\\\\n","\t 2022-10-08 19:46:31 & 2022-10-29 20:52:50 & 1818379 secs\\\\\n","\t 2022-10-08 19:11:54 & 2022-10-29 20:04:45 & 1817571 secs\\\\\n","\t 2022-10-09 02:19:39 & 2022-10-29 20:59:34 & 1795195 secs\\\\\n","\t 2022-10-09 11:34:45 & 2022-10-29 21:00:58 & 1761973 secs\\\\\n","\t 2022-10-09 13:02:55 & 2022-10-29 21:02:05 & 1756750 secs\\\\\n","\t 2022-10-09 14:57:49 & 2022-10-29 21:03:44 & 1749955 secs\\\\\n","\t 2022-10-01 10:42:04 & 2022-10-20 10:10:06 & 1639682 secs\\\\\n","\t 2022-10-01 10:41:54 & 2022-10-20 10:09:20 & 1639646 secs\\\\\n","\t 2022-10-15 20:30:04 & 2022-11-03 04:58:47 & 1585723 secs\\\\\n","\t 2022-10-09 18:52:58 & 2022-10-27 04:42:40 & 1504182 secs\\\\\n","\t 2022-10-07 06:35:02 & 2022-10-22 19:17:55 & 1341773 secs\\\\\n","\t 2022-10-07 16:01:41 & 2022-10-22 20:31:48 & 1312207 secs\\\\\n","\t 2022-10-07 16:02:16 & 2022-10-22 20:31:53 & 1312177 secs\\\\\n","\t 2022-10-15 05:50:06 & 2022-10-29 20:54:16 & 1263850 secs\\\\\n","\t 2022-10-15 09:59:36 & 2022-10-29 20:56:14 & 1248998 secs\\\\\n","\t 2022-10-07 18:45:32 & 2022-10-22 04:45:53 & 1245621 secs\\\\\n","\t 2022-10-02 06:53:14 & 2022-10-16 09:34:17 & 1219263 secs\\\\\n","\t 2022-10-02 18:42:44 & 2022-10-16 09:34:57 & 1176733 secs\\\\\n","\t 2022-10-05 08:16:12 & 2022-10-18 07:24:09 & 1120077 secs\\\\\n","\t 2022-10-12 12:07:32 & 2022-10-24 09:01:36 & 1025644 secs\\\\\n","\t 2022-10-11 14:29:26 & 2022-10-23 02:01:03 & 991897 secs\\\\\n","\t 2022-10-09 19:00:23 & 2022-10-21 06:12:10 & 990707 secs\\\\\n","\t 2022-10-11 16:07:20 & 2022-10-23 02:05:01 & 986261 secs\\\\\n","\t 2022-10-14 16:00:52 & 2022-10-25 18:05:12 & 957860 secs\\\\\n","\t 2022-10-12 10:02:19 & 2022-10-23 09:22:58 & 948039 secs\\\\\n","\t 2022-10-06 15:49:00 & 2022-10-17 06:40:32 & 917492 secs\\\\\n","\t 2022-10-06 16:00:23 & 2022-10-17 06:46:43 & 917180 secs\\\\\n","\t ⋮ & ⋮ & ⋮\\\\\n","\t 2022-10-25 12:29:35 & 2022-10-25 12:29:35 & 0 secs\\\\\n","\t 2022-10-06 18:12:59 & 2022-10-06 18:12:59 & 0 secs\\\\\n","\t 2022-10-20 15:37:49 & 2022-10-20 15:37:49 & 0 secs\\\\\n","\t 2022-10-01 18:44:24 & 2022-10-01 18:44:24 & 0 secs\\\\\n","\t 2022-10-29 09:34:50 & 2022-10-29 09:34:50 & 0 secs\\\\\n","\t 2022-10-03 17:08:54 & 2022-10-03 17:08:54 & 0 secs\\\\\n","\t 2022-10-09 17:49:34 & 2022-10-09 17:49:34 & 0 secs\\\\\n","\t 2022-10-28 20:42:34 & 2022-10-28 20:42:34 & 0 secs\\\\\n","\t 2022-10-01 17:01:15 & 2022-10-01 17:01:15 & 0 secs\\\\\n","\t 2022-10-14 07:13:37 & 2022-10-14 07:13:37 & 0 secs\\\\\n","\t 2022-10-16 10:34:34 & 2022-10-16 10:34:34 & 0 secs\\\\\n","\t 2022-10-09 16:40:23 & 2022-10-09 16:40:23 & 0 secs\\\\\n","\t 2022-10-12 17:28:05 & 2022-10-12 17:28:05 & 0 secs\\\\\n","\t 2022-10-21 11:26:32 & 2022-10-21 11:26:32 & 0 secs\\\\\n","\t 2022-10-12 08:05:13 & 2022-10-12 08:05:13 & 0 secs\\\\\n","\t 2022-10-07 15:40:18 & 2022-10-07 15:40:18 & 0 secs\\\\\n","\t 2022-10-07 15:19:21 & 2022-10-07 15:19:21 & 0 secs\\\\\n","\t 2022-10-23 17:01:13 & 2022-10-23 17:01:13 & 0 secs\\\\\n","\t 2022-10-13 11:17:28 & 2022-10-13 11:17:28 & 0 secs\\\\\n","\t 2022-10-18 15:40:17 & 2022-10-18 15:40:17 & 0 secs\\\\\n","\t 2022-10-17 17:03:25 & 2022-10-17 17:03:25 & 0 secs\\\\\n","\t 2022-10-09 07:59:30 & 2022-10-09 07:59:30 & 0 secs\\\\\n","\t 2022-10-11 11:27:34 & 2022-10-11 11:27:34 & 0 secs\\\\\n","\t 2022-10-05 18:25:33 & 2022-10-05 18:25:33 & 0 secs\\\\\n","\t 2022-10-30 15:13:23 & 2022-10-30 15:13:23 & 0 secs\\\\\n","\t 2022-10-26 16:04:39 & 2022-10-26 16:04:39 & 0 secs\\\\\n","\t 2022-10-21 19:29:00 & 2022-10-21 19:28:59 & -1 secs\\\\\n","\t 2022-10-24 17:03:29 & 2022-10-24 17:03:28 & -1 secs\\\\\n","\t 2022-10-03 08:55:01 & 2022-10-03 08:54:45 & -16 secs\\\\\n","\t 2022-10-13 14:42:10 & 2022-10-13 11:53:28 & -10122 secs\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 558685 × 3\n","\n","| started_at <dttm> | ended_at <dttm> | ride_length <drtn> |\n","|---|---|---|\n","| 2022-10-01 15:04:38 | 2022-10-30 08:51:53 | 2483235 secs |\n","| 2022-10-09 11:24:11 | 2022-10-31 04:33:40 | 1876169 secs |\n","| 2022-10-01 14:33:51 | 2022-10-23 04:47:04 | 1865593 secs |\n","| 2022-10-08 19:46:31 | 2022-10-29 20:52:50 | 1818379 secs |\n","| 2022-10-08 19:11:54 | 2022-10-29 20:04:45 | 1817571 secs |\n","| 2022-10-09 02:19:39 | 2022-10-29 20:59:34 | 1795195 secs |\n","| 2022-10-09 11:34:45 | 2022-10-29 21:00:58 | 1761973 secs |\n","| 2022-10-09 13:02:55 | 2022-10-29 21:02:05 | 1756750 secs |\n","| 2022-10-09 14:57:49 | 2022-10-29 21:03:44 | 1749955 secs |\n","| 2022-10-01 10:42:04 | 2022-10-20 10:10:06 | 1639682 secs |\n","| 2022-10-01 10:41:54 | 2022-10-20 10:09:20 | 1639646 secs |\n","| 2022-10-15 20:30:04 | 2022-11-03 04:58:47 | 1585723 secs |\n","| 2022-10-09 18:52:58 | 2022-10-27 04:42:40 | 1504182 secs |\n","| 2022-10-07 06:35:02 | 2022-10-22 19:17:55 | 1341773 secs |\n","| 2022-10-07 16:01:41 | 2022-10-22 20:31:48 | 1312207 secs |\n","| 2022-10-07 16:02:16 | 2022-10-22 20:31:53 | 1312177 secs |\n","| 2022-10-15 05:50:06 | 2022-10-29 20:54:16 | 1263850 secs |\n","| 2022-10-15 09:59:36 | 2022-10-29 20:56:14 | 1248998 secs |\n","| 2022-10-07 18:45:32 | 2022-10-22 04:45:53 | 1245621 secs |\n","| 2022-10-02 06:53:14 | 2022-10-16 09:34:17 | 1219263 secs |\n","| 2022-10-02 18:42:44 | 2022-10-16 09:34:57 | 1176733 secs |\n","| 2022-10-05 08:16:12 | 2022-10-18 07:24:09 | 1120077 secs |\n","| 2022-10-12 12:07:32 | 2022-10-24 09:01:36 | 1025644 secs |\n","| 2022-10-11 14:29:26 | 2022-10-23 02:01:03 | 991897 secs |\n","| 2022-10-09 19:00:23 | 2022-10-21 06:12:10 | 990707 secs |\n","| 2022-10-11 16:07:20 | 2022-10-23 02:05:01 | 986261 secs |\n","| 2022-10-14 16:00:52 | 2022-10-25 18:05:12 | 957860 secs |\n","| 2022-10-12 10:02:19 | 2022-10-23 09:22:58 | 948039 secs |\n","| 2022-10-06 15:49:00 | 2022-10-17 06:40:32 | 917492 secs |\n","| 2022-10-06 16:00:23 | 2022-10-17 06:46:43 | 917180 secs |\n","| ⋮ | ⋮ | ⋮ |\n","| 2022-10-25 12:29:35 | 2022-10-25 12:29:35 | 0 secs |\n","| 2022-10-06 18:12:59 | 2022-10-06 18:12:59 | 0 secs |\n","| 2022-10-20 15:37:49 | 2022-10-20 15:37:49 | 0 secs |\n","| 2022-10-01 18:44:24 | 2022-10-01 18:44:24 | 0 secs |\n","| 2022-10-29 09:34:50 | 2022-10-29 09:34:50 | 0 secs |\n","| 2022-10-03 17:08:54 | 2022-10-03 17:08:54 | 0 secs |\n","| 2022-10-09 17:49:34 | 2022-10-09 17:49:34 | 0 secs |\n","| 2022-10-28 20:42:34 | 2022-10-28 20:42:34 | 0 secs |\n","| 2022-10-01 17:01:15 | 2022-10-01 17:01:15 | 0 secs |\n","| 2022-10-14 07:13:37 | 2022-10-14 07:13:37 | 0 secs |\n","| 2022-10-16 10:34:34 | 2022-10-16 10:34:34 | 0 secs |\n","| 2022-10-09 16:40:23 | 2022-10-09 16:40:23 | 0 secs |\n","| 2022-10-12 17:28:05 | 2022-10-12 17:28:05 | 0 secs |\n","| 2022-10-21 11:26:32 | 2022-10-21 11:26:32 | 0 secs |\n","| 2022-10-12 08:05:13 | 2022-10-12 08:05:13 | 0 secs |\n","| 2022-10-07 15:40:18 | 2022-10-07 15:40:18 | 0 secs |\n","| 2022-10-07 15:19:21 | 2022-10-07 15:19:21 | 0 secs |\n","| 2022-10-23 17:01:13 | 2022-10-23 17:01:13 | 0 secs |\n","| 2022-10-13 11:17:28 | 2022-10-13 11:17:28 | 0 secs |\n","| 2022-10-18 15:40:17 | 2022-10-18 15:40:17 | 0 secs |\n","| 2022-10-17 17:03:25 | 2022-10-17 17:03:25 | 0 secs |\n","| 2022-10-09 07:59:30 | 2022-10-09 07:59:30 | 0 secs |\n","| 2022-10-11 11:27:34 | 2022-10-11 11:27:34 | 0 secs |\n","| 2022-10-05 18:25:33 | 2022-10-05 18:25:33 | 0 secs |\n","| 2022-10-30 15:13:23 | 2022-10-30 15:13:23 | 0 secs |\n","| 2022-10-26 16:04:39 | 2022-10-26 16:04:39 | 0 secs |\n","| 2022-10-21 19:29:00 | 2022-10-21 19:28:59 | -1 secs |\n","| 2022-10-24 17:03:29 | 2022-10-24 17:03:28 | -1 secs |\n","| 2022-10-03 08:55:01 | 2022-10-03 08:54:45 | -16 secs |\n","| 2022-10-13 14:42:10 | 2022-10-13 11:53:28 | -10122 secs |\n","\n"],"text/plain":[" started_at ended_at ride_length \n","1 2022-10-01 15:04:38 2022-10-30 08:51:53 2483235 secs\n","2 2022-10-09 11:24:11 2022-10-31 04:33:40 1876169 secs\n","3 2022-10-01 14:33:51 2022-10-23 04:47:04 1865593 secs\n","4 2022-10-08 19:46:31 2022-10-29 20:52:50 1818379 secs\n","5 2022-10-08 19:11:54 2022-10-29 20:04:45 1817571 secs\n","6 2022-10-09 02:19:39 2022-10-29 20:59:34 1795195 secs\n","7 2022-10-09 11:34:45 2022-10-29 21:00:58 1761973 secs\n","8 2022-10-09 13:02:55 2022-10-29 21:02:05 1756750 secs\n","9 2022-10-09 14:57:49 2022-10-29 21:03:44 1749955 secs\n","10 2022-10-01 10:42:04 2022-10-20 10:10:06 1639682 secs\n","11 2022-10-01 10:41:54 2022-10-20 10:09:20 1639646 secs\n","12 2022-10-15 20:30:04 2022-11-03 04:58:47 1585723 secs\n","13 2022-10-09 18:52:58 2022-10-27 04:42:40 1504182 secs\n","14 2022-10-07 06:35:02 2022-10-22 19:17:55 1341773 secs\n","15 2022-10-07 16:01:41 2022-10-22 20:31:48 1312207 secs\n","16 2022-10-07 16:02:16 2022-10-22 20:31:53 1312177 secs\n","17 2022-10-15 05:50:06 2022-10-29 20:54:16 1263850 secs\n","18 2022-10-15 09:59:36 2022-10-29 20:56:14 1248998 secs\n","19 2022-10-07 18:45:32 2022-10-22 04:45:53 1245621 secs\n","20 2022-10-02 06:53:14 2022-10-16 09:34:17 1219263 secs\n","21 2022-10-02 18:42:44 2022-10-16 09:34:57 1176733 secs\n","22 2022-10-05 08:16:12 2022-10-18 07:24:09 1120077 secs\n","23 2022-10-12 12:07:32 2022-10-24 09:01:36 1025644 secs\n","24 2022-10-11 14:29:26 2022-10-23 02:01:03 991897 secs\n","25 2022-10-09 19:00:23 2022-10-21 06:12:10 990707 secs\n","26 2022-10-11 16:07:20 2022-10-23 02:05:01 986261 secs\n","27 2022-10-14 16:00:52 2022-10-25 18:05:12 957860 secs\n","28 2022-10-12 10:02:19 2022-10-23 09:22:58 948039 secs\n","29 2022-10-06 15:49:00 2022-10-17 06:40:32 917492 secs\n","30 2022-10-06 16:00:23 2022-10-17 06:46:43 917180 secs\n","⋮ ⋮ ⋮ ⋮ \n","558656 2022-10-25 12:29:35 2022-10-25 12:29:35 0 secs \n","558657 2022-10-06 18:12:59 2022-10-06 18:12:59 0 secs \n","558658 2022-10-20 15:37:49 2022-10-20 15:37:49 0 secs \n","558659 2022-10-01 18:44:24 2022-10-01 18:44:24 0 secs \n","558660 2022-10-29 09:34:50 2022-10-29 09:34:50 0 secs \n","558661 2022-10-03 17:08:54 2022-10-03 17:08:54 0 secs \n","558662 2022-10-09 17:49:34 2022-10-09 17:49:34 0 secs \n","558663 2022-10-28 20:42:34 2022-10-28 20:42:34 0 secs \n","558664 2022-10-01 17:01:15 2022-10-01 17:01:15 0 secs \n","558665 2022-10-14 07:13:37 2022-10-14 07:13:37 0 secs \n","558666 2022-10-16 10:34:34 2022-10-16 10:34:34 0 secs \n","558667 2022-10-09 16:40:23 2022-10-09 16:40:23 0 secs \n","558668 2022-10-12 17:28:05 2022-10-12 17:28:05 0 secs \n","558669 2022-10-21 11:26:32 2022-10-21 11:26:32 0 secs \n","558670 2022-10-12 08:05:13 2022-10-12 08:05:13 0 secs \n","558671 2022-10-07 15:40:18 2022-10-07 15:40:18 0 secs \n","558672 2022-10-07 15:19:21 2022-10-07 15:19:21 0 secs \n","558673 2022-10-23 17:01:13 2022-10-23 17:01:13 0 secs \n","558674 2022-10-13 11:17:28 2022-10-13 11:17:28 0 secs \n","558675 2022-10-18 15:40:17 2022-10-18 15:40:17 0 secs \n","558676 2022-10-17 17:03:25 2022-10-17 17:03:25 0 secs \n","558677 2022-10-09 07:59:30 2022-10-09 07:59:30 0 secs \n","558678 2022-10-11 11:27:34 2022-10-11 11:27:34 0 secs \n","558679 2022-10-05 18:25:33 2022-10-05 18:25:33 0 secs \n","558680 2022-10-30 15:13:23 2022-10-30 15:13:23 0 secs \n","558681 2022-10-26 16:04:39 2022-10-26 16:04:39 0 secs \n","558682 2022-10-21 19:29:00 2022-10-21 19:28:59 -1 secs \n","558683 2022-10-24 17:03:29 2022-10-24 17:03:28 -1 secs \n","558684 2022-10-03 08:55:01 2022-10-03 08:54:45 -16 secs \n","558685 2022-10-13 14:42:10 2022-10-13 11:53:28 -10122 secs "]},"metadata":{},"output_type":"display_data"}],"source":["df_11 %>% \n"," select(started_at,ended_at) %>% \n"," mutate(ride_length=difftime(ended_at,started_at,units,\"secs\")) %>% \n","arrange(desc(ride_length))\n","# we see ride_length greater than 24 hs and negative ride lenght \n","# we will need cleaning this outsiders\n"]},{"cell_type":"markdown","id":"a3f5dbb2","metadata":{"papermill":{"duration":0.018858,"end_time":"2023-01-19T07:55:30.496035","exception":false,"start_time":"2023-01-19T07:55:30.477177","status":"completed"},"tags":[]},"source":["### day of weekday "]},{"cell_type":"markdown","id":"8e962726","metadata":{"papermill":{"duration":0.018876,"end_time":"2023-01-19T07:55:30.533567","exception":false,"start_time":"2023-01-19T07:55:30.514691","status":"completed"},"tags":[]},"source":["There is an important metric, with this we can obtain the median and mode weekday, in order to get insight, about which day has strongest demand from riders."]},{"cell_type":"code","execution_count":11,"id":"8f96aff2","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:30.574765Z","iopub.status.busy":"2023-01-19T07:55:30.573041Z","iopub.status.idle":"2023-01-19T07:55:31.16103Z","shell.execute_reply":"2023-01-19T07:55:31.15921Z"},"papermill":{"duration":0.61158,"end_time":"2023-01-19T07:55:31.163746","exception":false,"start_time":"2023-01-19T07:55:30.552166","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 558685 × 2</caption>\n","<thead>\n","\t<tr><th scope=col>started_at</th><th scope=col>day_of_week</th></tr>\n","\t<tr><th scope=col><dttm></th><th scope=col><ord></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>2022-10-14 17:13:30</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-01 16:29:26</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-19 18:55:40</td><td>Wednesday</td></tr>\n","\t<tr><td>2022-10-31 07:52:36</td><td>Monday </td></tr>\n","\t<tr><td>2022-10-13 18:41:03</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-13 15:53:27</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-06 15:51:21</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-26 17:30:10</td><td>Wednesday</td></tr>\n","\t<tr><td>2022-10-22 09:47:56</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-24 12:39:47</td><td>Monday </td></tr>\n","\t<tr><td>2022-10-01 07:41:27</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-13 18:00:15</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-30 14:48:15</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-23 09:12:46</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-10 17:31:32</td><td>Monday </td></tr>\n","\t<tr><td>2022-10-07 18:46:03</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-02 01:06:20</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-09 14:38:20</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-21 09:51:25</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-01 09:43:22</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-28 12:28:01</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-02 08:17:51</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-09 08:36:40</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-22 17:48:21</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-06 07:12:15</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-13 07:50:59</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-27 19:44:20</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-27 16:10:06</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-23 15:57:51</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-16 10:39:38</td><td>Sunday </td></tr>\n","\t<tr><td>⋮</td><td>⋮</td></tr>\n","\t<tr><td>2022-10-15 14:25:55</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-22 11:45:36</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-09 08:46:51</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-29 10:32:34</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-19 15:07:34</td><td>Wednesday</td></tr>\n","\t<tr><td>2022-10-30 11:41:59</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-18 21:56:49</td><td>Tuesday </td></tr>\n","\t<tr><td>2022-10-05 13:18:15</td><td>Wednesday</td></tr>\n","\t<tr><td>2022-10-18 14:30:58</td><td>Tuesday </td></tr>\n","\t<tr><td>2022-10-28 13:22:41</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-30 16:35:18</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-30 23:16:31</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-21 08:50:25</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-10 17:00:33</td><td>Monday </td></tr>\n","\t<tr><td>2022-10-22 13:16:48</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-20 18:21:05</td><td>Thursday </td></tr>\n","\t<tr><td>2022-10-07 17:01:45</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-28 16:22:14</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-01 21:57:14</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-28 10:54:37</td><td>Friday </td></tr>\n","\t<tr><td>2022-10-22 18:32:35</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-19 07:27:19</td><td>Wednesday</td></tr>\n","\t<tr><td>2022-10-22 13:17:09</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-16 14:50:27</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-24 17:45:38</td><td>Monday </td></tr>\n","\t<tr><td>2022-10-30 01:41:29</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-30 01:41:54</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-15 09:34:11</td><td>Saturday </td></tr>\n","\t<tr><td>2022-10-09 10:21:34</td><td>Sunday </td></tr>\n","\t<tr><td>2022-10-22 13:17:13</td><td>Saturday </td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 558685 × 2\n","\\begin{tabular}{ll}\n"," started\\_at & day\\_of\\_week\\\\\n"," <dttm> & <ord>\\\\\n","\\hline\n","\t 2022-10-14 17:13:30 & Friday \\\\\n","\t 2022-10-01 16:29:26 & Saturday \\\\\n","\t 2022-10-19 18:55:40 & Wednesday\\\\\n","\t 2022-10-31 07:52:36 & Monday \\\\\n","\t 2022-10-13 18:41:03 & Thursday \\\\\n","\t 2022-10-13 15:53:27 & Thursday \\\\\n","\t 2022-10-06 15:51:21 & Thursday \\\\\n","\t 2022-10-26 17:30:10 & Wednesday\\\\\n","\t 2022-10-22 09:47:56 & Saturday \\\\\n","\t 2022-10-24 12:39:47 & Monday \\\\\n","\t 2022-10-01 07:41:27 & Saturday \\\\\n","\t 2022-10-13 18:00:15 & Thursday \\\\\n","\t 2022-10-30 14:48:15 & Sunday \\\\\n","\t 2022-10-23 09:12:46 & Sunday \\\\\n","\t 2022-10-10 17:31:32 & Monday \\\\\n","\t 2022-10-07 18:46:03 & Friday \\\\\n","\t 2022-10-02 01:06:20 & Sunday \\\\\n","\t 2022-10-09 14:38:20 & Sunday \\\\\n","\t 2022-10-21 09:51:25 & Friday \\\\\n","\t 2022-10-01 09:43:22 & Saturday \\\\\n","\t 2022-10-28 12:28:01 & Friday \\\\\n","\t 2022-10-02 08:17:51 & Sunday \\\\\n","\t 2022-10-09 08:36:40 & Sunday \\\\\n","\t 2022-10-22 17:48:21 & Saturday \\\\\n","\t 2022-10-06 07:12:15 & Thursday \\\\\n","\t 2022-10-13 07:50:59 & Thursday \\\\\n","\t 2022-10-27 19:44:20 & Thursday \\\\\n","\t 2022-10-27 16:10:06 & Thursday \\\\\n","\t 2022-10-23 15:57:51 & Sunday \\\\\n","\t 2022-10-16 10:39:38 & Sunday \\\\\n","\t ⋮ & ⋮\\\\\n","\t 2022-10-15 14:25:55 & Saturday \\\\\n","\t 2022-10-22 11:45:36 & Saturday \\\\\n","\t 2022-10-09 08:46:51 & Sunday \\\\\n","\t 2022-10-29 10:32:34 & Saturday \\\\\n","\t 2022-10-19 15:07:34 & Wednesday\\\\\n","\t 2022-10-30 11:41:59 & Sunday \\\\\n","\t 2022-10-18 21:56:49 & Tuesday \\\\\n","\t 2022-10-05 13:18:15 & Wednesday\\\\\n","\t 2022-10-18 14:30:58 & Tuesday \\\\\n","\t 2022-10-28 13:22:41 & Friday \\\\\n","\t 2022-10-30 16:35:18 & Sunday \\\\\n","\t 2022-10-30 23:16:31 & Sunday \\\\\n","\t 2022-10-21 08:50:25 & Friday \\\\\n","\t 2022-10-10 17:00:33 & Monday \\\\\n","\t 2022-10-22 13:16:48 & Saturday \\\\\n","\t 2022-10-20 18:21:05 & Thursday \\\\\n","\t 2022-10-07 17:01:45 & Friday \\\\\n","\t 2022-10-28 16:22:14 & Friday \\\\\n","\t 2022-10-01 21:57:14 & Saturday \\\\\n","\t 2022-10-28 10:54:37 & Friday \\\\\n","\t 2022-10-22 18:32:35 & Saturday \\\\\n","\t 2022-10-19 07:27:19 & Wednesday\\\\\n","\t 2022-10-22 13:17:09 & Saturday \\\\\n","\t 2022-10-16 14:50:27 & Sunday \\\\\n","\t 2022-10-24 17:45:38 & Monday \\\\\n","\t 2022-10-30 01:41:29 & Sunday \\\\\n","\t 2022-10-30 01:41:54 & Sunday \\\\\n","\t 2022-10-15 09:34:11 & Saturday \\\\\n","\t 2022-10-09 10:21:34 & Sunday \\\\\n","\t 2022-10-22 13:17:13 & Saturday \\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 558685 × 2\n","\n","| started_at <dttm> | day_of_week <ord> |\n","|---|---|\n","| 2022-10-14 17:13:30 | Friday |\n","| 2022-10-01 16:29:26 | Saturday |\n","| 2022-10-19 18:55:40 | Wednesday |\n","| 2022-10-31 07:52:36 | Monday |\n","| 2022-10-13 18:41:03 | Thursday |\n","| 2022-10-13 15:53:27 | Thursday |\n","| 2022-10-06 15:51:21 | Thursday |\n","| 2022-10-26 17:30:10 | Wednesday |\n","| 2022-10-22 09:47:56 | Saturday |\n","| 2022-10-24 12:39:47 | Monday |\n","| 2022-10-01 07:41:27 | Saturday |\n","| 2022-10-13 18:00:15 | Thursday |\n","| 2022-10-30 14:48:15 | Sunday |\n","| 2022-10-23 09:12:46 | Sunday |\n","| 2022-10-10 17:31:32 | Monday |\n","| 2022-10-07 18:46:03 | Friday |\n","| 2022-10-02 01:06:20 | Sunday |\n","| 2022-10-09 14:38:20 | Sunday |\n","| 2022-10-21 09:51:25 | Friday |\n","| 2022-10-01 09:43:22 | Saturday |\n","| 2022-10-28 12:28:01 | Friday |\n","| 2022-10-02 08:17:51 | Sunday |\n","| 2022-10-09 08:36:40 | Sunday |\n","| 2022-10-22 17:48:21 | Saturday |\n","| 2022-10-06 07:12:15 | Thursday |\n","| 2022-10-13 07:50:59 | Thursday |\n","| 2022-10-27 19:44:20 | Thursday |\n","| 2022-10-27 16:10:06 | Thursday |\n","| 2022-10-23 15:57:51 | Sunday |\n","| 2022-10-16 10:39:38 | Sunday |\n","| ⋮ | ⋮ |\n","| 2022-10-15 14:25:55 | Saturday |\n","| 2022-10-22 11:45:36 | Saturday |\n","| 2022-10-09 08:46:51 | Sunday |\n","| 2022-10-29 10:32:34 | Saturday |\n","| 2022-10-19 15:07:34 | Wednesday |\n","| 2022-10-30 11:41:59 | Sunday |\n","| 2022-10-18 21:56:49 | Tuesday |\n","| 2022-10-05 13:18:15 | Wednesday |\n","| 2022-10-18 14:30:58 | Tuesday |\n","| 2022-10-28 13:22:41 | Friday |\n","| 2022-10-30 16:35:18 | Sunday |\n","| 2022-10-30 23:16:31 | Sunday |\n","| 2022-10-21 08:50:25 | Friday |\n","| 2022-10-10 17:00:33 | Monday |\n","| 2022-10-22 13:16:48 | Saturday |\n","| 2022-10-20 18:21:05 | Thursday |\n","| 2022-10-07 17:01:45 | Friday |\n","| 2022-10-28 16:22:14 | Friday |\n","| 2022-10-01 21:57:14 | Saturday |\n","| 2022-10-28 10:54:37 | Friday |\n","| 2022-10-22 18:32:35 | Saturday |\n","| 2022-10-19 07:27:19 | Wednesday |\n","| 2022-10-22 13:17:09 | Saturday |\n","| 2022-10-16 14:50:27 | Sunday |\n","| 2022-10-24 17:45:38 | Monday |\n","| 2022-10-30 01:41:29 | Sunday |\n","| 2022-10-30 01:41:54 | Sunday |\n","| 2022-10-15 09:34:11 | Saturday |\n","| 2022-10-09 10:21:34 | Sunday |\n","| 2022-10-22 13:17:13 | Saturday |\n","\n"],"text/plain":[" started_at day_of_week\n","1 2022-10-14 17:13:30 Friday \n","2 2022-10-01 16:29:26 Saturday \n","3 2022-10-19 18:55:40 Wednesday \n","4 2022-10-31 07:52:36 Monday \n","5 2022-10-13 18:41:03 Thursday \n","6 2022-10-13 15:53:27 Thursday \n","7 2022-10-06 15:51:21 Thursday \n","8 2022-10-26 17:30:10 Wednesday \n","9 2022-10-22 09:47:56 Saturday \n","10 2022-10-24 12:39:47 Monday \n","11 2022-10-01 07:41:27 Saturday \n","12 2022-10-13 18:00:15 Thursday \n","13 2022-10-30 14:48:15 Sunday \n","14 2022-10-23 09:12:46 Sunday \n","15 2022-10-10 17:31:32 Monday \n","16 2022-10-07 18:46:03 Friday \n","17 2022-10-02 01:06:20 Sunday \n","18 2022-10-09 14:38:20 Sunday \n","19 2022-10-21 09:51:25 Friday \n","20 2022-10-01 09:43:22 Saturday \n","21 2022-10-28 12:28:01 Friday \n","22 2022-10-02 08:17:51 Sunday \n","23 2022-10-09 08:36:40 Sunday \n","24 2022-10-22 17:48:21 Saturday \n","25 2022-10-06 07:12:15 Thursday \n","26 2022-10-13 07:50:59 Thursday \n","27 2022-10-27 19:44:20 Thursday \n","28 2022-10-27 16:10:06 Thursday \n","29 2022-10-23 15:57:51 Sunday \n","30 2022-10-16 10:39:38 Sunday \n","⋮ ⋮ ⋮ \n","558656 2022-10-15 14:25:55 Saturday \n","558657 2022-10-22 11:45:36 Saturday \n","558658 2022-10-09 08:46:51 Sunday \n","558659 2022-10-29 10:32:34 Saturday \n","558660 2022-10-19 15:07:34 Wednesday \n","558661 2022-10-30 11:41:59 Sunday \n","558662 2022-10-18 21:56:49 Tuesday \n","558663 2022-10-05 13:18:15 Wednesday \n","558664 2022-10-18 14:30:58 Tuesday \n","558665 2022-10-28 13:22:41 Friday \n","558666 2022-10-30 16:35:18 Sunday \n","558667 2022-10-30 23:16:31 Sunday \n","558668 2022-10-21 08:50:25 Friday \n","558669 2022-10-10 17:00:33 Monday \n","558670 2022-10-22 13:16:48 Saturday \n","558671 2022-10-20 18:21:05 Thursday \n","558672 2022-10-07 17:01:45 Friday \n","558673 2022-10-28 16:22:14 Friday \n","558674 2022-10-01 21:57:14 Saturday \n","558675 2022-10-28 10:54:37 Friday \n","558676 2022-10-22 18:32:35 Saturday \n","558677 2022-10-19 07:27:19 Wednesday \n","558678 2022-10-22 13:17:09 Saturday \n","558679 2022-10-16 14:50:27 Sunday \n","558680 2022-10-24 17:45:38 Monday \n","558681 2022-10-30 01:41:29 Sunday \n","558682 2022-10-30 01:41:54 Sunday \n","558683 2022-10-15 09:34:11 Saturday \n","558684 2022-10-09 10:21:34 Sunday \n","558685 2022-10-22 13:17:13 Saturday "]},"metadata":{},"output_type":"display_data"}],"source":["df_11 %>% \n"," select(started_at) %>% \n"," mutate(day_of_week=(wday(started_at,label=TRUE,abbr=FALSE)))\n"]},{"cell_type":"markdown","id":"7b61821a","metadata":{"papermill":{"duration":0.028329,"end_time":"2023-01-19T07:55:31.211554","exception":false,"start_time":"2023-01-19T07:55:31.183225","status":"completed"},"tags":[]},"source":["### month of year"]},{"cell_type":"markdown","id":"5659cb30","metadata":{"papermill":{"duration":0.019517,"end_time":"2023-01-19T07:55:31.250952","exception":false,"start_time":"2023-01-19T07:55:31.231435","status":"completed"},"tags":[]},"source":["There is another important metric, because we can obtain the median and mode month, in order to get insight, about to unveil a pattern or trend, is there one or more months with strong or weak demand?."]},{"cell_type":"code","execution_count":12,"id":"8e28182e","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:31.293876Z","iopub.status.busy":"2023-01-19T07:55:31.292275Z","iopub.status.idle":"2023-01-19T07:55:32.470993Z","shell.execute_reply":"2023-01-19T07:55:32.469173Z"},"papermill":{"duration":1.203544,"end_time":"2023-01-19T07:55:32.473536","exception":false,"start_time":"2023-01-19T07:55:31.269992","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 558685 × 2</caption>\n","<thead>\n","\t<tr><th scope=col>started_at</th><th scope=col>month_of_year</th></tr>\n","\t<tr><th scope=col><dttm></th><th scope=col><ord></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>2022-10-14 17:13:30</td><td>October</td></tr>\n","\t<tr><td>2022-10-01 16:29:26</td><td>October</td></tr>\n","\t<tr><td>2022-10-19 18:55:40</td><td>October</td></tr>\n","\t<tr><td>2022-10-31 07:52:36</td><td>October</td></tr>\n","\t<tr><td>2022-10-13 18:41:03</td><td>October</td></tr>\n","\t<tr><td>2022-10-13 15:53:27</td><td>October</td></tr>\n","\t<tr><td>2022-10-06 15:51:21</td><td>October</td></tr>\n","\t<tr><td>2022-10-26 17:30:10</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 09:47:56</td><td>October</td></tr>\n","\t<tr><td>2022-10-24 12:39:47</td><td>October</td></tr>\n","\t<tr><td>2022-10-01 07:41:27</td><td>October</td></tr>\n","\t<tr><td>2022-10-13 18:00:15</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 14:48:15</td><td>October</td></tr>\n","\t<tr><td>2022-10-23 09:12:46</td><td>October</td></tr>\n","\t<tr><td>2022-10-10 17:31:32</td><td>October</td></tr>\n","\t<tr><td>2022-10-07 18:46:03</td><td>October</td></tr>\n","\t<tr><td>2022-10-02 01:06:20</td><td>October</td></tr>\n","\t<tr><td>2022-10-09 14:38:20</td><td>October</td></tr>\n","\t<tr><td>2022-10-21 09:51:25</td><td>October</td></tr>\n","\t<tr><td>2022-10-01 09:43:22</td><td>October</td></tr>\n","\t<tr><td>2022-10-28 12:28:01</td><td>October</td></tr>\n","\t<tr><td>2022-10-02 08:17:51</td><td>October</td></tr>\n","\t<tr><td>2022-10-09 08:36:40</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 17:48:21</td><td>October</td></tr>\n","\t<tr><td>2022-10-06 07:12:15</td><td>October</td></tr>\n","\t<tr><td>2022-10-13 07:50:59</td><td>October</td></tr>\n","\t<tr><td>2022-10-27 19:44:20</td><td>October</td></tr>\n","\t<tr><td>2022-10-27 16:10:06</td><td>October</td></tr>\n","\t<tr><td>2022-10-23 15:57:51</td><td>October</td></tr>\n","\t<tr><td>2022-10-16 10:39:38</td><td>October</td></tr>\n","\t<tr><td>⋮</td><td>⋮</td></tr>\n","\t<tr><td>2022-10-15 14:25:55</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 11:45:36</td><td>October</td></tr>\n","\t<tr><td>2022-10-09 08:46:51</td><td>October</td></tr>\n","\t<tr><td>2022-10-29 10:32:34</td><td>October</td></tr>\n","\t<tr><td>2022-10-19 15:07:34</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 11:41:59</td><td>October</td></tr>\n","\t<tr><td>2022-10-18 21:56:49</td><td>October</td></tr>\n","\t<tr><td>2022-10-05 13:18:15</td><td>October</td></tr>\n","\t<tr><td>2022-10-18 14:30:58</td><td>October</td></tr>\n","\t<tr><td>2022-10-28 13:22:41</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 16:35:18</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 23:16:31</td><td>October</td></tr>\n","\t<tr><td>2022-10-21 08:50:25</td><td>October</td></tr>\n","\t<tr><td>2022-10-10 17:00:33</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 13:16:48</td><td>October</td></tr>\n","\t<tr><td>2022-10-20 18:21:05</td><td>October</td></tr>\n","\t<tr><td>2022-10-07 17:01:45</td><td>October</td></tr>\n","\t<tr><td>2022-10-28 16:22:14</td><td>October</td></tr>\n","\t<tr><td>2022-10-01 21:57:14</td><td>October</td></tr>\n","\t<tr><td>2022-10-28 10:54:37</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 18:32:35</td><td>October</td></tr>\n","\t<tr><td>2022-10-19 07:27:19</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 13:17:09</td><td>October</td></tr>\n","\t<tr><td>2022-10-16 14:50:27</td><td>October</td></tr>\n","\t<tr><td>2022-10-24 17:45:38</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 01:41:29</td><td>October</td></tr>\n","\t<tr><td>2022-10-30 01:41:54</td><td>October</td></tr>\n","\t<tr><td>2022-10-15 09:34:11</td><td>October</td></tr>\n","\t<tr><td>2022-10-09 10:21:34</td><td>October</td></tr>\n","\t<tr><td>2022-10-22 13:17:13</td><td>October</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 558685 × 2\n","\\begin{tabular}{ll}\n"," started\\_at & month\\_of\\_year\\\\\n"," <dttm> & <ord>\\\\\n","\\hline\n","\t 2022-10-14 17:13:30 & October\\\\\n","\t 2022-10-01 16:29:26 & October\\\\\n","\t 2022-10-19 18:55:40 & October\\\\\n","\t 2022-10-31 07:52:36 & October\\\\\n","\t 2022-10-13 18:41:03 & October\\\\\n","\t 2022-10-13 15:53:27 & October\\\\\n","\t 2022-10-06 15:51:21 & October\\\\\n","\t 2022-10-26 17:30:10 & October\\\\\n","\t 2022-10-22 09:47:56 & October\\\\\n","\t 2022-10-24 12:39:47 & October\\\\\n","\t 2022-10-01 07:41:27 & October\\\\\n","\t 2022-10-13 18:00:15 & October\\\\\n","\t 2022-10-30 14:48:15 & October\\\\\n","\t 2022-10-23 09:12:46 & October\\\\\n","\t 2022-10-10 17:31:32 & October\\\\\n","\t 2022-10-07 18:46:03 & October\\\\\n","\t 2022-10-02 01:06:20 & October\\\\\n","\t 2022-10-09 14:38:20 & October\\\\\n","\t 2022-10-21 09:51:25 & October\\\\\n","\t 2022-10-01 09:43:22 & October\\\\\n","\t 2022-10-28 12:28:01 & October\\\\\n","\t 2022-10-02 08:17:51 & October\\\\\n","\t 2022-10-09 08:36:40 & October\\\\\n","\t 2022-10-22 17:48:21 & October\\\\\n","\t 2022-10-06 07:12:15 & October\\\\\n","\t 2022-10-13 07:50:59 & October\\\\\n","\t 2022-10-27 19:44:20 & October\\\\\n","\t 2022-10-27 16:10:06 & October\\\\\n","\t 2022-10-23 15:57:51 & October\\\\\n","\t 2022-10-16 10:39:38 & October\\\\\n","\t ⋮ & ⋮\\\\\n","\t 2022-10-15 14:25:55 & October\\\\\n","\t 2022-10-22 11:45:36 & October\\\\\n","\t 2022-10-09 08:46:51 & October\\\\\n","\t 2022-10-29 10:32:34 & October\\\\\n","\t 2022-10-19 15:07:34 & October\\\\\n","\t 2022-10-30 11:41:59 & October\\\\\n","\t 2022-10-18 21:56:49 & October\\\\\n","\t 2022-10-05 13:18:15 & October\\\\\n","\t 2022-10-18 14:30:58 & October\\\\\n","\t 2022-10-28 13:22:41 & October\\\\\n","\t 2022-10-30 16:35:18 & October\\\\\n","\t 2022-10-30 23:16:31 & October\\\\\n","\t 2022-10-21 08:50:25 & October\\\\\n","\t 2022-10-10 17:00:33 & October\\\\\n","\t 2022-10-22 13:16:48 & October\\\\\n","\t 2022-10-20 18:21:05 & October\\\\\n","\t 2022-10-07 17:01:45 & October\\\\\n","\t 2022-10-28 16:22:14 & October\\\\\n","\t 2022-10-01 21:57:14 & October\\\\\n","\t 2022-10-28 10:54:37 & October\\\\\n","\t 2022-10-22 18:32:35 & October\\\\\n","\t 2022-10-19 07:27:19 & October\\\\\n","\t 2022-10-22 13:17:09 & October\\\\\n","\t 2022-10-16 14:50:27 & October\\\\\n","\t 2022-10-24 17:45:38 & October\\\\\n","\t 2022-10-30 01:41:29 & October\\\\\n","\t 2022-10-30 01:41:54 & October\\\\\n","\t 2022-10-15 09:34:11 & October\\\\\n","\t 2022-10-09 10:21:34 & October\\\\\n","\t 2022-10-22 13:17:13 & October\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 558685 × 2\n","\n","| started_at <dttm> | month_of_year <ord> |\n","|---|---|\n","| 2022-10-14 17:13:30 | October |\n","| 2022-10-01 16:29:26 | October |\n","| 2022-10-19 18:55:40 | October |\n","| 2022-10-31 07:52:36 | October |\n","| 2022-10-13 18:41:03 | October |\n","| 2022-10-13 15:53:27 | October |\n","| 2022-10-06 15:51:21 | October |\n","| 2022-10-26 17:30:10 | October |\n","| 2022-10-22 09:47:56 | October |\n","| 2022-10-24 12:39:47 | October |\n","| 2022-10-01 07:41:27 | October |\n","| 2022-10-13 18:00:15 | October |\n","| 2022-10-30 14:48:15 | October |\n","| 2022-10-23 09:12:46 | October |\n","| 2022-10-10 17:31:32 | October |\n","| 2022-10-07 18:46:03 | October |\n","| 2022-10-02 01:06:20 | October |\n","| 2022-10-09 14:38:20 | October |\n","| 2022-10-21 09:51:25 | October |\n","| 2022-10-01 09:43:22 | October |\n","| 2022-10-28 12:28:01 | October |\n","| 2022-10-02 08:17:51 | October |\n","| 2022-10-09 08:36:40 | October |\n","| 2022-10-22 17:48:21 | October |\n","| 2022-10-06 07:12:15 | October |\n","| 2022-10-13 07:50:59 | October |\n","| 2022-10-27 19:44:20 | October |\n","| 2022-10-27 16:10:06 | October |\n","| 2022-10-23 15:57:51 | October |\n","| 2022-10-16 10:39:38 | October |\n","| ⋮ | ⋮ |\n","| 2022-10-15 14:25:55 | October |\n","| 2022-10-22 11:45:36 | October |\n","| 2022-10-09 08:46:51 | October |\n","| 2022-10-29 10:32:34 | October |\n","| 2022-10-19 15:07:34 | October |\n","| 2022-10-30 11:41:59 | October |\n","| 2022-10-18 21:56:49 | October |\n","| 2022-10-05 13:18:15 | October |\n","| 2022-10-18 14:30:58 | October |\n","| 2022-10-28 13:22:41 | October |\n","| 2022-10-30 16:35:18 | October |\n","| 2022-10-30 23:16:31 | October |\n","| 2022-10-21 08:50:25 | October |\n","| 2022-10-10 17:00:33 | October |\n","| 2022-10-22 13:16:48 | October |\n","| 2022-10-20 18:21:05 | October |\n","| 2022-10-07 17:01:45 | October |\n","| 2022-10-28 16:22:14 | October |\n","| 2022-10-01 21:57:14 | October |\n","| 2022-10-28 10:54:37 | October |\n","| 2022-10-22 18:32:35 | October |\n","| 2022-10-19 07:27:19 | October |\n","| 2022-10-22 13:17:09 | October |\n","| 2022-10-16 14:50:27 | October |\n","| 2022-10-24 17:45:38 | October |\n","| 2022-10-30 01:41:29 | October |\n","| 2022-10-30 01:41:54 | October |\n","| 2022-10-15 09:34:11 | October |\n","| 2022-10-09 10:21:34 | October |\n","| 2022-10-22 13:17:13 | October |\n","\n"],"text/plain":[" started_at month_of_year\n","1 2022-10-14 17:13:30 October \n","2 2022-10-01 16:29:26 October \n","3 2022-10-19 18:55:40 October \n","4 2022-10-31 07:52:36 October \n","5 2022-10-13 18:41:03 October \n","6 2022-10-13 15:53:27 October \n","7 2022-10-06 15:51:21 October \n","8 2022-10-26 17:30:10 October \n","9 2022-10-22 09:47:56 October \n","10 2022-10-24 12:39:47 October \n","11 2022-10-01 07:41:27 October \n","12 2022-10-13 18:00:15 October \n","13 2022-10-30 14:48:15 October \n","14 2022-10-23 09:12:46 October \n","15 2022-10-10 17:31:32 October \n","16 2022-10-07 18:46:03 October \n","17 2022-10-02 01:06:20 October \n","18 2022-10-09 14:38:20 October \n","19 2022-10-21 09:51:25 October \n","20 2022-10-01 09:43:22 October \n","21 2022-10-28 12:28:01 October \n","22 2022-10-02 08:17:51 October \n","23 2022-10-09 08:36:40 October \n","24 2022-10-22 17:48:21 October \n","25 2022-10-06 07:12:15 October \n","26 2022-10-13 07:50:59 October \n","27 2022-10-27 19:44:20 October \n","28 2022-10-27 16:10:06 October \n","29 2022-10-23 15:57:51 October \n","30 2022-10-16 10:39:38 October \n","⋮ ⋮ ⋮ \n","558656 2022-10-15 14:25:55 October \n","558657 2022-10-22 11:45:36 October \n","558658 2022-10-09 08:46:51 October \n","558659 2022-10-29 10:32:34 October \n","558660 2022-10-19 15:07:34 October \n","558661 2022-10-30 11:41:59 October \n","558662 2022-10-18 21:56:49 October \n","558663 2022-10-05 13:18:15 October \n","558664 2022-10-18 14:30:58 October \n","558665 2022-10-28 13:22:41 October \n","558666 2022-10-30 16:35:18 October \n","558667 2022-10-30 23:16:31 October \n","558668 2022-10-21 08:50:25 October \n","558669 2022-10-10 17:00:33 October \n","558670 2022-10-22 13:16:48 October \n","558671 2022-10-20 18:21:05 October \n","558672 2022-10-07 17:01:45 October \n","558673 2022-10-28 16:22:14 October \n","558674 2022-10-01 21:57:14 October \n","558675 2022-10-28 10:54:37 October \n","558676 2022-10-22 18:32:35 October \n","558677 2022-10-19 07:27:19 October \n","558678 2022-10-22 13:17:09 October \n","558679 2022-10-16 14:50:27 October \n","558680 2022-10-24 17:45:38 October \n","558681 2022-10-30 01:41:29 October \n","558682 2022-10-30 01:41:54 October \n","558683 2022-10-15 09:34:11 October \n","558684 2022-10-09 10:21:34 October \n","558685 2022-10-22 13:17:13 October "]},"metadata":{},"output_type":"display_data"}],"source":["df_11 %>% \n"," select(started_at) %>% \n"," mutate(month_of_year=(month(started_at,label=TRUE,abbr=FALSE)))\n"]},{"cell_type":"markdown","id":"be5100a1","metadata":{"papermill":{"duration":0.020181,"end_time":"2023-01-19T07:55:32.513746","exception":false,"start_time":"2023-01-19T07:55:32.493565","status":"completed"},"tags":[]},"source":["## Actual - Main dataframe \n","Now we'll apply the new metrics to the whole dataset."]},{"cell_type":"code","execution_count":13,"id":"e5ffb011","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:55:32.559004Z","iopub.status.busy":"2023-01-19T07:55:32.557406Z","iopub.status.idle":"2023-01-19T07:56:20.530776Z","shell.execute_reply":"2023-01-19T07:56:20.528776Z"},"papermill":{"duration":47.999573,"end_time":"2023-01-19T07:56:20.53486","exception":false,"start_time":"2023-01-19T07:55:32.535287","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v1 <- df_merged %>% # we create main dataframe\n"," select(member_casual,rideable_type,started_at,ended_at,start_station_name, end_station_name) %>% \n"," mutate(ride_length=interval(started_at, ended_at)/minutes(),day_of_week=(wday(started_at,label=TRUE,abbr=FALSE))) %>% \n"," mutate(month_of_year=(month(started_at,label=TRUE,abbr=FALSE))) %>% \n"," filter(ride_length > 0) %>% # exclude started_at lower than ended_at\n"," filter(ride_length < 1440) # exclude ended_at highter than 24 hs\n"]},{"cell_type":"code","execution_count":14,"id":"ef6aab64","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:56:20.580845Z","iopub.status.busy":"2023-01-19T07:56:20.578915Z","iopub.status.idle":"2023-01-19T07:56:23.115485Z","shell.execute_reply":"2023-01-19T07:56:23.113049Z"},"papermill":{"duration":2.562422,"end_time":"2023-01-19T07:56:23.118979","exception":false,"start_time":"2023-01-19T07:56:20.556557","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Rows: 5,727,518\n","Columns: 9\n","$ member_casual \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"casual\", \"member\", \"member\", \"member\", \"…\n","$ rideable_type \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"electric_bike\", \"electric_bike\", \"electric_bike\", …\n","$ started_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-07 15:06:07, 2021-12-11 03:43:29, 2021-12-…\n","$ ended_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-07 15:13:42, 2021-12-11 04:10:23, 2021-12-…\n","$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Laflin St & Cullerton St\", \"LaSalle Dr & Huron St\"…\n","$ end_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Morgan St & Polk St\", \"Clarendon Ave & Leland Ave\"…\n","$ ride_length \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 7.583333, 26.900000, 12.766667, 14.716667, 20.26666…\n","$ day_of_week \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m Tuesday, Saturday, Wednesday, Sunday, Thursday, Wed…\n","$ month_of_year \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m December, December, December, December, December, D…\n"]},{"data":{"text/plain":[" member_casual rideable_type started_at \n"," Length:5727518 Length:5727518 Min. :2021-12-01 00:00:01 \n"," Class :character Class :character 1st Qu.:2022-05-17 11:53:50 \n"," Mode :character Mode :character Median :2022-07-13 22:12:55 \n"," Mean :2022-07-06 05:55:19 \n"," 3rd Qu.:2022-09-07 17:59:38 \n"," Max. :2022-11-30 23:56:11 \n"," \n"," ended_at start_station_name end_station_name \n"," Min. :2021-12-01 00:02:40 Length:5727518 Length:5727518 \n"," 1st Qu.:2022-05-17 12:12:00 Class :character Class :character \n"," Median :2022-07-13 22:28:01 Mode :character Mode :character \n"," Mean :2022-07-06 06:11:37 \n"," 3rd Qu.:2022-09-07 18:14:10 \n"," Max. :2022-12-01 11:45:53 \n"," \n"," ride_length day_of_week month_of_year \n"," Min. : 0.0167 Sunday :781527 July : 822557 \n"," 1st Qu.: 5.8333 Monday :756698 August : 785121 \n"," Median : 10.3000 Tuesday :782037 June : 768161 \n"," Mean : 16.2861 Wednesday:816490 September: 700640 \n"," 3rd Qu.: 18.4667 Thursday :853333 May : 634148 \n"," Max. :1439.9333 Friday :816532 October : 558198 \n"," Saturday :920901 (Other) :1458693 "]},"metadata":{},"output_type":"display_data"}],"source":["glimpse(df_merged_v1)\n","summary(df_merged_v1)"]},{"cell_type":"markdown","id":"7de23098","metadata":{"papermill":{"duration":0.019981,"end_time":"2023-01-19T07:56:23.158797","exception":false,"start_time":"2023-01-19T07:56:23.138816","status":"completed"},"tags":[]},"source":["Note: in this case we filter out ride_length not valid. total 4.016 rows"]},{"cell_type":"markdown","id":"73a54bc9","metadata":{"papermill":{"duration":0.019952,"end_time":"2023-01-19T07:56:23.19925","exception":false,"start_time":"2023-01-19T07:56:23.179298","status":"completed"},"tags":[]},"source":["### filter trips ended_at minus started as more than 24 hs"]},{"cell_type":"code","execution_count":15,"id":"1647ea9b","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:56:23.244163Z","iopub.status.busy":"2023-01-19T07:56:23.241905Z","iopub.status.idle":"2023-01-19T07:57:09.31986Z","shell.execute_reply":"2023-01-19T07:57:09.317924Z"},"papermill":{"duration":46.10466,"end_time":"2023-01-19T07:57:09.323907","exception":false,"start_time":"2023-01-19T07:56:23.219247","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v2 <- df_merged %>% # filter trips for more than 24 hs\n"," select(member_casual,rideable_type,started_at,ended_at,start_station_name, end_station_name) %>% \n"," mutate(ride_length=interval(started_at, ended_at)/minutes(),day_of_week=(wday(started_at,label=TRUE,abbr=FALSE))) %>%\n"," mutate(month_of_year=(month(started_at,label=TRUE,abbr=FALSE))) %>% \n"," filter(ride_length > 1440) "]},{"cell_type":"code","execution_count":16,"id":"17009420","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:09.368711Z","iopub.status.busy":"2023-01-19T07:57:09.367128Z","iopub.status.idle":"2023-01-19T07:57:09.400323Z","shell.execute_reply":"2023-01-19T07:57:09.398469Z"},"papermill":{"duration":0.05813,"end_time":"2023-01-19T07:57:09.402995","exception":false,"start_time":"2023-01-19T07:57:09.344865","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Rows: 5,395\n","Columns: 9\n","$ member_casual \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"casual\", \"casual\", \"casual\", \"casual\", \"…\n","$ rideable_type \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"classic_bike\", \"classic_bike\", \"cl…\n","$ started_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-11 02:47:33, 2021-12-20 22:47:53, 2021-12-…\n","$ ended_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-12 03:47:27, 2021-12-21 23:47:41, 2021-12-…\n","$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Halsted St & Willow St\", \"Michigan Ave & 8th St\", …\n","$ end_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m NA, NA, NA, NA, \"California Ave & Lake St\", \"Adler …\n","$ ride_length \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 1499.900, 1499.800, 1499.917, 1499.917, 1499.900, 5…\n","$ day_of_week \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m Saturday, Monday, Friday, Thursday, Wednesday, Thur…\n","$ month_of_year \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m December, December, December, December, December, D…\n"]},{"data":{"text/plain":[" member_casual rideable_type started_at \n"," Length:5395 Length:5395 Min. :2021-12-01 07:28:31 \n"," Class :character Class :character 1st Qu.:2022-05-23 09:07:58 \n"," Mode :character Mode :character Median :2022-07-04 23:28:16 \n"," Mean :2022-07-04 17:45:32 \n"," 3rd Qu.:2022-08-27 16:23:33 \n"," Max. :2022-11-28 17:28:53 \n"," \n"," ended_at start_station_name end_station_name \n"," Min. :2021-12-02 08:28:32 Length:5395 Length:5395 \n"," 1st Qu.:2022-05-25 17:30:34 Class :character Class :character \n"," Median :2022-07-07 19:43:52 Mode :character Mode :character \n"," Mean :2022-07-07 01:35:39 \n"," 3rd Qu.:2022-08-29 16:36:18 \n"," Max. :2022-12-01 04:42:30 \n"," \n"," ride_length day_of_week month_of_year \n"," Min. : 1441 Sunday :1006 June : 977 \n"," 1st Qu.: 1500 Monday : 650 July : 859 \n"," Median : 1500 Tuesday : 614 August : 734 \n"," Mean : 3350 Wednesday: 585 May : 662 \n"," 3rd Qu.: 2141 Thursday : 673 September: 627 \n"," Max. :41387 Friday : 803 October : 422 \n"," Saturday :1064 (Other) :1114 "]},"metadata":{},"output_type":"display_data"}],"source":["glimpse(df_merged_v2)\n","summary(df_merged_v2)"]},{"cell_type":"markdown","id":"a74a7da2","metadata":{"papermill":{"duration":0.022902,"end_time":"2023-01-19T07:57:09.446825","exception":false,"start_time":"2023-01-19T07:57:09.423923","status":"completed"},"tags":[]},"source":["### filter trips ended_at lower than started_at"]},{"cell_type":"markdown","id":"20ce3068","metadata":{"papermill":{"duration":0.021037,"end_time":"2023-01-19T07:57:09.488493","exception":false,"start_time":"2023-01-19T07:57:09.467456","status":"completed"},"tags":[]},"source":["Note: in this case we filter out ride_length not valid. total 653 rows"]},{"cell_type":"code","execution_count":17,"id":"28a15fd0","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:09.535177Z","iopub.status.busy":"2023-01-19T07:57:09.533445Z","iopub.status.idle":"2023-01-19T07:57:49.508363Z","shell.execute_reply":"2023-01-19T07:57:49.506451Z"},"papermill":{"duration":40.000735,"end_time":"2023-01-19T07:57:49.51104","exception":false,"start_time":"2023-01-19T07:57:09.510305","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v3 <- df_merged %>% # filter trips ended_at <started_at\n"," select(member_casual,rideable_type,started_at,ended_at,start_station_name, end_station_name) %>% \n"," mutate(ride_length=interval(started_at, ended_at)/minutes(),day_of_week=(wday(started_at,label=TRUE,abbr=FALSE))) %>% \n"," mutate(month_of_year=(month(started_at,label=TRUE,abbr=FALSE))) %>% \n"," filter(ride_length <= 0) # ended_at <started_at"]},{"cell_type":"code","execution_count":18,"id":"8d223319","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:49.556881Z","iopub.status.busy":"2023-01-19T07:57:49.555149Z","iopub.status.idle":"2023-01-19T07:57:49.587695Z","shell.execute_reply":"2023-01-19T07:57:49.585875Z"},"papermill":{"duration":0.058339,"end_time":"2023-01-19T07:57:49.590171","exception":false,"start_time":"2023-01-19T07:57:49.531832","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Rows: 538\n","Columns: 9\n","$ member_casual \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"member\", \"member\", \"casual\", \"casual\", \"member\", \"…\n","$ rideable_type \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"classic_bike\", \"classic_bike\", \"electric_bike\", \"e…\n","$ started_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-12 16:32:15, 2021-12-01 17:36:30, 2021-12-…\n","$ ended_at \u001b[3m\u001b[90m<dttm>\u001b[39m\u001b[23m 2021-12-12 16:32:15, 2021-12-01 17:36:30, 2021-12-…\n","$ start_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Mies van der Rohe Way & Chicago Ave\", \"Clinton St …\n","$ end_station_name \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m \"Mies van der Rohe Way & Chicago Ave\", \"Clinton St …\n","$ ride_length \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …\n","$ day_of_week \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m Sunday, Wednesday, Sunday, Saturday, Tuesday, Satur…\n","$ month_of_year \u001b[3m\u001b[90m<ord>\u001b[39m\u001b[23m December, December, December, December, December, D…\n"]},{"data":{"text/plain":[" member_casual rideable_type started_at \n"," Length:538 Length:538 Min. :2021-12-01 17:36:30 \n"," Class :character Class :character 1st Qu.:2022-06-03 00:24:13 \n"," Mode :character Mode :character Median :2022-08-02 11:22:26 \n"," Mean :2022-07-23 01:57:12 \n"," 3rd Qu.:2022-09-24 20:56:19 \n"," Max. :2022-11-29 20:37:02 \n"," \n"," ended_at start_station_name end_station_name \n"," Min. :2021-12-01 17:36:30 Length:538 Length:538 \n"," 1st Qu.:2022-06-03 00:24:13 Class :character Class :character \n"," Median :2022-08-02 11:22:26 Mode :character Mode :character \n"," Mean :2022-07-23 01:32:45 \n"," 3rd Qu.:2022-09-24 17:39:28 \n"," Max. :2022-11-29 20:37:02 \n"," \n"," ride_length day_of_week month_of_year\n"," Min. :-10353.35 Sunday :99 August : 77 \n"," 1st Qu.: 0.00 Monday :59 July : 72 \n"," Median : 0.00 Tuesday :82 September: 72 \n"," Mean : -24.46 Wednesday:50 June : 66 \n"," 3rd Qu.: 0.00 Thursday :80 October : 65 \n"," Max. : 0.00 Friday :72 November : 58 \n"," Saturday :96 (Other) :128 "]},"metadata":{},"output_type":"display_data"}],"source":["glimpse(df_merged_v3)\n","summary(df_merged_v3)"]},{"cell_type":"markdown","id":"df82ac3e","metadata":{"papermill":{"duration":0.022508,"end_time":"2023-01-19T07:57:49.634885","exception":false,"start_time":"2023-01-19T07:57:49.612377","status":"completed"},"tags":[]},"source":["# Analysis\n","### Number of trips by casual riders and annual members"]},{"cell_type":"markdown","id":"c736a757","metadata":{"papermill":{"duration":0.020883,"end_time":"2023-01-19T07:57:49.678594","exception":false,"start_time":"2023-01-19T07:57:49.657711","status":"completed"},"tags":[]},"source":["Now we filtering out wrong data (4016+653=4669), we select variables to begin our analisys and get more insight about dataset.\n","Now this case we will use dataframe : df_merged_v1"]},{"cell_type":"markdown","id":"efaf3911","metadata":{"papermill":{"duration":0.021861,"end_time":"2023-01-19T07:57:49.722754","exception":false,"start_time":"2023-01-19T07:57:49.700893","status":"completed"},"tags":[]},"source":["We can see annual member riding more than casual riders, but their median ride length is lower than casual riders."]},{"cell_type":"code","execution_count":19,"id":"6655db90","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:49.769487Z","iopub.status.busy":"2023-01-19T07:57:49.767699Z","iopub.status.idle":"2023-01-19T07:57:50.280386Z","shell.execute_reply":"2023-01-19T07:57:50.278688Z"},"papermill":{"duration":0.539254,"end_time":"2023-01-19T07:57:50.282771","exception":false,"start_time":"2023-01-19T07:57:49.743517","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 2 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>member_casual</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><chr></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>casual</td><td>2341924</td><td>13.033333</td><td>21.90795</td><td>1439.933</td></tr>\n","\t<tr><td>member</td><td>3385594</td><td> 8.833333</td><td>12.39728</td><td>1439.833</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 2 × 5\n","\\begin{tabular}{lllll}\n"," member\\_casual & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <chr> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t casual & 2341924 & 13.033333 & 21.90795 & 1439.933\\\\\n","\t member & 3385594 & 8.833333 & 12.39728 & 1439.833\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 2 × 5\n","\n","| member_casual <chr> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| casual | 2341924 | 13.033333 | 21.90795 | 1439.933 |\n","| member | 3385594 | 8.833333 | 12.39728 | 1439.833 |\n","\n"],"text/plain":[" member_casual number_trips median(ride_length) avg_ride_length\n","1 casual 2341924 13.033333 21.90795 \n","2 member 3385594 8.833333 12.39728 \n"," max_ride_length\n","1 1439.933 \n","2 1439.833 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," group_by(member_casual) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"917e9579","metadata":{"papermill":{"duration":0.023815,"end_time":"2023-01-19T07:57:50.328506","exception":false,"start_time":"2023-01-19T07:57:50.304691","status":"completed"},"tags":[]},"source":["### Number of trips by rideable type for casual riders and annual members"]},{"cell_type":"markdown","id":"065509db","metadata":{"papermill":{"duration":0.021919,"end_time":"2023-01-19T07:57:50.372713","exception":false,"start_time":"2023-01-19T07:57:50.350794","status":"completed"},"tags":[]},"source":["there are three rideable type, classic bike has more demand in year 2021."]},{"cell_type":"code","execution_count":20,"id":"e2ea1c98","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:50.420511Z","iopub.status.busy":"2023-01-19T07:57:50.418985Z","iopub.status.idle":"2023-01-19T07:57:51.444228Z","shell.execute_reply":"2023-01-19T07:57:51.442376Z"},"papermill":{"duration":1.051912,"end_time":"2023-01-19T07:57:51.446685","exception":false,"start_time":"2023-01-19T07:57:50.394773","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 3 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>rideable_type</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><chr></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>classic_bike </td><td> 895927</td><td>14.55000</td><td>24.48903</td><td>1439.9333</td></tr>\n","\t<tr><td>docked_bike </td><td> 178422</td><td>27.71667</td><td>49.20277</td><td>1439.5667</td></tr>\n","\t<tr><td>electric_bike</td><td>1267575</td><td>10.98333</td><td>16.24164</td><td> 480.4333</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 3 × 5\n","\\begin{tabular}{lllll}\n"," rideable\\_type & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <chr> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t classic\\_bike & 895927 & 14.55000 & 24.48903 & 1439.9333\\\\\n","\t docked\\_bike & 178422 & 27.71667 & 49.20277 & 1439.5667\\\\\n","\t electric\\_bike & 1267575 & 10.98333 & 16.24164 & 480.4333\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 3 × 5\n","\n","| rideable_type <chr> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| classic_bike | 895927 | 14.55000 | 24.48903 | 1439.9333 |\n","| docked_bike | 178422 | 27.71667 | 49.20277 | 1439.5667 |\n","| electric_bike | 1267575 | 10.98333 | 16.24164 | 480.4333 |\n","\n"],"text/plain":[" rideable_type number_trips median(ride_length) avg_ride_length\n","1 classic_bike 895927 14.55000 24.48903 \n","2 docked_bike 178422 27.71667 49.20277 \n","3 electric_bike 1267575 10.98333 16.24164 \n"," max_ride_length\n","1 1439.9333 \n","2 1439.5667 \n","3 480.4333 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," filter(member_casual==\"casual\" ) %>% \n"," group_by(rideable_type ) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"ed56d3ad","metadata":{"papermill":{"duration":0.021624,"end_time":"2023-01-19T07:57:51.489455","exception":false,"start_time":"2023-01-19T07:57:51.467831","status":"completed"},"tags":[]},"source":["### rideable type - annual members"]},{"cell_type":"markdown","id":"9f53a78f","metadata":{"papermill":{"duration":0.023242,"end_time":"2023-01-19T07:57:51.533993","exception":false,"start_time":"2023-01-19T07:57:51.510751","status":"completed"},"tags":[]},"source":["filtering by riders, we can see anual members, don't use docked by by use classic by a median of 10 minutes by year"]},{"cell_type":"code","execution_count":21,"id":"36436b4d","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:51.582221Z","iopub.status.busy":"2023-01-19T07:57:51.58044Z","iopub.status.idle":"2023-01-19T07:57:52.147353Z","shell.execute_reply":"2023-01-19T07:57:52.145496Z"},"papermill":{"duration":0.593184,"end_time":"2023-01-19T07:57:52.149775","exception":false,"start_time":"2023-01-19T07:57:51.556591","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 2 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>rideable_type</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_leng_min</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><chr></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>classic_bike </td><td>1729100</td><td>9.4</td><td>13.29009</td><td>1439.833</td></tr>\n","\t<tr><td>electric_bike</td><td>1656494</td><td>8.3</td><td>11.46534</td><td> 614.400</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 2 × 5\n","\\begin{tabular}{lllll}\n"," rideable\\_type & number\\_trips & median(ride\\_length) & avg\\_ride\\_leng\\_min & max\\_ride\\_length\\\\\n"," <chr> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t classic\\_bike & 1729100 & 9.4 & 13.29009 & 1439.833\\\\\n","\t electric\\_bike & 1656494 & 8.3 & 11.46534 & 614.400\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 2 × 5\n","\n","| rideable_type <chr> | number_trips <int> | median(ride_length) <dbl> | avg_ride_leng_min <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| classic_bike | 1729100 | 9.4 | 13.29009 | 1439.833 |\n","| electric_bike | 1656494 | 8.3 | 11.46534 | 614.400 |\n","\n"],"text/plain":[" rideable_type number_trips median(ride_length) avg_ride_leng_min\n","1 classic_bike 1729100 9.4 13.29009 \n","2 electric_bike 1656494 8.3 11.46534 \n"," max_ride_length\n","1 1439.833 \n","2 614.400 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," filter(member_casual==\"member\") %>% \n"," group_by(rideable_type ) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_leng_min=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"db54f893","metadata":{"papermill":{"duration":0.022245,"end_time":"2023-01-19T07:57:52.194873","exception":false,"start_time":"2023-01-19T07:57:52.172628","status":"completed"},"tags":[]},"source":["## Number of riders by weekday\n","### casual riders\n"]},{"cell_type":"markdown","id":"444ec3af","metadata":{"papermill":{"duration":0.021203,"end_time":"2023-01-19T07:57:52.23832","exception":false,"start_time":"2023-01-19T07:57:52.217117","status":"completed"},"tags":[]},"source":["Now we will focuse in a weekday, and we can see casual riders demands bikes Saturday and Sunday, when the take a bike for about an average 30 minuts by trip"]},{"cell_type":"code","execution_count":22,"id":"bfd0be48","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:52.28554Z","iopub.status.busy":"2023-01-19T07:57:52.283767Z","iopub.status.idle":"2023-01-19T07:57:52.732626Z","shell.execute_reply":"2023-01-19T07:57:52.730629Z"},"papermill":{"duration":0.47552,"end_time":"2023-01-19T07:57:52.735476","exception":false,"start_time":"2023-01-19T07:57:52.259956","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 7 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>day_of_week</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><ord></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>Sunday </td><td>391166</td><td>15.01667</td><td>25.04149</td><td>1439.367</td></tr>\n","\t<tr><td>Monday </td><td>279889</td><td>12.91667</td><td>22.42066</td><td>1435.917</td></tr>\n","\t<tr><td>Tuesday </td><td>263528</td><td>11.58333</td><td>19.69828</td><td>1437.900</td></tr>\n","\t<tr><td>Wednesday</td><td>278877</td><td>11.46667</td><td>18.90769</td><td>1437.583</td></tr>\n","\t<tr><td>Thursday </td><td>313140</td><td>11.80000</td><td>19.56086</td><td>1436.917</td></tr>\n","\t<tr><td>Friday </td><td>339756</td><td>12.48333</td><td>20.51695</td><td>1438.817</td></tr>\n","\t<tr><td>Saturday </td><td>475568</td><td>14.98333</td><td>24.55181</td><td>1439.933</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 7 × 5\n","\\begin{tabular}{lllll}\n"," day\\_of\\_week & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <ord> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t Sunday & 391166 & 15.01667 & 25.04149 & 1439.367\\\\\n","\t Monday & 279889 & 12.91667 & 22.42066 & 1435.917\\\\\n","\t Tuesday & 263528 & 11.58333 & 19.69828 & 1437.900\\\\\n","\t Wednesday & 278877 & 11.46667 & 18.90769 & 1437.583\\\\\n","\t Thursday & 313140 & 11.80000 & 19.56086 & 1436.917\\\\\n","\t Friday & 339756 & 12.48333 & 20.51695 & 1438.817\\\\\n","\t Saturday & 475568 & 14.98333 & 24.55181 & 1439.933\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 7 × 5\n","\n","| day_of_week <ord> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| Sunday | 391166 | 15.01667 | 25.04149 | 1439.367 |\n","| Monday | 279889 | 12.91667 | 22.42066 | 1435.917 |\n","| Tuesday | 263528 | 11.58333 | 19.69828 | 1437.900 |\n","| Wednesday | 278877 | 11.46667 | 18.90769 | 1437.583 |\n","| Thursday | 313140 | 11.80000 | 19.56086 | 1436.917 |\n","| Friday | 339756 | 12.48333 | 20.51695 | 1438.817 |\n","| Saturday | 475568 | 14.98333 | 24.55181 | 1439.933 |\n","\n"],"text/plain":[" day_of_week number_trips median(ride_length) avg_ride_length max_ride_length\n","1 Sunday 391166 15.01667 25.04149 1439.367 \n","2 Monday 279889 12.91667 22.42066 1435.917 \n","3 Tuesday 263528 11.58333 19.69828 1437.900 \n","4 Wednesday 278877 11.46667 18.90769 1437.583 \n","5 Thursday 313140 11.80000 19.56086 1436.917 \n","6 Friday 339756 12.48333 20.51695 1438.817 \n","7 Saturday 475568 14.98333 24.55181 1439.933 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," filter(member_casual==\"casual\") %>% \n"," group_by(day_of_week) %>% \n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n","# median, avg,and max_ride_length in minuts"]},{"cell_type":"markdown","id":"9e6cdf65","metadata":{"papermill":{"duration":0.02182,"end_time":"2023-01-19T07:57:52.779317","exception":false,"start_time":"2023-01-19T07:57:52.757497","status":"completed"},"tags":[]},"source":["\n","### annual member \n"]},{"cell_type":"markdown","id":"ce84dd25","metadata":{"papermill":{"duration":0.023061,"end_time":"2023-01-19T07:57:52.823649","exception":false,"start_time":"2023-01-19T07:57:52.800588","status":"completed"},"tags":[]},"source":["Continues focussed in a weekday, the annual members demand remain stable on labourable days, they take a trip for about 10 minutes, only Saturday and Sunday number of trips fall slightly."]},{"cell_type":"code","execution_count":23,"id":"9156e7ba","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:52.876232Z","iopub.status.busy":"2023-01-19T07:57:52.87434Z","iopub.status.idle":"2023-01-19T07:57:53.968789Z","shell.execute_reply":"2023-01-19T07:57:53.96689Z"},"papermill":{"duration":1.124217,"end_time":"2023-01-19T07:57:53.971572","exception":false,"start_time":"2023-01-19T07:57:52.847355","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 7 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>day_of_week</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><ord></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>Sunday </td><td>390361</td><td>9.550000</td><td>13.68801</td><td>1377.483</td></tr>\n","\t<tr><td>Monday </td><td>476809</td><td>8.466667</td><td>11.98140</td><td>1402.950</td></tr>\n","\t<tr><td>Tuesday </td><td>518509</td><td>8.483333</td><td>11.82175</td><td>1436.333</td></tr>\n","\t<tr><td>Wednesday</td><td>537613</td><td>8.583333</td><td>11.79554</td><td>1418.933</td></tr>\n","\t<tr><td>Thursday </td><td>540193</td><td>8.683333</td><td>12.00374</td><td>1434.750</td></tr>\n","\t<tr><td>Friday </td><td>476776</td><td>8.716667</td><td>12.19848</td><td>1435.467</td></tr>\n","\t<tr><td>Saturday </td><td>445333</td><td>9.833333</td><td>13.79790</td><td>1439.833</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 7 × 5\n","\\begin{tabular}{lllll}\n"," day\\_of\\_week & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <ord> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t Sunday & 390361 & 9.550000 & 13.68801 & 1377.483\\\\\n","\t Monday & 476809 & 8.466667 & 11.98140 & 1402.950\\\\\n","\t Tuesday & 518509 & 8.483333 & 11.82175 & 1436.333\\\\\n","\t Wednesday & 537613 & 8.583333 & 11.79554 & 1418.933\\\\\n","\t Thursday & 540193 & 8.683333 & 12.00374 & 1434.750\\\\\n","\t Friday & 476776 & 8.716667 & 12.19848 & 1435.467\\\\\n","\t Saturday & 445333 & 9.833333 & 13.79790 & 1439.833\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 7 × 5\n","\n","| day_of_week <ord> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| Sunday | 390361 | 9.550000 | 13.68801 | 1377.483 |\n","| Monday | 476809 | 8.466667 | 11.98140 | 1402.950 |\n","| Tuesday | 518509 | 8.483333 | 11.82175 | 1436.333 |\n","| Wednesday | 537613 | 8.583333 | 11.79554 | 1418.933 |\n","| Thursday | 540193 | 8.683333 | 12.00374 | 1434.750 |\n","| Friday | 476776 | 8.716667 | 12.19848 | 1435.467 |\n","| Saturday | 445333 | 9.833333 | 13.79790 | 1439.833 |\n","\n"],"text/plain":[" day_of_week number_trips median(ride_length) avg_ride_length max_ride_length\n","1 Sunday 390361 9.550000 13.68801 1377.483 \n","2 Monday 476809 8.466667 11.98140 1402.950 \n","3 Tuesday 518509 8.483333 11.82175 1436.333 \n","4 Wednesday 537613 8.583333 11.79554 1418.933 \n","5 Thursday 540193 8.683333 12.00374 1434.750 \n","6 Friday 476776 8.716667 12.19848 1435.467 \n","7 Saturday 445333 9.833333 13.79790 1439.833 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% filter(member_casual==\"member\") %>% \n"," group_by(day_of_week) %>% \n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"d44a3655","metadata":{"papermill":{"duration":0.023413,"end_time":"2023-01-19T07:57:54.017685","exception":false,"start_time":"2023-01-19T07:57:53.994272","status":"completed"},"tags":[]},"source":["\n","## Number of trips by month year 2021\n","### casual riders"]},{"cell_type":"markdown","id":"619d2c49","metadata":{"papermill":{"duration":0.021843,"end_time":"2023-01-19T07:57:54.061919","exception":false,"start_time":"2023-01-19T07:57:54.040076","status":"completed"},"tags":[]},"source":["Now we focuss on months, firstly we take casual riders segment and we can see that every month has different demand, in winter demand decrease and increase in summer, July has a highest demand in the year and January the lowest demand, able days, with hot wheter average ride length its about 30 minutes, on the other hand we can see that every month there are casual riders that take a bike until 24 hours."]},{"cell_type":"code","execution_count":24,"id":"bab4754b","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:54.111095Z","iopub.status.busy":"2023-01-19T07:57:54.109339Z","iopub.status.idle":"2023-01-19T07:57:54.540565Z","shell.execute_reply":"2023-01-19T07:57:54.5386Z"},"papermill":{"duration":0.459271,"end_time":"2023-01-19T07:57:54.54316","exception":false,"start_time":"2023-01-19T07:57:54.083889","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 12 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>month_of_year</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><ord></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>January </td><td> 18450</td><td>10.083333</td><td>17.42806</td><td>1379.617</td></tr>\n","\t<tr><td>February </td><td> 21350</td><td>10.866667</td><td>19.57922</td><td>1387.550</td></tr>\n","\t<tr><td>March </td><td> 89647</td><td>14.183333</td><td>24.21397</td><td>1435.367</td></tr>\n","\t<tr><td>April </td><td>126121</td><td>13.800000</td><td>23.23836</td><td>1438.583</td></tr>\n","\t<tr><td>May </td><td>279801</td><td>15.250000</td><td>25.53077</td><td>1437.700</td></tr>\n","\t<tr><td>June </td><td>368141</td><td>14.316667</td><td>23.40488</td><td>1438.817</td></tr>\n","\t<tr><td>July </td><td>405237</td><td>14.016667</td><td>23.17882</td><td>1439.367</td></tr>\n","\t<tr><td>August </td><td>358241</td><td>12.950000</td><td>21.47497</td><td>1436.950</td></tr>\n","\t<tr><td>September</td><td>296133</td><td>12.016667</td><td>20.04681</td><td>1439.567</td></tr>\n","\t<tr><td>October </td><td>208639</td><td>10.783333</td><td>18.47106</td><td>1433.867</td></tr>\n","\t<tr><td>November </td><td>100570</td><td> 9.216667</td><td>15.54523</td><td>1439.933</td></tr>\n","\t<tr><td>December </td><td> 69594</td><td>10.933333</td><td>18.19083</td><td>1431.883</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 12 × 5\n","\\begin{tabular}{lllll}\n"," month\\_of\\_year & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <ord> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t January & 18450 & 10.083333 & 17.42806 & 1379.617\\\\\n","\t February & 21350 & 10.866667 & 19.57922 & 1387.550\\\\\n","\t March & 89647 & 14.183333 & 24.21397 & 1435.367\\\\\n","\t April & 126121 & 13.800000 & 23.23836 & 1438.583\\\\\n","\t May & 279801 & 15.250000 & 25.53077 & 1437.700\\\\\n","\t June & 368141 & 14.316667 & 23.40488 & 1438.817\\\\\n","\t July & 405237 & 14.016667 & 23.17882 & 1439.367\\\\\n","\t August & 358241 & 12.950000 & 21.47497 & 1436.950\\\\\n","\t September & 296133 & 12.016667 & 20.04681 & 1439.567\\\\\n","\t October & 208639 & 10.783333 & 18.47106 & 1433.867\\\\\n","\t November & 100570 & 9.216667 & 15.54523 & 1439.933\\\\\n","\t December & 69594 & 10.933333 & 18.19083 & 1431.883\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 12 × 5\n","\n","| month_of_year <ord> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| January | 18450 | 10.083333 | 17.42806 | 1379.617 |\n","| February | 21350 | 10.866667 | 19.57922 | 1387.550 |\n","| March | 89647 | 14.183333 | 24.21397 | 1435.367 |\n","| April | 126121 | 13.800000 | 23.23836 | 1438.583 |\n","| May | 279801 | 15.250000 | 25.53077 | 1437.700 |\n","| June | 368141 | 14.316667 | 23.40488 | 1438.817 |\n","| July | 405237 | 14.016667 | 23.17882 | 1439.367 |\n","| August | 358241 | 12.950000 | 21.47497 | 1436.950 |\n","| September | 296133 | 12.016667 | 20.04681 | 1439.567 |\n","| October | 208639 | 10.783333 | 18.47106 | 1433.867 |\n","| November | 100570 | 9.216667 | 15.54523 | 1439.933 |\n","| December | 69594 | 10.933333 | 18.19083 | 1431.883 |\n","\n"],"text/plain":[" month_of_year number_trips median(ride_length) avg_ride_length\n","1 January 18450 10.083333 17.42806 \n","2 February 21350 10.866667 19.57922 \n","3 March 89647 14.183333 24.21397 \n","4 April 126121 13.800000 23.23836 \n","5 May 279801 15.250000 25.53077 \n","6 June 368141 14.316667 23.40488 \n","7 July 405237 14.016667 23.17882 \n","8 August 358241 12.950000 21.47497 \n","9 September 296133 12.016667 20.04681 \n","10 October 208639 10.783333 18.47106 \n","11 November 100570 9.216667 15.54523 \n","12 December 69594 10.933333 18.19083 \n"," max_ride_length\n","1 1379.617 \n","2 1387.550 \n","3 1435.367 \n","4 1438.583 \n","5 1437.700 \n","6 1438.817 \n","7 1439.367 \n","8 1436.950 \n","9 1439.567 \n","10 1433.867 \n","11 1439.933 \n","12 1431.883 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," filter(member_casual==\"casual\") %>% \n"," group_by(month_of_year) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"eaecd1c8","metadata":{"papermill":{"duration":0.024339,"end_time":"2023-01-19T07:57:54.590351","exception":false,"start_time":"2023-01-19T07:57:54.566012","status":"completed"},"tags":[]},"source":["\n","### annual members"]},{"cell_type":"markdown","id":"7e5db49b","metadata":{"papermill":{"duration":0.022046,"end_time":"2023-01-19T07:57:54.634532","exception":false,"start_time":"2023-01-19T07:57:54.612486","status":"completed"},"tags":[]},"source":["Now we analyse annual members segment and we can see that every month has different demand,number of trips are higher than casual riders segment, August has a highest demand in the year and January the lowest demand, in spring and summer average ride length its about 10 minutes, on the other hand we can see that every month there are annual members that take a bike until 24 hours."]},{"cell_type":"code","execution_count":25,"id":"6af3e9b5","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:54.683201Z","iopub.status.busy":"2023-01-19T07:57:54.681495Z","iopub.status.idle":"2023-01-19T07:57:55.780385Z","shell.execute_reply":"2023-01-19T07:57:55.777924Z"},"papermill":{"duration":1.127357,"end_time":"2023-01-19T07:57:55.783802","exception":false,"start_time":"2023-01-19T07:57:54.656445","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 12 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>month_of_year</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><ord></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>January </td><td> 85227</td><td> 7.483333</td><td>11.61517</td><td>1380.000</td></tr>\n","\t<tr><td>February </td><td> 94166</td><td> 7.516667</td><td>11.02688</td><td>1373.383</td></tr>\n","\t<tr><td>March </td><td>194115</td><td> 8.033333</td><td>11.69101</td><td>1435.467</td></tr>\n","\t<tr><td>April </td><td>244794</td><td> 7.883333</td><td>11.33549</td><td>1421.200</td></tr>\n","\t<tr><td>May </td><td>354347</td><td> 9.383333</td><td>13.04896</td><td>1393.783</td></tr>\n","\t<tr><td>June </td><td>400020</td><td>10.033333</td><td>13.64379</td><td>1323.533</td></tr>\n","\t<tr><td>July </td><td>417320</td><td> 9.900000</td><td>13.42408</td><td>1361.867</td></tr>\n","\t<tr><td>August </td><td>426880</td><td> 9.600000</td><td>13.07563</td><td>1418.933</td></tr>\n","\t<tr><td>September</td><td>404507</td><td> 9.100000</td><td>12.62430</td><td>1415.450</td></tr>\n","\t<tr><td>October </td><td>349559</td><td> 8.100000</td><td>11.53454</td><td>1436.333</td></tr>\n","\t<tr><td>November </td><td>236891</td><td> 7.633333</td><td>10.85640</td><td>1439.833</td></tr>\n","\t<tr><td>December </td><td>177768</td><td> 7.616667</td><td>10.82142</td><td>1230.867</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 12 × 5\n","\\begin{tabular}{lllll}\n"," month\\_of\\_year & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <ord> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t January & 85227 & 7.483333 & 11.61517 & 1380.000\\\\\n","\t February & 94166 & 7.516667 & 11.02688 & 1373.383\\\\\n","\t March & 194115 & 8.033333 & 11.69101 & 1435.467\\\\\n","\t April & 244794 & 7.883333 & 11.33549 & 1421.200\\\\\n","\t May & 354347 & 9.383333 & 13.04896 & 1393.783\\\\\n","\t June & 400020 & 10.033333 & 13.64379 & 1323.533\\\\\n","\t July & 417320 & 9.900000 & 13.42408 & 1361.867\\\\\n","\t August & 426880 & 9.600000 & 13.07563 & 1418.933\\\\\n","\t September & 404507 & 9.100000 & 12.62430 & 1415.450\\\\\n","\t October & 349559 & 8.100000 & 11.53454 & 1436.333\\\\\n","\t November & 236891 & 7.633333 & 10.85640 & 1439.833\\\\\n","\t December & 177768 & 7.616667 & 10.82142 & 1230.867\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 12 × 5\n","\n","| month_of_year <ord> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| January | 85227 | 7.483333 | 11.61517 | 1380.000 |\n","| February | 94166 | 7.516667 | 11.02688 | 1373.383 |\n","| March | 194115 | 8.033333 | 11.69101 | 1435.467 |\n","| April | 244794 | 7.883333 | 11.33549 | 1421.200 |\n","| May | 354347 | 9.383333 | 13.04896 | 1393.783 |\n","| June | 400020 | 10.033333 | 13.64379 | 1323.533 |\n","| July | 417320 | 9.900000 | 13.42408 | 1361.867 |\n","| August | 426880 | 9.600000 | 13.07563 | 1418.933 |\n","| September | 404507 | 9.100000 | 12.62430 | 1415.450 |\n","| October | 349559 | 8.100000 | 11.53454 | 1436.333 |\n","| November | 236891 | 7.633333 | 10.85640 | 1439.833 |\n","| December | 177768 | 7.616667 | 10.82142 | 1230.867 |\n","\n"],"text/plain":[" month_of_year number_trips median(ride_length) avg_ride_length\n","1 January 85227 7.483333 11.61517 \n","2 February 94166 7.516667 11.02688 \n","3 March 194115 8.033333 11.69101 \n","4 April 244794 7.883333 11.33549 \n","5 May 354347 9.383333 13.04896 \n","6 June 400020 10.033333 13.64379 \n","7 July 417320 9.900000 13.42408 \n","8 August 426880 9.600000 13.07563 \n","9 September 404507 9.100000 12.62430 \n","10 October 349559 8.100000 11.53454 \n","11 November 236891 7.633333 10.85640 \n","12 December 177768 7.616667 10.82142 \n"," max_ride_length\n","1 1380.000 \n","2 1373.383 \n","3 1435.467 \n","4 1421.200 \n","5 1393.783 \n","6 1323.533 \n","7 1361.867 \n","8 1418.933 \n","9 1415.450 \n","10 1436.333 \n","11 1439.833 \n","12 1230.867 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," filter(member_casual==\"member\") %>% \n"," group_by(month_of_year) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"8d6322ca","metadata":{"papermill":{"duration":0.023911,"end_time":"2023-01-19T07:57:55.829983","exception":false,"start_time":"2023-01-19T07:57:55.806072","status":"completed"},"tags":[]},"source":["## casual riders and annual members\n","### rides by month\n"]},{"cell_type":"markdown","id":"d59f587b","metadata":{"papermill":{"duration":0.024143,"end_time":"2023-01-19T07:57:55.88409","exception":false,"start_time":"2023-01-19T07:57:55.859947","status":"completed"},"tags":[]},"source":["The monthly trend shows us that trips are very low in January, 103,677 trips, then reach a peek on July, with 822,557 trips, remain stable in summer"]},{"cell_type":"code","execution_count":26,"id":"e2d19a3d","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:55.935043Z","iopub.status.busy":"2023-01-19T07:57:55.933313Z","iopub.status.idle":"2023-01-19T07:57:56.331763Z","shell.execute_reply":"2023-01-19T07:57:56.329304Z"},"papermill":{"duration":0.428508,"end_time":"2023-01-19T07:57:56.335162","exception":false,"start_time":"2023-01-19T07:57:55.906654","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 12 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>month_of_year</th><th scope=col>number_trips</th><th scope=col>median(ride_length)</th><th scope=col>avg_ride_length</th><th scope=col>max_ride_length</th></tr>\n","\t<tr><th scope=col><ord></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>January </td><td>103677</td><td> 7.866667</td><td>12.64961</td><td>1380.000</td></tr>\n","\t<tr><td>February </td><td>115516</td><td> 8.033333</td><td>12.60755</td><td>1387.550</td></tr>\n","\t<tr><td>March </td><td>283762</td><td> 9.466667</td><td>15.64731</td><td>1435.467</td></tr>\n","\t<tr><td>April </td><td>370915</td><td> 9.450000</td><td>15.38278</td><td>1438.583</td></tr>\n","\t<tr><td>May </td><td>634148</td><td>11.550000</td><td>18.55623</td><td>1437.700</td></tr>\n","\t<tr><td>June </td><td>768161</td><td>11.866667</td><td>18.32179</td><td>1438.817</td></tr>\n","\t<tr><td>July </td><td>822557</td><td>11.683333</td><td>18.22980</td><td>1439.367</td></tr>\n","\t<tr><td>August </td><td>785121</td><td>10.950000</td><td>16.90815</td><td>1436.950</td></tr>\n","\t<tr><td>September</td><td>700640</td><td>10.200000</td><td>15.76150</td><td>1439.567</td></tr>\n","\t<tr><td>October </td><td>558198</td><td> 9.000000</td><td>14.12722</td><td>1436.333</td></tr>\n","\t<tr><td>November </td><td>337461</td><td> 8.050000</td><td>12.25376</td><td>1439.933</td></tr>\n","\t<tr><td>December </td><td>247362</td><td> 8.433333</td><td>12.89477</td><td>1431.883</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 12 × 5\n","\\begin{tabular}{lllll}\n"," month\\_of\\_year & number\\_trips & median(ride\\_length) & avg\\_ride\\_length & max\\_ride\\_length\\\\\n"," <ord> & <int> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t January & 103677 & 7.866667 & 12.64961 & 1380.000\\\\\n","\t February & 115516 & 8.033333 & 12.60755 & 1387.550\\\\\n","\t March & 283762 & 9.466667 & 15.64731 & 1435.467\\\\\n","\t April & 370915 & 9.450000 & 15.38278 & 1438.583\\\\\n","\t May & 634148 & 11.550000 & 18.55623 & 1437.700\\\\\n","\t June & 768161 & 11.866667 & 18.32179 & 1438.817\\\\\n","\t July & 822557 & 11.683333 & 18.22980 & 1439.367\\\\\n","\t August & 785121 & 10.950000 & 16.90815 & 1436.950\\\\\n","\t September & 700640 & 10.200000 & 15.76150 & 1439.567\\\\\n","\t October & 558198 & 9.000000 & 14.12722 & 1436.333\\\\\n","\t November & 337461 & 8.050000 & 12.25376 & 1439.933\\\\\n","\t December & 247362 & 8.433333 & 12.89477 & 1431.883\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 12 × 5\n","\n","| month_of_year <ord> | number_trips <int> | median(ride_length) <dbl> | avg_ride_length <dbl> | max_ride_length <dbl> |\n","|---|---|---|---|---|\n","| January | 103677 | 7.866667 | 12.64961 | 1380.000 |\n","| February | 115516 | 8.033333 | 12.60755 | 1387.550 |\n","| March | 283762 | 9.466667 | 15.64731 | 1435.467 |\n","| April | 370915 | 9.450000 | 15.38278 | 1438.583 |\n","| May | 634148 | 11.550000 | 18.55623 | 1437.700 |\n","| June | 768161 | 11.866667 | 18.32179 | 1438.817 |\n","| July | 822557 | 11.683333 | 18.22980 | 1439.367 |\n","| August | 785121 | 10.950000 | 16.90815 | 1436.950 |\n","| September | 700640 | 10.200000 | 15.76150 | 1439.567 |\n","| October | 558198 | 9.000000 | 14.12722 | 1436.333 |\n","| November | 337461 | 8.050000 | 12.25376 | 1439.933 |\n","| December | 247362 | 8.433333 | 12.89477 | 1431.883 |\n","\n"],"text/plain":[" month_of_year number_trips median(ride_length) avg_ride_length\n","1 January 103677 7.866667 12.64961 \n","2 February 115516 8.033333 12.60755 \n","3 March 283762 9.466667 15.64731 \n","4 April 370915 9.450000 15.38278 \n","5 May 634148 11.550000 18.55623 \n","6 June 768161 11.866667 18.32179 \n","7 July 822557 11.683333 18.22980 \n","8 August 785121 10.950000 16.90815 \n","9 September 700640 10.200000 15.76150 \n","10 October 558198 9.000000 14.12722 \n","11 November 337461 8.050000 12.25376 \n","12 December 247362 8.433333 12.89477 \n"," max_ride_length\n","1 1380.000 \n","2 1387.550 \n","3 1435.467 \n","4 1438.583 \n","5 1437.700 \n","6 1438.817 \n","7 1439.367 \n","8 1436.950 \n","9 1439.567 \n","10 1436.333 \n","11 1439.933 \n","12 1431.883 "]},"metadata":{},"output_type":"display_data"}],"source":["df_merged_v1 %>% \n"," group_by(month_of_year ) %>%\n"," summarise(number_trips=n(), median(ride_length), avg_ride_length=mean(ride_length), max_ride_length=max(ride_length))\n"]},{"cell_type":"markdown","id":"84268a3b","metadata":{"papermill":{"duration":0.023209,"end_time":"2023-01-19T07:57:56.380844","exception":false,"start_time":"2023-01-19T07:57:56.357635","status":"completed"},"tags":[]},"source":["## Locations:\n","### Create dataframe df_merged_v5 that only consists of member_casual, srtat latitude & longitude, end latitude & longitude\n","Note: This will be used in Tableau to create an interactive map and to view the concentration of riders"]},{"cell_type":"code","execution_count":27,"id":"553f1107","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:57:56.431197Z","iopub.status.busy":"2023-01-19T07:57:56.429483Z","iopub.status.idle":"2023-01-19T07:58:34.910745Z","shell.execute_reply":"2023-01-19T07:58:34.908776Z"},"papermill":{"duration":38.510262,"end_time":"2023-01-19T07:58:34.914986","exception":false,"start_time":"2023-01-19T07:57:56.404724","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v5 <- df_merged %>% # filter trips for more than 24 hs\n"," select(member_casual,start_lat, start_lng, end_lat, end_lng) %>%\n"," na_if(\"\") %>% #Replace blanks with NA\n"," na.omit() # Remove NA"]},{"cell_type":"code","execution_count":28,"id":"e115a874","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:34.966995Z","iopub.status.busy":"2023-01-19T07:58:34.965368Z","iopub.status.idle":"2023-01-19T07:58:36.28107Z","shell.execute_reply":"2023-01-19T07:58:36.279142Z"},"papermill":{"duration":1.344124,"end_time":"2023-01-19T07:58:36.28374","exception":false,"start_time":"2023-01-19T07:58:34.939616","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":[" member_casual start_lat start_lng end_lat \n"," Length:5727577 Min. :41.64 Min. :-87.84 Min. : 0.00 \n"," Class :character 1st Qu.:41.88 1st Qu.:-87.66 1st Qu.:41.88 \n"," Mode :character Median :41.90 Median :-87.64 Median :41.90 \n"," Mean :41.90 Mean :-87.65 Mean :41.90 \n"," 3rd Qu.:41.93 3rd Qu.:-87.63 3rd Qu.:41.93 \n"," Max. :45.64 Max. :-73.80 Max. :42.37 \n"," end_lng \n"," Min. :-88.14 \n"," 1st Qu.:-87.66 \n"," Median :-87.64 \n"," Mean :-87.65 \n"," 3rd Qu.:-87.63 \n"," Max. : 0.00 "]},"metadata":{},"output_type":"display_data"}],"source":["summary(df_merged_v5)"]},{"cell_type":"code","execution_count":29,"id":"d1f20fd5","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:36.336869Z","iopub.status.busy":"2023-01-19T07:58:36.335176Z","iopub.status.idle":"2023-01-19T07:58:36.362261Z","shell.execute_reply":"2023-01-19T07:58:36.360379Z"},"papermill":{"duration":0.057181,"end_time":"2023-01-19T07:58:36.36499","exception":false,"start_time":"2023-01-19T07:58:36.307809","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<table class=\"dataframe\">\n","<caption>A tibble: 6 × 5</caption>\n","<thead>\n","\t<tr><th scope=col>member_casual</th><th scope=col>start_lat</th><th scope=col>start_lng</th><th scope=col>end_lat</th><th scope=col>end_lng</th></tr>\n","\t<tr><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n","</thead>\n","<tbody>\n","\t<tr><td>member</td><td>41.85483</td><td>-87.66366</td><td>41.87197</td><td>-87.65097</td></tr>\n","\t<tr><td>casual</td><td>41.89441</td><td>-87.63233</td><td>41.96797</td><td>-87.65000</td></tr>\n","\t<tr><td>member</td><td>41.89936</td><td>-87.64852</td><td>41.93758</td><td>-87.64410</td></tr>\n","\t<tr><td>member</td><td>41.89939</td><td>-87.64854</td><td>41.89488</td><td>-87.63233</td></tr>\n","\t<tr><td>member</td><td>41.89558</td><td>-87.68202</td><td>41.93125</td><td>-87.64434</td></tr>\n","\t<tr><td>member</td><td>41.86038</td><td>-87.62581</td><td>41.87260</td><td>-87.63350</td></tr>\n","</tbody>\n","</table>\n"],"text/latex":["A tibble: 6 × 5\n","\\begin{tabular}{lllll}\n"," member\\_casual & start\\_lat & start\\_lng & end\\_lat & end\\_lng\\\\\n"," <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n","\\hline\n","\t member & 41.85483 & -87.66366 & 41.87197 & -87.65097\\\\\n","\t casual & 41.89441 & -87.63233 & 41.96797 & -87.65000\\\\\n","\t member & 41.89936 & -87.64852 & 41.93758 & -87.64410\\\\\n","\t member & 41.89939 & -87.64854 & 41.89488 & -87.63233\\\\\n","\t member & 41.89558 & -87.68202 & 41.93125 & -87.64434\\\\\n","\t member & 41.86038 & -87.62581 & 41.87260 & -87.63350\\\\\n","\\end{tabular}\n"],"text/markdown":["\n","A tibble: 6 × 5\n","\n","| member_casual <chr> | start_lat <dbl> | start_lng <dbl> | end_lat <dbl> | end_lng <dbl> |\n","|---|---|---|---|---|\n","| member | 41.85483 | -87.66366 | 41.87197 | -87.65097 |\n","| casual | 41.89441 | -87.63233 | 41.96797 | -87.65000 |\n","| member | 41.89936 | -87.64852 | 41.93758 | -87.64410 |\n","| member | 41.89939 | -87.64854 | 41.89488 | -87.63233 |\n","| member | 41.89558 | -87.68202 | 41.93125 | -87.64434 |\n","| member | 41.86038 | -87.62581 | 41.87260 | -87.63350 |\n","\n"],"text/plain":[" member_casual start_lat start_lng end_lat end_lng \n","1 member 41.85483 -87.66366 41.87197 -87.65097\n","2 casual 41.89441 -87.63233 41.96797 -87.65000\n","3 member 41.89936 -87.64852 41.93758 -87.64410\n","4 member 41.89939 -87.64854 41.89488 -87.63233\n","5 member 41.89558 -87.68202 41.93125 -87.64434\n","6 member 41.86038 -87.62581 41.87260 -87.63350"]},"metadata":{},"output_type":"display_data"}],"source":["head(df_merged_v5)"]},{"cell_type":"markdown","id":"6540ff74","metadata":{"papermill":{"duration":0.027735,"end_time":"2023-01-19T07:58:36.416514","exception":false,"start_time":"2023-01-19T07:58:36.388779","status":"completed"},"tags":[]},"source":["# Data Visualization\n","## casual riders and annual members by rideable type"]},{"cell_type":"markdown","id":"7158698b","metadata":{"papermill":{"duration":0.024199,"end_time":"2023-01-19T07:58:36.464024","exception":false,"start_time":"2023-01-19T07:58:36.439825","status":"completed"},"tags":[]},"source":["Annual members trips are higher than casual riders in total year 2021"]},{"cell_type":"code","execution_count":30,"id":"14e0f085","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:36.515295Z","iopub.status.busy":"2023-01-19T07:58:36.513244Z","iopub.status.idle":"2023-01-19T07:58:41.966328Z","shell.execute_reply":"2023-01-19T07:58:41.964299Z"},"papermill":{"duration":5.482027,"end_time":"2023-01-19T07:58:41.969198","exception":false,"start_time":"2023-01-19T07:58:36.487171","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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CAIgh2AAAAiiDYAQAAKIJgBwAAoAiCHQAAgCII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\ndgAAAJL4P1FXrmNjwuDaAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+geom_bar(mapping = aes(x=member_casual,fill=rideable_type))+ \n","labs(title= \"Annual trips\",subtitle = \"total riders\", caption = \"Vignesh Naidu - Google Capstone Project\")\n","\n"]},{"cell_type":"markdown","id":"45c7f1dc","metadata":{"papermill":{"duration":0.025299,"end_time":"2023-01-19T07:58:42.019485","exception":false,"start_time":"2023-01-19T07:58:41.994186","status":"completed"},"tags":[]},"source":["## Weekday trend\n","### casual riders"]},{"cell_type":"markdown","id":"dfc4c6e3","metadata":{"papermill":{"duration":0.025891,"end_time":"2023-01-19T07:58:42.069978","exception":false,"start_time":"2023-01-19T07:58:42.044087","status":"completed"},"tags":[]},"source":["Casual riders has more trips in Saturday an Sunday and rent diferent rideable type of bikes."]},{"cell_type":"code","execution_count":31,"id":"9a0d0af9","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:42.122137Z","iopub.status.busy":"2023-01-19T07:58:42.120391Z","iopub.status.idle":"2023-01-19T07:58:44.583004Z","shell.execute_reply":"2023-01-19T07:58:44.581111Z"},"papermill":{"duration":2.491627,"end_time":"2023-01-19T07:58:44.586076","exception":false,"start_time":"2023-01-19T07:58:42.094449","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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xPEm56wLwi+0EN8On/882cMhIZrfDbiGe+wAAAA0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= aes(x=day_of_week,fill=rideable_type),filter(df_merged_v1,df_merged_v1$member_casual==\"casual\"))+ theme(axis.text.x = element_text(angle = 45))+ \n"," labs(title= \"weekday trend\",subtitle = \"count of trips by casual customers\", caption = \"Vignesh Naidu - Google Capstone Project\")\n"]},{"cell_type":"markdown","id":"2ac5115a","metadata":{"papermill":{"duration":0.024763,"end_time":"2023-01-19T07:58:44.636054","exception":false,"start_time":"2023-01-19T07:58:44.611291","status":"completed"},"tags":[]},"source":["## weekdays trend \n","### annual members"]},{"cell_type":"markdown","id":"79670612","metadata":{"papermill":{"duration":0.025084,"end_time":"2023-01-19T07:58:44.685465","exception":false,"start_time":"2023-01-19T07:58:44.660381","status":"completed"},"tags":[]},"source":["For annual members demand is more stable every weekday, segment prefers classic bike, and they take less electric bikes for their trips."]},{"cell_type":"code","execution_count":32,"id":"24714fd7","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:44.737892Z","iopub.status.busy":"2023-01-19T07:58:44.736135Z","iopub.status.idle":"2023-01-19T07:58:48.253166Z","shell.execute_reply":"2023-01-19T07:58:48.251324Z"},"papermill":{"duration":3.547043,"end_time":"2023-01-19T07:58:48.256685","exception":false,"start_time":"2023-01-19T07:58:44.709642","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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AAAogmAHAACgCIIdAACAIgh2AAAAiiDYAQAAKIJg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\nm9SNU4ZuOPfW0tryw+Pz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bQFQh2AFAEXOxXTmzaSDUYdmOYq75FK/UqdRhKeDOT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\nAAAASUVORK5CYII="},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+geom_bar(mapping = aes(x=day_of_week,fill=rideable_type),filter(df_merged_v1,df_merged_v1$member_casual==\"member\"))+ theme(axis.text.x = element_text(angle = 45))+ \n"," labs(title= \"weeky trend\",subtitle = \"count of trips by anual members customers\", caption = \"Vignesh Naidu - Google Capstone Project\")\n","\n"]},{"cell_type":"markdown","id":"c780af03","metadata":{"papermill":{"duration":0.025653,"end_time":"2023-01-19T07:58:48.309022","exception":false,"start_time":"2023-01-19T07:58:48.283369","status":"completed"},"tags":[]},"source":["## Annual trend - month by month\n","### casual members"]},{"cell_type":"markdown","id":"0826e3ce","metadata":{"papermill":{"duration":0.02703,"end_time":"2023-01-19T07:58:48.361249","exception":false,"start_time":"2023-01-19T07:58:48.334219","status":"completed"},"tags":[]},"source":["Casual Members increase number of trips between May and October, this segment use three rideable type of bikes."]},{"cell_type":"code","execution_count":33,"id":"a46a79f5","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:48.415674Z","iopub.status.busy":"2023-01-19T07:58:48.41383Z","iopub.status.idle":"2023-01-19T07:58:51.248674Z","shell.execute_reply":"2023-01-19T07:58:51.246595Z"},"papermill":{"duration":2.864919,"end_time":"2023-01-19T07:58:51.251648","exception":false,"start_time":"2023-01-19T07:58:48.386729","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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SHu7351IPSJ1kVFJDH66OGohA5PBotI+JmlI986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C1er7eGa3vHH7ultF76rfmOwM3wnHPU+89qhXJoeZ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\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UVRT36LIEh2zN1CqmZiF/XV6sPPSrWf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\nTAQmdgghhBBCJgITO4QQQgghE/E/jJa7CGtJrPoAAAAASUVORK5CYII="},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+geom_bar(mapping = aes(x=month_of_year,fill=rideable_type),filter(df_merged_v1,df_merged_v1$member_casual==\"casual\"))+ theme(axis.text.x = element_text(angle = 45))+ \n"," labs(title= \"Annual trend\",subtitle = \"count of trips by casual riders\", caption = \"Vignesh Naidu - Google Capstone Project\")\n"]},{"cell_type":"markdown","id":"e92ff012","metadata":{"papermill":{"duration":0.026923,"end_time":"2023-01-19T07:58:51.305558","exception":false,"start_time":"2023-01-19T07:58:51.278635","status":"completed"},"tags":[]},"source":["### annual members"]},{"cell_type":"markdown","id":"2aef645c","metadata":{"papermill":{"duration":0.026709,"end_time":"2023-01-19T07:58:51.35856","exception":false,"start_time":"2023-01-19T07:58:51.331851","status":"completed"},"tags":[]},"source":["Annual Members increase number of trips between June and October, this segment use only classic and electric rideable type of bikes."]},{"cell_type":"code","execution_count":34,"id":"9f1b1fe7","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:51.416837Z","iopub.status.busy":"2023-01-19T07:58:51.414856Z","iopub.status.idle":"2023-01-19T07:58:54.776223Z","shell.execute_reply":"2023-01-19T07:58:54.774237Z"},"papermill":{"duration":3.392756,"end_time":"2023-01-19T07:58:54.778668","exception":false,"start_time":"2023-01-19T07:58:51.385912","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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nO36et+XA5I2zp8zalDS8rm9NRERifg9wjOozYCe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\n+Iudoo4E3RR23lbHhhAiiZPWnr+4Fd9c9dZfo76vywCfYt7sbBcX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cEr5p7P7tu/as//ynWdKywqOVkI9ed9XKSW7eSZcaZMefuBWoirb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\nAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+geom_bar(mapping = aes(x=day_of_week,fill=rideable_type),filter(df_merged_v1,df_merged_v1$member_casual==\"member\"))+ theme(axis.text.x = element_text(angle = 45))+ \n"," labs(title= \"Annual trend\",subtitle = \"count of trips by members\", caption = \"Vignesh Naidu - Google Capstone Project\")\n"]},{"cell_type":"markdown","id":"b1ecaa10","metadata":{"papermill":{"duration":0.028076,"end_time":"2023-01-19T07:58:54.834366","exception":false,"start_time":"2023-01-19T07:58:54.80629","status":"completed"},"tags":[]},"source":["## Annual \n","### casual riders and annual members and rideable type\n","### weekday\n","\n"]},{"cell_type":"markdown","id":"d102d53b","metadata":{"papermill":{"duration":0.027304,"end_time":"2023-01-19T07:58:54.889081","exception":false,"start_time":"2023-01-19T07:58:54.861777","status":"completed"},"tags":[]},"source":["Casual riders has more number of trips on Saturday and Sunday. Annual members has a more stable level of trips every weekday"]},{"cell_type":"code","execution_count":35,"id":"71927beb","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:58:54.948057Z","iopub.status.busy":"2023-01-19T07:58:54.946274Z","iopub.status.idle":"2023-01-19T07:59:02.73293Z","shell.execute_reply":"2023-01-19T07:59:02.730891Z"},"papermill":{"duration":7.819642,"end_time":"2023-01-19T07:59:02.73613","exception":false,"start_time":"2023-01-19T07:58:54.916488","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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W1b011KRIH+3dbsm/SvxljDs1V11dqtPz77uG1x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\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idF7xkYSezUBSlLRnevIhVzRbLaTXtHXC38+3di1fg61R04LLads1vgoBl3cwrf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\n/wNGC+t31WJCogAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+geom_bar(mapping = aes(x=member_casual,fill=rideable_type))+ \n","facet_wrap(~day_of_week)+ labs(title= \"Annual trips\",subtitle = \"count of trips by weekday\", caption = \"Vignesh Naidu - Google Capstone Project\")\n"]},{"cell_type":"markdown","id":"3f4c6ce6","metadata":{"papermill":{"duration":0.030314,"end_time":"2023-01-19T07:59:02.794851","exception":false,"start_time":"2023-01-19T07:59:02.764537","status":"completed"},"tags":[]},"source":["## month by casual riders and annual members and rideable type\n","### by weekday"]},{"cell_type":"markdown","id":"abdd1f3a","metadata":{"papermill":{"duration":0.029121,"end_time":"2023-01-19T07:59:02.852949","exception":false,"start_time":"2023-01-19T07:59:02.823828","status":"completed"},"tags":[]},"source":["Annual members has a stable demand every month, only January a Februry has lower demand, but Casual riders has more number of trips on June, July, August and September. The day with highest demand in the year is Saturday on July, the second best is Sunday on August."]},{"cell_type":"code","execution_count":36,"id":"2b529ed5","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:59:02.91366Z","iopub.status.busy":"2023-01-19T07:59:02.911856Z","iopub.status.idle":"2023-01-19T07:59:11.584826Z","shell.execute_reply":"2023-01-19T07:59:11.582844Z"},"papermill":{"duration":8.707522,"end_time":"2023-01-19T07:59:11.588787","exception":false,"start_time":"2023-01-19T07:59:02.881265","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAABmJLR0QA/wD/AP+gvaeTAAAg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ZTNcUKF0JXmcvGTDhg0Lt8+Czz8yWBBR7Rve+THe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qUm+1dREgvdWluz8EjHh1CS4PD/PwcmVcGriMlM9igWDSTS1Zs6OBfl5Do6ORFNz\nsLUSScQWVvZEUxN/q6mVF+VYWbKLyyREU5OJZeX5eRybRkRTk1Z8MNWjGFPpRFPzae5ekJ/H\nseYQTc3VwVYkERsZmxNNTSRD/3FqoEGDmyeIsbe3DwwMbNWqlampaeUkEsXDP4ScduKvrecb\nBwTZGdBc/T07t2qaeWzejnchy0a0QghlXzr6tn14oBu3b88OZmZmCJGMKKjdgEGWLCoiUexc\nvArvHI9LSGocENTEwppCQldW/v3OJ6JHS/OyzNunT9/oM2lK1w7t2Gw2QsjI3sWCRUUIYemH\nuN/nLV1/VGZiVlbw+so/t+Rrp1Xc3bt/781HDwsyM538+wwJ7ShfsBosEyTsPypjmOHX1/63\n8Lc3+l4TJo9x/HA94ZRw0lhe+/Z+7du2qfmzQgihVau2sIfNjnQzRQhRGMbc4JDcf/bsPpzc\nOCConUfT1q1bB3dob2FhUe2yZZU3avUIbtO8dQd+1zb5ma8zsj849B7q51T9h24erJ+y8Zbp\n/zau6N/rh9Ce/iVPz+3YdbFxQFBz367ubEq5VL9r/0kDg5toTs3Ozi4gIMDX1/fjrtAutQz/\nPh1cW0T1DDI3NyeU2vGT1/tMmtJVvisQUgRXa2o5r984+fcZ2ruzYkEtU5sQ+0Pbtu382rW1\ntLQklFpbj6ZcbqvgQH9LS0tCqRVmvXmbVewcMcK/qf3XSM3GxsbPz69NmzYfd4V2qWUG9gtw\n8YnsGWxpaUkotcPHr/eZNKVboJ+VlRWh1DJevHTy7zM0vItiQS1TGx/T09e3TYC/n8oFNafW\nokXLoMAADodDKLWinLdvMovdokb5N2v0NVLjcDjt27dv167dxy3SLrWsjtEBTb3Ce3bkcDiE\nUks4cr3PpCndgwM4HA6h1F6mPHPy7zMsIsTa2ppQamNH92jVqnVAgL/KBTWl5uni7e0TFBhg\nY2NDKLXi3IzXmUWe/WMCXKrv/y+SGtA19X2Rn44oynm9alx/5StYj43sN25TKsZY8O7WiMj+\nr4SSmkvJJMW7V80I7T1o/MQJkb34isVf7p/F5/eeNGtWZGiv5YeeqlzjvbWxEdEL0oorMMai\nD282zBletbjs/pn9G9b9nXgnU3ObHx9cHRXWa+i4WUeTn8unPN8+od/oBM1LzeobPik+TXlK\n1pVZkcNGh0WOU2xjrZct33h0enh46ITfjysuW1bcFPJx54iFGONpUb1nnM34OFEm/H14lPJ+\n/hyQGqQGqWFITQ1IDTRQUNh9rvrqeuryfpZVJJ3Ys3Thz/OXrTpzR76sDGMsLZdijAtTzg4K\n67X1UaHKJWveGqZ4TMWtYcRv1Kp5x+6e6UO3PyrUppesA0gNUoPUFM8BqVXNBKkBXQCF3ef6\nr7ueKkTfzzKpcPNPQyIGTdsSv3/zqpl8Pv/385kYY2H+1ZGR0dNnTAjvFbbqwH0VC0qKti0e\nw+eH8ni8Xy9kSUV5S0dGRQ6emXjvuUBQnHolYXDv0F1pxTUWU33Df/SUQ/LHP73h/5Pb72WS\nYrEoLyxyYq5IqlUvSRykBqlBahhSUwKpAZ0Bhd3n+i+7HsLHhUrenJgd3n9OZoUEY3xp88yo\nkUvfCCVZWUIsE984kfD35l3JT/JULnjhl5FRo1c8zBQUZb7DGEuE7wuLs3b9MrkXn8/j8XqF\nDdx84pHKBQmdfFEM44QxPjNv6ITfDg2NPY8x1raXJAhSg9QgNQypKYHUgM6Awu5zfc434YS6\nnjoeF1Y5MLLv1MPpWKnPKni4LnKgpjF7McZSUV5vPv9wXhnGWFKWEbd2Xjif3ys0evfd/PLC\ntw8eP8v9IFLV1LqffMEYlxfen9ovPDQ8JqNCIm+DNr0kIZCaqqZCapUgNUjtO08NNGhQ2H0u\nol3PgTULhvYbMHHeupdlYkJdT52PC+VOxfaPXfdQ0WdhjMty43k8XrFE9fDj8itzpaLc8F78\nebuOnz+6LaZP2Jj5626nPj+0dHifoVs0rKtuJ19k4sJtK7cVimW4qucaOG2jonkaesk6gNRq\ngtQUIDVIDX/fqYEGDQo7wure9cgkaycNiJ68/MDhhAVj+vYZvjBNINbyYKtux4XKcm+u4vND\nIwYueFPVw+ZcWxzWZ7K6X5WRX5mLtbg1rLwgRSj75GnqdvJFIkxbMKxP9NR1yj3XoJmb1HWs\nhEBqkBqkpgCp1fSdpwZ0CRR2BNW165FJigX5x3pHTSuSyDDGUlHObxP6yxfHWhxs1eG4sFr3\nirHs5B/T+L36/Ln/bGpqyo1T24eHh65U6lyUm6q4MlcxDau9NUy2cnDkyKUnlZ+hzidfxIKv\n03NBapAapPYpSA1SA7oKCjsCPqfrOTNvaMyin/vHnFFMqbZ4rYgNqqSqe8UYP/ln65TR0Twe\nL2pgbPylZ+qaqrgyV0HDrWGCrOTTz4pkkkLFUSnRky9ipU5JZc+1MilH887RAFKD1CC1qpZC\nah/bCqkBXQWFHQGf0/VUXe46KqtCcZD3cfFCsarjLUKDKik9gebuFWMsEFRo0dTKK3OrnlTT\nrWEysfDknKEjFu6R91yErow5OntI7K+HP+25nk+MDFP0XOLSXA2trRWkBqlhSA1jDKkploPU\ngE6DnxQjwM67yZXtCbkl6UGhPQ0oJIQQiaLv2zkg8+LubUdTO/fooEcmqVuWqscJ7Ohy78zp\nI3c+dOrYkkH+uDgi2bRyr/7TNFhWvmVOzK5rH1q0bWkofLFr+7Y8TlAbB8PygqSxw+dcvXNp\ne3yi78D5YwKbIITEgkczRi0zatvBzoCGEDq7cMzvd0ul4qD+POdqjeR29jOlkWk0Fb8biCXv\nd6ze17i1t4G+vKmnjt4uUTQVkci2Td1btPSyt2DVXDb+p1G5If2LT+04kioL6eBJo+j7dQsS\np13cuiN+f8LBxBuvu4z4ebBv5Q9PycS5v09fhLzb2zJL31TQPb05Zzf/efqtXtd2rvJVkWlm\nbvRbh8/fPH+3qEfnVnSGPuGolEBqkBqC1CA1SA18P+q7smwAat6LNGj6RuVrGqSinL2H7qlc\n9O7pA+tWr9iw48DDdwJM5JIIYoMqVR/rkvixL5Erc4W5t2dN+p/8aFX5chNh7q2JUb0VR6VY\n3cmXqtaumhw9+e9HGGNBVnJsZKjyUWn6oamDpl04clTFcKBagtSqzQmpqQOpQWr4+0sN6DAo\n7GpXt3uRZFLB5rkje/cdu/zXVdNjo3uFDth69rn2ixMeVElVz6V191pJmytzhbm3J0X1Hr5g\nl1AqwzUuN6nZc6kkrcgaGR7K54dfzqy8C6yy5/rfgSKxrCj92rjI0Dkn32h4hlpBapAapIYh\ntY/rg9TA9wIKO63U4V6kl/E/RkQveFd5eaz0atwyPr/XzkeFisU1X+5KdFAlXGMcc227VyJX\n5lbrs/DHY9+Pl5to03OVvj0187cj1X4tUZCVPC6qN58fxueHrth9Qc1hMwGQmvxRSA1Sg9Qw\npAa+G1DYqUX0/Txvzkbl7/A3Dukz5UC68hOeWzQ8ctCaymdTdbmr8hkNQoMqqRvHXJueS/sr\nc2v2WRhjiUzFUJnynuv326p/gUep3dV/CVtSlnHl/Ll/XxXVsqCGp4TUIDVITUW7ITVIDXwv\noLBTi9C9SMsHRvJ4PMVDGOO44VEjl99WfsKSjHV8fqiGg8NPz2hoO6gS1jSOee3HvjWv3sAY\npx+eyu/Fj566TtFDqeyzMpN3Dh63UiiV1ewfRcXZKlYmq0jcv2nWzFlr912WVk1R9FzC/Nta\njmugAaQGqWFIDUNqn4DUwHcF7oqtJBPnn0vYFbfv4JXbT2RslybmTHt3K+3vRbIW3TibIuMI\nUg7dfB/csZUemWRu/Dw+4aBlu26OJnT5PIUPjp25b9k3omPlGkUyEuWTe8TIVFO/LtwnB//e\nnZQf3LFV83YhPmxB4olD8QeP335e0nPM3JHBTVQ2ftWqLexhsyPdTBFCFIYxNzgk9589uw8n\nNw4IamJh37FbsJ+TpboNpxnYBwc0rralxan/XCsbHNGhmYcrRz7bswN/7n+QxeUP7dDMXD4l\n69quCb/sbxURE+BqKb+j7fqeLQl3SzoFt2CQSRSGQbUVYVnZ5rmxcUn5Ph5Wt4/vv5hjHNKm\nKYlE8fAPIaedWL/xYOKJc8X2Hds2rr6gBpAapAapQWqQGgCfqO/K8ptQXnBvztDIqJjpG7Zs\nXvbjkKkbLsuna38vklSUMzI89LeL/0yM6l15QCmTxM0f0Sts4LaTye9ysx5dThgRHrr6YpZ8\nfmHu7ckDhp7LKcO1ndHAtQ2qhLUbx1yY/6/iYFEmKTqwZv7gvgMmL9z4plyCtbsy98KmGXx+\nrzWnnmOMM5N3RvTirzzy+JPdqPHYN3XbpMghSzKEEoxxytpYHo838feTiqPS5NP7Tt95rXkz\nq4HUIDVITWUjIbVqtPylBw3BNcTUwHcLCjsskxTOiY6IWZEgqPwKXap8KkD7e5FexE/pM3RN\nWe4tpZ5LfHHPioERoTweLzRy+K7EylHFq31pX+sZjVrVOo65tEK6bniU4kTA8QXD+sbMij+w\nf+7IqKhRyxSfN7VemSvvuX7ZvrZmn1XZcvVDZc7rFz47OQdjLCpNie079Maj08PDQyf8fjwv\n88HTUsI/XA2pQWqQmrpGQmrVaJMaxlhzcA0rNfA9g8IOvzk5M7zvLIFSZyWTlN44s2/t6lXb\n9ycKpDIt70WSH5JuSn0vVO65MJbJhBnv8hRL1bwUQ8trOD7x6ZDrmscxlx/7nki9Jr/Io7Ds\nXXjUdPmQ65KKzBWx/RSfN9pcmXth0wwejzdk1mkN80hFef/E/7Vo7qzFK/64kPJePnH7qL5T\n96TJZMI1sf3+vJqFMX65bzKPx+Pz+auvqLrcRCNIDVKD1DCk9uVSO5dTprgSTl1wDSg18D2D\na+zQkzXb/zUfGhliI/83/eaJ5fMXHLqcIiaJ71w6k/iUFcHr3LWnfyM7x579YsMCXMkkVJ53\nZ/bUVVnIwMnJjl41ljqJou8mu7V5V2bffr2CAp2S9+44eLMwuGMrPQrNyJAln6s8785P45e+\nFMpG/Ty7qVHllSVaXsOhUHPI9XybHlPG8FSOYy5fY4lrz7Fh3Tt1cr2+Z0vCrSwyxX8A3xkh\nRKYYtu3S/vXZndtPpLUJaWfCNG7k4GhpwtSwu5q07GwteHDh6ukC01a+zmY1Z6govL9wwsxz\nGdjD07ki/cb9EtuQVo0RQq7+np1bNc08Nm/Hu5BlI1ohhLIvHS0btSCmR1++jwWkBqlBapBa\nPaYW060Fk1V1JZya4Nr16Nrc2alBpAa+a/VdWda/1PWxoZHjLt5Le5t6Y+38WD6/15zf418V\nVWCM8+5t4PP5j2vciBQ/MZrH4/Xu2zs8etzWw5cVl2XID0m3PivCVTe0q7ptavvSkVHKd7DL\naTijIcy9vb/qqA5rGHK9xjjm6gdVGqH8K4eKQ9Ki2kZ7V1C+mkSZ5rM2GONjI/uN25SKMRa8\nuzUisr/yBS6EQGqQGqQGqSkW/FKp4S8UXL2nBr5nUNhhiTBt5qAIHo/H4/EGTVpy5WmB0kPp\nPB7v7PvyGou8WToyKnzA1Lgj8bPG9Ovdb8ymAxcKRVKM8fM9U6JGbpDPJsy9tXb/ncq/8z72\nIPLFVfZcKs9oxMX0C+8/50NVh6LlkOvyPovPDz3/7pO11BxUCWMsqcjco3HI9ZpU9ly1nrV5\nuX8Wn9970qxZkaG9lh96SmiNyiA1DKlBapAaxvhLp4a/UHD1mxr4nkFhhzHG0orcaxcSb9x/\nVe3gKf3kvLDISSrHB6/suaJnPioqe3T5yNyxA8KiRm3Ydy63NHNkeOj2F8XV5hfm3lq4PF5x\nXKiu51J5DUfJ6yO9+Pype1Ll/2oz5LrmY1/tf5BRswubZizYc0t5SvL46MHTP45N9erG8enD\nIvm9+oybND6czx/682GMZffP7N+w7u/EO5mfs2oMqdUVpFY5HVLT2veWGv5CwdVvauC7BYVd\nNR/fw2+S46NC+X+qH7hS0XM9Lq7AWJqSdHz+hIGhkSNmDIroO2pzrWtS13OplPRbDD+0//0S\nEdZiyHVtjn2/1OdNNXU4a/MlQGqfBVJTBqlp9L2khr9OcPWUGvi+wM0TymRxi0bv/bdY+iHj\n0vGdv+1MbBu9cFJXJ3Vzk6nG7bu0eXVu354j9z07B7o5uwV1D+U2oj1/+czcjx/Y3EbzyuSL\np184vP9QcuOAIDsDmvKj5QX/ZmG2MZ0s/9e2ZetbRw6ff8QK6+yqb9vWJP/ujdTnUqaZEUX0\n8vbJFWvPc0cvbu9oJJ9ZIsi8l22z5Ke+TDJJ3VoUA2Y+tw1uZ6+PvhBTT7eHpw8fOXXixNnL\nhSz38bMWDenR2oRJQQgxTCzi959050c4MqlfanUIIUjt80FqkJp2vqPU0NcJrj5SA9+f+q4s\nvy3pV+Nnjh8WFRU1fuaiM3ffabPIp0elhKk8XpRUZEyK6h0aPmTd/gslVQeLebfW8Xi87VXX\nCD/5Z+uU0dE8Hi9qYGz8pWd1WAvWOKhSndXhrM1ngtQ+H6QGqWnje0sNf4Xg/vvUwPcGCrsv\n4Iv0XIv2f3Kd7JbY/qHhw0ZHhUUOnppw8bH8vb5v+qDwAfOVL7ytdcj1amvR8rzGF0LgrM1/\nD1JTA1LDGFL7oiA1Nb7p1EDDBadivwDFGYen1DYBriZ1WNy/e5dAT1vlia6tjQ4cvhj2yyou\n+U389q0nbr9m27t05nud2rfnLrV1Z/fK4ZFoNAqhRqo7r/EVEDtr89+D1FSB1CC1Lw9SU+Vb\nTw00XFDYfRnyrifAvfpQmVoikZkIIZlISqJUXjJC03eyfXNh29EPM3+ezOvcsuR58pYtW+9l\nGfK88ZkDx5vzeZZad1jKjZT3XGk0bgd3dt2aqjWSEVV463Li+Su3S6g20WPnDOrU9CuvkTBI\nrQZIDVL7KiC1GhpAaqCBImGM67sNACGEyvPvzJrwq1XIoJiB3YwpJISQtPz5sP7TPKf+Nc3P\nCiFU8Czp781/Jz0twlhi7j16y6KedVsRlhSTqMZfsunfMUitIYLUGiJIDQAtwTd23woSwiUF\n6adPHD9x9p6+nWtTG2Myld2ccm/r1ishYSF6ZBKL3civC5/biPn2eYplm6613lOmdkVkTb+H\nAwiB1BoiSK0hgtQA0BJ8Y/dtKUq7tnH9xqvPCpz9eo+PHdCEVT47ekh59yWrBrl+nAmLEIle\nf20E1UFqDRGk1hBBagDUCr6x+7Ywzez9Qnp5WpJvnjqUcPBsmaHjwC60HRt3N+fxLelVl4yQ\nCF87Ar4qSK0hgtQaIkgNgFrBN3bfKJko/8TOTduPXdd3bMd6e6PCI2bL/K713ShQC0itIYLU\nGiJIDQB1oLD7pgky7v+9Yf25h1kkEmn2rv2+hnB+oQGA1BoiSK0hgtQAqAkKuwbgedLh05n2\n4yO59d0QQACk1hBBag0RpAaAMijsAAAAAAB0BLm+GwAAAAAAAL4MKOwAAAAAAHQEFHYAAAAA\nADoCCjsAAAAAAB0BhR0AAAAAgI6Awg4AAAAAQEdAYQcAAAAAoCOgsAMAAAAA0BFQ2AEAAAAA\n6Ago7AAAAAAAdAQUdgAAAAAAOgIKOwAAAAAAHQGFHQAAAACAjoDCDgAAAABAR0BhBwAAAACg\nI6CwAwAAAADQEVDYAQAAAADoCCjsAAAAAAB0BBR2AAAAAAA6Ago7AAAAAAAdAYUdAAAAAICO\ngMIOAAAAAEBHQGEHAAAAAKAjoLADAACqJp7RAAAWRklEQVQAANARUNgBAAAAAOgIKOwAAAAA\nAHQEFHYAAAAAADoCCjsAAAAAAB0BhR0AAAAAgI6Awg4AAAAAQEdAYQcAAAAAoCOgsAMAAAAA\n0BFQ2AEAAAAA6Ago7AAAAAAAdAQUdgDosg+v55BIpAFPC+u3GXvn9LO3MDB3Hla/zVDmoU+3\naXeqvlsBAABfGLW+GwAA0HGC7E19l8Q3Cf3x14hu9d0WAADQcVDYAQC+LmHeCYTQyD9+HmJv\nWN9tAQAAHQenYgEAXxeWyRBCDDKpvhsCAAC6Dwo7AHTKrfjlnVs5GzLpbOumfSeuzhXJlB9N\nOfpnaFBLc2N9Kl3P2slr8PQ/CiUYIZSyzo9EIq15V6o0r6yTqZ6BtbZXxeXc2DegezsLEwO6\nvnGz1p0Xbrson37Yw8LS5xhC6Ec7Q32LyFqfZ4mzKZVhUybD8n/fnu5BIpGM7KcrZrjUvymJ\nRNqWU4YQKn19eVLfro0sTBj6Zq4tOi7466Ts02erdYZKWLSyrxuZwpgal6Ll9gIAwDcKAwB0\nxYO1UQghJrvF0HEzp8VEN9OnmXo7I4T6pxZgjN8cjyWTSCauQT/OXrB0wdzoEA+EUNMBxzHG\n5e8TySSSx4TriqcqfrUUIeS/PkWb9ebe+p8RlUzTbzY4dvqCGeM7u5oghDrPuYgxzrl6fu+6\ntgihkbsOnT1/r9anerymLUJoyesP8n+Pd7FHCJEprCyRVD5lgKU+w8gPY1z67pCTHo3GajJk\n7I+L582IDHRECPkM2qp4Ks0zuLNo1m1PYoyxTLx6gAeJTJu485E2GwsAAN8yKOwA0BES4XNL\nOoVlxXv0QSSfUpqR6MKiKQq77R7mVGaj1+USxSKTbQ312Dz535PsDPXMeigeOhPlRCIzbpeI\ntFizrI8li8Zyu5wlkP8vFedNbWFOIjMvF1dgjHPv8xBCv2aUaLMVgpwdCCHu0vvyf0NMmVZB\nbRFCk54WYozFgn8pJJJD6BmM8XwPNo3llpwvVCx7aIoPQmhxWpH8X80zVBZ2MvHawc1JJNr4\n7f9q0zwAAPjGQWEHgI7ISopACIWefqM88eaPzRWFneB9QUFhqeIhmbQ01saAadJJ/u+T9X4I\noc1ZpfKH3Fg0c69V2qy3LC8BIeQ58bryxMKUHxFCwfvSMMHCDmPsZ8wwcVyMMa74cA0hNOjm\nY0MKufnUmxjjnJsDEEIj7uaKBY8oJJJ8okJF0SWEUPMfb2KMa53BnUXjtD22fpgPQqhJr8Na\ntg0AAL5xcI0dADoi90o6QqhvS3PliU5DWyj+ZpmYlb248tuiWSMGRnUJbGPPZq/L/HhRnWO/\nRWQSac3vqQih/AfTU8rEIaujtFlv+fvTCCHHQQ7KEw3sByGEsv7JrsOGzA2y/vDm10KJrPDh\nShKJ8pNns8l2hq/3HUQIPVl1g0w1WuTBLi88JcX435W+JCUMk0CEUPG/xQihWmdACOXdjR67\n45WvCePt6djkD6I6NBUAAL41MNwJADqCTCUjhKrde0pmmir+PjC1U+RvF2xbdOQFt/3Br9vU\nhd7vRnUZl1v5KMM4eJKdwYa/l6Nl+89NPkJlNPojgKPdmnHNSSQSFSGEJSoeqlWLucGyI1t/\nSf/QbfVdlkU/Vz1qr4EOi5etyRUv2XQ+08RpIYdOLiXTEULNp2/5X0ebaoszjH0QQqjWGRDC\nMtLSk/8OM9li6TsvKmLj23/G1aG1AADwbanvrwwBAF9G9o0ohFDvs2+VJ6ZsbI8Q6p9aUPHh\nGoVEatTzL+VHtzQzU5yKxRin/OWPENqZ8dyCRmnCP6rlesvy9iGEmk+5oTzx/dOfEEIddj7H\nxE/FSireGlDIzX+8OcBS36nPBYxx0cvZCKHx9y+TSaSALU8xxmLhcwqJ5DYqSXlBcVlKfHz8\nxSyBNjO4s2icNsfl0z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hyXC9J8ig62N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\nAwAAAADQEf8HO0LTxzDey6oAAAAASUVORK5CYII="},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["ggplot(df_merged_v1)+ geom_bar(mapping = aes(x=day_of_week,fill=member_casual))+ facet_wrap(~month_of_year)+ theme(axis.text.x = element_text(angle = 45))+ \n"," labs(title= \"Total 2021 by month\",subtitle = \"trips by casual riders and annual memebrs\", caption = \"Vignesh Naidu - Google Capstone Project\")\n"]},{"cell_type":"markdown","id":"efedefd5","metadata":{"papermill":{"duration":0.03139,"end_time":"2023-01-19T07:59:11.651137","exception":false,"start_time":"2023-01-19T07:59:11.619747","status":"completed"},"tags":[]},"source":["# Summary \n","1. Annual members and casual riders use differ ways the bikes, segment of annual members show annual demand more stable, cassual rideres segment show a higher demand on summer, with the highest demand on Saturday of July and Sunday of August. Only casual riders segment uses docked bikes.\n","2. Demand is very hight in summer season, casual riders should assure a bike in this month of very high demand \n","3. During the Saturday on July and the Sunday on August there are a good opportunity to develop a digital campaign to become since casual riders to annual members, these days have high demand of bikes."]},{"cell_type":"markdown","id":"75ad47fa","metadata":{"papermill":{"duration":0.030316,"end_time":"2023-01-19T07:59:11.712556","exception":false,"start_time":"2023-01-19T07:59:11.68224","status":"completed"},"tags":[]},"source":["# Export \n","## clean data"]},{"cell_type":"markdown","id":"716d35bf","metadata":{"papermill":{"duration":0.031368,"end_time":"2023-01-19T07:59:11.773003","exception":false,"start_time":"2023-01-19T07:59:11.741635","status":"completed"},"tags":[]},"source":["Create a file to prepare a presentation for our stakeholders. \n","To download files add your path. (\"C:\\users\\Your_USERNAME\\desktop..\") / you can simply download your file from the output section"]},{"cell_type":"code","execution_count":37,"id":"233c2ebe","metadata":{"execution":{"iopub.execute_input":"2023-01-19T07:59:11.837205Z","iopub.status.busy":"2023-01-19T07:59:11.835471Z","iopub.status.idle":"2023-01-19T08:00:58.827811Z","shell.execute_reply":"2023-01-19T08:00:58.825656Z"},"papermill":{"duration":107.026926,"end_time":"2023-01-19T08:00:58.830448","exception":false,"start_time":"2023-01-19T07:59:11.803522","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v1 %>% \n"," write.csv(\"df_mergedv1.csv\")\n","# add path"]},{"cell_type":"code","execution_count":38,"id":"7cb612d6","metadata":{"execution":{"iopub.execute_input":"2023-01-19T08:00:58.895868Z","iopub.status.busy":"2023-01-19T08:00:58.893949Z","iopub.status.idle":"2023-01-19T08:01:42.875375Z","shell.execute_reply":"2023-01-19T08:01:42.873417Z"},"papermill":{"duration":44.018229,"end_time":"2023-01-19T08:01:42.878407","exception":false,"start_time":"2023-01-19T08:00:58.860178","status":"completed"},"tags":[]},"outputs":[],"source":["df_merged_v5 %>% \n"," write.csv(\"df_mergedv5.csv\")\n","# add path"]},{"cell_type":"code","execution_count":39,"id":"1c5f0502","metadata":{"execution":{"iopub.execute_input":"2023-01-19T08:01:42.943993Z","iopub.status.busy":"2023-01-19T08:01:42.941936Z","iopub.status.idle":"2023-01-19T08:01:42.955931Z","shell.execute_reply":"2023-01-19T08:01:42.954144Z"},"papermill":{"duration":0.048683,"end_time":"2023-01-19T08:01:42.958405","exception":false,"start_time":"2023-01-19T08:01:42.909722","status":"completed"},"tags":[]},"outputs":[],"source":["# Share\n","## Visualize presentation in Tableau"]},{"cell_type":"markdown","id":"b4b18679","metadata":{"papermill":{"duration":0.030516,"end_time":"2023-01-19T08:01:43.018224","exception":false,"start_time":"2023-01-19T08:01:42.987708","status":"completed"},"tags":[]},"source":["I developed few visualizations in Public Tableau \n","* https://public.tableau.com/app/profile/vignesh3092/viz/Vignesh_GDACapstoneProject/Sheet6\n","* https://public.tableau.com/app/profile/vignesh3092/viz/GDACapstoneProject-LocationDemographic/Dashboard1 \n","\n","I used Google Slides to create a presentation to our stakeholders and team members \n","* https://docs.google.com/presentation/d/e/2PACX-1vTZusHYD4E7aASSw1JhrGYRUoAi7JPYpVLxuQEU1_0k4Yh2eUMfpOAqSSfOs3BAzjgQMQXfgsCt9YF1/pub?start=false&loop=false"]}],"metadata":{"kernelspec":{"display_name":"R","language":"R","name":"ir"},"language_info":{"codemirror_mode":"r","file_extension":".r","mimetype":"text/x-r-source","name":"R","pygments_lexer":"r","version":"4.0.5"},"papermill":{"default_parameters":{},"duration":443.914002,"end_time":"2023-01-19T08:01:43.5727","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-01-19T07:54:19.658698","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}