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Create_database.R
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Create_database.R
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library(tidyverse)
library(RSQLite)
# this script takes the downloaded data and builds a single SQLite database from it
# must run download_instruction.sh shell scripts to download the data
# before running this script
# connect to database -----------------------------------------------------
# connect to database NYC.db; if it doesn't exist this will
# create it in the working directory
conn <- dbConnect(RSQLite::SQLite(), "NYC.db")
# helper functions --------------------------------------------------------
# function to convert text to proper case
toproper <- function(name) paste0(toupper(substr(name, 1, 1)), tolower(substring(name, 2)))
# read in turnstile data --------------------------------------------------
#function to read in the data, format, and then combine to one dataframe
read_subway_files <- function(year) {
#year should be last two digits; e.g. 19 for 2019
stopifnot(is.character(year), nchar(year) == 2)
filenames <- list.files(
path = "Subway-turnstiles/Data/",
pattern = paste0("turnstile_",
as.character(year),
".....txt"),
all.files = FALSE,
full.names = FALSE,
recursive = FALSE,
ignore.case = FALSE
)
# read in all those files and combine into one data.frame
map_df(filenames, function(filename) {
NameDF <-
read_csv(
file = paste0("Subway-turnstiles/Data/", filename),
col_types = cols(
`C/A` = col_character(),
DATE = col_date(format = "%m/%d/%Y"),
DESC = col_character(),
DIVISION = col_character(),
ENTRIES = col_double(),
EXITS = col_double(),
SCP = col_character(),
STATION = col_character(),
TIME = col_time(format = "%H:%M:%S"),
UNIT = col_character()
)
)
# add identifier
NameDF$Source.file <- filename
return(NameDF)
})
}
# read in the turnstile files to form the table and append subsequent files.
# final result is a database with tables for each year
years.to.import <- 2019:2020
sapply(years.to.import, function(year) {
# read in the files
df <- read_subway_files(year = substr(as.character(year), 3, 4))
# clean up the column names
names(df) <- sapply(names(df), toproper)
names(df)[1] <- "Booth"
names(df)[3] <- "SCP"
table.name <- paste0("turnstile.", year)
# create table if it doesn't exist
if (!dbExistsTable(conn, table.name)) {
dbWriteTable(
conn = conn,
name = table.name,
value = df,
overwrite = TRUE,
field.types = c(
Booth = "text",
Unit = "text",
SCP = "text",
Station = "text",
Linename = "text",
Division = "text",
Date = "int",
Time = "int",
Desc = "text",
Entries = "real",
Exits = "real",
Source.file = "text"
)
)
} else {
# append the file to the table if the table already exists
dbWriteTable(
conn = conn,
name = table.name,
value = df,
append = TRUE
)
}
})
# list all the tables available in the database
dbListTables(conn)
# test a query
tbl(conn, "turnstile.2020") %>%
group_by(Source.file) %>%
summarize(n.rows = n())
# read in the Citi bike data ---------------------------------------------------------------
# the following files have different date formats and need to be accounted for a case-when statement
# these files have dateformat of "%m/%d/%Y %H:%M"
date.format.1 <- c(
"20150[1-3]-citibike-tripdata.csv",
"201506-citibike-tripdata.csv"
)
# these files have dateformat of "%m/%d/%Y %H:%M:%S"
date.format.2 <- c(
"201409-citibike-tripdata.csv",
"20141[0-2]-citibike-tripdata.csv",
"20150[4-5]-citibike-tripdata.csv",
"20150[7-9]-citibike-tripdata.csv",
"20151[0-2]-citibike-tripdata.csv",
"20160[1-9]-citibike-tripdata.csv"
)
#function to read in the data, format, and then combine to one dataframe
read_citibike_files <- function(year) {
#year should be four digits; e.g. 2019
stopifnot(nchar(year) == 4)
# list all the files in the directory that match this year
filenames <- list.files(path = "Citi-bike/Data/",
pattern = paste0("^", year, ".*csv$"))
# read in all those files and combine into one data.frame
map_df(filenames, function(filename) {
# some files have different date formats and need to be adjusted
# date.format.# is a list of file names (regex patterns) that the filename
# can match to. If it does match, then it returns a new date.format
date.format <-
case_when(
any(sapply(date.format.1, function(pattern) grepl(pattern, filename))) ~ "%m/%d/%Y %H:%M",
any(sapply(date.format.2, function(pattern) grepl(pattern, filename))) ~ "%m/%d/%Y %H:%M:%S",
TRUE ~ "%Y-%m-%d %H:%M:%S"
)
# read in the data but for certain pre-determined files that have inconsistent column names
if (grepl("20161[0-2]-citibike-tripdata.csv", filename) |
grepl("20170[1-3]-citibike-tripdata.csv", filename)) {
# if the filename name is in that list then use a different set of column names
NameDF <-
read_csv(
file = paste0("Citi-bike/Data/", filename),
col_types = cols(
`Birth Year` = col_double(),
`Start Station ID` = col_double(),
`End Station ID` = col_double(),
`Start Station Name` = col_character(),
`End Station Name` = col_character(),
`Start Station Latitude` = col_double(),
`End Station Latitude` = col_double(),
`Start Station Longitude` = col_double(),
`End Station Longitude` = col_double(),
`Bike ID` = col_double(),
`User Type` = col_character(),
`Start Time` = col_datetime(format = date.format),
`Stop Time` = col_datetime(format = date.format),
`Trip Duration` = col_integer(),
Gender = col_double()
)
)
# need to change names to the more common convention so columns aren't duplicated
# note that the order here must match the column order
colnames(NameDF) <- c(
"tripduration",
"starttime",
"stoptime",
"start station id",
"start station name",
"start station latitude",
"start station longitude",
"end station id",
"end station name",
"end station latitude",
"end station longitude",
"bikeid",
"usertype",
"birth year",
"gender"
)
} else {
# for all other files, use the default names
NameDF <-
read_csv(
file = paste0("Citi-bike/Data/", filename),
col_types = cols(
`birth year` = col_double(),
`start station id` = col_double(),
`end station id` = col_double(),
`start station name` = col_character(),
`end station name` = col_character(),
`start station latitude` = col_double(),
`end station latitude` = col_double(),
`start station longitude` = col_double(),
`end station longitude` = col_double(),
bikeid = col_double(),
usertype = col_character(),
starttime = col_datetime(format = date.format),
stoptime = col_datetime(format = date.format),
tripduration = col_integer(),
gender = col_double()
)
)
}
# add identifier
NameDF$Source.file <- filename
return(NameDF)
})
}
# read in the citibike files by month, cleanup, then append to the database
# final result is a database with tables for each year
years.to.import <- 2013:2020
sapply(years.to.import, function(year) {
# read in the files
df <- read_citibike_files(year = year)
# clean up the data
names(df) <- str_replace_all(names(df), " ", ".")
names(df) <- sapply(names(df), toproper)
df$Gender <- factor(
df$Gender,
levels = c(0, 1, 2),
labels = c("Unknown", "Male", "Female")
)
# set the table name by year
table.name <- paste0("citibike.", year)
# create table if it doesn't exist
if (!dbExistsTable(conn, table.name)) {
dbWriteTable(
conn = conn,
name = table.name,
value = df,
overwrite = TRUE,
field.types = c(
Tripduration = "int",
Starttime = "int",
Stoptime = "int",
Start.station.id = "int",
Start.station.name = "text",
Start.station.latitude = "real",
Start.station.longitude = "real",
End.station.id = "int",
End.station.name = "text",
End.station.latitude = "real",
End.station.longitude = "real",
Bikeid = "int",
Usertype = "text",
Birth.year = "int",
Gender = "text",
Source.file = "text"
)
)
} else{
# append the file to the table if the table already exists
dbWriteTable(
conn = conn,
name = table.name,
value = df,
append = TRUE
)
}
})
# check to see if any tables have issues with the starttime column
dbListTables(conn) %>%
grep("citibike*", ., value = T) %>%
sapply(., function(table) {
tbl(conn, table) %>%
filter(is.na(starttime)) %>%
collect() %>%
summarize(n())
})
# check number of rows per table
dbListTables(conn) %>%
grep("citibike*", ., value = T) %>%
sapply(., function(table) {
tbl(conn, table) %>%
tally() %>%
collect() %>%
as.numeric()
})
# list all the tables available in the database
dbListTables(conn)
# test a query
tbl(conn, "citibike.2019") %>%
group_by(Source.file) %>%
summarize(n.rows = n())
# remove all tables from the database
# sapply(dbListTables(conn), function(table) dbRemoveTable(conn, table))
# remove citibike tables
# dbListTables(conn) %>% grep("citi", ., value = T) %>% sapply(., function(table) dbRemoveTable(conn, table))