hummell_dplyr.Rmd

---
title: "Tidyverse - Dplyr"
author: "Joshua Hummell"
date: "4/8/2021"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Tidyverse Vignette
```{r message=FALSE}
library(dplyr)
```

#### Hands down my favorite R package in Tidyverse is Dplyr
Dplyr allows for easy data manipulation and, therefore, is highly useful for everyday work!  



```{r}
murders <- read.csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/murder_2016/murder_2015_final.csv')
```


Select data columns with ease
```{r}
murders %>% select(state)
```
easily filter data
```{r}
murders %>% 
  filter(city == 'Baltimore')
```

Easily Aggregate Date

```{r}
state <- murders %>% select(state, change) %>% group_by(state) %>% summarise(state_totals = sum(change)) %>% arrange(desc(state_totals))
state
```
  
and even join data
 
```{r}
states_pop <- read.csv('https://raw.githubusercontent.com/jhumms/DATA607/main/state_populations.csv')
colnames(states_pop) <- tolower(colnames(states_pop))

murders_state <- left_join(state, states_pop, by='state')

murders_state
```
and, if that weren't enough, you can even make aggregations across columns very easily!

```{r}
murders_state$population <- as.numeric(murders_state$population)

murders_state %>% mutate(murder_rate_by_pop = (state_totals / population) *100) %>% arrange(desc(murder_rate_by_pop))
```