Rathish_TydeVerse.Rmd

---
title: "TidyVerse Assignment"
author: "Rathish Sasidharan"
date: "4/9/2021"
output: html_document
---

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

## Overview
In this vignette we will explore various tidyverse packages like tidyr,dplyr,ggplot2

Please find the link to the article.

[A Handful Of Cities Are Driving 2016’s Rise In Murders](https://fivethirtyeight.com/features/a-handful-of-cities-are-driving-2016s-rise-in-murders/)

## Prerequisites
Load required packages

```{r message=FALSE}
library(ggplot2)
library(dplyr)
#library(tidyr)

```


### Loading data frame 
Read the  data CSV file into data frames
```{r}
#Read the data from git
murder2016 <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/murder_2016/murder_2016_prelim.csv")

```

## Subset columns

select {dplyr} function is used to subset  data frame by columns
```{r}
murder2016_colsubset <- murder2016 %>% select(-c('source','as_of'))
```


## Rename columns

rename {dplyr} function is used to rename data frame  columns
```{r}
murder2016_colsubset <- murder2016_colsubset %>% rename(murders_2015=X2015_murders,murders_2016=X2016_murders)
```


## Filter data
filter {dplyr} is used to subset data frame by column value
```{r}
murder2016Fltr<- murder2016_colsubset %>%
    filter(murders_2016 >50 & change >10)
```


## Sort the data
arrange {dplyr} function is used to sort the data frame by column values 
```{r}
murder2016Fltr %>% arrange(murders_2016)
murder2016Fltr %>% arrange(desc(murders_2016))

```



##  Modify columns
mutate {dplyr} - this function is used to create/modify/delete columns
```{r}
murder2016Rt<- murder2016Fltr %>% mutate(increaseRt=change/murders_2016 *100)

murder2016Rt

```


## Plot a graph

bar plot for crime rates

```{r}

murder2016_5row <- head(murder2016Rt,5) 

ggplot(data=murder2016_5row, aes(x=city, y=increaseRt,color=city,fill=city)) +  geom_bar(stat="identity") +labs(title='Crime Rate Across major cities 2016',x='City',y='Crime Rate increase') + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+geom_text(aes(label=increaseRt), position=position_dodge(width=0.9), vjust=-0.25)

```

# Extension (Vic Chan)

We can also to see which city has the highest crime rates for 2015 and 2016 and see if there has been any change on which city has the highest crime rate


```{r}
murder2016Rt %>%
  ggplot(aes(x=reorder(city, murders_2015), y=murders_2015)) + geom_bar(stat="identity") + coord_flip()
```

```{r}
murder2016Rt %>%
  ggplot(aes(x=reorder(city, murders_2016), y=murders_2016)) + geom_bar(stat="identity") + coord_flip()
```

We can see that Chicago and Houston still had the highest murder rate, but Houston murder rate drastically decreased in 2016 while Chicago murder rate stayed relatively the same.