Crime Data Assigment

Loading Libraries & Data

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(magrittr)
library(tidyverse)

## -- Attaching packages ---------------------------------------------------- tidyverse 1.2.1 --

## v tibble  1.4.2     v purrr   0.2.4
## v tidyr   0.7.2     v stringr 1.2.0
## v tibble  1.4.2     v forcats 0.2.0

## -- Conflicts ------------------------------------------------------- tidyverse_conflicts() --
## x tidyr::extract()   masks magrittr::extract()
## x dplyr::filter()    masks stats::filter()
## x dplyr::lag()       masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()

CRIME_DATA <- read_csv("C:/Users/Meghan/Documents/CRIME DATA.csv")

## Parsed with column specification:
## cols(
##   .default = col_integer(),
##   Geo_Name = col_character(),
##   Geo_QName = col_character(),
##   SE_A002_001 = col_double(),
##   SE_T011_001 = col_double(),
##   SE_T011_002 = col_double(),
##   SE_T011_003 = col_double(),
##   SE_T011_004 = col_double(),
##   SE_T011_005 = col_double(),
##   SE_T011_006 = col_double(),
##   SE_T011_007 = col_double(),
##   SE_T011_008 = col_double(),
##   SE_T011_009 = col_double(),
##   SE_T011_010 = col_double(),
##   SE_T011_011 = col_double(),
##   SE_T011_012 = col_double(),
##   SE_T011_013 = col_double(),
##   SE_T011_014 = col_double(),
##   SE_T011_015 = col_double(),
##   SE_T011_016 = col_double(),
##   SE_T011_017 = col_double()
##   # ... with 106 more columns
## )

## See spec(...) for full column specifications.

Headers

head(CRIME_DATA)

Selecting Variables to Keep

CRIME_DATA <- select(CRIME_DATA, Geo_FIPS, Geo_Name, Geo_QName, SE_T001_001, SE_T002_001, SE_A001_001, SE_T010_001)

Renaming Variables

• Total Crimes
• Violet and Property Crimes
• Arson
• Arrests

CRIME_DATA <- rename(CRIME_DATA, 
              "var.FIPS" = Geo_FIPS,
              "State" = Geo_Name, 
              "State2" = Geo_QName,
              "TotalCrimes" = SE_T001_001,
              "ViolentandPropertyCrimes" = SE_T002_001,
              "Arson" = SE_A001_001,
              "Arrests" = SE_T010_001)

Mean Summary of Crimes in Different States

summarize (CRIME_DATA, meanTotalCrimes = mean(ViolentandPropertyCrimes, na.rm = TRUE))

summarize (CRIME_DATA, meanTotalCrimes = mean(Arson, na.rm = TRUE))

summarize (CRIME_DATA, meanTotalCrimes = mean(Arrests, na.rm = TRUE))

Running another head to check out the renamed and cleaned data

head(CRIME_DATA)

Bar Charts of Different Crimes For Each State

ggplot(data=CRIME_DATA)+
  geom_col(aes(x=State,y=TotalCrimes))+
  coord_flip()

ggplot(data=CRIME_DATA)+
  geom_col(aes(x=State,y=TotalCrimes))+
  coord_flip()

ggplot(data=CRIME_DATA)+
  geom_col(aes(x=State,y=Arson))+
  coord_flip()

ggplot(data=CRIME_DATA)+
  geom_col(aes(x=State,y=Arrests))+
  coord_flip()