Data_607_TidyVerse_CREATE_Assignment

Which State Has The Worst Drivers?

###DATA BACKGROUND

This dataset, “bad-drivers” is from Fivethirtyeight. This data shows America’s bad drivers in all the states, involved in collisions.

The data contains the following fields:

State

Number of drivers involved in fatal collisions per billion miles

Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding

Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired

Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted

Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents

Car Insurance Premiums (\() #### Losses incurred by insurance companies for collisions per insured driver (\))

###RESOURCES

Data is collected by the National Highway Traffic Safety Administration 2009 and 2012 (NHTSA) (https://www-fars.nhtsa.dot.gov/Main/index.aspx) and National Association of Insurance Commissioners 2010 & 2011(website was not provided).

Data is by Mona Chalabi from FiveThirtyEight and is available online here: https://github.com/fivethirtyeight/data/tree/master/bad-drivers For this project, the data was uploaded by calling the URL and using the read_table() function.

###PACKAGES

Package used is tidyverse. It includes readr, dplyr, tidyr, ggplot2, stringr, tibble, forcats and purr packages.

#Upload tidyverse, ggplot2, and dplyr libraries.

library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
#> ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
#> ✔ tibble  3.1.8     ✔ dplyr   1.0.9
#> ✔ tidyr   1.2.0     ✔ stringr 1.4.1
#> ✔ readr   2.1.2     ✔ forcats 0.5.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
library(ggplot2)
library(dplyr)
library(infer)

###ANALYSIS

###SECTION 1:LOAD THE DATA

read_csv() function is from readr package, used for reading flat file data with comma separated values.

# Load the dataset from fivethirtyeight.com and read the dataset with read.csv.

theUrl <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv"

worst_drivers <- read.csv(file=theUrl, header=TRUE, sep=",")

head(worst_drivers)
#>        State Number.of.drivers.involved.in.fatal.collisions.per.billion.miles
#> 1    Alabama                                                             18.8
#> 2     Alaska                                                             18.1
#> 3    Arizona                                                             18.6
#> 4   Arkansas                                                             22.4
#> 5 California                                                             12.0
#> 6   Colorado                                                             13.6
#>   Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Speeding
#> 1                                                                   39
#> 2                                                                   41
#> 3                                                                   35
#> 4                                                                   18
#> 5                                                                   35
#> 6                                                                   37
#>   Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Alcohol.Impaired
#> 1                                                                           30
#> 2                                                                           25
#> 3                                                                           28
#> 4                                                                           26
#> 5                                                                           28
#> 6                                                                           28
#>   Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Not.Distracted
#> 1                                                                         96
#> 2                                                                         90
#> 3                                                                         84
#> 4                                                                         94
#> 5                                                                         91
#> 6                                                                         79
#>   Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Had.Not.Been.Involved.In.Any.Previous.Accidents
#> 1                                                                                                     80
#> 2                                                                                                     94
#> 3                                                                                                     96
#> 4                                                                                                     95
#> 5                                                                                                     89
#> 6                                                                                                     95
#>   Car.Insurance.Premiums....
#> 1                     784.55
#> 2                    1053.48
#> 3                     899.47
#> 4                     827.34
#> 5                     878.41
#> 6                     835.50
#>   Losses.incurred.by.insurance.companies.for.collisions.per.insured.driver....
#> 1                                                                       145.08
#> 2                                                                       133.93
#> 3                                                                       110.35
#> 4                                                                       142.39
#> 5                                                                       165.63
#> 6                                                                       139.91

###SECTION 2: EXPLORATORY DATA ANALYSIS/CHECKING ASSUMPTION ###SECTION 3: SUMMARY STATISTICS

glimpse() function is from tibble package, used to see column names, how many rows, how many columns, and type of variables in a data frame

# Checking the columns name and how many rows and columns and variables type in that particular column. 

glimpse(worst_drivers)
#> Rows: 51
#> Columns: 8
#> $ State                                                                                                  <chr> …
#> $ Number.of.drivers.involved.in.fatal.collisions.per.billion.miles                                       <dbl> …
#> $ Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Speeding                                   <int> …
#> $ Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Alcohol.Impaired                           <int> …
#> $ Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Were.Not.Distracted                             <int> …
#> $ Percentage.Of.Drivers.Involved.In.Fatal.Collisions.Who.Had.Not.Been.Involved.In.Any.Previous.Accidents <int> …
#> $ Car.Insurance.Premiums....                                                                             <dbl> …
#> $ Losses.incurred.by.insurance.companies.for.collisions.per.insured.driver....                           <dbl> …

colnames() is from dplyr package. Is used to obtain or set the names of the row and column of a matrix-like object, respectively.

# Rename the columns for easier reading.

colnames(worst_drivers) <- c("STATE", 
                           "DRIVERS_INVOLVED", 
                           "PERC_DRIVERS_SPEED", 
                           "PERC_DRIVERS_ALCHO", 
                           "PERC_DRIVERS_NOT_DIST", 
                           "PERC_DRIVERS_NO_ACC", 
                           "INS_PREM", 
                           "LOSS_INSCOMP")

glimpse(worst_drivers)
#> Rows: 51
#> Columns: 8
#> $ STATE                 <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "Cal…
#> $ DRIVERS_INVOLVED      <dbl> 18.8, 18.1, 18.6, 22.4, 12.0, 13.6, 10.8, 16.2, …
#> $ PERC_DRIVERS_SPEED    <int> 39, 41, 35, 18, 35, 37, 46, 38, 34, 21, 19, 54, …
#> $ PERC_DRIVERS_ALCHO    <int> 30, 25, 28, 26, 28, 28, 36, 30, 27, 29, 25, 41, …
#> $ PERC_DRIVERS_NOT_DIST <int> 96, 90, 84, 94, 91, 79, 87, 87, 100, 92, 95, 82,…
#> $ PERC_DRIVERS_NO_ACC   <int> 80, 94, 96, 95, 89, 95, 82, 99, 100, 94, 93, 87,…
#> $ INS_PREM              <dbl> 784.55, 1053.48, 899.47, 827.34, 878.41, 835.50,…
#> $ LOSS_INSCOMP          <dbl> 145.08, 133.93, 110.35, 142.39, 165.63, 139.91, …

DATA WRANGLING/VISUALIZATION USING DPLYR, TIDYR, AND GGPLOT2

Columns PERC_DRIVERS_SPEED, PERC_DRIVERS_ALCHO, PERC_DRIVERS_NOT_DIST, PERC_DRIVERS_NO_ACC are percentages of DRIVERS_INVOLVED. I will mutate new columns DRIVERS_SPEED, DRIVERS_ALCHO, DRIVERS_NOT_DIST, DRIVERS_NO_ACC by taking the given percentage of DRIVERS_INVOLVED column.

mutate() function is from dplyr package, adds new variables and preserves existing ones.

# Create new column DRIVERS_SPEED which will be (DRIVERS_INVOLVED*PERC_DRIVERS_SPEED)/100

percent_worst_drivers <- worst_drivers %>% 
  mutate(DRIVERS_SPEED=(DRIVERS_INVOLVED*PERC_DRIVERS_SPEED)/100) %>% 
  mutate(DRIVERS_ALCHO=(DRIVERS_INVOLVED*PERC_DRIVERS_ALCHO)/100) %>% 
  mutate(DRIVERS_NOT_DIST=(DRIVERS_INVOLVED*PERC_DRIVERS_NOT_DIST)/100) %>% 
  mutate(DRIVERS_NO_ACC=(DRIVERS_INVOLVED*PERC_DRIVERS_NO_ACC)/100)

glimpse(percent_worst_drivers)
#> Rows: 51
#> Columns: 12
#> $ STATE                 <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "Cal…
#> $ DRIVERS_INVOLVED      <dbl> 18.8, 18.1, 18.6, 22.4, 12.0, 13.6, 10.8, 16.2, …
#> $ PERC_DRIVERS_SPEED    <int> 39, 41, 35, 18, 35, 37, 46, 38, 34, 21, 19, 54, …
#> $ PERC_DRIVERS_ALCHO    <int> 30, 25, 28, 26, 28, 28, 36, 30, 27, 29, 25, 41, …
#> $ PERC_DRIVERS_NOT_DIST <int> 96, 90, 84, 94, 91, 79, 87, 87, 100, 92, 95, 82,…
#> $ PERC_DRIVERS_NO_ACC   <int> 80, 94, 96, 95, 89, 95, 82, 99, 100, 94, 93, 87,…
#> $ INS_PREM              <dbl> 784.55, 1053.48, 899.47, 827.34, 878.41, 835.50,…
#> $ LOSS_INSCOMP          <dbl> 145.08, 133.93, 110.35, 142.39, 165.63, 139.91, …
#> $ DRIVERS_SPEED         <dbl> 7.332, 7.421, 6.510, 4.032, 4.200, 5.032, 4.968,…
#> $ DRIVERS_ALCHO         <dbl> 5.640, 4.525, 5.208, 5.824, 3.360, 3.808, 3.888,…
#> $ DRIVERS_NOT_DIST      <dbl> 18.048, 16.290, 15.624, 21.056, 10.920, 10.744, …
#> $ DRIVERS_NO_ACC        <dbl> 15.040, 17.014, 17.856, 21.280, 10.680, 12.920, …

###SECTION 4: VISUALIZING THE RESULTS

Create a stacked bar plot using ggplot() method having states on X axis and DRIVERS_SPEED and DRIVERS_INVOLVED stacked together on Y axis. We use select() method to get required columns. The gather() method to make data long for DRIVERS_INVOLVED and DRIVERS_SPEED and finally use ggplot() to draw stacked bar plot.

select() function is from dplyr package that keeps only the variables we mention.

gather() function is from tidyr package that takes multiple columns and collapses into key-value pairs, duplicating all other columns as needed.

ggplot() All ggplot2 plots begin with a call to ggplot().

percent_worst_drivers %>% 
  select(STATE, DRIVERS_INVOLVED, DRIVERS_SPEED) %>% 
  gather(type, value, DRIVERS_INVOLVED:DRIVERS_SPEED) %>% 
  ggplot(., aes(x = STATE,y = value, fill = type)) + 
  geom_bar(position = "stack", stat="identity") + 
  scale_fill_manual(values = c("red", "darkred")) + 
  ylab("Drivers involved in Fatal collision while Speeding") + 
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

Below plot of for STATE vs INS_PREMIUM that used ggplot() method to creat a bar plot.

percent_worst_drivers %>% 
  ggplot(., aes(x = STATE,y = INS_PREM)) + 
  geom_bar(position = "stack", stat="identity") + 
  ylab("Car Insurance Premium") + 
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

###CONCLUSION

I discussed various packages and their functions to explore bad drivers dataset. For complete set details refer (https://www.tidyverse.org/).

Resources:

https://www.tidyverse.org/packages/

https://fivethirtyeight.com/features/which-state-has-the-worst-drivers/

###SESSION INFORMATION

options(width = 100)
devtools::session_info()
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#>  date     2022-10-30
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Data_607_TidyVerse_CREATE_Assignment_Vignette.rmd

Enid Roman

2022-10-30