Challenge 2

Code

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to

1. read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
2. provide summary statistics for different interesting groups within the data, and interpret those statistics

Read in the Data

• railroad*.csv or StateCounty2012.xls ⭐

Code

library(tidyverse)
railroad<-read_csv("_data/railroad_2012_clean_county.csv")
head(railroad)

# A tibble: 6 × 3
  state county               total_employees
  <chr> <chr>                          <dbl>
1 AE    APO                                2
2 AK    ANCHORAGE                          7
3 AK    FAIRBANKS NORTH STAR               2
4 AK    JUNEAU                             3
5 AK    MATANUSKA-SUSITNA                  2
6 AK    SITKA                              1

Describe the data

As mentioned in challenge 1 we can see that there are three variables in this table: state, county and total employees.

The data seems to be tabulating the number of railroad employees in different counties.

Provide Grouped Summary Statistics

The total number of employees across all counties is: 255,432

Code

summarise(railroad, total_employees = sum(total_employees))

# A tibble: 1 × 1
  total_employees
            <dbl>
1          255432

We can find the county with the largest number of employees: 8207 in Cook County Illinois

Code

summarise(railroad, most_employees=max(total_employees))

# A tibble: 1 × 1
  most_employees
           <dbl>
1           8207

We can take a look and see how the largest number of employees might correlate to the largest states by area.

1st: Alaska, 665,400 square miles

2nd: Texas, 268,597 square miles

3rd: California, 163,696 square miles

4th: Montana, 147,040 square miles

Code

filter(railroad, state=="AK")%>%
  summarise(AK_total_employees=sum(total_employees))

# A tibble: 1 × 1
  AK_total_employees
               <dbl>
1                103

Code

filter(railroad, state=="TX")%>%
  summarise(TX_total_employees=sum(total_employees))

# A tibble: 1 × 1
  TX_total_employees
               <dbl>
1              19839

Code

filter(railroad, state=="CA")%>%
  summarise(CA_total_employees=sum(total_employees))

# A tibble: 1 × 1
  CA_total_employees
               <dbl>
1              13137

Code

filter(railroad, state=="MT")%>%
  summarise(MT_total_employees=sum(total_employees))

# A tibble: 1 × 1
  MT_total_employees
               <dbl>
1               3327

And also how the largest number of employees might correlate to the largest states by population*

1st: California, 37,253,956
2nd: Texas, 25,145,561
3rd: New York, 19,378,102
4th: Florida, 18,801,310

This time let’s combine in one table:

Code

By_population<-railroad%>%
  group_by(state)%>%
  summarise(total_employees = sum(total_employees))
By_population[By_population$state %in% c("CA","TX","NY","FL"),]

# A tibble: 4 × 2
  state total_employees
  <chr>           <dbl>
1 CA              13137
2 FL               7419
3 NY              17050
4 TX              19839

Interpretation

I looked at the four states with the largest amount of total employees, I ran the data to see if there was any correlation between either population or square miles of the state. Based on a cursory look at the initial data there doesn’t seem to be a direct correlation between the number of railroad employees and populaton nor a correlation with the size of the state.