Challenge 1

Code

library(tidyverse)

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

Challenge Overview

Today’s challenge is to

1. read in a dataset, and

2. describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

Read in one (or more) of the following data sets, using the correct R package and command.

• railroad_2012_clean_county.csv ⭐
• birds.csv ⭐⭐
• FAOstat*.csv ⭐⭐
• wild_bird_data.xlsx ⭐⭐⭐
• StateCounty2012.xls ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.

Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

Railroad

Code

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

# A tibble: 2,930 × 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
 7 AK    SKAGWAY MUNICIPALITY              88
 8 AL    AUTAUGA                          102
 9 AL    BALDWIN                          143
10 AL    BARBOUR                            1
# … with 2,920 more rows

Code

railroad %>%
  summary(railroad)

    state              county          total_employees  
 Length:2930        Length:2930        Min.   :   1.00  
 Class :character   Class :character   1st Qu.:   7.00  
 Mode  :character   Mode  :character   Median :  21.00  
                                       Mean   :  87.18  
                                       3rd Qu.:  65.00  
                                       Max.   :8207.00  

The dataset of railroad across the United States includes three columns–state, county and total employees in each county – and 2930 counties.Each county at least maintains one employees while the maximum number is 8207. The average of total employees each county hires is 81.18. However, the median of it is 27.00, which reflects that most counties maintain a small station. The numbers of 1st Qu. and 3rd Qu. further confirms the finding.

Code

# top 10 largest sizes of total employees
max_railroad = railroad %>%
  arrange(desc(`total_employees`)) %>%
  slice(1:10)
max_railroad

# A tibble: 10 × 3
   state county           total_employees
   <chr> <chr>                      <dbl>
 1 IL    COOK                        8207
 2 TX    TARRANT                     4235
 3 NE    DOUGLAS                     3797
 4 NY    SUFFOLK                     3685
 5 VA    INDEPENDENT CITY            3249
 6 FL    DUVAL                       3073
 7 CA    SAN BERNARDINO              2888
 8 CA    LOS ANGELES                 2545
 9 TX    HARRIS                      2535
10 NE    LINCOLN                     2289

Cook county, IL, hires the largest size of employees and reaches to 8207 while Tarrant, TX, is second to it with 4235 employees.

Code

# the total employees of each state and arrange them in the descending order
railroad %>% group_by(state) %>% summarise(total=sum(total_employees)) %>% arrange(desc(total)) %>% slice(1:10)

# A tibble: 10 × 2
   state total
   <chr> <dbl>
 1 TX    19839
 2 IL    19131
 3 NY    17050
 4 NE    13176
 5 CA    13137
 6 PA    12769
 7 OH     9056
 8 GA     8605
 9 IN     8537
10 MO     8419

Although Cook county hires the largest size of employees, Illinois is not the NO.1 in terms of total employees. Texas outnumbers it by several hundreds employees.