Challenge 1 Solution

Code

#| load necessary libraries
library(tidyverse)
library(readxl)


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.

Code

# load in data and select relevant objects for analysis

railroads <- read_csv("_data/railroad_2012_clean_county.csv")
state <- select(railroads, 'state')
county <- select(railroads, 'county')
wild_birds <- read_xlsx("_data/wild_bird_data.xlsx", skip=1)
state_county <- read_xls("_data/StateCounty2012.xls", skip=3)
state_county_trimmed <- select(state_county, 'STATE', 'COUNTY', 'TOTAL')

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).

Railroads

Code

head(railroads)

# 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

Code

summary(railroads)

    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  

Code

unique(state, print = 53)

# A tibble: 53 × 1
   state
   <chr>
 1 AE   
 2 AK   
 3 AL   
 4 AP   
 5 AR   
 6 AZ   
 7 CA   
 8 CO   
 9 CT   
10 DC   
# … with 43 more rows

Code

unique (county, print = 1709)

# A tibble: 1,709 × 1
   county              
   <chr>               
 1 APO                 
 2 ANCHORAGE           
 3 FAIRBANKS NORTH STAR
 4 JUNEAU              
 5 MATANUSKA-SUSITNA   
 6 SITKA               
 7 SKAGWAY MUNICIPALITY
 8 AUTAUGA             
 9 BALDWIN             
10 BARBOUR             
# … with 1,699 more rows

“railroad_2012_county.csv” is a comma separated value file containing information about the number of railroad employees in 2012 in each county in each state. There are 2930 unique cases but only 1709 unique county names, so further analysis would always have to include the state-county combination in order to accruately portray the data.

Wild Birds

Code

head(wild_birds)

# A tibble: 6 × 2
  `Wet body weight [g]` `Population size`
                  <dbl>             <dbl>
1                  5.46           532194.
2                  7.76          3165107.
3                  8.64          2592997.
4                 10.7           3524193.
5                  7.42           389806.
6                  9.12           604766.

Code

summary(wild_birds)

 Wet body weight [g] Population size  
 Min.   :   5.459    Min.   :      5  
 1st Qu.:  18.620    1st Qu.:   1821  
 Median :  69.232    Median :  24353  
 Mean   : 363.694    Mean   : 382874  
 3rd Qu.: 309.826    3rd Qu.: 198515  
 Max.   :9639.845    Max.   :5093378  

“wild_bird_data.xlsx” is an Excel file with 146 observations of 2 variables about the weight and population size of wild birds. Something not included is how these observations are differentiated (species, geography, or both). The first line does include a reference to a paper, so locating the paper would help provide more context if further analysis was needed.

Railroad Data 2

Code

head(state_county_trimmed)

# A tibble: 6 × 3
  STATE     COUNTY               TOTAL
  <chr>     <chr>                <dbl>
1 AE        APO                      2
2 AE Total1 <NA>                     2
3 AK        ANCHORAGE                7
4 AK        FAIRBANKS NORTH STAR     2
5 AK        JUNEAU                   3
6 AK        MATANUSKA-SUSITNA        2

Code

summary(state_county_trimmed)

    STATE              COUNTY              TOTAL         
 Length:2990        Length:2990        Min.   :     1.0  
 Class :character   Class :character   1st Qu.:     7.0  
 Mode  :character   Mode  :character   Median :    22.0  
                                       Mean   :   256.9  
                                       3rd Qu.:    71.0  
                                       Max.   :255432.0  
                                       NA's   :5         

“StateCounty2012.xls” is an Excel file with the same data as the first csv file reviewed for this exercise before it has been cleaned. There was an extraneous title row and lots of white space surrounding the data. There are also total rows embedded in the data that would need to be removed before analysis. I’m not quite confident enough yet to do that, but I’m sure I will be soon.