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.xlsx ⭐⭐⭐⭐

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

library(readxl)
StateCounty <- read_excel("_data/StateCounty2012.xls",
                              skip = 4,
                              col_names = c("state","delete","country","delete","empolyees")) %>%
 select(!contains("delete")) %>%
 filter(!str_detect(state,"Total"))
StateCounty

# A tibble: 2,933 × 3
   state country              empolyees
   <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 2,923 more rows
# ℹ Use `print(n = ...)` to see more rows

Code

#Remove the last two observations 
StateCounty <- head(StateCounty, -2)

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).

Code

#Find state variables
StateCounty %>%
  select(state) %>%
  distinct()

# A tibble: 54 × 1
   state
   <chr>
 1 AE   
 2 AK   
 3 AL   
 4 AP   
 5 AR   
 6 AZ   
 7 CA   
 8 CO   
 9 CT   
10 DC   
# … with 44 more rows
# ℹ Use `print(n = ...)` to see more rows