Challenge 1

Reading in data and creating a post

Code

library(tidyverse)
library(readr)
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

# Function call to read a csv file
railroad_2012_clean_county <- read_csv("_data/railroad_2012_clean_county.csv")

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

# Displaying the first few records from the dataset
railroad_2012_clean_county

# 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

From the above output, we can see that there are 3 main attributes in the dataset namely - state, county and total_employees. Judging by the dataset file name and the underlying data, we can make a fair guess that the total_employees column describes the total number of employees working for railroad at a state-county granularity.

Code

mean(railroad_2012_clean_county$total_employees)

[1] 87.17816

Code

median(railroad_2012_clean_county$total_employees)

[1] 21

From the output of the above two functions we can see that the mean number of employees at state-county level is 87.17 and the median is 21