Challenge 1 Instructions

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 ⭐⭐⭐

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)
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

Railroad Dataset-

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

railroad%>%
  select(county)%>%
  n_distinct(.)

[1] 1709

Code

railroad%>%
  select(county)%>%
  distinct()

# 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

Code

railroad%>%
  select(state)%>%
  n_distinct(.)

[1] 53

Code

railroad%>%
  select(state)%>%
  distinct()

# 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

railroad%>%
  select(total_employees)%>%
  n_distinct(.)

[1] 404

Code

railroad%>%
  select(total_employees)%>%
  distinct()

# A tibble: 404 × 1
   total_employees
             <dbl>
 1               2
 2               7
 3               3
 4               1
 5              88
 6             102
 7             143
 8              25
 9             154
10              13
# … with 394 more rows

Birds Dataset-

Code

library(readxl)
birds<-read_csv("_data/birds.csv")
birds

# A tibble: 30,977 × 14
   Domain Cod…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year
   <chr>        <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl>
 1 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1961  1961
 2 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1962  1962
 3 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1963  1963
 4 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1964  1964
 5 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1965  1965
 6 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1966  1966
 7 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1967  1967
 8 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1968  1968
 9 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1969  1969
10 QA           Live …       2 Afgh…    5112 Stocks     1057 Chic…    1970  1970
# … with 30,967 more rows, 4 more variables: Unit <chr>, Value <dbl>,
#   Flag <chr>, `Flag Description` <chr>, and abbreviated variable names
#   ¹​`Domain Code`, ²​`Area Code`, ³​`Element Code`, ⁴​`Item Code`, ⁵​`Year Code`

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