Code
library(tidyverse)
library(readxl)
library(summarytools)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 1
challenge_1
railroads
wildbirds
Reading in data and creating a post
Challenge Overview
Today’s challenge is to
read in a dataset, and
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.
Reading the railroad dataset
Code
#Reading the railroad_2012_clean_county dataset
railroad <- read_csv("_data/railroad_2012_clean_county.csv")Printing first few rows of the railroad dataset
Code
#Printing only few rows
head(railroad)# 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 1Reading the wild bird dataset
Code
#Reading the wild_bird_data dataset
wildbird <- read_excel("_data/wild_bird_data.xlsx", skip=1)Printing first few rows of the wildbird dataset
Code
#Printing only few rows
head(wildbird)# 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.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
dfSummary(railroad)Data Frame Summary
railroad
Dimensions: 2930 x 3
Duplicates: 0
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No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- ----------------- -------------------------- --------------------- -------------------- ---------- ---------
1 state 1. TX 221 ( 7.5%) I 2930 0
[character] 2. GA 152 ( 5.2%) I (100.0%) (0.0%)
3. KY 119 ( 4.1%)
4. MO 115 ( 3.9%)
5. IL 103 ( 3.5%)
6. IA 99 ( 3.4%)
7. KS 95 ( 3.2%)
8. NC 94 ( 3.2%)
9. IN 92 ( 3.1%)
10. VA 92 ( 3.1%)
[ 43 others ] 1748 (59.7%) IIIIIIIIIII
2 county 1. WASHINGTON 31 ( 1.1%) 2930 0
[character] 2. JEFFERSON 26 ( 0.9%) (100.0%) (0.0%)
3. FRANKLIN 24 ( 0.8%)
4. LINCOLN 24 ( 0.8%)
5. JACKSON 22 ( 0.8%)
6. MADISON 19 ( 0.6%)
7. MONTGOMERY 18 ( 0.6%)
8. CLAY 17 ( 0.6%)
9. MARION 17 ( 0.6%)
10. MONROE 17 ( 0.6%)
[ 1699 others ] 2715 (92.7%) IIIIIIIIIIIIIIIIII
3 total_employees Mean (sd) : 87.2 (283.6) 404 distinct values : 2930 0
[numeric] min < med < max: : (100.0%) (0.0%)
1 < 21 < 8207 :
IQR (CV) : 58 (3.3) :
:
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dfSummary(wildbird)Data Frame Summary
wildbird
Dimensions: 146 x 2
Duplicates: 0
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No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- --------------------- ------------------------------- --------------------- ------- ---------- ---------
1 Wet body weight [g] Mean (sd) : 363.7 (983.5) 146 distinct values : 146 0
[numeric] min < med < max: : (100.0%) (0.0%)
5.5 < 69.2 < 9639.8 :
IQR (CV) : 291.2 (2.7) :
: .
2 Population size Mean (sd) : 382874 (951938.7) 146 distinct values : 146 0
[numeric] min < med < max: : (100.0%) (0.0%)
4.9 < 24353.2 < 5093378 :
IQR (CV) : 196693.8 (2.5) :
: .
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