Challenge 3

challenge_3

animal_weight

Tidy Data: Pivoting

Author

Lindsay Jones

Published

August 17, 2022

Code

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
identify what needs to be done to tidy the current data
anticipate the shape of pivoted data
pivot the data into tidy format using pivot_longer

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

animal_weights.csv ⭐
eggs_tidy.csv ⭐⭐ or organicpoultry.xls ⭐⭐⭐
australian_marriage*.xlsx ⭐⭐⭐
USA Households*.xlsx ⭐⭐⭐⭐
sce_labor_chart_data_public.csv 🌟🌟🌟🌟🌟

Code

animal_weight<-read_csv("_data/animal_weight.csv",
                        show_col_types = FALSE)
print(animal_weight)

# A tibble: 9 × 17
  IPCC A…¹ Cattl…² Cattl…³ Buffa…⁴ Swine…⁵ Swine…⁶ Chick…⁷ Chick…⁸ Ducks Turkeys
  <chr>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>   <dbl>
1 Indian …     275     110     295      28      28     0.9     1.8   2.7     6.8
2 Eastern…     550     391     380      50     180     0.9     1.8   2.7     6.8
3 Africa       275     173     380      28      28     0.9     1.8   2.7     6.8
4 Oceania      500     330     380      45     180     0.9     1.8   2.7     6.8
5 Western…     600     420     380      50     198     0.9     1.8   2.7     6.8
6 Latin A…     400     305     380      28      28     0.9     1.8   2.7     6.8
7 Asia         350     391     380      50     180     0.9     1.8   2.7     6.8
8 Middle …     275     173     380      28      28     0.9     1.8   2.7     6.8
9 Norther…     604     389     380      46     198     0.9     1.8   2.7     6.8
# … with 7 more variables: Sheep <dbl>, Goats <dbl>, Horses <dbl>, Asses <dbl>,
#   Mules <dbl>, Camels <dbl>, Llamas <dbl>, and abbreviated variable names
#   ¹`IPCC Area`, ²`Cattle - dairy`, ³`Cattle - non-dairy`, ⁴Buffaloes,
#   ⁵`Swine - market`, ⁶`Swine - breeding`, ⁷`Chicken - Broilers`,
#   ⁸`Chicken - Layers`
# ℹ Use `colnames()` to see all variable names

Briefly describe the data

This data set contains the average weight of 17 different groups of animals in 9 regions of the world. This data set is not tidy because the variables (animal species and average weight) are not set as the columns. Using pivot_longer will fix that.

Challenge: Pivot the Chosen Data

Code

aw_pivot <- pivot_longer(animal_weight, 
             "Cattle - dairy":"Llamas", 
             names_to = "Species",
             values_to = "Weight")
print(aw_pivot)

# A tibble: 144 × 3
   `IPCC Area`         Species            Weight
   <chr>               <chr>               <dbl>
 1 Indian Subcontinent Cattle - dairy      275  
 2 Indian Subcontinent Cattle - non-dairy  110  
 3 Indian Subcontinent Buffaloes           295  
 4 Indian Subcontinent Swine - market       28  
 5 Indian Subcontinent Swine - breeding     28  
 6 Indian Subcontinent Chicken - Broilers    0.9
 7 Indian Subcontinent Chicken - Layers      1.8
 8 Indian Subcontinent Ducks                 2.7
 9 Indian Subcontinent Turkeys               6.8
10 Indian Subcontinent Sheep                28  
# … with 134 more rows
# ℹ Use `print(n = ...)` to see more rows

This format makes it more difficult to look at the data for each country, but this could be solved using a few different functions. If we wanted to examine both types of cattle in every country:

Code

aw_pivot %>%
  filter(grepl('Cattle', Species))

# A tibble: 18 × 3
   `IPCC Area`         Species            Weight
   <chr>               <chr>               <dbl>
 1 Indian Subcontinent Cattle - dairy        275
 2 Indian Subcontinent Cattle - non-dairy    110
 3 Eastern Europe      Cattle - dairy        550
 4 Eastern Europe      Cattle - non-dairy    391
 5 Africa              Cattle - dairy        275
 6 Africa              Cattle - non-dairy    173
 7 Oceania             Cattle - dairy        500
 8 Oceania             Cattle - non-dairy    330
 9 Western Europe      Cattle - dairy        600
10 Western Europe      Cattle - non-dairy    420
11 Latin America       Cattle - dairy        400
12 Latin America       Cattle - non-dairy    305
13 Asia                Cattle - dairy        350
14 Asia                Cattle - non-dairy    391
15 Middle east         Cattle - dairy        275
16 Middle east         Cattle - non-dairy    173
17 Northern America    Cattle - dairy        604
18 Northern America    Cattle - non-dairy    389