Challenge 3: Pivoting

Code

library(tidyverse)
library(readr)
knitr::opts_chunk$set(echo = TRUE)

Code

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

# 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`, ⁴​Buffaloes,
#   ⁵​`Swine - market`, ⁶​`Swine - breeding`, ⁷​`Chicken - Broilers`,
#   ⁸​`Chicken - Layers`

The data for each animal for each area is in one row, I want to pivot the data from the columns to rows using pivot_longer, so that I can group by each area and get summary statistics for each area. These statistics cannot be calculated easily in the present format, for this reason I want to pivot the data. There are 16 lifestock types across the 9 regions which when we pivot to rows, the total number of results will be 16*9=144 rows

Code

pivot_longer(animal_weights, 2:ncol(animal_weights), names_to = "animal", values_to = "weight")

# A tibble: 144 × 3
   `IPCC Area`         animal             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

After pivoting the data, the dimension of the tibble is 144x3 which is as expected