Challenge 3

challenge_3

animal_weights

Author

Tracy Tien

Published

December 1, 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 ⭐

Code

#Read in the data
animal_weight <- read_csv("_data/animal_weight.csv")
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`

Code

spec(animal_weight)

cols(
  `IPCC Area` = col_character(),
  `Cattle - dairy` = col_double(),
  `Cattle - non-dairy` = col_double(),
  Buffaloes = col_double(),
  `Swine - market` = col_double(),
  `Swine - breeding` = col_double(),
  `Chicken - Broilers` = col_double(),
  `Chicken - Layers` = col_double(),
  Ducks = col_double(),
  Turkeys = col_double(),
  Sheep = col_double(),
  Goats = col_double(),
  Horses = col_double(),
  Asses = col_double(),
  Mules = col_double(),
  Camels = col_double(),
  Llamas = col_double()
)

Code

#Load packages
library(tidyverse)
#install.packages("psych")
library(psych)

describe(animal_weight)

                   vars n   mean     sd median trimmed    mad   min   max range
IPCC Area*            1 9   5.00   2.74    5.0    5.00   2.97   1.0   9.0   8.0
Cattle - dairy        2 9 425.44 140.39  400.0  425.44 185.32 275.0 604.0 329.0
Cattle - non-dairy    3 9 298.00 116.26  330.0  298.00  90.44 110.0 420.0 310.0
Buffaloes             4 9 370.56  28.33  380.0  370.56   0.00 295.0 380.0  85.0
Swine - market        5 9  39.22  10.79   45.0   39.22   7.41  28.0  50.0  22.0
Swine - breeding      6 9 116.44  84.19  180.0  116.44  26.69  28.0 198.0 170.0
Chicken - Broilers    7 9   0.90   0.00    0.9    0.90   0.00   0.9   0.9   0.0
Chicken - Layers      8 9   1.80   0.00    1.8    1.80   0.00   1.8   1.8   0.0
Ducks                 9 9   2.70   0.00    2.7    2.70   0.00   2.7   2.7   0.0
Turkeys              10 9   6.80   0.00    6.8    6.80   0.00   6.8   6.8   0.0
Sheep                11 9  39.39  10.80   48.5   39.39   0.00  28.0  48.5  20.5
Goats                12 9  34.72   4.48   38.5   34.72   0.00  30.0  38.5   8.5
Horses               13 9 315.22  73.26  377.0  315.22   0.00 238.0 377.0 139.0
Asses                14 9 130.00   0.00  130.0  130.00   0.00 130.0 130.0   0.0
Mules                15 9 130.00   0.00  130.0  130.00   0.00 130.0 130.0   0.0
Camels               16 9 217.00   0.00  217.0  217.00   0.00 217.0 217.0   0.0
Llamas               17 9 217.00   0.00  217.0  217.00   0.00 217.0 217.0   0.0
                    skew kurtosis    se
IPCC Area*          0.00    -1.60  0.91
Cattle - dairy      0.11    -1.92 46.80
Cattle - non-dairy -0.45    -1.68 38.75
Buffaloes          -2.07     2.63  9.44
Swine - market     -0.12    -2.12  3.60
Swine - breeding   -0.17    -2.15 28.06
Chicken - Broilers   NaN      NaN  0.00
Chicken - Layers     NaN      NaN  0.00
Ducks                NaN      NaN  0.00
Turkeys              NaN      NaN  0.00
Sheep              -0.19    -2.17  3.60
Goats              -0.19    -2.17  1.49
Horses             -0.19    -2.17 24.42
Asses                NaN      NaN  0.00
Mules                NaN      NaN  0.00
Camels               NaN      NaN  0.00
Llamas               NaN      NaN  0.00

Briefly describe the data

The animal_weight dataset contains 16 types (including subtypes, such as dairy cattle vs. non-dairy cattle) of livestock weight information for 9 IPCC (Intergovernmental Panel on Climate Change) areas.

Anticipate the End Result

The first step in pivoting the data is to try to come up with a concrete vision of what the end product should look like - that way you will know whether or not your pivoting was successful.

One easy way to do this is to think about the dimensions of your current data (tibble, dataframe, or matrix), and then calculate what the dimensions of the pivoted data should be.

Suppose you have a dataset with \(n\) rows and \(k\) variables. In our example, 3 of the variables are used to identify a case, so you will be pivoting \(k-3\) variables into a longer format where the \(k-3\) variable names will move into the names_to variable and the current values in each of those columns will move into the values_to variable. Therefore, we would expect \(n * (k-3)\) rows in the pivoted dataframe!

Example: find current and future data dimensions

Lets see if this works with a simple example.

Code

df<-tibble(country = rep(c("Mexico", "USA", "France"),2),
           year = rep(c(1980,1990), 3), 
           trade = rep(c("NAFTA", "NAFTA", "EU"),2),
           outgoing = rnorm(6, mean=1000, sd=500),
           incoming = rlogis(6, location=1000, 
                             scale = 400))
df

# A tibble: 6 × 5
  country  year trade outgoing incoming
  <chr>   <dbl> <chr>    <dbl>    <dbl>
1 Mexico   1980 NAFTA    1007.     896.
2 USA      1990 NAFTA     501.     526.
3 France   1980 EU       1268.    1085.
4 Mexico   1990 NAFTA     748.     357.
5 USA      1980 NAFTA    1022.    1191.
6 France   1990 EU       1215.    2224.

Code

#existing rows/cases
nrow(df)

[1] 6

Code

#existing columns/cases
ncol(df)

[1] 5

Code

#expected rows/cases
nrow(df) * (ncol(df)-3)

[1] 12

Code

# expected columns 
3 + 2

[1] 5

Or simple example has \(n = 6\) rows and \(k - 3 = 2\) variables being pivoted, so we expect a new dataframe to have \(n * 2 = 12\) rows x \(3 + 2 = 5\) columns.

Challenge: Describe the final dimensions

The current dimensions of animal_weight are 9 rows of IPCC Areas, and 17 columns (types of livestock). 9*(17-1)=144 rows, and we will want each row to have the IPCC region, type of livestock, and the weight. This means there should be 3 columns.

Code

#Existing rows/cases
nrow(animal_weight)

[1] 9

Code

#Existing columns/cases
ncol(animal_weight)

[1] 17

Code

#Expected rows/cases
nrow(animal_weight) * (ncol(animal_weight)-1) #subtract first column IPCC Area

[1] 144

Any additional comments?

Pivot the Data

Now we will pivot the data, and compare our pivoted data dimensions to the dimensions calculated above as a “sanity” check.

Example

Code

df<-pivot_longer(df, col = c(outgoing, incoming),
                 names_to="trade_direction",
                 values_to = "trade_value")
df

# A tibble: 12 × 5
   country  year trade trade_direction trade_value
   <chr>   <dbl> <chr> <chr>                 <dbl>
 1 Mexico   1980 NAFTA outgoing              1007.
 2 Mexico   1980 NAFTA incoming               896.
 3 USA      1990 NAFTA outgoing               501.
 4 USA      1990 NAFTA incoming               526.
 5 France   1980 EU    outgoing              1268.
 6 France   1980 EU    incoming              1085.
 7 Mexico   1990 NAFTA outgoing               748.
 8 Mexico   1990 NAFTA incoming               357.
 9 USA      1980 NAFTA outgoing              1022.
10 USA      1980 NAFTA incoming              1191.
11 France   1990 EU    outgoing              1215.
12 France   1990 EU    incoming              2224.

Yes, once it is pivoted long, our resulting data are \(12x5\) - exactly what we expected!

Challenge: Pivot the Chosen Data

Using pivot_longer to transform the data where each row represents a livestock type with its weight within an IPCC region.

Code

animal_weight_pivot_longer <- pivot_longer(animal_weight,
                                           cols = -`IPCC Area`, #the - means except for this column?
                                           names_to = "livestock type",
                                           values_to = "weight")
animal_weight_pivot_longer

# A tibble: 144 × 3
   `IPCC Area`         `livestock type`   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