Challenge 3 Instructions

challenge_3

animal_weights

eggs

australian_marriage

usa_households

sce_labor

Author

Prajakti Kapade

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 organiceggpoultry.xls ⭐⭐⭐
australian_marriage*.xls ⭐⭐⭐
USA Households*.xlsx ⭐⭐⭐⭐
sce_labor_chart_data_public.xlsx 🌟🌟🌟🌟🌟

Code

data <-read.csv('_data/animal_weight.csv')
data

            IPCC.Area Cattle...dairy Cattle...non.dairy Buffaloes
1 Indian Subcontinent            275                110       295
2      Eastern Europe            550                391       380
3              Africa            275                173       380
4             Oceania            500                330       380
5      Western Europe            600                420       380
6       Latin America            400                305       380
7                Asia            350                391       380
8         Middle east            275                173       380
9    Northern America            604                389       380
  Swine...market Swine...breeding Chicken...Broilers Chicken...Layers Ducks
1             28               28                0.9              1.8   2.7
2             50              180                0.9              1.8   2.7
3             28               28                0.9              1.8   2.7
4             45              180                0.9              1.8   2.7
5             50              198                0.9              1.8   2.7
6             28               28                0.9              1.8   2.7
7             50              180                0.9              1.8   2.7
8             28               28                0.9              1.8   2.7
9             46              198                0.9              1.8   2.7
  Turkeys Sheep Goats Horses Asses Mules Camels Llamas
1     6.8  28.0  30.0    238   130   130    217    217
2     6.8  48.5  38.5    377   130   130    217    217
3     6.8  28.0  30.0    238   130   130    217    217
4     6.8  48.5  38.5    377   130   130    217    217
5     6.8  48.5  38.5    377   130   130    217    217
6     6.8  28.0  30.0    238   130   130    217    217
7     6.8  48.5  38.5    377   130   130    217    217
8     6.8  28.0  30.0    238   130   130    217    217
9     6.8  48.5  38.5    377   130   130    217    217

Briefly describe the data

Describe the data, and be sure to comment on why you are planning to pivot it to make it “tidy”

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    2272.    1668.
2 USA      1990 NAFTA     724.     844.
3 France   1980 EU        765.    1069.
4 Mexico   1990 NAFTA     866.    1587.
5 USA      1980 NAFTA     952.     922.
6 France   1990 EU       1680.    -134.

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

Document your work here.

The area is the only variable that is used to decsribe the case, other values are just weights of the animals which need to be pivoted and converted to a single column, so that it can be neat.

Code

data

            IPCC.Area Cattle...dairy Cattle...non.dairy Buffaloes
1 Indian Subcontinent            275                110       295
2      Eastern Europe            550                391       380
3              Africa            275                173       380
4             Oceania            500                330       380
5      Western Europe            600                420       380
6       Latin America            400                305       380
7                Asia            350                391       380
8         Middle east            275                173       380
9    Northern America            604                389       380
  Swine...market Swine...breeding Chicken...Broilers Chicken...Layers Ducks
1             28               28                0.9              1.8   2.7
2             50              180                0.9              1.8   2.7
3             28               28                0.9              1.8   2.7
4             45              180                0.9              1.8   2.7
5             50              198                0.9              1.8   2.7
6             28               28                0.9              1.8   2.7
7             50              180                0.9              1.8   2.7
8             28               28                0.9              1.8   2.7
9             46              198                0.9              1.8   2.7
  Turkeys Sheep Goats Horses Asses Mules Camels Llamas
1     6.8  28.0  30.0    238   130   130    217    217
2     6.8  48.5  38.5    377   130   130    217    217
3     6.8  28.0  30.0    238   130   130    217    217
4     6.8  48.5  38.5    377   130   130    217    217
5     6.8  48.5  38.5    377   130   130    217    217
6     6.8  28.0  30.0    238   130   130    217    217
7     6.8  48.5  38.5    377   130   130    217    217
8     6.8  28.0  30.0    238   130   130    217    217
9     6.8  48.5  38.5    377   130   130    217    217

Code

nrow(data)

[1] 9

Code

ncol(data)

[1] 17

Code

#no of changes to be made i.e. values to be moved
nrow(data) * (ncol(data)-1)

[1] 144

Any additional comments?

The table will change into a cleaner table, though there will be very high number of values to be moved.

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              2272.
 2 Mexico   1980 NAFTA incoming              1668.
 3 USA      1990 NAFTA outgoing               724.
 4 USA      1990 NAFTA incoming               844.
 5 France   1980 EU    outgoing               765.
 6 France   1980 EU    incoming              1069.
 7 Mexico   1990 NAFTA outgoing               866.
 8 Mexico   1990 NAFTA incoming              1587.
 9 USA      1980 NAFTA outgoing               952.
10 USA      1980 NAFTA incoming               922.
11 France   1990 EU    outgoing              1680.
12 France   1990 EU    incoming              -134.

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

Challenge: Pivot the Chosen Data

Document your work here. What will a new “case” be once you have pivoted the data? How does it meet requirements for tidy data?

Code

names(data)

 [1] "IPCC.Area"          "Cattle...dairy"     "Cattle...non.dairy"
 [4] "Buffaloes"          "Swine...market"     "Swine...breeding"  
 [7] "Chicken...Broilers" "Chicken...Layers"   "Ducks"             
[10] "Turkeys"            "Sheep"              "Goats"             
[13] "Horses"             "Asses"              "Mules"             
[16] "Camels"             "Llamas"

Code

data<-pivot_longer(data, col = c("Cattle...dairy","Cattle...non.dairy","Buffaloes","Swine...market","Swine...breeding","Chicken...Broilers","Chicken...Layers","Ducks","Turkeys","Sheep","Goats","Horses","Asses","Mules","Camels","Llamas"),
                 names_to="animal_name",
                 values_to = "weight_value")
data

# A tibble: 144 × 3
   IPCC.Area           animal_name        weight_value
   <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

Any additional comments? The table looks like tidier and has lesser columns but more descriptive of the weight each animal has in every area.