Challenge 3

challenge_3

animal_weights

eggs

australian_marriage

usa_households

sce_labor

Author

Janhvi Joshi

Published

November 5, 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

eggs_tidy <- read_csv('_data/eggs_tidy.csv')
eggs_tidy

# A tibble: 120 × 6
   month      year large_half_dozen large_dozen extra_large_half_dozen extra_l…¹
   <chr>     <dbl>            <dbl>       <dbl>                  <dbl>     <dbl>
 1 January    2004             126         230                    132       230 
 2 February   2004             128.        226.                   134.      230 
 3 March      2004             131         225                    137       230 
 4 April      2004             131         225                    137       234.
 5 May        2004             131         225                    137       236 
 6 June       2004             134.        231.                   137       241 
 7 July       2004             134.        234.                   137       241 
 8 August     2004             134.        234.                   137       241 
 9 September  2004             130.        234.                   136.      241 
10 October    2004             128.        234.                   136.      241 
# … with 110 more rows, and abbreviated variable name ¹extra_large_dozen

Code

summary(eggs_tidy)

    month                year      large_half_dozen  large_dozen   
 Length:120         Min.   :2004   Min.   :126.0    Min.   :225.0  
 Class :character   1st Qu.:2006   1st Qu.:129.4    1st Qu.:233.5  
 Mode  :character   Median :2008   Median :174.5    Median :267.5  
                    Mean   :2008   Mean   :155.2    Mean   :254.2  
                    3rd Qu.:2011   3rd Qu.:174.5    3rd Qu.:268.0  
                    Max.   :2013   Max.   :178.0    Max.   :277.5  
 extra_large_half_dozen extra_large_dozen
 Min.   :132.0          Min.   :230.0    
 1st Qu.:135.8          1st Qu.:241.5    
 Median :185.5          Median :285.5    
 Mean   :164.2          Mean   :266.8    
 3rd Qu.:185.5          3rd Qu.:285.5    
 Max.   :188.1          Max.   :290.0

Briefly describe the data

This dataset has 120 rows and 6 columns and describes the prices of different types of eggs from year 2004 to 2013. 4 types of eggs are described - large_half_dozen, large_dozen, extra_large_half_dozen, extra_large_dozen. I chose to tidy this dataset because it currently stores the price of 2 types of eggs (large and extra large) for both dozen and half dozen, but all are stored in 4 different columns. It would be better to have one row for one eggs size and quantity. This would help in analysing trends of how the prices different sizes eggs changed over the years and in what quantities.

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     910.    2026.
2 USA      1990 NAFTA    1546.    2008.
3 France   1980 EU       1082.    1436.
4 Mexico   1990 NAFTA    1575.     432.
5 USA      1980 NAFTA     909.    1283.
6 France   1990 EU        655.    1787.

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

After pivoting the table, I expect the dataset the dataset to have the month, year, size of the egg and the quantity of the eggs as columns. The resulting dataset will have data 4 times longer. The columns will change from 6 to 5.

Code

#existing rows/cases
nrow(eggs_tidy)

[1] 120

Code

#existing columns/cases
ncol(eggs_tidy)

[1] 6

Code

#expected rows/cases
nrow(eggs_tidy) * (ncol(eggs_tidy)-2)

[1] 480

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               910.
 2 Mexico   1980 NAFTA incoming              2026.
 3 USA      1990 NAFTA outgoing              1546.
 4 USA      1990 NAFTA incoming              2008.
 5 France   1980 EU    outgoing              1082.
 6 France   1980 EU    incoming              1436.
 7 Mexico   1990 NAFTA outgoing              1575.
 8 Mexico   1990 NAFTA incoming               432.
 9 USA      1980 NAFTA outgoing               909.
10 USA      1980 NAFTA incoming              1283.
11 France   1990 EU    outgoing               655.
12 France   1990 EU    incoming              1787.

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

Challenge: Pivot the Chosen Data

As expected, the resulting data is 4 times longer (from 120 -> 480). The number of columns has been reduced by 1 from 6 -> 5. Now, we have a single record of one egg size and quantity per row and helps in easy understanding of data for future analysis.

Code

eggs_longer <- eggs_tidy%>%
  pivot_longer(cols=contains("large"),
               names_to = c("Size", "Quantity"),
               names_sep="_",
               values_to = "Cost"
  )
eggs_longer

# A tibble: 480 × 5
   month     year Size  Quantity  Cost
   <chr>    <dbl> <chr> <chr>    <dbl>
 1 January   2004 large half      126 
 2 January   2004 large dozen     230 
 3 January   2004 extra large     132 
 4 January   2004 extra large     230 
 5 February  2004 large half      128.
 6 February  2004 large dozen     226.
 7 February  2004 extra large     134.
 8 February  2004 extra large     230 
 9 March     2004 large half      131 
10 March     2004 large dozen     225 
# … with 470 more rows

Any additional comments? Yes, another optimization could be to take an average of the cost of a size and quantity combination for each month for better trend analysis.