Challenge 3 Instructions

Tidy Data: Pivoting

Code

library(tidyverse)

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

Challenge Overview

Today’s challenge is to:

1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
2. identify what needs to be done to tidy the current data
3. anticipate the shape of pivoted data
4. 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 🌟🌟🌟🌟🌟

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     663.     248.
2 USA      1990 NAFTA    1501.    1865.
3 France   1980 EU        660.    1654.
4 Mexico   1990 NAFTA     928.    1335.
5 USA      1980 NAFTA    1461.    2074.
6 France   1990 EU       1114.    1843.

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.

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               663.
 2 Mexico   1980 NAFTA incoming               248.
 3 USA      1990 NAFTA outgoing              1501.
 4 USA      1990 NAFTA incoming              1865.
 5 France   1980 EU    outgoing               660.
 6 France   1980 EU    incoming              1654.
 7 Mexico   1990 NAFTA outgoing               928.
 8 Mexico   1990 NAFTA incoming              1335.
 9 USA      1980 NAFTA outgoing              1461.
10 USA      1980 NAFTA incoming              2074.
11 France   1990 EU    outgoing              1114.
12 France   1990 EU    incoming              1843.

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?

Any additional comments?