Challenge 3 Instructions

Tidy Data: Pivoting

Code

library(tidyverse)

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

Challenge Overview

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

First, we’ll read in the data set eggs_tidy.csv

Code

  #Read in data
  eggs <- readr::read_csv("_data/eggs_tidy.csv")
  #Number of rows and columns:
  nrow(eggs)

[1] 120

Code

  ncol(eggs)

[1] 6

Code

  #Data:
  eggs

Briefly describe the data

The data set appears to show the number of sets of a dozen and half dozen large and extra large eggs, likely on a specific farm. Each observation/row shows data from a specific month. The data has 120 rows and 6 columns.

Because the columns other than year and month all measure number of eggs, we can pivot the data to combine the eggs together, and add the new columns type and amount to maintain the dozen/half dozen information and the amount information. Based on this, we know that the pivot will have four (2+2) columns.

We are effectively turning the existing columns large_half_dozen, large_dozen, extra_large_half_dozen, and extra_large_dozen into rows with the resulting rate of four rows in the pivot to one row in the existing data (four rows for a given month). This means that our data should have the following number of rows:

Code

nrow(eggs)*4

[1] 480

Pivot the Data

Now we will pivot the data.

Code

eggs<-
  eggs%>%
    pivot_longer(col=c(large_half_dozen,large_dozen,extra_large_half_dozen,extra_large_dozen),
                                 names_to="type",
                                 values_to="amount")
eggs

Here, we see that we do indeed have 480 rows and 4 columns.

A new “case” in the pivoted data will contain month and year data, as well as the type (dozen/half dozen and large/extra large) and amount.

In our pivoted data, each variable forms a column and each observation forms a row. All values have their own cells. The data is easier to examine because of this.