Challenge 4

More data wrangling: pivoting

Author

Danny Holt

Published

June 12, 2023

Code

library(tidyverse)
library(readxl)
library(lubridate, warn.conflicts = FALSE)

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

Challenge Overview

Today’s challenge is to:

identify variables that need to be mutated
mutate variables and sanity check all mutations

Read in data

First, we’ll read in poultry_tidy.xlsx.

Code

  poultry <- read_excel("_data/poultry_tidy.xlsx")
  poultry

ABCDEFGHIJ0123456789

Product <chr>	Year <dbl>	Month <chr>	Price_Dollar <dbl>
Whole	2013	January	2.38500
Whole	2013	February	2.38500
Whole	2013	March	2.38500
Whole	2013	April	2.38500
Whole	2013	May	2.38500
Whole	2013	June	2.38500
Whole	2013	July	2.38500
Whole	2013	August	2.38500
Whole	2013	September	2.38500
Whole	2013	October	2.38500

Briefly describe the data

The data tells prices for different poultry cuts in months from 2004 to 2013. The prices appear to be per unit of weight (unit not specified).

Tidy Data (as needed)

The data is already tidy. Each variable forms a column and each observation forms a row. All values have their own cells.

Identify variables that need to be mutated

We can use mutate() and lubridate to create one properly coded date variable.

First, let’s combine Month and Year into one Date variable

Code

poultry <- poultry %>%
  # combine month and year
  mutate(date_prelim=str_c(`Month`," ",`Year`))

Next, we’ll use lubridate to create a properly coded Date variable.

Code

# create month year stamp
my_st<-stamp("02-2019", orders = "my")

poultry <- poultry %>%
  # recode string to date
  mutate(Date=my_st(my(date_prelim))) %>%
  # remove unnecessary variables
  select(Product,Date,Price_Dollar)
poultry

ABCDEFGHIJ0123456789

Product <chr>	Date <chr>	Price_Dollar <dbl>
Whole	01-2013	2.38500
Whole	02-2013	2.38500
Whole	03-2013	2.38500
Whole	04-2013	2.38500
Whole	05-2013	2.38500
Whole	06-2013	2.38500
Whole	07-2013	2.38500
Whole	08-2013	2.38500
Whole	09-2013	2.38500
Whole	10-2013	2.38500