Challenge 4

challenge_4

poultry_tidy

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

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

---
title: "Challenge 4"
author: "Danny Holt"
description: "More data wrangling: pivoting"
date: "6/12/2023"
format:
  html:
    df-print: paged
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_4
  - poultry_tidy
---

```{r}
#| label: setup
#| warning: false
#| message: false

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:

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

## Read in data
First, we'll read in poultry_tidy.xlsx.
```{r}
  poultry <- read_excel("_data/poultry_tidy.xlsx")
  poultry
```

### 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
```{r}
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.
```{r}
# 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
```