Challenge 4 Instructions

challenge_4

abc_poll

eggs

fed_rates

hotel_bookings

debt

More data wrangling: pivoting

Author

Sean Conway

Published

May 30, 2023

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)
tidy data (as needed, including sanity checks)
identify variables that need to be mutated
mutate variables and sanity check all mutations

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

abc_poll.csv ⭐
poultry_tidy.xlsx or organiceggpoultry.xls⭐⭐
FedFundsRate.csv⭐⭐⭐
hotel_bookings.csv⭐⭐⭐⭐
debt_in_trillions.xlsx ⭐⭐⭐⭐⭐

Briefly describe the data

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

Any additional comments?

Identify variables that need to be mutated

Are there any variables that require mutation to be usable in your analysis stream? For example, are all time variables correctly coded as dates? Are all string variables reduced and cleaned to sensible categories? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

Any additional comments?

---
title: "Challenge 4 Instructions"
author: "Sean Conway"
description: "More data wrangling: pivoting"
date: "5/30/2023"
format:
  html:
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_4
  - abc_poll
  - eggs
  - fed_rates
  - hotel_bookings
  - debt
---

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

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)  tidy data (as needed, including sanity checks)
3)  identify variables that need to be mutated
4)  mutate variables and sanity check all mutations

## Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

-   abc_poll.csv ⭐
-   poultry_tidy.xlsx or organiceggpoultry.xls⭐⭐
-   FedFundsRate.csv⭐⭐⭐
-   hotel_bookings.csv⭐⭐⭐⭐
-   debt_in_trillions.xlsx ⭐⭐⭐⭐⭐

```{r}
```

### Briefly describe the data

## Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

```{r}
```

Any additional comments?

## Identify variables that need to be mutated

Are there any variables that require mutation to be usable in your analysis stream? For example, are all time variables correctly coded as dates? Are all string variables reduced and cleaned to sensible categories? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

```{r}
```

Any additional comments?