Challenge 4 Will Munson

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. 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.csv⭐⭐
• FedFundsRate.csv⭐⭐⭐
• hotel_bookings.csv⭐⭐⭐⭐
• debt_in_trillions ⭐⭐⭐⭐⭐

Code

poultry_tidy<-read_csv("_data/poultry_tidy.csv",
                        show_col_types = FALSE)

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?

I believe the data is already tidy. Each value has its own column, and there's not much to do. However, I noticed that the year column seems to be backwards, and the price values don't round up to two digits. I'm not sure if that's a problem.

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.

Code

poultry_tidy %>%
  arrange(Product, Year) %>%
  mutate_at(vars(Price_Dollar), funs(round(., digit = 2)))

# A tibble: 600 × 4
   Product     Year Month     Price_Dollar
   <chr>      <dbl> <chr>            <dbl>
 1 B/S Breast  2004 January           6.46
 2 B/S Breast  2004 February          6.42
 3 B/S Breast  2004 March             6.42
 4 B/S Breast  2004 April             6.42
 5 B/S Breast  2004 May               6.42
 6 B/S Breast  2004 June              6.41
 7 B/S Breast  2004 July              6.42
 8 B/S Breast  2004 August            6.42
 9 B/S Breast  2004 September         6.42
10 B/S Breast  2004 October           6.42
# … with 590 more rows
# ℹ Use `print(n = ...)` to see more rows

Any additional comments?

While the data might be tidy, you should also check and make sure the values are entered in a way that makes sense. Otherwise, there's