library(tidyverse)
::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) knitr
Challenge 3
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)
- identify what needs to be done to tidy the current data
- anticipate the shape of pivoted data
- pivot the data into tidy format using
pivot_longer
Read in data
Read in one (or more) of the following datasets, using the correct R package and command.
- animal_weights.csv ⭐
- eggs_tidy.csv ⭐⭐ or organicpoultry.xls ⭐⭐⭐
- australian_marriage*.xlsx ⭐⭐⭐
- USA Households*.xlsx ⭐⭐⭐⭐
- sce_labor_chart_data_public.csv 🌟🌟🌟🌟🌟
library(readr)
<- read_csv("_data/australian_marriage_tidy.csv")
australian_marriage_tidy View(australian_marriage_tidy)
Briefly describe the data
Describe the data, and be sure to comment on why you are planning to pivot it to make it “tidy”
Anticipate the End Result
The first step in pivoting the data is to try to come up with a concrete vision of what the end product should look like - that way you will know whether or not your pivoting was successful.
One easy way to do this is to think about the dimensions of your current data (tibble, dataframe, or matrix), and then calculate what the dimensions of the pivoted data should be.
Suppose you have a dataset with \(n\) rows and \(k\) variables. In our example, 3 of the variables are used to identify a case, so you will be pivoting \(k-3\) variables into a longer format where the \(k-3\) variable names will move into the names_to
variable and the current values in each of those columns will move into the values_to
variable. Therefore, we would expect \(n * (k-3)\) rows in the pivoted dataframe!
Example: find current and future data dimensions
Lets see if this works with a simple example.
<-tibble(country = rep(c("Mexico", "USA", "France"),2),
dfyear = rep(c(1980,1990), 3),
trade = rep(c("NAFTA", "NAFTA", "EU"),2),
outgoing = rnorm(6, mean=1000, sd=500),
incoming = rlogis(6, location=1000,
scale = 400))
df
# A tibble: 6 × 5
country year trade outgoing incoming
<chr> <dbl> <chr> <dbl> <dbl>
1 Mexico 1980 NAFTA -1.14 1059.
2 USA 1990 NAFTA 1286. 809.
3 France 1980 EU 839. -649.
4 Mexico 1990 NAFTA 1136. 13.9
5 USA 1980 NAFTA 786. 2506.
6 France 1990 EU 1268. 708.
#existing rows/cases
nrow(df)
[1] 6
#existing columns/cases
ncol(df)
[1] 5
#expected rows/cases
nrow(df) * (ncol(df)-3)
[1] 12
# expected columns
3 + 2
[1] 5
Or simple example has \(n = 6\) rows and \(k - 3 = 2\) variables being pivoted, so we expect a new dataframe to have \(n * 2 = 12\) rows x \(3 + 2 = 5\) columns.
Challenge: Describe the final dimensions
Document your work here.
print(australian_marriage_tidy, n = 5, width = 4)
# A
# tibble:
# 16
# ×
# 4
# … with 11 more rows, and 4 more variables: territory <chr>, resp <chr>, count <dbl>, percent <dbl>
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#existing rows
nrow(australian_marriage_tidy)
[1] 16
#existing columns
ncol(australian_marriage_tidy)
[1] 4
#existing cases
nrow(australian_marriage_tidy) * ncol(australian_marriage_tidy)
[1] 64
<- select(australian_marriage_tidy, ) Marriage
Any additional comments?
Pivot the Data
Now we will pivot the data, and compare our pivoted data dimensions to the dimensions calculated above as a “sanity” check.
Example
<-pivot_longer(df, col = c(outgoing, incoming),
dfnames_to="trade_direction",
values_to = "trade_value")
df
# A tibble: 12 × 5
country year trade trade_direction trade_value
<chr> <dbl> <chr> <chr> <dbl>
1 Mexico 1980 NAFTA outgoing -1.14
2 Mexico 1980 NAFTA incoming 1059.
3 USA 1990 NAFTA outgoing 1286.
4 USA 1990 NAFTA incoming 809.
5 France 1980 EU outgoing 839.
6 France 1980 EU incoming -649.
7 Mexico 1990 NAFTA outgoing 1136.
8 Mexico 1990 NAFTA incoming 13.9
9 USA 1980 NAFTA outgoing 786.
10 USA 1980 NAFTA incoming 2506.
11 France 1990 EU outgoing 1268.
12 France 1990 EU incoming 708.
Yes, once it is pivoted long, our resulting data are \(12x5\) - exactly what we expected!
Challenge: Pivot the Chosen Data
Document your work here. What will a new “case” be once you have pivoted the data? How does it meet requirements for tidy data?
Any additional comments?