Code
library(tidyverse)
::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) knitr
Lai Wei
August 17, 2022
Today’s challenge is to:
pivot_longer
Read in one (or more) of the following datasets, using the correct R package and command.
Describe the data, and be sure to comment on why you are planning to pivot it to make it “tidy”
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!
Lets see if this works with a simple example.
# A tibble: 6 × 5
country year trade outgoing incoming
<chr> <dbl> <chr> <dbl> <dbl>
1 Mexico 1980 NAFTA 1236. 2306.
2 USA 1990 NAFTA 728. 1510.
3 France 1980 EU 212. 798.
4 Mexico 1990 NAFTA 1167. 2398.
5 USA 1980 NAFTA 1333. 1480.
6 France 1990 EU 1525. 564.
[1] 6
[1] 5
[1] 12
[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.
Document your work here.
# A tibble: 9 × 17
IPCC A…¹ Cattl…² Cattl…³ Buffa…⁴ Swine…⁵ Swine…⁶ Chick…⁷ Chick…⁸ Ducks Turkeys
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Indian … 275 110 295 28 28 0.9 1.8 2.7 6.8
2 Eastern… 550 391 380 50 180 0.9 1.8 2.7 6.8
3 Africa 275 173 380 28 28 0.9 1.8 2.7 6.8
4 Oceania 500 330 380 45 180 0.9 1.8 2.7 6.8
5 Western… 600 420 380 50 198 0.9 1.8 2.7 6.8
6 Latin A… 400 305 380 28 28 0.9 1.8 2.7 6.8
7 Asia 350 391 380 50 180 0.9 1.8 2.7 6.8
8 Middle … 275 173 380 28 28 0.9 1.8 2.7 6.8
9 Norther… 604 389 380 46 198 0.9 1.8 2.7 6.8
# … with 7 more variables: Sheep <dbl>, Goats <dbl>, Horses <dbl>, Asses <dbl>,
# Mules <dbl>, Camels <dbl>, Llamas <dbl>, and abbreviated variable names
# ¹`IPCC Area`, ²`Cattle - dairy`, ³`Cattle - non-dairy`, ⁴Buffaloes,
# ⁵`Swine - market`, ⁶`Swine - breeding`, ⁷`Chicken - Broilers`,
# ⁸`Chicken - Layers`
# ℹ Use `colnames()` to see all variable names
[1] "IPCC Area" "Cattle - dairy" "Cattle - non-dairy"
[4] "Buffaloes" "Swine - market" "Swine - breeding"
[7] "Chicken - Broilers" "Chicken - Layers" "Ducks"
[10] "Turkeys" "Sheep" "Goats"
[13] "Horses" "Asses" "Mules"
[16] "Camels" "Llamas"
Any additional comments?
Now we will pivot the data, and compare our pivoted data dimensions to the dimensions calculated above as a “sanity” check.
# A tibble: 9 × 17
IPCC A…¹ Cattl…² Cattl…³ Buffa…⁴ Swine…⁵ Swine…⁶ Chick…⁷ Chick…⁸ Ducks Turkeys
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Indian … 275 110 295 28 28 0.9 1.8 2.7 6.8
2 Eastern… 550 391 380 50 180 0.9 1.8 2.7 6.8
3 Africa 275 173 380 28 28 0.9 1.8 2.7 6.8
4 Oceania 500 330 380 45 180 0.9 1.8 2.7 6.8
5 Western… 600 420 380 50 198 0.9 1.8 2.7 6.8
6 Latin A… 400 305 380 28 28 0.9 1.8 2.7 6.8
7 Asia 350 391 380 50 180 0.9 1.8 2.7 6.8
8 Middle … 275 173 380 28 28 0.9 1.8 2.7 6.8
9 Norther… 604 389 380 46 198 0.9 1.8 2.7 6.8
# … with 7 more variables: Sheep <dbl>, Goats <dbl>, Horses <dbl>, Asses <dbl>,
# Mules <dbl>, Camels <dbl>, Llamas <dbl>, and abbreviated variable names
# ¹`IPCC Area`, ²`Cattle - dairy`, ³`Cattle - non-dairy`, ⁴Buffaloes,
# ⁵`Swine - market`, ⁶`Swine - breeding`, ⁷`Chicken - Broilers`,
# ⁸`Chicken - Layers`
# ℹ Use `colnames()` to see all variable names
[1] 9
[1] 17
Yes, once it is pivoted long, our resulting data are \(12x5\) - exactly what we expected!
Document your work here. What will a new “case” be once you have pivoted the data? How does it meet requirements for tidy data?
[1] 153
[1] 432
Any additional comments?
---
title: "Challenge 3"
author: "Lai Wei"
desription: "Tidy Data: Pivoting"
date: "08/17/2022"
format:
html:
toc: true
code-fold: true
code-copy: true
code-tools: true
categories:
- challenge_3
---
```{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. identify what needs to be done to tidy the current data
3. anticipate the shape of pivoted data
4. 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 🌟🌟🌟🌟🌟
```{r}
animal_weight<-read_csv("_data/animal_weight.csv",
show_col_types = FALSE)
```
### 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.
```{r}
#| tbl-cap: Example
df<-tibble(country = rep(c("Mexico", "USA", "France"),2),
year = 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
#existing rows/cases
nrow(df)
#existing columns/cases
ncol(df)
#expected rows/cases
nrow(df) * (ncol(df)-3)
# expected columns
3 + 2
```
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.
```{r}
animal_weight<-read_csv("_data/animal_weight.csv",
show_col_types = FALSE)
#import the data
animal_weight
#get names of columns
colnames(animal_weight)
```
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
```{r}
#| tbl-cap: Animal Weight Example
animal_weight_longer<-pivot_longer(animal_weight,
col = -'IPCC Area',
names_to = "Livesstock",
values_to = "Weight")
animal_weight
#number of rows of data
nrow(animal_weight)
#number of columns of data
ncol(animal_weight)
```
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?
```{r}
#number of total cases
nrow(animal_weight) * ncol(animal_weight)
# expected columns
3 * 144
```
Any additional comments?