Code
library(tidyverse)
::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) knitr
Yakub Rabiutheen
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 × 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
# … 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
.
Because all of the Data can be grouped in one Livestock column, we only need one Column for Live
# A tibble: 144 × 3
`IPCC Area` Livestock Weight
<chr> <chr> <dbl>
1 Indian Subcontinent Cattle - dairy 275
2 Indian Subcontinent Cattle - non-dairy 110
3 Indian Subcontinent Buffaloes 295
4 Indian Subcontinent Swine - market 28
5 Indian Subcontinent Swine - breeding 28
6 Indian Subcontinent Chicken - Broilers 0.9
7 Indian Subcontinent Chicken - Layers 1.8
8 Indian Subcontinent Ducks 2.7
9 Indian Subcontinent Turkeys 6.8
10 Indian Subcontinent Sheep 28
# … with 134 more rows
# ℹ Use `print(n = ...)` to see more rows
---
title: "Challenge 3 Instructions"
author: "Yakub Rabiutheen"
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")
```
### 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
head(animal_weight)
```
.
Because all of the Data can be grouped in one Livestock column, we only need one Column for Live
```{r}
animal_pivot<-pivot_longer(animal_weight,
col=-`IPCC Area`,
names_to = "Livestock",
values_to = "Weight")
animal_pivot
```