Code
library(tidyverse)
::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) knitr
Jerin Jacob
August 17, 2022
# 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
The animal weight data has the weight of 16 types of livestocks collected from 9 geographical areas.
The pivoted dataset will have 144 rows/ cases and 3 columns of region, animal type and weight.
# 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"
author: " Jerin Jacob"
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)
```
## Read in data
```{r}
animal_weight<-read_csv("_data/animal_weight.csv",
show_col_types = FALSE)
animal_weight
```
## Describing the data
The animal weight data has the weight of 16 types of livestocks collected from 9 geographical areas.
The pivoted dataset will have 144 rows/ cases and 3 columns of region, animal type and weight.
```{r}
animal_weight_longer<-pivot_longer(animal_weight,
col=-`IPCC Area`,
names_to = "Livestock",
values_to = "Weight")
animal_weight_longer
```