Challenge 8

challenge_8

faostat

Joining Data

Author

Noah Dixon

Published

June 27, 2023

library(tidyverse)
library(readxl)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Using the read.csv function we can read the FAOSTAT_egg_chicken and FAOSTAT_country_groups data into data frames.

egg_chicken <- read.csv("_data/FAOSTAT_egg_chicken.csv")
egg_chicken

country_groups <- read.csv("_data/FAOSTAT_country_groups.csv")
country_groups

Briefly describe the data

The FAOSTAT_egg_chicken data set shows the number of eggs sold in countries around the world for the years 1961-2018. The FAOSTAT_country_groups data frame shows the country group for each country (ex. country: Algeria, country group: Africa).

Tidy Data (as needed)

For the sake of this challenge, we are only going to look at the eggs sold in 2018 in units of “1000 head”. We will use the subset function to select only rows with these conditions in the egg_chicken data frame, and then only select the columns we are interested in (the Area, Year, and Value (number of sold eggs)).

egg_chicken <- subset(egg_chicken, Year == 2018 & Unit == "1000 Head") %>%
  select(Area, Year, Value)
egg_chicken

In the country_groups data frame, we will tidy the data to only select the country group and country columns.

country_groups <- country_groups %>%
  select(Country.Group, Country)
country_groups

Join Data

Using the right_join function we can add the country group to each row in the egg_chicken data frame.

joined_data <- right_join(country_groups, egg_chicken, by = c("Country" = "Area"))
joined_data

Now, we will filter only the rows where the country group is in the following list: Europe, Americas, Asia, Oceania, Africa.

filtered_data <- joined_data %>%
  filter(Country.Group %in% c("Europe", "Americas", "Asia", "Oceania", "Africa"))
filtered_data

Now, we can group the data by these regions.

grouped_data <- filtered_data %>%
  rename(Region = Country.Group) %>%
  group_by(Region) %>%
  summarize(EggsSold = sum(Value))
grouped_data

Now, we can visualize the joined and grouped data to show how many eggs were sold in each region in 2018.

ggplot(grouped_data, aes(x = Region, y = EggsSold, fill = Region)) +
  geom_bar(stat = "identity") + 
  labs(title = "Eggs Sold in 2018", x = "Region", y = "Thousands of Eggs") + 
  guides(fill = FALSE)

--- title: "Challenge 8" author: "Noah Dixon" description: "Joining Data" date: "6/27/2023" format: html: df-print: paged toc: true code-copy: true code-tools: true categories: - challenge_8 - faostat --- ```{r} #| label: setup #| warning: false #| message: false library(tidyverse) library(readxl) library(ggplot2) knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) ``` ## Challenge Overview Using the read.csv function we can read the FAOSTAT_egg_chicken and FAOSTAT_country_groups data into data frames. ```{r} #| label: read data egg_chicken <- read.csv("_data/FAOSTAT_egg_chicken.csv") egg_chicken country_groups <- read.csv("_data/FAOSTAT_country_groups.csv") country_groups ``` ### Briefly describe the data The FAOSTAT_egg_chicken data set shows the number of eggs sold in countries around the world for the years 1961-2018. The FAOSTAT_country_groups data frame shows the country group for each country (ex. country: Algeria, country group: Africa). ## Tidy Data (as needed) For the sake of this challenge, we are only going to look at the eggs sold in 2018 in units of "1000 head". We will use the subset function to select only rows with these conditions in the egg_chicken data frame, and then only select the columns we are interested in (the Area, Year, and Value (number of sold eggs)). ```{r} #| label: tidy egg_chicken egg_chicken <- subset(egg_chicken, Year == 2018 & Unit == "1000 Head") %>% select(Area, Year, Value) egg_chicken ``` In the country_groups data frame, we will tidy the data to only select the country group and country columns. ```{r} #| label: tidy country_groups country_groups <- country_groups %>% select(Country.Group, Country) country_groups ``` ## Join Data Using the right_join function we can add the country group to each row in the egg_chicken data frame. ```{r} #| label: join data joined_data <- right_join(country_groups, egg_chicken, by = c("Country" = "Area")) joined_data ``` Now, we will filter only the rows where the country group is in the following list: Europe, Americas, Asia, Oceania, Africa. ```{r} #| label: filter joined data filtered_data <- joined_data %>% filter(Country.Group %in% c("Europe", "Americas", "Asia", "Oceania", "Africa")) filtered_data ``` Now, we can group the data by these regions. ```{r} #| label: group data grouped_data <- filtered_data %>% rename(Region = Country.Group) %>% group_by(Region) %>% summarize(EggsSold = sum(Value)) grouped_data ``` Now, we can visualize the joined and grouped data to show how many eggs were sold in each region in 2018. ```{r} #| label: plot yes reponses ggplot(grouped_data, aes(x = Region, y = EggsSold, fill = Region)) + geom_bar(stat = "identity") + labs(title = "Eggs Sold in 2018", x = "Region", y = "Thousands of Eggs") + guides(fill = FALSE) ```