Generated by summarytools 1.0.1 (R version 4.2.1) 2022-08-31
dim(faostat_country)
[1] 1943 7
The faostat_country dataset has 1943 rows and 7 columns. It contains information on each country name and unique code, the country groups they belong to, as well as codes for ‘M49’, ‘ISO2’, and ‘ISO3’. Only the ‘ISO2’ column has 8 missing values.
Tidy Data
Tidying faostat_country
faostat_country seems tidy enough to work with.
Tidying faostat_egg
# turning 'Element' and 'Flag Description' into factor typefaostat_egg$Element <-as.factor(faostat_egg$Element)faostat_egg$'Flag Description'<-as.factor(faostat_egg$'Flag Description')# deleting 'Year Code'faostat_egg <- faostat_egg %>%select(-'Year Code')
Tidying faostat_livestock
# turning 'Element' and 'Flag Description' into factor typefaostat_livestock$Element <-as.factor(faostat_livestock$Element)faostat_livestock$'Flag Description'<-as.factor(faostat_livestock$'Flag Description')# deleting 'Year Code'faostat_livestock <- faostat_livestock %>%select(-'Year Code')
Tidying faostat_cattle
# turning 'Element' and 'Flag Description' into factor typefaostat_cattle$Element <-as.factor(faostat_cattle$Element)faostat_cattle$'Flag Description'<-as.factor(faostat_cattle$'Flag Description')# deleting 'Year Code'faostat_cattle <- faostat_cattle %>%select(-'Year Code')
Join Data
Be sure to include a sanity check, and double-check that case count is correct!
Joining faostat_egg and faostat_livestock by rowbinding:
# A tibble: 28 × 3
# Groups: Area Code [28]
`Area Code` Area n
<dbl> <chr> <int>
1 5000 World 696
2 5100 Africa 696
3 5101 Eastern Africa 696
4 5102 Middle Africa 638
5 5103 Northern Africa 696
6 5104 Southern Africa 638
7 5105 Western Africa 638
8 5200 Americas 638
9 5203 Northern America 580
10 5204 Central America 580
# … with 18 more rows
# ℹ Use `print(n = ...)` to see more rows
The anti_join() on egg_livestock and faostat_country reveals that there are 17346 observations in egg_livestock that did not find a match in faostat_country. join_anti has 13 columns, the same as the egg_livestock dataset.
Analyze Data
I’m looking to compare egg production to camel livestock production in Bahrain and Qatar using a line graph with facet wrap.
# A tibble: 216 × 5
Year Area Item Element Value
<dbl> <chr> <chr> <fct> <dbl>
1 1961 Bahrain Eggs, hen, in shell Production 60
2 1962 Bahrain Eggs, hen, in shell Production 60
3 1963 Bahrain Eggs, hen, in shell Production 60
4 1964 Bahrain Eggs, hen, in shell Production 60
5 1965 Bahrain Eggs, hen, in shell Production 70
6 1966 Bahrain Eggs, hen, in shell Production 70
7 1967 Bahrain Eggs, hen, in shell Production 80
8 1968 Bahrain Eggs, hen, in shell Production 80
9 1969 Bahrain Eggs, hen, in shell Production 80
10 1970 Bahrain Eggs, hen, in shell Production 80
# … with 206 more rows
# ℹ Use `print(n = ...)` to see more rows
egg_livestock_filt %>%ggplot(aes(x=Year, y=Value, color=Area)) +geom_line() +labs(title ="Egg Production versus Camel Stocks (1961-2018)", x ="Year", y ="Value") +facet_wrap(~ Element) +theme_minimal()
Over time, egg production in Qatar exceeded that in Bahrain. Camel livestock production in Qatar evidently far-exceeds stocks in Bahrain too.
Source Code
---title: "Challenge 8"author: "Ananya Pujary"description: "Joining Data"date: "08/25/2022"format: html: toc: true code-copy: true code-tools: truecategories: - challenge_8 - faostat---```{r}#| label: setup#| warning: false#| message: falselibrary(tidyverse)library(ggplot2)library(summarytools)library(dplyr)knitr::opts_chunk$set(echo =TRUE, warning=FALSE, message=FALSE)```## Read in dataReading in the faostat datasets.```{r}#| label: reading in the datafaostat_egg <-read_csv("_data/FAOSTAT_egg_chicken.csv")faostat_livestock <-read_csv("_data/FAOSTAT_livestock.csv")faostat_cattle <-read_csv("_data/FAOSTAT_cattle_dairy.csv")faostat_country <-read_csv ("_data/FAOSTAT_country_groups.csv")```### Briefly describe the data```{r}#| label: data description 1print(summarytools::dfSummary(faostat_egg, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.50, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')dim(faostat_egg)```The `faostat_egg` dataset has 38170 rows and 13 columns. There are 40 missing values in the 'Values' column and 7548 missing values in 'Flag'.```{r}#| label: data description 2print(summarytools::dfSummary(faostat_livestock, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.50, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')dim(faostat_livestock)```There are 82116 rows and 13 columns in the `faostat_livestock` dataset. It has missing values too (1301 in 'Value' and 38270 in 'Flag').```{r}#| label: data description 3print(summarytools::dfSummary(faostat_country, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.50, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')dim(faostat_country)```The `faostat_country` dataset has 1943 rows and 7 columns. It contains information on each country name and unique code, the country groups they belong to, as well as codes for 'M49', 'ISO2', and 'ISO3'. Only the 'ISO2' column has 8 missing values.## Tidy Data### Tidying `faostat_country``faostat_country` seems tidy enough to work with.### Tidying `faostat_egg````{r}#| label: tidy data 1# turning 'Element' and 'Flag Description' into factor typefaostat_egg$Element <-as.factor(faostat_egg$Element)faostat_egg$'Flag Description'<-as.factor(faostat_egg$'Flag Description')# deleting 'Year Code'faostat_egg <- faostat_egg %>%select(-'Year Code')```### Tidying `faostat_livestock````{r}#| label: tidy data 2# turning 'Element' and 'Flag Description' into factor typefaostat_livestock$Element <-as.factor(faostat_livestock$Element)faostat_livestock$'Flag Description'<-as.factor(faostat_livestock$'Flag Description')# deleting 'Year Code'faostat_livestock <- faostat_livestock %>%select(-'Year Code')```### Tidying `faostat_cattle````{r}#| label: tidy data 3# turning 'Element' and 'Flag Description' into factor typefaostat_cattle$Element <-as.factor(faostat_cattle$Element)faostat_cattle$'Flag Description'<-as.factor(faostat_cattle$'Flag Description')# deleting 'Year Code'faostat_cattle <- faostat_cattle %>%select(-'Year Code')```## Join DataBe sure to include a sanity check, and double-check that case count is correct!Joining `faostat_egg` and `faostat_livestock` by rowbinding:```{r}#| label: join data 1egg_livestock <-bind_rows(faostat_egg,faostat_livestock)dim(egg_livestock)```Both datasets had 13 columns and the combined number of rows in the new dataset is $$38170 + 82116 = 120286$$.Joining `faostat_cattle` and `faostat_livestock`by rowbinding:```{r}#| label: join data 2cattle_livestock <-bind_rows(faostat_cattle,faostat_livestock)dim(cattle_livestock)```Both datasets had 13 columns and the combined number of rows in the new dataset is $$36449 + 82116 = 118565$$.```{r}#| label: join data 3#figuring out the primary key for 'faostat_country'faostat_country %>%count('Country Code') %>%filter(n >1)faostat_countryegg_livestock %>%count('Area Code') %>%filter(n >1)unique(faostat_country$`Country Code`)unique(egg_livestock$`Area Code`)```So 'Country Code' can be used as the primary key for the `faostat_country` dataset to connect to the `egg_livestock` dataset.```{r}#| label: join data 4join_anti <- egg_livestock %>%anti_join(faostat_country, by =c("Area Code"="Country Code")) dim(join_anti)unique(join_anti$`Area Code`)unique(egg_livestock$`Area Code`)check1 <- join_anti %>%select(`Area Code`,`Area`) %>%group_by(`Area Code`,`Area`) %>%tally()check1```The anti_join() on `egg_livestock` and `faostat_country` reveals that there are 17346 observations in `egg_livestock` that did not find a match in `faostat_country`. `join_anti` has 13 columns, the same as the `egg_livestock` dataset.## Analyze DataI'm looking to compare egg production to camel livestock production in Bahrain and Qatar using a line graph with facet wrap.```{r}#| label: analyze data 1egg_livestock_filt <- egg_livestock %>%select(Year,Area,Item,Element,Value) %>%filter(Area=="Bahrain"|Area=="Qatar") %>%filter(Element=="Production"| Item=="Camels")egg_livestock_filt``````{r}#| label: analyze data 2egg_livestock_filt %>%ggplot(aes(x=Year, y=Value, color=Area)) +geom_line() +labs(title ="Egg Production versus Camel Stocks (1961-2018)", x ="Year", y ="Value") +facet_wrap(~ Element) +theme_minimal()```Over time, egg production in Qatar exceeded that in Bahrain. Camel livestock production in Qatar evidently far-exceeds stocks in Bahrain too.
