PA 5: Military Spending

Author

The names of your group members go here!

library(readxl) 
library(tidyverse)

Today you will be tidying messy data to explore the relationship between countries of the world and military spending. You can find the gov_spending_per_capita.xlsx data included in the data folder.

This task is complex. It requires many different types of abilities. Everyone will be good at some of these abilities but nobody will be good at all of them. In order to produce the best product possible, you will need to use the skills of each member of your group.

Data Description

We will be using data from the Stockholm International Peace Research Institute (SIPRI). The SIPRI Military Expenditure Database is an open source data set containing time series on the military spending of countries from 1949–2019. The database is updated annually, which may include updates to data from previous years.

Military expenditure is presented in many ways:

in local currency and in US $ (both from 2018 and current);
in terms of financial years and calendar years;
as a share of GDP and per capita.

The availability of data varies considerably by country, but we note that data is available from at least the late 1950s for a majority of countries that were independent at the time. Estimates for regional military expenditure have been extended backwards depending on availability of data, but no estimates for total world military expenditure are available before 1988 due to the lack of data from the Soviet Union.

SIPRI military expenditure data is based on open sources only.

Data Import and Cleaning

First, you should notice that there are ten different sheets included in the dataset. We are interested in the sheet labeled “Share of Govt. spending”, which contains information about the share of all government spending that is allocated to the military.

Next, you’ll notice that there are notes about the data in the first six rows. Ugh! Also notice that the last six rows are footnotes about the data. Ugh!

Rather than copying this one sheet into a new Excel file and deleting the first and last few rows, let’s learn something new about the read_xlsx() function!

The read_xlsx() function has several useful arguments:

sheet: specify the name of the sheet that you want to use. The name must be passed in as a string (in quotations)!
skip: specify the number of rows you want to skip before reading in the data.
n_max: specify the maximum number of rows of data to read in.
na: specify the ways that NA is coded in the data, formatted c("a","b")

1. Comment the code below to read the military expenditures data into your workspace, identifying why each setting was chosen.

military <- read_xlsx("data/gov_spending_per_capita.xlsx",  #comment
                      sheet = "Share of Govt. spending",    #comment
                      skip  = 7,                            #comment
                      n_max = 191,                          #comment
                      na = c("xxx", ". ."))                  #comment

I would highly recommend you open the dataset in Excel, so you can see the data layout!

Filtering Unwanted Rows

If you give the Country column a look, you’ll see there are names of continents and regions included. These names are only included to make it simpler to find countries, as they contain no data.

Luckily for us, these region names were also stored in the “Regional totals” sheet. We can use the Region column of this dataset to filter out the names we don’t want.

Run the code below to read in the “Regional totals” data.

cont_region <- read_xlsx("data/gov_spending_per_capita.xlsx", 
                      sheet = "Regional totals", 
                      skip = 14,
                      n_max = 36) |> 
  filter(Region != "World total (including Iraq)", 
         Region != "World total (excluding Iraq)")

We can use the function pull() to extract just the values of the column Region.

regions <- cont_region |> 
  pull(Region)

Then we can use that to filter out or exclude the rows that contain regions instead of countries.

military_clean <- military |> 
  filter(!Country %in% regions)

2. Write a sentence describing what the line of code filter(!Country %in% regions) is doing in the context of the data.

Insert Answer Here

Canvas Question #1

3. Complete the code below to figure out what four regions were NOT removed from the military_clean data set?

Hint: the regions that were not removed have missing values for every column except Country.

military_clean |> 
  filter(if_all(.cols = _________,   #hint: what is the easiest way to include every column except `Country`
                .fns = __________)   #hint: what function in R (there are several) tests if a value is missing or is NA?
         )

Error in parse(text = input): <text>:2:26: unexpected input
1: military_clean |> 
2:   filter(if_all(.cols = __
                            ^

Data Organization

We are interested in comparing the military expenditures of countries in Eastern Europe. Our desired plot looks something like this:

Desired plot: Countries from Eastern Europe adn their military spending.

Unfortunately, if we want a point representing the spending for every country and year, we need every year to be a single column!

To tidy a dataset like this, we need to pivot the columns of years from wide format to long format. To do this process we need three arguments:

cols: The set of columns that represent values, not variables. In these data, those are all the columns from 1988 to 2019.
names_to: The name of the variable that should be created to move these columns into. In these data, this could be "year".
values_to: The name of the variable that should be created to move these column’s values into. In these data, this could be labeled "spending".

These form the three required arguments for the pivot_longer() function.

4. Pivot the cleaned up military data set to a “longer” orientation. Save this new “long” version as a new object called military_long.
Hint: Do not overwrite your cleaned up dataset!

Data Visualization

Now that we’ve transformed the data, let’s create a plot to explore military spending across Eastern European countries.

5. Create side-by-side boxplots to explore the military spending between Eastern European countries.

Hint 1: You will need to remove all other countries except the Eastern European ones before initiating your plot.

Hint 2: Place the Country variable on an axis that makes it easier to read the labels

Hint 3: Make sure you change the plot title and axis labels to accurately represent the plot.

# Countries to include in the plot! 
eastern_europe <- c("Armenia", 
                    "Azerbaijan",
                    "Belarus", 
                    "Georgia", 
                    "Moldova", 
                    "Russia", 
                    "Ukraine")

# Hint - look at the code chunk `filter-regions` and Question 2 for code similar to what you will want to use to filter out just these countries.

Canvas Question 2 & Question 3

6. Looking at the plot you created above, which Eastern European country had the second highest median military expenditure?.

7. Looking at the plot you created above, which Eastern European country had the largest variability in military expenditures over time?

--- title: "PA 5: Military Spending" author: "The names of your group members go here!" format: html embed-resources: true code-tools: true toc: true editor: source execute: error: true echo: true message: false warning: false --- ```{r} #| label: setup library(readxl) library(tidyverse) ``` Today you will be tidying messy data to explore the relationship between countries of the world and military spending. You can find the `gov_spending_per_capita.xlsx` data included in the `data` folder. ***This task is complex. It requires many different types of abilities. Everyone will be good at some of these abilities but nobody will be good at all of them. In order to produce the best product possible, you will need to use the skills of each member of your group.***  ## Data Description We will be using data from the Stockholm International Peace Research Institute (SIPRI). The SIPRI Military Expenditure Database is an open source data set containing time series on the military spending of countries from 1949--2019. The database is updated annually, which may include updates to data from previous years. Military expenditure is presented in many ways: + in local currency and in US $ (both from 2018 and current); + in terms of financial years and calendar years; + as a share of GDP and per capita. The availability of data varies considerably by country, but we note that data is available from at least the late 1950s for a majority of countries that were independent at the time. Estimates for regional military expenditure have been extended backwards depending on availability of data, but no estimates for total world military expenditure are available before 1988 due to the lack of data from the Soviet Union. SIPRI military expenditure data is based on open sources only. ## Data Import and Cleaning First, you should notice that there are ten different sheets included in the dataset. We are interested in the sheet labeled *"Share of Govt. spending"*, which contains information about the share of all government spending that is allocated to the military. Next, you'll notice that there are notes about the data in the first six rows. Ugh! Also notice that the last six rows are footnotes about the data. **Ugh**! Rather than copying this one sheet into a new Excel file and deleting the first and last few rows, let's learn something new about the `read_xlsx()` function! The `read_xlsx()` function has several useful arguments: + `sheet`: specify the name of the sheet that you want to use. The name must be passed in as a string (in quotations)! + `skip`: specify the number of rows you want to skip *before* reading in the data. + `n_max`: specify the maximum number of rows of data to read in. + `na`: specify the ways that `NA` is coded in the data, formatted `c("a","b")` **1. Comment the code below to read the military expenditures data into your workspace, identifying why each setting was chosen.** ```{r} #| label: read-in-military-data military <- read_xlsx("data/gov_spending_per_capita.xlsx", #comment sheet = "Share of Govt. spending", #comment skip = 7, #comment n_max = 191, #comment na = c("xxx", ". .")) #comment ``` *I would highly recommend you open the dataset in Excel, so you can see the data layout!*  ## Filtering Unwanted Rows If you give the `Country` column a look, you'll see there are names of **continents and regions** included. These names are only included to make it simpler to find countries, as they contain no data. Luckily for us, these region names were also stored in the *"Regional totals"* sheet. We can use the `Region` column of this dataset to filter out the names we don't want. Run the code below to read in the *"Regional totals"* data. ```{r} #| label: regional-totals cont_region <- read_xlsx("data/gov_spending_per_capita.xlsx", sheet = "Regional totals", skip = 14, n_max = 36) |> filter(Region != "World total (including Iraq)", Region != "World total (excluding Iraq)") ``` We can use the function `pull()` to extract just the values of the column `Region`. ```{r} #| label: id-regions regions <- cont_region |> pull(Region) ``` Then we can use that to filter out or exclude the rows that contain regions instead of countries. ```{r} #| label: filter-regions military_clean <- military |> filter(!Country %in% regions) ``` **2. Write a sentence describing what the line of code `filter(!Country %in% regions)` is doing in the context of the data.** >Insert Answer Here ### Canvas Question #1 **3. Complete the code below to figure out what four regions were NOT removed from the `military_clean` data set?** *Hint: the regions that were not removed have missing values for every column except `Country`.* ```{r} #| label: inspecting-what-regions-were-not-removed military_clean |> filter(if_all(.cols = _________, #hint: what is the easiest way to include every column except `Country` .fns = __________) #hint: what function in R (there are several) tests if a value is missing or is NA? ) ``` ## Data Organization  We are interested in comparing the military expenditures of countries in Eastern Europe. Our desired plot looks something like this: ![Desired plot: Countries from Eastern Europe adn their military spending.](images/military-expenditure-plot.png) Unfortunately, if we want a point representing the spending for every country and year, we need every year to be a **single column**! To tidy a dataset like this, we need to pivot the columns of years from wide format to long format. To do this process we need three arguments: + `cols`: The set of columns that represent values, not variables. In these data, those are all the columns from `1988` to `2019`. + `names_to`: The name of the variable that should be created to move these columns into. In these data, this could be `"year"`. + `values_to`: The name of the variable that should be created to move these column's values into. In these data, this could be labeled `"spending"`. These form the three required arguments for the `pivot_longer()` function. **4. Pivot the cleaned up `military` data set to a "longer" orientation. Save this new "long" version as a new object called `military_long`.** Hint: **Do not** overwrite your cleaned up dataset! ```{r} #| label: pivoting-military-data-longer ``` ## Data Visualization  Now that we've transformed the data, let's create a plot to explore military spending across Eastern European countries. **5. Create side-by-side boxplots to explore the military spending between Eastern European countries.** Hint 1: You will need to remove all other countries except the Eastern European ones before initiating your plot. Hint 2: Place the `Country` variable on an axis that makes it easier to read the labels Hint 3: Make sure you change the plot title and axis labels to accurately represent the plot. ```{r} #| label: side-by-side-boxplots-east-europe # Countries to include in the plot! eastern_europe <- c("Armenia", "Azerbaijan", "Belarus", "Georgia", "Moldova", "Russia", "Ukraine") # Hint - look at the code chunk `filter-regions` and Question 2 for code similar to what you will want to use to filter out just these countries. ``` ### Canvas Question 2 & Question 3 **6. Looking at the plot you created above, which Eastern European country had the second highest median military expenditure?.** **7. Looking at the plot you created above, which Eastern European country had the largest variability in military expenditures over time?**