R Coding Basics

September 22, 2024

R Programming Basics

What Can R Do?

R is a powerful language for data analysis and visualization
It is also a general-purpose programming language
Does everything from web development to machine learning
It is open-source and has a large community of users and developers

R as a Calculator

R can be used as a simple calculator
You can perform arithmetic operations on numbers

# Addi a number and store it to a value
sum_of_2plus2 <- 2 + 2


sum_of_2plus2

[1] 4

Some Common Arithmetic Operators

+ addition
- subtraction
* multiplication
/ division
^ exponentiation (also **)

Functions

A function is a set of instructions that produces some output
In R, you can use built-in functions to perform specific tasks
For example, you can use the mean() function to calculate the average of a set of numbers
To do this you have to use the combine function c() to create a vector of numbers

Create a vector of numbers and take the mean…

# Create a vector of numbers
numbers <- c(1, 2, 3, 4, 5)

# Calculate the mean of the numbers
mean(numbers)

[1] 3

Some Common Base R Functions

mean() calculates the mean of a set of numbers
median() calculates the median of a set of numbers
sd() calculates the standard deviation of a set of numbers
sum() calculates the sum of a set of numbers
length() calculates the length of a vector
max() and min() calculate the maximum and minimum values of a vector
round() rounds a number to a specified number of decimal places
sqrt() calculates the square root of a number
log() calculates the natural logarithm of a number
exp() calculates the exponential of a number
abs() calculates the absolute value of a number

Packages

From Functions to Packages

A function is a set of instructions
- read_csv() is a function
- ggplot() is a function
A package is a collection of functions
- readr is a package that contains the read_csv() function
- ggplot2 is a package that contains the ggplot() function
Use install.packages() to install packages
Use library() to load packages
You can install packages from CRAN

The Tidyverse

The Tidyverse is a collection of data science packages
It is also considered a dialect of R
In this class, we will be using many Tidyverse packages
- ggplot2 for data visualization
- readr for reading data
- dplyr for data manipulation
- tidyr for data tidying
- Etc.
At first we will load the packages independently, e.g. library(ggplot2)
Later we will load them all at once with library(tidyverse)

Objects

What is an Object?

An object in R is a data structure used to store data
It can vary from simple scalar types to more complex data structures like vectors, lists, or data frames
Objects hold not only data but also information about the type of data and the operations that can be performed on them
Every entity in R is considered an object, making R a language based around the manipulation of objects

How to Store Data

In R, you can store data in objects using the assignment operator <-
The object name is on the left of <-, and the data or value you wish to assign to the object is on the right
Then you can print the object to the console using the object name

# Store the value 42 in the object my_number
my_number <- 42

# Print the value of my_number
my_number

[1] 42

When to Store Data in Objects

Note that you don’t always have to store data in objects
You should mostly store data in objects when you want to use the data later
If you only need to use the data once, you can just use the data directly

# Add two numbers without storing them in an object
2 + 2

[1] 4

Your Turn!

Go to the Week 2 module on Posit Cloud
Work through the first set of challenges there

05:00

Three Examples

Let’s get going . . .

Let’s open up the Getting Started module on Posit Cloud…

And work through the examples there.

Example: Make a map!

library(leaflet)
leaflet() %>% 
  addTiles() %>%   # Add default OpenStreetMap map tiles
  addMarkers(lat = 38.90243843683386, lng =  -77.0443814477152, 
             label = "Elliott School of International Affairs")

05:00

Example: Plotting Democracy Over Time

# Load the packages
library(vdemlite)
library(ggplot2)

# Use vdemlite to extract democracy scores for France and INdia
dem_data <- fetchdem(indicators = "v2x_polyarchy",
                     countries = c("FRA", "IND"))

# And now we can plot the data
ggplot(dem_data, aes(y = v2x_polyarchy, x = year, color=country_name)) +
  geom_line() +
  theme_minimal() +
  xlab("Year") +
  ylab("Electoral Democracy Index") +
  ggtitle("Electoral Democracy, 1970-2022") +
  geom_hline(yintercept = .5, linetype = "dashed", color = "grey") +
   scale_color_manual(name="Country", values=c("#E69F00", "#56B4E9")) +
  ylim(0, 1)

05:00

Example: UN Voting Trends

library(tidyverse)
library(unvotes)
library(lubridate)
library(scales)
library(pacman)

unvotes <- un_votes %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid")

unvotes %>%
  # then filter out to only include the countries we want
  filter(country %in% c("South Africa", "United States", "France")) %>%
  # then make sure R understands the the year variable is a data
  mutate(year = year(date)) %>%
  # Then group the data by country and year
  group_by(country, year, issue) %>%
  # then take the average Yes votes for each country_year
  summarize(percent_yes = mean(vote == "yes")) %>%
  # then make a nice plot
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~issue) +
  scale_y_continuous(labels = percent) +
  scale_color_manual( values=c("#E69F00", "#56B4E9", "#009E73")) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  )

05:00