POLISCI 3325G - Data Science for Political Science

1.1 Running your code using command + enter

If you type code directly into the console, it will be executed automatically when you press enter. However, code is typically written in your script and sent into the console using command + enter at either extremity of the code line. You can also use the run icon in the RStudio environment, but this is less efficient.

answer <- 1+1 # Creating an object called answer 

answer # Displaying the value of our "answer" object

## [1] 2

Our code (which appears in the first rectangle) is a typical example of what can be written within a script. Similarly, ## [1] 2 is the output that would appear in your console by executing these code lines.

1.2 Writing a comment using #

Writing comments is an essential part of being a good data scientist. Not only does it make it easier for others to understand your code, but it also allows you to remember why you used specific functions. Characters following a # in your script are considered to be comments, and won’t be executed by R when you run your code.

# This is a comment

## This is also a comment

print("Hello World") 

## [1] "Hello World"

You can use as many # as you’d like to make your script more readable. Please note that if you use 5 # or more, you will create a chunk (or a code subsection). This technique can be useful when working with longer scripts.

1.3 Installing a package using install.packages()

R is an open-source programming language, which means that anyone with sufficient coding abilities can contribute to the R community by creating their own package. These packages work like “extension packs” for the basic functions which are already included when you first download R. install.packages() is one of these basic functions: it allows you to download any package listed in the R repository (https://cran.r-project.org/).

# install.packages("tidyverse") 

# Tidyverse is a collection of R packages designed for data science. 
# All packages share an underlying design philosophy, grammar, and data structure. 
# Please install this package, as it will be useful in this class. 
# For more information, see: https://www.tidyverse.org/packages/

Please note that R is sensitive to a variety of syntax elements. For instance, if you tried to run install.packages(tidyverse) (i.e. without “…”), an error message would appear. Likewise, R is case sensitive—so if you create an object called “myName”, the programming language won’t recognize “myname”.

1.4 Loading a package using library()

Once you install a package, it remains dormant within RStudio. In order to activate the functions encompassed within that package, you need to load it within your library using the library() function.

library(tidyverse) 

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

3.1 Types of Objects

Objects are entities with different attributes. A sequence of elements is called a vector, and a sequence of vectors is called a data frame.

3.2 Classes of Objects

Objects can have a variety of classes in R. Today, we will focus on the three main classes: character, numeric and logical.

myLogObject <- TRUE # Creating a logical object 
myLogObject # Displaying the object 

## [1] TRUE

class(myLogObject) # Displaying the class of the "myLogObject" object

## [1] "logical"

myNumObject <- 1 # Creating a numeric object 
myNumObject # Displaying the object 

## [1] 1

class(myNumObject) # Displaying the class of the "myNumObject" object

## [1] "numeric"

myCharObject <- "1" # Creating a character object
myCharObject # Displaying the object 

## [1] "1"

class(myCharObject) # Displaying the class of the "myCharObject" object

## [1] "character"