The RStudio interface

RStudio is an integrated development environment (IDE) for R. Think of RStudio as a front that allows us to interact, compile, and render R code in a more instinctive way. The following image shows what the standard RStudio interface looks like:

RStudio Interface

1. Console: The console provides a means to interact directly with R. You can type some code at the console and when you press ENTER, R will run that code. Depending on what you type, you may see some output in the console or if you make a mistake, you may get a warning or an error message.

2. Script editor: You will utilize the script editor to complete your assignments. The script editor will be the space where files will be displayed. For example, once you download and open the bi-weekly assignment .Rmd template, it will appear here. The editor is a where you should place code you care about, since the code from the console cannot be saved into a script.

3. Environment: This area holds the abstractions you have created with your code. If you run myresult <- 5+3+2, the myresult object will appear there.

4. Plots and files: This area will be where graphic output will be generated. Additionally, if you write a question mark before any function, (i.e. ?mean) the online documentation will be displayed here.

Getting Help 🔍

The key to learning R is: Google! We can give you an overview over basic R functions, but to really learn R you will have to actively use it yourself, trouble shoot, ask questions, and google! Help pages such as http://stackoverflow.com offer a rich archive of questions and answers by the R community. For example, if you google “recode data in r” you will find a variety of useful websites explaining how to do this on the first page of the search results. Also, don’t be surprised if you find a variety of different ways to execute the same task.

RStudio also has a useful help menu. In addition, you can get information on any function or integrated data set in R through the console, for example:

?plot

In addition, there are a lot of free R comprehensive guides, such as Quick-R at http://www.statmethods.net or the R cookbook at http://www.cookbook-r.com.

Executing a line of code

To execute a single line of code. In RStudio, with the curser in the line you want R to execute,

Press command + return (on macOS) or Crtl + Enter (on Windows).

To execute multiple lines of code at once, highlight the respective portion of the code and then run it using the operations above.

Arithmetic in R

You can use R as a calculator!

4*9

## [1] 36

sqrt(144)

## [1] 12

Logical operators

Logical operators are incredibly helpful for any type of exploratory analysis, data cleaning and/or visualization task.

4 > 2

## [1] TRUE

4 <= 2

## [1] FALSE

Assigning values to objects

A few things to remember

• Do not use special characters such as $ or %. Common symbols that are used in variable names include . or _.
• Remember that R is case sensitive.
• To assign values to objects, we use the assignment operator <-. Sometimes you will also see = as the assignment operator. This is a matter of preference and subject to debate among R programmers. Personally, I use <- to assign values to objects and = within functions.
• The # symbol is used for commenting and demarcation. Any code following # will not be executed.

Below, R stores the result of the calculation in an object named result. We can access the value by referring to the object name.

result <- 5/3
result

## [1] 1.666667

If we assign a different value to the object, the value of the object will be changed.

result <- 5-3
result

## [1] 2

Data Types in R

There are four main variable types you should be familiar with:

• Numerical: Any number. An integer is a numerical variable without any decimals, while a double is a numerical variable with decimals.
• Character: This is what Stata (and other programming languages such as Python) calls a string. We typically store any alphanumeric data that is not ordered as a character vector.
• Logical: A collection of TRUE and FALSE values.
• Factor: Think about it as an ordinal variable, i.e. an ordered categorical variable.

Data Structures in R

R has many data structures, the most important ones for now are:

• Vectors
• Lists
• Matrices
• Data frames
• Factors

Here is a short overview with a few related operations that you can apply to the different structures.

my_vector <- c(1, 2, 3)
my_character_vector <- c("Welcome", "everyone")
length(my_vector)

## [1] 3

str(my_character_vector)

##  chr [1:2] "Welcome" "everyone"

my_matrix <- matrix(nrow=3, ncol=3)
dim(my_matrix)

## [1] 3 3

my_list <- list(1, "a", TRUE)
my_list[2]

## [[1]]
## [1] "a"

my_list[[2]]

## [1] "a"

my_df <- data.frame(id = letters[1:10], x = 1:10, y = 11:20)
my_df

##    id  x  y
## 1   a  1 11
## 2   b  2 12
## 3   c  3 13
## 4   d  4 14
## 5   e  5 15
## 6   f  6 16
## 7   g  7 17
## 8   h  8 18
## 9   i  9 19
## 10  j 10 20

head(my_df)
tail(my_df)
dim(my_df)
nrow(my_df)
ncol(my_df)
str(my_df)
names(my_df)

# direct creation of factors
my_factor <- factor(c("single","single","married","married","single"))

# turning vectors into factors
my_vector <- c("single","single","married","married","single")
my_factor <- as.factor(my_vector)

levels(my_factor)

## [1] "married" "single"

Vector operations

You can do a variety of things like have R print out particular values or ranges of values in a vector, replace values, add additional values, etc. We will not get into all of these operations today, but be aware that (for all practical purposes) if you can think of a vector manipulation operation, R can probably do it.

We can do arithmatic operations on vectors! Let’s use the vector of population counts we created earlier and double it.

pop1

## [1] 1379 1324  323  261  208

pop1_double <- pop1 * 2
pop1_double

## [1] 2758 2648  646  522  416

Exercise 4: What do you think this will do?

res <- pop1 + pop2

Exercise 5: And this?

pop_c <- c(pop1, pop2)

Functions

There are a number of special functions that operate on vectors and allow us to compute measures of location and dispersion of our data.

min(pop)

## [1] 127

max(pop)

## [1] 1379

mean(pop)

## [1] 430.6

Exercise 6: Using functions in R, how else could we compute the mean population value?

Accessing elements of vectors - Indexing I

There are many ways to access elements that are stored in an object. Here, we will focus on a method called indexing, using square brackets as an operator.

Below, we use square brackets and the index 1 to access the first element of the top 5 population vector and the corresponding country name vector.

pop1[1]

## [1] 1379

cname[1]

## [1] "CHN"

We can use indexing to access multiple elements of a vector. For example, below we use indexing to implicitly print the second and fifth elements of the population and the country name vectors, respectively.

pop[c(2,5)]

## [1] 1324  208

cname[c(2,5)]

## [1] "IND" "BRA"

We can assign the first element of the population vector to a new object called first.

first <- pop[1]

Below, we make a copy of the country name vector and delete the last element. Note, that we can use the length() function to achieve the highest level of generalizability in our code. Using length(), we do not need to know the index of the last element of out vector to drop the last element.

cname_copy <- cname
## Option 1: Dropping the 5th element
cname_copy[-5]

## [1] "CHN" "IND" "USA" "IDN"

## Option 2 (for generalizability): Getting the last element and dropping it.
length(cname_copy)

## [1] 5

cname_copy[-length(cname_copy)]

## [1] "CHN" "IND" "USA" "IDN"

Indexing can be used to alter values in a vector. Suppose, we notice that we wrongly entered the second element of the regime type vector (or the regime type changed).

regime

## [1] "Autocracy" "FlawedDem" "FullDem"   "FlawedDem" "FlawedDem"

regime[2] <- "FullDem"
regime

## [1] "Autocracy" "FullDem"   "FullDem"   "FlawedDem" "FlawedDem"

Exercise 7: We made even more mistakes when entering the data! We want to subtract 10 from the third and fifth element of the top 5 population vector. How would you do it?

More functions

The myriad of functions that are either built-in to base R or parts of user-written packages are the greatest stength of R. For most applications we encounter in our daily programming practice, R already has a function, or someone smart wrote one. Below, we introduce a few additional helpful functions from base R.

Let’s create a vector of indices for our top 5 population data.

cindex <- seq(from = 1, to = length(pop1), by = 1)
cindex

## [1] 1 2 3 4 5

Suppose we wanted to only print a sequence of even numbers between 2 and 10. We can do so by adjusting the by operator.

seq(2, 10, 2)

## [1]  2  4  6  8 10

We can use the rep() function to repeat data.

rep(30, 5)

## [1] 30 30 30 30 30

Suppose, we wanted to record whether we had completed the data collection process for the top 10 most populous countries. First, suppose we completed the process on every second country.

completed <- rep(c("yes","no"), 5)
completed

##  [1] "yes" "no"  "yes" "no"  "yes" "no"  "yes" "no"  "yes" "no"

Now suppose that we have completed the data collection process for the first 5 countries, but not the latter 5 countries (we don’t have their names, location, or regime type yet).

completed2 <- rep(c("yes","no"), each = 5)
completed2

##  [1] "yes" "yes" "yes" "yes" "yes" "no"  "no"  "no"  "no"  "no"

We can give our data informative labels. Let’s use the country names vector as labels for our top 5 population vector.

names(pop1)

## NULL

cname

## [1] "CHN" "IND" "USA" "IDN" "BRA"

names(pop1) <- cname
names(pop1)

## [1] "CHN" "IND" "USA" "IDN" "BRA"

pop1

##  CHN  IND  USA  IDN  BRA 
## 1379 1324  323  261  208

We can use labels to access data using indexing and logical operators. Suppose, we wanted to access the population count for Brazil in our top 5 population data.

pop1[names(pop1) == "BRA"]

## BRA 
## 208

Exercise 8 Access all top 5 population ratings that are greater or equal than the mean value of population ratings.

Exercise 9 Access all top 5 population ratings that are less than the population of the most populous country, but not the US.

Operating on multiple vectors simultaneously

We did not work with data frames yet, but remember that our data input is ordered. The first element of the pop1 vector corresponds with the first element of the cname, regime, and asia vectors. We can use this to run more sophisticated queries on our data.

Suppose, we wanted to know the regime type of Indonesia. Given that our vectors are ordered, we can use indexing to extract the data. First, let’s see what happens if we run a simple logical query.

cname == "IDN"

## [1] FALSE FALSE FALSE  TRUE FALSE

regime[cname == "IDN"]

## [1] "FlawedDem"

We can also use the which() function that returns the index of the vector element.

which(cname == "IDN")

## [1] 4

regime[which(cname == "IDN")]

## [1] "FlawedDem"

Using logical statements, we can run more complex queries. Below, we print the population count for all Asian countries within the top 5 most populous countries that are not autocracies.

pop1[asia == T & regime != "Autocracy"]

##  IND  IDN 
## 1324  261

R Packages 📦

For the most part, R Packages are collections of code and functions that leverage R programming to expand on the basic functionalities. There are a plethora of packages in R designed to facilitate the completion of tasks, built by the R community. In later parts of this course, you will also learn how to write packages yourself.

Unlike other programming languages, in R you only need to install a package once. The following times you will only need to load the package. As a good practice I recommend running the code to install packages only in your R console, not in the code editor. You can install a package with the following syntax

install.packages("name_of_your_package") #note that the quotation marks are mandatory at this stage

Once the package has been installed, you just need to “call it” every time you want to use it in a file by running:

library("name_of_your_package") #either of this lines will require the package
library(name_of_your_package) #library understands the code with, or without, quotation marks

It is extremely important that you do not have any lines installing packages for your assignments because the file will fail to knit

Working directory

The working directory is just a file path on your computer that sets the default location of any files you read into R, or save out of R. Normally, when you open RStudio it will have a default directory (a folder in your computer). You can check you directory by running getwd() in your console:

#this is the default in my case
getwd()
#[1] "/Users/l.oswald"

When your RStudio is closed and you open a file from your finder in MacOS or file explorer in Windows, the default working directory will be the folder where the file is hosted

setwd("/path/to/your/directory") #in macOS
setwd("c:/path/to/your/directory") #in windows

#install.packages("foreign") #alternatively use "Install" button 
library(foreign)

Importing data

Most data formats we commonly use are not native to R and need to be imported. Luckily, there is a variety of packages available to import just about any non-native format. One of the essential libraries is called foreign and includes functions to import .csv, .dta (Stata), .dat, ,.sav (SPSS), etc.

mydata <- read.csv("data/ACLED_countries.csv",
                   stringsAsFactors = F)

Accessing elements of a data frame - Indexing II

As a rule, whenever we use two-dimensional indexing in R, the order is: [row, column]. To access the first row of the data frame, we specify the row we want to see and leave the column slot following the comma empty.

mydata[1, ]

##       country        region nconflict nconflict_no_fatalities fatalities
## 1 Afghanistan Southern Asia     40765                   23946     119973

We can use the concatenate function c() to access multiple rows (or columns) at once. Below we print out the first and second row of the dataframe.

mydata[c(1,2), ]

##       country        region nconflict nconflict_no_fatalities fatalities
## 1 Afghanistan Southern Asia     40765                   23946     119973
## 2     Albania        Europe       582                     581          1

We can also access a range of rows by separating the minimum and maximum value with a :. Below we print out the first five rows of the dataframe.

mydata[1:5,]

##       country                    region nconflict nconflict_no_fatalities
## 1 Afghanistan             Southern Asia     40765                   23946
## 2     Albania                    Europe       582                     581
## 3     Algeria           Northern Africa      7362                    5321
## 4      Angola             Middle Africa      1108                     728
## 5     Armenia Caucasus and Central Asia      3118                    3110
##   fatalities
## 1     119973
## 2          1
## 3       8451
## 4      13788
## 5          8

If we try to access a data point that is out of bounds, R returns the value NULL.

mydata[3,7]

## NULL

Exercise 10 Access the element of the dataframe mydata that is stored in row 1, column 1.

Exercise 11 Access the element of the data frame mydata that is stored in column 3, row 100.

The $ operator

The $ operator in R is used to specify a variable within a data frame. This is an alternative to indexing.

which(colnames(mydata) == "nconflict")

## [1] 3

mydata[,3]

##   [1] 40765   582  7362  1108  3118 12627  1861 16802   293   215   459    52
##  [13]   765  2105  7760  1919  2621  4625   912   411   312 14519   112 10260
##  [25]    50   191   190  5122   215   197  1446   858  1312  1121   151 67561
##  [37]  3371  5708 22354  1580  1838   491   374  6427   289    39   319    41
##  [49]  2956    83  1294  8857  1365   512   384  3193   598   444   213  2177
##  [61]  1250  9070   682  5143   971 15011   275     9 54496  6568  9690     5
##  [73]   243  1134  3734   370  6096   963  1275  1079 29667 11667  6227     2
##  [85]  3855 12528 70734    68   782  7420   321  5756     1 10334    21  4866
##  [97] 30131     4   102   503 45298  1148  5891

mydata$nconflict

##   [1] 40765   582  7362  1108  3118 12627  1861 16802   293   215   459    52
##  [13]   765  2105  7760  1919  2621  4625   912   411   312 14519   112 10260
##  [25]    50   191   190  5122   215   197  1446   858  1312  1121   151 67561
##  [37]  3371  5708 22354  1580  1838   491   374  6427   289    39   319    41
##  [49]  2956    83  1294  8857  1365   512   384  3193   598   444   213  2177
##  [61]  1250  9070   682  5143   971 15011   275     9 54496  6568  9690     5
##  [73]   243  1134  3734   370  6096   963  1275  1079 29667 11667  6227     2
##  [85]  3855 12528 70734    68   782  7420   321  5756     1 10334    21  4866
##  [97] 30131     4   102   503 45298  1148  5891

table() function

The table() function can be used to tabularize one or more variables. For example, let’s find out how many observations (i.e. individual countries) we have per region.

table(mydata$region)

## 
## Caucasus and Central Asia            Eastern Africa                    Europe 
##                         8                        13                        15 
##             Middle Africa               Middle East           Northern Africa 
##                         8                        15                         7 
##        South-Eastern Asia           Southern Africa             Southern Asia 
##                         8                         8                         6 
##            Western Africa 
##                        15

Using logical operations, we can create more complex tabularizations. For example, below, we show how many countries have above average number of conflict events per region.

summary(mydata$nconflict)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.0   315.5  1250.0  6216.3  5993.5 70734.0

table(mydata$region, mydata$nconflict > mean(mydata$nconflict))

##                            
##                             FALSE TRUE
##   Caucasus and Central Asia     7    1
##   Eastern Africa                9    4
##   Europe                       14    1
##   Middle Africa                 7    1
##   Middle East                  10    5
##   Northern Africa               3    4
##   South-Eastern Asia            5    3
##   Southern Africa               7    1
##   Southern Asia                 2    4
##   Western Africa               14    1

Exercise 12: How would you access all elements of the variable country using indexing rather than the $ operator?

Exercise 13: How would you find the maximum value for number of conflict events using the $ operator?

Exercise 14: Print the country that corresponds to the maximum value for conflict events using the $ operator and indexing!

Exercise 15: Print out every second element from the variable country using indexing methods and the sequence function seq().

NAs in R

NA is how R denotes missing values. For certain functions, NAs cause problems.

vec <- c(4, 1, 2, NA, 3)
mean(vec) #Result is NA!

## [1] NA

sum(vec) #Result is NA!

## [1] NA

We can tell R to remove the NA and execute the function on the remainder of the data.

mean(vec, na.rm = T)

## [1] 2.5

sum(vec, na.rm = T)

## [1] 10

Adding observations

First, let’s add another observation to the data. Suppose we wanted to add an observation for Germany, which will be a missing value. We can use the same operations we used for vectors to add data. Here, we will use the rbind() function to do so. rbind() stands for “row bind.” Save the output in a new data frame!

obs <- c("Germany", "Europe", NA, NA, NA)
mydata_new <- rbind(mydata, obs)
dim(mydata_new)

## [1] 104   5

Adding variables

We can also create new variables that use information from the existing data. If we know the number of conflict events without fatalities by country, we can calculate the number of conflict events with fatalities to generate the variable nconflict_fatalities. By using the $ operator, we can directly assign the new variable to the data frame mydata_new.

mydata$nconflict_fatalities <- mydata$nconflict - mydata$nconflict_no_fatalities
head(mydata, 3) #prints out the first 3 rows of the data frame

##       country          region nconflict nconflict_no_fatalities fatalities
## 1 Afghanistan   Southern Asia     40765                   23946     119973
## 2     Albania          Europe       582                     581          1
## 3     Algeria Northern Africa      7362                    5321       8451
##   nconflict_fatalities
## 1                16819
## 2                    1
## 3                 2041

We could also compute the average number of fatalities per conflict, computed as the sum of fatalities (fatalities variable) divided by the number of conflicts (nconflict variable).

mydata$av_fatalities <- mydata$fatalities/mydata$nconflict

Subsetting data

Suppose we want to figure out which country in Northern Africa has the highest number of riot and protest events. We can figure this out by first subsetting our dataset to only include countries in the region, then looking up the maximum value for nconflict. Below, we assign the output to a new object called mydata_na.

mydata_na <- mydata[mydata$region == "Northern Africa",]
max(mydata_na$nconflict)

## [1] 12528

mydata_na$country[mydata_na$nconflict == max(mydata_na$nconflict)]

## [1] "Sudan"

Saving data

Suppose we wanted to save this newly created data frame. We have multiple options to do so. If we wanted to save it as a native .RData format, we would run the following command.

# Make sure you specified the right working directory!
save(mydata, file = "mydata_new.RData")

Most of the time, however, we would want to save our data in formats that can be read by other programs as well. .csv is an obvious choice.

write.csv(mydata_new, file = "mydata_new.csv")

Dealing with errors in R

Errors in R occur when code is used in a way that it is not intended. For example when you try to add two character strings, you will get the following error:

"hello" + "world"
Error in "hello" + "world": non-numeric argument to binary operator

Normally, when something has gone wrong with your program, R will notify you of an error in the console. There are errors that will prevent the code from running, while others will only produce warning messages. In the following case, the code will run, but you will notice that the string “three” is turned into a NA.

as.numeric(c("1", "2", "three"))
Warning: NAs introduced by coercion
[1]  1  2 NA

Since we will be utilizing widely used packages and functions in the course of the semester, the errors that you may come across in the process of completing your assignments will be common for other R users. Most errors occur because of typos. A Google search of the error message can take you a long way as well. Most of the times the first entry on stackoverflow.com will solve the problem.

The double colon operator ::

You may have noted in the previous section that the functions were preceded by their package name and two colons, for example: readr::read_rds(). The double colon operator :: helps us ensure that we select functions from a particular package. We utilize the operator to explicitly state where the function is coming. This may become even more important when you are doing data analysis as part of a team further in your careers. Though it is likely that this will not be a problem during the course, we can try to employ the following convention package_name::function() to ensure that we will not encounter errors in our knitting process:

dplyr::select()

Let’s look at what happens when we load tidyverse.

library(tidyverse)

#── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 #──
#✓ ggplot2 3.3.2     ✓ purrr   0.3.4
#✓ tibble  3.0.3     ✓ dplyr   1.0.2
#✓ tidyr   1.1.2     ✓ stringr 1.4.0
#✓ readr   1.3.1     ✓ forcats 0.5.0
#── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() #──
#x dplyr::filter() masks stats::filter()
#x dplyr::lag()    masks stats::lag()

You may notice that R points out some conflicts where some functions are being masked. The default in this machine will become the filter() from the dplyr package during this session. If you were to run some code that is based on the filter() from the stats package, your code will probably result in errors.

Script structure

When structuring your scripts, remember a few things

• Libraries go first
• Hard coded variables (e.g. when reading in data) go second
• Relative paths over absolute paths (give your friends and colleagues a chance to execute your scripts without trouble)

Naming conventions

In summary, we can say: always use easy to interpret names and don’t use whitespaces in file or variable names! Some good examples:

• my_file.R
• my_vector <- c(1,2,3)
• this_function()

Spacing

It is somewhat easier to read if you generally leave a space after a comma

• my_function(1:10, c(2, 4))

Repetition

When you start copying and pasting code (creating a lot of redundancy), you might want to consider slimming down your code by creating a function. Functions? Let us leave this as a note of caution and return to functions and efficient code in one of our very last sessions! :-)

Actually learning R 🎒

Again, the key to learning R is: Google! We can only give you an overview over basic R functions, but to really learn R you will have to actively use it yourself, trouble shoot, ask questions, and google! It is very likely that someone else has had the exact same or just similar enough issue before and that the R community has answered it with 5+ different solutions years ago. 😉

To-do for next week!

For those of you who have yet to create a Github account, do so now.

Next week’s session will be about version control and project management with Git and Github. Please download and install Github desktop on your machine! See you next week! 💻