Introduction to R

1.1 What is R?

Technically R is an expression language with a very simple syntax. It is also perfect for exploration. It can be used in any kind of analysis work, as it has many tools and is also very extensible. Additionally, it is a perfect fit for big data solutions. You can do whatever you want with R such as if you have tons of picture/maps you can convert and combine to Pdf(s). You can also make presentation,send email, write report and even you can make your own website with R.In summary, it is a powerful, extensible environment. It has a wide range of statistics and general data analysis and visualization capabilities including-

• Data handling, wrangling, and storage
• Wide array of statistical methods and graphical techniques available
• Easy to install on any platform and use (and it’s free!)
• Open source with a large and growing community of peers
• R Studio: Graphical user interface, not just a command prompt

1.2 R and RStudio

RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.

RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux) or in a browser connected to RStudio Server or RStudio Server Pro (Debian/Ubuntu, Red Hat/CentOS, and SUSE Linux).

Install R & RStudio: Download both R and RStudio. Install R first and then RStudio.

1.3 R Studio interface

• Top left: Script editor (where you write your scripts)
• Bottom left: Console (the “engine”, i.e. where you run your scripts)
• Top right: Environment/history (where you see active objects in your session)
• Bottom right: Files/plots/packages/help

1.4 Working with “projects” (.Rproj)

RStudio projects make it straight-forward to divide your work into multiple contexts, each with their own working directory, workspace, history, and source documents.

Click on “File >> New Project” to create a new project file. Once a project file is created, open it (which opens a new R session) and open your scripts from within R (via the bottom right “Files” pane).

A major advantage of working with project files is that they automatically set the working directory to where the respective project documents are saved. When you load in datasets from your project folder, it means the document path will be relative and therefore much cleaner.

More info on projects is found here.

1.5 Caution:: Before writing a R script

1. It is case sensitive, as are most UNIX based packages, so “A” and “a” are different symbols and would refer to different variables. For example, if you declare a variable with “name” and then very next line you try to recall the variable with “Name,” it wouldn’t work

name <- c("Shantosh","Shorouq","Natacha","Mehedi")

name

[1] "Shantosh" "Shorouq"  "Natacha"  "Mehedi"  

Name

Error: object 'Name' not found

2. Sometimes it is difficult to find out the problem in the calculation. For example, below, we have a dataset that contains the monthly income of IOM staff. We have to calculate annual income from the dataset

In the data frame, income data are stored as a string rather than numeric, but to calculate the annual income, we need to convert it to numeric value as you can not sum/multiply a value with string.

df <- data.frame(
  name = c("Mariia","Ahmad","Sakar"),
  monthly_income = c(800,800,".")
)

df %>% kable()

df$monthly_income %>% class()

[1] "character"

So to calculate the total income, we have to make it numeric value by as.numeric(). However, Then you will see another error in the annual income of Sakar. There is a “.” in the monthly income tab, which seems not to be correct. Ideally, it should be stopped when converting “.” to a numeric value, but R did not make the code stoped; instead, it made the value NA. Sometimes, it makes errors in calculation, especially when you have tons of data, and it is nearly impossible with eye to find such a problem.

df_With_annual_income <- df %>% mutate(
  monthly_income = as.numeric(monthly_income)
)

df_With_annual_income %>% kable()

Basic of R

2.1 Importing data in R

Typically, you do not create your own data frame in R, but load in a dataset, which you then use to do calculations on.

Generally, it is good practice to work with CSV files rather than Excel sheets. As needed, you define the character separating cells in your CSV file, and specify strings that should be treated as missing values (or NA):

data <- read.csv("data/example_data.csv")
data <- read.csv("data/example_data.csv", na.strings = c("NA", ""), sep = ",")

You can find more documentation on data input functions here.

Specifying NAs when importing datasets

When importing a dataset into R, make sure you specify the NA values in the na.strings as needed. Failing to do so may affect your analysis later on, as blanks ("") are regular values that will be evaluated and therefore might skew your results.

data <- read.csv("data/example_data.csv", na.strings = c("NA", ""))

2.2 Basic terms

• Operator - symbol performing specific mathematical or logical manipulation
• Variable - column of data in a data frame
• Observation - row of data in a data frame
• Datapoint - single cell value
• Vector - sequence of data elements of the same basic type
• Matrix - same as a vector, but two-dimensional
• Dataframe (df) - R equivalent to an Excel/SPSS table dataset

2.3 Basic data structure (vectors)

A vector is the most common and basic data structure in R, the workhorse of R. It is a collection of values of the same basic type. The c() function returns a vector.

# define the vector
myVector <- c(1, 50, 9, 42)

# display the vector
myVector 

[1]  1 50  9 42

govs <- c("Odessa", "Kyiv", "Kharkiv", "Kherson", "Lviv", "Zaporizhzhia")
govs 

[1] "Odessa"       "Kyiv"         "Kharkiv"      "Kherson"      "Lviv"        
[6] "Zaporizhzhia"

back to top

2.4 Operators

2.4.1 Assignment

• <-, <<-, = : Leftwards assignment
• ->, ->> : Rightwards assignment

element <- "this is a value"
element

[1] "this is a value"

2.4.3 Arithmetic

• + : Addition
  
• - : Subtraction
  
• * : Multiplication
  
• / : Division
  
• ^ : Exponent
  
• %% : Modulus (remainder from division)
  
• %/% : Integer division

Examples of arithmetic operators:

3 - 2

[1] 1

v <- c(2, 5, 6)
t <- c(8, 3, 4)
v + t

[1] 10  8 10

5^2 * 2

[1] 50

2.4.4 Relational

• < : Less than
• > : Greater than
• <= : Less than or equal to
• >= : Greater than or equal to
• == : Equal to
• != : Not equal to

Examples of relational operators:

1 == 2

[1] FALSE

4 > 3

[1] TRUE

"a" != "b"

[1] TRUE

v <- c(2, 5, 6, 9)
t <- c(8, 3, 4, 9)
v > t

[1] FALSE  TRUE  TRUE FALSE

2.4.5 Logical

• ! : Logical NOT
• & : Element-wise logical AND
• && : Logical AND
• | : Element-wise logical OR
• || : Logical OR

1 & 3 < 4

[1] TRUE

1 & 3 > 4

[1] FALSE

1 | 3 < 2

[1] TRUE

2.4.6 Other operators

• : operator - creates the series of numbers in sequence for a vector

v <- 2:8
v

[1] 2 3 4 5 6 7 8

• %in% - used to identify if an element belongs to a vector

v <- 5
t <- c(1:7)

v %in% t

[1] TRUE

9 %in% t

[1] FALSE

back to top

2.5 Data types

2.5.1 Character

The character class is your typical string, a set of one or more letters.

myString <- "Hello R!"
myString

[1] "Hello R!"

You can check the class associated with the object we defined above, by wrapping it in the class() function.

class(myString)

[1] "character"

2.5.2 Numeric

Indicates numeric values like 10, 15.6, -48792.54989827 and so on.

myNum <- 10.4343
myNum

[1] 10.4343

class(myNum)

[1] "numeric"

2.5.3 Integer

Integers are whole numbers, though they get autocoerced (changed) into numerics when saved into variables.

myInt <- 209173987
class(myInt)

[1] "numeric"

myInt <- as.integer(myInt)
class(myInt)

[1] "integer"

myInt <- 209173987L
class(myInt)

[1] "integer"

2.5.4 Logical

Logical types (booleans) are the same as in most other languages and can be two things – either true, or false. True can be represented with TRUE or T while false is FALSE or F.

Logical_1 <- TRUE 
class(Logical_1)

[1] "logical"

Logical_2 <- 2 < 1 
class(Logical_2)

[1] "logical"

2.5.5 Factors

A factor is a type of vector that is used to store categorical data. Each unique category is referred to as a factor level (category = level).

Strlevels <- c("low", "high", "medium", "high", "low", "medium", "high")
Factorlevels <- factor(Strlevels)
Factorlevels

[1] low    high   medium high   low    medium high  
Levels: high low medium

class(Factorlevels)

[1] "factor"

2.5.6 Data frame

A data frame is the most common way of storing data in R and, generally, is the data structure most often used for data analyses. Under the hood, a data frame is a list of equal-length vectors. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows. As a result, data frames can store different classes of objects in each column (i.e. numeric, character, factor). Typically, data frames have column headers (i.e. variable names).

In essence, the easiest way to think of a data frame is as an Excel worksheet that contains columns of different types of data but are all of equal length rows.

Example

Let’s define a data frame, using the data.frame() function, and then have a look at it:

df <- data.frame(col1 = 1:3, 
                 col2 = c("this", "is", "text"), 
                 variable_name3 = c(TRUE, FALSE, TRUE), 
                 Variable.4 = c(2.5, 4.2, pi))

df

  col1 col2 variable_name3 Variable.4
1    1 this           TRUE   2.500000
2    2   is          FALSE   4.200000
3    3 text           TRUE   3.141593

class(df$col1)

[1] "integer"

class(df$col2)

[1] "character"

class(df$variable_name3)

[1] "logical"

class(df$Variable.4)

[1] "numeric"

Intermediate skills

install.packages("dplyr")

library(dplyr)