# This is a comment
# Anything we type after '#' consider as a comment
1+1 #this is a proper way to write a comment after writing a line of code.[1] 2Getting Started with R
Starting up
1) Commenting in R console and script
We can make a comment for every codes that we write in R script by typing ’#“.
It is a good practice to write a comment at the end of the codes that we write. This is to prevent miss-understanding about the code in future. This can be done as follows:
We can make all above as comment at once by selecting all codes and hit “ctrl + shift + c”
# comment will not be executed in R console
# this is another example that comment is not executed in R console:
# 1 + 12) Explaining output on console
We can type code in the console and it will print the output in the same console. The code “print("Mohammad Nasir Abdullah")” is the code chunk and the result [1] "Mohammad Nasir Abdullah" is the output produced by R.
print("Mohammad Nasir Abdullah")[1] "Mohammad Nasir Abdullah"3) Object assignation
You can create variables from within the R environment and from files on your computer. R uses “=” or “<-” to assign values to a variable name.
Example 1: Assign using “=”
x = 2
print(x)[1] 2Example 2: Assign using “<-”
y <- 2
print(y)[1] 24) R is case sensitive
One of the fundamental aspects to understand while working with R is its case sensitivity. In R, the identifiers such as variable names, function names, and other object names are case-sensitive. This means that Variable, variable, and VaRiAbLe are considered different identifiers in R.
It is crucial to maintain consistent capitalization to ensure that your code works as expected.
Example 1:
# Case sensitivity in R
Variable <- 1
variable <- 2
#The following will output 1, not 2
print(Variable)[1] 1#The following will output 2, not 1
print(variable)[1] 2In this example, Variable and variable are treated as two distinct objects, each holding different values.
5) Listing the objects in the workspace
The operations from previous sections led to the creation of several simple R objects. These objects are stored in the current R workspace. A list of all objects in the current workspace can be printed to the screen using objects() function.
objects()[1] "variable" "Variable" "x" "y" A synonym for objects() is ls(). Remember that if we quit our R session without saving the workspace image,then these objects will disappear. If we save the workspace image, then the workspace will be restored at out next R session.
Data Types in R
One of the fundamental concepts in any programming language is the data type. In R, there are several basic data types that are used to define the type of data that can be stored and manipulated.
Basic data types in R:
1) Numeric: represent both integer and decimal numbers.
x <- 23.5
y <- 42) Character: represents strings or text.
name <- "Mohammad Nasir Abdullah"
greeting <- "Hello, World!"3) Logical: represents boolean values (“TRUE” or “FALSE”).
is_true <- TRUE
is_false <- FALSE4) Complex: represents complex numbers.
z <- 3 + 4i5) Raw: represents raw bytes
raw_data <- charToRaw("Hello")Data Structures in R
While the basic data types define a single value, R offers several data structure to store collections of values:
1) Vectors: A one-dimensional array that can hold elements of the same data type.
numeric_vector <- c(1,2,3,4,5,6)
character_vector <- c("apple", "banana", "cherry")2) Matrix: A two-dimensional array where elements are arranged in rows and columns
matrix_data <- matrix(1:6, nrow=2, ncol=3)3) List: A collection that can hold elements of different data types.
my_list <- list(name="Nasir", age=16, score=c(100,99,100))4) Data frame: A table-like structure where columns can be of different data types
students <- data.frame(Name=c("Ali", "Abu", "Ahmad"),
Age = c(28, 39,10),
Grade = c("A", "A","A+"))5) Factor: Used to represent categorical data
gender <- factor(c("Male", "Female", "Male"))Vector
Vector is a basic data structure that holds elements of the same type. It is a sequence of data elements. For example, a numeric vector holds only numeric data, and a character vector holds only character data.
1) Creating a vector
You can create a vector in R using the c() function.
#Example
numeric_vector <- c(1,2,3,4,5,6)
character_vector <- c("A","B", "C", "D", "E", "F")They symbol can be used to create sequences of increasing (or decreasing) values. For example:
numbers5to20 <- 5:20
numbers5to20 [1] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20vectors can be joined together (i.e: concatenated) with the c() function. For example, note what happens when we type:
c(numbers5to20, numeric_vector) [1] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6we can append number5to20 to the end of the end of numeric_vector, and then append the decreasing sequence from 4 to 1:
a.mess <- c(numeric_vector, numbers5to20, 4:1)
a.mess [1] 1 2 3 4 5 6 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 4 3 2
[26] 12) Extracting elements from vectors
A nice way to display the 22nd element of a.mess is to use square brackets [ ] to extract just that element:
#Extract 22nd element from a.mess
a.mess[22][1] 20We can extract more than one element at a time. For example, the 3rd, 6th, and 7th elements of a.mess are:
#Extract 3rd, 6th, and 7th Elements
a.mess[c(3,6,7)][1] 3 6 5To get the 3rd through 7th elements of a.mess, just type:
a.mess[3:7][1] 3 4 5 6 5Negative indices can be used to omit certain element(s). For Example:
# To omit 3rd element in a.mess
a.mess[-3] [1] 1 2 4 5 6 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 4 3 2 1#To omit 2nd to 10th elements in a.mess
a.mess[-c(2,10)] [1] 1 3 4 5 6 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 4 3 2 1Do not mix positive and negative indices. To see what happens, observe:
a.mess[c(-2, 10)]
#Error in a.mess[c(-2, 10)] : only 0's may be mixed with negative subscripts Always be careful to make sure that vector indices are integer. When fractional values are used, they will be truncated towards 0.
a.mess[0.5]
#numeric(0)3) Vector arithmetic
Arithmetic can be done on R vectors. For example, we can multiply all elements of numeric_vector by 3.
#Multiplication
numeric_vector * 3[1] 3 6 9 12 15 18Note that the computation is performed element wise. Addition (+), substraction (-), and division (/) by a constant have the same kind of effect. For example:
#Substraction
numeric_vector - 5 [1] -4 -3 -2 -1 0 1#Division
numeric_vector / 2[1] 0.5 1.0 1.5 2.0 2.5 3.0#Addition
numeric_vector + 2[1] 3 4 5 6 7 8Next, consider taking the 3rd power of the elements of numeric_vector:
numeric_vector^3[1] 1 8 27 64 125 2164) Characters vectors
Scalars and vectors can be made up of strings of characters instead of numbers. All elements of a vectors must be of the same type. For Example:
colors <- c("red", "yellow", "blue")
more.colors <- c(colors, "green", "magenta", "pink") #This appended some new elements to colors#An attempt to mix data types in a vector
new <- c("green", "yellow", 1)To see the contents of more.colors and new, simply type;
more.colors [1] "red" "yellow" "blue" "green" "magenta" "pink" new[1] "green" "yellow" "1" 5) Factor vector
Factor offer an alternative way to store character data. For example, a factor with 4 elements and having the 2 levels control and treatment can be create using:
grp <- c("control", "treatment", "control", "treatment")
grp [1] "control" "treatment" "control" "treatment"#set as factor
grp <- as.factor(grp)
grp[1] control treatment control treatment
Levels: control treatmentFactors can be an efficient way to storing character data when there are repeated among the vector elements. This is because the levels of a factor are internally coded as integers. To see what the codes are for our factor, we can type:
as.integer(grp)[1] 1 2 1 2The labels for the levels are stored just once each, rather than being repeated. The codes are indices of the vector of levels:
levels(grp)[1] "control" "treatment"The levels() function can be used to change factor labels as well. For example, suppose we wish to change the “control” label to “placebo”.
levels(grp)[1] <- "placebo"An important use for factors is to list all possible values, even if some are not present. For example:
gender <- factor(c("Female", "Female", "Female"), levels = c("Female", "Male"))
gender[1] Female Female Female
Levels: Female MaleIt shows that there are two possible values for gender, but only one is present in our vector.
Data Frames
Data sets frequently consist of more than one column of data, where each column represents measurements of a single variable. Each row usually represents a single observation. This format is referred to as case-by-variable format.
Most data sets are stored in R as data frames. These are like matrices, but with the columns having their own names.
A data frame is one of the most commonly used data structures in R, especially for data analysis and statistical modelling. Conceptually, it can be thought of as a table or a spreadsheet, where you have rows representing observations and columns representing variables. A data frame is similar to a matrix, but with the added flexibility that different columns can contain different types of data (eg: numeric, character, factor).
Features:
Mixed Data Types: Unlike matrices, data frames can store different classes of objects in each column.Column Names: Columns in a data frame can have names, which makes accessing and manipulating data easier and more intuitive.Row Names: By default, rows have index names (from 1 to the number of rows), but these can also be explicitly set to other values.
Creation:
A data frame can be created using the data.frame() function:
df <- data.frame(Name = c("Ali", "Abu", "Ahmad"),
Age = c(9, 6, 2),
Score = c(82, 93, 92))
df Name Age Score
1 Ali 9 82
2 Abu 6 93
3 Ahmad 2 92Indexing:
Columns: You can access a column in a data frame using
$operator or double square brackets[[…]].#Extracting first row df[1, ]Name Age Score 1 Ali 9 82Rows: Rows can be accessed using single square brackets
[…].#Extracting 3rd row df[3, ]Name Age Score 3 Ahmad 2 92
Subsetting: You can subset data frames using conditions
#Extracting data that contain more than 90 df[df$Score > 90, ]Name Age Score 2 Abu 6 93 3 Ahmad 2 92
Useful functions
head() and tail(): Display the first or last part of a data frame.str(): Provides the structure of a data frame, showing the data type of each column and the first few entries.summary(): Gives a statistical summary of all columns in a data frame.dim(): Returns the dimensions (number of rows and columns) of a data frame.rownames()andcolnames(): Get or set the row or column names of a data frame.merge(): Merges two data frames by common columns or row names.
Examples
1) head() and tail()
These functions display the first or last part of a data frame, respectively. By default, they show six rows.
# Create a sample data frame
df <- data.frame(Name = c("Ali", "Abu", "Ahmad", "Aminah", "Rosnah", "Rozanae", "Rohana"),
Age = c(25, 32, 29, 24, 27, 31, 23),
Score = c(85, 90, 93, 87, 78, 91, 82))
# Display the first few rows
head(df) Name Age Score
1 Ali 25 85
2 Abu 32 90
3 Ahmad 29 93
4 Aminah 24 87
5 Rosnah 27 78
6 Rozanae 31 91# Display the last few rows
tail(df) Name Age Score
2 Abu 32 90
3 Ahmad 29 93
4 Aminah 24 87
5 Rosnah 27 78
6 Rozanae 31 91
7 Rohana 23 822) str()
This function provides a concise display of the structure of an object, such as a data frame.
# Display the structure of df
str(df)'data.frame': 7 obs. of 3 variables:
$ Name : chr "Ali" "Abu" "Ahmad" "Aminah" ...
$ Age : num 25 32 29 24 27 31 23
$ Score: num 85 90 93 87 78 91 823) summary()
Gives a statistical summary of all columns in a data frame.
# Get a summary of df
summary(df) Name Age Score
Length:7 Min. :23.00 Min. :78.00
Class :character 1st Qu.:24.50 1st Qu.:83.50
Mode :character Median :27.00 Median :87.00
Mean :27.29 Mean :86.57
3rd Qu.:30.00 3rd Qu.:90.50
Max. :32.00 Max. :93.00 4) dim()
Returns the dimensions of an object.
# Get the dimensions of df (number of rows and columns)
dim(df)[1] 7 35) rownames() and colnames()
Retrieve or set the row or column names of a data frame.
# Get row names of df
rownames(df)[1] "1" "2" "3" "4" "5" "6" "7"# Get column names of df
colnames(df)[1] "Name" "Age" "Score"# Set new row names for df
rownames(df) <- c("A", "B", "C", "D", "E", "F", "G")6) merge()
Merge two data frames by common columns or row names.
# Create another sample data frame
df2 <- data.frame(Name = c("Ali", "Abu", "Rosnah", "Rohana"),
Grade = c("A", "B", "A", "C"))
# Merge df and df2 by the "Name" column
merged_df <- merge(df, df2, by="Name")
print(merged_df) Name Age Score Grade
1 Abu 32 90 B
2 Ali 25 85 A
3 Rohana 23 82 C
4 Rosnah 27 78 ALet’s use mtcars data set.
This dataset comprises various specifications and details about different car models from the 1970s.
1. Quick Glance at the Dataset
First, let’s take a quick look at the mtcars dataset:
head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 12. Structure of the Dataset (str())
Examining the structure of mtcars:
str(mtcars)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...3. Summary of the Dataset (summary())
Providing a statistical summary:
summary(mtcars) mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000 4. Dimensions of the Dataset (dim())
Checking the number of rows and columns:
dim(mtcars)[1] 32 115. Column Names (colnames())
Retrieving the names of the columns:
colnames(mtcars) #same as names(mtcars) [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
[11] "carb"7. Subsetting Example
Extracting data for cars with 6 cylinders and horsepower (hp) greater than 150:
mtcars[mtcars$cyl == 6 & mtcars$hp > 150, ] mpg cyl disp hp drat wt qsec vs am gear carb
Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
Exercise!
Create a numeric variable “
num_var” with the value “42.5”.Create a character variable “
char_var” with the value “R is fun!”.Print the data type “
char_var”.Create a list “
student_info” with the following elements:“
name”: “Mohammad Nasir Abdullah”“
age”: 18“
grades”: a numeric vector with values “99, 100, 89”.
Create a data frame “
df_students” with the following columns“
Name”: “John”, “Pablo”“
Age”: “22”, “30”“
Grade”: “A”, “C”
Create a numeric vector “
vec_num” with the values “5,10,15,20”.Extract the second and third elements from “
vec_num”.Create a vector “
vec_seq” that contains a sequence of number from 1 to 10.Create a vector “
vec_rand” with 5 random numbers between 1 and 100.Create a character vector “
vec_char” with the values “apple”, “banana”, “cherry”.