Graphical Representations in R

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Many graphical representations in R Language are available for qualitative and quantitative data types. This post will only discuss graphical representations in R such as histograms, bar plots, and box plots.

Creating Histogram in R

To visualize a single variable, the histogram can be drawn using the hist() function in R. The use of histograms is to judge the shape and distribution of data in a graphical way. Histograms are also used to check the normality of the variable.

Let us attach the data from iris dataset.

attach(iris)
head(iris)
hist(Petal.Width)

We can enhance the histogram by using some arguments/parameters related to the hist() function in R. For example,

hist(Petal.Width,
  xlab = "Petal Width",
  ylab = "Frequency",
  main = "Histogram of Petal Width from Iris Data set",
  breaks = 10,
  col = "dodgerblue",
  border = "orange")
Graphical Representations in R Language

If these arguments are not provided, R will attempt to intelligently guess them, especially the number of breaks. See the YouTube tutorial for graphical representations of the histogram.

Creating Barplots in R

The bar plots are the best choice for visual inspection of a categorical variable (or a numeric variable with a finite number of values), or a rank variable. Usually, one can use bar plots for comparison purposes. The barplot() function can be used for visual inspection of a categorical variable.

library(mtcars)
barplot( table(cyl) )
barplot(table(cyl),
  ylab = "Frequency",
  xlab = "Cylinders (4, 6, 8)",
  main = "Number of cylinders ",
  col = "green",
  border = "blue")

Creating Boxplots in R

One can use Boxplots to visualize the normality, skewness, and existence of outliers in the data based on five-number summary statistics.

boxplot(mpg)
boxplot(Petal.Width)
boxplot(Petal.Length)

However, one can compare a numerical variable for different values of a categorical/grouping variable. For example,

boxplot(mpg ~ cyl, data = mtcars)
Graphical Representations in R Boxplot

The reads the formula mpg ~ cyl as: “Plot the mpg variable against the cyl variable using the dataset mtcars. The symbol ~ used to specify a formula in R.

boxplot(mpg ~ cyl, data =mtcars,
  xlab = "Cylinders",
  ylab = "Miles per Gallon",
  pch = 20,
  cex = 2,
  col = "pink",
  border = "black")
Graphical-representation-in-r

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switch Statement in R

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In R language, the switch statements allow a variable to be tested for equality against a list of values. Each value in a list is called a case, and the variable being switched on is checked for each case. R switch is almost the same as the if statement regarding working functionality.

The basic syntax is

Basic Syntax of Switch Statement in R Language

switch(expression,
     case 1,
     case 2,
     case 3,
     .
     .
)

The expression values are tested against multiple cases (case1, case2, …, casen). The one-line syntax is,

switch statement in r

An R Language Switch statement allows a default statement can also be added. The default statement will be executed when the Expression value is not matching with any of the case statements.

The following example is a simple command-line type calculator using R.

Simple Calculator Example

number1  <- 30
number2  <- 20
operator <- readline(prompt = "Enter any ARITHMETIC OPERATOR (+, -, *, ^, /, %/%, %%)!: ")

switch(operator,
       "+" = print(paste("Addition (number1+number2) = ", number1 + number2)),
       "-" = print(paste("Subtraction (number1-number2) = ", number1 - number2)),
       "*" = print(paste("Multiplication (number1*number2) = ", number1 * number2)),
       "^" = print(paste("Exponent (number1^number2) = ", number1 ^ number2)),
       "/" = print(paste("Division (number1/number2) = ", number1 / number2)),
       "%/%" = print(paste("Integer Division (number1 %/% number2) = ", number1 %/% number2)),
       "%%" = print(paste("Division (number1 %% number2) = ", number1 %% number2))
)

From the above example, one can easily compute some basic computations on two numbers. The operation on these two numbers depends on the input given to readline( ) the function and the expression in the switch. The operator value from readline() is matched with the options (cases) in the switch statement and results are displayed when matched.

Probability under the F-Curve

Consider another example, for different probabilities, the area under the curve for an F-curve can be selected using the switch as given below.

# q contains the probability under the curve for a F-curve
q <- c(0.25, 0.5, 0.75, 0.999)
test = 3
v1 = 10
v2 = 20

switch(test,
      "1" = print (qf(q[1], df1=v1, df2=v2, lower.tail = T) ),
      "2" = print (qf(q[2], df1=v1, df2=v2, lower.tail = T) ),
      "3" = print (qf(q[3], df1=v1, df2=v2, lower.tail = T) ),
      "4" = print (qf(q[4], df1=v1, df2=v2, lower.tail = T) )
)

The code above will produce F-table values for different probability values.

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if Statement in R: if-else, if-else-if Conditional Statement

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General Introduction to if Statement in R

In R Language, the if statement(s) is a conditional control used for making a decision. The if statement in R is used to run the block of statements when a certain condition is met. Therefore, if statements are also called conditional statements.

if Statement in R

An if statement consists of a boolean expression followed by one or more statements. The basic syntax for creating a if statement is

if ( boolean expression ) {
      # statements 
      # these statements will execute only if the boolean expression is true
}

The code inside the brackets of the if statement will be executed if the boolean expression in parenthesis evaluates to be true. For example,

x <- 3.0L
if ( is.integer(x) ){
   print("x is an integer")
}

if-else Statement

An if statement can be followed by an optional else statement. The else statement executes only when the boolean expression in the parenthesis of the if statement evaluates to false. The basic syntax of a if-else statement is

if (boolean expression){
        # statement(s) will execute if the boolean expression is true 
}
else{
      # statemenet(s) will execute if boolean expression is false
}
if statement in R syntax

For example,

x <- 31
if ( x %% 2 == 0 ){
   print("X is even")
}else{
  print("X is odd")
}
if Statement in R

if-else-if Statement

An if statement can be followed by an optional else-if-else statement, which is very useful for testing various conditions using a single if-else-if statement. The basic syntax for creating an if-else-if is

if ( boolean expression-1 ){
      # statements execute when the boolean expression-1 is true
} else if ( boolean expression-2 ) {
     # statements execute when the boolean expression-2 is true
} else if ( boolean expression-3 ){
    # statements execute when the boolean expression-3 is true
} else {
    # statements execute when none of the above condition is true
}

For example,

# Consider durbin watson statistics
d = -2
if (d == 2){
   print("no autocorrelation")
} else if (d > 0 &amp; d &lt; 2){
   print("Positive autocorrelation")
} else if (d > 2){
   print("successive error terms are negatively correlated")
} else {
  print("d is less than 0")
}

The value of $d (d=-2)$ will be compared with the expression’s result in the parenthesis of if or else if statement. From all of the if or else if statements only one statement will be true. In the example, $d=-2$ does not match with any of the if (or else if statement), therefore, the last statement (that is else statement) will be executed.

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