Syntax

print(x)

Arguments

• x = The object can be of any type, including vectors, matrices, data frames, and lists.

Example 1: Print Values using print() function

#printing values
print("R Programming Guide")

Output

[1] "R Programming Guide"

Example 2: Print Variables using print() function

#creating a variable
x <- 25
#printing variable
print(x)

Output

[1] 25

Example 3: Print a Vector using print() function

#creating a vector
x <- c(1:5)
#printing vector
print(x)

Output

[1] 1 2 3 4 5

Example 4: Print a List using print() function

#creating a list
x <- list('Bruce','Wayne',1.6,'Martial Arts')
#printing list
print(x)

Output

[[1]]
[1] "Bruce"

[[2]]
[1] "Wayne"

[[3]]
[1] 1.6

[[4]]
[1] "Martial Arts"

Example 5: Print a Data Frame using print() Function

#printing the preloaded iris data frame
print(iris)

Output

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Using hist() Function to Create Stacked Histogram in R

You should use the hist() function to create a stacked histogram when you have two different variables that are to be plotted on the same graph. The hist() is a built-in function in R that takes a vector as input and creates a histogram.

To create a stacked histogram of two variables, we need to call the hist() function twice, as there are two variables. When you call the hist() function a second time, make sure to include the “add” argument. This argument plots the first and the second histogram on a single plot.

Syntax

hist(x, col=NULL,add=TRUE)

Arguments

• x = It is the vector of data that you want to create the histogram from.
• col = It is the color of the bars.
• add = TRUE to merge the histograms.

Example

In this example, we will create a stacked histogram from two different variables.

 #creating 1st variable
x = c(50,40,45,20,25)
#creating 2nd variable
y = c(15,5,40,10,45)
#creating histogram of x
hist(x,col = "green")
#creating histogram of y and stacking x
hist(y,col = "red",add=TRUE)

Output

In the above example, we created a histogram of variable x and stacked it on top of the histogram of variable y.

Using ggplot() Function to Create Stacked Histogram in R

While the hist() function is used when you have two different variables, ggplot() is used when you have one variable with different categories. ggplot() is not a built-in function in R.

Syntax of ggplot()

ggplot(data=, aes(x=, fill=)) + geom_histogram()

Argument

• data = It is the data frame you want to use to create the plot.
• aes() = It is used to specify the variable to use on axes.
• x = The variable to map on the x-axis.
• fill = It defines the categorical variable based on which you want to color the histogram.
• geom_histogram() = It specifies that you want to create a histogram.

Installing ggplot2

ggplot() comes from the ggplot2 package. To install and load the package, run the following code:

# install ggplot2 package
install.packages('ggplot2')
# load ggplot2 package
library('ggplot2')

Example

To demonstrate how to use the ggplot() to create a stacked histogram, we will use the PlantGrowth data set. This data set is included with the base R installation. The data set contains information on the experiments to compare yields. It has two columns: weight and group.

In this example, we will plot the histogram of mpg column using the ggplot() function.

ggplot(data=PlantGrowth, aes(x=weight,fill=group)) + geom_histogram()

Output

In the above example, we created a plot containing stacked histograms. We specified the weight column to be plotted and then colored the histogram based on the column group.

The post How to Create Stacked Histograms in R appeared first on Coding Campus.

Method #1: Create an empty vector using the c() function

The combine or c() function in R is used to create vectors by combining multiple values. To create an empty vector using the c() function, do not pass anything inside the parentheses.

Syntax

vector name <- c()

Here, “vector name” can be any name you want to give to the empty vector.

Example

x <- c()
x

Output

NULL

In the above example, the output is NULL which denotes that the vector is empty.

Method #2: Create an empty vector using the vector() function

To create an empty vector using the vector() function, do not pass anything inside the parentheses.

Syntax

vector name <- vector()

Here, “vector name” can be any name you want to give to the empty vector.

Example

v <- vector()
v

Output

logical(0)

In the above example, logical(0) denotes an empty vector.

Method #3: Create an empty vector using a NULL object

You can assign NULL to an existing or a new vector to create an empty vector.

Example

If you have a vector x = c(1, 2, 3), you can convert it into an empty vector using the following code:

x <-c(1,2,3,5)
#converting to an empty vector
x <- NULL
x

Output

NULL

In the above example, we converted an existing non-empty vector to an empty vector by assigning the NULL object.

Method #4: Create an empty vector using the rep() function

rep() function in R repeats the specified object a specified number of times. It is used to create a vector with a specified number of entries. To create an empty vector using the rep() function, do not pass anything inside the parentheses.

Syntax

vector name <- rep()

Here, “vector name” can be any name you want to give to the empty vector.

Example

y <- rep()
y

Output

NULL

Method #5: Create an empty vector using the numeric() function

The numeric() function creates a numeric vector of a defined length. To create an empty vector using the numeric() function, do not pass anything inside the parentheses.

Syntax

vector name <- numeric()

Here, “vector name” can be any name you want to give to the empty vector.

Example

a <- numeric()
a

Output

numeric(0)

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Syntax

cos(x)

Argument

• x = It is the numeric vector you want to calculate the cosine of.

Here are some examples of the cos() function in R:

Example 1: Calculate the cosine of a number

#calculating the cosine value of a number
cos(0)
#calculating the cosine value of a number stored in a variable
x = 5
cos(x)

Output

[1] 1
[1] 0.2836622

Example 2: Calculate the cosine of a vector

#calculating the cosine value of a vector
x = (c(0,1, 2))
cos(x)

Output

[1] 1.0000000 0.5403023 -0.4161468

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Calculate the Square of a Single Value in R

There are 3 ways to calculate the square of a vector in R.

Method #1: Using the ^ operator

To calculate the square of a single value, use the ^ operator.

For example, to calculate the square of 5, you would use the following code:

5^2

Output

[1] 25

Method #2:  Using the * operator

You can square a number using the multiplication operator, also known as an asterisk symbol (‘*’). To use it, take the number and multiply it by itself. For example, to take the square of 5, you can use the following formula:

5*5

Output

[1] 25

Method #3: Using the ** operator

You can also square a number using the double asterisk ** operator. So we type the number to square, then **, and end with 2. For example, to get the square of 5, use the following formula:

5**2

Output

[1] 25

Calculate the Square of a Vector in R

There are three ways to calculate the square of a vector in R.

Method #1: Using the ^ operator

To calculate the square of a vector, use the ^ operator.

For example, if you have a vector x = c(1,2,3), you can calculate the square of each element in the vector using the following code:

x = c(1,2,3)
x^2

Output

[1] 1 4 9

Method #2: Using the * operator

Alternatively, you can use the * operator to square a vector.

Syntax

vector * vector

For example, if you have a vector x = c(1,2,3) you would use this code:

x = c(1,2,3)
x*x

Output

[1] 1 4 9

Method #3: Using the ** operator

Use the ** operator to square a vector.

Syntax

vector ** 2

For example, if you have a vector x = c(1,2,3) you would use this code:

x = c(1,2,3)
x**2

Output

[1] 1 4 9

Calculate the Square of a Matrix in R

To calculate the square of a matrix in R, use the ^ operator with the matrix.

For example, if you have a matrix A = matrix(1:9, 3, 3) you can calculate the square of each element in the matrix using the following code:

A = matrix(1:9, 3, 3)
A^2

Output

[1] 1 4 9 16 25 36 49 64 81

Calculate the Square of a Data Frame in R

To calculate the square of a data frame in R, use the ^ operator with the data frame.

For example, if you have a data frame df, you can calculate the square of each element in the data frame using the following code:

df = data.frame(x = c(1,2,3), y = c(4,5,6))
df^2

Output

  x y
1 1 16
2 4 25
3 9 36

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Syntax

dim(x)

Arguments

• x = a vector, matrix, array, or data frame

Using dim() to get the dimension of a Vector

A vector is a one-dimensional data structure. To get the dimension of a vector, you need to specify the vector as an argument to the dim() function. It will always return NULL.

Example

#creating a vector
x <- 1:3
#printing vector
x
#getting the dimension of a vector
dim(x)

Output

[1] 1 2 3
NULL

Using dim() to get the dimension of a Matrix

A matrix is a two-dimensional array. To get the dimension of a matrix, you need to specify the matrix as an argument to the dim() function. It will return a vector of two integers that indicate the number of rows and columns in the matrix.

Example

#creating a matrix
y <- matrix(1:9, nrow=3, ncol=3)
#printing matrix
y
#getting the dimension of a matrix
dim(y)

Output

[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9

[1] 3 3

Using dim() to get the dimension of an Array

An array is a three-dimensional data structure. To get the dimension of an array, you need to specify the array as an argument to the dim() function. It will return a vector of three integers that indicate the size of the array.

Example

#creating an array
a <- array(1:8, c(2, 2, 2))
#printing array
a
#getting the dimension of the array
dim(a)

Output

, , 1
[,1] [,2]
[1,] 1 3
[2,] 2 4

, , 2
[,1] [,2]
[1,] 5 7
[2,] 6 8

[1] 2 2 2

Using dim() to get the dimension of a Data Frame

A data frame is a two-dimensional data structure. To get the dimension of a data frame, you need to specify the data frame as an argument to the dim() function. It will return a vector of two integers that indicate the number of rows and columns in the data frame.

Example

#creating a data frame
df <- data.frame(x=1:3, y=4:6)
#printing data frame
df
#getting the dimension of the data frame
dim(df)

Output

x y
1 1 4
2 2 5
3 3 6

[1] 3 2

Here, 3 indicates the number of rows in the data frame, and 2 indicates the number of columns.

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Syntax

sample(x, size, replace, probability)

Arguments

• x = a vector of data points from which you want to select a random sample.
• size = the number of data points you want to select.
• replace = can be TRUE or FALSE. If TRUE, the values can be repeated in the sample. By default, it is set to FALSE.
• prob = a vector of probability weights.

Now that we’ve gone over the basics of the function, let’s look at some examples of how to use it.

Example 1: Creating a sample without replacement

We can use the sample() function to select a random sample of data points without replacement. This means that once a data point is selected, it will not be selected again. Let’s say we have a vector x that contains 100 data points. To select a random sample of 10 data points from this vector, we use the following code:

#creating a vector of 100 values
x = c(1:100)
#generating a sample of 10 values
sample(x, size = 10)

Output

[1] 10 62 56 97 64 9 26 87 52 41

In this example, the function returned a vector containing ten randomly selected data points from x, where no data point was selected more than once. Also, note that values are not repeated because, by default, the replace argument is set to be FALSE.

Example 2: Creating a sample with replacement

Let’s take the same example used in Example 1. We can use the sample() function to select a random sample of 10 data points from this vector with replacement of the values using the following code:

#creating a vector of 100 values
x = c(1:100)
#generating a sample of 10 values
sample(x, size = 10, replace=TRUE)

Output

[1] 28 34 32 68 67 91 88 2 14 88

In this example, the function returned a vector containing ten randomly selected data points from x. Also, note that values may be repeated because we have set the replace argument as TRUE. In this example, 88 got repeated twice.

Example 3: Creating a sample with uneven probabilities

In the following example, we will define each element’s probabilities in the data set. Kindly note that the probability value should be between 0 to 1.

#creating a vector of 5 values
x = c(25,50,75,100,125)
#generating a sample of 3 values
sample(x, size = 3, replace=TRUE, prob = c(0.7,0.3,0.8,0.6,0.7))

Output

[1] 75 25 25

In the above example, we have defined the probability weights of each element using the prob argument. Notice that the value 75 has the highest probability of 0.8 and thus was represented in the sample output.

Example 4: Reordering the elements inside a vector

If you use the sample() function without defining the size, the function will return the same data set but with reordering it in ascending order.

#creating a vector of 5 values
x = c(25,50,75,100,125)
#reordering the data
sample(x)

Output

[1] 25 50 100 125 75

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dnorm() syntax

dnorm(x, mean = 0, sd = 1)

Arguments

• x = numeric value to test for probability
• mean = mean of the distribution
• sd = standard deviation of the distribution

The mean and sd arguments are optional. If you do not pass a value to them, they will take the default values as mean = 0 and sd = 1.

Example 1: dnorm() function with a single value

Suppose we want to calculate the probability density function of 5 with a mean of 10 and a standard deviation of 3.

x <- dnorm(5, mean=10, sd=3)

Output

[1] 0.03315905

Example 2: dnorm() Function with a vector of values

Say we want to calculate the probability density function of each element inside a vector with a mean of 10 and a standard deviation of 3.

#creating a vector
my_data <-c(1,2,3,4,5)
#calculating probability density function of the vector
x <- dnorm(my_data, mean=10, sd=3)
x

Output

[1] 0.001477283 0.003798662 0.008740630 0.017996989 0.033159046

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Syntax

tryCatch(expr, ..., finally)
tryCatch({
expr
}, error = function(e) {
code for handling error
}, warning = function(w) {
code for handling warning
}, finally = {
clean-up code
}

Arguments

• expr = expression to be evaluated.
• error = error handling function. It is called if an error occurs when evaluating the expression.
• warning = warning handling function. it is called if a warning occurs when evaluating the expression.
• finally = function to run after the execution of the tryCatch function. This argument is optional.

Example 1

In this example, we evaluate an expression that causes an error. Then, we will use the tryCatch() function to handle the error.

# creating an error by dividing a character by an integer
x/12

Output

Error in x/12: non-numeric argument to binary operator

#using the tryCatch() function to handle the error
tryCatch({
expr = x/12 #expression
}, error = function(e) {
print("There is an error!") #printing a message when prompted with an error
}, finally ={
print("code executed") #printing a message after code execution
})

Output

[1] "There is an error!"
[1] "code executed"

Example 2

In this example, we evaluate an expression that will cause a warning. Then, we will use the tryCatch() function to handle the warning.

# taking a square root of a negative number generates a warning
sqrt(-1)

Output

[1] NaN
Warning message:
In sqrt(-1) : NaNs produced

#using the tryCatch() function to handle the warning
tryCatch({
expr = sqrt(-1) #expression
}, warning = function(w) {
print("There is a warning!") #printing a message when prompted with a warning
}, finally ={
print("code executed") #printing a message after code execution
})

Output

[1] "There is a warning!"
[1] "code executed"

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There are two ways to find the natural log in R. This guide shows you how to use both methods.

Method #1: Calculating Natural Log using log() function

log() function comes preloaded in R. It allows the users to input the base of the log. But, if you don’t mention the base, the log() function will, by default, calculate the natural logarithm.

Syntax of log() Function

log(x, base)

Arguments

• x = a numeric or complex vector
• base = base of the log

Example 1: Calculating Natural Log using the log() function

#calculating natural log of 5
log(5)

Output

[1] 1.609438

Example 2: Calculating Natural Log of a vector using the log() function

#creating a vector
x <- c(1,2,3,4,5)
#calculating natural log of 5
log(x)

Output

[1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379

In this example, the log() function calculates each element’s natural log inside the vector.

Method 2: Calculating Natural Log using ln() function

Installing SciViews

ln() comes from the SciViews package. To install and load the package, run the following code:

install.packages('SciViews')
library('SciViews')

Syntax of ln() Function

ln(x)

Arguments

• x = a numeric or complex vector.

Example 1: Calculating Natural Log using the ln() Function

#loading the SciViews package
library('SciViews')
#calculating natural log of 5
ln(5)

Output

[1] 1.609438

Example 2: Calculating Natural Log of a Vector using the log() Function

#loading the SciViews package
library('SciViews')
#creating a vector
x <- c(1,2,3,4,5)
#calculating natural log of 5
ln(x)

Output

[1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379

In this example, the ln() function calculates each element’s natural log inside the vector.

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