Syntax

The syntax for floor division is straightforward:

result = numerator // denominator

Where numerator is the number to be divided, and denominator is the number by which you divide.

How Does Floor Division Work?

Integer Division

When both operands are integers, the result of the // operator is also an integer. It divides the first operand by the second and then floors the result to the nearest integer towards negative infinity.

Example 1: Positive Integers

result = 7 // 2
print(result)  # Output: 3

In this example, 7 divided by 2 is 3.5. Flooring 3.5 gives us 3.

Example 2: Negative Integers

result = -7 // 2
print(result)  # Output: -4

result = 7.0 // 2
print(result)  # Output: 3.0

result = 7 // 2.0
print(result)  # Output: 3.0

result = -7.0 // 2
print(result)  # Output: -4.0

items = [1, 2, 3, 4, 5, 6]
half_length = len(items) // 2
print(half_length)  # Output: 3
first_half = items[:half_length]
second_half = items[half_length:]
print(first_half)  # Output: [1, 2, 3]
print(second_half)  # Output: [4, 5, 6]

total_items = 17
items_per_box = 5
full_boxes = total_items // items_per_box
print(full_boxes)  # Output: 3

The post What Does // Mean in Python? A Beginner’s Guide to Floor Division appeared first on Coding Campus.

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

The post cos() Function in R appeared first on Coding Campus.

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

The post How to Calculate Square in R appeared first on Coding Campus.

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.

The post dim() in R appeared first on Coding Campus.

We’ve compiled a list of methods you can use to locate missing files in Linux. As always, we include tools for both more experienced users and beginners, and we hope that these suggestions allow you to avoid the traumatic loss of important data!

Using the grep command to find a file by its contents

Grep has been part of Unix for so long that you will often see the term used as a verb in Linux discussions online (it’s generally synonymous with “search locally”). It can be thought of as the ultimate “pattern recognition tool”. However, Grep should only be used to search for the contents of a file rather than finding items using their filename.

Now, let’s look at how grep is actually used. Let’s say you had a file in your ~/Documents directory called ‘Give.txt’. The contents of the file are as follows:

What's real can't die
You only get what you give
You're gonna get what you give
Don't give up
Just don't be afraid to leave

Of course, these are the lyrics to a famous song by New Radicals. It’s an excellent example in this case, as it highlights how useful grep is and a few of its behaviors that might appear idiosyncratic at first to new users.

From our Documents directory, we’re going to execute the following command:

grep you give.txt

Press return, and you’ll see that it returns every line with the word “you” in it:

Now, let’s try another command (remember to include the escaped apostrophe):

grep don't give.txt

And here is our result:

As you can see, the fifth line was identified; however, because grep is case-sensitive by default, the fourth line (“Don’t give up”) was not returned.

Now, we’ll use the “-i” flag to turn off case-sensitivity in our following query (I’ve also now used quotations to avoid relying on the backslash character this time):

grep -i "don't" give.txt

This time, we can see that every instance of the word – regardless of case – is brought back. If you use the “-i” flag when searching for “you”, you’ll also notice that it now highlights the capitalized appearances of the word as well:

Using the find command to find a file by its name

While grep allows users to query the contents of files on their computer, the find tool provides an efficient way to locate items based on their actual filename.

The most basic use of the find command is as follows:

find .

The period (‘.’) tells the command to return every file and subdirectory found if we search from the present working directory. Essentially, all this is is a good starting point.

However, there are a variety of additional arguments which allow us to get a lot more specific with how we use the find tool. For example, the following command will return all subdirectories from the present location (with ‘d’ standing for “directories”):

find . -type d

Alternatively, we can return only files by typing:

find . -type f

The find command becomes especially useful for locating misplaced files when switching out the “-type” option with “-name”. For example, we can return all ePub files (either in the current directory or within its subdirectories) by using:

find . -name "*.epub"

Or, we can find all ePub files with the word “great” in the name by typing:

find . -name "*great*epub"

And, as with the grep tool, we can choose to make our query case-insensitive:

find . -iname "*great*epub"

And, if we want to narrow it down even further, we can combine options too:

find . -type f -iname "*GrEat*"

The above example is slightly redundant, as we only have files with the word ‘great’ in their title. However, if the ~/Downloads folder also had a subdirectory called “Great Expectations”, it would be omitted from our results as we have specifically included the ‘-type f’ option.

Find files using Catfish

For those who prefer not to use the command line, you can use a GTK3 application called Catfish. This can be installed from the software repository of your Linux distribution by typing:

sudo apt-get install catfish -y

Other popular choices for Linux users who prefer to use GUI to locate files include fsearch, Cerebro, and Terrier.

Catfish can provide complex searches (based on criteria like date of modification, file type, and so on). While it’s not quite as powerful as grep or find, it should suit most users fine.

The post How to Find a File in Linux appeared first on Coding Campus.

Check CentOS Using RPM (Easiest)

The RPM Package Manager provides the quickest and easiest way to find out which version of CentOS is running.

1. From the home screen, click ‘Activities’ in the top left-hand corner.

   

2. Then, type ‘terminal’ into the search box which appears. Now, click the terminal button.

   

3. In the terminal window, enter the following command:

rpm -q centos-linux-release

Then hit enter.

As you can see, the exact version of CentOS (including the major release number, as well as the revision).

Check CentOS Version Using Your Release File

You might not know this, but there’s actually a text file included in your CentOS installation. It’s located in the ‘/etc/’ directory, and it’s very handy for checking your release version.

1. From the home screen, click ‘Activities’ in the top left-hand corner.

   

2. Now, click the Terminal icon which appears on the left-hand sidebar.

   

3. In the window which opens, enter the following command:

cat /etc/centos-release

Then hit enter.

There’s also a way to find out more details about your release, by using the following command instead:

cat /etc/os-release

And hit enter.

As you can see, this command doesn’t provide your exact release number, but it does provide other useful information such as the ‘Platform_ID’ and the ‘Pretty Name’.

Check the ‘Pretty Name’ Using the GUI

If you’re also content just to know which major release of CentOS you’re using, you can do this using the graphical interface. This method might also be useful if – for whatever reason – you can’t use the terminal.

1. Again, click ‘activities’ in the top left-hand corner.

   

2. Then, type ‘version’ into the search box which appears.

   

3. You can now see the release of CentOS you’re currently running. Although less detailed, this method only requires using the GUI.

Bonus Tip: You can click the ‘View Updates’ button in the bottom right-hand area of the above screenshot to begin updating your current installation (if needed).

The post How to Check CentOS Version appeared first on Coding Campus.

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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