R Language Introduction and R Language Examples

R is a programming language and software environment for statistical computing and graphics. It is free and open-source. It first appeared in 1993 and has gone through a number of releases. Today R is widely used for data analysis among statisticians and data scientists.

Further, R is a language designed specifically for data analysis and plotting. This differentiates R from Python in the sense that you may find algorithms and functions in R that are not yet covered in Python.

RStudio

RStudio is a free environment for R scripting. You can download RStudio at https://www.rstudio.com/.

RStudio workspace consists of 4 panes:

1. Source Pane for viewing and writing R scripts
2. Console Pane for live coding
3. Environment/History Pane for exploring active variables or viewing older commands
4. Files/Plots/Packages/Help Pane for viewing plots and help and exploring files or packages

You can download the content of this article in the R file here and follow along.

Arithmetic operators in R Language

We can test the console with basic arithmetic operators.

For example:

> x=10
> y=2

> x+y
[1] 12

> x-y
[1] 8

> x*2
[1] 20

> x/y
[1] 5

> x^y
[1] 100

> x**y
[1] 100

> x%%y
[1] 0

Relational operators in R

Similarly, we use the following relational operators:

For example:

> x=10
> y=2

> x<y
[1] FALSE
> x>y
[1] TRUE
> x<=y
[1] FALSE
> x>=y
[1] TRUE
> x==y
[1] FALSE
> x!=y
[1] TRUE

Math functions

Certainly, there is an abundance of available math functions. Few examples are listed in the table below.

Help on R Language functions

We can find help for any function by typing help(function_name). For example, help for log function:

> help(log)

Datasets (DataFrames) and basic commands in R

R comes equipped with sample datasets that can be used to analyze and study data. For Instance the Iris dataset, which contains information on Iris plant. Moreover, it specifies measurements for four features measured for three variants of Iris flower (setosa, virginica, versicolor). All measurements are given in centimeters.

Calling the Iris Dataset

> iris

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species

1            5.1         3.5          1.4         0.2     setosa

2            4.9         3.0          1.4         0.2     setosa

3            4.7         3.2          1.3         0.2     setosa

4            4.6         3.1          1.5         0.2     setosa

5            5.0         3.6          1.4         0.2     setosa

6            5.4         3.9          1.7         0.4     setosa

7            4.6         3.4          1.4         0.3     setosa

8            5.0         3.4          1.5         0.2     setosa

9            4.4         2.9          1.4         0.2     setosa

10           4.9         3.1          1.5         0.1     setosa

11           5.4         3.7          1.5         0.2     setosa

12           4.8         3.4          1.6         0.2     setosa

Exploring Column Names in the Iris Dataset

> # Column names
> names(iris)

 [1] “Sepal.Length” “Sepal.Width”  “Petal.Length” “Petal.Width”  “Species”   

Returning an Object Deffinition for the Iris dataset

> # We usually work with objects of class data.frame, a table look-alike with columns and rows
> class(iris)

[1] “data.frame”

Returning the Dimensions of the Dataset with R Language

> # Dimension
> dim(iris)

[1] 150   5

Return First 6 Rows of a Dataset

> # First 6 rows of dataframe
> head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species

1          5.1         3.5          1.4         0.2  setosa

2          4.9         3.0          1.4         0.2  setosa

3          4.7         3.2          1.3         0.2  setosa

4          4.6         3.1          1.5         0.2  setosa

5          5.0         3.6          1.4         0.2  setosa

6          5.4         3.9          1.7         0.4  setosa

Return a Specific Number of First Rows with R Language

> # Specifying number of first rows
> head(iris, 10)

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species

1           5.1         3.5          1.4         0.2  setosa

2           4.9         3.0          1.4         0.2  setosa

3           4.7         3.2          1.3         0.2  setosa

4           4.6         3.1          1.5         0.2  setosa

5           5.0         3.6          1.4         0.2  setosa

6           5.4         3.9          1.7         0.4  setosa

7           4.6         3.4          1.4         0.3  setosa

8           5.0         3.4          1.5         0.2  setosa

9           4.4         2.9          1.4         0.2  setosa

10          4.9         3.1          1.5         0.1  setosa

Return the Last 6 rows of a Dataset/DataFrame

This is a sort of a logical function. If the first six rows was head then this one is…

> # Last 6 rows of dataframe
> tail(iris)

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species

145          6.7         3.3          5.7         2.5 virginica

146          6.7         3.0          5.2         2.3 virginica

147          6.3         2.5          5.0         1.9 virginica

148          6.5         3.0          5.2         2.0 virginica

149          6.2         3.4          5.4         2.3 virginica

150          5.9         3.0          5.1         1.8 virginica

Return the Last 3 rows of a Dataset/DataFrame

In the same vein as the head example.

> # Specifying number of last rows
> tail(iris, 3)

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species

148          6.5         3.0          5.2         2.0 virginica

149          6.2         3.4          5.4         2.3 virginica

150          5.9         3.0          5.1         1.8 virginica

Descriptive statistics

Let’s look at the basic statistics commands like min, max, range, median, mode, standard deviation, and quantile. We can get them all by simply using the summary() function.

> # Show dataframe statistics 
> summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width  

 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100 

 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300 

 Median :5.800   Median :3.000   Median :4.350   Median :1.300 

 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199 

 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800 

 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500 

       Species 

 setosa    :50 

 versicolor:50 

 virginica :50 

Or we can examine them separately.

> min(iris$Sepal.Length)
[1] 4.3

> max(iris$Sepal.Length)
[1] 7.9

> range(iris$Sepal.Length)
[1] 4.3 7.9

> mean(iris$Sepal.Length)
[1] 5.843333

> median(iris$Sepal.Length)
[1] 5.8

> mode(iris$Sepal.Length)
[1] "numeric"

> sd(iris$Sepal.Length)
[1] 0.8280661

> quantile(iris$Sepal.Length)
  0%  25%  50%  75% 100% 
 4.3  5.1  5.8  6.4  7.9 

Plots: meet ggplot2, library for stunning graphics

R can plot data on its own, but the dedicated library you’ll really want to use is ggplot2. With ggplot2 you can create beautiful print quality and publication-ready data visualizations.

Ggplot2 is based on the grammar of graphics idea, which basically means each part of code stands for a component or a layer. As a result, we can add components together using +. The basic structure of any plot looks like this:

ggplot(data = , aes(x =, y = )) + geom_name()

1. ggplot(): creates an object and assigns columns from data dataframe to x and y axes
2. geom_name(): plots the data in desired geometry, for example point, line, histogram, boxplot

Point plot

Let’s plot a basic point plot.

We create a plot object using ggplot2, input the iris dataframe as data and assign the Sepal.Length column to x axis and Sepal.Width to y axis.

Further, add geom_point() to plot the points to the plot.

> library(ggplot2)
> ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point()

We often want to separate data according to the field in our data. We specify Species column as our color argument with color = Species.

> # Add colors to groups
> ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) + geom_point()

Histogram in R Language

A histogram organizes a group of data points into ranges and plots the frequency of occurrence within each range. In short, this is the chart behind all normalized curves you will ever encounter. However, it should be said that for the best results while using a histogram settings will have to be manually adjusted most of the time.

> ggplot(iris, aes(x = Sepal.Length)) + geom_histogram()

To add color by group we specify Species column as our fill argument with fill = Species. For example, see the code below.

>  # Add colors to groups
> ggplot(iris, aes(x = Sepal.Length, fill = Species)) + geom_histogram()

Boxplot

Boxplot illustrates the distribution of data based on descriptive statistics: minimum, first quartile, median, third quartile, and maximum. In other words, it offers a lot of statistical information in one plot. Consequently, this is the chart of choice for a lot of Analysts and Statisticians.

> ggplot(iris, aes(x = Species, y = Sepal.Length)) + 
	geom_boxplot()

Saving Plots as images or as PDF in R Language

Plots can be saved as images or PDFs directly in plot viewer by selecting Export > Save as Image or Save as PDF.

Alternatively, you can use function ggsave(filename, path). For example:

> ggsave(filename = "my_ggplot.png", path = "C:/temp")

If the path argument is omitted, it will default to the current working directory.

Learn more about R Language

In conclusion, this was a quick overview of R basics. To learn more, stand by for our upcoming R Academy posts and Videos where we’ll go in-depth and cover all the relevant topics of R language.