Basics of R
3.3 (13 ratings)
601 students enrolled

# Basics of R

A comprehensive guide for beginners in R
3.3 (13 ratings)
601 students enrolled
Created by Nisha Kiran
Last updated 1/2016
English
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This course includes
• 43 mins on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Write programs in R
• Use basic statistics in R to perform various analyses
• Gain insights into data by subsetting the data and plotting in R
Course content
Expand all 22 lectures 42:53
+ Introduction
2 lectures 02:22

In this lecture, we discuss how to create objects in R. As well as we cover listing and deletion of objects in R.

Preview 01:44
+ Data Types
5 lectures 07:41

There are four basic data types in R. In this lecture, we look at 3 main data types in R.

Preview 01:02

A vector is a sequence of data elements of the same data type. Here we cover the creation of vectors of each data type in R.

Preview 01:29

A matrix is a collection of data elements of the same data type in a two-dimensional rectangular layout. Here we cover the matrix construction in R.

Preview 01:20

When a variable can take only a set of values, it is necessary in most circumstances to treat it as a categorical variable with different levels. In this lecture, we see how to create factors and look at summary and levels of a factor.

Preview 02:06

Another way information is stored in R is using data frame. Here we look at how to create data frames in R.

Preview 01:44
+ Operations
2 lectures 04:02

This lecture covers the various operations like addition, subtraction, multiplication, division and indexing on vectors.

Operations on vectors
02:37

This lecture covers how to do indexing on a matrix and access its elements.

Operations on matrices
01:25
+ Input in R
3 lectures 06:24

This lecture covers how one can read a comma separated values file in R into a data frame and access the data.

01:46

In this lecture, we will work with the iris data set and look at various commands used to access rows and columns in the dataset.

Working with datasets
02:11

In this lecture, we will look at how to subset the dataset. Subsetting the dataset can be useful in scenarios where we need to answer questions about the dataset.

Subsetting the dataset
02:27
Quiz 1
4 questions
+ Plots in R
4 lectures 07:32

In this lecture, we look at how to produce scatter plots. Scatter plots provide graphical relationship between two numeric variables.

Scatter plots
01:36

In this lecture, we will look at how to produce histograms in R. Histograms are a very common plot in R used to plot frequency that the data appears within certain ranges.

Histograms
01:47

Boxplots provide graphical view of median, quartiles, maximum and minimum in the data set. In this lecture, we cover how to produce boxplots.

Boxplots
01:24

Bar charts and pie charts are used for summarizing distribution of categorical variable. In this lecture, we will look at how to produce bar charts and pie charts.

Bar charts and Pie charts
02:45
+ Basic Statistics in R
3 lectures 09:08

We look at the four functions which are used to generate values associated with normal distribution in this lecture.

Normal distribution
04:27

In this lecture, we look at the various commands used to generate binomial distribution. The commands we look at are dbinom, pbinom, qbinom, and rbinom.

Binomial distribution
02:37

In this lecture, we discuss the lm() command to perform the least square regression. We also look at the abline() and summary() functions.

Linear Regression
02:04
+ Functions and loops
3 lectures 05:47

In this lecture, we look at three ways to loop in R. They are for loop, while loop and repeat loop. We look at an example of each.

Loops in R
02:39

In this lecture, we give a basic introduction to functions in R. We work on an example on how to write functions in R.

Functions in R
01:15

In this lecture we look at the apply function and work on two examples to use the apply() function.

apply() function
01:53
Quiz 2
4 questions
Requirements