Statistics with R - Beginner Level
4.3 (1,069 ratings)
65,764 students enrolled

# Statistics with R - Beginner Level

Basic statistical analyses using the R program
Bestseller
4.3 (1,069 ratings)
65,764 students enrolled
Created by Bogdan Anastasiei
Last updated 3/2016
English
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This course includes
• 3 hours on-demand video
• 13 articles
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• manipulate data in R (filter and sort data sets, recode and compute variables)
• compute statistical indicators (mean, median, mode etc.)
• determine skewness and kurtosis
• get statistical indicators by subgroups of the population
• build frequency tables
• build cross-tables
• create histograms and cumulative frequency charts
• build column charts, mean plot charts and scatterplot charts
• build boxplot diagrams
• check the normality assumption for a data series
• detect the outliers in a data series
• perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit)
Requirements
• R and R studio
• knowledge of basic statistics
Description

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!
Who this course is for:
• students
• PhD candidates
• University teachers
• anyone looking for a job in the statistical analysis field
• anyone who is passionate about quantitative analysis
Course content
Expand all 46 lectures 02:48:45
+ Data Manipulation in R
9 lectures 34:10

How to filter your data frames with brackets (in base R).

Preview 07:56

How to filter your data frames using subsets.

Filtering Data With the Subset Command
05:07

How to filter your data set using the dplyr package

Filtering Data With dplyr
04:03

How to recode categorical variables in R

Recoding Categorical Variables
05:46

How to recode continuous variables in R

Recoding Continuous Variables
05:04

How to sort data sets using various criteria

Sorting Data Frames
04:10

How to compute new variables based on the existing ones

Compute New Variables
01:52

All the codes used in the lectures 2-8, for your reference

R Codes File for the First Chapter
00:06

Practical exercises for the lectures 2-8

Practical Exercises for the First Chapter
00:06
+ Descriptive Statistics
11 lectures 30:08

How to compute the statistical indicators (mean, median, standard deviation etc.) in base R

Using Base R to Generate Statistical Indicators
03:36

How to compute statistical indicators with the psych package

Descriptive Statistics with the psych Package
03:42

How to compute statistical indicators using the pastecs package

Descriptive Statistics with the pastecs Package
04:56

How to compute skewness and kurtosis in R

Determining the Skewness and Kurtosis
01:35

How to detemine the quantiles of a distribution

Computing Quantiles
02:15

How to compute the mode of a distribution

Determining the Mode
01:29

How to compute the statistical indicators by groups using the DoBy package

Getting the Statistical Indicators by Group with DoBy
05:12

How to compute the statistical indicators with the DescribeBy package

Getting the Statistical Indicators by Group with DescribeBy
02:42

How to compute the statistical indicators with the stats package

Getting the Statistical Indicators by Group with stats
04:29

All the codes used in the lectures 11-19, for your reference

R Codes File for the Second Chapter
00:06

Practical exercises for the lectures 11-19

Practical Exercises for the Second Chapter
00:06
+ Creating Frequency Tables and Cross Tables
6 lectures 17:36

How to build frequency tables

Frequency Tables in Base R
06:40

How to build frequency tables using the package plyr

Frequency Tables with plyr
05:00

Creating cross-tables with the xtabs command

Building Cross Tables using xtabs
01:24

Creating cross-tables with the CrossTable command

Building Cross Tables with CrossTable
04:20
All the codes used in the lectures 22-25, for your reference
R Codes File for the Third Chapter
00:06
Practical exercises for the lectures 22-25
Practical Exercises for the Third Chapter
00:06
+ Building Charts
8 lectures 55:38

How to create a histogram for your distribution

Histograms
07:09

How to create cumulative frequency line charts

Cumulative Frequency Line Charts
11:20

How to build column charts

Column Charts
05:36

How to build mean plot charts

Mean Plot Charts
13:57

How to build scatterplot charts

Scatterplot Charts
11:22

How to build boxplot charts

Boxplot Charts
06:02
All the codes used in the lectures 28-33, for your reference
R Codes File for the Fourth Chapter
00:06

Practical exercises for the lectures 28-33

Practical Exercises for the Fourth Chapter
00:06
+ Checking Assumptions
5 lectures 08:15

How to check for normality using numerical methods

Checking the Normality Assumption - Numerical Method
02:22

How to check for normality using graphical methods

Checking the Normality Assumption - Graphical Methods
03:36

How to detect the extreme values in your data series

Detecting the Outliers
02:05

All the codes used in the lectures 36-38, for your reference

R Codes File for the Fifth Chapter
00:06
Practical exercises for the lectures 36-38
Practical Exercises for the Fifth Chapter
00:06
+ Performing Univariate Analyses
5 lectures 17:08

How to run and interpret the one-sample t test

One-Sample T Test
03:41

How to run and interpret the binomial test

Binomial Test
06:07

How to perform the chi-square test for goodness-of-fit

Chi-Square Test For Goodness-of-Fit
07:08

All the codes used in the lectures 41-43, for your reference

R Codes File for the Sixth Chapter
00:06
Practical exercises for the lectures 41-43
Practical Exercises for the Sixth Chapter
00:06
+ Course Materials
1 lecture 00:03