Statistics with R - Beginner Level
- Description
- Curriculum
- FAQ
- Reviews
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!
-
2
Filtering Data Using BracketsHow to filter your data frames with brackets (in base R).
-
3
Filtering Data With the Subset Command
How to filter your data frames using subsets.
-
4
Filtering Data With dplyr
How to filter your data set using the dplyr package
-
5
Recoding Categorical Variables
How to recode categorical variables in R
-
6
Recoding Continuous Variables
How to recode continuous variables in R
-
7
Sorting Data Frames
How to sort data sets using various criteria
-
8
Compute New Variables
How to compute new variables based on the existing ones
-
9
R Codes File for the First ChapterAll the codes used in the lectures 2-8, for your reference
-
10
Practical Exercises for the First Chapter
Practical exercises for the lectures 2-8
-
11
Using Base R to Generate Statistical Indicators
How to compute the statistical indicators (mean, median, standard deviation etc.) in base R
-
12
Descriptive Statistics with the psych Package
How to compute statistical indicators with the psych package
-
13
Descriptive Statistics with the pastecs Package
How to compute statistical indicators using the pastecs package
-
14
Determining the Skewness and Kurtosis
How to compute skewness and kurtosis in R
-
15
Computing Quantiles
How to detemine the quantiles of a distribution
-
16
Determining the Mode
How to compute the mode of a distribution
-
17
Getting the Statistical Indicators by Group with DoBy
How to compute the statistical indicators by groups using the DoBy package
-
18
Getting the Statistical Indicators by Group with DescribeBy
How to compute the statistical indicators with the DescribeBy package
-
19
Getting the Statistical Indicators by Group with stats
How to compute the statistical indicators with the stats package
-
20
R Codes File for the Second Chapter
All the codes used in the lectures 11-19, for your reference
-
21
Practical Exercises for the Second Chapter
Practical exercises for the lectures 11-19
-
22
Frequency Tables in Base R
How to build frequency tables
-
23
Frequency Tables with plyr
How to build frequency tables using the package plyr
-
24
Building Cross Tables using xtabs
Creating cross-tables with the xtabs command
-
25
Building Cross Tables with CrossTable
Creating cross-tables with the CrossTable command
-
26
R Codes File for the Third ChapterAll the codes used in the lectures 22-25, for your reference
-
27
Practical Exercises for the Third ChapterPractical exercises for the lectures 22-25
-
28
Histograms
How to create a histogram for your distribution
-
29
Cumulative Frequency Line Charts
How to create cumulative frequency line charts
-
30
Column Charts
How to build column charts
-
31
Mean Plot Charts
How to build mean plot charts
-
32
Scatterplot Charts
How to build scatterplot charts
-
33
Boxplot Charts
How to build boxplot charts
-
34
R Codes File for the Fourth ChapterAll the codes used in the lectures 28-33, for your reference
-
35
Practical Exercises for the Fourth Chapter
Practical exercises for the lectures 28-33
-
36
Checking the Normality Assumption - Numerical Method
How to check for normality using numerical methods
-
37
Checking the Normality Assumption - Graphical Methods
How to check for normality using graphical methods
-
38
Detecting the Outliers
How to detect the extreme values in your data series
-
39
R Codes File for the Fifth Chapter
All the codes used in the lectures 36-38, for your reference
-
40
Practical Exercises for the Fifth ChapterPractical exercises for the lectures 36-38
-
41
One-Sample T Test
How to run and interpret the one-sample t test
-
42
Binomial Test
How to run and interpret the binomial test
-
43
Chi-Square Test For Goodness-of-Fit
How to perform the chi-square test for goodness-of-fit
-
44
R Codes File for the Sixth Chapter
All the codes used in the lectures 41-43, for your reference
-
45
Practical Exercises for the Sixth ChapterPractical exercises for the lectures 41-43
