R Basic Course

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R is a popular programming language, It is free and open source. R has been widely used in the field of statistical data analysis, econometrics, machine learning , just name a few in the last almost three decades. R is regarded as the easy-to-use and easy-to-learn statistics software, as well as its abundance of packages in almost all the areas in data science. In this course, we provide the fundamentals of R programming skills, and hope controlling these techniques can greatly help you smoothly practice R programming in your study discipline. We do not assume that students in this course have used R before, as the course will start from the very beginning of learning R. We assume, however, students have basic knowledge of data management (e.g. continuous variable, categorical variables, csv file), and entrance statistics knowledge (e.g. mean, variance). And we think also that you have basic knowledge in using a computer. The R Basic Course will teach programming In R from installation of program to basic and advanced data management skills, and is suitable for anyone who has no or very little experience in R programming before, and may have never used any other statistical software either. And of course people with such knowledge or experience will feel grasping the R language even easier and quicker.

The course takes 2 days on weekend, about 10 hours in total. Course fee is 16 USD.

The course lectures are distributed in 5 sections, and the following lists show the course content details:

Section 1 Get started with R environment
Introduction and outline
Installation of R and RStudio
Get started working R
Working with R packages
Simple example

Section 2 R data structure, create datasets
Introduction of R data structure, Vector
Matrix
Array
Create data frame using data.frame()
Create Data frame using read.table()
List
Factor
Create dataset,data input csv file(read table, read csv)
Using with()
Object functions
Reading Excel file in R
create, index and modify Data Frame
create, index and modify list
Generating Vector and Matrix of Random Numbers in R

Section 3 Basic data management
Creating new variables
Recoding variables
Renaming variables
Handling missing value
Date values
Type conversion
Sorting data
Merging datasets
Subsetting datasets
Modify dataframes transform() Function
with() and within()
An working example

Section 4 Advanced data management
R mathematical functions
Statistical functions
Probability functions
Character functions
Using Apply functions
Solution to Working example
control flow
Create your own function
Transposing data object
Aggregating data
Descriptive statistics
sample() function
Table function family: table(),xtabs(),prop.table, margin.table,ftable
Data Reshaping in R Programming
An Working example

Section 5 Using R graphic with ggplot2 package
Introduction of ggplot2
Start building a graph and Add Geoms in ggplot()
Using Grouping
Using Scales
Using Facets
Formulate Labels ,
Formulate Themes
Graphs as objects
Saving graphs
Bar charts
Pie charts
Histograms
Box plots
Kernel density plots
Scatter plots

Assignment (homework)

You can watch video on YouTube of preview of the R tutorial.