Lecturer: Ph.D. Bonsang Koo (bsk245@gmail.com)
Teaching Assistant: Changdong Oh(lamancha91@gmail.com)
July 31, 2017 ~ August 4, 2017
This lecture aims to introduce R for students who want to learn statistical analysis and reproducible research in R. Contents of the lecture are as follows.
Day 1: Basic Interface, Instructions, and Functions of R
The R user interface
Object Programming
assignment, help() and print()
Description of basic types of objects (vectors, lists, matrices, data frames) and classes (numeric, integer, logical, char, complex)
Vectors
length(), head(), tail(), sort(), sum(), rep()
Indices, logical indices
R library
How to install and Use R packages?
Day 2: Read and Write Data in R
Vectors and Matrices
Data Frames
various delimited input files
extracting data from R objects
Reading data files written in other programs (Excel, SPSS, Stata, etc.)
writing R objects to files
Factors
Characters
Day 3: Basic Visualization
Histograms
Scatter Plots
Lines
Texts
Legends
Setting plot parameters
Advanced Plotting
Lattice
trellis
ggplot2
Day 4: Basic Statistics
Mean
Standard Deviation
Summary
Table
correlation
t.test
chi-square test
Linear Models
Using R for simulation
Creating User-defined functions in R.
Day 5: Data Managing
Subscripting
Character Manipulation
Data Aggregation
Reshaping Data
Recoding Data
Creating New Variables
Transforming the Data
Converting Continuous Variables into Categorical ones
Merging multiple Datasets
apply, lapply, sapply
Introduction to more advanced data science with R
text data
image data
network data
bioinformatics