← Back to courses
Data Science and AI
R Programming for Data Analysis
Vectors, data frames, statistical models and graphics
A classroom course in R, the language built for statistics. The modules follow the chapters of the R project's own manual, An Introduction to R (R 4.6): numbers and vectors, objects, factors, arrays and matrices, lists and data frames, reading data from files, probability distributions, loops and conditional execution, writing functions, statistical models, graphical procedures and packages. This course prepares for no exam.
11Modules
BeginnerLevel
2 monthsDuration
COURSE THUMBNAIL
What you will be able to do
Work with vectors, matrices, lists and data frames
Read data from files and prepare it for analysis
Write your own functions and control flow
Fit and interpret a statistical model
Produce clear charts of your results
Course outline
01
Introduction and preliminaries: R and RStudio
02
Simple manipulations: numbers and vectors
03
Objects, their modes and attributes; factors
04
Arrays and matrices
05
Lists and data frames
06
Reading data from files
07
Probability distributions
08
Grouping, loops and conditional execution; writing your own functions
09
Statistical models in R
10
Graphical procedures
11
Packages; a project on your own dataset
Tools you will use
RRStudioCRAN packages
What you need
General computer skills
School-level mathematics; no programming experience needed
