R Training Course

✅ History of R

✅ Advantages and disadvantages

✅ Downloading and installing

✅ How to find documentation

✅ Using the R console

✅ Getting help

✅ Learning about the environment

✅ Writing and executing scripts

✅ Object oriented programming

✅ Introduction to vectorized calculations

✅ Introduction to data frames

✅ Installing packages

✅ Working directory

✅ Saving your work

✅ Variables and assignment

✅ Data types

✅ Data structures

✅ Indexing, subsetting

✅ Assigning new values

✅ Viewing data and summaries

✅ Naming conventions

✅ Objects

✅Built-in data

✅ Reading data from structured text files

✅ Reading data using ODBC

✅ Renaming columns

✅ Adding new columns

✅ Binning data (continuous to categorical)

✅ Combining categorical values

✅ Transforming variables

✅ Handling missing data

✅ Long to wide and back

✅ Merging datasets together

✅ Stacking datasets together (concatenation)

✅ Date and date-time classes in R

✅ Formatting dates for modeling

✅ Truth testing

✅ Branching

✅ Looping

✅ Parameters

✅ Return values

✅ Variable scope

✅Sapply, lapply, apply

✅Continuous data

✅ Categorical data

✅ Group by calculations with dplyr

✅ Melting and casting data

✅ Bivariate correlation

✅ T-test and non-parametric equivalents

✅ Chi-squared test

✅ Base graphics system in R

✅ Scatterplots, histograms, barcharts, box and whiskers, dotplots

✅ Labels, legends, titles, axes

✅ Exporting graphics to different formats

✅ Understanding the grammar of graphics

✅ Quick plots (qplot function)

✅ Building graphics by pieces
(ggplot function)

✅ Linear and logistic models

✅ Regression plots

✅ Confounding / interaction in regression

✅ Scoring new data from models (prediction)