Audience
Anyone whose role demands the statistical analysis of data sets by using R.
Prerequisites
There are no prerequisites.
Duration
Two days. Lecture presentations are supported by examples. Participants will install R and gain hands-on experience of analysing data sets using R.
Course Objectives
After taking this course, participants should be able to:
- Understand the statistical concepts underlying the analysis of datasets.
- Install R and RStudio.
- Import data sets into RStudio and manipulate the contents.
- Use R to produce appropriate visualisations.
- Perform initial data analysis using appropriate statistical tests.
Course Content
Introduction to R
Installation of R and RStudio
Basic R syntax
Data types (vectors, lists, matrices, arrays, data frames)
Operators and variables
Packages
Working with Data
Importing data into R
Categorical and numeric variables
Dealing with missing values
Indexing and subsetting data
Recoding and summarising
Data Visualisation
Histograms and bar charts
Pie charts
Line graphs
Scatterplots and boxplots
Exploratory Data Analysis
Generic data analysis process
Data distributions
Mean, median and mode
Standard deviation, variance
Percentiles, outliers
Exploring Categorical Data
Chi-square distribution
Goodness of fit
Contingency tables
Testing for independence
Exploring Numeric Data
Association and correlation
Covariation, Pearson Correlation
Linear regression
Prediction using regression
Multiple regression
Nonlinear regression