This is the complete study package for Computational Statistics & R Programming —
using R, the open-source statistical computing language, for data handling, descriptive
statistics, visualization, hypothesis testing and regression. Each topic shows the exact R
code with sample outputs and two worked examples per concept.
Pre-requisite: The earlier subjects. Familiarity with Descriptive Statistics,
the tests in Inferential Statistics, and regression from Statistical Methods. You should install
R from cran.r-project.org and ideally RStudio from posit.co.
Course Outcomes
Import and preprocess statistical datasets in R efficiently.
Demonstrate proficiency in basic data manipulation for statistical preparation.
Apply R functions to handle real-world data issues like outliers and inconsistencies.
Compute and interpret descriptive statistics using R commands and packages.
Analyse data distributions and identify patterns or anomalies statistically.
Generate summary reports for datasets to support preliminary statistical insights.
J. Chambers (2008) — Software for Data Analysis: Programming with R, Springer.
M. J. Crawley (2017) — The R Book, John Wiley & Sons.
N. Matloff (2011) — The Art of R Programming, No Starch Press.
Mark Gardener (2012) — Beginning R — The Statistical Programming Language, John Wiley & Sons.
Purohit, Gore & Deshmukh (2008) — Statistics Using R, Narosa Publishing House.
W. N. Venables & D. M. Smith — An Introduction to R (online).
Zumel & Mount (2014) — Practical Data Science with R, Manning.
Quick install: Open R or RStudio and type
install.packages(c("psych", "moments", "dplyr", "ggplot2", "readxl"))
to install the most-used packages used throughout this course.