Learn R: Statistical Analysis

Standard Course
£400 +VAT
per delegate
Course duration: 1 Day

After the course, attendees will be able to apply various statistical methods for both continuous and discrete data, and be able to interpret the statistical output generated in R.

The course will cover both descriptive and graphical data exploration techniques, hypothesis testing, sampling methods and the comparison of two groups of data. It will cover:

  • Exploratory Data Analysis – exploring discrete and continuous data, data profiling and handling missing data
  • Exploring Data Graphically – supporting descriptive statistics with visualisations, using ggplot2
  • Statistical Analysis – Learn how to conduct a hypothesis test, sample data, examine the normal distribution and perform Students t-tests for paired and independent samples

Who is it for?

This course is for statisticians, data analysts and scientists wishing to explore and analyse data using R. The course is appropriate for those making use of statistics in all industries.


This course is focused on techniques for data exploration and statistical analysis in R. The attendees are expected to have prior knowledge of R data structures, the main functions from the {dplyr} package, and the family of apply functions from base R language. These topics are covered our in Learn R: Introduction and Learn R: Programming courses. Having some experience with {ggplot2} visualisation package is advantageous but not required 

The courses are taught using the following versions of R language and R packages.

R version: 4.2.1

R packages:

  • Lubridate 1.9.2
  • Dplyr 1.1.4
  • Ggplot2 4.4
  • Moments 0.14.1

If your organization uses a different version of R  or the above listed packages, please contact us for more information on the minimal technical requirements for this course.

What you will learn

  • EDA for continuous data
  • EDA for discrete data
  • Data profiling, data quality and missing data

  • Graphically exploring data

  • Sampling Data
  • Normal Distribution
  • Hypothesis testing
  • Students T-tests (independent, paired)
  • Non-parametric tests

Learn R: Statistical Analysis Course Dates

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