Short course
R
R is the reference language of applied statistics. Designed by statisticians for statisticians, it natively offers everything other tools add through extensions: linear and non-linear models, time series, multivariate analysis, complex survey designs, epidemiology. RStudio, its working environment, makes it a comfortable and readable tool, even for someone new to programming.
- Duration
- 5 sessions, 18 hours
- Format
- In person or online
- Credential
- Certificate of attendance
Tuition79,000 XOF
By the end of the programme, you will be able to
- Install and use R and RStudio, and organise an analysis project properly
- Understand R's data structures: vectors, factors, lists, data frames
- Import data from Excel, CSV, SPSS, Stata or a mobile data collection platform
- Clean and transform a dataset with tidyverse functions
- Produce descriptive statistics and publication-ready summary tables
- Carry out and interpret common statistical tests and a linear or logistic regression
- Build professional-quality charts with ggplot2
- Automate repetitive processing with functions and loops
- Produce a reproducible report in Word, PDF or HTML with R Markdown or Quarto
- Document and archive your work so that it can still be rerun months later
Who it is for
Target audience
The course is primarily for people who produce or use data in a professional or academic context: monitoring and evaluation officers, statisticians, researchers, PhD students, epidemiologists, public health staff, economists, demographers, research consultants. The fee is the same for everyone, whatever the profession or organisation.
Prerequisites
Three requirements:
- Hold at least a three-year degree (BAC+3), completed or in the final year, in any field
- Failing that level, already be familiar with data analysis in a professional context: processing survey datasets, producing statistics, monitoring indicators
- Have a personal laptop, on which R and RStudio will be installed during the first session
No programming experience is required. However, the level requirement above is not a formality: in eighteen hours, it is not possible to build both the logical reflexes of coding and an understanding of statistical methods. Prior experience of data analysis, in Excel, SPSS or any other tool, is the best passport for following this course comfortably.
Detailed program
5 sessions · 18 h of teaching
- Installing R and then RStudio on each participant's computer
- The difference between R, the language, and RStudio, the working environment
- The four RStudio panes: console, script, environment, files and plots
- Writing in the console or in a script, and why the script quickly becomes essential
- RStudio projects: organising a working folder and putting an end to broken file paths
- Objects and assignment, the logic of naming
- Basic types: numeric, character, logical, factor
- Vectors, creating them and manipulating them by index
- Functions: calling them, passing arguments, reading the built-in help
- Installing and loading a package from CRAN
- Reading an error message and a warning, and telling them apart
- The most common beginner mistakes in R and how to fix them
- Guided exercise: a first script calculating indicators, commented line by line
- The data frame, the central structure of any analysis in R
- Lists and matrices, and the situations where they are really useful
- Factors: coding a categorical variable, ordering its categories, renaming its levels
- Handling missing values in R and how functions behave with them
- Loading a CSV file and controlling the separator, decimal mark and encoding
- Loading an Excel workbook with the readxl package, sheet by sheet
- Reading an SPSS, Stata or SAS dataset with the haven package, keeping value labels
- Reading an export from KoboToolbox or ODK
- Exploring a loaded dataset: dimensions, structure, summary, unique values
- Checking the quality of a dataset before any analysis
- The pipe, the operator that makes code readable from left to right
- Conditions and loops, and why they are often replaced by vectorised functions
- Writing your own function so you never copy the same block of code again
- Exporting a result to CSV or Excel
- Guided exercise: load and diagnose three datasets in different formats
- What the tidyverse is and why it transformed the way R is used
- Selecting columns and filtering rows with dplyr
- Sorting, renaming and reordering variables
- Creating and recoding variables, including with multiple conditions
- Grouping a continuous variable into classes
- Grouping by category and aggregating: counts, means, sums, custom indicators
- Summaries by subgroup and multi-level summary tables
- Merging two datasets on a common key and understanding the different types of joins
- Stacking several files with the same structure
- Moving from wide to long format with tidyr, and back
- Cleaning text with stringr: spaces, case, accents, pattern extraction
- Handling dates with lubridate: conversion, extraction, calculating durations
- Detecting and handling duplicates and outliers
- Producing a cross-tabulation ready to export to Word
- Guided exercise: clean and summarise a survey dataset of several thousand observations
- Descriptive statistics suited to each type of variable
- Publication-ready results tables with gtsummary or flextable
- Checking a distribution: histogram, box plot, normality test
- The chi-square test of independence and its conditions of use
- The t-test and its non-parametric alternative
- Analysis of variance and multiple comparisons
- Pearson and Spearman correlations
- Linear regression: fitting, coefficients, model quality, residual diagnostics
- Logistic regression and reading odds ratios
- Writing up a statistical result correctly, without over-interpreting
- The grammar of graphics and the logic of ggplot2
- Building a chart step by step: data, aesthetics, geometries, scales, theme
- Essential charts: bars, lines, histogram, box plot, scatter plot
- Breaking a chart down by subgroup with facets
- Customising titles, legends, colours and fonts, then exporting in high resolution
- Guided exercise: produce the complete, illustrated statistical analysis of a real dataset
- The principle of the reproducible document: the report regenerates when the data changes
- Introduction to R Markdown and Quarto, and choosing between them
- Structuring a document: text, code chunks, display options
- Inserting a table and a chart produced by code, without copying and pasting
- Calling a calculated value directly within a sentence of the text
- Generating the report in Word, PDF and HTML from the same source file
- Applying a Word template that meets your organisation's standards
- Group application: the group works through a complete case, from raw data to final report
- Regular checkpoints to compare solutions and discuss differing approaches
- The trainer moves between workstations throughout the session
- Final summary: the standard approach to repeat with any new dataset
Assessment and certification
There is no final assessment in this course. As the training is short and intensive, it aims to build practical skills, not to validate a level through an exam.
A training attestation is given to each participant at the end of the fifth session. It is issued by Waaldé Impact, signed by the trainer, and states the course title, the 18-hour duration, the dates and the list of modules followed.
Upcoming sessions
Next dates being prepared
Leave your email: we will let you know as soon as registration opens.
Frequently asked questions
79,000 XOF per participant. Training for a team or an organisation is quoted on request.
5 sessions, 18 hours.
Yes: you can attend onsite or online, as you prefer.
Three requirements: hold at least a three-year degree (BAC+3), completed or in the final year, in any field ; failing that level, already be familiar with data analysis in a professional context: processing survey datasets, producing statistics, monitoring indicators ; have a personal laptop, on which R and RStudio will be installed during the first session ; no programming experience is required. However, the level requirement above is not a formality: in eighteen hours, it is not possible to build both the logical reflexes of coding and an understanding of statistical methods. Prior experience of data analysis, in Excel, SPSS or any other tool, is the best passport for following this…
A certificate of attendance is issued at the end of the course.
Fill in the online enrolment request: no payment is taken on the site. The firm reviews your application, then contacts you to confirm your place and the payment terms. No session is scheduled yet: leave your email on this page to be told when registration opens.
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