R Software Training in Kenya — Statistical Computing & Data Analysis
R is an open-source, extensible statistical computing language and environment available for Windows, Macintosh, UNIX, and Linux — completely free to download and use. It acts as a powerful alternative to traditional packages such as SPSS, SAS, and Stata, but with far greater flexibility, exceptional visualisation through ggplot2, and over 20,000 freely available packages. Our expert-led training takes you from your very first command in RStudio through data wrangling, visualisation, and advanced statistical modelling for research.
Course Details
R & RStudio are 100% free to download and use
What is R & Why Should Researchers Learn It?
R is an open-source, extensible programming language and statistical computing environment available for free on Windows, Macintosh, UNIX, and Linux. It serves as a powerful alternative to SPSS, SAS, and Stata — offering far greater flexibility through its vast ecosystem of freely available packages covering every area of statistical analysis.
Unlike commercial software requiring expensive licences, R is completely free. This makes it an increasingly preferred tool for academic researchers and professionals in Kenya — particularly for research requiring advanced statistical techniques and high-quality data visualisation. R was created specifically to help researchers and analysts apply statistical methods to socio-economic, health, business, and scientific research problems.
Through packages such as ggplot2, dplyr, tidyr, and the Tidyverse, R provides powerful tools for data manipulation, exploratory analysis, regression modelling, and publication-quality graphics that far surpass what SPSS or Excel can produce.
| Feature | SPSS | Stata | R ✓ | SAS |
|---|---|---|---|---|
| Cost | Expensive | Affordable | Free | Expensive |
| Visualisation | Good | Very Good | Excellent | Good |
| Packages | Limited | Good | 20,000+ | Limited |
| Reproducible Reports | Limited | Limited | R Markdown | Limited |
| Machine Learning | Basic | Basic | Full support | Good |
| Learning Curve | Gradual | Gradual | Moderate | Steep |
ggplot2
Publication-quality visualisation
dplyr
Fast data manipulation
tidyr
Reshaping and tidying data
readr
Importing CSV and data files
tidyverse
Core data science collection
R Markdown
Reproducible research reports
What You Will Achieve
By the end of this training, participants will have the practical R skills needed to perform rigorous statistical analysis for research, academic work, and professional data analysis.
Apply R software in practical analysis and research of socio-economic problems at school and in the workplace — using R to clean, transform, analyse, and visualise real research datasets effectively.
Apply R and complementary software in academic projects and thesis work — generating statistically rigorous results for Masters and PhD dissertations across social sciences, economics, public health, and business.
Use R’s Tidyverse ecosystem (dplyr, ggplot2, tidyr, readr) to efficiently import, clean, wrangle, and visualise data from real-world research datasets using best-practice reproducible workflows.
Conduct statistical inference and modelling in R — including descriptive statistics, correlation, regression, ANOVA, and hypothesis testing — with correct interpretation for thesis chapters.
Training Mode & Approach
Class-Based Training
Structured, expert-led sessions online via video call or in person at our Nairobi offices, with step-by-step instruction throughout.
Practical R Use
Every session is hands-on in RStudio. You write real R code, run live analyses, and work directly on your own or provided research datasets.
Take-Away Assignments
Each module includes structured practice assignments to reinforce learning and apply R techniques to your own research data.
One-on-One Sessions
Individual sessions available on request — ideal for personalised guidance on your specific research project or thesis dataset.
The Three-Module Learning Path
Each module builds progressively — from R and RStudio fundamentals through data wrangling and visualisation to statistical modelling and advanced analysis.
Introduction to R & RStudio
R and RStudio setup, R syntax basics, data types, vectors, data frames, importing data, and foundational data manipulation commands.
Data Wrangling & Visualisation
dplyr for data manipulation, tidyr for reshaping, ggplot2 for publication-quality charts, and exploratory data analysis techniques.
Statistical Analysis & Modelling
Descriptive statistics, hypothesis testing, correlation, regression, ANOVA, and R Markdown for reproducible research reports.
Detailed Module Content
Every topic is covered hands-on in RStudio using real datasets. Each module is Ksh 5,000 per individual and runs for 5 hours.
Introduction to R & RStudio Basics
R environment · Syntax · Data types · Vectors · Data frames · Data import & export
Introduction to R & RStudio
R Syntax & Data Types
Data Frames — The Research Dataset in R
Importing & Exporting Data
Data Wrangling & Visualisation with Tidyverse
dplyr · tidyr · ggplot2 · Data cleaning · Reshaping · Exploratory analysis
Data Manipulation with dplyr
Data Reshaping with tidyr
Data Visualisation with ggplot2
Statistical Analysis & Modelling in R
Descriptive stats · Hypothesis testing · Correlation · Regression · ANOVA · R Markdown
Descriptive Statistics
Hypothesis Testing
Correlation & Regression Analysis
ANOVA & Reproducible Reports
Common R Commands You Will Learn
Our training is completely hands-on. You write and run real R code in RStudio from day one — directly applicable to your own research datasets.
# Load the Tidyverse
library(tidyverse)
library(readxl)
library(haven)
# Import from CSV
df <- read_csv("mydata.csv")
# Import from Excel
df <- read_excel("mydata.xlsx")
# Import from SPSS .sav
df <- read_sav("mydata.sav")
# Explore the dataset
glimpse(df)
summary(df)
head(df, 10)
# Filter, select, mutate with pipe
df_clean <- df %>%
filter(age > 18) %>%
select(id, age, income,
education) %>%
mutate(
log_income = log(income),
edu_group = case_when(
education < 12 ~ "Primary",
education < 16 ~ "Secondary",
TRUE ~ "Tertiary"
)
)
# Grouped summary statistics
df %>%
group_by(edu_group) %>%
summarise(
mean_inc = mean(income,
na.rm = TRUE),
n = n()
)
# Scatter plot with regression line
ggplot(df, aes(x = age,
y = income)) +
geom_point(color = "steelblue",
alpha = 0.6) +
geom_smooth(method = "lm",
se = TRUE) +
labs(
title = "Age vs Income",
x = "Age (years)",
y = "Monthly Income (Ksh)"
) +
theme_minimal()
# Save for thesis (300 dpi)
ggsave("plot.png", dpi = 300)
# Descriptive statistics
psych::describe(df$income)
# Shapiro-Wilk normality test
shapiro.test(df$income)
# Pearson correlation
cor.test(df$age, df$income,
method = "pearson")
# Multiple linear regression
model <- lm(income ~ age +
education + gender, data = df)
summary(model)
# One-way ANOVA
fit <- aov(income ~ edu_group,
data = df)
TukeyHSD(fit)
Key R Capabilities You Will Master
R’s combination of statistical power, free availability, and exceptional visualisation makes it one of the most valuable tools a researcher in Kenya can learn.
Completely Free & Open Source
R and RStudio are both 100% free — no licence fees, no annual renewals needed. Install on any computer and use for life at zero cost.
Excellent Data Visualisation
ggplot2 produces publication-quality, highly customisable charts far superior to SPSS or Excel — perfect for theses and journal submissions.
Powerful Data Wrangling
The dplyr and tidyr packages make data cleaning, filtering, reshaping, and transformation intuitive and fast — even for complex, messy datasets.
Comprehensive Statistical Analysis
From basic descriptive statistics to advanced regression, ANOVA, and multivariate analysis — R covers every technique required for postgraduate research.
Reproducible Research with R Markdown
Combine code, results, and narrative in one document — generating Word, PDF, or HTML reports automatically. Essential for transparent thesis writing.
20,000+ Free Packages
CRAN hosts over 20,000 R packages covering every analytical need — from basic statistics to machine learning and bioinformatics — all completely free.
Who Should Attend R Software Training?
R is valuable for researchers who want advanced visualisation, greater analytical flexibility, and a tool that will never cost them a licence fee.
Masters & PhD Students
Need to perform data analysis for your dissertation but want a more flexible, free alternative to SPSS? R fully covers everything required for Chapters 4 and 5 — regression, ANOVA, hypothesis testing, and publication-quality charts.
Social Science & Development Researchers
R was specifically designed for socio-economic and social science data analysis. Whether analysing household surveys, poverty data, or community health datasets — R handles it all.
Economists & Finance Professionals
Need advanced econometric techniques beyond SPSS? R’s packages for panel data, time-series, and regression modelling give you complete analytical freedom.
Public Health Researchers
R is widely used for epidemiological analysis, survival analysis, and logistic regression — ideal for public health, clinical research, and health policy dissertations.
SPSS Users Looking for More Flexibility
Already know SPSS but feeling limited? R offers everything SPSS does — and much more — for free. We cover transitioning your existing SPSS .sav files into R using the haven package from day one.
Data Analysts & Professionals
Need to add R to your professional skill set? Our training equips you with job-ready data wrangling, visualisation, and reporting skills using Tidyverse and R Markdown.
Frequently Asked Questions
Everything you need to know before enrolling in our R software training programme.
Book Your R Software Training Session
Fill in our booking form or contact us directly by phone or WhatsApp. Our team is available Monday to Saturday to confirm your slot and answer any questions.
Have Questions? Contact Us Directly
We are happy to help you decide which module to start with or discuss how R fits your specific research needs.
