R Cookbook : Proven Recipes for Data Analysis, Statistics, and Graphics

R is the world’s most widely used statistical computing language. It is the specific tool that Kenya’s most data-capable researchers, economists, biostatisticians, public health analysts, actuaries, and data scientists use to transform raw data into the specific insights, the specific visualisations, and the specific statistical conclusions that drive decisions in government, in healthcare, in finance, in academia, and in Kenya’s rapidly growing technology sector.

And the specific problem with R — the particular reason that even experienced R users regularly spend hours searching for solutions to problems they have already solved before — is that R’s extraordinary flexibility and power comes with the specific complexity of a language that rewards deep knowledge. The specific code that solves today’s data problem is not always immediately obvious. The specific function that performs the specific statistical test you need is not always easy to find. The specific ggplot2 syntax that produces the specific visualisation your report requires is not always immediately remembered.

R Cookbook: Proven Recipes for Data Analysis, Statistics & Graphics — Second Edition, published by O’Reilly, the most trusted publisher of technical computing references in the world — is the specific solution to that specific problem. Not a textbook. Not a tutorial. A cookbook: hundreds of specific, proven, ready-to-run recipes that each solve a specific R problem, each explained clearly, each immediately applicable to the specific data challenge the reader is facing right now.

At Ksh 100, the most practically useful R reference available to every Kenyan data professional.

What This Book Covers:

Getting Started with R — The Foundation Recipes:

Installation and Setup:

• The specific recipes for installing R and RStudio on every major operating system; the particular package installation workflow (install.packages, library, require) that every R user needs as their first reflex; the specific CRAN repository selection and the specific Bioconductor installation for biological and genomic data that many Kenyan health researchers specifically need
• The specific workspace management recipes — how to set working directories, how to manage the R environment, how to save and load workspaces, and the specific R project structure using RStudio Projects that keeps complex analytical work organised and reproducible; the particular .Rprofile and .Renvisrc customisation that makes every R session start exactly as needed
• The specific package ecosystem navigation — how to find the specific package that solves a specific problem; the particular CRAN Task Views that organise packages by domain (econometrics, clinical trials, social sciences, spatial analysis) and that are the specific most efficient entry point for the Kenyan researcher entering a new analytical domain

R Basics:

• The specific data type recipes — how to work with R’s core data types (numeric, integer, character, logical, factor, Date, POSIXct) and the specific coercion rules that determine how R converts between them; the particular NA handling recipes that are essential for every real-world dataset, which always contains missing values, and which R handles through a specific logic that differs from every other programming language
• The specific vector and matrix recipes — the foundational data structures of R; how to create, subset, modify, and operate on vectors and matrices with the specific vectorised operations that make R both powerful and occasionally surprising; the specific recycling rule and when it helps versus when it silently produces wrong results
• The specific list recipes — R’s most flexible and most confusing data structure; the particular single-bracket versus double-bracket subsetting distinction that confuses every new R user; when lists are the right data structure and when data frames are more appropriate

Data Management — The Most-Used Recipes:

Data Frames:

• The specific data frame creation, inspection, and manipulation recipes that form the core of most R analytical workflows — how to create data frames from scratch, from CSV files, from Excel files, from databases, and from API responses; the specific str(), head(), tail(), summary(), and dim() inspection functions that every R analyst needs as their first move with any new dataset
• The specific dplyr recipes — the particular filter(), select(), mutate(), arrange(), group_by(), and summarise() verbs of the tidyverse’s most important package; how these six functions, combined with the pipe operator (%>%), solve the vast majority of data manipulation problems that any analyst encounters; the specific join recipes (left_join, inner_join, full_join, anti_join) that combine data frames from multiple sources
• The specific tidyr recipes — the particular pivot_longer() and pivot_wider() functions that reshape data between wide and long formats; the specific separate() and unite() functions; and the specific complete() and fill() functions for handling missing combinations and missing values in the specific way that tidy data analysis requires

Reading and Writing Data:

• The specific file import recipes — reading CSV files with read.csv() and the faster read_csv() from readr; reading Excel files with readxl; reading SPSS, Stata, and SAS files with haven (particularly relevant for Kenyan academic researchers who receive data in these formats from collaborators); reading data from databases with DBI and RODBC; reading JSON and XML from web APIs
• The specific data export recipes — writing to CSV, to Excel, to database tables, and to the specific formats that Kenyan data professionals are most commonly required to deliver; the particular write.csv() versus write_csv() differences and when each is more appropriate
• The specific large data handling recipes — how to work with datasets that are larger than R’s typical in-memory workflow can comfortably handle; the specific data.table package recipes for high-performance data manipulation that outperforms dplyr by orders of magnitude on large Kenyan government and health datasets

Statistics — The Complete Recipe Collection:

Descriptive Statistics:

• The specific summary statistic recipes — mean(), median(), sd(), var(), range(), quantile(), and the specific fivenum() and summary() functions that produce the complete descriptive picture of any variable; the specific weighted mean and weighted median recipes for survey data where observations have different sampling probabilities
• The specific frequency table and cross-tabulation recipes — table(), prop.table(), and the specific xtabs() and ftable() functions that produce the specific contingency tables that categorical data analysis requires; the specific addmargins() function for adding row and column totals
• The specific correlation recipes — cor() and cor.test() for the specific Pearson, Spearman, and Kendall correlation coefficients; the specific correlation matrix for multiple variables; the specific corrplot visualisation for correlation matrices that is standard in Kenyan academic publications

Probability and Distributions:

• The specific probability distribution recipes — R’s consistent d/p/q/r function naming convention for every standard distribution (dnorm/pnorm/qnorm/rnorm for normal; dbinom/pbinom/qbinom/rbinom for binomial; dpois/ppois/qpois/rpois for Poisson) and the specific use cases for each function type (density, cumulative probability, quantile, random generation)
• The specific normal distribution recipes — the specific z-score calculations, the specific probability calculations (what proportion of values fall between x and y?), and the specific quantile calculations (what value corresponds to the 95th percentile?) that are the most frequently needed probability calculations in Kenyan health, education, and economics research
• The specific distribution testing recipes — the Shapiro-Wilk test (shapiro.test()), the Kolmogorov-Smirnov test (ks.test()), and the specific Q-Q plot (qqnorm(), qqline()) for assessing normality — the specific prerequisite assessment that every parametric statistical test requires

Hypothesis Testing:

• The specific t-test recipes — one-sample t-test, independent samples t-test, and paired t-test (t.test()) with the specific one-tailed versus two-tailed options; how to interpret the specific output including the p-value, the confidence interval, and the test statistic; the specific Welch correction for unequal variances that R applies by default
• The specific non-parametric test recipes — Wilcoxon signed-rank test, Mann-Whitney U test (wilcox.test()), Kruskal-Wallis test (kruskal.test()), and the specific Friedman test for the specific research situations where the normal distribution assumption cannot be justified; particularly relevant for Kenyan health research where small samples and skewed outcomes are common
• The specific chi-square test recipes — chisq.test() for the specific test of independence in contingency tables and the specific goodness-of-fit test; the specific Fisher’s exact test (fisher.test()) for the specific small-sample situations where chi-square is unreliable; the specific expected count check that determines which test is appropriate

Analysis of Variance:

• The specific one-way ANOVA recipes — aov() and the specific summary(), TukeyHSD(), and pairwise.t.test() post-hoc procedures that identify which specific group differences are statistically significant after a significant overall F-test; the specific assumption checking recipes (homogeneity of variance with Levene’s test, normality of residuals)
• The specific two-way ANOVA recipes — factorial designs with interaction effects; how to interpret the specific interaction plot and the specific significance of interaction terms; the specific Kenyan research application in agricultural trials, clinical studies, and educational research where multiple factors are simultaneously examined
• The specific repeated measures ANOVA recipes — within-subjects designs that are standard in Kenyan clinical and psychological research; the specific sphericity assumption and the Mauchly test; the Greenhouse-Geisser and Huynh-Feldt corrections

Regression Analysis:

• The specific simple linear regression recipes — lm(), the specific summary.lm() output interpretation (coefficients, standard errors, t-values, p-values, R-squared, F-statistic), and the specific diagnostic plots (plot.lm()) that assess the four regression assumptions; the specific predict() function for generating predicted values and confidence intervals
• The specific multiple regression recipes — the particular variable selection approaches (stepwise with step(), best subsets with leaps package), the specific multicollinearity assessment with vif() from the car package, and the specific interaction term specification; the specific standardised coefficient calculation for comparing effect sizes across predictors with different scales
• The specific logistic regression recipes — glm() with family=binomial for the specific binary outcome research that is standard in Kenyan health (disease presence/absence), education (pass/fail), and economics (employment/unemployment) research; the specific odds ratio calculation and confidence intervals; the specific model fit assessment with Hosmer-Lemeshow test and ROC curve

Graphics and Visualisation — Publication-Ready Plots:

Base R Graphics:

• The specific base R plotting recipes — plot(), hist(), boxplot(), barplot(), pie() (and why pie charts are almost always the wrong choice), and the specific par() settings that control plot appearance; the specific multi-panel layout recipes with par(mfrow=) and layout()
• The specific customisation recipes — titles with main=, axis labels with xlab= and ylab=, colour with col=, point type with pch=, line type with lty=, and the specific legend() function for multi-series plots; how to produce publication-quality base R graphics with minimal code

ggplot2 — The Professional Visualisation Standard:

• The specific ggplot2 grammar of graphics — the particular ggplot() + geom_() + scale_() + theme_() + facet_() structure that makes ggplot2 simultaneously the most powerful and the most learnable R visualisation system; how to read and construct ggplot2 code by understanding its specific grammar rather than memorising specific plot types
• The specific most-needed ggplot2 recipes — geom_point() for scatter plots, geom_line() for time series, geom_bar() and geom_col() for bar charts, geom_histogram() and geom_density() for distributions, geom_boxplot() and geom_violin() for group comparisons, geom_smooth() for trend lines with confidence bands; each with the specific aesthetic mappings, the specific scale choices, and the specific theme settings that produce publication-quality output
• The specific ggplot2 publication formatting recipes — theme_classic(), theme_bw(), and the specific manual theme customisation that matches the specific formatting requirements of Kenyan and international academic journals; the specific ggsave() recipe for exporting plots at the specific resolution, the specific dimensions, and the specific file format that journal submission requires

Advanced Topics — Power User Recipes:

String Manipulation:

• The specific string handling recipes using base R (paste(), paste0(), sprintf(), substr(), gsub(), grep()) and the specific stringr package (str_detect(), str_extract(), str_replace(), str_split()) that makes string manipulation more consistent and more readable; the specific regular expression recipes for the specific pattern matching that text data cleaning requires

Date and Time:

• The specific date and time handling recipes — as.Date(), as.POSIXct(), format(), difftime(), and the specific lubridate package (ymd(), dmy(), mdy(), year(), month(), day(), floor_date(), ceiling_date()) that makes date manipulation in R genuinely straightforward; particularly relevant for Kenyan health and financial data where date-based calculations are essential

Functional Programming:

• The specific apply family recipes — apply(), sapply(), lapply(), tapply(), and mapply() for applying functions across data structures without explicit loops; the specific purrr package alternatives (map(), map_df(), walk()) that are more consistent and more readable for complex functional programming tasks
• The specific function writing recipes — how to write clean, reusable R functions; the specific default argument syntax, the specific error handling with tryCatch(), and the specific documentation conventions that make custom functions maintainable and shareable

Why Kenyan Data Professionals Are Buying This Book:

R is the primary statistical computing tool of Kenya’s most analytically sophisticated institutions — the Kenya National Bureau of Statistics, the Kenya Medical Research Institute, Kenyan university statistics and economics departments, international development organisations operating in Kenya (World Bank, USAID, WHO), and Kenya’s growing data science and fintech community. The specific researcher who can use R fluently — who can move from raw data to analysis to publication-quality visualisation without reaching for SPSS or paying for a statistician — is the specific researcher whose analytical capacity most directly determines the quality and the speed of their research output.

R Cookbook is the specific reference that bridges the gap between knowing R and using R fluently — the particular resource that the Kenyan data professional reaches for when they know what they want to do but need the specific syntax to do it efficiently.

At Ksh 100, the most practically useful R data analysis reference available to every Kenyan researcher and data professional.

Who This Book Is For:

• Kenyan university students in statistics, economics, public health, bioinformatics, data science, and any quantitative research discipline who use R for coursework, dissertations, and research projects
• Kenyan academic researchers who conduct quantitative research and who want the specific ready-to-run R code for the specific analyses their research requires without spending hours searching documentation
• Kenyan public health and epidemiology professionals at KEMRI, county health departments, and international health organisations who analyse health data in R and who need the specific statistical and visualisation recipes that health data analysis requires
• Kenyan data scientists, analysts, and business intelligence professionals in Kenya’s growing technology sector who use R for data analysis and reporting
• Every reader of Basic SPSS Tutorial, Data Analysis in Management with SPSS Software (Verma), and How to Write a Great Research Paper who wants to add R fluency to their quantitative research toolkit

📖 Authors: J.D. Long & Paul Teetor
🏢 Publisher: O’Reilly (Second Edition)
📄 Format: PDF eBook (instant download via WhatsApp or email)
💰 Price: Ksh 100 only
🚀 Delivery: Instant after M-Pesa payment confirmation
👉 Order now on cliffmatt.co.ke — Pay via M-Pesa, receive your PDF instantly.