Data Analysis

R Analysis: A Reproducible Workflow for Statistical Research

GOSPELTRADER Research Desk · 11 September 2026 · 12 min read

Quick answer

A reliable R analysis follows five stages: import and inspect, clean with tidyverse verbs, describe, model with the appropriate function (t.test, aov, lm, glm), then validate with residual diagnostics — all inside a scripted, version-controlled project so the entire result can be reproduced with one command.

Why R over point-and-click tools

R's advantage is not the statistics — it is the audit trail. Every decision is a line of code, so an analysis can be re-run months later, corrected in one place, and inspected by a reviewer. That property is what makes an analysis defensible.

• Reproducibility: one script regenerates every table and figure.
• Coverage: mixed models, survival analysis, SEM, time series and Bayesian methods in one environment.
• Reporting: R Markdown or Quarto produces the document and the analysis together.
• Cost: free, with no licence constraints for institutions.

Import, inspect and clean

Cleaning is the majority of the work. The pattern below covers the operations that appear in nearly every research dataset: type correction, reverse coding, missing handling and scale construction.

library(tidyverse)

raw <- read_csv("data/survey.csv")

glimpse(raw)
summary(raw)
colSums(is.na(raw))

clean <- raw |>
  distinct() |>
  mutate(
    gender  = factor(gender, levels = c(1, 2), labels = c("Male", "Female")),
    q3r     = 6 - q3,                      # reverse-score a 5-point item
    satisfaction = rowMeans(across(c(q1, q2, q3r, q4)), na.rm = TRUE)
  ) |>
  filter(!is.na(satisfaction), age >= 18)

# Internal consistency
psych::alpha(clean |> select(q1, q2, q3r, q4))$total$raw_alpha

Descriptives and group comparison

Report the group summary and the test together; a p-value without means and standard deviations is uninterpretable.

clean |>
  group_by(gender) |>
  summarise(n = n(), M = mean(satisfaction), SD = sd(satisfaction))

# Assumptions
shapiro.test(clean$satisfaction)          # normality
car::leveneTest(satisfaction ~ gender, clean)  # equal variance

t.test(satisfaction ~ gender, data = clean, var.equal = FALSE)
effectsize::cohens_d(satisfaction ~ gender, data = clean)

Modelling and diagnostics

For a continuous outcome use lm(); for a binary outcome use glm(family = binomial). Diagnostics are not optional — the four-panel residual plot answers linearity, normality of residuals, homoscedasticity and influence in one view.

fit <- lm(productivity ~ training_hours + experience + department, data = clean)

summary(fit)                 # coefficients, R², F
confint(fit)                 # 95% CI for each B
car::vif(fit)                # multicollinearity, keep < 5
par(mfrow = c(2, 2)); plot(fit)   # residual diagnostics
lmtest::bptest(fit)          # Breusch-Pagan heteroskedasticity

# Model comparison
fit2 <- update(fit, . ~ . + tenure)
anova(fit, fit2)             # nested F-test
AIC(fit, fit2)               # lower AIC preferred

Choosing the R function for the job

A quick mapping from question to function.

Analysis	Function / package	Key output
Two-group mean comparison	t.test()	t, df, p, CI
Three+ groups	aov() + TukeyHSD()	F, p, pairwise CIs
Non-parametric comparison	wilcox.test(), kruskal.test()	W / H, p
Linear prediction	lm()	β, R², F
Binary outcome	glm(family = binomial) + exp(coef())	Odds ratios
Count outcome	glm(family = poisson) / MASS::glm.nb()	IRR
Repeated measures / nested data	lme4::lmer()	Fixed + random effects
Factor structure	psych::fa(), lavaan::cfa()	Loadings, fit indices
Forecasting	forecast::auto.arima()	AIC, forecast intervals

Make the analysis reproducible

Keep raw data read-only, write every transformation in a script, set a seed for any random procedure, and pin package versions with renv. Render the report with Quarto so numbers in the text are computed, never typed.

set.seed(2026)
renv::init()      # lock the package library
quarto::quarto_render("report.qmd")

Project layout
  data/raw/        # never edited
  data/processed/  # script output only
  R/01-clean.R  R/02-analyse.R  R/03-figures.R
  report.qmd

Frequently asked questions

Is R harder to learn than SPSS?

The first week is harder, the sixth month is far easier. Once a script exists, re-running an entire analysis after a data correction takes seconds in R and hours of repeated clicking in SPSS. For one-off coursework SPSS is quicker; for a research programme R pays back rapidly.

Which R packages are essential for research analysis?

tidyverse for data manipulation and graphics, psych for reliability and factor analysis, car and lmtest for regression diagnostics, effectsize for effect sizes, lme4 for mixed models, forecast for time series, lavaan for SEM, and gtsummary for publication-ready tables.

How do I report R results in APA style?

Report the same elements as any other software: statistic, degrees of freedom, exact p-value and an effect size, for example t(178) = 2.41, p = .017, d = 0.36. Packages such as report and gtsummary generate APA-formatted sentences and tables directly from the fitted model.

Can R handle large datasets?

Yes, within memory limits. For datasets beyond a few million rows use data.table or arrow for on-disk processing, or connect R directly to a database with DBI and dplyr so aggregation happens server-side.

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