Meet the Expert
Shruti Sharma
Academic Writing Coach & Research Data Analysis Expert
- Practical R and RStudio experience for PhD dissertation data analysis
- Specialises in tidyverse, ggplot2, psych package, and lavaan SEM for academic research
- Guided 100+ researchers in transitioning from SPSS to R for their dissertation analysis
R is a free, open-source statistical programming language used by researchers worldwide for data analysis, statistical modelling, and data visualisation. Combined with RStudio, it provides a powerful, flexible environment for conducting any statistical analysis needed for a PhD dissertation or academic research paper.
Why Use R for Academic Research?
R Software for Research at a Glance
Open source; no licence fees
Any statistical method is available
Highly customisable, journal-ready figures
Full analysis trail; R Markdown reports
Works on all major operating systems
Stack Overflow, RStudio Community, CRAN
Step 1: Installing R and RStudio
- Download and install R from cran.r-project.org — choose your operating system (Windows/Mac/Linux).
- Download and install RStudio Desktop (free) from posit.co/download/rstudio-desktop.
- Open RStudio — you will see four panes: Source (script editor), Console, Environment/History, and Plots/Packages/Help.
- Install essential packages: type in the Console:
install.packages(c("tidyverse", "psych", "car", "haven", "readxl"))
Step 2: Importing Data into R
R can import data from multiple formats:
| Data Format | Package | R Command |
|---|---|---|
| CSV file | Base R / readr | data <- read.csv("file.csv") or read_csv("file.csv") |
| Excel (.xlsx) | readxl | data <- read_excel("file.xlsx") |
| SPSS (.sav) | haven | data <- read_sav("file.sav") |
| STATA (.dta) | haven | data <- read_dta("file.dta") |
| R Data (.RData) | Base R | load("file.RData") |
Step 3: Descriptive Statistics in R
Use the psych package for a comprehensive descriptive statistics summary:
library(psych)
describe(data)
This produces: n, mean, SD, median, min, max, range, skewness, kurtosis, and standard error for all numeric variables.
For frequencies of categorical variables:
table(data$gender)
prop.table(table(data$gender)) * 100
Step 4: Common Statistical Tests in R
| Test | R Function | Example |
|---|---|---|
| Independent t-test | t.test() | t.test(score ~ group, data = df) |
| One-way ANOVA | aov() | aov(score ~ group, data = df) |
| Pearson Correlation | cor.test() | cor.test(df$x, df$y) |
| Multiple Regression | lm() | lm(outcome ~ pred1 + pred2, data = df) |
| Chi-square Test | chisq.test() | chisq.test(table(df$var1, df$var2)) |
| Reliability (alpha) | alpha() in psych | alpha(df[, c("item1","item2","item3")]) |
Step 5: Data Visualisation with ggplot2
The ggplot2 package produces publication-quality visualisations. Basic examples:
- Histogram:
ggplot(df, aes(x = score)) + geom_histogram(bins = 20, fill = "steelblue") - Bar chart:
ggplot(df, aes(x = group, fill = group)) + geom_bar() - Scatterplot:
ggplot(df, aes(x = pred, y = outcome)) + geom_point() + geom_smooth(method = "lm") - Box plot:
ggplot(df, aes(x = group, y = score)) + geom_boxplot()
Using R Markdown for Reproducible Dissertation Analysis
R Markdown (.Rmd) allows you to combine R code, output, and narrative text in a single document that can be rendered as HTML, PDF, or Word. This is ideal for creating a fully reproducible analysis appendix for your dissertation. Write your R code in chunks, run the analysis, and the output (tables, figures, test results) appears directly in the document. Many top journals now require or recommend R Markdown for transparent, reproducible research.
Need R analysis support for your PhD dissertation? Our data analysis specialists at Thesis Ace Writers provide R coding, analysis interpretation, and results chapter writing services.
Related Reading from Thesis Ace Writers
Struggling with R for your dissertation? Contact Thesis Ace Writers for hands-on R analysis support from data import to publication-quality results.
Frequently Asked Questions
Click a question to expand the answer.
R is a free, open-source programming language and environment for statistical computing and graphics. It is widely used in academic research, data science, and bioinformatics. Researchers use R because it is free, highly customisable, has thousands of statistical packages (CRAN), produces publication-quality graphs, and is increasingly required or preferred in top academic journals. It can perform any statistical analysis available in paid software like SPSS or STATA.
R is the underlying statistical computing language. RStudio is an Integrated Development Environment (IDE) that makes R much easier to use — it provides a user-friendly interface with a script editor, console, environment viewer, and plots pane. You need to install R first, then install RStudio. Almost all academic R users work in RStudio. Think of R as the engine and RStudio as the dashboard.
Key R packages for academic research: tidyverse (data manipulation and ggplot2 visualisation), psych (descriptive statistics, reliability, factor analysis), car (regression diagnostics, ANOVA), lme4 (mixed-effects models), lavaan (structural equation modelling), survival (survival analysis), ggplot2 (data visualisation), readxl (import Excel data), haven (import SPSS/STATA data), and rmarkdown (create reproducible research reports). Install packages with install.packages('package_name').
R is free and more powerful; SPSS is paid and more user-friendly for beginners. R requires coding (scripting); SPSS has a point-and-click interface. R produces more customisable, publication-quality graphics; SPSS graphics are basic. R has a wider range of advanced statistical methods; SPSS covers standard analyses well. Both are accepted for PhD dissertations. If your university provides SPSS, use it for standard analyses; consider R for complex models, advanced visualisations, or when you want free, reproducible analysis.
Basic R knowledge is increasingly expected in quantitative PhD programmes in social sciences, psychology, economics, and public health. You do not need to be a programmer — basic data import, cleaning, and analysis tasks require only a small set of commands. Start with the tidyverse ecosystem and the psych package, which are well-documented and beginner-friendly. Many free resources (R for Data Science by Hadley Wickham, swirl interactive tutorials) make learning R accessible.