Yes—you can learn R programming and do substantial statistical work before installing any contributed packages. Install the official R distribution, practice the language and its built-in facilities, and add packages only when a task needs capabilities outside the standard installation. “No packages” does not mean an empty program: R always has its base package, and a normal startup can attach other standard packages.
What “without packages” means in R
R is a free software environment for statistical computing and graphics. In everyday conversation, “no packages” usually means that you have not installed separately maintained contributed packages such as those from CRAN. The R language, base functions, and the standard distribution are still available.
A normal R session may attach standard packages at startup. To start a strictly package-free session—apart from base—set the default package list to empty:
options(defaultPackages = character())This changes startup attachments; it does not remove R’s built-in language or base functionality. You can also inspect what is attached with:
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search()Install R, not a package collection
Download the official R distribution for your operating system. R runs on Unix-like systems, Windows, and macOS. An IDE such as RStudio is optional: it provides an editor and interface, while R itself is the interpreter and statistical environment. You can learn from the R console or a plain .R script.
The latest release listed by the R Project at the time of this article is R 4.6.1, released 24 June 2026. Record the R version used for exercises, screenshots, and reproducible examples because startup behavior and documentation can change.
R.version.stringA package-free learning path
1. Expressions, arithmetic, and assignment
R evaluates expressions and returns objects. Start with arithmetic, comparisons, and assignment using the conventional left arrow:
2 + 3
x <- 10
x * 4
x == 10= also assigns in many contexts, but learning <- makes assignment visually distinct from argument matching in function calls.
2. Atomic vectors and indexing
Vectors are R’s fundamental one-dimensional data structure. Learn numeric, character, and logical vectors, then select values by position, condition, or name:
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names(scores) <- c("Ana", "Bo", "Chen", "Dee")
scores[2]
scores[scores >= 80]
scores[c("Ana", "Dee")]Understand one-based indexing, negative indices for exclusion, and the difference between a missing value and a value that is not present.
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3. Matrices, arrays, lists, and data frames
Use matrices and arrays for homogeneous data, lists for heterogeneous objects, and data frames for rectangular tables whose columns may have different types:
m <- matrix(1:6, nrow = 2)
record <- list(id = 7, name = "Ana", active = TRUE)
people <- data.frame(
name = c("Ana", "Bo"),
score = c(88, 76)
)
people$scorePractice selecting rows and columns with [row, column], [[ ]] for one list element, and $ for a named component.
4. Missing values, coercion, and recycling
NA represents a missing value and can propagate through calculations. Test for it explicitly:
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values <- c(4, NA, 9)
is.na(values)
mean(values, na.rm = TRUE)R may coerce mixed vectors to a common type, and shorter vectors can be recycled in operations. Check types and lengths rather than guessing:
typeof(values)
length(values)
as.character(c(1, 2, 3))5. Conditions and control flow
Use if/else for decisions and for, while, and repeat for explicit iteration. break exits a loop and next skips to its next iteration:
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for (score in scores) {
if (score >= 80) {
print("pass")
} else {
print("review")
}
}6. Functions and environments
Write small functions with named arguments and a clear return value:
percentage <- function(x, total = 100) {
100 * x / total
}
percentage(42)R uses lexical scoping: a function looks for names in its own environment and then in enclosing environments. You do not need advanced environment programming initially, but recognizing this rule explains why a function can use an argument, a local variable, or a name defined outside it.
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Practice the functions that make a package-free workflow useful:
sum(),mean(),median(),min(), andmax()for numerical summaries.length(),table(), andsummary()for structure and distributions.- Built-in model functions such as
lm()for many standard analyses.
summary(people$score)
table(people$score >= 80)
fit <- lm(score ~ 1, data = people)
summary(fit)Not every statistical method is included in base or the standard distribution. Treat the available models as a strong foundation, not a promise that every specialist technique is package-free.
8. Base graphics
Graphics are part of the standard R learning path. Begin with:
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plot(people$score)
hist(people$score)
boxplot(people$score)
barplot(table(people$score >= 80))
plot(scores)
lines(scores)These functions let you inspect data and communicate results before learning a separate graphics system.
The Tool Desk
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- Read data you can access through base R connections and built-in functions, then inspect and clean vectors and data frames.
- Transform columns with indexing, conditional logic, arithmetic, and functions you write yourself.
- Compute descriptive statistics and fit many conventional statistical models.
- Automate work in scripts and reusable functions.
- Create exploratory and presentation-ready plots with base graphics.
- Use R’s own help system to learn unfamiliar functions.
The exact set of immediately available functions depends on your R version and which standard packages are attached, so state those details when sharing code.
Use R’s built-in help before searching elsewhere
R includes a complete discovery workflow:
?meanorhelp(mean)opens a function’s help page.help.start()opens the local HTML documentation index.apropos("plot")searches names containing a term.example(plot)runs examples from a help page.RSiteSearch("robust regression")searches broader R documentation.
Read the Usage, Arguments, Value, Details, and Examples sections; then run a small example and change one input at a time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When packages should enter your workflow
Packages add functions, data, and documentation. Installation and attachment are separate operations:
install.packages("packageName") # downloads and installs
library(packageName) # attaches it for this sessionDelay installation until you understand expressions, objects, indexing, control flow, functions, and help. Add a package when it provides a capability or workflow that the standard distribution does not, or when a project explicitly requires it.
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Base R and package-based work compared
| Decision axis | Base or standard R | Contributed-package workflow |
|---|---|---|
| Availability | Present with the R distribution and its standard components | Requires installation and external dependencies |
| Learning objective | Language fundamentals and core computing concepts | Task-specific productivity and specialized methods |
| Data manipulation | Explicit indexing and base functions | Higher-level verbs supplied by packages |
| Graphics | Base graphics such as plot() and hist() |
Additional graphics systems and extensions |
| Maintenance | Fewer external dependencies | Richer capabilities but a changing dependency ecosystem |
A practical first exercise
- Start R and record
R.version.string. - Create a numeric vector of ten observations with
c(). - Give it names and select values by position, name, and a logical condition.
- Insert an
NA, comparemean(x)withmean(x, na.rm = TRUE), and explain the difference. - Write a function that returns a standardized value or percentage.
- Summarize the vector with
summary()andtable(). - Draw a histogram and a boxplot, then save the commands in an
.Rscript. - Use
?function_nameandexample(function_name)for one function you have not used.
Frequently Asked Questions
Can I learn R before learning tidyverse?
Yes. Base R teaches the underlying objects, indexing, evaluation, functions, and control flow that package-based interfaces build on. You can add tidyverse or another package system when its workflow solves a real need.
Is RStudio required to learn R?
No. RStudio is an optional IDE. The R interpreter, console, scripts, help pages, and graphics work without it.
Does no packages mean no statistical models?
No. R includes many standard summaries and models, including functions such as lm(). Specialist methods may require contributed packages.
The Bottom Line
Start with official R, learn its objects, indexing, control flow, functions, summaries, graphics, and help system, and add packages deliberately rather than treating them as a prerequisite.
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