What are the basic R commands?
R commands are the basis for data analysis and statistical modeling in the R environment. They provide the tools and flexibility to read data, identify patterns and make informed decisions.
What are R commands?
R commands are used in R programming to perform specific tasks or initiate actions in the R environment. These commands make it possible to analyse data, perform statistical calculations, or create visualisations. R commands can be entered and processed in the R command line or in R scripts. It’s important to distinguish R commands from functions in R.
R functions are blocks of code defined and named in R that perform specific tasks. These can include the use of R operators and R data to accept arguments or output return values. This means that functions can store, process and return data associated with different R data types .
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An overview of R commands
The following R commands list provides an overview of different application areas in R programming. Depending on your specific needs and projects, you can pick and match the commands that suit you.
Data manipulation and processing

read.csv()
: Read data from a CSV file 
data.frame()
: Create a data framework 
subset()
: Filter data based on specific conditions 
merge()
: Merge data from different data frames 
aggregate()
: Aggregate data based on specific criteria 
transform()
: Create new variables in a data frame 
sort()
: Sort vectors or data frames 
unique()
: Identify unique values in a vector or column
Data visualisation

plot()
: Create scatter plots and other basic plot types 
hist()
: Create histograms 
barplot()
: Create bar charts 
boxplot()
: Create box plots 
ggplot2::ggplot()
: Create more sophisticated and customisable visualisations with the ggplot2 package
Statistical analysis

summary()
: Get a summary of data, including statistical metrics 
lm()
: Perform linear regressions 
t.test()
: Perform Ttests for hypothesis testing 
cor()
: Calculate correlation coefficients between variables 
anova()
: Perform analysis of variance (ANOVA) 
chisq.test()
: Perform chisquare tests
Data processing

ifelse()
: Perform condition evaluations and conditional expressions 
apply()
: Apply a function to matrices or data frames 
dplyr::filter()
: Filter data in data frames with the dplyr package 
dplyr::mutate()
: Create new variables in data frames with the dplyr package 
lapply()
,sapply()
,mapply()
: Apply functions to lists or vectors
Data import and export

readRDS()
,saveRDS()
: Read and save R data objects 
write.csv()
,read.table()
: Export and import data in various formats
Statistical graphs and charts

qqnorm()
,qqline()
: Create quantilequantile diagrams 
plot()
,acf()
: Display autocorrelation diagrams 
density()
: Display density functions and histograms 
heatmap()
: Create heat maps
R command examples
The following code examples show you how to use basic R commands for different purposes. Depending on your data and analysis needs, you can customise and extend these commands.
Reading data from a CSV file
data < read.csv("data.csv")
RRead.csv()
is a command for reading data from a CSV file in R. In our example, the imported data is stored in the variable data
. This command is useful for importing external data into R and making it available for analysis.
Creating a scatter plot
plot(data$X, data$Y, main="Scatter plot")
RPlot()
is one of the R commands for creating charts and graphs in R. Here, a scatter plot is drawn showing the relationship between the variables X
and Y
from the data
data frame. The argument main
defines the diagram title.
Performing linear regression
regression_model < lm(Y ~ X, data=data)
RIn this example, we’ll perform a linear regression to model the relationship between the variables X
and Y
from the data
data frame. The lm()
command is used to calculate a linear regression in R. The result of the regression is stored in the variable regression_model
and can be used for further analysis.
Filtering data with the dplyr package
filtered_data < dplyr::filter(data, column > 10)
RThe command dplyr::filter()
is derived from the dplyr package and used for data manipulation. The dplyr package offers powerful data filtering capabilities. We get the variable filtered_data
by selecting rows from the data frame data
where the value in the column is greater than 10.
Creating quantilequantile diagrams
qqnorm(data$Variable)
qqline(data$Variable)
RYou can use qqnorm()
to plot a quantilequantile diagram in R. In this example, a quantilequantile diagram for the variable variable
is drawn from data
. qqline()
adds a reference line to compare the distribution with a normal distribution.
If you are just getting started with R, we recommend checking out our tutorial on R programming. Here, you’ll find useful tips and basic information to get started with the language. For more tips and learning the basics of programming, our Digital Guide article on learning how to code has got you covered.