R commands are the foundation of data analysis and statistical modeling in the R environment. They provide the tools and flexibility to understand data, detect patterns, and make informed decisions.
R commands: what are they?¶
R commands (or R commands in English) are instructions used in R programming to execute specific tasks or initiate tasks in the R environment. These commands allow you toanalyze data, perform statistical calculations or create visualizations. R commands can be entered and processed in the R command line or in R scripts. It is important to distinguish commands from R functions.
R functions are code blocks defined and designated under R, which perform specific tasks. They can include the use of R operators and R data to accept arguments or display return values. This means that functions can save, process, and return data that is associated with different R data types.
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R commands: list of different commands¶
The following list of R commands gives you an overview of the different application areas in R programming. Depending on your specific projects and requirements, you can select and combine the appropriate R commands.
Data handling and processing¶

read.csv()
: reading data from a CSV file 
data.frame()
: creation of a data frame 
subset()
: filtering data based on specific conditions 
merge()
: merging data from different data frames 
aggregate()
: data aggregation based on specific criteria 
transform()
: creation of new variables in a data frame 
sort()
: sorting vectors or data frames 
unique()
: identifying unique values in a vector or column
Data visualization¶

plot()
: creating scatterplots and other basic types of diagrams 
hist()
: creation of histograms 
barplot()
: creating bar charts 
boxplot()
: creating boxplots 
ggplot2::ggplot()
: for more demanding and customizable visualizations with the ggplot2 package
Statistical analyzes¶

summary()
: preparation of a collection of data, including key statistical figures 
lm()
: running linear regressions 
t.test()
: running T tests to test hypotheses 
cor()
: calculation of correlation coefficients between variables 
anova()
: performing analyzes of variance (ANOVA) 
chisq.test()
: for chisquare tests
Data processing¶

ifelse()
: for condition evaluations and conditional expressions 
apply()
: application of a function to matrices or data frames 
dplyr::filter()
: filtering data in a data frame with the dplyr package 
dplyr::mutate()
: creation of new variables in data frames with the dplyr package 
lapply()
,sapply()
,mapply()
: for applying functions to lists or vectors
Importing and exporting data¶

readRDS()
,saveRDS()
: reading and saving R data objects 
write.csv()
,read.table()
: export and import of data in different formats
Statistical charts and graphs¶

qqnorm()
,qqline()
: for creating quantilequantile diagrams 
plot()
,acf()
: representation of autocorrelation diagrams 
density()
: representation of density functions and histograms 
heatmap()
: creation of density maps
R commands: usage examples¶
The following code examples demonstrate the use of major R commands in various application domains. According to your data and analytics requirementsyou can adapt and expand these commands.
Reading data from a CSV file¶
data < read.csv("donnees.csv")
R
Read.csv()
is a command allowing you to read the data present in a CSV file, in R. In our example, the data read is saved in the variable data
. This command is useful for importing external data into R and for making analyzes available.
Creating a Scatter Plot¶
plot(data$X, data$Y, main="DiagrammeDispersion")
R
Plot()
is an R command for creating charts and graphs in R. In our example, a scatter plot is created to represent the relationship between variables X
And Y
of the data frame data
. The argument main
sets the title of the diagram.
Running a Linear Regression¶
regression_model < lm(Y ~ X, data=data)
R
In this example, we run a linear regression in order to model the relationship between variables X
And Y
in the data frame data
. The command lm()
is used to calculate a linear regression in R. The result of the regression is saved in the variable regression_model
and can be used for other analyses.
Filtering data with the dplyr package¶
filtered_data < dplyr::filter(data, column > 10)
R
The command dplyr::filter()
comes from dplyr package and will be used for data manipulation. The dplyr package provides powerful functions for data filtering. We obtain the variable filtered_data
by selecting the lines of the data frame data
for which the value of the column column
is greater than 10.
Creating quantilequantile plots¶
qqnorm(data$Variable)
qqline(data$Variable)
R
You can use qqnorm()
to represent a quantilequantile diagram in R. In this example, a quantilequantile diagram is represented for the variable Variable
of data
. qqline()
adds a reference line to compare the distribution with a normal distribution.
We recommend that all beginners check out the R programming overview tutorial. There you will find plenty of tips and the basic knowledge needed to progress with the R programming language. More tips and basics are available in our article “Learn programming: basic principles” from the Digital Guide.