R commands: overview of the main commands

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.

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)
• chi-sq.test() : for chi-square 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 quantile-quantile 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")

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")

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)

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)

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 quantile-quantile plots¶

qqnorm(data$Variable)
qqline(data$Variable)

You can use qqnorm() to represent a quantile-quantile diagram in R. In this example, a quantile-quantile 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.