# 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 **analyze data, perform statistical calculations, or create visualizations**. 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](https://www.ionos.com/digitalguide/websites/web-development/r-functions/).

R functions are **blocks of code defined and named in R** that perform specific tasks. These can include the use of [R operators](https://www.ionos.com/digitalguide/websites/web-development/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](https://www.ionos.com/digitalguide/websites/web-development/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 visualization

- `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 customizable visualizations with the ggplot2 package

### Statistical analysis

- `summary()`: Get a summary of data, including statistical metrics
- `lm()`: Perform linear regressions
- `t.test()`: Perform T-tests for hypothesis testing
- `cor()`: Calculate correlation coefficients between variables
- `anova()`: Perform analysis of variance (ANOVA)
- `chi-sq.test()`: Perform chi-square 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 quantile-quantile 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 customize and extend these commands.

### Reading data from a CSV file

```R
data <- read.csv("data.csv")
```

`Read.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

```R
plot(data$X, data$Y, main="Scatter plot")
```

[Plot()](https://www.ionos.com/digitalguide/websites/web-development/r-plot/) 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

```R
regression_model <- lm(Y ~ X, data=data)
```

In 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

```R
filtered_data <- dplyr::filter(data, column > 10)
```

The 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 quantile-quantile diagrams

```R
qqnorm(data$Variable)
qqline(data$Variable)
```

You can use `qqnorm()` to plot a **quantile-quantile diagram** in R. In this example, a quantile-quantile 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](https://www.ionos.com/digitalguide/websites/web-development/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](https://www.ionos.com/digitalguide/websites/web-development/learn-to-code-an-intro-on-how-to-get-the-basics-down/) has got you covered.


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