Overview of R Commands
The following R commands provide an overview of different application areas in R programming. Depending on our specific needs and projects, we can pick and match the commands that suits.
1. Reading and Writing Commands
Reading and writing data are fundamental tasks in data analysis and manipulation. In R, several functions and packages can help you handle different types of data sources.
- read.csv(): Read data from a CSV file.
- write.csv(): Write data to a CSV file.
2. Dataframe Operations Commands
Dataframe operations in R are essential for data manipulation and analysis. Here are some common operations you might perform on data frames using base R and the dplyr package, which is part of the tidyverse collection.
- data.frame(): Create a data frame.
- 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.
3. Applying Functions Commands
Applying functions to data frames is a powerful technique in R for data transformation and analysis. Here are various ways to apply functions to data frames.
- apply(): Apply a function to rows or columns of matrices or data frames.
- lapply(), sapply(), mapply(): Apply functions to lists or vectors.
4. Using dplyr for Data Manipulation
dplyr is a powerful package in R designed to make data manipulation easy and intuitive. It provides a set of verbs that allow you to solve the most common data manipulation challenges:.
- dplyr::filter(): Filter data in data frames.
- dplyr::mutate(): Create new variables in data frames.
- dplyr::select(): Select specific columns from a data frame.
- dplyr::summarize(): Summarize data by applying functions.
R Program Commands
R is a powerful programming language and environment designed for statistical computing and data analysis. It is widely used by statisticians, data scientists, and researchers for its extensive capabilities in handling data, performing statistical analysis, and creating visualizations.
Table of Content
- Overview of R Commands
- Data Visualizations commands
- Statistical Analysis commands
- Data Import and Export commands
- Control Structures and Conditionals commands
- Data Structures in R