- Getting started with R Language
- Awesome Book
- Awesome Community
- Awesome Course
- Awesome Tutorial
- Awesome YouTube
- *apply family of functions (functionals)
- Bulk File Loading
- Combining multiple `data.frames` (`lapply`, `mapply`)
- Use anonymous functions with apply
- Using built-in functionals
- Using user-defined functionals
- .Rprofile
- Aggregating data frames
- Analyze tweets with R
- ANOVA
- Arima Models
- Arithmetic Operators
- Bar Chart
- Base Plotting
- Bibliography in RMD
- boxplot
- caret
- Classes
- Cleaning data
- Code profiling
- Coercion
- Color schemes for graphics
- Column wise operation
- Combinatorics
- Control flow structures
- Creating packages with devtools
- Creating reports with RMarkdown
- Creating vectors
- Data acquisition
- Data frames
- data.table
- Date and Time
- Date-time classes (POSIXct and POSIXlt)
- Debugging
- Distribution Functions
- dplyr
- Expression: parse + eval
- Extracting and Listing Files in Compressed Archives
- Factors
- Fault-tolerant/resilient code
- Feature Selection in R -- Removing Extraneous Features
- Formula
- Fourier Series and Transformations
- Functional programming
- Generalized linear models
- Get user input
- ggplot2
- GPU-accelerated computing
- Hashmaps
- heatmap and heatmap.2
- Hierarchical clustering with hclust
- Hierarchical Linear Modeling
- I/O for database tables
- I/O for foreign tables (Excel, SAS, SPSS, Stata)
- I/O for geographic data (shapefiles, etc.)
- I/O for raster images
- I/O for R's binary format
- Implement State Machine Pattern using S4 Class
- Input and output
- Inspecting packages
- Installing packages
- Introduction to Geographical Maps
- Introspection
- JSON
- Linear Models (Regression)
- Lists
- lubridate
- Machine learning
- Matrices
- Meta: Documentation Guidelines
- Missing values
- Modifying strings by substitution
- Natural language processing
- Network analysis with the igraph package
- Non-standard evaluation and standard evaluation
- Numeric classes and storage modes
- Object-Oriented Programming in R
- Parallel processing
- Pattern Matching and Replacement
- Performing a Permutation Test
- Pipe operators (%>% and others)
- Pivot and unpivot with data.table
- Probability Distributions with R
- Publishing
- R code vectorization best practices
- R in LaTeX with knitr
- R Markdown Notebooks (from RStudio)
- R memento by examples
- Random Forest Algorithm
- Random Numbers Generator
- Randomization
- Raster and Image Analysis
- Rcpp
- Reading and writing strings
- Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
- Recycling
- Regular Expression Syntax in R
- Regular Expressions (regex)
- Reproducible R
- Reshape using tidyr
- Reshaping data between long and wide forms
- RESTful R Services
- RMarkdown and knitr presentation
- RODBC
- roxygen2
- Run-length encoding
- Scope of variables
- Set operations
- Shiny
- Solving ODEs in R
- Spark API (SparkR)
- spatial analysis
- Speeding up tough-to-vectorize code
- Split function
- sqldf
- Standardize analyses by writing standalone R scripts
- String manipulation with stringi package
- strsplit function
- Subsetting
- Survival analysis
- Text mining
- The character class
- The Date class
- The logical class
- tidyverse
- Time Series and Forecasting
- Updating R and the package library
- Updating R version
- Using pipe assignment in your own package %<>%: How to ?
- Using texreg to export models in a paper-ready way
- Variables
- Web Crawling in R
- Web scraping and parsing
- Writing functions in R
- xgboost

A function in the `*apply`

family is an abstraction of a `for`

loop. Compared with the `for`

loops `*apply`

functions have the following advantages:

- Require less code to write.
- Doesn't have an iteration counter.
- Doesn't use temporary variables to store intermediate results.

However `for`

loops are more general and can give us more control allowing to achieve complex computations that are not always trivial to do using `*apply`

functions.

The relationship between `for`

loops and `*apply`

functions is explained in the documentation for `for`

loops.

`*apply`

FamilyThe `*apply`

family of functions contains several variants of the same principle that differ based primarily on the kind of output they return.

function | Input | Output |
---|---|---|

`apply` | `matrix` , `data.frame` , or `array` | vector or matrix (depending on the length of each element returned) |

`sapply` | vector or `list` | vector or matrix (depending on the length of each element returned) |

`lapply` | vector or `list` | `list` |

`vapply` | vector or `list | vector or matrix (depending on the length of each element returned) of the user-designated class |

`mapply` | multiple vectors, `lists` or a combination | `list` |

See "Examples" to see how each of these functions is used.

- Bulk File Loading
- Combining multiple `data.frames` (`lapply`, `mapply`)
- Use anonymous functions with apply
- Using built-in functionals
- Using user-defined functionals

This modified text is an extract of the original Stack Overflow Documentation created by following contributors and released under CC BY-SA 3.0

This website is not affiliated with Stack Overflow

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