R Language
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- *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
R Language *apply family of functions (functionals)
Remarks
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.
Members of the *apply Family
The *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.
*apply family of functions (functionals) Related Examples
- Bulk File Loading
- Combining multiple `data.frames` (`lapply`, `mapply`)
- Use anonymous functions with apply
- Using built-in functionals
- Using user-defined functionals
