R Language
- Getting started with R Language
- Awesome Book
- Awesome Community
- Awesome Course
- Awesome Tutorial
- Awesome YouTube
- *apply family of functions (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)
- Basic use and chaining
- Assignment with %<>%
- Creating side effects with %T>%
- Exposing contents with %$%
- Functional sequences
- Using the pipe with dplyr and ggplot2
- 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 Pipe operators (%>% and others) Assignment with %<>%
Example
The magrittr package contains a compound assignment infix-operator, %<>%, that updates a value by first piping it into one or more rhs expressions and then assigning the result. This eliminates the need to type an object name twice (once on each side of the assignment operator <-). %<>% must be the first infix-operator in a chain:
library(magrittr)
library(dplyr)
df <- mtcars
Instead of writing
df <- df %>% select(1:3) %>% filter(mpg > 20, cyl == 6)
or
df %>% select(1:3) %>% filter(mpg > 20, cyl == 6) -> df
The compound assignment operator will both pipe and reassign df:
df %<>% select(1:3) %>% filter(mpg > 20, cyl == 6)
