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)
- Pivot and unpivot with data.table
- Pivot and unpivot tabular data with data.table - I
- Pivot and unpivot tabular data with data.table - II
- 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 Pivot and unpivot with data.table
Syntax
- Melt with
melt(DT, id.vars=c(..), variable.name="CategoryLabel", value.name="Value") - Cast with
dcast(DT, LHS ~ RHS, value.var="Value", fun.aggregate=sum)
Parameters
| Parameter | Details |
|---|---|
| id.vars | tell melt which columns to retain |
| variable.name | tell melt what to call the column with category labels |
| value.name | tell melt what to call the column that has values associated with category labels |
| value.var | tell dcast where to find the values to cast in columns |
| formula | tell dcast which columns to retain to form a unique record identifier (LHS) and which one holds the category labels (RHS) |
| fun.aggregate | specify the function to use when the casting operation generates a list of values in each cell |
Remarks
Much of what goes into conditioning data to build models or visualizations can be accomplished with data.table. As compare to other options, data.table offers advantages of speed and flexibility.
Pivot and unpivot with data.table Related Examples
- Pivot and unpivot tabular data with data.table - I
- Pivot and unpivot tabular data with data.table - II
