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
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- boxplot
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- Column wise operation
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- 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
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- Feature Selection in R -- Removing Extraneous Features
- Formula
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- Functional programming
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- Get user input
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- heatmap and heatmap.2
- Hierarchical clustering with hclust
- Hierarchical Linear Modeling
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- 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
- Difference between period and duration
- Instants
- Intervals, Durations and Periods
- Manipulating date and time in lubridate
- Parsing date and time in lubridate
- Parsing dates and datetimes from strings with lubridate
- Rounding dates
- Time Zones
- 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 lubridate Manipulating date and time in lubridate
Example
date <- now()
date
## "2016-07-22 03:42:35 IST"
year(date)
## 2016
minute(date)
## 42
wday(date, label = T, abbr = T)
# [1] Fri
# Levels: Sun < Mon < Tues < Wed < Thurs < Fri < Sat
day(date) <- 31
## "2016-07-31 03:42:35 IST"
# If an element is set to a larger value than it supports, the difference
# will roll over into the next higher element
day(date) <- 32
## "2016-08-01 03:42:35 IST"
