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
- 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
- Check R Version
- Deciding on the old packages
- Installing from R Website
- Updating from within R using installr Package
- Updating Packages
- 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 Updating R version Deciding on the old packages
Example
Once the installation is finished click the Finish button.
Now it asks if you want to copy your packages fro the older version of R to Newer version of R. Once you choose yes all the package are copied to the newer version of R.
After that you can choose if you still want to keep the old packages or delete.
You can even move your Rprofile.site from older version to keep all your customised settings.
