LearnR_Packages

Packages

GPT-4 explains what an R package is:

“In R and RStudio, a”package” is a collection of functions, data sets, and documentation bundled together. A package provides a way to distribute and share R code and related material with others. They can be thought of as an extension or add-on to the base R system, allowing users to tap into specialized functionality for specific tasks or domains.

The R community has developed thousands of packages, which are available on the Comprehensive R Archive Network (CRAN) and other repositories. These packages cover a vast range of topics and methods, from statistical techniques and data manipulation to graphical display and interactive applications.

In RStudio, you can easily install and manage packages using the “Packages” pane, which provides a user-friendly interface for these tasks. Once a package is installed, you need to use the library() function to load it into your current R session to access its functions and datasets.”

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Packages How To

To install a new package:

install.packages("")

To check which packages are installed:

installed.packages()

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Package Links

Here are links to helpful lists of packages or descriptions of particularly useful packages.

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Posit Recommended Packages - Quick list of useful R packages

Quick list of useful R packages_by Posit

R-ArcGIS Bridge

Cloud.R-Project.Org CRAN Packages

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Darrell’s Most Used Packages (updated periodically)

To install a package:

install.packages()

The packages I’m using most these days:

Data Cleaning and Handling in R

# All Purpose Data
install.packages("tidyverse")

# Data Visualization 
install.packages("ggplot2") # Coursera Preferred Option
install.packages("Plotly")
install.packages("Lattice")
install.packages("RGL")
install.packages("Dygraphs")
install.packages("Leaflet")
install.packages("Highcharter")
install.packages("Patchwork")
install.packages("gganimate")
install.packages("ggridges")


#Database
install.packages("DBI")
install.packages("odbc")
install.packages("RODBC")


#SQL
install.packages("sqldf")

Example Use Case:
  sqldf("SELECT * FROM Orange ORDER BY age ASC, circumference DESC LIMIT 5")

SEE: 
  
  1.3. Importing from SQL Databases
  https://tomaztsql.wordpress.com/2020/04/27/essential-list-of-useful-r-packages-for-data-scientists/

AND:
    SQL in R
    https://dept.stat.lsa.umich.edu/~jerrick/courses/stat701/notes/sql.html

    
    
# HTML
install.packages("shiny")
install.packages("htmltools")



#TBD...

install.packages("")
install.packages("")
install.packages("")
install.packages("")

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R & SQL Server Databases - Part 1 Writing R in RStudio, with a SQL Database

GPT-4: If you’re working with SQL Server in R, there are several R packages that can help you connect to, retrieve, and manipulate data. Here are some of the most widely-used ones:

1. RODBC: This is one of the older and more established packages for ODBC database connections in R. As the name implies, it uses the ODBC (Open Database Connectivity) drivers to establish connections.

    install.packages("RODBC")

-   Usage:

    library(RODBC)
    connection <- odbcConnect("DSN_NAME", uid="YOUR_USER_ID", pwd="YOUR_PASSWORD")
    data <- sqlFetch(connection, "TABLE_NAME")
    close(connection)

2. DBI and odbc: DBI is a modern database interface for R, and the odbc package, which builds on DBI, provides a way to establish ODBC connections.

    install.packages("DBI")
    install.packages("odbc")

-   Usage:

    library(DBI)
    library(odbc)
    connection <- dbConnect(odbc(), "DSN_NAME", UID="YOUR_USER_ID", PWD="YOUR_PASSWORD")
    data <- dbGetQuery(connection, "SELECT * FROM TABLE_NAME")
    dbDisconnect(connection)

3. RSQLServer: This package is a DBI-compliant interface specifically for SQL Server. It might be more intuitive if you’re exclusively working with SQL Server.

    # Using devtools to install from GitHub
    devtools::install_github('imanbio/RSQLServer')

• Usage:

    library(RSQLServer)
    connection <- dbConnect(RSQLServer::SQLServer(), url="jdbc:sqlserver://SERVER_NAME;databaseName=DB_NAME", user="YOUR_USER_ID", password="YOUR_PASSWORD")
    data <- dbGetQuery(connection, "SELECT * FROM TABLE_NAME")
    dbDisconnect(connection)

4. pool: This is an auxiliary package that can be used in combination with the others. It allows you to manage and optimize your database connections using connection pooling, which can be particularly helpful when building Shiny apps that connect to databases.

    install.packages("pool")

When selecting a package, consider the specific features you need, as well as any dependencies. For example, RODBC might require configuration of an ODBC Data Source Name (DSN), while RSQLServer uses JDBC. In general, the DBI and odbc combination is widely adopted and offers a good mix of features and ease of use.

### R & SQL Server Databases - Part 2 Writing SQL in RStudio
#### See Images paired with these instructions:

HTML in RStudio

GPT-4 on RStudio tools for HTML:

Shiny: It’s an R package that allows for the creation of interactive web apps. While it primarily uses its own system of UI components, you can integrate raw HTML and CSS if needed.

R Markdown with HTML Output: When you create an R Markdown document in RStudio, you can knit the document to produce an HTML output. Within the R Markdown document, you can mix Markdown, R code, and even raw HTML.

htmltools: This R package provides functions and tools for working with HTML. It can be particularly useful when you want to generate dynamic HTML content based on data analysis in R.

install.packages("shiny")

install.packages("htmltools")

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# UNDER CONSTRUCTION………….

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install.packages("")

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install.packages("")

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