Powerful, flexible and well-thought-out dataviz package following 'grammar of graphics' syntax to create static graphics, but be prepared for a steep learning curve.
CRAN. CRAN. It also offers compression that doesn't slow data access too much, as well as the ability to import a specific range of rows (by row number). CRAN. ggplot2 lets you use the grammar of graphics to build layered, customizable plots. The package names in the table are clickable if you want more information.
d <- diamonds[sample(nrow(diamonds), 1000), ], R wrapper for the robust and well documented Highcharts JavaScript library, one of my favorite choices for presentation-quality interactive graphics. CRAN. RColorBrewer helps you select color palettes for your visualizations.
ggmap - Download street maps straight from Google maps and use them as a background in your ggplots.
A collection of packages for modeling and machine learning using, Click here to start a new community discussion, Installing and Configuring Python with RStudio. It also has some nice tabulating tools, like adding a total row, as well as generating tables with percentages and easy crosstabs. Windows only: Update your installed version of R from within R. On CRAN. There are a number of useful functions in here. R packages are collections of functions and data sets developed by the community.
CRAN.
Run SQL queries on your data frame with sqldf. R interface to the dimple JavaScript library with numerous customization options. If you've copied data from the Web, a spreadsheet, or other source into your clipboard, datapasta lets you paste it into R. df_paste() to create a data frame, vector_paste() to create a vector.
CRAN. quantmod - Tools for downloading financial data, plotting common charts, and doing technical analysis. CRAN. Turn any R function into a host-able API with a line or two of code. XLConnect, xlsx - These packages help you read and write Micorsoft Excel files from R. You can also just export your spreadsheets from Excel as .csv's. And, its get_dupes() function is an elegant way of finding duplicate rows in data frames, either based on one column, several columns, or entire rows.
See the, data import, data visualization, data analysis. For example, if you are usually working with data frames, probably you will have heard about dplyr or data.table, two of the most popular R packages. Package names, like pretty much everything else in R, are case sensitive. This is pretty much pure fun.
I don't do much forecasting analysis; but if I did, I'd start with this package. I've seen some nice (if sometimes sluggish) apps and it's got many enthusiasts. ggplot2. You can also read about the entire package development process online in Hadley Wickham's R Packages book. Write R code in your markdown reports. If you need to write to an Excel file as well as read, this package is easy to use and offers a lot of options for formatting your spreadsheet. This makes for an elegant paste() replacement. CRAN.
Add R to a markdown document and easily generate reports in HTML, Word and other formats.
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It's also built for I/O speed. While RStudio has since added a list-viewing option, this HTML widget still offers an elegant way to view complex nested lists within R. GitHub timelyportfolio/listviewer. CRAN. Scatterplot (Hadley Wickham/Tidyverse) While it's relatively easy to create standard plots …
tabyl(mydf, sort = TRUE) %>% adorn_totals("row"), car's recode function makes it easy to bin continuous numerical data into categories or factors. CRAN. Her book Practical R for Mass Communication and Journalism was published in December 2018.
CRAN. odbc - Use any ODBC driver with the odbc package to connect R to your database. This makes it easy to download numerical and geospatial info in R-ready format.
Be sure to include a reproducible example of your issue. package development, package installation, devtools has a slew of functions aimed at helping you create your own R packages, such as automatically running all example code in your help files to make sure everything works. Extremely handy if you want to move beyond built-in and RColorBrewer options. If you've ever wanted to tear your hair out over an Excel file with merged cells, data in column headers, headers mixed in data, and key information in color coding, this is the package for you. The result? It even includes fuzzy matching if you misspell a package name!
Base R handles most of these functions; but if you have huge files, these packages offer faster and standardized way to read CSVs and similar files into R. readr has been around for awhile; vroom is a speedier alternative, useful for larger data sets.
maps - Easy to use map polygons for plots. tidyverse - An opinionated collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures.
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