An alternative to t-tests, producing posterior estimates for group means and standard deviations and their differences and effect sizes. Unsubscribe at any time. data.table - An alternative way to organize data sets for very, very fast operations.
R Markdown - The perfect workflow for reproducible reporting. Basic data cleaning made easy, such as finding duplicates by multiple columns, making R-friendly column names and removing empty columns. R can handle plain text files – no package required. tidymodels - A collection of packages for modeling and machine learning using tidyverse principles. If Shiny is too complex and involved for your needs, this package offers a simpler (if somewhat less robust) solution based on R Markdown. To install a package from CRAN, use the command install.packages("packagename") -- of course substituting the actual package name for packagename and putting it in quotation marks. Thanks for subscribing! 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. CRAN. GitHub. If you want to use data from a Google API in an R project and there's not yet a specific package for that API, this is the place to turn for authenticating CRAN. Part of JournalDev IT Services Private Limited. CRAN. A GIS game-changer for R. CRAN. dplyr - Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. GitHub.
This interface to the Google Maps Direction and Distance Matrix APIs let you analyze and map distances and driving routes. Yihui Xie & others (knitr), RStudio (rmarkdown).
How to reshape data with tidyr’s new pivot functions, How to add color to your ggplot2 text in R, Mozilla sees 'collateral damage' in DOJ antitrust fight with Google, Warning: Multiple Windows 10 retirements ahead, 8 fast fixes for common Chrome OS problems. While I often prefer dplyr, data.table has many fans for its speed with large data sets and concise syntax. This package is another that aims to solve one problem, and solve it well: package installation. Recent functionality adds support for simple features, interactive maps and creating leaflet objects. ggplot2. read_csv(myfile.csv) or vroom(myfile.csv), Hadley Wickham (readr), Jim Hester (vroom). CRAN. cSplit(mydata, "multi_val_column", sep = ",", direction = "long"). I don't use this package often for its main purpose of pulling down background map tiles, it's also useful for geocoding addresses with the Google Maps API with its geocode and mutate_geocode functions.
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