computerworld-bi (english)

  1. R tip: Learn dplyr’s case_when() function

    In this second episode of Do More with R, Sharon Machlis, director of Editorial Data & Analytics at IDG Communications, shows how dplyr's case_when() function helps avoid a lot of nested ifelse statements
  2. 10 annoying things about Android P (that'll hopefully be fixed soon)

    All right, gang: The honeymoon's officially over. We've oohed. We've ahhed. We've talked about Android P's most noteworthy productivity features. Now it's time to step back, get real, and talk about some of the software's lessimpressive elements — because the truth is, for all of its positives, Android P has an awful lot of, well, awful stuff.

    Now, let's be clear: Perspective here is critical. This is only the first public beta of the Android P release, so these sorts of rough edges are absolutely to be expected. We'll hold onto hope that Google will iron out the kinks and get all these details fixed up and figured out by the time the final Android P software rolls around later this summer. Otherwise, we might find ourselves feeling a bit of Lollipop déjà vu.

    To read this article in full, please click here

  3. R tip: How to create easy interactive scatter plots with taucharts

    In this first episode of Do More with R, Sharon Machlis, director of Editorial Data & Analytics at IDG Communications, demonstrates how easy it is to use the R language to create an interactive scatter plot with multiple trendline choices
  4. Great R packages for data import, wrangling and visualization

    One of the great things about R is the thousands of packages users have written to solve specific problems in various disciplines -- analyzing everything from weather or financial data to the human genome -- not to mention analyzing computer security-breach data.

    Some tasks are common to almost all users, though, regardless of subject area: data import, data wrangling and data visualization. The table below show my favorite go-to packages for one of these three tasks (plus a few miscellaneous ones tossed in). The package names in the table are clickable if you want more information. To find out more about a package once you've installed it, type help(package = "packagename") in your R console (of course substituting the actual package name ).

    To read this article in full, please click here

  5. Useful R functions you might not know

    Almost every R user knows about popular packages like dplyr and ggplot2. But with 10,000+ packages on CRAN and yet more on GitHub, it's not always easy to unearth libraries with great R functions. One of the best way to find cool, new-to-you R code is to see what other useRs have discovered. So, I'm sharing a few of my discoveries -- and hope you'll share some of yours in return (contact info below).

    Choose a ColorBrewer palette from an interactive app. Need a color scheme for a map or app? ColorBrewer is well known as a source for pre-configured palettes, and the RColorBrewer package imports those into R. But it's not always easy to remember what's available. The tmaptools package's palette_explorer creates an interactive application that shows you the possibilities.

    To read this article in full, please click here

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