computerworld-bi (english)

  1. These R packages import sports, weather, stock data and more

    There are lots of good reasons you might want to analyze public data, from detecting salary trends in government data to uncovering insights about a potential investment (or your favorite sports team).

    But before you can run analyses and visualize trends, you need to have the data. The packages listed below make it easy to find economic, sports, weather, political and other publicly available data and import it directly into R -- in a format that's ready for you to work your analytics magic.

    Packages that are on CRAN can be installed on your system by using the R command install.packages("packageName") -- you only need to run this once. GitHub packages are best installed with the devtools package -- install that once with install.packages("devtools") and then use that to install packages from GitHub using the format devtools::install_github("repositoryName/packageName"). Once installed, you can load a package into your working session once each session using the format library("packageName").

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  2. 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 ).

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  3. Why Elon Musk says 50% of all cars will be electric by 2027

    It’s time to hit the reset button on the gas engine. As you may already know, the electric car is now much more viable than it was 10 years ago—there are charging stations in every major city scattered everywhere, particularly at hotels and along major highways. One glance at just the Tesla supercharger network of 900 stations proves that point.

    Yet, to reach the point where more than half of all new cars are fully electric by 2027—as Elon Musk predicted recently—there needs to be a massive undertaking that only the enterprise can understand. It is not a consumer endeavor but one that must be backed by IT, similar to an ERP roll-out or a massive Windows deployment.

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  4. R Consortium survey seeks input from R users

  5. Better question, but even harder to answer

    It's the mid-1990s, and this programmer pilot fish has just been hired as a manager at a company where most employees are still learning how to use their new desktop computers.

    "At best, my boss's knowledge of how to use a PC was limited to e-mail and web browsing," fish says. "My prior experience as a data analyst and Cobol programmer gave me an edge when it came to computers over all my new peers.

    "One day, my boss asked me if I could create a few revenue charts for him. I replied that it wouldn't be a problem. He asked me how long it would take. I estimated it would take a couple of days.

    "'A couple of days!' he cried out. 'Why so long? Can't you just push a button?'

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