The Data Science Lab Program-Defined Functions in R The three most common open source technologies for writing data science programs are Python, SciLab, and R. Here's how to write program-defined ...

For setup, the code below loads several libraries I need and then uses base R’s list.files() function to return a sorted vector with names of all the files in my data directory.

R also contains a load of more sophisticated functions that let you do analyses with one or two commands: probability distributions, correlations, significance tests, regressions, ANOVA (analysis ...

For example: dplyr:: filter (mtcars, mpg > 30) Note the column name, mpg, is unquoted. That feature hasn’t been handy, though, if you want to write your own R functions using the tidyverse.

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.

Among my developer colleagues, the three most common ways to perform data science tasks with open source tools are using the R language, using the Python language, and using the SciLab (or roughly ...