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.

This online data science specialization is designed for anyone interested in learning how to program in R. You will learn the basics of R, including imputing data, performing basic analysis, graphing, ...

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 ...