These notes have hardly scratched the surface of R, which has many more statistical functions. These include functions to calculate the density, cdf, and inverse cdf of distributions such as chi-squared, t, F, lognormal, logistic and others.
survival library includes methods for the estimation
of survival curves, tests of differences between survival curves, and
Cox proportional hazards models. The library
code for fitting generalized linear mixed effect models, including
multilevel models. Many new statistical procedures are first made
available to the research community in the form of R functions.
To produce really nice graphs consider installing the
ggplot2 package. To draw a plot you specify a data frame,
aesthetics that map variables to aspects of the graph, and geometries
that specify whether to use points, lines, or other primitives. You fill
find more information at https://ggplot2.tidyverse.org/
For data management I recommend that you install the
dplyr package, which includes tools for adding new
variables, selecting cases or variables (rows or columns), as well as
summarizing and re-arranging your data. Check the overview at https://dplyr.tidyverse.org/.
You can also run
install all the packages in the tidyverse, including
dplyr, as well as
tidyr (for help tidying data),
reading rectangular data like csv files),
purrr (for an
alternative to loops),
tibble (for tidy data frames),
stringr (for working with strings) and
(for working with factors). Learn more at https://www.tidyverse.org/packages/.
In addition, R is a full-fledged programming language, with a rich complement of mathematical functions, matrix operations and control structures. It is very easy to write your own functions. To learn more about programming R, I recommend Wickham (2019)’s Advanced R book.
R is an interpreted language but it is reasonably fast, particularly if you take advantage of the fact that operations are vectorized, and try to avoid looping. Where efficiency is crucial you can always write a function in a compiled language such as C++ and then call it from R. Some of my work on multilevel generalized linear models used this approach.
Last, but most certainly not least, you will want to learn about dynamic documents using R Markdown. The basic idea here is to combine a narrative written in Markdown with R code, an approach that has excellent support in R Studio. The definite book on the subject is Xie, Allaire, and Grolemund (2019).
This tutorial has been written in R Markdown. You can download the
introducingR.Rmd, the bibliography file
introducingR.bib, and the image file
RStudioIDE.png from GitHub.
To reproduce the PDF document you also need
the same source. To generate an HTML document instead, change the output
specification near the top of the script.