Welcome to a new challenge - an empty green meadow is waiting to be explored and developed!
R is a pretty strange programming language, it does not follow the usual conventions of a programming language and is not intuitive, or at least not in the beginning. I am familiar with quite a few programming languages, however R is nothing like them (in which other programming language do you prefer the assignment operator "<-" over a "="??). However as R is currently considered the statistics reference programm (even more popular than SPSS) and in addition it is open source, it is worth looking at it.
Here are some tips if you think about starting to learn R:
- Install R first, play around with the console. You will find out that it is not very handy. E.g. R does not use line delimiter (";", ".") after statements, R only permits a maximum line length is 80 characters, ...
- After you found out, that R is quite strange, get the RStudio (e.g. here). The RStudio is really helpful, it provides short cuts for the most used commands, a simple structure and a pretty nice user interface.
- Make yourself familiar with Google's R Style Guide, so you do make a fool out of yourself chatting with experts. Also you learn a lot of R's specialities (so far I considered "." a bad choice for a letter in identifyiers as you expect it to point to a subattribute, however in R it is accepted and "_" is the bad choice...)
- Remember command "rm(list = ls())" which is used to clear the current workspace (yes, there is a current workspace in which locally created variables live!)
- Look for free online courses (there are tons of them)
- Get familiar with the shortcuts "ALT + -" and "Strg + L"
- Find information on available packages on the sites of CRAN, have a look especially on this crantastic page that allows you to search for (popular) packages
- Use the predefined data sets in R (see "data()" for an overview), they will often be used in examples and it feels good to already know them
- Use command "View(..)" regularily on your data to get a clean picture of it
- When you use "require("packageName")", remember to use "detach("package:packageName", unload = TRUE)" at the end (use those commands together in RStudio)
- use command demo() to get an overview on the demos included in R. To execute a demos use the same command with one of the given arguments (e.g. demo(colors)).
- use fix() on a data frame to correct manually
- use transform() and the powerful (s)apply() on your data frames
This post is being updated regularily.
0 Comment