2. What is R?
“R is a free software environment for
statistical computing and graphics.”
www.r-project.org
3. Why R?
R takes time to learn and use.
So why should I bother?
There are more user-friendly
programmes, right?
4. 12 reasons to learn R
1. Rigour and strategy in data analysis – not
“thinking after clicking”.
2. Automatizing repeated calculations can
save time in the end.
3. A lot of stuff is simply not feasible in menu
driven software.
5. 12 reasons to learn R
4. R is not only about software, it is also an online
community of user support and collaboration.
5. Scripts make it easy to communicate about
your problem. Important for collaborative
research!
6. Research becomes replicable when scripts are
published.
6. 12 reasons to learn R
7. R packages represent state-of-the-art in
many academic fields.
8. Graphics in R are very good.
9. R stimulates learning – graduation from
user to developer.
7. 12 reasons to learn R
10. R is free, which saves you money. Or R may
be the only option when budgets are
restricted.
11. R encourages to freely explore new
methods and learn about them.
12. Knowing to work with R is a valuable and
transferable skill.
9. Resources to learn R
My two picks for you
http://pj.freefaculty.org/R/Rtips.html
10. Some R packages of interest
RNCEP – Reanalysis data
clim.pact – Downscaling climate data
GhcnDaily – daily weather data
weatherData (R-Forge) – Daily weather data
and derived bioclimatic variables relevant to
plant growth
raster – gives access to WorldClim data
11. And a lot more here...
http://cran.r-project.org/other-docs.html
13. Choose a mirror nearby and then...
Binaries
“When downloading, a completely functional
program without any installer is also often
called program binary, or binaries (as opposed
to the source code).”
(Wikipedia)
14. And finally,
you can download R...
When downloading has finished, run the installer
19. Exercises: running code
Type a second line with another calculation
(use “-”, “/”, or “*”) and click “Run” again.
Select only one line with the mouse or Shift +
arrows. Then click “Run”.
Save your first code to a separate folder
“Rexercises”.
20. Following exercises
In the next exercises, we will develop a script.
Just copy every new line and add it to your script,
without erasing the previous part.
If you want to make a comment in your script,
put a # before that line. Like this:
#important to remember: use # to comment
21. If the exercises are a bit silly...
...that’s because you are learning.
22. Vector
Type a new line with the expression
1:10
in the script and run this line.
A concatenation of values is called a vector.
23. Making a new variable
If we send 1:10 into the console it will only
print the outcome. To “store” this vector, we
need to do the following.
a <- 1:10
new variable “a” assign vector values 1 to 10
25. Other ways of making vectors
d <- c(1, 6, 9)
d
class(d)
f <- LETTERS
f
class(f)
What is the difference between d and f?
26. Functions
Actually, we have already seen functions!
Functions consist of a name followed by one
or more arguments. Commas and brackets
complete the expression.
class(f)
c(d,f)
name argument
27. Cheat sheet
When you use R, you will become familiar
with the most common functions.
If you need a less common function, there are
ways to discover the right one.
For now, use the cheat sheet to look up the
functions you need.
28. Getting help on functions
This will open help pages for the functions in your
browser.
?c
?class
Especially the examples are often helpful.
Just copy and paste the code into the console and
see with your own eyes what happens!
29. Matrices
We have already met the vector.
If we put two or more vector together as
columns, we get a matrix.
X <- c(1,2,3)
Y <- c(8,9,7)
Z <- c(4,2,8)
M <- cbind(X, Y, Z)
How many columns and rows does M have?
30. Data frames
Matrices must consist of values of the same class. But often
datasets consist of a mix of different types of variables (real
numbers and groups). This is the job of data frames.
L <- c(“a”, “b”, “c”)
Df <- data.frame(X,Y,Z,L)
Visualize Df like this:
str(Df)
What would happen if you tried to make a matrix out of
these same vectors instead? Try and see.
31. Getting data into R
?read.csv
CSV files are a relatively trouble-free way of
getting data into R.
It is a fairly common format.
You can make a CSV file in any spreadsheet
software.
32. Create a CSV file
First name Family name Sex Age
John Travolta Male 57
Elijah Wood Male 30
Nicole Kidman Female 44
Keira Knightley Female 26
Add your own favorite actor, too.
Open the file with Notepad.
Make sure the values are separated by commas.
33. Now use R to read it
Now read it into R.
actors <- read.csv(yourfile.csv)
str(actors)
34. Subsetting
There are many ways of selecting only part of a
data frame. Observe carefully what happens.
actors[1:2,]
actors[,1:2]
actors[“Age”]
actors[c(“Name”, “Age”)]
subset(actors, Age> 40)
Now create a new data frame with the actors
younger than 45.
