R Basics  Day 1  afternoon
Introduction to R and RStudio

R is a language and environment for statistical computing and graphics.

Extracted from http://nicercode.github.io/intro/
Writing code is fast becoming a key  if not the most important  skill for doing research in the 21st century. As scientists, we live in extraordinary times. The amount of data (information) available to us is increasingly exponentially, allowing for rapid advances in our understanding of the world around us.
The amount of information contained in a standard scientific paper also seems to be on the rise. Researchers therefore need to be able to handle ever larger amounts of data to ask novel questions and get papers published.
Yet, the standard tools used by many biologists  point and click programs for manipulating data, doing stats and making plots  do not allow us to scaleup our analyses to match data availability, at least not without many, many more ‘clicks’.
The following material was also based on their introduction to R.

Install R for windows

Install R for Linux (ubuntu)
sudo addaptrepository "deb http://cran.stat.ucla.edu/bin/linux/ubuntu precise/" sudo aptget update sudo aptget install rbase

Install RStudio
Getting started with RStudio

Load R studio however you do that on your platform.

RStudio has four panes:

Bottom left – this is the R interpreter. If you type code here, it is “evaluated” so that you get an answer.

Top left – editor for writing longer pieces of code.

Top right will tell you things about objects in the workspace. We’ll get to this soon, but this will be things like data objects, or functions that will process them. It is completely unrelated to the file system. The “History” tab will keep an eye on what you’ve done.

Will display files, plots, packages, and help information, usually as needed.

Using R as calculator

Enter the following in the bottom left
1 + 100 [1] 101

Lets try something more complex
3 + 5 * 2 ^ 2 [1] 23 (3 + 5) * 2 [1] 16
See ?Arithmetic for more information, you can also get there by ?“+” (note the quotes)

The usual sort of comparison operators are available:
1 == 1 # equality (note two equals signs, read as "is equal to") [1] TRUE 1 != 2 # inequality (read as "is not equal to") [1] TRUE 1 < 2 # less than [1] TRUE 1 <= 1 # less than or equal to [1] TRUE 1 > 0 # greater than [1] TRUE 1 >= 9 # greater than or equal to [1] TRUE
See ?Comparison for more information (you can also get there by help(“==”).

Really small numbers get a scientific notation:
2/10000 [1] 2e04
Read eXX as “multiplied by 10^XX”, so 2e4 is 2 * 10^(4).
Mathematical functions

R has many built in mathematical functions that will work as you would expect:
sin(1) # trig functions [1] 0.8415 asin(1) # inverse sin (also for cos and tan) [1] 1.571 log(1) # natural logarithm [1] 0 log10(10) # base10 logarithm [1] 1 log2(100) # base2 logarithm [1] 6.644 exp(0.5) # e^(1/2) [1] 1.649
Variables and assignment

You can assign values to variables using the assignment operator <, like this:
x < 1/40

And now the variable x contains the value 0.025:
x [1] 0.025

note that it does not contain the fraction 1/40, it contains a decimal approximation of this fraction. This appears exact in this case, but it is not. These decimal approximations are called “floating point numbers” and at some point you will probably end up having to learn more about them than you’d like.

Look up at the top right pane of RStudio, and you’ll see that this has appeared in the “Workspace” pane. Our variable x can be used in place of a number in any calculation that expects a number.
log(x) [1] 3.689 sin(x) [1] 0.025

The right hand side of the assignment can be any valid R expression.

Notice that assignment does not print a value.
x < 100

Notice also that variables can be reassigned (x used to contain the value 0.025 and and now it has the value 100). Assignment values can contain the variable being assigned to: What will x contain after running this?
x < x + 1

The right hand side is fully evaluated before the assignment occurs.

Variable names can contain letters, numbers, underscores and periods. The cannot start with a number. They cannot contain spaces at all. Different people use different conventions for long varaible names, these include
 periods.between.words
 underscores_between_words
 camelCaseToSeparateWords
What you use is up to you, but be consistent.
Exercise 1
Compute the difference in years between now and the year that you started at University. Divide this by the difference between now and the year when you were born. Multiply this by 100 to get the percentage of your life spent at university. Use parentheses if you need them, use assignment if you need it.
This problem is as much about thinking about formalising the ingredients of a problem as much as actually getting the syntax correct.
Vectors

R was designed for people who do data analysis and generally you have more than one piece of data. As a result in R all data types are actually vectors. So the number ‘1’ is actually a vector of numbers that happens to be of length 1.
1 [1] 1 length(1) [1] 1

To build a vector, use the c function (c stands for “concatenate”).
x < c(1, 2, 40, 1234)

We have assigned this vector to the variable x.
x [1] 1 2 40 1234 length(x) [1] 4

Notice how RStudio has updated its description of x. If you click it, you’ll get an option to alter it, which is rarely what you want to do.

This is a deep piece of engineering in the design; most of R thinks quite happily in terms of vectors. If you wanted to double all the values in the vector, just multiply it by 2:
2 * x [1] 2 4 80 2468
Vector functions

You can get the maximum value…
max(x) [1] 1234

…minimum value…
min(x) [1] 1

…sum…
sum(x) [1] 1277

…mean value…
mean(x) [1] 319.2

…and so on. There are huge numbers of functions that operate on vectors. It is more common that functions will than that they won’t.

Vectors can be summed together:
y < c(0.1, 0.2, 0.3, 0.4) x + y [1] 1.1 2.2 40.3 1234.4

And they can be concatenated together:
c(x, y) [1] 1.0 2.0 40.0 1234.0 0.1 0.2 0.3 0.4

and scalars can be added to them
x + 0.1 [1] 1.1 2.1 40.1 1234.1

Be careful though: if you add/multiply together vectors that are of different lengths, but the lengths factor, R will silently “recycle” the length of the shorter one:
x [1] 1 2 40 1234 x * c(2, 2) [1] 2 4 80 2468
(note how the first and third element have been multiplied by 2 while the second and fourth element are multiplied by 2).

If the lengths do not factor (i.e., the length of the shorter vector is not a factor of the length of the longer vector) you will get a warning, but the calculation will happen anyway:
x * c(2, 0, 2) Warning: longer object length is not a multiple of shorter object length [1] 2 0 80 2468

This is almost never what you want. Pay attention to warnings. Note that Warnings are different to Errors. We just saw a warning, where what happened is (probably) undesirable but not fatal. You’ll get Errors where what happened has been deemed unrecoverable. For example
x + z # fails because there is no variable z Error: object 'z' not found

Just as with the scalars, as well as doing arithmetic operators we can do comparisons. This returns a new vector of TRUE and FALSE indicating which elements are less than 10:
x < 10 [1] TRUE TRUE FALSE FALSE

You can do vectorvector comparisons too:
x < y # all false as y is quite small. [1] FALSE FALSE FALSE FALSE

And combined arithmetic operations with comparison operations. Both sides of the expression are fully evaluated before the comparison takes place.
x > 1/y [1] FALSE FALSE TRUE TRUE

Be careful with comparisons: This compares the first element with 20, the second with 20, the third with 20 and the fourth with 20.
x >= c(20, 20) [1] TRUE FALSE TRUE TRUE

This does nothing sensible, really, and warns you again:
x == c(2, 0, 2) Warning: longer object length is not a multiple of shorter object length [1] FALSE FALSE FALSE FALSE

All the comparison operators work in fairly predictable ways:
x == 40 [1] FALSE FALSE TRUE FALSE x != 2 [1] TRUE FALSE TRUE TRUE

Sequences are easy to make, and often useful. Integer sequences can be made with the colon operator:
3:10 # sequence 3, 4, ..., 10 [1] 3 4 5 6 7 8 9 10

Which also works backwards
10:3 # the reverse [1] 10 9 8 7 6 5 4 3

Step in different sizes
seq(3, 10, by=2) [1] 3 5 7 9 seq(3, 10, length=10) [1] 3.000 3.778 4.556 5.333 6.111 6.889 7.667 8.444 9.222 10.000

Now we will see the meaning of the [1] term – this indicates that you’re looking at the first element of a vector. If you make a really long vector, you’ll see new numbers:
seq(3, by=2, length=100) [1] 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 [18] 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 [35] 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 [52] 105 107 109 111 113 115 117 119 121 123 125 127 129 131 133 135 137 [69] 139 141 143 145 147 149 151 153 155 157 159 161 163 165 167 169 171 [86] 173 175 177 179 181 183 185 187 189 191 193 195 197 199 201
Exercise 2
One thing you can do with sequences is you can very informally look at convergent sequences. For example, the sum of squares of the reciprocals of integers:
$$\frac{1}{1} + \frac{1}{4} + \frac{1}{9} + \frac{1}{16}$$
$$\frac{1}{1} + \frac{1}{2^2} + \frac{1}{3^2} + \frac{1}{4^2} + \cdots + \frac{1}{n^2}$$

What is the sum of the first four squares?

What is the sum of the first 100?

…of the first 10,000?

If x is the answer to 3, what is the square root of 6x?
A possible solution
1 + 1/4 + 1/9 + 1/16 # starting to get tedious to type
[1] 1.424
squares < (1:100)^2
sum(1/squares)
[1] 1.635
sum(1 / (1:10000)^2)
[1] 1.645
x < sum(1 / (1:10000)^2)
sqrt(x * 6)
[1] 3.141
Data frames
Let us remember the power law
$$L(r)=A r^\eta$$

then
A < 0.1 r < (1:100) L < A*r^eta

oops error
eta < 1.1 L < A*r^eta

we can do a plot of the data
plot(r,L)

Or using R’s “formulae”
plot(log(L)~log(r))
Read this as “log(L) is a function of log(r)”.

we can put all together in a data frame
pl < data.frame(r,L,logL=log(L),logr=log(r))
The pl variable contains a data.frame object. It is a number of columns of the same length, arranged like a matrix or table.
head(pl) # first rows of the data frame names(pl) # the names of columns nrow(pl) # the number of rows ncol(pl) # the number of columns pl$L # obtain one column

we can do a regression to obtain the exponent of our power law
lm(logL~logr,data=pl)

“lm” stands for linear model and is a general function for doing all kinds of linear statistica models.
Exercise 3
First delete all variables to start over
rm(list=ls())
Generate a data.frame with different parameters for the power law and 1000 points.

A possible answer
pl < data.frame(r=seq(1,100,0.1)) pl$L< 0.4*pl$r^1.3 pl$logL <log(pl$L) pl$logr <log(pl$r) lm(logL~log(r),data=pl)
Writing functions

Writing functions is one of the most important steps to use all the power of R.

Remember the power law of cumulative biomass in lake Constanz, we have to calculate $L(>r)$
pl$r >=1 pl$r >=2 pl[pl$r >=2,] # select the rows with the column r>2 pl[pl$r >=99,] pl[pl$r >=99,]$L # Get only the column that we are interested sum(pl[pl$r >=99,]$L)

We can define a function to calculate this using a variable
cumPowerL < function(x) { sum(pl[pl$r >=x,]$L) } cumPowerL(99) cumPowerL(1) cumPowerL(pl$r)

If we want to apply the function to a vector we have to use:
sapply(pl$r,cumPowerL)
we can add a variable to our data.frame
pl$cumL < sapply(pl$r,cumPowerL) plot(log(cumL)~logr,data=pl)

It seems that the power law is defined for $logr<3$
pl1 < pl[pl$logr<3,] lm(log(cumL)~logr,data=pl1) lm(log(cumL)~logr,data=pl) summary(lm(log(cumL)~logr,data=pl1))

One way to not type the same (or copy) commands is assign a variable
lm1 < lm(log(cumL)~logr,data=pl1) lm0 < lm(log(cumL)~logr,data=pl) summary(lm1) summary(lm0) abline(lm1,col="red") abline(lm0,col="blue")

We previously said that if we have a power law
$$L(r) \propto r^{\eta}$$
the cumulative distribution should be
$$L(>r) \propto r^{\eta+1}$$
but here this result is only approximate.
Exercise 4
 Fit the cumulative power law to the first 50 values in the data frame