Running R code

The R interpreter is controlled by typing in plain-text commands at its command-line prompt >|. R commands are just text, so you can prepare a chunk of them in a text-editor, and then copy-and-paste them directly into R to run them immediately. Try copying and pasting the line below at the prompt:

print("Hello world")

Lo and behold, R will spit out what you knew it would:

[1] "Hello world"

R comes with its own simple text-editor, which you can open using the “File…New script” menu option. You can edit R code in this box, execute everything that is currently in the editor by pressing Ctrl-A and then Ctrl-R, and save the code using “File…Save as” for later reloading and use with “File…Open script”. Avoid preparing R scripts in Word: by default, Word ‘corrects’ quote characters (“…”) to smart-quotes (“…”) which R does not understand.

Even more critically, avoid copying-and-pasting chunks of R code you do not understand: blindly shovelling data into a black-box and assuming the output is correct and meaningful will eventually lead to embarrassing catastrophe.

If you enter data into R directly by typing at the >| prompt, R will just spit it back out at you, preceded by a numeric label [1].

42
[1] 42

To store a number for later use, you need to assign it to a variable, such as a, with the <-assignment operator (“gets-arrow“):

a<-42

R doesn’t output anything here, but if you want to see the value you have stored in the variable named a, you can print() it:

print( a )
[1] 42

You can also just type:

a
[1] 42

with the same effect. Variable names should be given meaningful names (which a probably isn’t). They must start with a letter, and should contain only letters, numb3r5, full.stops and under_scores. They are case-sensitive, so the variable nubbin is different from the variable Nubbin or the variable NUBBIN.
In R, it is very common to want to handle vectors, which are ordered lists of numbers (or of other kinds of value) stored in a variable. You can create these using the c() function (‘c’ for concatenate):

radii<-c( 1, 1.2, 3, 3.6, 7, 9 )

# The white-space between the listed items is optional, but aids readability.
# These lines starting with a hash '#' are comments. They are ignored by R
# but can be useful to explain to yourself and others what the code is doing.
# Blank lines are also ignored.

radii
[1] 1.0 1.2 3.0 3.6 7.0 9.0

Longer vectors will be output over multiple lines, with the numerical labels indicating the index of the first item on that row. Vectors are one example of a number of different kinds of object which R can use to store different kinds of data. We’ll see data frame objects later. The usefulness of vectors becomes obvious when you find that R has functions like mean(), max() and min():

mean( radii )
[1] 4.133333
max( radii )
[1] 9
min( radii )
[1] 1

You can extract an item from a vector using square brackets to index into the vector like this:

fifth.item<-radii[5]
print( fifth.item )
[1] 7

You can create simple vectors of integers from one number to another using the colon (:) syntax.

one.to.ten<-c(1:10)
print(one.to.ten)
 [1]  1  2  3  4  5  6  7  8  9 10

R can perform mathematical calculations on numbers and objects:

a+3
[1] 45

If you perform a calculation on a single vector, the calculation is applied individually to each item in the vector, and the resulting values can be captured into a new vector like this:

areas<-pi*(radii^2)
areas
[1]   3.141593   4.523893  28.274334  40.715041 153.938040 254.469005

The usual mathematical operators (+, -, *, /, ^), shorthand for scientific notation (1E6) and functions (sin(), sqrt(), exp(), log()) will do what you expect. Note that log() returns natural log, not base-10 log.
As you create vectors and other kinds of object, you will begin to fill up R’s namespace with variable names. To see what you currently have in memory, use:

ls()
 [1] "asellus.gills"      "dog.whelks"         "enzyme.kinetics"   
 [4] "fly.agarics"        "main"               "radii"             
 [7] "reaction.rate"      "students"           "sycamore.seeds"

To delete moribund objects use:

rm( students )

To delete everything, use:

rm( list = ls() )

It is easy to find help on R from its own HTML documentation. If you know the name of the function you want help on, you can use either of these:

help( "t.test" )
?t.test

If you don’t know the name of the function, you can search for a term in the documentation with:

help.search( "chi squared" )

The result of this search contains a link to the documentation for stats::chisq.test(), amongst other things.

When you make a mistake in R, it will tell you so:

2+lg( 3 )
Error: could not find function "lg"

It can be frustrating to find that this is due to a simple typo, particularly if this is a long line of code. However, it is easy to recall, edit and replay the last command you typed: press the ↑ arrow key to scroll through the last command(s) you typed, use the ← and → arrow keys to move to the place you want to edit, edit the text, and then press the “Enter” key to re-run the command.

Exercises

  1. The molar absorbance coefficient (ϵ) of riboflavin at 440 nm is 54×103 L mol−1 cm−1. In a cuvette with a path-length (l) of 1 cm, what is the absorbance (A) of a 10 µM solution? A = ϵ C l. Resist the temptation to do this with a calculator: use R as your calculator.
  2. What is the volume (V) of an Escherichia coli cell, in cubic micrometres, if you model it as a cylinder of height (h) 2 µm and radius (r) 0.25 µm? V = π r2 h.
  3. In the data below of A260 values from a protein-estimation practical, what is the largest value? The smallest value? The mean value? The median value? Create a vector called A260.sorted containing the values in ascending order so you can manually check these results. What is the thirteenth highest value? You can copy-and-paste the data below into R directly (and in so doing, you may note something helpful).
0.457, 0.314, 0.298, 0.284, 0.298, 0.42, 0.266, 0.285, 0.31, 0.288, 0.312,
0.284, 0.31, 0.255, 0.297, 0.293, 0.274, 0.253, 0.331, 0.243, 0.314, 0.269,
0.711, 0.46, 0.23, 0.314, 0.336, 0.255, 0.307, 0.243, 0.42, 0.302, 0.46,
0.297, 0.284, 0.283, 0.282, 0.231, 0.266, 0.228, 0.228, 0.402, 0.282,
0.312, 0.26, 0.247, 0.283, 0.288, 0.302, 0.252, 0.902, 0.336, 0.247,
0.231, 0.261, 0.283, 0.307, 0.457, 0.274, 0.288, 0.288, 0.461, 0.293,
0.314, 0.404
  1. What do seq(0,10,2), rep(10,3) and A260[A260>0.5] do?
  2. The mass of wood in a pine-tree of girth (circumference at ground-level) c, and of height h can be roughly approximated by considering the pine tree as a cone of volume V =c2 h / 12 π , and of the same density as water (i.e. 1 t m−3). What are the masses of the following trees? We haven’t covered the syntax for this explicitly, but see if you can work it out. R is a lot more intuitive than you might think.
Girth / m Height / m
1 10
3.5 18
1.8 25
9 50
  1. What is the sum of all the numbers from 1 to 100?

Answers

  1. Absorbance of riboflavin solution:
54E3*10E-6
[1] 0.54
  1. Volume of an Escherichia coli cell:
pi*0.25^2*2
[1] 0.3926991
  1. A260 values:
A260<-c(
0.457, 0.314, 0.298, 0.284, 0.298, 0.42, 0.266, 0.285, 0.31, 0.288, 0.312,
0.284, 0.31, 0.255, 0.297, 0.293, 0.274, 0.253, 0.331, 0.243, 0.314, 0.269,
0.711, 0.46, 0.23, 0.314, 0.336, 0.255, 0.307, 0.243, 0.42, 0.302, 0.46,
0.297, 0.284, 0.283, 0.282, 0.231, 0.266, 0.228, 0.228, 0.402, 0.282,
0.312, 0.26, 0.247, 0.283, 0.288, 0.302, 0.252, 0.902, 0.336, 0.247,
0.231, 0.261, 0.283, 0.307, 0.457, 0.274, 0.288, 0.288, 0.461, 0.293,
0.314, 0.404
)
# You'll notice you can paste this line-by-line: as R knows the expression
# isn't finished until you close the ')' parenthesis at the end, it will
# prompt you with a '+' rather than a '>' until you have entered the ')'
# These lines with hash '#' signs are also valid R syntax: R ignores
# lines starting with '#', and treats them as comments

max(A260)
[1] 0.902
min(A260)
[1] 0.228
mean(A260)
[1] 0.3194769
median(A260)
[1] 0.288
A260.sorted<-sort(A260)
A260.sorted[13]
[1] 0.255
print(A260.sorted)
[1] 0.228 0.228 0.230 0.231 0.231 0.243 0.243 0.247 0.247 0.252 0.253 0.255
[13] 0.255 0.260 0.261 0.266 0.266 0.269 0.274 0.274 0.282 0.282 0.283 0.283
[25] 0.283 0.284 0.284 0.284 0.285 0.288 0.288 0.288 0.288 0.293 0.293 0.297
[37] 0.297 0.298 0.298 0.302 0.302 0.307 0.307 0.310 0.310 0.312 0.312 0.314
[49] 0.314 0.314 0.314 0.331 0.336 0.336 0.402 0.404 0.420 0.420 0.457 0.457
[61] 0.460 0.460 0.461 0.711 0.902
  1. Useful vector functions
# Construct a vector containing integers from 0 to 10, but in steps of 2
seq(0,10,2)
[1]  0  2  4  6  8 10
# Construct a vector containing three repeats of the number 10
rep(10,3)
[1] 10 10 10
# Return just the numbers in a vector greater than some value
A260[A260>0.5]
[1] 0.711 0.902
  1. Mass of wood in pine-trees:
c<-c(1, 3.5, 1.8, 9)
h<-c(10, 18, 25, 50)
M<-1*c^2*h/(12*pi)
# Multiplication of two vectors of the same length in R results in
# a new vector of the same length containing the item-wise products.
# R can do 'vector multiplication' in the mathematical sense too (dot/cross)
# but using a different syntax
print(M)
[1]   0.2652582   5.8489442   2.1485917 107.4295866
  1. Sum from 1 to 100
sum(c(1:100))
[1] 5050

Next up…Kinds of data.

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