R-Studio

Question 1

what can R be used for and and what is it very powerful in doing?

Answer 1

R is very powerful in analysing biological data efficiently, it can be used as a calculator, statistical modelling, to make graphs, for programming and much more

Question 2

what type of language is R?

Answer 2

R is an object-oriented programming language i.e. one creates objects, gives them names - names that can be used for statistical tests or to make graphs/tables

we use a user friendly version: R-Studio

Question 3

what do we do before we start processing data in R?

Answer 3

we must prepare our data in Excel before we export it to R

Question 4

what are the four quadrants in the R-Studio:

Answer 4

console - where you write the code
graphical window - where the graphs show up
bottom right - variables, objects & formulas

Question 5

how do you assign a value to a letter or word in order transform it into an object?

Answer 5

[name of object] <- [value to assign to object

Question 6

what are the operators in R-Studio?

Answer 6

+ = addition
* = multiplication
/ = division
^ = raised to the power
sqrt = square root

Question 7

how can we make R-Studio remember something?

Answer 7

through creating objects using the “<-“ method

Question 8

why do we need to keep an eye on capitalisation?

Answer 8

because R is case-sensitive

Question 9

how do we export our data from excel to R-Studio?

Answer 9

through creating a data frame using the following code:

beetle <- read.table(“location of excel file with desired data”, header = T, stringsAsFactors = T)

Question 10

state what the following line of code does and dissect and explain each component of it:

beetle <- read.table(“C:/teaching/stats/lecture3/beetle_behaviour.txt” , header = T, stringsAsFactors = T)

Answer 10

this line of code steals all of your data from an excel sheet and places it neatly into R-Studio

beetle <- = simply the name of the data frame

read.table(…) = command to read in data frame with location of excel sheet

header = T (short for header = true): first row contains variable names

stringsAsFactors = T converts text in a data table to categorical variable (,factor’)

Question 11

what do you always add at the end of your file name?

Answer 11

file name.txt

Question 12

what is the purpose of the lines “header = T” & “stringsAsFactors = T”?

Answer 12

they confirm to the graph that the heading and columns are to be read correctly - T meaning ‘true’ where “header = T” confirms that the top row is the names of the variables and “stringsAsFactors = T” confirms that the factors (data points) lie beneath the headers in columns

Question 13

what must you do once you have typed out your “read in data frame” line?

Answer 13

you must attach the data frame to the work space memory, i.e. make the data accessible through pressing enter after you’ve typed it to then, on the next line write: “attach(name of data frame)” which in this case would be: attach(beetle)

Question 14

what do hashtags mean in R-Studio?

Answer 14

in R-Studio hashtags are used to make comments and will have no effect on the actual code

Question 15

make the data accessible in R:

Answer 15

attach(name of data frame)

Question 16

once you have made your data accessible to R through entering attach(name of data-frame) what must you enter and what will that give you?

Answer 16

once the data-frame is accessible, you type names(beetle) which will give you an output containing all of the variable names

Question 17

once you have inputted your names(beetle) line to give you the name of all your variables, what must you now do and why?

Answer 17

immediately after names(beetle) you type head(beetle) which will automatically show the first rows of your data frame (the headings)

Question 18

first three steps following the uploading of your excel file and why each step is important:

Answer 18

[beetle is the name of data-frame]

attach(beetle): this allows for your data to become accessible to the code
names(beetle): this gives the names of the variables in our data-frame “beetle”
head(beetle): this only shows the first rows of our data frame

Question 19

how do you calculate the probability of a normal distribution in R?

Answer 19

using the command “pnorm”

Question 20

what does R not like when it comes to excel sheets of data?

Answer 20

1) it does not like empty cells, therefore you must delete them of type n/a

2) it does not like headings with spaces, therefore if your header has two words - separate them with an underscore

Question 21

get excel sheet ready for R:

Answer 21

(1) make sure that all measures/observations of one variable are in one column
(2) remove all spaces within your table and replace those by ‘_’
(3) try and give short and distinct variable names (as we may need to type them later)
(4) replace all empty cells, i.e. missing values, by ‘NA’

Question 22

how can you test for normal distributions in R using the shapiro-wilk test?

Answer 22

shapiro.test(variable name)

where you will be given a W & P value where W is your test statistic and P is your probability value

if your p value is larger than 0.05 then you can conclude that your data is of a normal distribution

Question 23

how can you get your critical value that is needed for comparison following the calculation of your chi-squared value in R-Studio?

Answer 23

qchisq(certainty [0.95], Degrees of Freedom)

> critical value

Question 24

how do you calculate chi-squared in R-Studio?

Answer 24

check through command: count

[#1. we add the data columnwise into a matrix, called ‘count’]

embryo <- matrix(c(38,14,11,51), nrow=2)

[#2. our chi-squared test]

chisq.test(embryo, correct = F)

answer - including: p-values, degrees of freedom (DF) etc

nrow = specified the number of rows our matrix should have
c = connect

Question 25

to understand and check the structure of your data-frame:

Answer 25

str(name of data-frame)

Question 26

how can you create a subset in R?

Answer 26

say we are only interested in the first and second column of our data frame, we write:

twocols<-genome[,1:2]

how many columns we want<-data-frame name[, 1st column:last column]

if we are only interested in the first three rows:

threerows <-genome[1:3,] threerows

Question 27

mean in R:

Answer 27

mean(variable name)

Question 28

median in R:

Answer 28

median(variable name)

Question 29

calculating variance in R:

Answer 29

tapply(variable name 1, variable name 2)

[can just do one variable if you wanted to though

Question 30

standard deviation in R:

Answer 30

sd(variable name)

Question 31

how can you use the shapiro-wilk test to see if your data is parametric (normally distributed) or non-parametric (non-normally-distributed)?

Answer 31

input: shapiro.test(variable)

this will give you a p-value, if your p-value is MORE than 0.05 this means that your data is normally distributed and therefore you accept the null hypothesis that the data is normally distributed

Question 32

calculating standard error in R:

Answer 32

SE<-function(x)sqrt(var(x)/length(x))

where “x” are your to-be plugged in values

then we use our new object “SE” as a command where: SE(variable name)

Question 33

what should you do if the following error message shows up: “Error in plot.new() : figure margins too large”?

Answer 33

move your mouse over the borders of the ‘Plots’ pane, i.e. the graphical window, until your cursor turns into a symbol consisting of four arrows; if you now press the mouse button, you can make the window larger; you can then re-send the above command and the graph should show)

Question 34

how do we change the names of graphical axis in R-Studio?

Answer 34

after your plot(y variable~x variable) you add a comma before closing the brackets and write:

las =1, ylab = “name of new y axis”, xlab = “name of new x axis”)

Question 35

[las = 1] means:

Answer 35

command for axis labelling, means that the axes labels are always horizontally written

Question 36

how do we change the names of our graph axis?

Answer 36

[plot(command, las = 1, ylab = “new y name”, xlab = “new x name”]

Question 37

if we have two levels factors on our x-axis (e.g. two box-and-whisker plots), how can we rename them?

Answer 37

to rename these pars of the graph you must (after your xlab = “new name” command for a new axis label) write names=c(“first new name” ”second new name ”)

Question 38

if the data table is very small, how can you plot the data into R manually?

Answer 38

temperature <- c(data-point-1, data-point-2, etc) activity<- c(data-point-1, data-point-2, etc)

Question 39

how can you change the range of the y-axis in R?

Answer 39

ylim=c(0,6),

in the above instance, this would make your data on the y axis now range from 0 → 6

Question 40

how can you change the size of your axis labels and values in R, where the default value is 1:

Answer 40

changing size of axis labels - cex.lab = 1.5, (0.5 bigger)

changing size of axis values - cex.axis = 1.5, (0.5 bigger)

Question 41

how can you change the appearance of your data-point symbols?

Answer 41

fill in the circle = …,pch=19)

colour the circle = …, col=“red” [if you want red]

Question 42

how must you separate every single command when writing out a line of code in R?

Answer 42

you must separate each one using a comma

Question 43

if you are done and happy with your graph, how can you export it to another programme?

Answer 43

(1) go to the tab “plots”

(2) export

(3) copy to clipboard

[you can also save your plot]

Question 44

how can we clean up after ourselves by removing all the objects we have created?

Answer 44

we can do this through pressing the little broomstick in the top right corner of the environment pane

Question 45

full and complete command needed to have a linear regression in R:

Answer 45

(1) data<-read.csv(“excel_sheet1.csv”, header = T, stringsAsFactor = T)

(2) attach(data)

(3) names(data)

(4) m1<-lm(y variable~x variable)
#”m1” is simply your model name

(5) summary.lm(m1)

(6) summary.aov(m1)

(7) plot(m1)

Question 46

complete linear regression R command:

Answer 46

(1) read.csv
(2) attach(file name)
(3) names(file name)
(4) m1<-lm(growth~tannin) #m1 is the name of your regression
(5) summary.lm(m1)
(6) summary.aov(m1)
(7) plot(m1)

(8) plot(y-variable~x-variable, pch=19, las=1)
(9) abline(lm(y-variable~x-variable)

Question 47

what singular command gives you the information to see the two mini graphs in linear regression that allow you to confirm parametric distributions?

Answer 47

plot(m1) #must be following certain previous commands

[gives one graph that must be “sky at night” and another graph which must have the data-symbols on or v.close to the line]

Question 48

how can you command R to give the pearsons correlation?

Answer 48

after attaching your data-frame and variable names(x):

cor.test(variable_1, variable_2, method = “pearson”)

note: doesn’t matter what way around your variables are - answer will be the same either way

Question 49

what do we receive from a pearsons cor.test command and how do you infer it?

Answer 49

you will get a p-value and a test statistic found underneath “cor” at the bottom of the output which is our correlation coefficient

(1) if the p value is smaller than <0.05 then we can assume that the two variables are correlated

(2) if the cor value if positive it means there is a positive correlation, if the cor value is negative it means there is a negative correlation

Question 50

how can you use R to calculate your spearman’s rank values?

Answer 50

we, once again use:

cor.test(variable one, variable two, method = “spearman”)

Question 51

χ²-tests enable us to judge whether:

Answer 51

the observed frequencies differ from the frequencies expected if the two variables were not associated

Question 52

what is the first thing you must do before conducting a chi-squared test in R?

Answer 52

you must first create a matrix for your data via command:

> matrix-name<- matrix(c(20,40,30,30,42,18), nrow = 2