Reading in data

In your working folder there will be a file called ukgas.csv. UKgas is a dataset of the quarterly UK gas consumption from 1960 Q1 to 1986 Q4, in millions of therms. It is native to R as a built in dataset but we will learn how to read a file in.

UKgas is a dataset of the quarterly UK gas consumption from 1960 Q1 to 1986 Q4, in millions of therms. It is native to R as a built in dataset but we will learn how to read a file in.

In R set your working directory - where all your work will be saved

#R
setwd("C://FILE_PATH_TO_DATA_AND_RSCRIPTS")

Download ukgs.csv and save it to your working directory set above

Remember to comment your code e.g.

#this is a comment

or even create code sections e.g.

###################################
#NEW SECTION
###################################

look up the read.csv function in the help file to learn how to read a .csv data file into R (web manual page).

?read.csv

read the data in and assign the data a name e.g. file1

file1<-read.csv("ukgas.csv")

you will see the newly created file1 variable in your top right Environment window

Exploring the data

In your script, comment what the following commands do:

file1 # this displays all data in file1
head(file1) # this displays xxxxxxxxx
tail(file1) # this displays xxxxxxxxxxxxx
file1 [1,] # this displays xxxxxxxxxxx
file1[1:5,] # displays xxxxxxxxxxx
file1[,1] # this displays xxxxxxxxxxxxxxxxxx
file1[,1:5] # this displays xxxxxxxxxxxx
file1[,'year'] # this displays xxxxxx
file1 [,'qtr1'] # this displays xxxxxx

You can assign your selected data to a new variable name using <- or =

year <- file1[,'year'] 
qtr1 = file1[,'qtr1']

It is best practice to use <- to assign a value to a new variable x rather than = which implies x equals the value. = is typically used to denote arguments within functions.

Plotting data

In R open up the manual page for the plot function:

?plot

DataSHIELD > Explore data: the basics > plot.png

make a basic plot. This will automatically appear in your plot window in the bottom right quadrant of R Studio.

plot(x = year, y = qtr1)

use the type argument to change the plot to lines
search the manual for the par function - this allows you to set additional parameters in graphs. In the manual page search (using Ctrl F) for the word color. Find and implement the argument for plotting colour - make your plotting colour ‘red’.

Writing the data

printing to file requires opening a graphics driver (e.g. pdf, png, jpg), the plot to be defined, and then once you have finished printing to file this device driver needs closing.
Open up the manual page for the png function to find out how to apply the function and then write the plot to file.

?png 
 
png(file="plot1.png") # opens the png printing driver, output to appear in plot1.png
plot(x = year, y = qtr1, type = 'b', col = 'red') # this plot is printed
dev.off() #prin driver closed

A .png will appear in your working directory viewable in bottom right quadrant in R Studio

DataSHIELD > Explore data: the basics > plot1.png

A pdf output can be printed using:

pdf (file="plot1.pdf", h=7, w=12) 
#where h is height in inches and w is width in inches
plot(x = year, y = qtr1, type = 'l', col = 'red')
dev.off()

Beautifying plots

R plots in layers. You start with a base plot using the plot function and then can add layers of extra data, regression lines, legends, text equations etc on top of it using:

points()
text()
lines()
legend()
abline()
Open up the manual page for points to learn how to add additional data to a plot.
Use points to add qtr2 data points to the qtr1 graph. Remember you will need to assign your qtr2 data to a new variable before you can plot it.

#look up points in help file
?points
 
#assign qtr2 data to variable called qtr2
qtr2 = file1[,'qtr2']
 
#plot qtr 1
plot(x = year, y = qtr1, type = 'l', col = 'red')
 
#add qtr2 data
points(x = year, y = qtr2, type = 'l', col = 'black')

The arguments that can be applied in the points function are very similar to the plot function.

Open up the manual page for the legend function. Use the information to add a legend to the plot.

#look up points in help file
?legend


#assign qtr2 data to variable called qtr2
qtr2 = file1[,'qtr2']

#plot qtr 1
plot(x = year, y = qtr1, type = 'l', col = 'red')

#add qtr2 data
points(x = year, y = qtr2, type = 'l', col = 'black')
 
#add legend
legend(x = 'topleft', y = NULL, legend = c('qtr1', 'qtr2'), col = c('red', 'black'), lty = 1)

Add qtr3 and qtr4 data to the plot. Print the final plot to file.

You will see that qtr 3 plots data off the y axis. Use the min and max functions respectively to identify the min of qtr3 and max of qtr1. Use the ylim argument in plot() to set the min and max y axis as the example below (denoted by i and j, respectively).

plot(x = year, y = qtr1, type = 'l', col = 'red', ylim=c(i,j))
points(x = year, y = qtr2, type = 'l', col = 'black')
points(x = year, y = qtr3, type = 'l', col = 'blue')
points(x = year, y = qtr4, type = 'l', col = 'green')
legend(x = 'topleft', y = NULL, legend = c('qtr1', 'qtr2','qtr3', 'qtr4'), col = c('red', 'black', 'blue', 'green'), lty = 1)

You have now completed Exploring the data: the basics. Make sure you:

comment your script appropriately
save your script somewhere sensible
your script should be similar to the example answer script.

Extended R Practical: Analysing simulated ALSPAC data