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Explore data: the basics

Explore data: the basics

07 Nov, 2018

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 and save it to your working directory set above

 

 

Tip: Comments

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

 

<- or =

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

?plot

  • 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

Tip: Printing a .pdf

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')

Tip: points()

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. 

Tip: Full plot

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)

 

 

info: Answer script

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.

Next section

Extended R Practical: Analysing simulated ALSPAC data