Your first R script

Now you have created the fileMyFirstScript.Ryou should put some header text at the start of the file to explain what the R script will do. This was described in tutorial 1.

(opens in a new window)Video Tutorial:Creating a new R script with RStudio (1 min)

The text should have a short explanation of the R script followed by your name and the date you wrote the R script. Each line should start with a#so that the text is not interpreted by R (this text is for humans so they understand what the file is intended to do). Here is an example,

# ********** Start of header **************
# Title: <The title of your R script> 
#
# Add a short description of the R script here.
#
# Author: <your name>  (email address)
# Date: <today's date>
#
# *********** End of header ****************

# Two common commands at the start of an R script are:
rm(list=ls())         # Clear R's memory

setwd('~/DataModule') # Set the working directory 
# Replace '~/DataModule' with the name of your own directory

# ******************************************
# Write your commands below. 
# Remember to use comments to explain your commands

Writing clear R scripts

An R script isn’t just telling the computer how to perform calculations on your data. It is also explaining your working to other human beings.

“Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.”– Donald E. Knuth

To make your R scripts usable by humans they must be clearlycommented(using the#symbol to start a comment) and clearly organised.

As you write an R script consider these questions:

• Does your R script look well organised (e.g. is it well spaced, are lines indented logically)?
• Could someone else read the R script and understand the basic idea?
• Could someone else modify your R script relatively easily?
• In a couple of months time could youquicklyread and edit your own R script?

Professional data analysts take clarity very seriously. Here are some links to R coding style guides:

1. Google’s style guide,(opens in a new window)https://google.github.io/styleguide/Rguide.xml
2. Hadley Wickham’s style guide,(opens in a new window)http://adv-r.had.co.nz/Style.html
3. (opens in a new window)http://www.stat.ubc.ca/~jenny/STAT545A/block19_codeFormattingOrganization.html
4. (opens in a new window)http://nicercode.github.io/blog/2013-04-05-why-nice-code/

Six guidelines for tidy data

When tidying your data you should ensure that:

1. each variable has its own column
2. each row is an observation
3. the top of each column contains the name of the variable
4. there are no blank columns or blank rows between data
5. all data in a column has the same type (e.g. it is all numerical data, or it is all text data)
6. data are consistent (e.g. if a binary variable can take values ‘Yes’ or ‘No’ then only these two values are allowed, with no alternatives such as ‘Y’ and ‘N’)

PDF Summary:This PDF document reiterates the concept of tidy data

The link to the PDF is:http://www.ucd.ie/ecomodel/pdf/TidyData.pdf

Poorly vs well formatted data

The data set shown in the figure below are an example of poorly formatted data. The data set contains data on the lead concentrations (ppm) from three species of fish (whitefish, sucker and trout). Two types of sample were collected: samples from fillets of fish and from whole fish. The data has three variables: lead concentration, species of fish and type of fish sample.

Figure: A poorly formatted data set. This file would be hard to import and analyse in this format.

How would you improve the format of the poorly formatted data shown in the figure? (Hint: use the six guidelines above)

The second figure shows some well formatted data that follows the tidy data guidelines: each column represents a single variable and each row an observation.

Figure: A well formatted data set. This file would be easy to import and analyse in this format. One column contains the data for one variable. These data are the worldwide occurences of Covid-19, downlaoded from the European Centre for Disease Prevention and Control,(opens in a new window)https://www.ecdc.europa.eu/en

Tibbles

A recent development (circa 2016) is an improved data frame called atibble. We will not discuss these new data frame objects here, but you can read about them at(opens in a new window)https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html.

Don’t Panic! Tibbles are very similar to data frames.

The important point to know is that if you use RStudio’s GUI interface to import data then your data will be stored in atibble, not adata frame.

Converting data to a CSV text file

Acomma separated valuesfile (CSV file) is the most common format for a text file that contains data.

Here are a few video tutorials on converting data into a CSV text file so that it is suitable for import into R.

(opens in a new window)Video Tutorial:Converting data from EXCEL to a CSV format (3 mins)

(opens in a new window)Video Tutorial:Converting data from Googlesheets to a CSV format (1 min)

Viewing text files

Before importing a text file into any software package it is a huge help if you can look at it in a text editor. Text files can contain characters that are normally invisible (e.g. spaces, tabs and end of line markers). If a text editor is going to be of use it must be able todisplay all the charactersin a file.

Three text editors that can do this are:

(opens in a new window)notepad++is a free program for Windows operating systems

(opens in a new window)BBeditis a free program for Mac OSX operating systems

(opens in a new window)emacsis a GNU opensource program primarily for Linux operating systems.

On Linux systems thecat -Acommand from the terminal is also useful.

Here are two video tutorials on this topic

(opens in a new window)Video Tutorial:Viewing data in a text file before importing into R (4 mins)

(opens in a new window)Video Tutorial:An overview of the common data text file formats (3 mins)

Data import examples

The data we’ll be importing are described athttp://www.ucd.ie/ecomodel/Resources/datasets_WebVersion.html

The files are:

• WOLF.CSV: This file is a text file of comma separated values.
• HEIGHT.CSV: This file is a text file of comma separated values.
• INSECT.TXT:This file is a text file of TAB delimited values.
• BEEKEEPER.TXT: This file is a text file with blank space delimiting the values.
• MALIN_HEAD.TXT: This file is a text file with TAB delimited values.

All these data files are simpletext filesthat differ in the character used to distinguish columns of data.

# Import WOLF.CSV file using read.table function
wolf = read.table('WOLF.CSV', header=TRUE, sep=',')

# Import HEIGHT.CSV file using read.table function
human = read.table('HEIGHT.CSV', header=TRUE, sep=',')

?read.table             # Display help page on read.table function

# Import INSECT.TXT file using read.table function (TAB delimited)
# skipping the first 3 lines (skip=3)
insect = read.table('INSECT.TXT', header=T, skip=3, sep='\t')

# Import MALIN_HEAD.TXT file using read.table function (TAB delimited)
rainfall = read.table('MALIN_HEAD.TXT', header=T, sep='\t')

# Import BEEKEEPER.TXT file using read.table function (white space delimited)
# skipping the first 6 lines (skip=6)
bees = read.table('BEEKEEPER.TXT', header=T, skip=6, sep='')

# Comma separated values
wolf = read.table('WOLF.CSV', header=TRUE, sep=',')
human = read.table('HEIGHT.CSV', header=TRUE, sep=',')

# TAB delimited values
insect = read.table('INSECT.TXT', header=T, skip=3, sep='\t')
rainfall = read.table('MALIN_HEAD.TXT', header=T, sep='\t')

# White space delimited values
bees = read.table('BEEKEEPER.TXT', header=T, skip=6, sep='')

# ******************************************
# Other packages for importing data --------
# The data.table package

library(data.table)    # Load the data.table package

# Import a CSV file
wolf2 = fread('WOLF.CSV')            
human2 = fread('HEIGHT.CSV')

# Import TAB delimited file
insect2 = fread('INSECT.TXT')
rainfall2 = fread('MALIN_HEAD.TXT')  

# Import white space delimited file
bees2 = fread('BEEKEEPER.TXT')