R for beginners: Installing Deducer in the R Windows GUI
A tutorial by D.M. Wiig
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More on this topic to be posted soon!
All posts by dmwiig
R for Beginners: Using R Commander in an Introductory Statistics Course
R for beginners: Using R Commander in introductory statistics courses
A tutorial by D. M. Wiig
As with previous tutorials in this series this document is an embedded Word documents. To view the document full screen click on the icon in the lower right corner of the window.
R for Beginners: Using R Commander for Basic t Tests and One Way ANOVA
R for Beginners: Using R Commander for Basic t Tests and One Way ANOVA
A tutorial by D. M. Wiig
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I hope that you found this tutorial informative. Stop back by to check for new installments. I have many currently in the writing stage.
R for Beginners: Using R Commander, Graphing and Correlation
A tutorial by Douglas M. Wiig
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R for Beginners: Installing and Using the R Commander GUI, Part One
A tutorial by D.M. Wiig
This tutorial is posted as an embedded Word document. To view the document full screen click on the button in the lower right corner of the window. Please note that you must be online for the full page Word document display to work.
R For Beginners: Installing and Using the R Console in a Windows Environment
An R tutorial by D. M. Wiig
This tutorial is posted as an embedded Word document. To view the document full screen click on the icon in the lower-right corner of the document window.
My next post covering installing and using the Rcommander GUI will be out in a day or two.
Using R to Create Ternary Diagrams: An Example Using 2016 Presidential Polling Data
An R Tutorial by D. M. Wiig
In previous tutorials I have discussed the basics of creating a ternary plot using the ggtern package using a simple hypothetical data frame containing five values. In a subsequent tutorial I discussed the application by creating a ternary graph using election results from the British House of Commons from the last half of the 20th century. This type of plot creates a very nice visual of the effects of a third party on the election outcome.
In this tutorial I will discuss using the same technique as applied to recent polling data from the ongoing 2016 U.S. presidential campaign. Before discussing the current election campaign I am going to refresh your memory relative to using the ggtern package.
Before running the script in this tutorial make sure that the packages ggplot, ggplot2, and ggtern are loaded into your R environment. Please also note the you will need a recent version of R that is version 3.1.x or newer. A very basic graph can be easily constructed. I will the use theoretical quantities XA , XB , and XC to demonstrate a basic ternary diagram. In this simple example I will create a sample of n=5 by entering the data from the keyboard into a data frame ‘sampfile.’ To invoke the editor use the following code:
###################################################
#create a sample file of n=5
###################################################
sampfile <-data.frame(Xa=numeric(0),Xb=numeric(0),Xc=numeric(0))
sampfile <-edit(sampfile)
###################################################
This will open up a data entry sheet with three columns labeled Xa, Xb, and Xc. The number that are entered do not matter for purposes of this illustration. The table I entered is as follows:
Xa Xb Xc
1 100 135 250
2 90 122 210
3 98 44 256
4 100 97 89
5 90 75 89
To produce a very basic ternary diagram with the above data set use the code segment:
##################################################
#do basic graph with sample data
##################################################
ggtern(data=sampfile, aes(x=Xa,y=Xb, z=Xc)) + geom_point()
##################################################
This produces the graph seen below:
The triangular representation of the dimensions Xa →Xb, Xc → Xa and Xb →Xc allow each case to be represented as a single point located relative to each of the three vectors. There are a large number of additions, modifications and tweaks that can be done to this basic pattern. In the next tutorial I will discuss generating a more elaborate ternary diagram using polling data from the current U.S. presidential campaign.
Thu US has a two party dominant system with several minor parties that regularly contest elections. In the current presidential election campaign there are the two major party candidates as well as two minor party candidates for the Libertarian and Green parties that are being included in the numerous public opinion polls that are being done nationally.
For purposes of this example I have added the percentages for these two minor parties together. This results in three variables that are being plotted, the percentage for Clinton (Democrat), Trump (Republican), and for the combined Johnson (Libertarian) and Stein (Green). By plotting the three variables over time on a ternary diagram we can visualize any changes in the mixture of support indicated for the candidates.
The poll data used in this project were taken from the web site RealClearPolitics.com for the time period from July 29 to August 18.¹ It should be noted that the poll numbers were not necessarily from the same polling organization for each date but all polls used were listed as being national in scope with a Clinton v. Trump v. Johnson v. Stein format.
Before working through this tutorial make sure that you have the ggplot, ggplot2, and ggtern packages loaded into your R environment.² I originally created the table shown above using Excel and then converted it into a *cvs format before importing it into R studio for analysis.³ The data can be entered directly via the R data editor as shown in the previous example. The code segment below was used to load the *csv format file:
####################################################Enter data into spreadsheet and save a a *csv file
#Load the data into a table using the read.table function
polldata <- read.table(“d:/16electiondata.csv”, header = TRUE, sep=”,”)
#Make sure the table is ok
View(polldata)
###################################################
date clinton trump johnson/stein
17-Aug 41 35 10
16-Aug 43 37 15
14-Aug 42 37 12
11-Aug 43 40 10
10-Aug 44 40 13
9-Aug 44 38 14
8-Aug 50 37 9
7-Aug 45 37 12
5-Aug 39 35 17
4-Aug 43 34 15
2-Aug 42 38 13
1-Aug 45 37 14
30-Jul 46 41 8
29-Jul 37 37 6
25-Jul 39 41 15
21-Jul 38 35 0
19-Jul 39 40 15
18-Jul 45 43 6
17-Jul 42 37 18
Once the data set is loaded use the following code to create the ternary diagram. Note that in this diagram we are using the base code as shown in the first tutorial with some additions that make the diagram easier to interpret such as the vector arrows and legend. The code segment is:
###################################################
#create ternary plot using percentage polled for each candidate for each polling period
#uses enhanced formatting for easier interpretation
#results of ggtern function are placed in variable ‘plot’ for rendering
###################################################
plot <- ggtern(data = polldata, aes(x = clinton, y = trump, z = johnson.stein)) +
geom_point(aes(fill = date),
size = 6,
shape = 21,
color = “black”) +
ggtitle(“2016 U.S. Presidential Election Polls”) +
labs(fill = “Date”) +
theme_rgbw() +
theme(legend.position = c(0,1),
legend.justification = c(1, 1))
###################################################
To show the diagram simply use:
###################################################
#now plot the diagram
###################################################
plot
###################################################
The resulting ternary diagram is:
Each point on the graph represents the percentage of support for each of the three candidates by the location of the point on the 3-way graph axes. This R routine provides a quick and straightforward method for representing a 3-dimensional relationship in two dimensions.
Code segments in this article were written using R Studio Version 0.98.993 running R version 3.1.1 in a Windows 7 environment.
Notes:
¹As indicated above the poll data used in this tutorial was located at http://realclearpolitics.com. This website is an excellent source of information about all aspects of American electoral politics.
² For additional information about ternary graphs see the website http://www.ggtern.com. See also the CRAN website at http://cran.r-project.org/web/packages/ggtern/ggtern.pdf.
³For information about using the IDE R Studio see the website https://www.rstudio.com.
Using R to Create Ternary Graphs
I am currently working on an updated posting of my tutorial
Ternary Diagrams Using R: An Example Using Election Outcomes.
The new tutorial will explore using ternary diagrams to track shifts in support for presidential candidates in the 2016 US presidential campaign.
Check back soon for the first installment!
R-Fiddle R Console and Data Editor: R Collaboration in the Cloud
R-Fiddle is a great tool to develop and test code segments or complete R programs. By accessing the R-Fiddle web site users have a fully functioning R console, code editor and discussion board all in one place. If a user has code uploaded that has been designated to share, other users can access the code and make suggestions or additions. Code can be run with full R support from your web browser.
Try the link below to test out R-Fiddle. I have uploaded a small program as a demo. Feel free to share your own projects, help others or try out code segments.
http://www.r-fiddle.org/#/embed?id=rtOt8yR3
Click in the link above to activate the R editor and R console.
Ternary Diagrams Using R: An Example Using Election Outcomes
Ternary Diagrams Using R: An Example Using Election Outcomes
A tutorial by D. M. Wiig
In part one of this tutorial I discussed creating a ternary diagram using a simple data frame that contained five hypothetical cases. In this tutorial I will expand on that foundation by creating a more informative ternary diagram using live data.
A useful application of this package in social science research is creating a visual display of parliamentary election outcomes. Specifically we can use a ternary graph to examine the distribution of seats in the British House of Commons over a period of time. Since the UK uses a proportional system to allocate seats in the House of Commons there can be a variety of outcomes in any given national election.
Since 1945 general elections in the UK have produced a division of seats among the Labour, Conservative, and various minor parties. To demonstrate how this division of seats can be shown over time data was collected for all of the general elections from the years 1945 to 2015. These data show the percentage of the popular vote won by each party and the number of seats allocated to that party based on the vote division(retrieved from http://www.ukpolitical.info). I have created a summary table of these results as follows:
Year Con Lab LD+Other SeatsCon SeatsLab SeatsOther
2015 36.9 30.4 32.7 331 232 95
2010 36.1 29 34.9 306 258 85
2005 35.2 32.4 32.4 355 198 92
2001 40.7 31.7 27.6 412 166 81
1997 43.2 30.7 26.1 418 165 76
1992 42.3 35.2 23.5 336 271 44
1987 42.2 30.8 27 375 229 48
1983 42.4 27.6 26.9 397 209 27
1979 43.9 36.9 15.8 339 268 28
1974 39.2 35.8 21.8 319 276 39
1974 37.1 37.9 20.1 301 296 38
1970 46.4 43 8.6 330 287 19
1966 47.9 41.9 8.5 363 253 25
1964 44.1 53.4 11.2 317 304 22
1959 49.4 43.8 5.9 365 258 19
1955 49.7 46.4 0 344 277 18
1951 48 48.8 2.5 321 295 18
1950 46.1 43.5 9.1 315 297 22
1945 47.8 39.8 1 393 213 57
The UK has a two party dominant system with a number of minor parties that regularly contest elections. As indicated above, a proportional representation method of allocating seats is used so these minor parties are able to gain some representation in the Commons. For readers interested in learning more about political parties in the UK there are a number of resources readily available at various online and other sources.
For purposes of this example I have added the popular vote of all minor parties together in the ‘LD+Other’ column, and the number of seats gained in the ‘SeatsOther’ column. By plotting the three variables ‘SeatsCon’, ‘SeatsLab’, and ‘SeatsOther’ by year on a ternary diagram we can visualize any changes in the mixture of seats won for the three groups. Before working through this tutorial make sure that you have the ggplot, ggplot2, and ggtern packages loaded into your R environment.
I originally created the table shown above using Excel and then imported it into R studio for analysis. If you are not using R studio you can enter the data via the R data editor as shown in the previous tutorial, or put the data into an Excel or LibreOffice spreadsheet and import it into R using the read.spss() function that I have discussed in earlier tutorials. You can also use any other method that you are familiar with to get the data into your R environment.
Once the data set is loaded use the following code to create the ternary diagram. Note that in this diagram we are using the base code as shown in the first tutorial with some additions that make the diagram easier to interpret such as the vector arrows and legend. The code segment is:
################################################### #create ternary plot using seats allocated by party for each election #uses enhanced formatting for easier interpretation #results of #ggtern function are placed in ‘plot for rendering ################################################### plot <- ggtern(data = ukvotedata, aes(x = SeatsCon, y = SeatsLab, z = SeatsOther)) +geom_point(aes(fill = Year), size = 4, shape = 21, color = “black”) + ggtitle(“Proportion of Seats Won 1945-2015”) + labs(fill = “Year”) + theme_rgbw() + theme(legend.position = c(0,1), legend.justification = c(0, 1)) ###################################################
To show the diagram simply use:
################################################### #now plot the diagram ################################################### plot ###################################################
The resulting ternary diagram is:
Each point on the graph represent the relative division of seats for each of the 19 elections in the table. The shading represents the year with the darkest being 1945 and the lightest 2015. The diagram clearly shows the trend toward more minor party representation and a move away from the two major parties over time. Indeed coalition governments resulted in several of the more recent elections due to the increase in minor party influence.
My purpose here is not to discuss UK politics but to show how ternary diagrams can be used in a social science application. With the many additions and extensions that are being added to the ggtern package it can be a very power device for graphical analysis.
R Statistics and Programming 