My next two live webinars done in partnership with Revolution Analytics are in January:

R for SAS, SPSS and Stata Users

Managing Data with R (updated to include dplyr, broom, tidyr, etc.)

Course outlines and registration for both is here.

My R for SAS, SPSS and Stata Users workshop is also now available as a self-paced interactive video workshop at DataCamp.com.

I do site visits in partnership with RStudio.com, whose software I recommend and use in every form of my training. If your company does its training through Xerox Learning Services, I also partner with them. For further details or to arrange a site visit, you can reach me at muenchen.bob@gmail.com.

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MEAN.5(Q1 TO Q10) asks for the mean only if at least five of the ten variables have valid values. Otherwise the result will be a missing value. This “.n” extension is also available for SPSS’ SUM, SD, VARIANCE, MIN and MAX functions.

Let’s now take a look at how to do this in R. First we’ll create some data with different numbers of missing values for each observations.

> q1 <- c(1, 1, 1) > q2 <- c(2, 2, NA) > q3 <- c(3, NA, NA) > df <- data.frame(q1, q2, q3) > df q1 q2 q3 1 1 2 3 2 1 2 NA 3 1 NA NA

R already has a mean function, but it lacks a function to count the number of valid values. A common way to do this in R is to use the is.na() function to generate a vector of TRUE/FALSE values for missing or not, respectively, then sum them. As with many software packages, R views TRUE as having the value 1 and FALSE as having a value of 0, so this approach gets us the number of missing values. The “!” symbol means “not” in R so !is.na() will find the number *non*-missing values. Here’s a function that does this:

> nvalid <- function(x) sum(!is.na(x)) > nvalid(q2) [1] 2

So it has found that there are two valid values for q2. This nvalid() function obviously works on vectors, but we need to apply it to the rows of our data frame. We can select the first three variables using df[1:3] and then pass the result into as.matrix() to make the rows easily accessible by R’s apply() function. The apply() function’s second argument is 1 indicating that we would like to compute the mean across rows (the value 2 would indicate columns). The final arguments are the functions to apply and any arguments they need.

> means <- apply(as.matrix(df[1:3]), 1, mean, na.rm = TRUE) > counts <- apply(as.matrix(df[1:3]), 1, nvalid) > means [1] 2.0 1.5 1.0 > counts [1] 3 2 1

We have our means and the counts of valid values, so all that remains is to choose our desired value of counts and accept the mean if the data have that value or greater, but return a missing value (NA) if not. This can be done using the ifelse() function, whose first argument is the logical condition, followed by the value desired when TRUE, then the value when false.

> means <- ifelse(counts >= 2, means, NA) > means [1] 2.0 1.5 NA

We’ve seen all the parts work, so all that remains is to put them together into a single function that has two arguments, one for the data frame and one for the n required.

mean.n <- function(df, n) { means <- apply(as.matrix(df), 1, mean, na.rm = TRUE) nvalid <- apply(as.matrix(df), 1, function(df) sum(!is.na(df))) ifelse(nvalid >= n, means, NA) }

Let’s test our function requiring 1, 2 and 3 valid values.

> df$mean1 <- mean.n(df[1:3], 1) > df$mean2 <- mean.n(df[1:3], 2) > df$mean3 <- mean.n(df[1:3], 3) > df q1 q2 q3 mean1 mean2 mean3 1 1 2 3 2.0 2.0 2 2 1 2 NA 1.5 1.5 NA 3 1 NA NA 1.0 NA NA

That looks good. You could apply this same idea to various other R functions such as sd() or var(). You could also apply it to sum() as SPSS does, but I rarely do that. If you were creating a scale score from a set of survey Likert items measuring agreement and a person replied “strongly agree” (a value of 5), to only half the items but skipped the others, would you want the resulting score to be a neutral value as the sum would imply, or “strongly agree” as the mean would indicate? The mean makes much more sense in most situations. Be careful though as there are standardized tests that require use of the sum.

If you’re an SPSS user looking to learn just enough R to use the two together, you might want to read this, or to learn more you could take one of my workshops. If you really want to dive into the details, you might consider reading my book, R for SAS and SPSS Users.

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Hadley Wickham’s dplyr and tidyr. The dplyr package almost completely replaces his popular plyr package for data manipulation. Most importantly for general R use, it makes it much easier to select variables. For example,

if your data included variables for race, gender, pretest, posttest, and four survey items q1 through q4, you could select various sets of variables using:

library("dplyr")select(mydata, race,gender) # Just those two variables.select(mydata,gender:posttest) # From gender through posttest.select(mydata, contains("test")) # Gets pretest & posttest.select(mydata,starts_with("q")) # Gets all vars staring with "q".select(mydata,ends_with("test")) # All vars ending with "test".select(mydata,num_range("q",1:4)) # q1 thru q4 regardless of location. select(mydata, matches("^q")) # Matches any regular expression.

As I show in my books, these were all possible in R before, but they required much more programming.

The tidyr package replaces Hadley’s popular reshape and reshape2 packages with a data reshaping approach that is simpler and more focused just on the reshaping process, especially converting from “wide” to “long” form and back.

I’ve integrated dplyr in to my workshop R for SAS, SPSS and Stata Users, and both tidyr and dplyr now play extensive roles in my Managing Data with R workshop. The next Virtual Instructor-led Classroom (webinar) version of those workshops I’m doing in partnership with Revolution Analytics during the week of October 6, 2014. I’m also available to teach them at your organization’s site in partnership with RStudio.com (contact me at Muenchen.bob@gmail.com to schedule a visit). These workshops will also soon be available 24/7 at Datacamp.com. “You’ll be able to take Bob’s popular workshops using an interactive combination of video and live exercises in the comfort of your own browser” said Jonathan Cornelissen, CEO of Datacamp.com.

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Here is my latest update to *The Popularity of Data Analysis Software*. To save you the trouble of reading all 25 pages of that article, the new section is below. The two most interesting nuggets it contains are:

- As I covered in my talk at the UseR 2014 meeting, it is very likely that during the summer of 2014, R became the most widely used analytics software for scholarly articles, ending a spectacular 16-year run by SPSS.
- Stata has probably passed Statistica in scholarly use, and its rapid rate of growth parallels that of R.

If you’d like to be alerted to future updates on this topic, you can follow me on Twitter, @BobMuenchen.

**Scholarly Articles**

The more popular a software package is, the more likely it will appear in scholarly publications as a topic and as a method of analysis. The software that is used in scholarly articles is what the next generation of analysts will graduate knowing, so it’s a good leading indicator of where things are headed. Google Scholar offers a way to measure such activity. However, no search of this magnitude is perfect and will include some irrelevant articles and reject some relevant ones. The details of the search terms I used are complex enough to move to a companion article, How to Search For Analytics Articles. Since Google regularly improves its search algorithm, I recollect the data for all years following the protocol described at http://librestats.com/2012/04/12/statistical-software-popularity-on-google-scholar/.

Figure 2a shows the number of articles found for each software package for all the years that Google Scholar can search. SPSS is by far the most dominant package, likely due to its balance between power and ease-of-use. SAS has around half as many, followed by MATLAB and R. Note that the general purpose software MATLAB, Java and Python are included only when found in combination with analytics terms, so view those as much rougher counts than the rest. Neither C nor C++ are included here because it’s very difficult to focus the search compared to the search for jobs above, whose job descriptions commonly include a clear target of skills in “C/C++” and “C or C++”.

From RapidMiner on down, the counts appear to be zero. That’s not the case, but relative to the others, it might as well be.

Figure 2b shows the number of articles for the most popular six classic statistics packages from 1995 through 2013 (the last complete year of data this graph was made). As in Figure 2a, SPSS has a clear lead, but you can see that its dominance peaked in 2007 and its use is now in sharp decline. SAS never came close to SPSS’ level of dominance, and it peaked in 2008.

Since SAS and SPSS dominate the vertical space in Figure 2a by such a wide margin, I removed those two packages and added the next two most popular statistics packages, Systat and JMP in Figure 2c. Freeing up so much space in the plot now allows us to see that the use of R is experiencing very rapid growth and is pulling away from the pack, solidifying its position in third place. In fact, extending the downward trend of SPSS and the upward trend of R make it likely that sometime during the summer of 2014 R became the most dominant package for analytics used in scholarly publications. Due to the lag caused by the publication process, getting articles online, indexing them, etc. we won’t be able to verify that this has happened until well into 2015 (correction: this said 2014 when originally posted).

After R, Statistica is in fourth place and growing, but at a much lower rate. Note that in the plots from previous years, Statistica was displayed as a flat line at the very bottom of the graph. That turned out to be a search-related artifact. Many academics who use Statistica don’t mention the package by software name but rather say something like, “we used the statistics package by Statsoft.”

Extrapolating from the trend lines, it is likely that the use of Stata among academics passed that of Statistica fairly early in 2014. The remaining three packages, Minitab, Systat and JMP are all growing but at a much lower rate than either R or Stata.

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Webinar Format:

– What is PMML?

– Building a predictive model in R and exporting it to PMML format

– Deploying a PMML model into a cloud-based Openscoring service

– Scoring Google Spreadsheet data

– Scoring PostgreSQL data

– Scoring Hadoop data

– Q&A

Speaker:

– Villu Ruusmann, Creator of jpmml and Founder of Openscoring.io

This event is brought to you by The Orange County R User Group.

Registration:

https://www3.gotomeeting.com/register/695356990

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Webinar Format:

– Introduction to the RSelenium R package

– Live Demonstration

– Question and Answer period

Date: May 21, 2014 at 10 am Pacific (California) time

Speaker:

John Harrison, RSelenium package author/maintainer

For more information on the RSelenium package, please visit this site:

http://cran.r-project.org/web/packages/RSelenium

Please note that in addition to attending from your laptop or desktop computer, you can also attend from a Wi-Fi connected iPhone, iPad, Android phone or Android tablet by installing the GoToMeeting App.

Registration is below:

https://www3.gotomeeting.com/register/724626654

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Webinar Format:

- Introduction to Joint Models and the JMBayes R package

– Live demonstration

– Question and Answer period

Speaker:

- Dimitris Rizopoulos, JMBayes Package Maintainer

For more information on the JMBayes package, please visit this site:

http://cran.r-project.org/package=JMbayes

Please note that in addition to attending from your laptop or desktop computer, you can also attend from a Wi-Fi connected iPhone, iPad, Android phone or Android tablet by installing the GoToMeeting App.

Registration:

https://www3.gotomeeting.com/register/187219462

This event is brought to you by The Orange County R User Group.

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**Growth in Capability**

The capability of analytics software has grown significantly over the years. It would be helpful to be able to plot the growth of each software package’s capabilities, but such data is hard to obtain. John Fox (2009) acquired it for R’s main distribution site http://cran.r-project.org/. I collected the data for later versions following his method.

Figure 8 shows that the growth in R packages is following a rapid parabolic arc (quadratic fit with R-squared=.998). The right-most point is for version 3.0.2, the last version released in 2013.

As rapid as this growth has been, these data represent only the main CRAN repository. R does have eight other software repositories, such as the one at http://www.bioconductor.org/ that are not included in this graph. A program run on 4/7/2014 counted 7,364 R packages at all major repositories, 5,323 of which were at CRAN. So the growth curve for the software at all repositories would be roughly 38% higher on the y-axis than the one shown in Figure 8. As with any analysis software, individuals also maintain their own separate collections typically available on their web sites.

To put this astonishing growth in perspective, let us compare it to the most dominant commercial package, SAS. In version, 9.3, SAS contains around 1,200 commands that are roughly equivalent to R functions (procs, functions etc. in Base, Stat, ETS, HP Forecasting, Graph, IML, Macro, OR, QC). R packages contain a median of 5 functions (Rasmus Bååth, 12/2012 personal communication). Therefore R has approximately 36,820 functions compared to SAS’s 1,200. *In fact, during 2013 alone, R added more functions/procs than SAS Institute has written in its entire history!* That’s 835 packages, counting only CRAN, or around 4,175 functions. Of course these are not perfectly equivalent. Some SAS procedures have many more options to control their output than R functions do. However, R functions can nest inside one another, creating nearly infinite combinations. Also, SAS is now out with version 9.4 and I have not repeated the arduous task of recounting its commands. If SAS Institute would provide the figure, I would be happy to list it here. While the comparison is not perfect, it does provide an interesting perspective on the size and growth rate of R.

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