Why R is Hard to Learn

[An updated version of this article is here]

The open source R software for analytics has a reputation for being hard to learn. It certainly can be, especially for people who are already familiar with similar packages such as SAS, SPSS or Stata. Training and documentation that leverages their existing knowledge and points out where their previous knowledge is likely to mislead them can save much of frustration. This is the approach used in my books, R for SAS and SPSS Users and R for Stata Users as well as the workshops that are based on them.

Here is a list of complaints about R that I commonly hear from people learning it. In the comments section below, I’d like to hear about things that drive you crazy about R.

Misleading Function or Parameter Names (data=, sort, if)

The most difficult time people have learning R is when functions don’t do the “obvious” thing. For example when sorting data, SAS, SPSS and Stata users all use commands appropriately named “sort.” Turning to R they look for such a command and, sure enough, there’s one named exactly that. However, it does not sort data sets! Instead it sorts individual variables, which is often a very dangerous thing to do. In R, the “order” function sorts data sets and it does so in a somewhat convoluted way. However there are add-on packages that have sorting functions that work just as SAS/SPSS/Stata users would expect.

Perhaps the biggest shock comes when the new R user discovers that sorting is often not even needed by R. When other packages require sorting before they can do three common tasks:

  1. Summarizing / aggregating data
  2. Repeating an analysis for each group (“by” or “split file” processing)
  3. Merging files by key variables

R does not need to sort files before any of these tasks! So while sorting is a very helpful thing to be able to do for other reasons, R does not require it for these common situations.

Nonstandard Output

R’s output is often quite sparse. For example, when doing crosstabulation, other packages routinely provide counts, cell percents, row/column percents and even marginal counts and percents. R’s built-in table function (e.g. table(a,b)) provides only counts. The reason for this is that such sparse output can be readily used as input to further analysis. Getting a bar plot of a crosstabulation is as simple as barplot( table(a,b) ). This piecemeal approach is what allows R to dispense with separate output management systems such as SAS’ ODS or SPSS’ OMS. However there are add-on packages that provide more comprehensive output that is essentially identical to that provided by other packages.

Too Many Commands

Other statistics packages have relatively few analysis commands but each of them have many options to control their output. R’s approach is quite the opposite which takes some getting used to. For example, when doing a linear regression in SAS or SPSS you usually specify everything in advance and then see all the output at once: equation coefficients, ANOVA table, and so on. However, when you create a model in R, one command (summary) will provide the parameter estimates while another (anova) provides the ANOVA table. There is even a command “coefficients” that gets only that part of the model. So there are more commands to learn but fewer options are needed for each.

R’s commands are also consistent, working across all the modeling types that they might apply to. For example the “predict” function works the same way for all types of models that might make predictions.

Sloppy Control of Variables

When I learned R, it came as quite a shock that in a single analysis you can include variables from multiple data sets. That usually requires that the observations be in identical order in each data set. Over the years I have had countless clients come in to merge data sets that they thought had observations in the same order, but were not! It’s always safer to merge by key variables (like ID) if possible. So by enabling such analyses R seems to be asking for disaster. I still recommend merging files when possible by key variables before doing an analysis.

So why does R allow this “sloppiness”? It does so because it provides very useful flexibility. For example, might plot regression lines of variable X against variable Y for each of three groups on the same plot. Then you can add group labels directly onto the graph. This lets you avoid a legend that makes your readers look back and forth between the legend and lines. The label data would contain only three variables: the group labels and the coordinates at which you wish them to appear. That’s a data set of only 3 observations so merging that with the main data set makes little sense.


R has loops to control program flow, but people (especially beginners) are told to avoid them. Since loops are so critical to applying the same function to multiple variables, this seems strange. R instead uses the “apply” family of functions. You tell R to apply the function to either rows or columns. It’s a mental adjustment to make, but the result is the same.

Functions That Act Like Procedures

Many other packages, including SAS, SPSS and Stata have procedures or commands that do typical data analyses which go “down” through all the observations. They also have functions that usually do a single calculation across rows, such as taking the mean of some scores for each observation in the data set. But R has only functions and those functions can do both. How does it get away with that? Functions may have a preference to go down rows or across columns but for many functions you can use the “apply” family of functions to force then to go in either direction. So it’s true that in R, functions act like procedures and functions. Coming from other software, that’s a wild new idea.

Naming and Renaming Variables is Way Too Complicated

Often when people learn how R names and renames its variables they, well, freak out. There are many ways to name and rename variables because R stores the names as a character variable. Think of all the ways you know how to fiddle with character variables and you’ll realize that if you could use them all to name or rename variables, you have way more flexibility than the other data analysis packages. However, how long did it take you to learn all those tricks? Probably quite a while! So until someone needs that much flexibility, I recommend simply using R to read variable names from the same source as you read the data. When you need to rename them, use an add-on package that will let you do so in a style that is similar to SAS, SPSS or Stata. An example is here. You can convert to R’s built-in approach when you need more flexibility.

Inability to Analyze Multiple Variables

One of the first functions beginners typically learn is mean(X). As you might guess, it gets the mean of the X variable’s values. That’s simple enough. It also seems likely that to get the mean of two variables, you would just enter mean(X, Y). However that’s wrong because functions in R typically accept only single objects. The solution is to put those two variables into a single object such as a data frame: mean( data.frame(x,y) ). So the generalization you need to make isn’t from one variable to multiple variables, but rather from one object (a variable) to another (a data set). Since other software packages are not object oriented, this is a mental adjustment people have to make when coming to R from other packages. (Note to R gurus: I could have used colMeans but it does not make this example as clear.)

Poor Ability to Select Variable Sets

Most data analysis packages allow you to select variables that are next to one another in the data set (e.g. A–Z or A TO Z). R generally lacks this useful ability. It does have a “subset” function that allows the form A:Z, but that form works only in that function. There are many various work-arounds for this problem but most do seem rather convoluted compared to other software. Nothing’s perfect!

Too Much Complexity

People complain that R has too much complexity overall compared to other software. This comes from the fact that you can start learning software like SAS and SPSS with relatively few commands: the basic ones to read and analyze data. However when you start to become more productive you then have to learn whole new languages! To help reduce repitition in your programs you’ll need to learn the macro language. To use the output from one procedure in another, you’ll need to learn an output management system like SAS ODS or SPSS OMS. To add new capabilities you need to learn a matrix language like SAS IML, SPSS Matrix or Stata Mata. Each of these languages has its own commands and rules. There are also steps for tranferring data or parameters from one language to another. R has no need for that added complexity because it integrates all these capabilities into R itself. So it’s true that beginners have to see more complexity in R. Howevever, as they learn more about R, they begin to realize that there is actually less complexity and more power in R!

Lack of Graphical User Interface (GUI)

Like most other packages R’s full power is only accessible through programming. However unlike the others, it does not offer a standard GUI to help non-programmers do analyses. The two which are most like SAS, SPSS and Stata are R Commander and Deducer. While they offer enough analytic methods to make it through an undergraduate degree in statistics, they lack control when compared to a powerful GUI such as those used by SPSS or JMP. Worse, beginners must initially see a programming environment and then figure out how to find, install, and activate either GUI. Given that GUIs are aimed at people with fewer computer skills, this is a problem.


Most of the issues described above are misunderstandings caused by expecting R to work like other software that the person already knows. What examples like this have you come across?


Thanks to Patrick Burns and Tal Galili for their suggestions that improved this post.

Poll Shows Open Source Almost Even with Commercial Analytics Software

The 2012 results of the annual KDnuggets poll are in. It shows R in first place with 30.7% of users reporting having used it for a real project. Excel is almost as popular. It seems out of place among so many more capable packages, but Excel is a tool that almost everyone has and knows how to use.

It’s interesting to note that four of the top five packages used were open source. While open source packages are clearly playing a major role in analytics, people still reported using more commercial software (1086) than open source (927).

For many other ways to measure analytic software popularity, see The Popularity of Data Analysis Software. I’ve just added this graph to that article.

Will 2015 be the Beginning of the End for SAS and SPSS?

[Since this was originally published in 2012, I’ve collected new data that renders this article obsolete. You can always see the most recent data here. -Bob Muenchen]

Learning to use a data analysis tool well takes significant effort, so people tend to continue using the tool they learned in college for much of their careers. As a result, the software used by professors and their students is likely to predict what the next generation of analysts will use for years to come. I track this trend, and many others, in my article The Popularity of Data Analysis Software. In the latest update (4/13/2012) I forecast that, if current trends continued, the use of the R software would exceed that of SAS for scholarly applications in 2015. That was based on the data shown in Figure 7a, which I repeat here:

Let’s take a more detailed look at what the future may hold for R, SAS and SPSS Statistics.

Here is the data from Google Scholar:

         R   SAS   SPSS
1995     8  8620   6450
1996     2  8670   7600
1997     6 10100   9930
1998    13 10900  14300
1999    26 12500  24300
2000    51 16800  42300
2001   133 22700  68400
2002   286 28100  88400
2003   627 40300  78600
2004  1180 51400 137000
2005  2180 58500 147000
2006  3430 64400 142000
2007  5060 62700 131000
2008  6960 59800 116000
2009  9220 52800  61400
2010 11300 43000  44500
2011 14600 32100  32000

ARIMA Forecasting

We can forecast the use of R using Rob Hyndman’s handy auto.arima function to forecast five years into the future:

> library("forecast")

> R_fit <- auto.arima(R)

> R_forecast <- forecast(R_fit, h=5)

> R_forecast

   Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
18          18258 17840 18676 17618 18898
19          22259 21245 23273 20709 23809
20          26589 24768 28409 23805 29373
21          31233 28393 34074 26889 35578
22          36180 32102 40258 29943 42417

We see that even if the use of SAS and SPSS were to remain at their current levels, R use would surpass their use in 2016 (Point Forecast column where 18-22 represent years 2012 -2016).

If we follow the same steps for SAS we get:

> SAS_fit <- auto.arima(SAS)

> SAS_forecast <- forecast(SAS_fit, h=5)

> SAS_forecast

   Point Forecast     Lo 80   Hi 80    Lo 95 Hi 95
18          21200  16975.53 25424.5  14739.2 27661
19          10300    853.79 19746.2  -4146.7 24747
20           -600 -16406.54 15206.5 -24774.0 23574
21         -11500 -34638.40 11638.4 -46887.1 23887
22         -22400 -53729.54  8929.5 -70314.4 25514

It appears that if the use of SAS continues to decline at its precipitous rate, all scholarly use of it will stop in 2014 (the number of articles published can’t be less than zero, so view the negatives as zero). I would bet Mitt Romney $10,000 that that is not going to happen!

I find the SPSS prediction the most interesting:

> SPSS_fit <- auto.arima(SPSS)

> SPSS_forecast <- forecast(SPSS_fit, h=5)

> SPSS_forecast

   Point Forecast   Lo 80 Hi 80   Lo 95  Hi 95
18        13653.2  -16301 43607  -32157  59463
19        -4693.6  -57399 48011  -85299  75912
20       -23040.4 -100510 54429 -141520  95439
21       -41387.2 -145925 63151 -201264 118490
22       -59734.0 -193590 74122 -264449 144981

The forecast has taken a logical approach of focusing on the steeper decline from 2005 through 2010 and predicting that this year (2012) is the last time SPSS will see use in scholarly publications. However the part of the graph that I find most interesting is the shift from 2010 to 2011, which shows SPSS use still declining but at a much slower rate.

Any forecasting book will warn you of the dangers of looking too far beyond the data and I think these forecasts do just that. The 2015 figure in the Popularity paper and in the title of this blog post came from an exponential smoothing approach that did not match the rate of acceleration as well as the ARIMA approach does.

Colbert Forecasting

While ARIMA forecasting has an impressive mathematical foundation it’s always fun to follow Stephen Colbert’s approach: go from the gut. So now I’ll present the future of analytics software that must be true, because it feels so right to me personally. This analysis has Colbert’s most important attribute: truthiness.

The growth in R’s use in scholarly work will continue for two more years at which point it will level off at around 25,000 articles in 2014.This growth will be driven by:

  • The continued rapid growth in add-on packages (Figure 10)
  • The attraction of R’s powerful language
  • The near monopoly R has on the latest analytic methods
  • Its free price
  • The freedom to teach with real-world examples from outside organizations, which is forbidden to academics by SAS and SPSS licenses (it benefits those organizations, so the vendors say they should have their own software license).

What will slow R’s growth is its lack of a graphical user interface that:

  • Is powerful
  • Is easy to use
  • Provides journal style output in word processor format
  • Is standard, i.e. widely accepted as The One to Use
  • Is open source

While programming has important advantages over GUI use, many people will not take the time needed to learn to program. Therefore they rarely come to fully understand those advantages. Conversely, programmers seldom take the time to fully master a GUI and so often underestimate its capabilities. Regardless of which is best, GUI users far outnumber programmers and, until resolved, this will limit R’s long term growth. There are GUIs for R, but so many to choose from that none becomes the clear leader (Deducer, R Commander, Rattle, Red-R, at least two from commercial companies and still more here.) If from this “GUI chaos” a clear leader were to emerge, then R could continue its rapid growth and end up as the most used package.

The use of SAS for scholarly work will continue to decline until it matches R at the 25,000 level. This is caused by competition from R and other packages (notably Stata) but also by SAS Instute’s self-inflicted GUI chaos.  For years they have offered too many GUIs such as SAS/Assist, SAS/Insight, IML/Studio, the Analyst application, Enterprise Guide, Enterprise Miner and  even JMP (which runs SAS nicely in recent versions). Professors looking to meet student demand for greater ease of use could not decide what to teach so they continued teaching SAS as a programming language. Even now that Enterprise Guide has evolved into a good GUI, many SAS users do not know what it is. If SAS Institute were to completely replace their default Display Manager System with Enterprise Guide, they could bend the curve and end up at a higher level of perhaps 27,000.

The use of SPSS for scholarly work will decline only slightly this year and will level off in 2013 because:

  • The people who needed advanced methods and were not happy calling R functions from within SPSS have already switched to R or Stata
  • The people who like to program and want a more flexible language than SPSS offers have already switched to R or Stata
  • The people who needed a more advanced GUI have already switched to JMP

The GUI users will stick with SPSS until a GUI as good (or close to as good) comes to R and becomes widely accepted. At The University of Tennessee where I work, that’s the great majority of SPSS users.

Stata’s growth will level off in 2013 at level that will leave it in fourth place. The other packages shown in Figure 7b will also level off around the same time, roughly maintaining their current place in the rankings. A possible exception is JMP, whose interface is radically superior to the the others for exploratory analysis. Its use could continue to grow, perhaps even replacing Stata for fourth place.

The future of Enterprise Miner and SPSS Modeler are tied to the success of each company’s more mainstream products, SAS and SPSS Statistics respectively. Use of those products is generally limited to one university class in data mining, while the other software discussed here is widely used in many classes.

So there you have it: the future of analytics revealed. No doubt each reader has found a wide range of things to disagree with, so I encourage you to follow the detailed blog at Librestats to collect your own data from Google Scholar and do your own set of forecasts. Or simply go from the gut!