Last February I reviewed the jamovi menu-based front end to R. I’ve reviewed five more user interfaces since then, and have developed a more comprehensive template to make it easier to compare them all. Now I’m cycling back to jamovi, using that template to write a far more comprehensive review. I’ve added this review to the previous set, and I’m releasing it as a blog post so that it will be syndicated on R-Bloggers, StatsBlogs, et al.
The ability to export data to a wide range of file types helps when you have to use multiple tools to complete a task. Research is commonly a team effort, and in my experience, it’s rare to have all team members prefer to use the same tool. For these reasons, GUIs such as BlueSky and Deducer offer many export formats. Others, such as R Commander and RKward can create only delimited text files.
A fairly unique feature of jamovi is that it doesn’t save just a dataset, but instead it saves the combination of a dataset plus its associated analyses. To save just the dataset, you use the menu (a.k.a. hamburger) menu to select “Export” then “Data.” The export formats supported are the same as those provided for import, except for the more rarely-used ones such as SAS xpt and SPSS por and zsav:
- Comma Separated Values (.csv)
- Plain text files (.txt)
- SPSS (.sav)
- SAS binary files (.sas7bdat)
It’s often said that 80% of data analysis time is spent preparing the data. Variables need to be transformed, recoded, or created; strings and dates need to be manipulated; missing values need to be handled; datasets need to be sorted, stacked, merged, aggregated, transposed, or reshaped (e.g. from “wide” format to “long” and back).
Some GUIs, such as BlueSky and R Commander can handle nearly all of these tasks. Others, such as RKWard handle only a few of these functions.
jamovi’s data management capabilities are minimal. You can transform or recode variables, and doing so across many variables is easy. The transformations are stored in the variable itself, making it easy to see what it was by double-clicking its name. However, the R code for the transformation is not available, even in with Syntax Mode turned on.
You can also filter cases to work on a subset of your data. However, jamovi can’t sort, stack, merge, aggregate, transpose, or reshape datasets. The lack of combining datasets may be a result of the fact that jamovi can only have one dataset open in a given session.
Menus & Dialog Boxes
The goal of pointing and clicking your way through an analysis is to save time by recognizing menu settings rather than performing the more difficult task of recalling programming commands. Some GUIs, such as BlueSky, make this easy by sticking to menu standards and using simpler dialog boxes; others, such as RKWard, use non-standard menus that are unique to it and hence require more learning.
jamovi uses standard menu choices for running steps listed on the Data and Analyses tabs. Dialog boxes appear and you select variables to place into their various roles. This is accomplished by either dragging the variable names or by selecting them and clicking an arrow located next to the particular role box. A unique feature of jamovi is that as soon as you fill in enough options to perform an analysis, its output appears instantly. There is no “OK” or “Run” button as the other GUIs reviewed here have. Thereafter, every option chosen adds to the output immediately; every option turned off is removed.
While nearly all GUIs keep your dialog box settings during your session, jamovi keeps those settings in its main “workspace” file. This allows you to return to a given analysis at a future date and try some model variations. You only need to click on the output of any analysis to have the dialog box appear to the right of it, complete with all settings intact.
Under the triple-dot menu on the upper right side of the screen, you can choose to run “Syntax Mode.” When you turn that on, the R syntax appears immediately, and when you turn it off, it vanishes just as quickly. Turning on syntax mode is the only way a jamovi user would be aware that R is doing the work in the background.
Output is saved by using the standard “Menu> Save" selection.
Documentation & Training
R GUIs provide simple task-by-task dialog boxes which generate much more complex code. So for a particular task, you might want to get help on 1) the dialog box’s settings, 2) the custom functions it uses (if any), and 3) the R functions that the custom functions use. Nearly all R GUIs provide all three levels of help when needed. The notable exception that is the R Commander, which lacks help on the dialog boxes themselves.
jamovi doesn’t offer any integrated help files, only the documentation described in the Documentation & Training section above. The search for help can become very confusing. For example, after doing the scatterplot shown in the next section, I wondered if the scat() function offered a facet argument, normally this would be an easy question to answer. My initial attempt was to go to RStudio, load jamovi’s jmv package knowing that I routinely get help from it. However, the scat() function is not built into jamovi (or jmv); it comes in the scatr add-on module. So I had to return to jamovi and install Rj Editor module. That module lets you execute R code from within jamovi. However, running “help(scat)” yielded no result. After so much confusion, I never was able to find any help on that function. Hopefully, this situation will improve as jamovi matures.
The various GUIs available for R handle graphics in several ways. Some, such as RKWard, focus on R’s built-in graphics. Others, such as BlueSky, focus on R’s popular ggplot graphics. GUIs also differ quite a lot in how they control the style of the graphs they generate. Ideally, you could set the style once, and then all graphs would follow it.
jamovi uses its own graphics functions to create plots. By default, they have the look of the popular ggplot2 package. jamovi is the only R GUI reviewed that lets you set the plot style in advance, and all future plots will use that style. It does this using four popular themes. jamovi also lets you choose color palettes in advance, from a set of eight.