Today I wondered how to remove an annotation in RQDA. It turns out, all you have to do is click on the annotation to view it, remove the text of the annotation (completely), and then save it.
Trying to do
install.packages("rgdal") on my GNU/Linux machine failed at first. It seems that my system had a couple of libraries not installed, so in the terminal:
sudo apt install libgdal-dev
sudo apt install libproj-dev
install.packages("rgdal") back in R.
Here’s how we can quite easily and flexibly create tables of descriptive statistics in R. Of course, we can simply use
summary(variable_name), but this is not what you’d include in a manuscript — so not what you want when compiling a document in knitr/Rmarkdown.
First, we identify the variables we want to summarize. Often our database includes many more variables:
vars <- c("variable_1", "variable_2", "variable_3")
Note that these are the variable names in quotes. Second, we use
lapply() to calculate whatever summary statistic we want. This is where flexibility kicks in: have you ever tried to include an interpolated median in such a table, just as easy as the mean in R. Here’s an example with the mean, minimum, maximum, and median:
v_mean <- lapply(dataset[vars], mean, na.rm=TRUE)
v_min <- lapply(dataset[vars], min, na.rm=TRUE)
v_max <- lapply(dataset[vars], max, na.rm=TRUE)
v_med <- lapply(dataset[vars], median, na.rm=TRUE)
Too many digits? We can use
round() to get rid of them. There’s actually an argument ‘digits’ in the
kable() command we’ll use in a minute that in principle allows rounding at the very end, but unfortunately it often fails on me. Rounding:
v_mean <- round(as.numeric(v_mean), 2)
Now we only need to bring the different summary statistics together:
v_tab <- cbind(mean=v_mean, min=v_min, max=v_max, median=v_med)
And add useful variable labels:
rownames(v_tab) <- c("Variable 1", "A description of variable 2", "Variable 3")
and we use
kable() to generate a decent table:
If this looks complicated, bear in mind that with no additional work you can change the order of the variables and include any summary statistics. That’s table A1 in the appendix sorted.
I have recently explored open-source approaches to computer-assisted qualitative data analysis (CAQDA). As is common with open-source software, there are several options available, but as is often also the case, not many of them can keep up with the commercial packages, or are abandoned.
Here I wanted to highlight just three options.
RQDA is built on top of R, which is perhaps not the most obvious choice — but can have advantages. The documentation is steadily improving, making it more apparent how RQDA has the main features we’ve come to expect from CAQDA software. I find it a bit fiddly with the many windows that tend to be opened, especially when working on a small screen.
Colloquium is Java-based, which makes it run almost everywhere. It offers a rather basic feature set, and tags can only be assigned to lines (which also implies that lines are the unit of analysis). Where it shines, though, is how it enables working in two languages in parallel.
CATMA is web-based, but runs without flash — so it should run pretty anywhere. It offers basic manual and automatic coding, but there’s one feature we really should care about: CATMA does TEI. This means that CATMA offers a standardized XML export that should be usable in the future, and facilitate sharing the documents as well as the accompanying coding. That’s quite exciting.
What I find difficult to judge at the moment, is whether TEI will be adopted by CAQDA software. Atlas.ti does some XML, but as far as I know it’s not TEI. And, would TEI be more useful to future researchers than a SQLite database like RQDA produces them?
Kosuke Imai has recently published a great introduction: Quantitative Social Science: An Introduction. Finally a
stats data analysis book that has arrived in the present! Yes, we can get away with very little mathematics and still do quantitative analysis. Yes, examples from published work are much more interesting than constructed toy examples. Yes, R can be accessible. Yes, we can talk about causality, measurement, and prediction (even Bayes) before getting to hypothesis testing. Yes, we can work with text and spatial data.