Real data not found in R/exams - r

Question

I am trying to develop an exam based on the results of a logit model fitted to a real data set. I try to load the data set, fit the model, and include some variables extracted from the model using the r varname syntax.
I first developed a small example using artificial data generated within the exercise. That worked fine and this is the corresponding Rmd file:
```{r data generation, echo = FALSE, results = "hide"}
library(tidyverse)
d <- tibble(y = rbinom(100, 1, 0.6), x1 = rnorm(100), x2=rnorm(100))
# randomize exams
nsize <- sample(50:150, 1)
sampled_dat <- sample(1:nrow(d), nsize, replace = TRUE)
fd <- d[sampled_dat, ]
fmodel <- glm(y ~ x1 + x2, data = fd, family = binomial("logit"))
```
Question
========
`r nrow(fd)`
```{r}
summary(fmodel)
```
Choose the correct answer.
Answerlist
----------
* sol1 `r nrow(fd)`
* sol2
Meta-information
================
exname: bdvDeviance
extype: schoice
exsolution: 10
exshuffle: TRUE
```
This worked as expected when launching
elearn_exam <- c("ess3.Rmd")
set.seed(1234567)
exams2nops(elearn_exam, n = 2, language = "en",
institution = "U", title = "Exam",
dir = "nops_pdf", name = "BDV", date = "2018-01-08", duplex = FALSE)
However, this is the analogous exercise loading a real data set:
```{r data generation, echo = FALSE, results = "hide"}
load("d.Rdata")
# randomize exams
nsize <- sample(180:250, 1)
sampled_dat <- sample(1:nrow(d), nsize, replace = TRUE)
fd <- d[sampled_dat, ]
logitModel <- glm(Adopted ~ CultArea + Trained + LabRice+ Education + ExtContact, data = fd, family=binomial("logit"))
```
Question
========
`r nrow(fd)`
Choose the correct answer.
Answerlist
----------
* When adding variables, the deviance did not change. The variables did not bring some useful information.
* sol2 `r nrow(fd)`
Meta-information
================
exname: bdvDeviance
extype: schoice
exsolution: 10
exshuffle: TRUE
```
This time, I get the following error:
> elearn_exam <- c("ess4.Rmd")
> set.seed(1234567)
> exams2nops(elearn_exam, n = 2, language = "en",
+ institution = "Uu", title = "Exam",
+ dir = "nops_pdf", name = "BDV_R", date = "2018-01-08", duplex = FALSE)
Quitting from lines 14-35 (ess4.Rmd)
Error in nrow(fd) : object 'fd' not found
I do not understand what the problem is in the second case. Apparently, the fd variable is not found when including it in r fd. The problem does not come from the regression because that works fine when knitting the Rmd file.

Answer 1

Your second example using the real data set just loads the corresponding data file via load("d.Rdata"), assuming that it is in the current working directory. However, when using any exams2xyz() interface, the exercises are processed in a temporary directory in order not to clutter the user's workspace. Hence, the d.Rdata file is not found in that directory and consequently cannot be loaded. And because of this problem, the fd object cannot be created and inserted. In short, the r fd code is working fine, the problem is loading the data.
To avoid this problem, you must either specify the full absolute path to your data file in load("/path/to/d.Rdata") or you need to copy the data to the temporary directory before loading it. For the latter, there is the convenience function include_supplement() that copies supplementary files to the temporary directory. By default, it takes them from the directory the exercise resides in. So you simply need to add:
include_supplement("d.Rdata")
before loading the data file. Note that when the file is not in the exercise directory itself but some sub-directory you can add the argument recursive = TRUE. Then sub-directories are searched recursively.