diff --git a/config.yaml b/config.yaml index 70f700b6..f1d95cc5 100644 --- a/config.yaml +++ b/config.yaml @@ -58,22 +58,22 @@ contact: 'kgahm@ucla.edu' # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - 1-intro-reproducible-examples.md - 2-understanding-your-code.md - 3-identify-the-problem.md - 4-minimal-reproducible-data.Rmd -- 5-minimal-reproducible-code.md +- 5-minimal-reproducible-code.Rmd - 6-asking-your-question.md # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # diff --git a/episodes/5-minimal-reproducible-code-draft.Rmd b/episodes/5-minimal-reproducible-code-draft.Rmd new file mode 100644 index 00000000..25f6ef05 --- /dev/null +++ b/episodes/5-minimal-reproducible-code-draft.Rmd @@ -0,0 +1,166 @@ +--- +title: "Minimal Reproducible Code" +teaching: 10 +exercises: 2 +--- + +::: questions +- Which part of my code is causing an error message or an incorrect result? +- I want to make my code minimal, but where do I even start? +- How do I make non-reproducible code reproducible? +- How do I tell whether a code snippet is reproducible or not? +::: + +::: objectives +- Identify the step that is generating the error +- Implement a stepwise approach to make minimal code +- Edit a piece of code to make it reproducible +- Evaluate whether a piece of code is reproducible as is or not. If not, identify what is missing. +::: + +In the last episode, we focused in on how to make minimal datasets that would reproduce a target piece of code, but we didn't talk much about the code itself. In each example, we already had a piece of code that needed a dataset. But how do you know which part of your code is the problem and should be focused on? + +At this point, we as researchers have been exploring our kangaroo rat data for a while and making several plots. Now we know how many kangaroo rats we can expect to catch, and we can move on to our second research question: Do the rodent exclusion plots actually working at keeping the target species out of certain areas? + +If the exclusion plots work, we would expect differences in abundance in the different plot types. Specifically, we'd expect to see fewer kangaroo rats overall in any of the exclusion plots than in the control. + +We start by making a plot to examine this + +```{r, include = F} +library(readr) +library(dplyr) +library(ggplot2) +library(stringr) +library(here) # XXX COME BACK TO THIS, SEE https://github.com/carpentries-incubator/R-help-reprexes/issues/61 +rodents <- read_csv(here("scripts/data/surveys_complete_77_89.csv")) +rodents <- rodents %>% + filter(taxa == "Rodent") +krats <- rodents %>% + filter(genus == "Dipodomys") +krats <- krats %>% + mutate(date = lubridate::ymd(paste(year, month, day, sep = "-"))) +krats <- krats %>% + mutate(time_period = ifelse(year < 1988, "early", "late")) +krats <- krats %>% + filter(species != "sp.") +krats_per_day <- krats %>% + group_by(date, year, species) %>% + summarize(n = n()) %>% + group_by(species) +``` + +To start figuring this out, we decide to make a plot of counts per day, per plot type, per species, per year. + +```{r} +counts_per_day <- krats %>% + group_by(year, plot_id, plot_type, month, day, species_id) %>% + summarize(count_per_day = n()) +``` + +Then, we use that information to visualize the distribution of counts per day in the different plot types to see if there's a difference overall. + +```{r} +counts_per_day %>% + ggplot(aes(x = plot_type, y = count_per_day, fill = species_id, group = interaction(plot_type, species_id)))+ + geom_boxplot(outlier.size = 0.5)+ + theme_minimal()+ + labs(title = "Kangaroo rat captures, all years", + x = "Plot type", + y = "Individuals per day", + fill = "Species") +``` + +Interestingly, we don't see a difference in the number of k-rats captured in the different plot types! We expected to catch more k-rats in the control plots (which don't exclude rodents) than in the various rodent exclosure plots, but the rates appear to be about the same. That doesn't bode well for the effectiveness of these experimental plots! + +You're really interested in this result, and you want to talk to your coworker about it. One of your coworkers, Taylor, asks to see the code you used to make this plot so that they can investigate it on their own. You say "No problem, I'll send the code over!" + +You send the following email: + +*Hi Taylor,* +*Here's the code I used to make that plot! I hope it works.* +``` +counts_per_day %>% + ggplot(aes(x = plot_type, y = count_per_day, fill = species_id, group = interaction(plot_type, species_id)))+ + geom_boxplot(outlier.size = 0.5)+ + theme_minimal()+ + labs(title = "Kangaroo rat captures, all years", + x = "Plot type", + y = "Individuals per day", + fill = "Species") +``` + +Unfortunately, Taylor soon writes back. +*Hey Sam,* +*That code didn't run properly for me. Maybe you need to include the data?* + +Of course! The data! That's important. +::: challenge +**Exercise:** On the Etherpad or in your own notes, identify the dataset that you'll need to send to Taylor so they can run your code. What are a few different ways that you could give him the data? What are the advantages or disadvantages to each? +::: +::: solution +XXX INSERT SOLUTION +::: + +You decide that instead of sending Taylor the modified file, you're going to send him more of the code so he can reproduce the entire thing himself. You look back over the code you've written so far. It's kind of messy! +XXX TODO: Add messy comments and other tangents to this script to make it long. +XXX TOOD: How do we show a script that includes line numbers? That will be important--they all need to have the same reference point for this challenge. +``` +# Note: your code might look a little different! That's okay. +library(readr) +library(dplyr) +library(ggplot2) +library(stringr) +library(here) # XXX COME BACK TO THIS, SEE https://github.com/carpentries-incubator/R-help-reprexes/issues/61 +rodents <- read_csv(here("scripts/data/surveys_complete_77_89.csv")) +rodents <- rodents %>% + filter(taxa == "Rodent") +krats <- rodents %>% + filter(genus == "Dipodomys") +krats <- krats %>% + mutate(date = lubridate::ymd(paste(year, month, day, sep = "-"))) +krats <- krats %>% + mutate(time_period = ifelse(year < 1988, "early", "late")) +krats <- krats %>% + filter(species != "sp.") +krats_per_day <- krats %>% + group_by(date, year, species) %>% + summarize(n = n()) %>% + group_by(species) +counts_per_day <- krats %>% + group_by(year, plot_id, plot_type, month, day, species_id) %>% + summarize(count_per_day = n()) +counts_per_day %>% + ggplot(aes(x = plot_type, y = count_per_day, fill = species_id, group = interaction(plot_type, species_id)))+ + geom_boxplot(outlier.size = 0.5)+ + theme_minimal()+ + labs(title = "Kangaroo rat captures, all years", + x = "Plot type", + y = "Individuals per day", + fill = "Species") +``` + +::: challenge +**Excercise:** Which lines of code in the script above does Taylor absolutely NEED to run in order to reproduce your plot? +Bonus: What are some lines of code that Taylor doesn't NEED to run but which might provide him useful extra context? + +(For this challenge, use the line numbers in the script above, even if your script looks slightly different. For extra practice, do the same exercise with your own script! Do you find any different answers?) +::: +::: solution +XXX INSERT SOLUTION, referencing line numbers +::: + +Taylor emails you back again: +*Hi Sam,* +*Thanks, that looks like it will probably work. Do I really have to install all those packages, though? I'm a little worried about running out of space on my computer.* + +You roll your eyes internally, but he's right--probably not all those packages are totally necessary! + +::: challenge +**Excercise:** Email Taylor back and tell him which packages he needs in order to run your code. +::: +::: solution +*Hi Taylor,* +*Good point. Yeah, you don't need all those packages. Some of them were from other parts of the code that I didn't include here, and there are some that I just forgot to remove when I stopped using them! You can just install {dplyr}, {ggplot2}, {here}, and {readr} and the code should run fine!* +*Sam* +::: + diff --git a/episodes/5-minimal-reproducible-code.Rmd b/episodes/5-minimal-reproducible-code.Rmd new file mode 100644 index 00000000..f8d4a851 --- /dev/null +++ b/episodes/5-minimal-reproducible-code.Rmd @@ -0,0 +1,436 @@ +--- +title: "Minimal Reproducible Code" +teaching: 40 +exercises: 35 +--- + +::: questions +- Why can't I just post my whole script? +- Which parts of my code are directly relevant to my problem? +- Which parts of my code are necessary in order for the problem area to run correctly? +- I feel overwhelmed by this script--where do I even start? +::: + +::: objectives +- Explain the value of a minimal code snippet. +- Simplify a script down to a minimal code example. +- Identify the problem area of the code. +- Identify supporting parts of the code that are essential to include. +- Identify pieces of code that can be removed without affecting the central functionality. +- Have a road map to follow to simplify your code. +::: + +You're excited by how much progress you're making in your research. You've made a lot of descriptive plots and gained some interesting insights into your data. Now you're excited to investigate whether the k-rat exclusion plots are actually working. You set to work writing a bunch of code to do this, using a combination of descriptive visualizations and linear models. + +So far, you've been saving all of your analysis in a script called "krat-analysis.R". At this point, it looks something like this: +```{r "krat-analysis.R"} +#| eval: false +#| code-fold: true +# Kangaroo rat analysis using the Portal data +# Created by: Research McResearchface +# Last updated: 2024-11-22 + +# Load packages to use in this script +library(readr) +library(dplyr) +library(ggplot2) +library(stringr) + +# Read in the data +rodents <- read_csv("scripts/data/surveys_complete_77_89.csv") + +### DATA WRANGLING #### +glimpse(rodents) # or click on the environment +str(rodents) # an alternative that does the same thing +head(rodents) # or open fully with View() or click in environment + +table(rodents$taxa) + +# Abundance distribution of taxa +rodents %>% + ggplot(aes(x=taxa))+ + geom_bar() + +# Examine NA values +## How do we find NAs anyway? ---- +head(is.na(rodents$taxa)) # logical--tells us when an observation is an NA (T or F) + +# Not very helpful. BUT +sum(is.na(rodents$taxa)) # sum considers T = 1 and F = 0 + +# Simplify down to just rodents +rodents <- rodents %>% + filter(taxa == "Rodent") +glimpse(rodents) + +# Just kangaroo rats because this is what we are studying +krats <- rodents %>% + filter(genus == "Dipodomys") +dim(krats) # okay, so that's a lot smaller, great. +glimpse(krats) + +# Prep for time analysis +# To examine trends over time, we'll need to create a date column +krats <- krats %>% + mutate(date = lubridate::ymd(paste(year, month, day, sep = "-"))) + +# Examine differences in different time periods +krats <- krats %>% + mutate(time_period = ifelse(year < 1988, "early", "late")) + +# Check that this went through; check for NAs +table(krats$time_period, exclude = NULL) # learned how to do this earlier + +### QUESTION 1: How many k-rats over time in the past? ### +# How many kangaroo rats of each species were found at the study site in past years (so you know what to expect for a sample size this year)? + +# Numbers over time by plot type +krats %>% + ggplot(aes(x = date, fill = plot_type)) + + geom_histogram()+ + facet_wrap(~species)+ + theme_bw()+ + scale_fill_viridis_d(option = "plasma")+ + geom_vline(aes(xintercept = lubridate::ymd("1988-01-01")), col = "dodgerblue") + +# Oops we gotta get rid of the unidentified k-rats +krats <- krats %>% + filter(species != "sp.") + +# Re-do the plot above +krats %>% + ggplot(aes(x = date, fill = plot_type)) + + geom_histogram()+ + facet_wrap(~species)+ + theme_bw()+ + scale_fill_viridis_d(option = "plasma")+ + geom_vline(aes(xintercept = lubridate::ymd("1988-01-01")), col = "dodgerblue") + +# How many individuals caught per day? +krats_per_day <- krats %>% + group_by(date, year, species) %>% + summarize(n = n()) %>% + group_by(species) + +krats_per_day %>% + ggplot(aes(x = species, y = n))+ + geom_boxplot(outlier.shape = NA)+ + geom_jitter(width = 0.2, alpha = 0.2, aes(col = year))+ + theme_classic()+ + ylab("Number per day")+ + xlab("Species") + +#### QUESTION 2: Do the k-rat exclusion plots work? ##### +# Do the k-rat exclusion plots work? (i.e. Does the abundance of each species differ by plot?) +# If the k-rat plots work, then we would expect: +# A. Fewer k-rats overall in any of the exclusion plots than in the control, with the fewest in the long-term k-rat exclusion plot +counts_per_day <- krats %>% + group_by(year, plot_id, plot_type, month, day, species_id) %>% + summarize(count_per_day = n()) + +counts_per_day %>% + ggplot(aes(x = plot_type, y = count_per_day, fill = species_id, group = interaction(plot_type, species_id)))+ + geom_boxplot(outlier.size = 0.5)+ + theme_minimal()+ + labs(title = "Kangaroo rat captures, all years", + x = "Plot type", + y = "Individuals per day", + fill = "Species") + +# B. For Spectabilis-specific exclosure, we expect a lower proportion of spectabilis there than in the other plots. +control_spectab <- krats %>% + filter(plot_type %in% c("Control", "Spectab exclosure")) + +prop_spectab <- control_spectab %>% + group_by(year, plot_type, species_id) %>% + summarize(total_count = n(), .groups = "drop_last") %>% + mutate(prop = total_count/sum(total_count)) %>% + filter(species_id == "DS") # keep only spectabilis + +prop_spectab %>% + ggplot(aes(x = year, y = prop, col = plot_type))+ + geom_point()+ + geom_line()+ + theme_minimal()+ + labs(title = "Spectab exclosures did not reduce proportion of\nspectab captures", + y = "Spectabilis proportion", + x = "Year", + color = "Plot type") + +#### MODELING #### +counts_mod <- lm(count_per_day ~ plot_type + species_id, data = counts_per_day) +summary(counts_mod) + +# with interaction term: +counts_mod_interact <- lm(count_per_day ~ plot_type*species_id, data = counts_per_day) +summary(counts_mod_interact) + +summary(counts_mod) +summary(counts_mod_interact) +``` + +## Why is it important to simplify code? + +Learning how to simplify your code is one of the most important parts of making a minimal reproducible example, asking others for help, and helping yourself. + +::::::::::::::::::::::::::::::::::::::::::: challenge +## Making sense of code + +Reflect on a time when you opened a coding project after a long time away from it. Or maybe you had to look through and try to run someone else's code. + +(If you have easy access to one of your past projects, maybe try opening it now and taking a look through it right now!) + +How do situations like this make you feel? Write some reflections on the Etherpad. + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: + +Debugging is a time when it's common to have to read through long and complex code (either your own or someone else's). That means that the person doing the debugging is likely to experience some of the emotions we just talked about. + +The more we can reduce the negative emotions and make the experience of solving errors easy and painless, the likelier you are to find solutions to your problems (or convince others to take the time to help you). Helpers are doing us a favor--why put barriers in their way? + +Let's illustrate the importance of simplifying our code by focusing on an error in the big long analysis script we created, shown above. Let's imagine we're getting ready to show these preliminary results to our advisor, but when we re-run the whole script, we realize there's a problem. + +[DESCRIPTION OF PROBLEM HERE] + +## A road map for simplifying your code + +In this episode, we're going to walk through a road map for breaking your code down to its simplest form while making sure that 1) it still runs, and 2) it reproduces the problem you care about solving. + +For now, we'll go through this road map step by step. At the end, we'll review the whole thing. One takeaway from this lesson is that there is a step by step process to follow, and you can refer back to it if you feel lost in the future. + +### Step 0. Create a separate script + +When we know there's a problem with our script, it helps to start solving it by examining smaller parts of the code in a separate script, instead of editing the original. + +:::::::::::::::::::::::::::::::::::::challenge +## A separate place for minimal code + +Create a new, blank R script and give it a name, such as "reprex-script.R" + +There are several ways to make an R script +- File > New File > R Script +- Click the white square with a green plus sign at the top left corner of your RStudio window +- Use a keyboard shortcut: Cmd + Shift + N (on a Mac) or Ctrl + Shift + N (on Windows) + +Once you've created the script, click the Save button to name and save it. + +This exercise should take about 2 minutes. +::::::::::::::::::::::::::::::::::::::::::: + +### Step 1. Identify the problem area + +Now that we have a script, let's zero in on what's broken. + +First, we should use some of the techniques we learned in the "Identify the Problem" episode and see if they help us solve our error. + +[MORE CONTENT THAT CALLS BACK TO PL'S EPISODE HERE] + +In this particular case, though, we weren't able to completely resolve our error. + +[WHY? maybe because it's not an error but a case of "the plot isn't returning what we want"? Or maybe it's an extra difficult error message that we can't find an easy answer to? + +I need to figure out what error to introduce into the script in the first place... that will determine the justification to use here.] + +(*Using the plot example for now*) + +Okay, so we know that the plot doesn't look the way we want it to. Which part of the code created that plot? One way to figure this out if we're not sure is to step through the code line by line. + +:::::::::::::::::::::::::::::::::::::callout +## Stepping through code, line by line + +Placing your cursor on a line of code and using the keyboard shortcut Cmd + Enter (Mac) or Ctrl + Enter (Windows) will run that line of code *and* it will automatically advance your cursor to the next line. This makes it easy to "step through" your code without having to click or highlight. +::::::::::::::::::::::::::::::::::::::::::: + +Yay, we found the trouble spot! Let's go ahead and copy that line of code and paste it over into the empty script we created, "reprex-script.R". + +### Step 2. Give context: functions and packages + +R code consists primarily of *variables* and *functions*. + +::::::::::::::::::::::::::::::::::::::::::: challenge +## Where do functions come from? + +When coding in R, we use a lot of different functions. Where do those functions come from? How can we make sure that our helpers have access to those sources? Take a moment to brainstorm. + +This exercise should take about 3 minutes. +::::::::::::::::::::::::::::::::::::::::::: +::: solution +Functions in R typically come from packages. Some packages, such as `{base}` and `{stats}`, are loaded in R by default, so you might not have realized that they are packages too. + +You can see a complete list of functions in `{base}` and `{stats}` by running `library(help = "base")` or `library(help = "stats")`. + +Some functions might be user-defined. In that case, you'll need to make sure to include the function definition in your reprex. +::: + +::::::::::::::::::::::::::::::::::::::::::: callout +## Finding functions + +Sometimes it can be hard to figure out where a function comes from. Especially if a function comes from a package you use frequently, you might not remember where it comes from! + +You can search for a function in the help docs with `??fun` (where "fun" is the name of the function). To explicitly declare which package a function comes from, you can use a double colon `::`--for example, `dplyr::select()`. Declaring the function with a double colon also allows you to use that function even if the package is not loaded, as long as it's installed. +::::::::::::::::::::::::::::::::::::::::::: + +The quickest way to make sure others have access to the functions contained in packages is to include a `library()` call in your reprex, so they know to load the package too. + +::::::::::::::::::::::::::::::::::::::::::: challenge +## Which packages are essential? + +In each of the following code snippets, identify the necessary packages (or other code) to make the example reproducible. + +- [Example (including an ambiguous function: `dplyr::select()` is a good one because it masks `plyr::select()`)] +- [Example where you have to look up which package a function comes from] +- [Example with a user-defined function that doesn't exist in any package] + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: +::: solution +FIXME +::: + +Looking through the problem area that we isolated, we can see that we'll need to load the following packages: FIXME +- `{package}` +- `{package}` +- `{package}` + +Let's go ahead and add those as `library()` calls to the top of our script. + +::::::::::::::::::::::::::::::::::::::::::: callout +## Installing vs. loading packages + +But what if our helper doesn't have all of these packages installed? Won't the code not be reproducible? + +Typically, we don't include `install.packages()` in our code for each of the packages that we include in the `library()` calls, because `install.packages()` is a one-time piece of code that doesn't need to be repeated every time the script is run. We assume that our helper will see `library(specialpackage)` and know that they need to go install "specialpackage" on their own. + +Technically, this makes that part of the code not reproducible! But it's also much more "polite". Our helper might have their own way of managing package versions, and forcing them to install a package when they run our code risks messing up our workflow. It is a common convention to stick with `library()` and let them figure it out from there. +FIXME this feels over-explained... pare it down! +::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::::::::: callout +## Installing packages conditionally + +There is an alternative approach to installing packages [insert content/example of the if(require()) thing--but note that explaining this properly requires explaining why require() is different from library(), why it returns a logical, etc. and is kind of a rabbit hole that I don't want to go down here.] +::::::::::::::::::::::::::::::::::::::::::: + +### Step 3. Give context: variables and datasets + +Isolating the problem area and loading the necessary packages and functions was an important step to making our example code self-contained. But we're still not done making the code minimal and reproducible. Almost certainly, our code snippet relies on variables, such as datasets, that our helper won't have access to. + +The piece of code that we copied over came from line [LINE NUMBER] of our analysis script. We had done a lot of analyses before then, including modifying datasets and creating intermediate objects/variables. + +Our code snippet depends on all those previous steps, so when we isolate it in a new script, it might not be able to run anymore. More importantly, when a helper doesn't have access to the rest of our script, the code might not run for them either. + +To fix this, we need to provide some additional context around our reprex so that it runs. + +::::::::::::::::::::::::::::::::::::::::::: challenge +## Identifying variables + +For each of the following code snippets, identify all the variables used + +- [Straightforward example] +- [Example where they use a built-in dataset but it contains a column that that dataset doesn't actually contain, i.e. because it's been modified previously. Might be good to use the `date` column that we put into `krats` for this] + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: +::: solution +FIXME +::: + +As you might have noticed, identifying these variables isn't always straightforward. Sometimes variables depend on other variables, and before you know it, you end up needing the entire script. + +Let's work together as a group to sketch out which variables depend on which others. A helpful way to do this is to start with the variables included in our code snippet and ask, for each one, "Where did this come from?" + +[Make a big dependency graph. The point is to illustrate that it gets very long and you can't always rely on this process to identify a simple way to include the needed variables.] + +How can we make sure that helpers can access these objects too, without providing them the entire long script? + +Theoretically, we could meticulously trace each object back and make sure to include the code to create all of its predecessors from the original data, which we would provide to our helper. But pretty soon, we might find that we're just giving the helper the original (long, complicated) script! + +As with other types of writing, creating a good minimal reprex takes hard work and time. + +> "I would have written a shorter letter, but I did not have the time." +> +> - Blaise Pascal, *Lettres Provinciales*, 1657 + + +::::::::::::::::::::::::::::::::::::::::::: callout +## Computational reproducibility + +Every object should be able to map back to either a file, a built-in dataset in R, or another intermediate step. If you found any variables where you weren't able to answer the "Where did this come from?" question, then that's a problem! Did you build a script that mistakenly relied on an object that was in your environment but was never properly defined? + +Mapping exercises like this can be a great way to check whether entire script is reproducible. Reproducibility is important in more cases than just debugging! More and more journals are requiring full analysis code to be posted, and if that code isn't reproducible, it will severely hamper other researchers' efforts to confirm and expand on what you've done. + +Various packages can help you keep track of your code and make it more reproducible. Check out the [`{targets}`](https://books.ropensci.org/targets/) and [`{renv}`](https://rstudio.github.io/renv/articles/renv.html) packages in particular if you're interested in learning more. +::::::::::::::::::::::::::::::::::::::::::: + +Luckily, we can make our lives easier if we realize that helpers don't always need the exact same variables and datasets, just reasonably good stand-ins. Let's think back to the last episode, where we talked about different ways to create minimal reproducible datasets. We can lean on those skills here to make our example reproducible and greatly reduce the amount of code that we need to include. + +:::::::::::::::::::::::::::::::::::::::::::challenge +## Incorporating minimal datasets + +Brainstorm some places in our reprex where you could use minimal reproducible data to make your problem area code snippet reproducible. + +Which of the techniques from the [data episode](LINK TO DATA EPISODE) will you choose in each case, and why? + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: +::: solution +FIXME +::: + +_**Using a minimal dataset simplifies not just your data but also your code, because it lets you avoid including data wrangling steps in your reprex!**_ + +### Step 4. Simplify + +We're almost done! Now we have code that runs because it includes the necessary `library()` calls and makes use of minimal datasets that still allow us to showcase the problem. Our script is almost ready to send to our helpers. + +But reading someone else's code can be slow! We want to make it very, very easy for our helper to see which part of the code is important to focus on. Let's see if there are any places where we can trim code down even more to eliminate distractions. + +Often, analysis code contains exploratory steps or other analyses that don't directly relate to the problem, such as calls to `head()`, `View()`, `str()`, or similar functions. (Exception: if you're using these directly to show things like dimension changes that help to illustrate the problem). + +Some other common examples are exploratory analyses, extra formatting added to plots, and [ANOTHER EXAMPLE]. + +When cutting these things, we have to be careful not to remove anything that would cause the code to no longer reproduce our problem. In general, it's a good idea to comment out the line you think is extraneous, re-run the code, and check that the focal problem persists before removing it entirely. + +:::::::::::::::::::::::::::::::::::::::::::challenge +## Trimming down the bells and whistles + +[Ex: removing various things, observing what happens, identifying whether or not we care about those things. (Need to include at least one that's tricky, like maybe it does change the actual values but it doesn't change their relationship to each other)] + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: +::: solution +FIXME +::: + +Great work! We've created a minimal reproducible example. In the next episode, we'll learn about `{reprex}`, a package that can help us double-check that our example is reproducible by running it in a clean environment. (As an added bonus, `{reprex}` will format our example nicely so it's easy to post to places like Slack, GitHub, and StackOverflow.) + +More on that soon. For now, let's review the road map that we just practiced. + +## Road map review +### Step 0. Create a separate script + - It helps to have a separate place to work on your minimal code snippet. + +### Step 1. Identify the problem area + - Which part of the code is causing the problem? Move it over to the reprex script so we can focus on it. + +### Step 2. Give context: functions and packages + - Make sure that helpers have access to all the functions they'll need to run your code snippet. + +### Step 3. Give context: variables and datasets + - Make sure that helpers have access to all the variables they'll need to run your code snippet, or reasonable stand-ins. + +### Step 4. Simplify + - Remove any extra code that isn't absolutely necessary to demonstrate your problem. + +:::::::::::::::::::::::::::::::::::::::::::challenge +## Reflection + +Let's take a moment to reflect on this process. + +- What's one thing you learned in this episode? An insight; a new skill; a process? + +- What is one thing you're still confused about? What questions do you have? + +This exercise should take about 5 minutes. +::::::::::::::::::::::::::::::::::::::::::: diff --git a/episodes/5-minimal-reproducible-code.md b/episodes/5-minimal-reproducible-code.md deleted file mode 100644 index 790b9638..00000000 --- a/episodes/5-minimal-reproducible-code.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: "Minimal Reproducible Code" -teaching: 10 -exercises: 2 ---- - -:::::::::::::::::::::::::::::::::::::: questions - -- To do - -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -- To do - -:::::::::::::::::::::::::::::::::::::::::::::::: - -Idea: take some of the same exercises from the understanding your code section and use those to get people to break down code into minimal chunks. Emphasize that it's not always going to be linear ( diff --git a/episodes/6-asking-your-question.md b/episodes/6-asking-your-question.md index 77134cf2..b6010c0e 100644 --- a/episodes/6-asking-your-question.md +++ b/episodes/6-asking-your-question.md @@ -6,6 +6,7 @@ exercises: 2 :::::::::::::::::::::::::::::::::::::: questions +- How can I make sure my minimal reproducible example will actually run correctly for someone else? - How can I easily share a reproducible example with a mentor or helper, or online? - How do I ask a good question? @@ -13,13 +14,16 @@ exercises: 2 ::::::::::::::::::::::::::::::::::::: objectives -- To do +- Use the reprex package to support making reproducible examples. +- Use the reprex package to format reprexes for posting online. +- Understand the benefits and drawbacks of different places to get help. +- Have a road map to follow when posting a question to make sure it's a good question. :::::::::::::::::::::::::::::::::::::::::::::::: - ::::::::::::::::::::::::::::::::::::: keypoints - The {reprex} package makes it easy to format and share your reproducible examples. +- Following a certain set of steps will make your questions clearer and likelier to get answered. :::::::::::::::::::::::::::::::::::::::::::::::: diff --git a/renv/activate.R b/renv/activate.R new file mode 100644 index 00000000..0eb51088 --- /dev/null +++ b/renv/activate.R @@ -0,0 +1,1305 @@ + +local({ + + # the requested version of renv + version <- "1.0.11" + attr(version, "sha") <- NULL + + # the project directory + project <- Sys.getenv("RENV_PROJECT") + if (!nzchar(project)) + project <- getwd() + + # use start-up diagnostics if enabled + diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE") + if (diagnostics) { + start <- Sys.time() + profile <- tempfile("renv-startup-", fileext = ".Rprof") + utils::Rprof(profile) + on.exit({ + utils::Rprof(NULL) + elapsed <- signif(difftime(Sys.time(), start, units = "auto"), digits = 2L) + writeLines(sprintf("- renv took %s to run the autoloader.", format(elapsed))) + writeLines(sprintf("- Profile: %s", profile)) + print(utils::summaryRprof(profile)) + }, add = TRUE) + } + + # figure out whether the autoloader is enabled + enabled <- local({ + + # first, check config option + override <- getOption("renv.config.autoloader.enabled") + if (!is.null(override)) + return(override) + + # if we're being run in a context where R_LIBS is already set, + # don't load -- presumably we're being run as a sub-process and + # the parent process has already set up library paths for us + rcmd <- Sys.getenv("R_CMD", unset = NA) + rlibs <- Sys.getenv("R_LIBS", unset = NA) + if (!is.na(rlibs) && !is.na(rcmd)) + return(FALSE) + + # next, check environment variables + # TODO: prefer using the configuration one in the future + envvars <- c( + "RENV_CONFIG_AUTOLOADER_ENABLED", + "RENV_AUTOLOADER_ENABLED", + "RENV_ACTIVATE_PROJECT" + ) + + for (envvar in envvars) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(tolower(envval) %in% c("true", "t", "1")) + } + + # enable by default + TRUE + + }) + + # bail if we're not enabled + if (!enabled) { + + # if we're not enabled, we might still need to manually load + # the user profile here + profile <- Sys.getenv("R_PROFILE_USER", unset = "~/.Rprofile") + if (file.exists(profile)) { + cfg <- Sys.getenv("RENV_CONFIG_USER_PROFILE", unset = "TRUE") + if (tolower(cfg) %in% c("true", "t", "1")) + sys.source(profile, envir = globalenv()) + } + + return(FALSE) + + } + + # avoid recursion + if (identical(getOption("renv.autoloader.running"), TRUE)) { + warning("ignoring recursive attempt to run renv autoloader") + return(invisible(TRUE)) + } + + # signal that we're loading renv during R startup + options(renv.autoloader.running = TRUE) + on.exit(options(renv.autoloader.running = NULL), add = TRUE) + + # signal that we've consented to use renv + options(renv.consent = TRUE) + + # load the 'utils' package eagerly -- this ensures that renv shims, which + # mask 'utils' packages, will come first on the search path + library(utils, lib.loc = .Library) + + # unload renv if it's already been loaded + if ("renv" %in% loadedNamespaces()) + unloadNamespace("renv") + + # load bootstrap tools + ansify <- function(text) { + if (renv_ansify_enabled()) + renv_ansify_enhanced(text) + else + renv_ansify_default(text) + } + + renv_ansify_enabled <- function() { + + override <- Sys.getenv("RENV_ANSIFY_ENABLED", unset = NA) + if (!is.na(override)) + return(as.logical(override)) + + pane <- Sys.getenv("RSTUDIO_CHILD_PROCESS_PANE", unset = NA) + if (identical(pane, "build")) + return(FALSE) + + testthat <- Sys.getenv("TESTTHAT", unset = "false") + if (tolower(testthat) %in% "true") + return(FALSE) + + iderun <- Sys.getenv("R_CLI_HAS_HYPERLINK_IDE_RUN", unset = "false") + if (tolower(iderun) %in% "false") + return(FALSE) + + TRUE + + } + + renv_ansify_default <- function(text) { + text + } + + renv_ansify_enhanced <- function(text) { + + # R help links + pattern <- "`\\?(renv::(?:[^`])+)`" + replacement <- "`\033]8;;ide:help:\\1\a?\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # runnable code + pattern <- "`(renv::(?:[^`])+)`" + replacement <- "`\033]8;;ide:run:\\1\a\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # return ansified text + text + + } + + renv_ansify_init <- function() { + + envir <- renv_envir_self() + if (renv_ansify_enabled()) + assign("ansify", renv_ansify_enhanced, envir = envir) + else + assign("ansify", renv_ansify_default, envir = envir) + + } + + `%||%` <- function(x, y) { + if (is.null(x)) y else x + } + + catf <- function(fmt, ..., appendLF = TRUE) { + + quiet <- getOption("renv.bootstrap.quiet", default = FALSE) + if (quiet) + return(invisible()) + + msg <- sprintf(fmt, ...) + cat(msg, file = stdout(), sep = if (appendLF) "\n" else "") + + invisible(msg) + + } + + header <- function(label, + ..., + prefix = "#", + suffix = "-", + n = min(getOption("width"), 78)) + { + label <- sprintf(label, ...) + n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L) + if (n <= 0) + return(paste(prefix, label)) + + tail <- paste(rep.int(suffix, n), collapse = "") + paste0(prefix, " ", label, " ", tail) + + } + + heredoc <- function(text, leave = 0) { + + # remove leading, trailing whitespace + trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text) + + # split into lines + lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]] + + # compute common indent + indent <- regexpr("[^[:space:]]", lines) + common <- min(setdiff(indent, -1L)) - leave + text <- paste(substring(lines, common), collapse = "\n") + + # substitute in ANSI links for executable renv code + ansify(text) + + } + + startswith <- function(string, prefix) { + substring(string, 1, nchar(prefix)) == prefix + } + + bootstrap <- function(version, library) { + + friendly <- renv_bootstrap_version_friendly(version) + section <- header(sprintf("Bootstrapping renv %s", friendly)) + catf(section) + + # attempt to download renv + catf("- Downloading renv ... ", appendLF = FALSE) + withCallingHandlers( + tarball <- renv_bootstrap_download(version), + error = function(err) { + catf("FAILED") + stop("failed to download:\n", conditionMessage(err)) + } + ) + catf("OK") + on.exit(unlink(tarball), add = TRUE) + + # now attempt to install + catf("- Installing renv ... ", appendLF = FALSE) + withCallingHandlers( + status <- renv_bootstrap_install(version, tarball, library), + error = function(err) { + catf("FAILED") + stop("failed to install:\n", conditionMessage(err)) + } + ) + catf("OK") + + # add empty line to break up bootstrapping from normal output + catf("") + + return(invisible()) + } + + renv_bootstrap_tests_running <- function() { + getOption("renv.tests.running", default = FALSE) + } + + renv_bootstrap_repos <- function() { + + # get CRAN repository + cran <- getOption("renv.repos.cran", "https://cloud.r-project.org") + + # check for repos override + repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA) + if (!is.na(repos)) { + + # check for RSPM; if set, use a fallback repository for renv + rspm <- Sys.getenv("RSPM", unset = NA) + if (identical(rspm, repos)) + repos <- c(RSPM = rspm, CRAN = cran) + + return(repos) + + } + + # check for lockfile repositories + repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity) + if (!inherits(repos, "error") && length(repos)) + return(repos) + + # retrieve current repos + repos <- getOption("repos") + + # ensure @CRAN@ entries are resolved + repos[repos == "@CRAN@"] <- cran + + # add in renv.bootstrap.repos if set + default <- c(FALLBACK = "https://cloud.r-project.org") + extra <- getOption("renv.bootstrap.repos", default = default) + repos <- c(repos, extra) + + # remove duplicates that might've snuck in + dupes <- duplicated(repos) | duplicated(names(repos)) + repos[!dupes] + + } + + renv_bootstrap_repos_lockfile <- function() { + + lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock") + if (!file.exists(lockpath)) + return(NULL) + + lockfile <- tryCatch(renv_json_read(lockpath), error = identity) + if (inherits(lockfile, "error")) { + warning(lockfile) + return(NULL) + } + + repos <- lockfile$R$Repositories + if (length(repos) == 0) + return(NULL) + + keys <- vapply(repos, `[[`, "Name", FUN.VALUE = character(1)) + vals <- vapply(repos, `[[`, "URL", FUN.VALUE = character(1)) + names(vals) <- keys + + return(vals) + + } + + renv_bootstrap_download <- function(version) { + + sha <- attr(version, "sha", exact = TRUE) + + methods <- if (!is.null(sha)) { + + # attempting to bootstrap a development version of renv + c( + function() renv_bootstrap_download_tarball(sha), + function() renv_bootstrap_download_github(sha) + ) + + } else { + + # attempting to bootstrap a release version of renv + c( + function() renv_bootstrap_download_tarball(version), + function() renv_bootstrap_download_cran_latest(version), + function() renv_bootstrap_download_cran_archive(version) + ) + + } + + for (method in methods) { + path <- tryCatch(method(), error = identity) + if (is.character(path) && file.exists(path)) + return(path) + } + + stop("All download methods failed") + + } + + renv_bootstrap_download_impl <- function(url, destfile) { + + mode <- "wb" + + # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715 + fixup <- + Sys.info()[["sysname"]] == "Windows" && + substring(url, 1L, 5L) == "file:" + + if (fixup) + mode <- "w+b" + + args <- list( + url = url, + destfile = destfile, + mode = mode, + quiet = TRUE + ) + + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(url) + if (length(headers) && is.character(headers)) + args$headers <- headers + } + + do.call(utils::download.file, args) + + } + + renv_bootstrap_download_custom_headers <- function(url) { + + headers <- getOption("renv.download.headers") + if (is.null(headers)) + return(character()) + + if (!is.function(headers)) + stopf("'renv.download.headers' is not a function") + + headers <- headers(url) + if (length(headers) == 0L) + return(character()) + + if (is.list(headers)) + headers <- unlist(headers, recursive = FALSE, use.names = TRUE) + + ok <- + is.character(headers) && + is.character(names(headers)) && + all(nzchar(names(headers))) + + if (!ok) + stop("invocation of 'renv.download.headers' did not return a named character vector") + + headers + + } + + renv_bootstrap_download_cran_latest <- function(version) { + + spec <- renv_bootstrap_download_cran_latest_find(version) + type <- spec$type + repos <- spec$repos + + baseurl <- utils::contrib.url(repos = repos, type = type) + ext <- if (identical(type, "source")) + ".tar.gz" + else if (Sys.info()[["sysname"]] == "Windows") + ".zip" + else + ".tgz" + name <- sprintf("renv_%s%s", version, ext) + url <- paste(baseurl, name, sep = "/") + + destfile <- file.path(tempdir(), name) + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (inherits(status, "condition")) + return(FALSE) + + # report success and return + destfile + + } + + renv_bootstrap_download_cran_latest_find <- function(version) { + + # check whether binaries are supported on this system + binary <- + getOption("renv.bootstrap.binary", default = TRUE) && + !identical(.Platform$pkgType, "source") && + !identical(getOption("pkgType"), "source") && + Sys.info()[["sysname"]] %in% c("Darwin", "Windows") + + types <- c(if (binary) "binary", "source") + + # iterate over types + repositories + for (type in types) { + for (repos in renv_bootstrap_repos()) { + + # build arguments for utils::available.packages() call + args <- list(type = type, repos = repos) + + # add custom headers if available -- note that + # utils::available.packages() will pass this to download.file() + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(repos) + if (length(headers) && is.character(headers)) + args$headers <- headers + } + + # retrieve package database + db <- tryCatch( + as.data.frame( + do.call(utils::available.packages, args), + stringsAsFactors = FALSE + ), + error = identity + ) + + if (inherits(db, "error")) + next + + # check for compatible entry + entry <- db[db$Package %in% "renv" & db$Version %in% version, ] + if (nrow(entry) == 0) + next + + # found it; return spec to caller + spec <- list(entry = entry, type = type, repos = repos) + return(spec) + + } + } + + # if we got here, we failed to find renv + fmt <- "renv %s is not available from your declared package repositories" + stop(sprintf(fmt, version)) + + } + + renv_bootstrap_download_cran_archive <- function(version) { + + name <- sprintf("renv_%s.tar.gz", version) + repos <- renv_bootstrap_repos() + urls <- file.path(repos, "src/contrib/Archive/renv", name) + destfile <- file.path(tempdir(), name) + + for (url in urls) { + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (identical(status, 0L)) + return(destfile) + + } + + return(FALSE) + + } + + renv_bootstrap_download_tarball <- function(version) { + + # if the user has provided the path to a tarball via + # an environment variable, then use it + tarball <- Sys.getenv("RENV_BOOTSTRAP_TARBALL", unset = NA) + if (is.na(tarball)) + return() + + # allow directories + if (dir.exists(tarball)) { + name <- sprintf("renv_%s.tar.gz", version) + tarball <- file.path(tarball, name) + } + + # bail if it doesn't exist + if (!file.exists(tarball)) { + + # let the user know we weren't able to honour their request + fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist." + msg <- sprintf(fmt, tarball) + warning(msg) + + # bail + return() + + } + + catf("- Using local tarball '%s'.", tarball) + tarball + + } + + renv_bootstrap_github_token <- function() { + for (envvar in c("GITHUB_TOKEN", "GITHUB_PAT", "GH_TOKEN")) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(envval) + } + } + + renv_bootstrap_download_github <- function(version) { + + enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE") + if (!identical(enabled, "TRUE")) + return(FALSE) + + # prepare download options + token <- renv_bootstrap_github_token() + if (nzchar(Sys.which("curl")) && nzchar(token)) { + fmt <- "--location --fail --header \"Authorization: token %s\"" + extra <- sprintf(fmt, token) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "curl", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } else if (nzchar(Sys.which("wget")) && nzchar(token)) { + fmt <- "--header=\"Authorization: token %s\"" + extra <- sprintf(fmt, token) + saved <- options("download.file.method", "download.file.extra") + options(download.file.method = "wget", download.file.extra = extra) + on.exit(do.call(base::options, saved), add = TRUE) + } + + url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version) + name <- sprintf("renv_%s.tar.gz", version) + destfile <- file.path(tempdir(), name) + + status <- tryCatch( + renv_bootstrap_download_impl(url, destfile), + condition = identity + ) + + if (!identical(status, 0L)) + return(FALSE) + + renv_bootstrap_download_augment(destfile) + + return(destfile) + + } + + # Add Sha to DESCRIPTION. This is stop gap until #890, after which we + # can use renv::install() to fully capture metadata. + renv_bootstrap_download_augment <- function(destfile) { + sha <- renv_bootstrap_git_extract_sha1_tar(destfile) + if (is.null(sha)) { + return() + } + + # Untar + tempdir <- tempfile("renv-github-") + on.exit(unlink(tempdir, recursive = TRUE), add = TRUE) + untar(destfile, exdir = tempdir) + pkgdir <- dir(tempdir, full.names = TRUE)[[1]] + + # Modify description + desc_path <- file.path(pkgdir, "DESCRIPTION") + desc_lines <- readLines(desc_path) + remotes_fields <- c( + "RemoteType: github", + "RemoteHost: api.github.com", + "RemoteRepo: renv", + "RemoteUsername: rstudio", + "RemotePkgRef: rstudio/renv", + paste("RemoteRef: ", sha), + paste("RemoteSha: ", sha) + ) + writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path) + + # Re-tar + local({ + old <- setwd(tempdir) + on.exit(setwd(old), add = TRUE) + + tar(destfile, compression = "gzip") + }) + invisible() + } + + # Extract the commit hash from a git archive. Git archives include the SHA1 + # hash as the comment field of the tarball pax extended header + # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html) + # For GitHub archives this should be the first header after the default one + # (512 byte) header. + renv_bootstrap_git_extract_sha1_tar <- function(bundle) { + + # open the bundle for reading + # We use gzcon for everything because (from ?gzcon) + # > Reading from a connection which does not supply a 'gzip' magic + # > header is equivalent to reading from the original connection + conn <- gzcon(file(bundle, open = "rb", raw = TRUE)) + on.exit(close(conn)) + + # The default pax header is 512 bytes long and the first pax extended header + # with the comment should be 51 bytes long + # `52 comment=` (11 chars) + 40 byte SHA1 hash + len <- 0x200 + 0x33 + res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len]) + + if (grepl("^52 comment=", res)) { + sub("52 comment=", "", res) + } else { + NULL + } + } + + renv_bootstrap_install <- function(version, tarball, library) { + + # attempt to install it into project library + dir.create(library, showWarnings = FALSE, recursive = TRUE) + output <- renv_bootstrap_install_impl(library, tarball) + + # check for successful install + status <- attr(output, "status") + if (is.null(status) || identical(status, 0L)) + return(status) + + # an error occurred; report it + header <- "installation of renv failed" + lines <- paste(rep.int("=", nchar(header)), collapse = "") + text <- paste(c(header, lines, output), collapse = "\n") + stop(text) + + } + + renv_bootstrap_install_impl <- function(library, tarball) { + + # invoke using system2 so we can capture and report output + bin <- R.home("bin") + exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R" + R <- file.path(bin, exe) + + args <- c( + "--vanilla", "CMD", "INSTALL", "--no-multiarch", + "-l", shQuote(path.expand(library)), + shQuote(path.expand(tarball)) + ) + + system2(R, args, stdout = TRUE, stderr = TRUE) + + } + + renv_bootstrap_platform_prefix <- function() { + + # construct version prefix + version <- paste(R.version$major, R.version$minor, sep = ".") + prefix <- paste("R", numeric_version(version)[1, 1:2], sep = "-") + + # include SVN revision for development versions of R + # (to avoid sharing platform-specific artefacts with released versions of R) + devel <- + identical(R.version[["status"]], "Under development (unstable)") || + identical(R.version[["nickname"]], "Unsuffered Consequences") + + if (devel) + prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r") + + # build list of path components + components <- c(prefix, R.version$platform) + + # include prefix if provided by user + prefix <- renv_bootstrap_platform_prefix_impl() + if (!is.na(prefix) && nzchar(prefix)) + components <- c(prefix, components) + + # build prefix + paste(components, collapse = "/") + + } + + renv_bootstrap_platform_prefix_impl <- function() { + + # if an explicit prefix has been supplied, use it + prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA) + if (!is.na(prefix)) + return(prefix) + + # if the user has requested an automatic prefix, generate it + auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA) + if (is.na(auto) && getRversion() >= "4.4.0") + auto <- "TRUE" + + if (auto %in% c("TRUE", "True", "true", "1")) + return(renv_bootstrap_platform_prefix_auto()) + + # empty string on failure + "" + + } + + renv_bootstrap_platform_prefix_auto <- function() { + + prefix <- tryCatch(renv_bootstrap_platform_os(), error = identity) + if (inherits(prefix, "error") || prefix %in% "unknown") { + + msg <- paste( + "failed to infer current operating system", + "please file a bug report at https://github.com/rstudio/renv/issues", + sep = "; " + ) + + warning(msg) + + } + + prefix + + } + + renv_bootstrap_platform_os <- function() { + + sysinfo <- Sys.info() + sysname <- sysinfo[["sysname"]] + + # handle Windows + macOS up front + if (sysname == "Windows") + return("windows") + else if (sysname == "Darwin") + return("macos") + + # check for os-release files + for (file in c("/etc/os-release", "/usr/lib/os-release")) + if (file.exists(file)) + return(renv_bootstrap_platform_os_via_os_release(file, sysinfo)) + + # check for redhat-release files + if (file.exists("/etc/redhat-release")) + return(renv_bootstrap_platform_os_via_redhat_release()) + + "unknown" + + } + + renv_bootstrap_platform_os_via_os_release <- function(file, sysinfo) { + + # read /etc/os-release + release <- utils::read.table( + file = file, + sep = "=", + quote = c("\"", "'"), + col.names = c("Key", "Value"), + comment.char = "#", + stringsAsFactors = FALSE + ) + + vars <- as.list(release$Value) + names(vars) <- release$Key + + # get os name + os <- tolower(sysinfo[["sysname"]]) + + # read id + id <- "unknown" + for (field in c("ID", "ID_LIKE")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + id <- vars[[field]] + break + } + } + + # read version + version <- "unknown" + for (field in c("UBUNTU_CODENAME", "VERSION_CODENAME", "VERSION_ID", "BUILD_ID")) { + if (field %in% names(vars) && nzchar(vars[[field]])) { + version <- vars[[field]] + break + } + } + + # join together + paste(c(os, id, version), collapse = "-") + + } + + renv_bootstrap_platform_os_via_redhat_release <- function() { + + # read /etc/redhat-release + contents <- readLines("/etc/redhat-release", warn = FALSE) + + # infer id + id <- if (grepl("centos", contents, ignore.case = TRUE)) + "centos" + else if (grepl("redhat", contents, ignore.case = TRUE)) + "redhat" + else + "unknown" + + # try to find a version component (very hacky) + version <- "unknown" + + parts <- strsplit(contents, "[[:space:]]")[[1L]] + for (part in parts) { + + nv <- tryCatch(numeric_version(part), error = identity) + if (inherits(nv, "error")) + next + + version <- nv[1, 1] + break + + } + + paste(c("linux", id, version), collapse = "-") + + } + + renv_bootstrap_library_root_name <- function(project) { + + # use project name as-is if requested + asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE") + if (asis) + return(basename(project)) + + # otherwise, disambiguate based on project's path + id <- substring(renv_bootstrap_hash_text(project), 1L, 8L) + paste(basename(project), id, sep = "-") + + } + + renv_bootstrap_library_root <- function(project) { + + prefix <- renv_bootstrap_profile_prefix() + + path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA) + if (!is.na(path)) + return(paste(c(path, prefix), collapse = "/")) + + path <- renv_bootstrap_library_root_impl(project) + if (!is.null(path)) { + name <- renv_bootstrap_library_root_name(project) + return(paste(c(path, prefix, name), collapse = "/")) + } + + renv_bootstrap_paths_renv("library", project = project) + + } + + renv_bootstrap_library_root_impl <- function(project) { + + root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA) + if (!is.na(root)) + return(root) + + type <- renv_bootstrap_project_type(project) + if (identical(type, "package")) { + userdir <- renv_bootstrap_user_dir() + return(file.path(userdir, "library")) + } + + } + + renv_bootstrap_validate_version <- function(version, description = NULL) { + + # resolve description file + # + # avoid passing lib.loc to `packageDescription()` below, since R will + # use the loaded version of the package by default anyhow. note that + # this function should only be called after 'renv' is loaded + # https://github.com/rstudio/renv/issues/1625 + description <- description %||% packageDescription("renv") + + # check whether requested version 'version' matches loaded version of renv + sha <- attr(version, "sha", exact = TRUE) + valid <- if (!is.null(sha)) + renv_bootstrap_validate_version_dev(sha, description) + else + renv_bootstrap_validate_version_release(version, description) + + if (valid) + return(TRUE) + + # the loaded version of renv doesn't match the requested version; + # give the user instructions on how to proceed + dev <- identical(description[["RemoteType"]], "github") + remote <- if (dev) + paste("rstudio/renv", description[["RemoteSha"]], sep = "@") + else + paste("renv", description[["Version"]], sep = "@") + + # display both loaded version + sha if available + friendly <- renv_bootstrap_version_friendly( + version = description[["Version"]], + sha = if (dev) description[["RemoteSha"]] + ) + + fmt <- heredoc(" + renv %1$s was loaded from project library, but this project is configured to use renv %2$s. + - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile. + - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library. + ") + catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote) + + FALSE + + } + + renv_bootstrap_validate_version_dev <- function(version, description) { + expected <- description[["RemoteSha"]] + is.character(expected) && startswith(expected, version) + } + + renv_bootstrap_validate_version_release <- function(version, description) { + expected <- description[["Version"]] + is.character(expected) && identical(expected, version) + } + + renv_bootstrap_hash_text <- function(text) { + + hashfile <- tempfile("renv-hash-") + on.exit(unlink(hashfile), add = TRUE) + + writeLines(text, con = hashfile) + tools::md5sum(hashfile) + + } + + renv_bootstrap_load <- function(project, libpath, version) { + + # try to load renv from the project library + if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) + return(FALSE) + + # warn if the version of renv loaded does not match + renv_bootstrap_validate_version(version) + + # execute renv load hooks, if any + hooks <- getHook("renv::autoload") + for (hook in hooks) + if (is.function(hook)) + tryCatch(hook(), error = warnify) + + # load the project + renv::load(project) + + TRUE + + } + + renv_bootstrap_profile_load <- function(project) { + + # if RENV_PROFILE is already set, just use that + profile <- Sys.getenv("RENV_PROFILE", unset = NA) + if (!is.na(profile) && nzchar(profile)) + return(profile) + + # check for a profile file (nothing to do if it doesn't exist) + path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project) + if (!file.exists(path)) + return(NULL) + + # read the profile, and set it if it exists + contents <- readLines(path, warn = FALSE) + if (length(contents) == 0L) + return(NULL) + + # set RENV_PROFILE + profile <- contents[[1L]] + if (!profile %in% c("", "default")) + Sys.setenv(RENV_PROFILE = profile) + + profile + + } + + renv_bootstrap_profile_prefix <- function() { + profile <- renv_bootstrap_profile_get() + if (!is.null(profile)) + return(file.path("profiles", profile, "renv")) + } + + renv_bootstrap_profile_get <- function() { + profile <- Sys.getenv("RENV_PROFILE", unset = "") + renv_bootstrap_profile_normalize(profile) + } + + renv_bootstrap_profile_set <- function(profile) { + profile <- renv_bootstrap_profile_normalize(profile) + if (is.null(profile)) + Sys.unsetenv("RENV_PROFILE") + else + Sys.setenv(RENV_PROFILE = profile) + } + + renv_bootstrap_profile_normalize <- function(profile) { + + if (is.null(profile) || profile %in% c("", "default")) + return(NULL) + + profile + + } + + renv_bootstrap_path_absolute <- function(path) { + + substr(path, 1L, 1L) %in% c("~", "/", "\\") || ( + substr(path, 1L, 1L) %in% c(letters, LETTERS) && + substr(path, 2L, 3L) %in% c(":/", ":\\") + ) + + } + + renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) { + renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv") + root <- if (renv_bootstrap_path_absolute(renv)) NULL else project + prefix <- if (profile) renv_bootstrap_profile_prefix() + components <- c(root, renv, prefix, ...) + paste(components, collapse = "/") + } + + renv_bootstrap_project_type <- function(path) { + + descpath <- file.path(path, "DESCRIPTION") + if (!file.exists(descpath)) + return("unknown") + + desc <- tryCatch( + read.dcf(descpath, all = TRUE), + error = identity + ) + + if (inherits(desc, "error")) + return("unknown") + + type <- desc$Type + if (!is.null(type)) + return(tolower(type)) + + package <- desc$Package + if (!is.null(package)) + return("package") + + "unknown" + + } + + renv_bootstrap_user_dir <- function() { + dir <- renv_bootstrap_user_dir_impl() + path.expand(chartr("\\", "/", dir)) + } + + renv_bootstrap_user_dir_impl <- function() { + + # use local override if set + override <- getOption("renv.userdir.override") + if (!is.null(override)) + return(override) + + # use R_user_dir if available + tools <- asNamespace("tools") + if (is.function(tools$R_user_dir)) + return(tools$R_user_dir("renv", "cache")) + + # try using our own backfill for older versions of R + envvars <- c("R_USER_CACHE_DIR", "XDG_CACHE_HOME") + for (envvar in envvars) { + root <- Sys.getenv(envvar, unset = NA) + if (!is.na(root)) + return(file.path(root, "R/renv")) + } + + # use platform-specific default fallbacks + if (Sys.info()[["sysname"]] == "Windows") + file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv") + else if (Sys.info()[["sysname"]] == "Darwin") + "~/Library/Caches/org.R-project.R/R/renv" + else + "~/.cache/R/renv" + + } + + renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) { + sha <- sha %||% attr(version, "sha", exact = TRUE) + parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L))) + paste(parts, collapse = "") + } + + renv_bootstrap_exec <- function(project, libpath, version) { + if (!renv_bootstrap_load(project, libpath, version)) + renv_bootstrap_run(version, libpath) + } + + renv_bootstrap_run <- function(version, libpath) { + + # perform bootstrap + bootstrap(version, libpath) + + # exit early if we're just testing bootstrap + if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA))) + return(TRUE) + + # try again to load + if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) { + return(renv::load(project = getwd())) + } + + # failed to download or load renv; warn the user + msg <- c( + "Failed to find an renv installation: the project will not be loaded.", + "Use `renv::activate()` to re-initialize the project." + ) + + warning(paste(msg, collapse = "\n"), call. = FALSE) + + } + + renv_json_read <- function(file = NULL, text = NULL) { + + jlerr <- NULL + + # if jsonlite is loaded, use that instead + if ("jsonlite" %in% loadedNamespaces()) { + + json <- tryCatch(renv_json_read_jsonlite(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + jlerr <- json + + } + + # otherwise, fall back to the default JSON reader + json <- tryCatch(renv_json_read_default(file, text), error = identity) + if (!inherits(json, "error")) + return(json) + + # report an error + if (!is.null(jlerr)) + stop(jlerr) + else + stop(json) + + } + + renv_json_read_jsonlite <- function(file = NULL, text = NULL) { + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + jsonlite::fromJSON(txt = text, simplifyVector = FALSE) + } + + renv_json_read_default <- function(file = NULL, text = NULL) { + + # find strings in the JSON + text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n") + pattern <- '["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]' + locs <- gregexpr(pattern, text, perl = TRUE)[[1]] + + # if any are found, replace them with placeholders + replaced <- text + strings <- character() + replacements <- character() + + if (!identical(c(locs), -1L)) { + + # get the string values + starts <- locs + ends <- locs + attr(locs, "match.length") - 1L + strings <- substring(text, starts, ends) + + # only keep those requiring escaping + strings <- grep("[[\\]{}:]", strings, perl = TRUE, value = TRUE) + + # compute replacements + replacements <- sprintf('"\032%i\032"', seq_along(strings)) + + # replace the strings + mapply(function(string, replacement) { + replaced <<- sub(string, replacement, replaced, fixed = TRUE) + }, strings, replacements) + + } + + # transform the JSON into something the R parser understands + transformed <- replaced + transformed <- gsub("{}", "`names<-`(list(), character())", transformed, fixed = TRUE) + transformed <- gsub("[[{]", "list(", transformed, perl = TRUE) + transformed <- gsub("[]}]", ")", transformed, perl = TRUE) + transformed <- gsub(":", "=", transformed, fixed = TRUE) + text <- paste(transformed, collapse = "\n") + + # parse it + json <- parse(text = text, keep.source = FALSE, srcfile = NULL)[[1L]] + + # construct map between source strings, replaced strings + map <- as.character(parse(text = strings)) + names(map) <- as.character(parse(text = replacements)) + + # convert to list + map <- as.list(map) + + # remap strings in object + remapped <- renv_json_read_remap(json, map) + + # evaluate + eval(remapped, envir = baseenv()) + + } + + renv_json_read_remap <- function(json, map) { + + # fix names + if (!is.null(names(json))) { + lhs <- match(names(json), names(map), nomatch = 0L) + rhs <- match(names(map), names(json), nomatch = 0L) + names(json)[rhs] <- map[lhs] + } + + # fix values + if (is.character(json)) + return(map[[json]] %||% json) + + # handle true, false, null + if (is.name(json)) { + text <- as.character(json) + if (text == "true") + return(TRUE) + else if (text == "false") + return(FALSE) + else if (text == "null") + return(NULL) + } + + # recurse + if (is.recursive(json)) { + for (i in seq_along(json)) { + json[i] <- list(renv_json_read_remap(json[[i]], map)) + } + } + + json + + } + + # load the renv profile, if any + renv_bootstrap_profile_load(project) + + # construct path to library root + root <- renv_bootstrap_library_root(project) + + # construct library prefix for platform + prefix <- renv_bootstrap_platform_prefix() + + # construct full libpath + libpath <- file.path(root, prefix) + + # run bootstrap code + renv_bootstrap_exec(project, libpath, version) + + invisible() + +}) diff --git a/renv/profile b/renv/profile new file mode 100644 index 00000000..6d4023b5 --- 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