Skip to main content

Lintr Bot, lintr’s Hester egg

(This article was first published on Maëlle, and kindly contributed to R-bloggers)

Remember my blog post about automatic tools for improving R packages? One of these tools is Jim Hester’s lintr, a package that performs static code analysis. In my experience it mostly helps identifying too long code lines and missing space, although it’s a bit more involved than that. In any case, lintr helps you maintain good code style, and as mentioned in that now old post of mine, you can add a lintr unit test to your package which will ensure you don’t get lazy over time.

Now say your package has a lintr unit test and lives on GitHub. What happens if someone makes a pull request and writes looong code lines? Continuous integration builds will fail but not only that… The contributor will get to know Lintr Bot, lintr’s Hester (Easter) egg!

Lintr Bot, a lazy little thing?

Lintr Bot has a GitHub profile. I am actually responsible for its avatar, because when I discovered it a while back I offered Jim to design it based on this free image. Now, Lintr Bot has a face but if you look at its GitHub activity, you might wonder, what does it even do? It’s perfectly fine for a human to have nearly no GitHub activity, but remember that Lintr Bot lives on GitHub only, so what does it do all day?

Well Lintr Bot goes around and comments on Pull Requests in R packages, see for instance this comment. As mentioned earlier, a contributor who forgets even a slight code style mistake will be reminded of it by Lintr Bot if the package has a lintr unit test. This is quite useful!

We might also wonder how often Lintr Bot actually works? After all, since GitHub activity timeline doesn’t show comments on Pull Requests, we’re at a loss when it comes to estimating how active this bot is…

Lintr Bot’s year in review

That’s where my ghrecipes package, presented in my blog post about Bob Rudis’ R packages, will come into play. I’ve been working on a function called spy which can return either Pull Requests or Issues an user commented on during a given period of time. As creepy as it might sound, it could help you trace back all the desperate comments you wrote when debugging something last week for instance, without your having had to track them in the first place. ghrecipe::spy is a work in progress, but let’s see what it can return me for Lintr Bot!

Note that there’s no way to filter for comments between a date range, well as far as I understand the “search” functionalities of GitHub V4 API, so I can only filter Pull Requests updated in a given range.

lintrbot_act <- ghrecipes::spy(user = "lintr-bot", type = "PullRequest",
                               updated_after = "2017-03-30",
                               updated_before = "2018-03-30")
lintrbot_act <- dplyr::filter(lintrbot_act,
                              as.Date(created_at) >= anytime::anydate("2017-03-30"))

We get 228 pull requests in one year! Lintr Bot is definitely not a lazy little thing!

Here are 5 Pull Requests that got a visit from Lintr Bot.

knitr::kable(lintrbot_act[1:5,])
owner repo title created_at state author url no_comments id
PredictiveEcology reproducible mergeCache 2018-03-29 18:29:33 MERGED eliotmcintire https://github.com/PredictiveEcology/reproducible/pull/18 2 18
PredictiveEcology SpaDES.core change objectSize to objSize – name conflict with R.oo::objectSize 2018-03-26 23:48:22 MERGED eliotmcintire https://github.com/PredictiveEcology/SpaDES.core/pull/57 1 57
PredictiveEcology reproducible memoise – loadFromRepo 2018-03-26 17:41:25 MERGED eliotmcintire https://github.com/PredictiveEcology/reproducible/pull/17 3 17
PredictiveEcology quickPlot thin fixes – bugfixes, SpatialPolygonsDataFrames 2018-03-26 05:32:12 MERGED eliotmcintire https://github.com/PredictiveEcology/quickPlot/pull/13 2 13
ptl93 AEDA added getEps 2018-03-25 17:12:44 MERGED MiGraber https://github.com/ptl93/AEDA/pull/39 5 39

Let’s have a look at the time series of comments.

library("ggplot2")
library("hrbrthemes")
ggplot(lintrbot_act) +
  geom_segment(aes(x = created_at, xend = created_at),
               y = 0, yend = 1) +
  theme_ipsum(base_size = 16,
              strip_text_size = 16) +
  ggtitle("Lintr Bot's work events",
          subtitle = "Each vertical line indicates Lintr Bot's commenting on a pull request")

plot of chunk unnamed-chunk-3

So it seems that Lintr Bot works quite regularly, with some intense periods! I won’t look at the number of repos over time to differentiate activity due to more activity in a few repos or due to several repos, but I’m curious to see who was helped by Lintr Bot.

library("magrittr")
dplyr::group_by(lintrbot_act, owner) %>%
  dplyr::summarize(repos = toString(unique(repo)), 
                   n = n()) %>%
  dplyr::arrange(- n) %>%
  dplyr::filter(n >= 5) %>%
  knitr::kable()
owner repos n
RTMC tmc-rstudio, tmc-r-tester, tmc-r 64
mlr-org mlr 38
PredictiveEcology reproducible, SpaDES.core, quickPlot, SpaDES.tools, SpaDES.shiny 26
Azure doAzureParallel 16
HealthCatalyst healthcareai-r 16
neuropsychology psycho.R 11
ropensci drake 11
hbc bcbioRNASeq, bcbioSingleCell 5

I must say that besides the recently onboarded rOpenSci package drake by Will Landau, I don’t know any of these repos!

Hire Lintr Bot?

What about you? Would you let that cute little bot help you?

Note, a good package to improve code style automatically is styler by Kirill Müller and Lorenz Walthert, so if Lintr Bot has a lot to tell you, give styler::style_pkg() a try!

To leave a comment for the author, please follow the link and comment on their blog: Maëlle.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


from R-bloggers https://ift.tt/2GoOmJc
via IFTTT
Labels:

Comments

Post a Comment

Popular posts from this blog

Solving Van der Pol equation with ivp_solve

Van der Pol’s differential equation is The equation describes a system with nonlinear damping, the degree of damping given by μ. If μ = 0 the system is linear and undamped, but for positive μ the system is nonlinear and damped. We will plot the phase portrait for the solution to Van der Pol’s equation in Python using SciPy’s new ODE solver ivp_solve . The function ivp_solve does not solve second-order systems of equations directly. It solves systems of first-order equations, but a second-order differential equation can be recast as a pair of first-order equations by introducing the first derivative as a new variable. Since y is the derivative of x , the phase portrait is just the plot of ( x , y ). If μ = 0, we have a simple harmonic oscillator and the phase portrait is simply a circle. For larger values of μ the solutions enter limiting cycles, but the cycles are more complicated than just circles. Here’s the Python code that made the plot. from scipy import linspace from ...
Post a Comment
Read more

Lawyer: 'Socialite Grifter' Anna Sorokin 'Had To Do It Her Way' (And Steal $275,000)

Opening statements were made in the "Socialite Grifter" trial on Wednesday, and both sides provided extremely different reasons why Anna Sorokin allegedly scammed a number of people and institutions out of $275,000. [ more › ] Gothamist https://ift.tt/2HXgI0E March 29, 2019 at 12:33AM
Post a Comment
Read more

5 Massively Important AI Features In Time Tracking Applications

Artificial intelligence has transformed the future of many industries. One area that has been under- investigated is the use of AI in time tracking technology. AI is Fundamentally Changing the Future of Time Tracking Technology A time tracking software is a worthy investment irrespective of the size of your organization. It generates accurate reports based on the amount of time your team spends working on a task. These reports facilitate planning of budgets for upcoming projects. Many AI tools are changing the nature of time management. MindSync AI discussed the pivotal role of AI in time management in a Medium article . Why is time tracking software important? It helps with keeping track of the hours being invested on a given task. This sheds light on the timeline for the overall project. It also helps in determining the productivity levels of the employees. This is one of the many reasons that AI is driving workplace productivity . But how can employers utilize it effectively? ...
Post a Comment
Read more