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Submission: colocr #243

Closed MahShaaban closed 5 years ago

MahShaaban commented 6 years ago

Summary

Package: colocr
Type: Package
Title: Conduct Co-localization Analysis of Fluorescence Microscopy Images
Version: 0.1.0
License: GPL-3
Authors@R: person("Mahmoud", "Ahmed",
    email = "mahmoud.s.fahmy@students.kasralainy.edu.eg",
    role = c("aut", "cre"))
URL: https://github.com/MahShaaban/colocr
BugReports: https://github.com/MahShaaban/colocr/issues
Description: Automate the co-localization analysis of fluoresence microscopy 
  images. Selecting regions of interest, extract pixel intensities from 
  the image channels and calculate different co-localization statistics.
Encoding: UTF-8
LazyData: true
Suggests: testthat,
    covr,
    knitr,
    rmarkdown,
    devtools
RoxygenNote: 6.0.1
Imports: imager,
  shiny,
  scales
VignetteBuilder: knitr

[e.g., "data extraction, because the package parses a scientific data file format"] data extraction

Requirements

Confirm each of the following by checking the box. This package:

Publication options

Detail

maelle commented 6 years ago

Thanks @mahshaaban! 😀

Before I proceed with regular checks since the package has a Shiny app could you have a look at our brand new specific guidance for Shiny apps especially their testing?

https://ropensci.github.io/dev_guide/building.html#interactivegraphical-interfaces

https://ropensci.github.io/dev_guide/building.html#testing

You can ask any question here / on discuss.ropensci.org / Slack (had you gotten an invite to our Slack yet?)

MahShaaban commented 6 years ago

Hey, @maelle

I had a look at links.

I don't think I've received any invites on slack.

maelle commented 6 years ago

:wave: @MahShaaban

I am already using the shinytest in the app

Ah sorry! But I don't see where in the tests? In particular, "shinytest" isn't written in DESCRIPTION like "testthat" is?

Code auto-generation sounds like a great idea, do you recommend any resource to get started on that? No, I'll research that and update this thread (and the guide, good point!)

I've now sent you an invite to the Slack.

MahShaaban commented 6 years ago

Thanks for the slack invite, @maelle

Regarding the shiny tests. The tests are in the app directory, inst/colocr_app/tests/. The reason is I wanted to test the app on Travis separately. If necessary, I will move the tests to run with the package testing.

maelle commented 6 years ago

To be honest I am very new to the testing ofShiny apps inside packages, and once your package is onboarded it'll probably be an example linked from the gitbook. 😸 Why test the app separately? What does it mean exactly? Your Travis config file looks normal to me 🤔

MahShaaban commented 6 years ago

I am also new to shinytest.

I wanted to test the app separately cuz I intended to make it stand-alone and I have a prototype of it on shinyapps.io, here.

I have the app as a separate repo on github, here. This repo is added to the package repo as a submodule. The app repo contains separate .travis.yml and tests/. This way I can link the app as a regular repo on Travis and it runs the tests in with every commit.

maelle commented 6 years ago

Ah interesting! Could you ask in the forum/Slack what best practice is? One thing that I still wonder about is whether your package's README should include a badge of the app's build status.

I'll make other checks as soon as I can.

MahShaaban commented 6 years ago

Alright. Will do. I think I can add the build status badge from travis, if this what you mean.

Thanks

maelle commented 6 years ago

Do you want to look into code generation before your package undergoes review? I tagged you in a commit, see links to apps generating code here.

maelle commented 6 years ago

goodpractice output

  ✖ use '<-' for assignment instead of '='. '<-' is
    the standard, and R users and developers are used it and it
    is easier to read your code for them if you use '<-'.

    R\methods.R:367:12
    R\methods.R:368:12
    R\methods.R:369:5

  ✖ avoid long code lines, it is bad for readability.
    Also, many people prefer editor windows that are about 80
    characters wide. Try make your lines shorter than 80
    characters

    inst\extdata\manual_analysis.R:6:1
    R\methods.R:378:1
    R\methods.R:379:1
    R\methods.R:380:1
    R\methods.R:381:1

and devtools::spell_check

WORD          FOUND IN
caculated     coloc_test.Rd:23
determin      roi_select.Rd:42
differen      roi_show.Rd:19
fluoresence   description:1
indecies      intensity_get.Rd:15
indicies      roi_show.Rd:16
manders       coloc_test.Rd:12
pearsons      coloc_test.Rd:12
reslution     roi_show.Rd:24
Thes          roi_select.Rd:36
MahShaaban commented 6 years ago

I made the following changes

maelle commented 6 years ago

Cool, I'll now look for reviewers.

Could you please add this badge to the README of your package:

[![](https://badges.ropensci.org/243_status.svg)](https://github.com/ropensci/onboarding/issues/243)
maelle commented 6 years ago

Editor checks:

Editor comments

Already tackled :wink:

Reviewers: @haozhu233 @seaaan Due date: 2018-09-03

maelle commented 6 years ago

Thanks @haozhu233 and @seaaan for accepting to review this package! Your reviews are due on 2018-09-03.

Here are links to our reviewer guide and review template (as you're both experienced rOpenSci reviewers/authors comments on the guide are welcome in e.g. Slack but don't feel obliged to review both the package and the reviewing guide :wink:).

seaaan commented 6 years ago

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

Documentation

The package includes all the following forms of documentation:

Functionality

Final approval (post-review)

Estimated hours spent reviewing: 3

Review Comments

I tested on two computers: Windows 10, running R 3.5.1 and Ubuntu 16.04 running R 3.4.4.

colocr provides an R-based workflow for a type of microscopy analysis. Specifically, images are taken of two different colors, typically with each color representing a different molecule of interest. With colocr, users can measure how much the two colors correlate in their intensity in different parts of the image.The steps are: select (and verify) regions of interest in the image, get (and view) the intensity of each pixel in each channel, and calculate two measures of correlation to assess co-localization of the two channels.

The method of colocalization analysis is not new; the purpose of colocr is to make it easier to perform in R (while also providing a GUI option) for more reproducible results. The package is pretty simple and easy to use for this specific task, as opposed to more complex packages like imager.

Below I have written comments based on the checkbox sections listed above. An overview of the most significant issues that I discovered:

More minor issues are named below. (Note that I have pointed out typos using this format: "the typo" -> "the correction".)

The package is pretty nicely done and most of my comments are relatively minor. Good job!

Documentation

Statement of need

Good overview!

"at loss" -> "at a loss" "straight forward" -> "straightforward"

(Note that those two corrections should also be made in the vignette.)

Installation

I don't understand what the "no submodules!" and "@development" mean in the installation guidelines. Can you explain (on the README) what that means and why?

It took me a while to get the package installed on Ubuntu. Specifically, imager wouldn't install because its dependencies Cairo and tiff wouldn't install until I installed some external-to-R dependencies. I think that if I had followed the installation instructions here it would have gone smoothly. It would be helpful for you to include a link to how to install imager on ubuntu in the installation instructions on github.

Vignette

Unsuccessful building of vignette

The vignette did not build successfully on either the Windows or the Ubuntu computer. This also prevented devtools::check() from succeeding. Specifically, I would get this error: 

pandoc.exe: Could not fetch
: openBinaryFile: does not exist (No such file or directory)
Error: processing vignette 'using_colocr.Rmd' failed with diagnostics:
pandoc document conversion failed with error 67
Execution halted

I figured out (via https://github.com/rstudio/rmarkdown/issues/228) that changing the header of the vignette to include the following lines would fix it: 

output:
  rmarkdown::html_vignette:
    self_contained: no

This fixed the issue on both the Windows and Ubuntu computers.

The vignette installed fine when installing with devtools::install_github(.., build_vignettes = TRUE).

Vignette accessibility

I suggest adding a link to an HTML version of the vignette on your github README. As it is now, I have to install the package before I can read the vignette, which is a barrier to me installing it because I can't see very well what it does. Either knit the vignette to a github markdown document and link to that or, once it's on CRAN, link to the web version of the vignette that CRAN hosts.

Vignette content

appropriatness -> appropriateness

acheived -> achieved

The reset of the anlysis -> the result (?) of the analysis

Pearson’s Coefficeint Correlation -> Pearson correlation coefficient

co-distrubuted -> co-distributed

straight forward -> straightforward

three steps only -> only three steps

functionionality -> functionality

encampasing -> encompassing

resuliontion -> resolution

Arguable -> arguably

seleciton -> selection

sampe -> sample

Although -> this sentence should be combined with the next one, otherwise "Although" doesn't make sense

rational -> rationale

separatly -> separately

through and formal -> thorough and formal

correltion -> correlation

Note that the vignette installation instructions differ from the README instructions.

Maybe hide the startup messages that appear after library(imager)

In the "Getting started" section, give some background on what the image is, what the "appropriate parameters" are and how you determine them and evaluate their appropriateness. What are the cells in the image and what are they stained for? What is the goal of the ROIs? At the example setting, the five cells in the upper middle all appear enclosed in one region together, is that the goal? I would have thought to make a separate regions for each cell.

Why do you use imager::highlight() instead of roi_show()?

Some of the background I am asking for in the "Getting started" section is provided in the "Detailed Example" section. I might just skip the "Getting started" section altogether and add a little more background to the "Detailed Example" section.

Why is the "Manders Overlap Coefficient" abbreviated with "SCC"? And why does the link go to a page about the Spearman's rank correlation coefficient?

Is the link to the shiny app running on shinyapps.io the same as the app that comes with the package?

"calling four different functions in order" but there are five listed

What are you looking for in the intensity_show plots?

Does the labeling of individual regions of interest just label the n largest contiguous regions? This is not totally clear from the vignette.

In the colocalization benchmark source, it would be helpful to know what results are expected from the particular images chosen. Does colocr achieve the expected results?

Function documentation

?roi_select: It would be nice to have a more thorough explanation of the parameters to roi_select than the help page provides, or a link to one at least. There's something funny in the details section where it says fill\codeclean, looks like in the .R file there's a stray slash on line 21.

?roi_show: in the details section, it says "a a low resolution"

?colocr::colocr-package and/or ?colocr::colocr should link to the main functions in the package, otherwise they're not very useful.

Examples

example("intensity_get") isn't very informative. I suggest printing out the head of each element of pix_int and explaining what they are. Also it says "call coloc_test" which is a mistake

example("intensity_show") runs slowly on my computer, can you subsample the image or something?

Community guidelines

There are no guidelines for how to contribute to the package.

FUNCTIONALITY

Installation

Installation from github mostly works fine. I had some issues as described in the documentation described above.

Functionality

The functionality described in the vignette and documentation is present and works just fine for the most part!

run_app() gives warnings and errors immediately on loading (argument is of length 0). It works once you load an image, but it's a bit disconcerting to see error messages.

One issue I thought about is what if you want to correlate the intensity of more than two channels?

Another issue I thought about is whether there could be a higher-level function to apply thresholds and get co-localization statistics for a bunch of images at once. I imagine relatively few cases where the user would want to repeat the code over and over for every image. Maybe a function that takes a vector of images and vectors for each argument to roi_select and then gives back the results of coloc_test for each image? You could also make functions for highlight and roi_show and intensity_show that could create some kind of report with each of the images.

I don't really understand how roi_select works with regard to channel 1 vs channel 2. Could you explain that somewhere, perhaps in the vignette? I.e. can you select an ROI based just on channel 1 and apply that to channel 1 and channel 2?

Maybe import and re-export the functions from imager that are needed for the vignette (and thus likely to be used by the user), so the user doesn't have to library(imager)

It would be nice to have more descriptive names for the results of coloc_test than $'p' and $r.

Suggested API

Currently, the basic workflow is to do: 

px <- roi_select(img, threshold = 90)
pix_int <- intensity_get(img, px)
coloc_test(pix_int)

It would be nice if you could do something like this: 

img %>% 
  roi_select(threshold = 90) %>% 
  intensity_get() %>% 
  coloc_test()

The benefits I see are that it's pipeable and that you don't have to separately keep track of px and pix_int in addition to img. Another benefit is that all the functions could return the same type of object. If you accept this option, you could drop the intensity_get() function all together by combining it with the roi_select function. You could make the functions pipeable and all return the same type by having each of the functions return a cimg with attributes containing px and pix_int that are used by the appropriate function or by making a new class as a subclass of cimg.

This is not a requirement by any means, just something that I think would be nice!

Performance

roi_select: with threshold = 100, you get a weird error when running highlight. With threshold > 100, you get a weird error when running roi_select, same with thresholds well below 0. I suggest enforcing 0 < threshold < 100 (i.e. throw an error if the provided value is outside that range). The errors are hard to interpret so it's not obvious that they're happening because of the value of threshold. Currently it just checks if it's numeric or not. Same for the other parameters as appropriate.

The app runs kind of slowly for me. It's not unusable by any means, but if there's some way you could speed it up (eg subsample the image to set the threshold and other parameters initially and then have a button to push to do the full analysis), that would be nice.

Automated checks

goodpractice::goodpractice()

goodpractice::goodpractice() gives me the following message on both Windows and Ubuntu: 

  ✖ write unit tests for all functions, and all package code in general. 87% of code lines are
    covered by test cases.

    R/methods.R:52:NA
    R/methods.R:53:NA
    R/methods.R:54:NA
    R/methods.R:62:NA
    R/methods.R:138:NA
    ... and 12 more lines

Ubuntu-only errors: 

  ✖ fix this R CMD check WARNING: LaTeX errors when creating PDF version. This typically
    indicates Rd problems.
  ✖ fix this R CMD check ERROR: Re-running with no redirection of stdout/stderr. Hmm ... looks
    like a package You may want to clean up by 'rm -rf /tmp/RtmpKNtM4d/Rd2pdf2e0446da511d'

Windows-only errors: 

Warning messages:
1: In MYPREPS[[prep]](state, quiet = quiet) :
  Prep step for cyclomatic complexity failed.
2: In MYPREPS[[prep]](state, quiet = quiet) :
  Prep step for rcmdcheck failed.
devtools::check()

Passes completely on Ubuntu once vignette issue is resolved (see Vignettes section).

It results in an error on Windows in the "Checking examples" section when it runs the example for .manders, I think because .manders isn't exported.

Build process

Works fine on Ubuntu once vignette issue is resolved (see Vignettes section)

I get these warnings on Windows: 

Rd warning: H:/R/colocr/man/roi_select.Rd:16: file link 'shrink' in package 'imager' does not exist and so has been treated as a topic
Rd warning: H:/R/colocr/man/roi_select.Rd:20: file link 'fill' in package 'imager' does not exist and so has been treated as a topic
Rd warning: H:/R/colocr/man/roi_select.Rd:38: file link 'shrink' in package 'imager' does not exist and so has been treated as a topic
Rd warning: H:/R/colocr/man/roi_select.Rd:39: file link 'fill' in package 'imager' does not exist and so has been treated as a topic
Tests

Tests pass.

Documentation

I get these warnings on Ubuntu: 

Skipping invalid path:  .labels_add.Rd 
Skipping invalid path:  .pearson.Rd 
Skipping invalid path:  .manders.Rd

Packaging guide

Section 1.3.2 suggests that a Shiny app should come with a mechanism to reproduce the choices made in the GUI, such as auto-generation of code. Maybe you can add a tab that generates the code that is run based on the user's selections?

Section 1.7 suggests creating a documentation website.

maelle commented 6 years ago

Thanks for your thorough review @seaaan! 😀

MahShaaban commented 6 years ago

Thanks, @seaaan for your review. I will follow with a reply to these issues.

I really like your suggested API. I made quick changes in a new branch called pipeable to try this approach. If it turned out this works better, I will rewrite the tests, app and the vignette.

maelle commented 6 years ago

@haozhu233 just a reminder that your review is due on 2018-09-03. Thank you! 😺

haozhu233 commented 6 years ago

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

Documentation

The package includes all the following forms of documentation:

Functionality

Final approval (post-review)

Estimated hours spent reviewing: 3

Review Comments

I reviewed the development branch of this package as it has a stand-alone folder for that shiny app. I noticed that there are some recent changes in the master branch but they are mostly about badges. I run all the checks on OSX 10.13.

This package provides a straightforward way of conducting co-localization calculation, a specific type of image analysis for microscopy images. There are only 5 main functions in this package but they are quite useful and easy to use. The author also provided a GUI in a shiny app which makes it even more user friendly. The package vignette was also well written and easy to follow. Although I personally don't have any experience with analyzing microscopy images, I was still able to follow the tutorial and achieve what was supposed to be done. I can imagine this package can be quite useful for this particular use case.

Here I'm listing out some of the most significant issues I found:

There are also a few minor issues that might be improved. I'm going to mention them below as well.

R codes

  1. The default value for n in roi_select was defined in .labels_add which is not an exported function. It confuses RStudio and promotes a code diagnosis warning that "the value of n is not defined".

  2. In coloc_test, it would be nice to standardize the type options with the terms used in the vignette. For example, "pearsons" can become "PCC" or "pcc", or vice versa. At the same time, the option "all" should be "both".

  3. The purpose of using S3 method here seems to be just sending out a warning message if class(img) != "cimg". It seems a little unnecessary. This logic can simply be replaced by a if statement which can even be modularized to a small utility function.

  4. It might be a good idea to split the contents of methods.R into multiple R files, like roi.R & intensity.R, based on their purposes.

  5. The function name run_app is too general. Consider name it differently. For example, colocr_app

Shiny App

  1. When there is no image input, there are some error message on the screen. To solve this problem, it's recommended to put req(img1()) at the very beginning of every output object. For, example, 
output$image_plot <- renderPlot({
    req(input$image1)
    par(mfrow=c(2,2), mar = rep(1, 4))
    roi_show(img = img1(),
                   px = px(),
                   labels = labs.px())
  })
  1. There is no need to define img1, labs.px, px and corr in reactiveValues. You can just start with things like this.
values <- reactiveValues()
  # load images
img1 <- reactive({
    load.image(input$image1$datapath) # note that I removed the value assignment
})
  1. For your question here (https://github.com/MahShaaban/colocr/blob/636435f57d034e02a9bc05cc7bd5d646e5dcb970/inst/colocr_app/app.R#L149), you can just say values$df <- data.frame() and that's enough.

For these three issues above, check out this modified version: https://gist.github.com/haozhu233/7a02bb4a03ccf4b5ef63acf40c53bbc7

  1. As I said above, it would be nice if users can upload a zipped file with many pictures. You can add two button for going back and forward and a simple counter to make it easy to use.

  2. It would be nice to add download to csv buttons to those two tables on the "Tabular View" tab.

  3. The correlation texts could be reformatted and become more readable.

Others

  1. I also feel like that a pkgdown site might be helpful according to the package guideline.
  2. I also really like @seaaan's suggestion on using the pipeable approach and am very excited to see it's getting implemented.
maelle commented 6 years ago

Thanks a lot for your review @haozhu233! 😀

@mahshaaban now both reviews are in.

MahShaaban commented 6 years ago

Thanks @haozhu233 for your review. I will work on the changes you suggested. Regarding your last comment, I made some changes to use the pipeable approach, here. I haven't merged the changes to the development branch yet. So please, let me know if you have any comments on that as well.

MahShaaban commented 6 years ago

I'd like first to thank you guys for your time and the thoughtful comments.

I've made a quite a few changes based on the comments and the suggestions above. So I will start by mentioning these then I will reply to each of the reviews separately.

The changes I made for these reviews are in a new branch called pipeable

To install this branch from github direclty please use

devtools::install_github('MahShaaban/colocr@pipeable')

Since the master still behind these changes and has the app directory as a submodule, please used the following to pull the repo and view it locally

$ git clone --recursive-submodules https://github.com/MahShaaban/colocr

Main changes

The main changes are:

img <- imager::load.image('path to image file')
img %>%
    roi_select(threshold = 50) %>%
    roi_test()

So all function takes a cimg as an input. roi_select returns a cimg with an attribute label. roi_show and roi_check return lists of 4 and 2 plots, respectively. Finally, roi_test returns a data.frame.

Here I reply to the different issues raised by both reviewers

First review (@seaaan)

Reviewer comments

Documentation

Functionality

Second review (@haozhu233)

Reviewer comments

R code

Shiny app

I really appreciate all the comments and the suggestions from both reviewers. They definately helped me improving the code.

maelle commented 6 years ago

Thanks @MahShaaban!

@seaaan @haozhu233 could you please have a look at @MahShaaban's answer? Thanks in advance!

haozhu233 commented 6 years ago

@MahShaaban Thanks for the responses. I just checked the branch and it looks a lot better. Here are some new issues I found.

MahShaaban commented 6 years ago

Thanks, @haozhu233

haozhu233 commented 6 years ago

@MahShaaban

Another tiny issue, can you reformat the UI part to make the codes look a little bit more structured?

seaaan commented 6 years ago

Nice changes! I have a few suggestions, but mostly it all looks good to me.

Processing multiple images

I like the functionality of processing multiple images.

It would be nice to return a data frame of one row per image instead of a list of data frames when roi_test is called on a list of images

Maybe provide a load_images() function that takes a directory (or list of files?) and calls lapply(., imager::load.image). You could also provide load_image() that just wraps imager::load.image() so the user doesn't have to run library(imager)

Vignette comments

"The reset of the analysis" --> the RESULT of the analysis?

"Arguable, selecting" --> ArguablY

"only semi-automate" --> only semi-automateS

The link to ImageJ doesn't work

"shrin" --> shrinK (in the argument to roi_select)

The example checking that the app and package output are equal returns FALSE. Use all.equal() instead of ==.

"regioins" --> regions

The first colocalization benchmark score image gives statistics relatively close to the expected. The second answer is very different! Comment in the vignette on why that is. It seems like ideally you should be able to reproduce the expected results?

ppc -> pcc (last code box)

Other

With regard to the @development and "no submodules!" on the README, I suggest that you explain on the README what those mean (since I didn't know, maybe other users will also wonder).

roi_show() returns (via imager::highlight) NULL (eg as displayed in the vignette). If you return invisible(NULL) instead, NULL won't print out but you'll still get the same effect. You could also invisibly return the img to continue the piping.

MahShaaban commented 6 years ago

Thanks, @haozhu233

MahShaaban commented 6 years ago

Hey, @seaaan Here are some changes I made in response to your last comment.

Processing multiple images

Vignette Comments

Other

seaaan commented 6 years ago

Nice work! Just a couple of things:

In the vignette, when you introduce analyzing a collection of images, it would be better to show how to use image_load with multiple file paths than to create the collection using list(img, img2) like it is now.

Also in the vignette, in the section where you're reproducing the analysis with the app and with code, the results aren't actually equivalent (i.e. all.equal(...)) returns a difference. That's confusing! Why aren't they equivalent?

MahShaaban commented 6 years ago

Thanks @seaaan

screen shot 2018-09-07 at 11 36 17 am

I am not sure what might be the reason why you get different results. This is the case at least after rounding to the 2nd or 3rd digits. One thing I can think of is that the system files in your local version of the package might be not updated.

seaaan commented 5 years ago

Okay, well it must be something on my end then. As long as it says TRUE in the CRAN vignette it should be fine. I don't have any further comments. Nice work!

maelle commented 5 years ago

Nice work @MahShaaban!

Thanks @seaaan!

@haozhu233 Thanks too! Did you have any further comment?

haozhu233 commented 5 years ago

@maelle Nope~ :) @MahShaaban has fixed all the questions and comments I got and I don't have any further comments. Great job!

maelle commented 5 years ago

I get this error for tests. Any clue? Is there any dependency with a minimal version not indicated in DESCRIPTION yet?

test-app.R:6: error: app works
Can't find variable: $
1: expect_pass(testApp(app_dir, compareImages = FALSE)) at C:\Users\Maelle\Documents\ropensci\colocr/tests/testthat/test-app.R:6
2: testApp(app_dir, compareImages = FALSE)
3: lapply(found_testnames, function(testname) {
       withr::local_dir(testsDir)
       withr::local_envvar(c(RSTUDIO = ""))
       withr::local_options(list(shinytest.app.dir = "appdir"))
       gc()
       env <- new.env(parent = .GlobalEnv)
       if (!quiet) {
           message(testname, " ", appendLF = FALSE)
       }
       source(testname, local = env)
   })
4: FUN(X[[i]], ...)
5: source(testname, local = env)
6: withVisible(eval(ei, envir))
7: eval(ei, envir)
8: eval(ei, envir)
9: ShinyDriver$new("../") at one_image.R:1
10: .subset2(public_bind_env, "initialize")(...)
11: sd_initialize(self, private, path, loadTimeout, checkNames, debug, phantomTimeout = phantomTimeout, 
       seed = seed, cleanLogs = cleanLogs, shinyOptions = shinyOptions)
12: private$web$executeScript(js)
13: session_executeScript(self, private, script, ...)
14: private$makeRequest("EXECUTE SCRIPT", list(script = script, args = args))
15: session_makeRequest(self, private, endpoint, data, params, headers)
16: report_error(response)
--------------------------------------------------------------------------------
√ |  6       | helpers.R [4.8 s]
√ | 32       | pipeable [205.1 s]

== Results =====================================================================
Duration: 223.1 s

OK:       38
Failed:   1
Warnings: 0
Skipped:  0
MahShaaban commented 5 years ago

I can't really understand what the problem is from the error. I have merged the branch we've been woking on to the master and it passed the checks locally and on travis. You may try a fresh clone and see if the error goes away.

maelle commented 5 years ago

I did a fresh clone and got it again, will try to extract a reprex.

maelle commented 5 years ago 
  library(shinytest)
  app_dir <- system.file('colocr_app/', package = 'colocr')
  expect_pass(testApp(app_dir, compareImages = FALSE))
#> Running one_image.R
#> Error in session_makeRequest(self, private, endpoint, data, params, headers): Can't find variable: $

Created on 2018-09-14 by the reprex package (v0.2.0).

Session info ``` r devtools::session_info() #> Session info ------------------------------------------------------------- #> setting value #> version R version 3.5.0 (2018-04-23) #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate English_United States.1252 #> tz Europe/Paris #> date 2018-09-14 #> Packages ----------------------------------------------------------------- #> package * version date source #> assertthat 0.2.0 2017-04-11 CRAN (R 3.5.0) #> backports 1.1.2 2017-12-13 CRAN (R 3.5.0) #> base * 3.5.0 2018-04-23 local #> base64enc 0.1-3 2015-07-28 CRAN (R 3.5.0) #> callr 3.0.0 2018-08-24 CRAN (R 3.5.1) #> compiler 3.5.0 2018-04-23 local #> crayon 1.3.4 2017-09-16 CRAN (R 3.5.0) #> curl 3.2 2018-03-28 CRAN (R 3.5.0) #> datasets * 3.5.0 2018-04-23 local #> debugme 1.1.0 2017-10-22 CRAN (R 3.5.0) #> devtools 1.13.5 2018-02-18 CRAN (R 3.5.0) #> digest 0.6.16 2018-08-22 CRAN (R 3.5.1) #> evaluate 0.11 2018-07-17 CRAN (R 3.5.1) #> graphics * 3.5.0 2018-04-23 local #> grDevices * 3.5.0 2018-04-23 local #> htmltools 0.3.6.9003 2018-09-04 Github (rstudio/htmltools@a9e969f) #> httpuv 1.4.4.1 2018-06-18 CRAN (R 3.5.0) #> httr 1.3.1 2017-08-20 CRAN (R 3.5.0) #> jsonlite 1.5 2017-06-01 CRAN (R 3.5.0) #> knitr 1.20 2018-02-20 CRAN (R 3.5.0) #> later 0.7.3 2018-06-08 CRAN (R 3.5.0) #> magrittr 1.5 2014-11-22 CRAN (R 3.5.0) #> memoise 1.1.0 2017-04-21 CRAN (R 3.5.0) #> methods * 3.5.0 2018-04-23 local #> mime 0.5 2016-07-07 CRAN (R 3.5.0) #> parsedate 1.1.3 2017-03-02 CRAN (R 3.5.0) #> pingr 1.1.2 2017-03-02 CRAN (R 3.5.0) #> png 0.1-7 2013-12-03 CRAN (R 3.5.0) #> processx 3.2.0 2018-08-16 CRAN (R 3.5.1) #> promises 1.0.1 2018-04-13 CRAN (R 3.5.0) #> ps 1.1.0 2018-08-10 CRAN (R 3.5.1) #> R6 2.2.2 2017-06-17 CRAN (R 3.5.0) #> Rcpp 0.12.18 2018-07-23 CRAN (R 3.5.0) #> rematch 1.0.1 2016-04-21 CRAN (R 3.4.0) #> rlang 0.2.2 2018-08-16 CRAN (R 3.5.1) #> rmarkdown 1.10 2018-06-11 CRAN (R 3.5.0) #> rprojroot 1.3-2 2018-01-03 CRAN (R 3.4.3) #> shiny 1.1.0 2018-05-17 CRAN (R 3.5.0) #> shinytest * 1.3.0 2018-06-07 Github (rstudio/shinytest@7f2b6a6) #> showimage 1.0.0 2018-01-24 CRAN (R 3.5.0) #> stats * 3.5.0 2018-04-23 local #> stringi 1.2.4 2018-07-23 local #> stringr 1.3.1 2018-05-10 CRAN (R 3.5.0) #> testthat 2.0.0 2017-12-13 CRAN (R 3.5.0) #> tools 3.5.0 2018-04-23 local #> utils * 3.5.0 2018-04-23 local #> webdriver 1.0.5 2018-04-11 CRAN (R 3.5.0) #> withr 2.1.2 2018-03-15 CRAN (R 3.4.4) #> xtable 1.8-2 2016-02-05 CRAN (R 3.4.0) #> yaml 2.2.0 2018-07-25 CRAN (R 3.5.1) ```
maelle commented 5 years ago

I get the same error with shinytest CRAN version.

library(shinytest)
#> Warning: package 'shinytest' was built under R version 3.5.1
#> Warning: package 'shinytest' was built under R version 3.5.1
  app_dir <- system.file('colocr_app/', package = 'colocr')
  expect_pass(testApp(app_dir, compareImages = FALSE))
#> Running one_image.R
#> Error in session_makeRequest(self, private, endpoint, data, params, headers): Can't find variable: $

Created on 2018-09-14 by the reprex package (v0.2.0).

Session info ``` r devtools::session_info() #> Session info ------------------------------------------------------------- #> setting value #> version R version 3.5.0 (2018-04-23) #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate English_United States.1252 #> tz Europe/Paris #> date 2018-09-14 #> Packages ----------------------------------------------------------------- #> package * version date source #> assertthat 0.2.0 2017-04-11 CRAN (R 3.5.0) #> backports 1.1.2 2017-12-13 CRAN (R 3.5.0) #> base * 3.5.0 2018-04-23 local #> base64enc 0.1-3 2015-07-28 CRAN (R 3.5.0) #> callr 3.0.0 2018-08-24 CRAN (R 3.5.1) #> compiler 3.5.0 2018-04-23 local #> crayon 1.3.4 2017-09-16 CRAN (R 3.5.0) #> curl 3.2 2018-03-28 CRAN (R 3.5.0) #> datasets * 3.5.0 2018-04-23 local #> debugme 1.1.0 2017-10-22 CRAN (R 3.5.0) #> devtools 1.13.5 2018-02-18 CRAN (R 3.5.0) #> digest 0.6.16 2018-08-22 CRAN (R 3.5.1) #> evaluate 0.11 2018-07-17 CRAN (R 3.5.1) #> graphics * 3.5.0 2018-04-23 local #> grDevices * 3.5.0 2018-04-23 local #> htmltools 0.3.6.9003 2018-09-04 Github (rstudio/htmltools@a9e969f) #> httpuv 1.4.4.1 2018-06-18 CRAN (R 3.5.0) #> httr 1.3.1 2017-08-20 CRAN (R 3.5.0) #> jsonlite 1.5 2017-06-01 CRAN (R 3.5.0) #> knitr 1.20 2018-02-20 CRAN (R 3.5.0) #> later 0.7.3 2018-06-08 CRAN (R 3.5.0) #> magrittr 1.5 2014-11-22 CRAN (R 3.5.0) #> memoise 1.1.0 2017-04-21 CRAN (R 3.5.0) #> methods * 3.5.0 2018-04-23 local #> mime 0.5 2016-07-07 CRAN (R 3.5.0) #> parsedate 1.1.3 2017-03-02 CRAN (R 3.5.0) #> pingr 1.1.2 2017-03-02 CRAN (R 3.5.0) #> png 0.1-7 2013-12-03 CRAN (R 3.5.0) #> processx 3.2.0 2018-08-16 CRAN (R 3.5.1) #> promises 1.0.1 2018-04-13 CRAN (R 3.5.0) #> ps 1.1.0 2018-08-10 CRAN (R 3.5.1) #> R6 2.2.2 2017-06-17 CRAN (R 3.5.0) #> Rcpp 0.12.18 2018-07-23 CRAN (R 3.5.0) #> rematch 1.0.1 2016-04-21 CRAN (R 3.4.0) #> rlang 0.2.2 2018-08-16 CRAN (R 3.5.1) #> rmarkdown 1.10 2018-06-11 CRAN (R 3.5.0) #> rprojroot 1.3-2 2018-01-03 CRAN (R 3.4.3) #> shiny 1.1.0 2018-05-17 CRAN (R 3.5.0) #> shinytest * 1.3.0 2018-05-07 CRAN (R 3.5.1) #> showimage 1.0.0 2018-01-24 CRAN (R 3.5.0) #> stats * 3.5.0 2018-04-23 local #> stringi 1.2.4 2018-07-23 local #> stringr 1.3.1 2018-05-10 CRAN (R 3.5.0) #> testthat 2.0.0 2017-12-13 CRAN (R 3.5.0) #> tools 3.5.0 2018-04-23 local #> utils * 3.5.0 2018-04-23 local #> webdriver 1.0.5 2018-04-11 CRAN (R 3.5.0) #> withr 2.1.2 2018-03-15 CRAN (R 3.4.4) #> xtable 1.8-2 2016-02-05 CRAN (R 3.4.0) #> yaml 2.2.0 2018-07-25 CRAN (R 3.5.1) ```
maelle commented 5 years ago

Oops so after re-installing htmltools (it was somehow corrupted), I was able to run the test. It told me the output was different. For the app screenshot it seems like a font difference. However it also shows me different results.

image

@MahShaaban could you please add testing on Appveyor to see what you get there? Sorry to notice all of this only now.

maelle commented 5 years ago

I actually see you have a failing Appveyor build, but it seems due to a wrong Appveyor config, which prevents further tests from running. I'll stop exploring on my own system now, I hope you can get the tests passing on Appveyor.

maelle commented 5 years ago

Besides, the link in your shinyapps.io badge, https://mahshaaban.shinyapps.io/colocr_app/, has a 404 status.

maelle commented 5 years ago

Reg the Appveyor build this webdriver setup might help https://github.com/rstudio/webdriver/pull/29/files

MahShaaban commented 5 years ago

I fixed the link. I can't get the tests to run on appveyor. I had a look at the issue from the webdriver repo. I don't get how they got it to pass, but if I have to guess I think they skipped that part of the test!

maelle commented 5 years ago

🤔 can you ask on the Slack workspace?

And any idea why I got different results?

MahShaaban commented 5 years ago

Okay. Will do.

I updated the image to match the expected. So the test for two images should pass with No changes now. My guess is I forgot to update the snapshot image the last time I ran the test.

maelle commented 5 years ago

But then why did the test pass on Travis?

MahShaaban commented 5 years ago

I think it passed on Travis because I use compareImages = FALSE in inst/colocr/runt_tests.R. I use this option because these images are screenshots and would be different from an OS to another.

maelle commented 5 years ago

Yes but what about the average PCC etc? (Am still new to shinytest)

MahShaaban commented 5 years ago

I am also new to shinytest :) I ran the tests locally again and got No changes!