Submitting author: @joethorley (Joseph Thorley)
Repository: https://github.com/bcgov/ssdtools
Version: v0.0.3
Editor: @arfon
Reviewer: @ibarraespinosa, @nanhung
Archive: 10.5281/zenodo.1651641
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paper.md
file include a list of authors with their affiliations?paper.md
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@ibarraespinosa, @nanhung - please carry out your review in this issue by updating the checklist above and giving feedback in this issue. The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html
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Overall, the package and paper look great. Just a few couples of questions and suggestions:
ssdfit_dist
and ssdfit_dists
rather than ssdfit
in your package. Why not combine these two functions to one and named it ssdfit?ggplot
with geom_xribbon
and geom_hcintercept
that you mentioned in summary. However, I don't know how to use geom_xribbon
. Perhaps you can provide an example in the reference manual. ggplot2
in reference?Thanks @nanhung
My responses are as follows
ssd_fit_dist
returns a object of class fitdist
while ssd_fit_dists
returns a object of class fitdists
which is a list of objects of class fitdist
The updated manual is at https://bcgov.github.io/ssdtools/articles/ssdtools-manual.html
Let me know if you require anymore information.
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My concern is since you can use ssd_fit_dists
to generate the same result as ssd_fit_dist
such as,
data <- ssdtools::boron_data
ssd_fit_dist(data, dist = c("lnorm"))
ssd_fit_dists(data, dists = c("lnorm"))
Why users need to use ssd_fit_dist
in their analysis?
Is it possible to use mutiple hc line in ssd_plot
, such as,
ssd_plot(boron_data, boron_pred, color = "Group", label = "Species", hc = c(5L, 50L),
xlab = "Concentration (mg/L)", ribbon = TRUE)
The current function only support 1 element.
@nanhung ssd_fit_dist()
is no longer exported to eliminate redundancy
@nanhung geom_hcintersect()
now takes multiple values
Just a suggestion. Can you add an assigment (like "group_by") in ssd_fit_dists
and predict
that can simplify the approach if the user wants to apply the grouping method.
Here is an example code. I haven't use the ssd_hc
in this case.
library(ssdtools)
library(tidyverse)
library(scales)
boron_dists_Fish <- boron_data %>% filter(Group == "Fish") %>% ssd_fit_dists()
boron_dists_Amphibian <- boron_data %>% filter(Group == "Amphibian") %>% ssd_fit_dists()
boron_dists_Invertebrate <- boron_data %>% filter(Group == "Invertebrate") %>% ssd_fit_dists()
boron_dists_Plant <- boron_data %>% filter(Group == "Plant") %>% ssd_fit_dists()
boron_pred_Fish <- predict(boron_dists_Fish)
boron_pred_Amphibian <- predict(boron_dists_Amphibian)
boron_pred_Invertebrate <- predict(boron_dists_Invertebrate)
boron_pred_Plant <- predict(boron_dists_Plant)
boron_pred_Fish$Group <- "Fish"
boron_pred_Amphibian$Group <- "Amphibian"
boron_pred_Invertebrate$Group <- "Invertebrate"
boron_pred_Plant$Group <- "Plant"
x <- do.call(rbind, list(boron_pred_Fish, boron_pred_Amphibian, boron_pred_Invertebrate, boron_pred_Plant))
gp <- ggplot(x, aes_string(x = "est")) +
geom_xribbon(aes_string(xmin = "lcl", xmax = "ucl", y = "percent/100"), alpha = 0.2) +
geom_ssd(data = boron_data, aes_string(x = "Conc")) +
facet_wrap(~Group) +
scale_y_continuous("Species Affected (%)", labels = percent) +
expand_limits(y = c(0, 1)) +
xlab("Concentration (mg/L)") +
coord_trans(x = "log10") +
geom_line(aes_string(y = "percent/100")) +
scale_x_continuous(breaks = scales::trans_breaks("log10", function(x) 10^x),
labels = comma_signif)
print(gp)
@nanhung It's a frequent request. I've added an example to the manual demonstrating a more elegant approach to grouping ie
library(ssdtools)
library(tidyverse)
boron_datas <- nest(boron_data, -Group)
boron_datas <- mutate(boron_datas,
Fit = map(data, ssd_fit_dists, dists = "lnorm"),
Prediction = map(Fit, predict))
boron_datas <- unnest(boron_datas, Prediction)
@nanhung I've also added to the manual a demonstration of how to plot HC5s for different groups
boron_hc5s <- filter(boron_datas, percent == 5)
gp %+% boron_datas +
facet_wrap(~Group) +
geom_hcintersect(data = boron_hc5s, aes(xintercept = est, yintercept = percent/100))
I'm trying to create the same plot in the example of ssd_plot
by geom_ssd
function. However, it seems that the geom_ssd
doesn't support the different color by groups.
# Defind single color
ggplot(boron_data, aes(x = Conc)) +
geom_ssd(colour = "red")
# Defind color by groups
ggplot(boron_data, aes(x = Conc)) +
geom_ssd(colour = Group)
try
ggplot(boron_data, aes(x = Conc)) +
geom_ssd(aes(colour = Group))
Hi @joethorley Interesting package. Demo is not running on line 42, consider adding ggplot2::autoplot(fits) or library(ggplto2) on ssdtools.R
Issue: https://github.com/bcgov/ssdtools/issues/31
@ibarraespinosa thanks - fixed
ahh - I just saw your pull request - I would have accepted had I seen it earlier
@ibarraespinosa I accepted your pull request because it is more elegant than what I did
Hi @joethorley. I found de package very interesting and also well documented with examples, tests and CI. I like that you added images in the help page. However, I found the paper hard to follow and the problem is not clearly stated. Also, there is a missing doi in the reference section.
The classes work very well with ggplot2 producing nice plots, however, it would be more gentle to the reader to see the code that generates the Figure 1: 'Species sensitivity distributions for sample species concentration values'.
In general, well done. After fixing that, it would be OK to accept the paper.
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Thanks @ibarraespinosa!
I've added the missing doi, added the code for the plot and modified the text to make the problem and functionality clearer.
Hi @joethorley. Great, now, could you please add this doi:
10.18637/jss.v064.i04
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@ibarraespinosa - done!
Excellent! @joethorley
I'm 100% @arfon
Thanks!
Thanks @nanhung and @ibarraespinosa for your helpful reviews!
@whedon accept
No archive DOI set. Exiting...
@joethorley - At this point could you make an archive of the reviewed software in Zenodo/figshare/other service and update this thread with the DOI of the archive? I can then move forward with accepting the submission.
@arfon the doi is
10.5281/zenodo.1651641
@whedon set 10.5281/zenodo.1651641 as archive
OK. 10.5281/zenodo.1651641 is the archive.
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@kyleniemeyer - for some reason the link to the archive doesn't work in the pdf although the link you specified is correct
Hi @joethorley, I just noticed that the affiliation info in the paper is slightly incomplete—could you extend it for both authors, e.g. with city and country info? (The way it would appear in a traditional article.)
Regarding the software DOI, it works ok here. The article DOI in the PDF will not be generated until the paper is actually published, though.
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@kyleniemeyer I've fixed the affiliations but note that although the Review and Repository links work the Archive link does not - can you confirm this specific link works at your end as the archive DOI is already live (unlike the paper DOI). The full address is https://doi.org/10.5281/zenodo.1651641
@joethorley I see what you mean. Hmm.
@whedon set 10.5281/zenodo.1651641 as archive
OK. 10.5281/zenodo.1651641 is the archive.
@whedon generate pdf
Attempting PDF compilation. Reticulating splines etc...
@arfon when you get a chance, could you check out the PDF generation? The archive link is not working in the PDF—seems like something is going wrong.
@kyleniemeyer - still no joy!
@arfon when you get a chance, could you check out the PDF generation? The archive link is not working in the PDF—seems like something is going wrong.
The archive link only gets compiled in once we're ready to accept.
@whedon accept
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@joethorley congrats on your paper acceptance into JOSS!
@ibarraespinosa & @nanhung thank you for your reviews, and thanks to @arfon for handling this submission.
:tada::tada::tada: Congratulations on your paper acceptance! :tada::tada::tada:
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@ibarraespinosa, @nanhung - please carry out your review in this issue by updating the checklist above and giving feedback in this issue. The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html
Any questions/concerns please let me know.