Gender Stats

Since the GenderAPI data seems a bit more comprehensive (see here), that's what we'll use going forward. This first block recapitulates what I did in the beginning of the last notebook.



In [4]:

    
using StatPlots


include("../src/dataimport.jl") # `importauthors()` and `getgenderprob()` functions

bio = importauthors("../data/pubdata/bio.csv", "bio")
comp = importauthors("../data/pubdata/comp.csv", "comp")
alldata = vcat(bio, comp)
bio = 0 # to free up memory
comp = 0

alldata[:Pfemale], alldata[:Count] = getgenderprob(alldata, "../data/genders/genderAPI_genders.json", :Author_First_Name)

pool!(alldata)
alldata = alldata[!isna(alldata[:Journal]), :] # remove rows where there's no Journal

alldata[1:5, :]









    Out[4]:




ID Date Journal Author_First_Name Author_Last_Name Author_Initials Position Title Dataset Pfemale Count
1 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Suwit Chotinun NA first PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND. bio 0.020000000000000018 306
2 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Prapas Patchanee NA last PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND. bio 0.030000000000000027 58
3 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Suvichai Rojanasthien NA second PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND. bio NA 0
4 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Pakpoom Tadee NA penultimate PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND. bio 0.020000000000000018 116
5 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Fred Unger NA other PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND. bio 0.040000000000000036 49394



In [6]:

    
means = by(alldata, [:Dataset], df -> DataFrame(MeanPF = mean(dropna(df[:Pfemale]))))









    Out[6]:




Dataset MeanPF
1 bio 0.34048754243415824
2 comp 0.29633429724211385



In [20]:

    
bar(means, :MeanPF,
        xaxis=("Dataset", ([1,2], means[:Dataset])),
        yaxis=("Percent Female", (0, 0.6), 0:0.1:0.6),
        legend=false,
        grid=false,
        title="Proportion of Female Authors")









    Out[20]:

Currently, the author positions are ordered in the dataframe in alphabetical order (first, last, other, penultimate, second), so I'm going to define a "less than" function to do a custom sort (thanks Stack Overflow!). This function needs to return true for the call x < y for the strings in the following order: ["first", "second", "other", "penultimate", "last"].



In [24]:

    
order = Dict(key => ix for (ix, key) in enumerate(["first", "second", "other", "penultimate", "last"]))


function authororder(pos1, pos2)
    return order[pos1] < order[pos2]
end

println(authororder("first", "second"))
println(authororder("second", "first"))









    



true
false

So now we can sort the dataframe using our custom function and the lt keyword.



In [31]:

    
sort!(alldata, cols=:Position, lt=authororder)
alldata[1:5, 1:7]









    Out[31]:




ID Date Journal Author_First_Name Author_Last_Name Author_Initials Position
1 26466425 2015-10-15 Southeast Asian J. Trop. Med. Public Health Suwit Chotinun NA first
2 26466421 2015-10-15 Southeast Asian J. Trop. Med. Public Health Chariya Chomvarin NA first
3 26466418 2015-10-15 Southeast Asian J. Trop. Med. Public Health Meng-Bin Tang NA first
4 26460400 2015-10-11 Ann. Hum. Genet. Christopher Steele D first
5 26255944 2015-08-10 Mutat. Res. Sara Skiöld NA first



In [32]:

    
byposition = by(alldata, [:Position, :Dataset], df -> mean(dropna(df[:Pfemale])))
sort!(byposition, cols=:Position, lt=authororder)
ys = hcat([byposition[byposition[:Dataset] .== x, :x1] for x in levels(byposition[:Dataset])]...)

groupedbar(ys, bar_position=:dodge,
        xaxis=("Author Position", (1:5, levels(alldata[:Position]))),
        yaxis=("Percent Female", (0, 0.6), 0:0.1:0.6),
        legend=false,
        grid=false,
        title="Proportion of Female Authors")









    Out[32]:

To reiterate, these data suggest:

Women are less likely to be authors than men
Women are less likely to be first authors than second authors
Women are less likely to be last authors than first authors
this recapitulates previously published data that women are under-represented in biology publishing.

New finding: It seems to be worse in computational biology than in all of biology, though not by as much as I expected.

Additional Annalysis

Let's see how this holds up. We can't do the "normal" sorts of statistics that folks often do (like T-tests, chi squared etc), since we're not taking a random sample of a population, we're looking at the whole population. An alternative is to use bootstrap analysis), where we randomly resample the data a bunch of times, and get statistics on those samples.

Happily, julia has a bootstrap package that does a lot of the work for us.



In [9]:

    
using Bootstrap

function getbootstrap(v, n=1000, i=0.95)
    bs = boot_basic(v, mean, n)
    return ci_basic(bs, i)
end









    Out[9]:





getbootstrap (generic function with 3 methods)

This function takes a vector of values v (like, say, a group of gender probabilities), and then samples them with replacement n times. In other words, if I had a vector t = [1, 2, 3, 4, 5] and I called getbootstrap(t), it would go through and take 5 samples at random... sometimes it would be [5, 2, 4, 3, 3], sometimes [1, 1, 1, 1, 1] etc. It would do this 1000 times, getting the mean each time.



In [17]:

    
t = [1,2,3,4,5]
getbootstrap(t)









    Out[17]:





Bootstrap Confidence Interval
  Estimate: 3.0
  Interval: [1.7999999999999998,4.2]
  Level:    0.95
  Method:   basic

The estimate for the mean is 3, just as it should be, but I also get a confidence interval. That means that 95% of the time, the mean is between 1.8 and 4.2

I can use this to look at the author genders:



In [18]:

    
by(alldata, [:Dataset, :Position]) do df
    ci = getbootstrap(dropna(df[:Pfemale]))
    return DataFrame(Mean=ci.t0, Lower=interval(ci)[1], Upper=interval(ci)[2])
end









    Out[18]:




Dataset Position Mean Lower Upper
1 bio first 0.375574751013497 0.37334818017881577 0.3777158577042995
2 bio last 0.24458769214547718 0.2425361884965018 0.24670336730005965
3 bio other 0.36808847346683826 0.3665970657841157 0.36957724436593176
4 bio penultimate 0.2793692129629628 0.2767264309764308 0.2817461016414139
5 bio second 0.3785907859078593 0.3759988278057385 0.38095996902826196
6 comp first 0.31633865814696477 0.3120813307403805 0.3204951034865952
7 comp last 0.2072917800762114 0.20321504717837066 0.21104291417165685
8 comp other 0.33070648163576183 0.3279606829163788 0.3336850914046319
9 comp penultimate 0.2361738867009997 0.23164189423118536 0.2408402864932704
10 comp second 0.32179097154072606 0.3167785468947146 0.32652382412729547

That code takes the data, subsets it by :Dataset and :Position (that's the first two columns) and then returns the mean and 95% confidence intervals for each subset.

We can make generic functions for this, which you can find in src/bootstrapping.jl.



In [19]:

    
function bystats(df::DataFrame, by1::Symbol, n=1000, i=0.95)
    by(df, by1) do df2
        ci = getbootstrap(dropna(df2[:Pfemale]), n, i)
        return DataFrame(Mean=ci.t0, Lower=interval(ci)[1], Upper=interval(ci)[2])
    end
end

function bystats(df::DataFrame, cols::Vector{Symbol}, n=1000, i=0.95)
    by(df, cols) do df2
        ci = getbootstrap(dropna(df2[:Pfemale]), n, i)
        return DataFrame(Mean=ci.t0, Lower=interval(ci)[1], Upper=interval(ci)[2])
    end
end









    Out[19]:





bystats (generic function with 6 methods)



In [27]:

    
big3 = alldata[
    (alldata[:Journal] .== utf8("Nature"))|
    (alldata[:Journal] .== utf8("Science"))|
    (alldata[:Journal] .== utf8("Cell")), :]


big3byposition = bystats(big3, [:Position, :Dataset])


plot(big3byposition, x=:Position, y=:Mean, color=:Dataset, ymin=:Lower, ymax=:Upper,
Scale.color_discrete_manual(my_colors...),
            Geom.bar(position = :dodge), Guide.title("Authors in Nature, Science and Cell"),
            Guide.YLabel("Percent Female"),
            Scale.x_discrete(levels=["first", "second", "other", "penultimate", "last"]),
            Theme(bar_spacing=2mm))









    Out[27]:



In [28]:

    
plos = alldata[map(x->contains(x, "PLoS"), alldata[:Journal]), :]

plosbyposition = by(plos, [:Position, :Dataset], df -> mean(dropna(df[:Pfemale])))

plot(plosbyposition, x=:Position, y=:x1, color=:Dataset,
Scale.color_discrete_manual(my_colors...),
                Geom.bar(position = :dodge), Guide.title("Authors in PLoS Journals"),
                Guide.YLabel("Percent Female"),
                Scale.x_discrete(levels=["first", "second", "other", "penultimate", "last"]),
                Theme(bar_spacing=2mm))









    Out[28]:

Based on Journal Specialty

Another way to do this is to split by journals that tend to publish computational biology articles vs those that are more generalist. Here we'll only use the articles in the "bio" dataset to avoid double-dipping (the "comp" dataset is almost entirely a subset of "bio")



In [29]:

    
bioids = Set(levels(alldata[alldata[:Dataset] .== "bio", :ID]))
compids = Set(levels(alldata[alldata[:Dataset] .== "comp", :ID]))

println("There are $(length(bioids)) articles in the \"bio\" dataset")
println("There are $(length(compids)) articles in the \"comp\" dataset")

dif = length(setdiff(compids, bioids))

println("There are $dif articles in the \"comp\" dataset that aren't in the \"bio\" dataset")









    



There are 202816 articles in the "bio" dataset
There are 42880 articles in the "comp" dataset
There are 236 articles in the "comp" dataset that aren't in the "bio" dataset



In [32]:

    
plosfocus = alldata[(alldata[:Dataset] .== "bio")&
    ((alldata[:Journal] .== utf8("PLoS Biol."))|
    (alldata[:Journal] .== utf8("PLoS Comput. Biol."))), :]

plosfocusbyposition = bystats(plosfocus, [:Position, :Journal])

plot(plosfocusbyposition, x=:Position, y=:Mean, color=:Journal,
                Scale.color_discrete_manual(my_colors...),
                Geom.bar(position = :dodge), Guide.title("Authors in Specified PLoS Journals"),
                Guide.YLabel("Percent Female"),
                Scale.x_discrete(levels=["first", "second", "other", "penultimate", "last"]),
                Theme(bar_spacing=2mm))









    Out[32]:



In [33]:

    
comppubs = alldata[(alldata[:Dataset] .== "bio")&
    ((alldata[:Journal] .== utf8("PLoS Comput. Biol."))| # impact factor 4.62
    (alldata[:Journal] .== utf8("Nucleic Acids Res."))| # 9.112
    (alldata[:Journal] .== utf8("BMC Bioinformatics"))| # 2.576
    (alldata[:Journal] .== utf8("Bioinformatics"))), :] # 4.981

genpubs = alldata[(alldata[:Dataset] .== "bio")&
    ((alldata[:Journal] .== utf8("Proc. Natl. Acad. Sci. U.S.A."))| # impact factor 9.674
    (alldata[:Journal] .== utf8("BMC Biol."))| # 9.112
    (alldata[:Journal] .== utf8("PLoS Biol."))| # 9.343
    (alldata[:Journal] .== utf8("Biol. Lett."))), :] # 3.248

comppubs[:JournalSet] = "comp"
genpubs[:JournalSet] = "gen"

pubs = vcat(comppubs, genpubs)
comppubs = 0
genpubs = 0


pubsbyposition = bystats(pubs, [:Position, :JournalSet])

plot(pubsbyposition, x=:Position, y=:Mean, color=:JournalSet,
                Scale.color_discrete_manual(my_colors...),
                Geom.bar(position = :dodge), Guide.title("Authors in Computational vs General Bio Publications"),
                Guide.YLabel("Percent Female"),
                Scale.x_discrete(levels=["first", "second", "other", "penultimate", "last"]),
                Theme(bar_spacing=2mm))









    Out[33]:

Subsetting Conclusions

With a few exceptions, each of these subsets show a similar trend: women are less likely to be authors in computational biology publications.

Changes over time

Previous work suggests that female authorship has been increasing over time. Let's see if the trends hold over time as well.



In [34]:

    
alldata[:Year] = map(x -> Dates.year(Date(x)), alldata[:Date])

byyear = bystats(alldata, [:Year, :Dataset])

plot(byyear, x=:Year, y=:Mean, color=:Dataset,
        Scale.color_discrete_manual(my_colors...),
        Geom.point, Geom.line, Guide.title("Female Authors Over Time"),
        Guide.YLabel("Percent Female"), Guide.XLabel("Year"))









    Out[34]:

What about in various author positions?



In [35]:

    
biobyyear = bystats(alldata[alldata[:Dataset] .== "bio", :], [:Year, :Position])

plot(biobyyear, x=:Year, y=:Mean, color=:Position,
        Scale.color_discrete_manual(my_colors...),
        Geom.point, Geom.line, Guide.title("Female Authors Over Time in Biology"),
        Guide.YLabel("Percent Female"), Guide.XLabel("Year"))









    



LoadError: ArgumentError: quantiles are undefined in presence of NaNs
while loading In[35], in expression starting on line 1

 in quantile! at statistics.jl:545
 in quantile at statistics.jl:609
 in ci_basic at /Users/KBLaptop/.julia/v0.4/Bootstrap/src/ci.jl:55
 in getbootstrap at In[9]:5
 in anonymous at In[19]:10
 in map at /Users/KBLaptop/.julia/v0.4/DataFrames/src/groupeddataframe/grouping.jl:181
 in bystats at In[19]:9 (repeats 2 times)



In [36]:

    
compbyyear = bystats(alldata[alldata[:Dataset] .== "comp", :], [:Year, :Position])

plot(compbyyear, x=:Year, y=:Mean, color=:Position,
        Scale.color_discrete_manual(my_colors...),
        Geom.point, Geom.line, Guide.title("Female Authors Over Time In Computational Biology"),
        Guide.YLabel("Percent Female"), Guide.XLabel("Year"))









    



LoadError: ArgumentError: quantiles are undefined in presence of NaNs
while loading In[36], in expression starting on line 1

 in quantile! at statistics.jl:545
 in quantile at statistics.jl:609
 in ci_basic at /Users/KBLaptop/.julia/v0.4/Bootstrap/src/ci.jl:55
 in getbootstrap at In[9]:5
 in anonymous at In[19]:10
 in map at /Users/KBLaptop/.julia/v0.4/DataFrames/src/groupeddataframe/grouping.jl:181
 in bystats at In[19]:9 (repeats 2 times)

Why does all the data suck before ~2002?



In [15]:

    
println("There are $(length(alldata[(alldata[:Year] .< 2002) & (alldata[:Dataset] .== "bio"), :Year])) pubs in the data set before 2002")
println("There are $(length(alldata[(alldata[:Year] .>= 2002) & (alldata[:Dataset] .== "bio"), :Year])) pubs in the data set after 2002")

for year in 1997:2015
    println("There are $(length(alldata[(alldata[:Year] .== year) & (alldata[:Dataset] .== "bio"), :Year])) pubs in the data set from $year")
end









    



There are 65587 pubs in the data set before 2002
There are 1046177 pubs in the data set after 2002
There are 6518 pubs in the data set from 1997
There are 9846 pubs in the data set from 1998
There are 11956 pubs in the data set from 1999
There are 15208 pubs in the data set from 2000
There are 22059 pubs in the data set from 2001
There are 24398 pubs in the data set from 2002
There are 33229 pubs in the data set from 2003
There are 45461 pubs in the data set from 2004
There are 54317 pubs in the data set from 2005
There are 58886 pubs in the data set from 2006
There are 65584 pubs in the data set from 2007
There are 73427 pubs in the data set from 2008
There are 84511 pubs in the data set from 2009
There are 99106 pubs in the data set from 2010
There are 110234 pubs in the data set from 2011
There are 119325 pubs in the data set from 2012
There are 124923 pubs in the data set from 2013
There are 133125 pubs in the data set from 2014
There are 19651 pubs in the data set from 2015

Data from the arXiv

Previous work suggests that women are far less likely to publish in computer science. Unfortunately, pubmed doesn't index computer science research.

The arXiv has preprints in many fields, including computer science. The sorts of papers posted here are likely to be different, so we can't compare directly to the stuff on pubmed, but there's also quantatative biology...



In [37]:

    
arxivcs = importauthors("../data/pubs/arxivcs.csv", "arxivcs")
arxivbio = importauthors("../data/pubs/arxivbio.csv", "arxivbio")

arxiv = vcat(arxivbio, arxivcs)
arxivcs = 0
arxivbio = 0

pool!(arxiv)
arxiv = arxiv[!isna(arxiv[:Author_Name]), :]

arxiv[:Pfemale], arxiv[:Count] = getgenderprob(arxiv, "../data/genders/genderAPI_genders.json", :Author_Name)


arxivbyposition = bystats(arxiv, [:Dataset, :Position])









    Out[37]:




Dataset Position Mean Lower Upper
1 arxivbio first 0.18387958963282955 0.17795801835853145 0.19044420446364302
2 arxivbio last 0.1477431463997271 0.14119141735866242 0.1544600443635538
3 arxivbio other 0.2645364647713224 0.25159851668726796 0.2773817676143384
4 arxivbio penultimate 0.19579630895420383 0.18280562200956957 0.20920189678742315
5 arxivbio second 0.20970474967907585 0.20037011736658727 0.21925201723821752
6 arxivcs first 0.15733544355165988 0.15493641749857975 0.15950626572518473
7 arxivcs last 0.15534637948196642 0.1528472001609591 0.15801725543766032
8 arxivcs other 0.1884365247155947 0.18213651464814276 0.194720980569818
9 arxivcs penultimate 0.1753966411268479 0.1698807948301217 0.18095756907360122
10 arxivcs second 0.17537953336346893 0.17152487916826045 0.17898814284224077



In [39]:

    
plot(arxivbyposition, x=:Position, y=:Mean, color=:Dataset,
        Scale.color_discrete_manual(my_colors...),
        Guide.title("Female Authors in arXiv"),
        Geom.bar(position = :dodge),
        Scale.x_discrete(levels=["first", "second", "other", "penultimate", "last"]),
        Theme(bar_spacing=2mm))









    Out[39]:



In [40]:

    
arxiv[:Year] = map(x -> Dates.year(Date(x)), arxiv[:Date])
arxivbyyear = bystats(arxiv, [:Year, :Dataset])

plot(arxivbyyear, x=:Year, y=:Mean, color=:Dataset,
        Scale.color_discrete_manual(my_colors...),
        Geom.point, Geom.line, Guide.title("Female Authors in PLoS Journals Over Time"),
        Guide.YLabel("Percent Female"), Guide.XLabel("Year"), Guide.yticks(ticks=[0:0.1:0.5]))









    Out[40]:

	ID	Date	Journal	Author_First_Name	Author_Last_Name	Author_Initials	Position	Title	Dataset	Pfemale	Count
1	26466425	2015-10-15	Southeast Asian J. Trop. Med. Public Health	Suwit	Chotinun	NA	first	PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND.	bio	0.020000000000000018	306
2	26466425	2015-10-15	Southeast Asian J. Trop. Med. Public Health	Prapas	Patchanee	NA	last	PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND.	bio	0.030000000000000027	58
3	26466425	2015-10-15	Southeast Asian J. Trop. Med. Public Health	Suvichai	Rojanasthien	NA	second	PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND.	bio	NA	0
4	26466425	2015-10-15	Southeast Asian J. Trop. Med. Public Health	Pakpoom	Tadee	NA	penultimate	PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND.	bio	0.020000000000000018	116
5	26466425	2015-10-15	Southeast Asian J. Trop. Med. Public Health	Fred	Unger	NA	other	PREVALENCE AND ANTIMICROBIAL RESISTANCE OF SALMONELLA ISOLATED FROM CARCASSES, PROCESSING FACILITIES AND THE ENVIRONMENT SURROUNDING SMALL SCALE POULTRY SLAUGHTERHOUSES IN THAILAND.	bio	0.040000000000000036	49394

	Dataset	Position	Mean	Lower	Upper
1	bio	first	0.375574751013497	0.37334818017881577	0.3777158577042995
2	bio	last	0.24458769214547718	0.2425361884965018	0.24670336730005965
3	bio	other	0.36808847346683826	0.3665970657841157	0.36957724436593176
4	bio	penultimate	0.2793692129629628	0.2767264309764308	0.2817461016414139
5	bio	second	0.3785907859078593	0.3759988278057385	0.38095996902826196
6	comp	first	0.31633865814696477	0.3120813307403805	0.3204951034865952
7	comp	last	0.2072917800762114	0.20321504717837066	0.21104291417165685
8	comp	other	0.33070648163576183	0.3279606829163788	0.3336850914046319
9	comp	penultimate	0.2361738867009997	0.23164189423118536	0.2408402864932704
10	comp	second	0.32179097154072606	0.3167785468947146	0.32652382412729547

	Dataset	Position	Mean	Lower	Upper
1	arxivbio	first	0.18387958963282955	0.17795801835853145	0.19044420446364302
2	arxivbio	last	0.1477431463997271	0.14119141735866242	0.1544600443635538
3	arxivbio	other	0.2645364647713224	0.25159851668726796	0.2773817676143384
4	arxivbio	penultimate	0.19579630895420383	0.18280562200956957	0.20920189678742315
5	arxivbio	second	0.20970474967907585	0.20037011736658727	0.21925201723821752
6	arxivcs	first	0.15733544355165988	0.15493641749857975	0.15950626572518473
7	arxivcs	last	0.15534637948196642	0.1528472001609591	0.15801725543766032
8	arxivcs	other	0.1884365247155947	0.18213651464814276	0.194720980569818
9	arxivcs	penultimate	0.1753966411268479	0.1698807948301217	0.18095756907360122
10	arxivcs	second	0.17537953336346893	0.17152487916826045	0.17898814284224077