Good Software - analysis notebook¶
Reproducing all panels from Figure 1¶
This notebook contains all Python code necessary to reproduce all panels from the Figure 1 of [our paper](./), starting from the raw dataset. If you are interested in reproducing our results, you can use Binder to get started immediately (no requirements). For more information, visit [our wiki page](./).
Github: https://github.com/smangul1/good.software/wiki
Manuscript: https://www.biorxiv.org/content/early/2018/10/25/452532
Twitter thread: https://twitter.com/serghei_mangul/status/1055561147431043072
Importing libraries¶
In [3]:
import pylab as pl
import numpy as np
import pandas as pd
Some definitions¶
In [4]:
# Legend for link statuses
legendStatus = { -1 : 'Time out', 1 : 'Accessible', 3 : 'Redirected', 4 : 'Broken' }
# Colors for each status
colors = { -1 : (1.0,0.6,0.2), 1 : (0.2,0.7,0.2), 3 : (0.2,0.4,1.0), 4 : (1.0,0.,0.) }
# Scheme 2:
colors2 = { 'bg_unreach' : (1.0,0.8,0.6),
'fg_unreach' : (0.8,0.,0.),
'bg_accessb' : (0.7,1.0,0.7),
'fg_accessb' : (0.0,0.0,0.8)
}
# Will only consider these years
initialYear = 2005
Util functions¶
In [5]:
def simpleaxis(ax):
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
Importing and parsing data¶
In [6]:
links = pd.read_csv('links.bulk.csv')
links.head()
Out[6]:
| type | journal | id | year | link | code | flag.uniqueness | |
|---|---|---|---|---|---|---|---|
| 0 | abstract | Nucleic_Acids_Res | 23155066.0 | 2013 | http://www.netlab.uky.edu/p/bioinfo/DiffSplice | 200 | 0 |
| 1 | abstract | Bioinformatics | 27273673.0 | 2017 | http://www.cs.helsinki.fi/u/lmsalmel/LoRMA/ | 302 | 0 |
| 2 | abstract | BMC_Bioinformatics | 20492656.0 | 2010 | http://www.cse.univdhaka.edu/~ashis/ppred/inde... | 301 | 0 |
| 3 | abstract | PLoS_Comput_Biol | 28212402.0 | 2017 | https://dongjunchung.github.io/GGPA/ | 301 | 0 |
| 4 | abstract | Bioinformatics | 28130230.0 | 2017 | https://github.com/bfosso/MetaShot | 301 | 0 |
In [7]:
links.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 48392 entries, 0 to 48391 Data columns (total 7 columns): type 48392 non-null object journal 48392 non-null object id 48093 non-null float64 year 48392 non-null int64 link 48392 non-null object code 48392 non-null int64 flag.uniqueness 48392 non-null int64 dtypes: float64(1), int64(3), object(3) memory usage: 2.6+ MB
Parsing from code into human-readable status:
- -1 : time out
- 0..299 : good
- 300..399 : redirected
- >= 400 : not found
To make it easier, let's map these codes to -1 for time out, 1 for accessible, 3 for redirected, and 4 for not found.
In [8]:
links = links[ links.year >= initialYear ]
links['status'] = ( links['code'] // 100 )
links.loc[ links['status'] > 4, 'status' ] = 4
links.loc[ (links['status'] >= 0) & (links['status'] < 3), 'status' ] = 1
Let's look at the histogram of statuses
In [9]:
statuses = np.array( links.status )
f, ax = pl.subplots(1, 1)
ax.hist( statuses, np.arange(-1.5,6,1), edgecolor=(0.2,0.2,0.2), lw=3,
color=(0.2,0.6,1.0) )
ax.set_ylabel('Count')
ax.set_xticks([-1, 1, 3, 4])
ax.set_xticklabels( ['Time out', 'Accessible', 'Redirected', 'Not found'], rotation=-45 )
pl.show()
In [10]:
count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])
print(count, bin_values)
total = float(count.sum())
for j in range(4):
print(count[j]/total)
(array([ 7487, 14394, 21355, 4039]), array([-1.2, -0.8, 1.8, 3.8, 4.8])) 0.15837123215230037 0.30447382337387624 0.451718667371761 0.0854362771020624
Filtering out repeated links¶
To avoid using repeated links, let's create another dataframe.
In [11]:
uniqueLinks = links[ links['flag.uniqueness'] == 0 ]
print(uniqueLinks.shape)
uniqueLinks = uniqueLinks.drop(columns=['flag.uniqueness'])
uniqueLinks.head()
(36702, 8)
Out[11]:
| type | journal | id | year | link | code | status | |
|---|---|---|---|---|---|---|---|
| 0 | abstract | Nucleic_Acids_Res | 23155066.0 | 2013 | http://www.netlab.uky.edu/p/bioinfo/DiffSplice | 200 | 1 |
| 1 | abstract | Bioinformatics | 27273673.0 | 2017 | http://www.cs.helsinki.fi/u/lmsalmel/LoRMA/ | 302 | 3 |
| 2 | abstract | BMC_Bioinformatics | 20492656.0 | 2010 | http://www.cse.univdhaka.edu/~ashis/ppred/inde... | 301 | 3 |
| 3 | abstract | PLoS_Comput_Biol | 28212402.0 | 2017 | https://dongjunchung.github.io/GGPA/ | 301 | 3 |
| 4 | abstract | Bioinformatics | 28130230.0 | 2017 | https://github.com/bfosso/MetaShot | 301 | 3 |
In [12]:
repeated_data = pd.concat(g for _, g in uniqueLinks.groupby("id") if len(g) > 1)
repeated_data.head(15)
Out[12]:
| type | journal | id | year | link | code | status | |
|---|---|---|---|---|---|---|---|
| 1516 | abstract | Nucleic_Acids_Res | 15608157.0 | 2005 | http://www.human-siRNA-database.net | 301 | 3 |
| 25875 | body | Nucleic_Acids_Res | 15608157.0 | 2005 | www.human-siRNA-database.net | -1 | -1 |
| 1771 | abstract | Nucleic_Acids_Res | 15608160.0 | 2005 | http://rfam.wustl.edu/ | -1 | -1 |
| 3406 | abstract | Nucleic_Acids_Res | 15608160.0 | 2005 | http://www.sanger.ac.uk/Software/Rfam/ | 301 | 3 |
| 11997 | body | Nucleic_Acids_Res | 15608160.0 | 2005 | http://infernal.wustl.edu/ | -1 | -1 |
| 2859 | abstract | Nucleic_Acids_Res | 15608162.0 | 2005 | http://siRNA.cgb.ki.se | 301 | 3 |
| 18563 | body | Nucleic_Acids_Res | 15608162.0 | 2005 | http://aosearch.cgb.ki.se | -1 | -1 |
| 3097 | abstract | Nucleic_Acids_Res | 15608164.0 | 2005 | http://alpha.protres.ru/trnadbase | -1 | -1 |
| 4575 | abstract | Nucleic_Acids_Res | 15608164.0 | 2005 | http://www.tRNA.uni-bayreuth.de | 200 | 1 |
| 7689 | abstract | Nucleic_Acids_Res | 15608166.0 | 2005 | http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB | 301 | 3 |
| 9325 | body | Nucleic_Acids_Res | 15608166.0 | 2005 | http://www.cs.ualberta.ca/~bioinfo/PA/ | 301 | 3 |
| 16974 | body | Nucleic_Acids_Res | 15608166.0 | 2005 | http://redpoll.pharmacy.ualberta.ca/CCDB | 301 | 3 |
| 4324 | abstract | Nucleic_Acids_Res | 15608172.0 | 2005 | http://motif.stanford.edu/eblocks/ | 404 | 4 |
| 5294 | abstract | Nucleic_Acids_Res | 15608172.0 | 2005 | http://otl.stanford.edu/ | 200 | 1 |
| 734 | abstract | Nucleic_Acids_Res | 15608174.0 | 2005 | http://ekhidna.biocenter.helsinki.fi:9801/sqgr... | -1 | -1 |
In [13]:
uniqueLinks.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 36702 entries, 0 to 48391 Data columns (total 7 columns): type 36702 non-null object journal 36702 non-null object id 36536 non-null float64 year 36702 non-null int64 link 36702 non-null object code 36702 non-null int64 status 36702 non-null int64 dtypes: float64(1), int64(3), object(3) memory usage: 2.2+ MB
In [14]:
uniqueLinks.describe()
Out[14]:
| id | year | code | status | |
|---|---|---|---|---|
| count | 3.653600e+04 | 36702.000000 | 36702.000000 | 36702.000000 |
| mean | 2.282358e+07 | 2012.014332 | 232.363413 | 1.849981 |
| std | 3.722272e+06 | 3.483438 | 120.649623 | 1.603584 |
| min | 1.560816e+07 | 2005.000000 | -1.000000 | -1.000000 |
| 25% | 1.978383e+07 | 2009.000000 | 200.000000 | 1.000000 |
| 50% | 2.318078e+07 | 2012.000000 | 301.000000 | 3.000000 |
| 75% | 2.595065e+07 | 2015.000000 | 301.000000 | 3.000000 |
| max | 2.916934e+07 | 2017.000000 | 999.000000 | 4.000000 |
In [15]:
print( 'Percent of repeated links: %.1f%%' % ( (1 - len(uniqueLinks)/len(links))*100. ) )
Percent of repeated links: 100.0%
In [16]:
statuses = np.array( uniqueLinks.status )
f, ax = pl.subplots(1, 1)
ax.hist( statuses, np.arange(-1.5,6,1), edgecolor=(0.2,0.2,0.2), lw=3,
color=(0.2,0.6,1.0) )
ax.set_ylabel('Count')
ax.set_xticks([-1, 1, 3, 4])
ax.set_xticklabels( ['Time out', 'Accessible', 'Redirected', 'Not found'], rotation=-45 )
pl.show()
In [17]:
np.unique(uniqueLinks.id).shape
Out[17]:
(19209,)
Fixing http -> https redirection¶
Due to increased security awareness in the past few years, a number of links marked as redirections are in fact just changing the http protocol to https. We will not consider them as redirection, if the rest of the URL is the same.
In [18]:
redirection_checking = pd.read_csv('http2https.redirected.csv',
header=None, names=['pubmed_id', 'url', 'status', 'https_redirect']
)
redirection_checking.head(10)
Out[18]:
| pubmed_id | url | status | https_redirect | |
|---|---|---|---|---|
| 0 | 27273673 | http://www.cs.helsinki.fi/u/lmsalmel/LoRMA/ | 302 | True |
| 1 | 20492656 | http://www.cse.univdhaka.edu/~ashis/ppred/inde... | 301 | -1 |
| 2 | 28212402 | https://dongjunchung.github.io/GGPA/ | 301 | True |
| 3 | 28130230 | https://github.com/bfosso/MetaShot | 301 | True |
| 4 | 20935049 | http://github.com | 301 | False |
| 5 | 21765097 | http://nwrce.org/pgat | 301 | False |
| 6 | 17939871 | http://kdbio.inesc-id.pt/~svinga/ep/ | 301 | False |
| 7 | 28453681 | http://opig.stats.ox.ac.uk/webapps/sphinx | 301 | False |
| 8 | 21546392 | http://www.microbio.me/boulderale | 301 | -1 |
| 9 | 22034521 | http://www.cmbi.ru.nl/pdb_redo | 302 | False |
In [19]:
for index, row in redirection_checking.iterrows():
if row['https_redirect'] == 'True' and uniqueLinks[ uniqueLinks.link == row['url']].shape[0] > 0:
idx = np.where(uniqueLinks.link == row['url'])[0][0]
uniqueLinks.iloc[idx,6] = 1
In [20]:
uniqueLinks.head()
Out[20]:
| type | journal | id | year | link | code | status | |
|---|---|---|---|---|---|---|---|
| 0 | abstract | Nucleic_Acids_Res | 23155066.0 | 2013 | http://www.netlab.uky.edu/p/bioinfo/DiffSplice | 200 | 1 |
| 1 | abstract | Bioinformatics | 27273673.0 | 2017 | http://www.cs.helsinki.fi/u/lmsalmel/LoRMA/ | 302 | 1 |
| 2 | abstract | BMC_Bioinformatics | 20492656.0 | 2010 | http://www.cse.univdhaka.edu/~ashis/ppred/inde... | 301 | 3 |
| 3 | abstract | PLoS_Comput_Biol | 28212402.0 | 2017 | https://dongjunchung.github.io/GGPA/ | 301 | 1 |
| 4 | abstract | Bioinformatics | 28130230.0 | 2017 | https://github.com/bfosso/MetaShot | 301 | 1 |
In [21]:
statuses = uniqueLinks.status
count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])
print(count, bin_values)
total = float(count.sum())
for j in range(4):
print(count[j]/total)
(array([ 6036, 10926, 16336, 3404]), array([-1.2, -0.8, 1.8, 3.8, 4.8])) 0.16445970246853034 0.2976949485041687 0.44509835976241074 0.0927469892648902
In [22]:
(count[0]+count[-1])/total
Out[22]:
0.25720669173342053
Constructing a dataframe with only abstracts¶
In [23]:
uniqueLinks_abs = uniqueLinks[ uniqueLinks.type == 'abstract' ]
uniqueLinks_abs.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 7183 entries, 0 to 7877 Data columns (total 7 columns): type 7183 non-null object journal 7183 non-null object id 7182 non-null float64 year 7183 non-null int64 link 7183 non-null object code 7183 non-null int64 status 7183 non-null int64 dtypes: float64(1), int64(3), object(3) memory usage: 448.9+ KB
Number of entries per journal¶
In [24]:
journals = np.array( uniqueLinks.journal )
journalTitles = np.unique(journals)
jhash = {}
for jid,journal in enumerate(journalTitles):
jhash[journal] = jid
listxs = np.arange(0,11,1)
journals_num = [ jhash[journal] for journal in journals ]
counts, bins = np.histogram( journals_num, bins=listxs )
newIDX = np.argsort(counts)
f, ax = pl.subplots(1, 1)
ax.barh( bins[0:-1], counts[newIDX], 0.8, edgecolor=(0.2,0.2,0.2), lw=2,
color=(0.2,0.6,1.0) )
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_top()
ax.get_yaxis().tick_left()
ax.text( 5000, 11.3, 'Entries count', fontsize=12 )
ax.set_yticks( listxs )
ax.set_yticklabels( journalTitles[newIDX], rotation=0, fontsize=12 )
pl.show()
print('Journal name \t\t Count')
for jid,journal in enumerate(journalTitles[newIDX]):
print('%s\t\t %d' %(journal[:15],counts[newIDX][jid]) )
Journal name Count Nat_Biotechnol 180 Genome_Med 352 Nat_Methods 403 Genome_Biol 904 BMC_Syst_Biol 912 Bioinformatics 3131 PLoS_Comput_Bio 3226 BMC_Bioinformat 6840 BMC_Genomics 7651 Nucleic_Acids_R 13103
Number of entries per abstract¶
In [25]:
journals = np.array( uniqueLinks_abs.journal )
journalTitles = np.unique(journals)
jhash = {}
for jid,journal in enumerate(journalTitles):
jhash[journal] = jid
listxs = np.arange(0,11,1)
journals_num = [ jhash[journal] for journal in journals ]
counts, bins = np.histogram( journals_num, bins=listxs )
newIDX = np.argsort(counts)
f, ax = pl.subplots(1, 1)
ax.barh( bins[0:-1], counts[newIDX], 0.8, edgecolor=(0.2,0.2,0.2), lw=2,
color=(0.2,0.6,1.0) )
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_top()
ax.get_yaxis().tick_left()
ax.text( 1200, 11.3, 'Entries count', fontsize=12 )
ax.set_yticks( listxs )
ax.set_yticklabels( journalTitles[newIDX], rotation=0, fontsize=12 )
pl.show()
print('Journal name \t\t Count')
for jid,journal in enumerate(journalTitles[newIDX]):
print('%s\t\t %d' %(journal[:15],counts[newIDX][jid]) )
Journal name Count Nat_Biotechnol 1 Genome_Med 18 Nat_Methods 20 Genome_Biol 60 BMC_Syst_Biol 104 PLoS_Comput_Bio 259 BMC_Genomics 386 BMC_Bioinformat 1161 Bioinformatics 1866 Nucleic_Acids_R 3308
Number of of links per year¶
In [26]:
uLinks_year = uniqueLinks.groupby(['year', 'status']).agg('count')
In [27]:
uLinks_year.head(12)
Out[27]:
| type | journal | id | link | code | ||
|---|---|---|---|---|---|---|
| year | status | |||||
| 2005 | -1 | 462 | 462 | 461 | 462 | 462 |
| 1 | 319 | 319 | 318 | 319 | 319 | |
| 3 | 549 | 549 | 535 | 549 | 549 | |
| 4 | 235 | 235 | 233 | 235 | 235 | |
| 2006 | -1 | 442 | 442 | 435 | 442 | 442 |
| 1 | 333 | 333 | 333 | 333 | 333 | |
| 3 | 591 | 591 | 585 | 591 | 591 | |
| 4 | 216 | 216 | 212 | 216 | 216 | |
| 2007 | -1 | 468 | 468 | 466 | 468 | 468 |
| 1 | 340 | 340 | 335 | 340 | 340 | |
| 3 | 603 | 603 | 588 | 603 | 603 | |
| 4 | 230 | 230 | 230 | 230 | 230 |
Grouping by status and retrieving the percent for each status per year.
In [28]:
tseries = {}
for j,status in enumerate([-1, 1, 3, 4]):
tseries[status] = np.array( uLinks_year.iloc[j::4].link )
Plotting
In [29]:
barsize=0.75
f, ax = pl.subplots(1, 1, figsize=(4,2.2) )
b = np.zeros( tseries[status].shape[0] )
years = np.arange(2005,2018,1)
for zlabel,status in enumerate([4,-1,1,3]):
ax.bar( years, tseries[status] + b, barsize, label=legendStatus[status],
color=colors[status], zorder=-1-zlabel )
b += tseries[status]
simpleaxis(ax)
ax.set_ylabel("Number of links")
ax.set_yticks( np.arange(0,4001,1000) )
ax.set_yticklabels( np.array( np.arange(0,4001,1000), dtype=int ), fontsize=8 )
ax.set_xlabel("Year")
ax.set_xticks( np.arange( 2005,2020,3 ) )
ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )
pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4,
handletextpad=0.2, columnspacing=0.8, handlelength=1)
pl.tight_layout()
pl.savefig('Figure_1_panels/NumLinks per year - all.pdf')
Percent of links per year¶
Normalizing the number of links for each year.
In [30]:
tseries_norm = np.zeros( tseries[-1].shape )
for status in [-1, 1, 3, 4]:
tseries_norm += tseries[status]
In [31]:
barsize=0.75
f, ax = pl.subplots(1, 1, figsize=(4,2.2) )
b = np.zeros( tseries[status].shape[0] )
years = np.arange(2005,2018,1)
for zlabel,status in enumerate([4,-1,1,3]):
ax.bar( years, ( tseries[status]/tseries_norm + b )*100.,
barsize, label=legendStatus[status],
color=colors[status], zorder=-1-zlabel )
b += tseries[status]/tseries_norm
simpleaxis(ax)
ax.set_ylabel("Percent of links (%)")
ax.set_yticks( np.arange(0,101,25) )
ax.set_yticklabels( np.array( np.arange(0,101,25), dtype=int ), fontsize=8 )
ax.set_xlabel("Year")
ax.set_xticks( np.arange( 2005,2020,3 ) )
ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )
pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4,
handletextpad=0.2, columnspacing=0.8, handlelength=1)
pl.tight_layout()
pl.savefig('Figure_1_panels/Percent per year - all.pdf')
Number of links as function of time¶
Counting total number per year
Plotting:
In [32]:
f, ax = pl.subplots( 1, 1, figsize=(3.4, 2.) )
pl.plot( years, tseries[1] + tseries[3], 'o-',
markersize=7, label='Accessible + Redirected', color=colors2['fg_accessb'],
markeredgecolor=colors2['fg_accessb'], markerfacecolor=colors2['bg_accessb'] )
pl.plot( years, tseries[-1] + tseries[4], 's-',
markersize=6, label='Broken + Time out', color=colors2['fg_unreach'],
markeredgecolor=colors2['fg_unreach'], markerfacecolor=colors2['bg_unreach'] )
simpleaxis(ax)
pl.ylabel('Number of links')
pl.ylim(0,4050)
pl.xlabel('Time (years)')
pl.xlim(2004.5,2017.5)
pl.legend(bbox_to_anchor=(0.9,1.5), frameon=False, handlelength=1.3)
pl.tight_layout()
pl.savefig('Figure_1_panels/Total per year - all.pdf')
Ratio between accessible links in 2017 in reference to that in 2005
In [33]:
numAccessible = tseries[1] + tseries[3]
ratio = numAccessible[-1] / numAccessible[0]
print('Ratio: %.2f' % ratio)
avgGrowthRatio = (numAccessible[-1] - numAccessible[0]) / (2017-2005) / numAccessible[0]
print( 'Average growth ratio: %.1f%%' % (avgGrowthRatio*100) )
Ratio: 3.00 Average growth ratio: 0.0%
Ratio between unreachable links in 2017 in reference to that in 2005
In [34]:
numUnreachable = tseries[-1] + tseries[4]
ratio = numUnreachable[-1] / numUnreachable[0]
print('Ratio: %.2f' % ratio)
avgGrowthRatio = (numUnreachable[-1] - numUnreachable[0]) / (2017-2005) / numUnreachable[0]
print( 'Average growth ratio: %.1f%%' % (avgGrowthRatio*100) )
Ratio: 0.00 Average growth ratio: -100.0%
Parsing Github and SourceForge¶
In [35]:
def count_status(statuses):
count, bin_values = np.histogram(statuses, bins = [-1.2,-0.8,1.8,3.8,4.8])
print(count, bin_values)
total = float(count.sum())
for j in range(4):
print(count[j]/total)
In [36]:
find_github = lambda x: 'github.com' in x.link
find_sourceforge = lambda x: 'sourceforge.net' in x.link
find_others = lambda x: ('github.com' not in x.link) and ('sourceforge.net' not in x.link)
uniqLabs_github = uniqueLinks_abs[ uniqueLinks_abs.apply(find_github, axis=1) ]
uniqLabs_sourceforge = uniqueLinks_abs[ uniqueLinks_abs.apply(find_sourceforge, axis=1) ]
uniqLabs_others = uniqueLinks_abs[ uniqueLinks_abs.apply(find_others, axis=1) ]
count_status(uniqLabs_github.status)
count_status(uniqLabs_sourceforge.status)
count_status(uniqLabs_others.status)
(array([ 3, 98, 252, 0]), array([-1.2, -0.8, 1.8, 3.8, 4.8])) 0.0084985835694051 0.2776203966005666 0.7138810198300283 0.0 (array([ 1, 107, 105, 1]), array([-1.2, -0.8, 1.8, 3.8, 4.8])) 0.004672897196261682 0.5 0.49065420560747663 0.004672897196261682 (array([1166, 2466, 2400, 584]), array([-1.2, -0.8, 1.8, 3.8, 4.8])) 0.17623941958887546 0.37273276904474 0.36275695284159615 0.08827085852478839
In [37]:
f, ax = pl.subplots( 1, 1, figsize=(1.5, 1.4) )
# let's make sure the order is maintained:
data_broken = np.array([0.08827085852478839, 0.004672897196261682,0])
data_timeout = np.array([0.17623941958887546, 0.004672897196261682, 0.0084985835694051])
data_accessible = np.array([0.4068923821039903, 0.7009345794392523, 0.9036827195467422])
data_redirected = np.array([0.32859733978234584, 0.2897196261682243, 0.08781869688385269])
colors = { -1 : (1.0,0.6,0.2), 1 : (0.2,0.7,0.2), 3 : (0.2,0.4,1.0), 4 : (1.0,0.,0.) }
pl.bar( [0,1,2], data_broken+data_timeout+data_accessible+data_redirected, 0.7, color=colors[3], lw=1.3, capsize=8 )
pl.bar( [0,1,2], data_broken+data_timeout+data_accessible, 0.7, color=colors[1], lw=1.3, capsize=8 )
pl.bar( [0,1,2], data_broken+data_timeout, 0.7, color=colors[-1], lw=1.3, capsize=8 )
pl.bar( [0,1,2], data_broken, 0.7, color=colors[4], lw=1.3, capsize=8 )
ax.set_ylim(0,1)
ax.set_ylabel('Percent of Links')
ax.set_xlabel('')
ax.set_xlim(-0.5,2.5)
ax.set_xticks([0,1,2])
ax.set_xticklabels( ['Others','Source\nForge','GitHub'],
rotation=-25, fontsize=9)
simpleaxis(ax)
pl.savefig('Figure_1_panels/Percent - github sourceforge.pdf')
In [38]:
f, ax = pl.subplots(1, 1, figsize=(2.,1.6))
data_broken = np.array([0.08827085852478839, 0.004672897196261682,0])*100
data_timeout = np.array([0.17623941958887546, 0.004672897196261682, 0.0084985835694051])*100
data_unreachable = data_broken + data_timeout
ax.barh( [0,1,2], data_unreachable, 0.6, edgecolor=(1.0,0.0,0.0), lw=0.1, color=(1.0,0.0,0.0) )
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.text( -0.05, -1.5, 'Unreachable links (%)', fontsize=12 )
ax.set_xticks( [0, 10, 20, 30] )
ax.set_yticks( [0, 1, 2] )
ax.set_yticklabels( ['Others', 'Source\nForge', 'GitHub'], rotation=0, fontsize=11 )
pl.savefig('Figure_1_panels/Percent - github sourceforge_I.pdf')
In [39]:
uniqLabs_github = uniqueLinks_abs[ uniqueLinks_abs.apply(find_github, axis=1) ]
tseries_norm = uniqueLinks_abs.groupby(['year']).agg('count').type
tseries_GH = uniqLabs_github.groupby(['year']).agg('count').type[:-1]/tseries_norm[5:-1]*100
years_gh = range(2010,2017)
tseries_SF = uniqLabs_sourceforge.groupby(['year']).agg('count').type/tseries_norm[:-1]*100
years_sf = range(2005,2017)
f, ax = pl.subplots( 1, 1, figsize=(2.4, 2.) )
gh_color=(0.0,0.0,0.5)
gh_incolor=(0.3,0.8,1.0)
pl.plot( years_gh, tseries_GH, 's-', zorder=2,
markersize=5, label='GitHub', color=gh_color,
markeredgecolor=gh_color, markerfacecolor=gh_incolor )
sf_color=(0.8,0.0,0.0)
sf_incolor=(1.0,0.8,0.8)
pl.plot( years_sf, tseries_SF, 'o-', zorder=1,
markersize=5, label='SourceForge', color=sf_color,
markeredgecolor=sf_color, markerfacecolor=sf_incolor )
simpleaxis(ax)
pl.ylabel('Percent of links')
pl.ylim(0,22)
ax.set_xticks([2005, 2010, 2015])
pl.xlabel('Year')
pl.xlim(2004.5,2016.5)
pl.legend(bbox_to_anchor=(0.85,1.), frameon=False, handlelength=1.3, fontsize=10)
pl.tight_layout()
pl.savefig('Figure_1_panels/Total per year - github.pdf')
Number of links per year - abstract only¶
In [40]:
uLinks_ya = uniqueLinks_abs.groupby(['year', 'status']).agg('count')
tseries_abs = {}
for j,status in enumerate([-1, 1, 3, 4]):
tseries_abs[status] = np.array( uLinks_ya.iloc[j::4].link )
In [41]:
barsize=0.75
f, ax = pl.subplots(1, 1, figsize=(4,2.2) )
b = np.zeros( tseries_abs[status].shape[0] )
years = np.arange(2005,2018,1)
for zlabel,status in enumerate([4,-1,1,3]):
ax.bar( years, tseries_abs[status] + b, barsize, label=legendStatus[status],
color=colors[status], zorder=-1-zlabel )
b += tseries_abs[status]
simpleaxis(ax)
ax.set_ylabel("Number of links")
#ax.set_yticks( np.arange(0,2001,500) )
#ax.set_yticklabels( np.array( np.arange(0,2001,500), dtype=int ), fontsize=8 )
ax.set_xlabel("Year")
ax.set_xticks( np.arange( 2005,2020,3 ) )
ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )
pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4,
handletextpad=0.2, columnspacing=0.8, handlelength=1)
pl.tight_layout()
pl.savefig('Figure_1_panels/NumLinks per year - Abstract.pdf')
In [42]:
tseries_norm = np.zeros( tseries_abs[-1].shape )
for status in [-1, 1, 3, 4]:
tseries_norm += tseries_abs[status]
In [43]:
barsize=0.75
f, ax = pl.subplots(1, 1, figsize=(4,2.2) )
b = np.zeros( tseries_abs[status].shape[0] )
years = np.arange(2005,2018,1)
for zlabel,status in enumerate([4,-1,1,3]):
ax.bar( years, 100*(tseries_abs[status] + b)/tseries_norm, barsize,
label=legendStatus[status],
color=colors[status], zorder=-1-zlabel )
b += tseries_abs[status]
simpleaxis(ax)
ax.set_ylabel("Percent of links (%)")
ax.set_yticks( np.arange(0,101,25) )
ax.set_yticklabels( np.array( np.arange(0,101,25), dtype=int ), fontsize=8 )
ax.set_xlabel("Year")
ax.set_xticks( np.arange( 2005,2020,3 ) )
ax.set_xticklabels( np.arange( 2005,2020,3 ), fontsize=8 )
pl.legend(bbox_to_anchor=(1,1.4), frameon=False, ncol=4,
handletextpad=0.2, columnspacing=0.8, handlelength=1)
pl.tight_layout()
pl.savefig('Figure_1_panels/Percent per year - Abstract.pdf')
Status vs IF of current journal¶
List of journals considered in this study:
In [44]:
journals = uniqueLinks.journal.unique()
print( journals )
['Nucleic_Acids_Res' 'Bioinformatics' 'BMC_Bioinformatics' 'PLoS_Comput_Biol' 'BMC_Genomics' 'BMC_Syst_Biol' 'Nat_Biotechnol' 'Genome_Biol' 'Nat_Methods' 'Genome_Med']
Impact factors were retrieved from this address: http://admin-apps.webofknowledge.com/JCR/JCR?RQ=HOME
In [45]:
IFdata = pd.read_csv('impact_factors_timeseries.csv')
IFdata = IFdata.fillna(0)
IFdata
Out[45]:
| year | BMC_Genomics | Nucleic_Acids_Res | Bioinformatics | BMC_Bioinformatics | PLoS_Comput_Biol | Genome_Biol | BMC_Syst_Biol | Nat_Biotechnol | Nat_Methods | Genome_Med | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016 | 3.729 | 10.162 | 7.307 | 2.448 | 4.542 | 11.908 | 2.303 | 41.667 | 25.062 | 7.071 |
| 1 | 2015 | 3.867 | 9.202 | 5.766 | 2.435 | 4.587 | 11.313 | 2.208 | 43.113 | 25.328 | 5.846 |
| 2 | 2014 | 3.986 | 9.112 | 4.981 | 2.576 | 4.620 | 10.810 | 2.435 | 41.514 | 32.072 | 5.809 |
| 3 | 2013 | 4.041 | 8.808 | 4.621 | 2.672 | 4.829 | 10.465 | 2.853 | 39.080 | 25.953 | 4.942 |
| 4 | 2012 | 4.397 | 8.278 | 5.323 | 3.024 | 4.867 | 10.288 | 2.982 | 32.438 | 23.565 | 3.906 |
| 5 | 2011 | 4.073 | 8.026 | 5.468 | 2.751 | 5.215 | 9.036 | 3.148 | 23.268 | 19.276 | 0.000 |
| 6 | 2010 | 4.206 | 7.836 | 4.877 | 3.029 | 5.515 | 6.885 | 3.565 | 31.090 | 20.721 | 0.000 |
| 7 | 2009 | 3.759 | 7.479 | 4.926 | 3.428 | 5.759 | 6.626 | 4.064 | 29.495 | 16.874 | 0.000 |
| 8 | 2008 | 3.926 | 6.878 | 4.328 | 3.781 | 5.895 | 6.153 | 3.706 | 22.297 | 13.651 | 0.000 |
| 9 | 2007 | 4.180 | 6.954 | 5.039 | 3.493 | 6.236 | 6.589 | 0.000 | 22.848 | 15.478 | 0.000 |
| 10 | 2006 | 4.029 | 6.317 | 4.894 | 3.617 | 4.914 | 7.172 | 0.000 | 22.672 | 14.959 | 0.000 |
| 11 | 2005 | 4.092 | 7.552 | 6.019 | 4.958 | 0.000 | 9.712 | 0.000 | 22.738 | 6.741 | 0.000 |
| 12 | 2004 | 3.250 | 7.260 | 5.742 | 5.423 | 0.000 | 0.000 | 0.000 | 22.355 | 0.000 | 0.000 |
| 13 | 2003 | 0.000 | 6.575 | 6.701 | 0.000 | 0.000 | 0.000 | 0.000 | 17.721 | 0.000 | 0.000 |
In [46]:
count = {}
Z = np.zeros( (10) )
for status in [1,3,-1,4]:
count[status] = np.zeros( (10) )
def journalID(journalName):
if journalName in journals:
return np.argwhere(journals == journalName)[0][0]
else: return -1
for index, row in uniqueLinks.iterrows():
jid = journalID(row.journal)
if jid != -1:
count[row['status']][ jid ] += 1
Z[ jid ] += 1
# Normalizing
for status in [1,3,-1,4]:
count[status] /= Z
In [47]:
IFs = np.array( IFdata.iloc[0] )[1:]
newIDX = np.argsort( IFs )
IFs = IFs[newIDX]
In [49]:
barsize=0.7
f, ax = pl.subplots(1, 1, figsize=(3.6,2.0) )
IFs_x = np.arange(10)
ax.bar( IFs_x, count[4][newIDX], barsize, label='Broken',
color=colors[4], zorder=-1 )
b = np.copy( count[4][newIDX] )
ax.bar( IFs_x, b+count[-1][newIDX], barsize, label='Time out',
color=colors[-1], zorder=-2 )
b += count[-1][newIDX]
ax.bar( IFs_x, b+count[1][newIDX], barsize, label='Good',
color=colors[1], zorder=-3 )
b += count[1][newIDX]
ax.bar( IFs_x, b+count[3][newIDX], barsize, label='Redirect',
color=colors[3], zorder=-4 )
simpleaxis(ax)
ax.set_ylabel("Percent (%)")
ax.set_yticks( np.arange(0,1.1,0.25) )
ax.set_yticklabels( np.array( np.arange(0,1.1,0.25)*100, dtype=int ), fontsize=8 )
ax.set_xlabel("Current Impact Factor")
ax.set_xticks( IFs_x )
ax.set_xticklabels( ['2.3','2.5','3.7','4.5','7.1','7.3','10','12','25','41'],
fontsize=8, )
pl.tight_layout()
pl.savefig('Figure_1_panels/Percent per IF.pdf')
Status vs IF - abstracts only¶
List of journals considered in this study:
In [50]:
journals = uniqueLinks_abs.journal.unique()
print( journals )
['Nucleic_Acids_Res' 'Bioinformatics' 'BMC_Bioinformatics' 'PLoS_Comput_Biol' 'BMC_Genomics' 'BMC_Syst_Biol' 'Nat_Biotechnol' 'Genome_Biol' 'Nat_Methods' 'Genome_Med']
Impact factors were retrieved from this address: http://admin-apps.webofknowledge.com/JCR/JCR?RQ=HOME
In [51]:
count = {}
Z = np.zeros( (10) )
for status in [1,3,-1,4]:
count[status] = np.zeros( (10) )
def journalID(journalName):
if journalName in journals:
return np.argwhere(journals == journalName)[0][0]
else: return -1
for index, row in uniqueLinks_abs.iterrows():
jid = journalID(row.journal)
if jid != -1:
count[row['status']][ jid ] += 1
Z[ jid ] += 1
# Normalizing
for status in [1,3,-1,4]:
count[status] /= Z
In [52]:
IFs = np.array( IFdata.iloc[0] )[1:]
newIDX = np.argsort( IFs )
IFs = IFs[newIDX]
In [53]:
barsize=0.7
f, ax = pl.subplots(1, 1, figsize=(3.5,2.2) )
IFs_x = np.arange(10)
ax.bar( IFs_x, count[4][newIDX], barsize, label='Broken',
color=colors[4], zorder=-1 )
b = np.copy( count[4][newIDX] )
ax.bar( IFs_x, b+count[-1][newIDX], barsize, label='Time out',
color=colors[-1], zorder=-2 )
b += count[-1][newIDX]
ax.bar( IFs_x, b+count[1][newIDX], barsize, label='Good',
color=colors[1], zorder=-3 )
b += count[1][newIDX]
ax.bar( IFs_x, b+count[3][newIDX], barsize, label='Redirect',
color=colors[3], zorder=-4 )
simpleaxis(ax)
ax.set_ylabel("Percent (%)")
ax.set_yticks( np.arange(0,1.1,0.25) )
ax.set_yticklabels( np.array( np.arange(0,1.1,0.25)*100, dtype=int ), fontsize=8 )
ax.set_xlabel("Current Impact Factor")
ax.set_xticks( IFs_x )
ax.set_xticklabels( ['2.3','2.5','3.7','4.5','7.1','7.3','10','12','25','41'],
fontsize=8, )
pl.tight_layout()
pl.savefig('Figure_1_panels/Percent per IF - abstracts.pdf')
Status vs IF at the time of publication¶
In [54]:
IFpub = {}
for status in [1,3,-1,4]:
IFpub[status] = []
for index, row in uniqueLinks.iterrows():
year = row.year
if ( year >= 2003 ) and ( year <= 2017 ) :
# there's no IF for 2017 yet
if year == 2017: year = 2016
yearIDX = -(year - 2003) + 13
IF_at_time = IFdata[row.journal].iloc[ yearIDX ]
IFpub[row.status].append( IF_at_time )
In [55]:
f, ax = pl.subplots( 1, 1, figsize=(2.8, 2.0) )
# let's make sure the order is maintained:
data = [IFpub[4], IFpub[-1], IFpub[1], IFpub[3]]
parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False,
showextrema=False, widths=0.7, points=200 )
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.3,0.9,0.3),(0.3,0.7,1.0)]
j = 0
for pc in parts['bodies']:
pc.set_facecolor(colors[j])
pc.set_edgecolor( (0.2,0.2,0.2) )
pc.set_edgecolors( (0.2,0.2,0.2) )
pc.set_alpha(1)
j += 1
def buttpos(x): return [x-0.05,x+0.05]
for j in range(4):
pl.plot(buttpos(j), [np.mean(data[j])]*2, color="k",
linewidth=6, solid_capstyle="butt", zorder=4)
for j in range(4):
ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )
ax.set_ylim(-3,14)
ax.set_ylabel('Impact factor')
ax.set_xlabel('')
ax.set_xticks([0,1,2,3])
ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'],
rotation=-25, fontsize=8 )
simpleaxis(ax)
pl.savefig('Figure_1_panels/IF vs status.pdf')
In [56]:
from scipy import stats
f_value, p_value = stats.f_oneway(data[0], data[1], data[3], data[2])
print(f_value, p_value)
(9.877125130203718, 1.659019515265056e-06)
Evolution of IF of each category over the years¶
In [57]:
IFpub = {}
Z = {}
for status in [1,3,-1,4]:
IFpub[status] = np.zeros(12)
Z[status] = np.zeros(12)
for index, row in uniqueLinks.iterrows():
year = row.year
if ( year >= 2003 ) and ( year <= 2017 ) :
# there's no IF for 2017 yet
if year == 2017: year = 2016
yearIDX = year - 2005
IF_at_time = IFdata[row.journal].iloc[ yearIDX ]
IFpub[row.status][yearIDX] += IF_at_time
Z[row.status][yearIDX] += 1
In [58]:
colors = { -1 : (1.0,0.6,0.2), 1 : (0.2,0.7,0.2), 3 : (0.2,0.4,1.0), 4 : (1.0,0.,0.) }
for s in [4,-1,1,3]:
pl.plot( IFpub[s]/Z[s], color=colors[s] )
Retrieving altmetric data¶
In [59]:
def parseAltmetricData(filename):
parsedData = {}
inputfile = open( filename, 'r' )
# skipping first row
inputfile.readline()
for line in inputfile:
lineSplit = line.split('\n')[0].split('[')
ls_comma = lineSplit[0].split(',')
pmid = int( ls_comma[0] )
parsedData[pmid] = {}
score = float( ls_comma[1] )
parsedData[pmid]['score'] = score
numReaders = int( ls_comma[2] )
parsedData[pmid]['numReaders'] = numReaders
cited = int( ls_comma[3] )
parsedData[pmid]['cited'] = cited
scopus = lineSplit[1][:-1]
parsedData[pmid]['scopus'] = scopus
return parsedData
dictAltmetric = parseAltmetricData('./links.bulk.altmetric.csv')
In [60]:
print( 'Number of Altmetric records: %d ' % (len(dictAltmetric.keys())) )
Number of Altmetric records: 13889
In [61]:
import matplotlib as mpl
mpl.rcParams['hatch.linewidth'] = 3 # previous pdf hatch linewidth
from statsmodels.stats.multicomp import MultiComparison
Altmetric score¶
In [62]:
scores = {}
for status in [1,3,-1,4]:
scores[status] = []
for index, row in uniqueLinks.iterrows():
if row.id in dictAltmetric.keys():
numYears = (year - 2003)
scores[ row.status ].append( dictAltmetric[row.id]['score'] )
In [63]:
f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.5) )
# let's make sure the order is maintained:
data = [scores[4], scores[-1], scores[1], scores[3]]
parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False,
showextrema=False, widths=0.7, points=500 )
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.3,0.9,0.3),(0.3,0.7,1.0)]
j = 0
for pc in parts['bodies']:
pc.set_facecolor(colors[j])
pc.set_edgecolor( (0.2,0.2,0.2) )
pc.set_edgecolors( (0.2,0.2,0.2) )
pc.set_alpha(1)
j += 1
def buttpos(x): return [x-0.05,x+0.05]
for j in range(4):
pl.plot(buttpos(j), [np.mean(data[j])]*2, color="k",
linewidth=6, solid_capstyle="butt", zorder=4)
for j in range(4):
ax.text( j-0.3, -2.5, 'n='+str(len(data[j])), fontsize=7 )
ax.set_ylim(-4,30)
ax.set_ylabel('Altmetric score')
ax.set_xlabel('')
ax.set_xticks([0,1,2,3])
ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'],
rotation=-25, fontsize=8)
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric Score vs status.pdf')
In [64]:
f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )
# let's make sure the order is maintained:
data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]
vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])),
np.std(scores[-1])/np.sqrt(len(scores[-1])),
np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.5,1.0,0.5)]
pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors,
edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors[2:], hatch="//",
edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',
edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )
ax.set_ylabel('Altmetric score')
ax.set_ylim(0,18)
ax.set_yticks([0,5,10,15])
ax.set_xlabel('')
ax.set_xticks([0,1,2])
ax.set_xlim(-0.5,2.5)
ax.set_xticklabels( ['Broken','Timeout','Accessible'],
rotation=-25, fontsize=9)
ax.text(1.3, 18, '*', fontsize=12)
ax.plot([2,2,0.5,0.5], [16,18,18,16], color='k', lw=1.5)
ax.plot([1,0], [16,16], color='k', lw=1.5)
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric Score vs status - barplot.pdf')
In [66]:
from scipy.stats.mstats import kruskalwallis
print(kruskalwallis(scores[-1],scores[4],scores[1],scores[3]))
lists = scores[-1]+scores[4]+scores[1]+scores[3]
categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]
mc = MultiComparison(lists, categories)
result = mc.tukeyhsd()
print(result)
print(mc.groupsunique)
KruskalResult(statistic=121.71348392841217, pvalue=3.2990385452748936e-26) Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- -1 1 2.4789 0.7074 4.2504 True -1 4 -2.0665 -4.7723 0.6393 False 1 4 -4.5454 -6.8068 -2.284 True -------------------------------------------- [-1 1 4]
Altmetric number of readers¶
In [67]:
scores = {}
for status in [1,3,-1,4]:
scores[status] = []
for index, row in uniqueLinks.iterrows():
if row.id in dictAltmetric.keys():
numYears = (year - 2003)
scores[ row.status ].append( dictAltmetric[row.id]['numReaders'] / numYears )
In [68]:
f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.8) )
# let's make sure the order is maintained:
data = [scores[4], scores[-1], scores[1], scores[3]]
parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False,
showextrema=False, widths=0.7, points=500 )
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.3,0.9,0.3),(0.3,0.7,1.0)]
j = 0
for pc in parts['bodies']:
pc.set_facecolor(colors[j])
pc.set_edgecolor( (0.2,0.2,0.2) )
pc.set_edgecolors( (0.2,0.2,0.2) )
pc.set_alpha(1)
j += 1
def buttpos(x): return [x-0.05,x+0.05]
for j in range(4):
pl.plot(buttpos(j), [np.mean(data[j])]*2, color="k",
linewidth=6, solid_capstyle="butt", zorder=4)
# for j in range(4):
# ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )
ax.set_ylim(0,20)
ax.set_ylabel('Altmetric readership')
ax.set_xlabel('')
ax.set_xticks([0,1,2,3])
ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'], rotation=-25 )
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric numReaders vs status.pdf')
In [70]:
f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )
# let's make sure the order is maintained:
data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]
vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])),
np.std(scores[-1])/np.sqrt(len(scores[-1])),
np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.5,1.0,0.5)]
pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors,
edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors[2:], hatch="//",
edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',
edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )
ax.set_ylim(0,10)
ax.set_ylabel('Altmetric \n readership')
ax.set_xlabel('')
ax.set_xlim(-0.5,2.5)
ax.set_xticks([0,1,2])
ax.set_xticklabels( ['Broken','Timeout','Accessible'],
rotation=-25, fontsize=9)
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric numReaders vs status - barplot.pdf')
In [71]:
lists = scores[-1]+scores[4]+scores[1]+scores[3]
categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]
mc = MultiComparison(lists, categories)
result = mc.tukeyhsd()
print(result)
print(mc.groupsunique)
Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================ group1 group2 meandiff lower upper reject -------------------------------------------- -1 1 -0.3351 -0.9904 0.3202 False -1 4 0.3794 -0.6216 1.3803 False 1 4 0.7145 -0.1221 1.551 False -------------------------------------------- [-1 1 4]
Altmetric citation¶
In [72]:
scores = {}
for status in [1,3,-1,4]:
scores[status] = []
for index, row in uniqueLinks.iterrows():
if row.id in dictAltmetric.keys():
numYears = (year - 2003)
scores[ row.status ].append( dictAltmetric[row.id]['cited'] / numYears )
In [73]:
f, ax = pl.subplots( 1, 1, figsize=(3.5, 1.5) )
# let's make sure the order is maintained:
data = [scores[4], scores[-1], scores[1], scores[3]]
parts = ax.violinplot( data, [0,1,2,3], showmeans=False, showmedians=False,
showextrema=False, widths=0.7, points=500 )
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.3,0.9,0.3),(0.3,0.7,1.0)]
j = 0
for pc in parts['bodies']:
pc.set_facecolor(colors[j])
pc.set_edgecolor( (0.2,0.2,0.2) )
pc.set_edgecolors( (0.2,0.2,0.2) )
pc.set_alpha(1)
j += 1
def buttpos(x): return [x-0.05,x+0.05]
for j in range(4):
pl.plot(buttpos(j), [np.mean(data[j])]*2, color="k",
linewidth=6, solid_capstyle="butt", zorder=4)
# for j in range(4):
# ax.text( j-0.4, -2, 'n='+str(len(data[j])), fontsize=7 )
ax.set_ylim(0,9)
ax.set_ylabel('Social media \n citations per year')
ax.set_yticks([0,2,4,6,8])
ax.set_xlabel('')
ax.set_xticks([0,1,2,3])
ax.set_xticklabels( ['Broken','Timeout','Accessible','Redirected'],
rotation=-25, fontsize=8 )
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric Cited vs status.pdf')
In [74]:
f, ax = pl.subplots( 1, 1, figsize=(1.8, 1.4) )
# let's make sure the order is maintained:
data = [ np.mean(scores[4]), np.mean(scores[-1]), np.mean(scores[1] + scores[3]) ]
vardata = [ np.std(scores[4])/np.sqrt(len(scores[4])),
np.std(scores[-1])/np.sqrt(len(scores[-1])),
np.std(scores[1] + scores[3])/np.sqrt(len(scores[1] + scores[3])) ]
colors=[(1.0,0.2,0.1),(1.0,0.6,0.2),(0.5,1.0,0.5)]
pl.bar( [0,1,2], data, 0.7, yerr=vardata, color=colors,
edgecolor=[(0.2,0.2,0.2)]*3, lw=1.3, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color=colors[2:], hatch="//",
edgecolor=[(0.1,0.4,1.0)]*3, lw=1.5, capsize=8 )
pl.bar( [2], data[2:], 0.7, yerr=vardata[2:], color='none',
edgecolor=[(0.0,0.0,0.0)]*3, lw=1.5, capsize=8 )
ax.set_ylim(0,5)
ax.set_ylabel('Social media \n citations per year')
ax.set_xlabel('')
ax.set_xlim(-0.5,2.5)
ax.set_xticks([0,1,2])
ax.set_xticklabels( ['Broken','Timeout','Accessible'],
rotation=-25, fontsize=9)
ax.text(1.3, 4.7, '*', fontsize=12)
ax.plot([2,2,0.5,0.5], [4,4.7,4.7,4], color='k', lw=1.5)
ax.plot([0,1], [4,4], color='k', lw=1.5)
simpleaxis(ax)
pl.savefig('Figure_1_panels/Altmetric Cited vs status - barplot.pdf')
In [75]:
lists = scores[-1]+scores[4]+scores[1]+scores[3]
categories = len(scores[-1])*[-1]+len(scores[4])*[4]+len(scores[1]+scores[3])*[1]
from scipy.stats.mstats import kruskalwallis
print(kruskalwallis(scores[-1],scores[4],scores[1],scores[3]))
mc = MultiComparison(lists, categories)
result = mc.tukeyhsd()
print(result)
print(mc.groupsunique)
KruskalResult(statistic=399.6966037767709, pvalue=2.5755683194906825e-86) Multiple Comparison of Means - Tukey HSD,FWER=0.05 ============================================= group1 group2 meandiff lower upper reject --------------------------------------------- -1 1 0.9307 0.3997 1.4617 True -1 4 -0.4639 -1.275 0.3472 False 1 4 -1.3946 -2.0725 -0.7168 True --------------------------------------------- [-1 1 4]