def figure3d():
"""Represents cost per JR5 download by provider
Reads in cost data from supplementary file for each provider
Cost per JR1 download = total cost per provider / # of JR5 downloads by provider
Represented as "big 7" providers, including Elsevier Freedom and Elsevier Subscribed titles
Chart Type: Bar Graph
Y-Axis: Cost (in dollars) per JR5 Download
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
1figr_U_Virginia_edit_Supp_Data, Total cost for 2017
X-Axis: Provider Name
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
1figr_U_Virginia_edit_Supp_Data, Total cost for 2017
"""
data = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
big7 = [
'Elsevier', 'Sage', 'Springer', 'Taylor & Francis', 'Wiley',
'Elsevier Freedom', 'Elsevier Subscribed'
]
stats_by_provider = []
for provider_name in big7:
subset_by_provider = data.loc[data['Provider'] == provider_name]
journals_data = subset_by_provider.groupby(
'Journal', as_index=False).sum().values.tolist()
for i in journals_data:
if i[0] == provider_name:
jr5_total = i[5]
stats_by_provider.append(
(i[0], jr5_total)) #i[0] = name of provider
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_freedom_jr5_downloads = elsevier_freedom_collection[
'Downloads JR5 2017 in 2017'].sum()
stats_by_provider.append(
('Elsevier Freedom', elsevier_freedom_jr5_downloads))
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_subscribed_jr5_downloads = elsevier_subscribed_titles[
'Downloads JR5 2017 in 2017'].sum()
stats_by_provider.append(
('Elsevier Subscribed', elsevier_subscribed_jr5_downloads))
#reads cost data per provider from the following supplementary file
cost_data = pd.read_excel('1figr_U_Virginia_edit_Supp_Data.xlsx')
cost_per_provider = cost_data.groupby(
['Package'], as_index=False).sum().values.tolist()
cost_per_jr5_download = []
for stat in stats_by_provider:
for cost in cost_per_provider:
if stat[0] == cost[0]: #first element in each item is the name
cost_per_jr5_download.append(cost[1] / stat[1])
#make plot
mpl.rcParams['ytick.major.width'] = 1
mpl.rcParams['xtick.major.width'] = 1
plt.figure(num=None, figsize=(8, 8))
plt.suptitle(f'Cost per Download, current year 2017 downloads (JR5)')
plot = plt.bar(big7, cost_per_jr5_download, width=.8, color='green')
plt.ylabel('Cost (dollars)')
plt.ylim(0, 37) #changes top and bottom limit of y axis in plot
plt.xticks(rotation=90)
for i in plot:
score = i.get_height()
plt.text(i.get_x() + i.get_width() / 2,
1.05 * score,
'${:,.2f}'.format(score),
ha='center',
va='bottom')
def figure1g():
"""Makes JR80, JR90, JR95 graph for all 6 big providers,
splitting elsevier into Elseveier Freedom and Elsevier Subscribed.
Plots References (Articles published by your institution, referenced by other authors)
JR80 = Journals which make up 80% of References
JR90 = Journals which make up 90% of References
JR95 = Journals which make up 95% of References
Chart Type: Stacked Bar Graph
Y-Axis: Percent of Total Titles
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Provider Name
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
data = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
stats_by_provider = []
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
#make stats for existing providers
for provider_name in providers:
subset_by_provider = data.loc[data['Provider'] == provider_name]
journals_data = subset_by_provider.groupby(
'Journal', as_index=False).sum().values.tolist()
for i in journals_data:
if i[0] == provider_name:
journals_data.remove(i) #removing aggregator column data
total_references = 0
total_journals = 0
for i in journals_data:
total_references += i[6]
total_journals += 1
reference_tuples = [(i[0], i[6]) for i in journals_data]
reference_tuples_sorted = sorted(
reference_tuples, key=lambda i: i[1],
reverse=True) #sorts on second element of reference_tuples
jr80_running_tally = 0 #represents 80% of collections use
jr90_running_tally = 0
jr95_running_tally = 0
jr80_highly_used_journals = [] #THIS HOLDS (JOURNAL NAME, REFERENCES)
jr90_highly_used_journals = []
jr95_highly_used_journals = []
for i in reference_tuples_sorted:
if jr80_running_tally < (total_references * 0.8):
jr80_highly_used_journals.append(i)
jr80_running_tally += i[1]
for i in reference_tuples_sorted:
if jr90_running_tally < (total_references * 0.9):
jr90_highly_used_journals.append(i)
jr90_running_tally += i[1]
for i in reference_tuples_sorted:
if jr95_running_tally < (total_references * 0.95):
jr95_highly_used_journals.append(i)
jr95_running_tally += i[1]
jr80_score = (len(jr80_highly_used_journals)) / (total_journals)
jr90_score = (len(jr90_highly_used_journals)) / (total_journals)
jr90_score = (jr90_score - jr80_score)
jr95_score = (len(jr95_highly_used_journals)) / (total_journals)
jr95_score = (jr95_score - (jr80_score + jr90_score))
total_score = (1 - (jr80_score + jr90_score + jr95_score))
stats_by_provider.append(
(provider_name, jr80_score, jr90_score, jr95_score, total_score,
len(jr80_highly_used_journals), len(jr90_highly_used_journals),
len(jr95_highly_used_journals)))
unmatched_collection_provider = rf.make_elsevier_unmatched_provider()
unmatched_collection_provider[
'Provider Name'] = 'Elsevier Unmatched' #need to create a column which holds provider name
subscribed_titles_provider = rf.make_elsevier_subscribed_titles_provider()
subscribed_titles_provider[
'Provider Name'] = 'Elsevier Subscribed' #need to create a column which holds provider name
freedom_collection_provider = rf.make_freedom_collection_provider()
freedom_collection_provider[
'Provider Name'] = 'Elsevier Freedom' #need to create a column which holds provider name
elsevier_providers = [
unmatched_collection_provider, subscribed_titles_provider,
freedom_collection_provider
]
for provider_name in elsevier_providers:
first_row = provider_name.iloc[1]
name = first_row[
'Provider Name'] #need string of provider name for stats_by_provider
journals_data = provider_name.groupby(
'Journal', as_index=False).sum().values.tolist()
total_references = 0
total_journals = 0
for i in journals_data:
total_references += i[6]
total_journals += 1
reference_tuples = [(i[0], i[6]) for i in journals_data]
reference_tuples_sorted = sorted(
reference_tuples, key=lambda i: i[1],
reverse=True) #sorts on second element of reference_tuples
jr80_running_tally = 0 #represents 80% of collections use
jr90_running_tally = 0
jr95_running_tally = 0
jr80_highly_used_journals = [] #THIS HOLDS (JOURNAL NAME, REFERENCES)
jr90_highly_used_journals = []
jr95_highly_used_journals = []
for i in reference_tuples_sorted:
if jr80_running_tally < (total_references * 0.8):
jr80_highly_used_journals.append(i)
jr80_running_tally += i[1]
for i in reference_tuples_sorted:
if jr90_running_tally < (total_references * 0.9):
jr90_highly_used_journals.append(i)
jr90_running_tally += i[1]
for i in reference_tuples_sorted:
if jr95_running_tally < (total_references * 0.95):
jr95_highly_used_journals.append(i)
jr95_running_tally += i[1]
jr80_score = (len(jr80_highly_used_journals)) / (total_journals)
jr90_score = (len(jr90_highly_used_journals)) / (total_journals)
jr90_score = (jr90_score - jr80_score)
jr95_score = (len(jr95_highly_used_journals)) / (total_journals)
jr95_score = (jr95_score - (jr80_score + jr90_score))
total_score = (1 - (jr80_score + jr90_score + jr95_score))
stats_by_provider.append(
(name, jr80_score, jr90_score, jr95_score, total_score,
len(jr80_highly_used_journals), len(jr90_highly_used_journals),
len(jr95_highly_used_journals)))
#make plot
plt.figure(num=None, figsize=(10, 10))
plt.suptitle('Percentage of Titles Referenced by Provider (References)')
plt.ylabel('Percent of total titles')
plt.gca().yaxis.set_major_formatter(
StrMethodFormatter('{x:,.0%}')) #formats y axis as %
jr80s = mpatches.Patch(color='violet', label='JR80 titles')
jr90s = mpatches.Patch(color='moccasin', label='JR90 titles')
jr95s = mpatches.Patch(color='paleturquoise', label='JR95 titles')
others = mpatches.Patch(color='silver', label='Total titles')
plt.legend(handles=[jr80s, jr90s, jr95s, others],
bbox_to_anchor=(1, 1)) #moves legend outside plot
plt.xticks(rotation=45)
#NEED TO ADD LABELS TO PLOTS
for i in stats_by_provider:
provider = i[0]
jr80 = i[1]
jr90 = i[2]
jr95 = i[3]
total_values = i[4]
plot1 = plt.bar(provider, jr80, color='violet')
plot2 = plt.bar(provider, jr90, bottom=jr80, color='moccasin')
plot3 = plt.bar(provider,
jr95,
bottom=(jr80 + jr90),
color='paleturquoise')
plot4 = plt.bar(provider,
total_values,
bottom=(jr80 + jr90 + jr95),
color='silver')
def figure2b():
"""A measurement of currency. Compares JR5 downloads to JR1 downloads for each of the big 7 providers.
JR5 downloads are 2017 articles downloaded in 2017.
JR1 downloads are all years articles downloaded in 2017.
We want to see what % of current articles people are downloading.
Adds the 'Elsevier Freedom Collection' and 'Elsevier Subscribed Titles', making it the big 7 providers
Chart Type: Bar Graph
Y-Axis: Percent of Total
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Provider Names
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
data = pd.read_excel(filename, sheet_name='Journals per Provider', skiprows=8)
big7 = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley', 'Elsevier Freedom Collection', 'Elsevier Subscribed Titles', 'Elsevier Unmatched']
percent_jr5_of_jr1 = []
for provider_name in big7:
subset_by_provider = data.loc[data['Provider'] == provider_name]
journals_data = subset_by_provider.groupby('Journal', as_index=False).sum().values.tolist()
for i in journals_data:
if i[0] == provider_name:
jr1_total = i[4]
jr5_total = i[5]
ratio = jr5_total/jr1_total
percent_jr5_of_jr1.append(ratio)
#make ratio of jr5 to jr1 downloads for elsevier freedom collection
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_freedom_jr5_downloads = elsevier_freedom_collection['Downloads JR5 2017 in 2017'].sum()
elsevier_freedom_jr1_downloads = elsevier_freedom_collection['Downloads JR1 2017'].sum()
elsevier_freedom_ratio = elsevier_freedom_jr5_downloads/elsevier_freedom_jr1_downloads
percent_jr5_of_jr1.append(elsevier_freedom_ratio)
#make ratio of jr5 to jr1 downloads for elsevier subscribed titles
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_subscribed_jr5_downloads = elsevier_subscribed_titles['Downloads JR5 2017 in 2017'].sum()
elsevier_subscribed_jr1_downloads = elsevier_subscribed_titles['Downloads JR1 2017'].sum()
elsevier_subscribed_ratio = elsevier_subscribed_jr5_downloads/elsevier_subscribed_jr1_downloads
percent_jr5_of_jr1.append(elsevier_subscribed_ratio)
#make ratio of jr5 to jr1 downloads for elsevier unmatched titles
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
elsevier_unmatched_jr5_downloads = elsevier_unmatched_titles['Downloads JR5 2017 in 2017'].sum()
elsevier_unmatched_jr1_downloads = elsevier_unmatched_titles['Downloads JR1 2017'].sum()
elsevier_unmatched_ratio = elsevier_unmatched_jr5_downloads/elsevier_unmatched_jr1_downloads
percent_jr5_of_jr1.append(elsevier_unmatched_ratio)
mpl.rcParams['ytick.major.width'] = 1
mpl.rcParams['xtick.major.width'] = 1
plt.figure(num=None, figsize=(8,8))
plt.suptitle(f'Percent JR5 downloads of JR1 downloads (for 2017)')
plot = plt.bar(big7, percent_jr5_of_jr1, width=.8, color='green')
plt.ylabel('Percent of Total')
plt.ylim(0, 1) #changes top and bottom limit of y axis in plot
plt.xticks(rotation=90)
plt.gca().yaxis.set_major_formatter(StrMethodFormatter('{x:,.0%}')) #formats y axis as %
for i in plot:
score = i.get_height()
plt.text(i.get_x() + i.get_width()/2,
1.05 * score,
'{:.1%}'.format(score),
ha='center',
va='bottom')
def figure4b_references():
""" Show references per year (2008-2017) by your institution's affiliated authors, separating Elsevier Freedom
and Elsevier Subscribed titles out from Elsevier as a whole.
Chart Type: Line Graph
Y-Axis: Number of References
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, References to journal/provider by your institution's authors (as measured in Scopus)
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
#this holds reference totals for all providers in the end, which is used to make final plot
ref_by_provider = []
#populate references totals for elsevier subset providers
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
ref_by_year = []
ref_2008 = provider_name['2008.1'].tolist()
ref_by_year.append(sum(ref_2008))
ref_2009 = provider_name['2009.1'].tolist()
ref_by_year.append(sum(ref_2009))
ref_2010 = provider_name['2010.1'].tolist()
ref_by_year.append(sum(ref_2010))
ref_2011 = provider_name['2011.1'].tolist()
ref_by_year.append(sum(ref_2011))
ref_2012 = provider_name['2012.1'].tolist()
ref_by_year.append(sum(ref_2012))
ref_2013 = provider_name['2013.1'].tolist()
ref_by_year.append(sum(ref_2013))
ref_2014 = provider_name['2014.1'].tolist()
ref_by_year.append(sum(ref_2014))
ref_2015 = provider_name['2015.1'].tolist()
ref_by_year.append(sum(ref_2015))
ref_2016 = provider_name['2016.1'].tolist()
ref_by_year.append(sum(ref_2016))
ref_2017 = provider_name['2017.1'].tolist()
ref_by_year.append(sum(ref_2017))
ref_by_provider.append(ref_by_year)
#populate references totals for other providers
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
for provider_name in providers:
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
ref_by_year = []
ref_2008 = subset_by_provider['2008.1'].tolist()
ref_by_year.append(ref_2008[0])
ref_2009 = subset_by_provider['2009.1'].tolist()
ref_by_year.append(ref_2009[0])
ref_2010 = subset_by_provider['2010.1'].tolist()
ref_by_year.append(ref_2010[0])
ref_2011 = subset_by_provider['2011.1'].tolist()
ref_by_year.append(ref_2011[0])
ref_2012 = subset_by_provider['2012.1'].tolist()
ref_by_year.append(ref_2012[0])
ref_2013 = subset_by_provider['2013.1'].tolist()
ref_by_year.append(ref_2013[0])
ref_2014 = subset_by_provider['2014.1'].tolist()
ref_by_year.append(ref_2014[0])
ref_2015 = subset_by_provider['2015.1'].tolist()
ref_by_year.append(ref_2015[0])
ref_2016 = subset_by_provider['2016.1'].tolist()
ref_by_year.append(ref_2016[0])
ref_2017 = subset_by_provider['2017.1'].tolist()
ref_by_year.append(ref_2017[0])
ref_by_provider.append(ref_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(
f'Number of References Made by {your_institution} Researchers by Provider'
)
plt.xlabel('Year')
plt.ylabel('Number References')
plt.ylim(0, 12000)
plt.plot(years,
ref_by_provider[0],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
ref_by_provider[1],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
ref_by_provider[2],
label='Elsevier Unmatched',
color='black')
plt.plot(years, ref_by_provider[3], label='Sage', color='blue')
plt.plot(years, ref_by_provider[4], label='Springer', color='green')
plt.plot(years,
ref_by_provider[5],
label='Taylor & Francis',
color='purple')
plt.plot(years, ref_by_provider[6], label='Wiley', color='orange')
plt.legend()
def figure4b_percentage():
""" Show percent references per year as a part of all references for each provider, separating Elsevier Freedom
and Elsevier Subscribed titles out from Elsevier as a whole.
Chart Type: Line Graph
Y-Axis: Number of References
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, References to journal/provider by your institution's authors (as measured in Scopus)
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
all_providers = original_1figr_dataset['Provider'].unique(
) #makes list of unique providers
#build total references for all providers by year
sum_2008 = 0
sum_2009 = 0
sum_2010 = 0
sum_2011 = 0
sum_2012 = 0
sum_2013 = 0
sum_2014 = 0
sum_2015 = 0
sum_2016 = 0
sum_2017 = 0
for provider_name in all_providers:
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
ref_2008 = subset_by_provider['2008.1'].tolist()
sum_2008 += ref_2008[0]
ref_2009 = subset_by_provider['2009.1'].tolist()
sum_2009 += ref_2009[0]
ref_2010 = subset_by_provider['2010.1'].tolist()
sum_2010 += ref_2010[0]
ref_2011 = subset_by_provider['2011.1'].tolist()
sum_2011 += ref_2011[0]
ref_2012 = subset_by_provider['2012.1'].tolist()
sum_2012 += ref_2012[0]
ref_2013 = subset_by_provider['2013.1'].tolist()
sum_2013 += ref_2013[0]
ref_2014 = subset_by_provider['2014.1'].tolist()
sum_2014 += ref_2014[0]
ref_2015 = subset_by_provider['2015.1'].tolist()
sum_2015 += ref_2015[0]
ref_2016 = subset_by_provider['2016.1'].tolist()
sum_2016 += ref_2016[0]
ref_2017 = subset_by_provider['2017.1'].tolist()
sum_2017 += ref_2017[0]
#build references by provider for each year
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
ref_by_provider = [
] #this holds percentage of total references for each year by provider, which is later plotted
for provider_name in providers:
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
ref_by_year = []
ref_2008 = subset_by_provider['2008.1'].tolist()
ref_by_year.append(ref_2008[0] / sum_2008)
ref_2009 = subset_by_provider['2009.1'].tolist()
ref_by_year.append(ref_2009[0] / sum_2009)
ref_2010 = subset_by_provider['2010.1'].tolist()
ref_by_year.append(ref_2010[0] / sum_2010)
ref_2011 = subset_by_provider['2011.1'].tolist()
ref_by_year.append(ref_2011[0] / sum_2011)
ref_2012 = subset_by_provider['2012.1'].tolist()
ref_by_year.append(ref_2012[0] / sum_2012)
ref_2013 = subset_by_provider['2013.1'].tolist()
ref_by_year.append(ref_2013[0] / sum_2013)
ref_2014 = subset_by_provider['2014.1'].tolist()
ref_by_year.append(ref_2014[0] / sum_2014)
ref_2015 = subset_by_provider['2015.1'].tolist()
ref_by_year.append(ref_2015[0] / sum_2015)
ref_2016 = subset_by_provider['2016.1'].tolist()
ref_by_year.append(ref_2016[0] / sum_2016)
ref_2017 = subset_by_provider['2017.1'].tolist()
ref_by_year.append(ref_2017[0] / sum_2017)
ref_by_provider.append(ref_by_year)
#Calculate number of references for Elsevier Freedom and Elsevier Subscribed titles
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
ref_by_year = []
ref_2008 = provider_name['2008.1'].tolist()
ref_by_year.append((sum(ref_2008)) / sum_2008)
ref_2009 = provider_name['2009.1'].tolist()
ref_by_year.append((sum(ref_2009)) / sum_2009)
ref_2010 = provider_name['2010.1'].tolist()
ref_by_year.append((sum(ref_2010)) / sum_2010)
ref_2011 = provider_name['2011.1'].tolist()
ref_by_year.append((sum(ref_2011)) / sum_2011)
ref_2012 = provider_name['2012.1'].tolist()
ref_by_year.append((sum(ref_2012)) / sum_2012)
ref_2013 = provider_name['2013.1'].tolist()
ref_by_year.append((sum(ref_2013)) / sum_2013)
ref_2014 = provider_name['2014.1'].tolist()
ref_by_year.append((sum(ref_2014)) / sum_2014)
ref_2015 = provider_name['2015.1'].tolist()
ref_by_year.append((sum(ref_2015)) / sum_2015)
ref_2016 = provider_name['2016.1'].tolist()
ref_by_year.append((sum(ref_2016)) / sum_2016)
ref_2017 = provider_name['2017.1'].tolist()
ref_by_year.append((sum(ref_2017)) / sum_2017)
ref_by_provider.append(ref_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(
f'Percent of All References Made by {your_institution} Authors')
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(
StrMethodFormatter('{x:,.0%}')) #formats y axis as %
plt.plot(years, ref_by_provider[0], label='Sage', color='blue')
plt.plot(years, ref_by_provider[1], label='Springer', color='green')
plt.plot(years,
ref_by_provider[2],
label='Taylor & Francis',
color='purple')
plt.plot(years, ref_by_provider[3], label='Wiley', color='orange')
plt.plot(years,
ref_by_provider[4],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
ref_by_provider[5],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
ref_by_provider[6],
label='Elsevier Unmatched',
color='black')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.8))
def figure6b_percent_oa_articles():
""" Show percent papers per year published open access per provider per year as a percentage of all papers
published per provider per year. Separates elsevier freedom an elsevier subscribed titles out from the whole
Chart Type: Line Graph
Y-Axis: Number of Papers
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, OA papers in 1findr per journal/provider (intersection with Scopus)
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
#holds percent papers published open access per year per provider
percent_oa_papers_by_provider = []
#populate % oa papers totals for other providers
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
for provider_name in providers:
percent_oa_papers_by_year = []
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
oa_papers_2008 = subset_by_provider['2008.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2008[0])
oa_papers_2009 = subset_by_provider['2009.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2009[0])
oa_papers_2010 = subset_by_provider['2010.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2010[0])
oa_papers_2011 = subset_by_provider['2011.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2011[0])
oa_papers_2012 = subset_by_provider['2012.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2012[0])
oa_papers_2013 = subset_by_provider['2013.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2013[0])
oa_papers_2014 = subset_by_provider['2014.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2014[0])
oa_papers_2015 = subset_by_provider['2015.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2015[0])
oa_papers_2016 = subset_by_provider['2016.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2016[0])
oa_papers_2017 = subset_by_provider['2017.3'].tolist()
percent_oa_papers_by_year.append(oa_papers_2017[0])
percent_oa_papers_by_provider.append(percent_oa_papers_by_year)
#populate % oa papers totals for elsevier freedom and elsevier subscribed titles
#this is # of OA papers divided by total papers
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
percent_oa_papers_by_year = []
oa_papers_2008 = provider_name['2008.2'].tolist()
total_2008 = provider_name['2008.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2008) / sum(total_2008))
oa_papers_2009 = provider_name['2009.2'].tolist()
total_2009 = provider_name['2009.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2009) / sum(total_2009))
oa_papers_2010 = provider_name['2010.2'].tolist()
total_2010 = provider_name['2010.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2010) / sum(total_2010))
oa_papers_2011 = provider_name['2010.2'].tolist()
total_2011 = provider_name['2011.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2011) / sum(total_2011))
oa_papers_2012 = provider_name['2012.2'].tolist()
total_2012 = provider_name['2012.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2012) / sum(total_2012))
oa_papers_2013 = provider_name['2013.2'].tolist()
total_2013 = provider_name['2013.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2013) / sum(total_2013))
oa_papers_2014 = provider_name['2014.2'].tolist()
total_2014 = provider_name['2014.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2014) / sum(total_2014))
oa_papers_2015 = provider_name['2015.2'].tolist()
total_2015 = provider_name['2015.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2015) / sum(total_2015))
oa_papers_2016 = provider_name['2016.2'].tolist()
total_2016 = provider_name['2016.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2016) / sum(total_2016))
oa_papers_2017 = provider_name['2017.2'].tolist()
total_2017 = provider_name['2017.4'].tolist()
percent_oa_papers_by_year.append(sum(oa_papers_2017) / sum(total_2017))
percent_oa_papers_by_provider.append(percent_oa_papers_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(f'Percent of all Open Access by provider')
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(
StrMethodFormatter('{x:,.0%}')) #formats y axis as %
plt.plot(years,
percent_oa_papers_by_provider[0],
label='Sage',
color='blue')
plt.plot(years,
percent_oa_papers_by_provider[1],
label='Springer',
color='green')
plt.plot(years,
percent_oa_papers_by_provider[2],
label='Taylor & Francis',
color='purple')
plt.plot(years,
percent_oa_papers_by_provider[3],
label='Wiley',
color='orange')
plt.plot(years,
percent_oa_papers_by_provider[4],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
percent_oa_papers_by_provider[5],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
percent_oa_papers_by_provider[6],
label='Elsevier Unmatched',
color='black')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.8))
def figure6b_oa_available_articles():
"""Show number Open Access (OA) available articles per provider over time (2008-2017) for provider,
separating our Elsevier subscribed and Elsevier Freedom collection from Elsevier as a whole
Chart Type: Line Graph
Y-Axis: Number of Open access Articles
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, OA papers in 1findr per journal/provider (intersection with Scopus)
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
oa_articles_by_provider = []
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
oa_articles_by_year = []
oa_articles_2008 = provider_name['2008.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2008))
oa_articles_2009 = provider_name['2009.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2009))
oa_articles_2010 = provider_name['2010.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2010))
oa_articles_2011 = provider_name['2011.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2011))
oa_articles_2012 = provider_name['2012.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2012))
oa_articles_2013 = provider_name['2013.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2013))
oa_articles_2014 = provider_name['2014.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2014))
oa_articles_2015 = provider_name['2015.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2015))
oa_articles_2016 = provider_name['2016.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2016))
oa_articles_2017 = provider_name['2017.2'].tolist()
oa_articles_by_year.append(sum(oa_articles_2017))
oa_articles_by_provider.append(oa_articles_by_year)
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
for provider_name in providers:
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
oa_articles_by_year = []
oa_articles_2008 = subset_by_provider['2008.2'].tolist()
oa_articles_by_year.append(oa_articles_2008[0])
oa_articles_2009 = subset_by_provider['2009.2'].tolist()
oa_articles_by_year.append(oa_articles_2009[0])
oa_articles_2010 = subset_by_provider['2010.2'].tolist()
oa_articles_by_year.append(oa_articles_2010[0])
oa_articles_2011 = subset_by_provider['2011.2'].tolist()
oa_articles_by_year.append(oa_articles_2011[0])
oa_articles_2012 = subset_by_provider['2012.2'].tolist()
oa_articles_by_year.append(oa_articles_2012[0])
oa_articles_2013 = subset_by_provider['2013.2'].tolist()
oa_articles_by_year.append(oa_articles_2013[0])
oa_articles_2014 = subset_by_provider['2014.2'].tolist()
oa_articles_by_year.append(oa_articles_2014[0])
oa_articles_2015 = subset_by_provider['2015.2'].tolist()
oa_articles_by_year.append(oa_articles_2015[0])
oa_articles_2016 = subset_by_provider['2016.2'].tolist()
oa_articles_by_year.append(oa_articles_2016[0])
oa_articles_2017 = subset_by_provider['2017.2'].tolist()
oa_articles_by_year.append(oa_articles_2017[0])
oa_articles_by_provider.append(oa_articles_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(f'Number of OA-Available Articles')
plt.xlabel('Year')
plt.ylabel('Number of Articles')
plt.plot(years,
oa_articles_by_provider[0],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
oa_articles_by_provider[1],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
oa_articles_by_provider[2],
label='Elsevier Unmatched',
color='black')
plt.plot(years, oa_articles_by_provider[3], label='Sage', color='blue')
plt.plot(years,
oa_articles_by_provider[4],
label='Springer',
color='green')
plt.plot(years,
oa_articles_by_provider[5],
label='Taylor & Francis',
color='purple')
plt.plot(years, oa_articles_by_provider[6], label='Wiley', color='orange')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.8))
def figure7e():
"""This is counting JR1 downloads by Domain for the subscribed titles and freedom collection providers
Chart Type: Dot Plot/Scatter Plot
Y-Axis: Domain
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Number of JR1 Downloads
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_data = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
subscribed_titles_provider = rf.make_elsevier_subscribed_titles_provider()
freedom_collection_provider = rf.make_freedom_collection_provider()
unique_domains = original_1figr_data['Domain'].unique().tolist()
for i in unique_domains: #this drops the 'nan' column from the unique domains
if type(i) == float:
unique_domains.remove(i)
unique_domains.sort()
stats_by_domain = []
#builds list of tuples containing (Domain name, # of subscribed titles JR1 downloads, # of Freedom titles downloads)
for domain in unique_domains:
subscribed_subset_by_domain = subscribed_titles_provider.loc[
subscribed_titles_provider['Domain'] == domain]
subscribed_jr1_downloads = subscribed_subset_by_domain[
'Downloads JR1 2017'].sum()
freedom_subset_by_domain = freedom_collection_provider.loc[
freedom_collection_provider['Domain'] == domain]
freedom_jr1_downloads = freedom_subset_by_domain[
'Downloads JR1 2017'].sum()
stats_by_domain.append(
(domain, subscribed_jr1_downloads, freedom_jr1_downloads))
#sorts domains by sum of total # of jr1 downloads
arrangement = sorted(stats_by_domain, key=lambda x: (x[1] + x[2]))
#splitting elements of tuples in arrangement into lists so it is easier to plot
domains_list = [i[0] for i in arrangement]
subscribed_total = [i[1] for i in arrangement]
freedom_total = [i[2] for i in arrangement]
#make plot
mpl.rcParams['ytick.major.width'] = 1
mpl.rcParams['xtick.major.width'] = 1
plt.figure(num=None, figsize=(8, 8))
plt.suptitle(f'JR1 downloads by Domain')
plot = plt.scatter(subscribed_total, domains_list, color='blue')
plot2 = plt.scatter(freedom_total, domains_list, color='orange')
subscribed_legend_label = mpatches.Patch(color='blue',
label='Elsevier Subscribed')
freedom_legend_label = mpatches.Patch(color='orange',
label='Elsevier Freedom')
plt.xlabel('Number of JR1 Downloads')
plt.legend(loc='lower right',
handles=[subscribed_legend_label, freedom_legend_label])
def figure5b_percentage():
""" Show percent papers per year published by UVA authors as a percentage of all papers for each provider, separating Elsevier Freedom
and Elsevier Subscribed titles out from Elsevier as a whole. All papers are the 'total papers in scopus' columns
Chart Type: Line Graph
Y-Axis: Number of Papers
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Total papers in Scopus per journal/provider
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
#holds percent papers per year published by UVA authors of total papers per provider
percent_papers_by_provider = []
#populate papers totals for other providers
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
for provider_name in providers:
percent_papers_by_year = []
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
papers_2008 = subset_by_provider[2008].tolist()
total_2008 = subset_by_provider['2008.4'].tolist()
percent_papers_by_year.append(sum(papers_2008) / sum(total_2008))
papers_2009 = subset_by_provider[2009].tolist()
total_2009 = subset_by_provider['2009.4'].tolist()
percent_papers_by_year.append(sum(papers_2009) / sum(total_2009))
papers_2010 = subset_by_provider[2010].tolist()
total_2010 = subset_by_provider['2010.4'].tolist()
percent_papers_by_year.append(sum(papers_2010) / sum(total_2010))
papers_2011 = subset_by_provider[2011].tolist()
total_2011 = subset_by_provider['2011.4'].tolist()
percent_papers_by_year.append(sum(papers_2011) / sum(total_2011))
papers_2012 = subset_by_provider[2012].tolist()
total_2012 = subset_by_provider['2012.4'].tolist()
percent_papers_by_year.append(sum(papers_2012) / sum(total_2012))
papers_2013 = subset_by_provider[2013].tolist()
total_2013 = subset_by_provider['2013.4'].tolist()
percent_papers_by_year.append(sum(papers_2013) / sum(total_2013))
papers_2014 = subset_by_provider[2014].tolist()
total_2014 = subset_by_provider['2014.4'].tolist()
percent_papers_by_year.append(sum(papers_2014) / sum(total_2014))
papers_2015 = subset_by_provider[2015].tolist()
total_2015 = subset_by_provider['2015.4'].tolist()
percent_papers_by_year.append(sum(papers_2015) / sum(total_2015))
papers_2016 = subset_by_provider[2016].tolist()
total_2016 = subset_by_provider['2016.4'].tolist()
percent_papers_by_year.append(sum(papers_2016) / sum(total_2016))
papers_2017 = subset_by_provider[2017].tolist()
total_2017 = subset_by_provider['2017.4'].tolist()
percent_papers_by_year.append(sum(papers_2017) / sum(total_2017))
percent_papers_by_provider.append(percent_papers_by_year)
#populate papers totals for elsevier freedom and elsevier subscribed providers
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
percent_papers_by_year = []
papers_2008 = provider_name[2008].tolist()
total_2008 = provider_name['2008.4'].tolist()
percent_papers_by_year.append(sum(papers_2008) / sum(total_2008))
papers_2009 = provider_name[2009].tolist()
total_2009 = provider_name['2009.4'].tolist()
percent_papers_by_year.append(sum(papers_2009) / sum(total_2009))
papers_2010 = provider_name[2010].tolist()
total_2010 = provider_name['2010.4'].tolist()
percent_papers_by_year.append(sum(papers_2010) / sum(total_2010))
papers_2011 = provider_name[2011].tolist()
total_2011 = provider_name['2011.4'].tolist()
percent_papers_by_year.append(sum(papers_2011) / sum(total_2011))
papers_2012 = provider_name[2012].tolist()
total_2012 = provider_name['2012.4'].tolist()
percent_papers_by_year.append(sum(papers_2012) / sum(total_2012))
papers_2013 = provider_name[2013].tolist()
total_2013 = provider_name['2013.4'].tolist()
percent_papers_by_year.append(sum(papers_2013) / sum(total_2013))
papers_2014 = provider_name[2014].tolist()
total_2014 = provider_name['2014.4'].tolist()
percent_papers_by_year.append(sum(papers_2014) / sum(total_2014))
papers_2015 = provider_name[2015].tolist()
total_2015 = provider_name['2015.4'].tolist()
percent_papers_by_year.append(sum(papers_2015) / sum(total_2015))
papers_2016 = provider_name[2016].tolist()
total_2016 = provider_name['2016.4'].tolist()
percent_papers_by_year.append(sum(papers_2016) / sum(total_2016))
papers_2017 = provider_name[2017].tolist()
total_2017 = provider_name['2017.4'].tolist()
percent_papers_by_year.append(sum(papers_2017) / sum(total_2017))
percent_papers_by_provider.append(percent_papers_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(
f'Percent of All Articles published by {your_institution} Authors')
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(
StrMethodFormatter('{x:,.2%}')) #formats y axis as %
plt.plot(years, percent_papers_by_provider[0], label='Sage', color='blue')
plt.plot(years,
percent_papers_by_provider[1],
label='Springer',
color='green')
plt.plot(years,
percent_papers_by_provider[2],
label='Taylor & Francis',
color='purple')
plt.plot(years,
percent_papers_by_provider[3],
label='Wiley',
color='orange')
plt.plot(years,
percent_papers_by_provider[4],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
percent_papers_by_provider[5],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
percent_papers_by_provider[6],
label='Elsevier Unmatched',
color='black')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.8))
def figure5b_papers():
"""Show papers per year (2008-2017) by your institution's affiliated authors, separating Elsevier Freedom
and Elsevier Subscribed titles out from Elsevier as a whole. Papers are publications
by you institution's affiliated authors.
Chart Type: Line Graph
Y-Axis: Number of Papers
Y-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Total papers in Scopus per journal/provider
Elsevier_2019, Subscribed Journal List 2019
X-Axis: Year
X-Axis Data Source: Original 1Figr Dataset, Journals Per Provider, Provider
Elsevier_2019, Subscribed Journal List 2019
"""
original_1figr_dataset = pd.read_excel(filename,
sheet_name='Journals per Provider',
skiprows=8)
elsevier_freedom_collection = rf.make_freedom_collection_provider()
elsevier_subscribed_titles = rf.make_elsevier_subscribed_titles_provider()
elsevier_unmatched_titles = rf.make_elsevier_unmatched_provider()
#this holds papers totals for all providers in the end, which is used to make final plot
papers_by_provider = []
#populate papers totals for elsevier subset providers
elsevier_providers = [
elsevier_freedom_collection, elsevier_subscribed_titles,
elsevier_unmatched_titles
]
for provider_name in elsevier_providers:
papers_by_year = []
papers_2008 = provider_name[2008].tolist()
papers_by_year.append(sum(papers_2008))
papers_2009 = provider_name[2009].tolist()
papers_by_year.append(sum(papers_2009))
papers_2010 = provider_name[2010].tolist()
papers_by_year.append(sum(papers_2010))
papers_2011 = provider_name[2011].tolist()
papers_by_year.append(sum(papers_2011))
papers_2012 = provider_name[2012].tolist()
papers_by_year.append(sum(papers_2012))
papers_2013 = provider_name[2013].tolist()
papers_by_year.append(sum(papers_2013))
papers_2014 = provider_name[2014].tolist()
papers_by_year.append(sum(papers_2014))
papers_2015 = provider_name[2015].tolist()
papers_by_year.append(sum(papers_2015))
papers_2016 = provider_name[2016].tolist()
papers_by_year.append(sum(papers_2016))
papers_2017 = provider_name[2017].tolist()
papers_by_year.append(sum(papers_2017))
papers_by_provider.append(papers_by_year)
#populate papers totals for other providers
providers = ['Sage', 'Springer', 'Taylor & Francis', 'Wiley']
for provider_name in providers:
subset_by_provider = original_1figr_dataset.loc[
original_1figr_dataset['Provider'] == provider_name]
papers_by_year = []
papers_2008 = subset_by_provider[2008].tolist()
papers_by_year.append(papers_2008[0])
papers_2009 = subset_by_provider[2009].tolist()
papers_by_year.append(papers_2009[0])
papers_2010 = subset_by_provider[2010].tolist()
papers_by_year.append(papers_2010[0])
papers_2011 = subset_by_provider[2011].tolist()
papers_by_year.append(papers_2011[0])
papers_2012 = subset_by_provider[2012].tolist()
papers_by_year.append(papers_2012[0])
papers_2013 = subset_by_provider[2013].tolist()
papers_by_year.append(papers_2013[0])
papers_2014 = subset_by_provider[2014].tolist()
papers_by_year.append(papers_2014[0])
papers_2015 = subset_by_provider[2015].tolist()
papers_by_year.append(papers_2015[0])
papers_2016 = subset_by_provider[2016].tolist()
papers_by_year.append(papers_2016[0])
papers_2017 = subset_by_provider[2017].tolist()
papers_by_year.append(papers_2017[0])
papers_by_provider.append(papers_by_year)
years = [
'2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016',
'2017'
]
plt.figure(num=None, figsize=(10, 10))
plt.suptitle(f'Number of Articles (papers) by {your_institution} Authors')
plt.xlabel('Year')
plt.ylabel('Number of Articles')
plt.plot(years,
papers_by_provider[0],
label='Elsevier Freedom',
color='red',
linestyle='dashed')
plt.plot(years,
papers_by_provider[1],
label='Elsevier Subscribed',
color='red')
plt.plot(years,
papers_by_provider[2],
label='Elsevier Unmatched Titles',
color='black')
plt.plot(years, papers_by_provider[3], label='Sage', color='blue')
plt.plot(years, papers_by_provider[4], label='Springer', color='green')
plt.plot(years,
papers_by_provider[5],
label='Taylor & Francis',
color='purple')
plt.plot(years, papers_by_provider[6], label='Wiley', color='orange')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.8))
