README.md
In dgrtwo/cord19: COVID-19 Open Research Dataset
cord19
The cord19 package shares the COVID-19 Open Research Dataset (CORD-19) in a tidy form that is easily analyzed within R.
Installation
Install the package from GitHub as follows:
remotes::install_github("dgrtwo/cord19")
Papers
The package turns the CORD-19 dataset into a set of tidy tables.
For example, the paper metadata is stored in cord19_papers.
library(dplyr)
library(cord19)
cord19_papers
#> # A tibble: 12,503 x 14
#> paper_id source title doi pmcid pubmed_id license abstract publish_time
#> <chr> <chr> <chr> <chr> <lgl> <dbl> <chr> <chr> <dbl>
#> 1 210a892… CZI Incu… 10.3… NA NA cc-by The geo… 2020
#> 2 e3b40cc… CZI Char… 10.3… NA 32093211 cc-by In Dece… 2020
#> 3 0df0d52… CZI An u… 10.1… NA NA cc-by-… The bas… 2020
#> 4 f242425… CZI Real… 10.1… NA NA cc-by-… The ini… 2020
#> 5 e1b336d… CZI COVI… 10.1… NA NA cc-by-… Cruise … 2020
#> 6 e923910… CZI Dist… 10.1… NA NA cc-by Coronav… 2020
#> 7 469ed0f… CZI Firs… 10.1… NA NA cc-by Similar… 2020
#> 8 4e550e0… CZI Effe… 10.2… NA NA cc-by We simu… 2020
#> 9 4bbb0c5… CZI Geno… 10.1… NA 32108862 cc-by-… SUMMARY… 2020
#> 10 c821803… CZI Case… 10.3… NA NA cc-by-… Since m… 2020
#> # … with 12,493 more rows, and 5 more variables: authors <chr>, journal <chr>,
#> # microsoft_academic_paper_id <dbl>, who_number_covidence <chr>,
#> # has_full_text <lgl>
# Learn how many papers came from each journal
cord19_papers %>%
count(journal, sort = TRUE)
#> # A tibble: 1,300 x 2
#> journal n
#> <chr> <int>
#> 1 PLoS One 1560
#> 2 Emerg Infect Dis 726
#> 3 Viruses 545
#> 4 <NA> 503
#> 5 Sci Rep 485
#> 6 PLoS Pathog 357
#> 7 Virol J 357
#> 8 BMC Infect Dis 246
#> 9 Front Immunol 210
#> 10 Front Microbiol 202
#> # … with 1,290 more rows
Full text
Most usefully, cord19_paragraphs has the full text of the papers, with one observation for each paragraph.
cord19_paragraphs
#> # A tibble: 364,755 x 4
#> paper_id paragraph section text
#> <chr> <int> <chr> <chr>
#> 1 0015023cc06b5362d332b… 1 <NA> VP3, and VP0 (which is further pro…
#> 2 0015023cc06b5362d332b… 2 70 The FMDV 5′ UTR is the largest kno…
#> 3 0015023cc06b5362d332b… 3 120 To introduce mutations into the PK…
#> 4 0015023cc06b5362d332b… 4 120 132 133 author/funder. All rights …
#> 5 0015023cc06b5362d332b… 5 120 The copyright holder for this prep…
#> 6 0015023cc06b5362d332b… 6 135 Mutations were then introduced int…
#> 7 0015023cc06b5362d332b… 7 136 To assess the effects of truncatio…
#> 8 0015023cc06b5362d332b… 8 144 Transcription reactions to produce…
#> 9 0015023cc06b5362d332b… 9 144 The copyright holder for this prep…
#> 10 0015023cc06b5362d332b… 10 144 The copyright holder for this prep…
#> # … with 364,745 more rows
# What are common sections
cord19_paragraphs %>%
count(section, sort = TRUE)
#> # A tibble: 79,531 x 2
#> section n
#> <chr> <int>
#> 1 Discussion 41868
#> 2 Introduction 24128
#> 3 <NA> 12503
#> 4 Results 11317
#> 5 Background 6709
#> 6 Conclusions 5328
#> 7 Methods 4167
#> 8 Materials And Methods 3677
#> 9 Conclusion 2872
#> 10 Statistical Analysis 2689
#> # … with 79,521 more rows
This allows for some analysis with a package like tidytext.
library(tidytext)
set.seed(2020)
# Sample 100 random papers
paper_words <- cord19_paragraphs %>%
filter(paper_id %in% sample(unique(paper_id), 100)) %>%
unnest_tokens(word, text) %>%
anti_join(stop_words, by = "word")
paper_words %>%
count(word, sort = TRUE)
#> # A tibble: 21,612 x 2
#> word n
#> <chr> <int>
#> 1 1 1556
#> 2 2 1366
#> 3 cells 1300
#> 4 virus 1184
#> 5 infection 1033
#> 6 3 920
#> 7 cell 854
#> 8 study 848
#> 9 viral 830
#> 10 data 773
#> # … with 21,602 more rows
Citations
This also includes the articles cited by each paper.
cord19_paper_citations
#> # A tibble: 605,650 x 9
#> paper_id ref_id title venue volume issn pages year doi
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 0015023cc06b5… b0 Genetic economy… PLOS … 13 "" "" 2017 <NA>
#> 2 0015023cc06b5… b2 A universal pro… BMC G… 604 "" "" 2014 <NA>
#> 3 0015023cc06b5… b3 Library prepara… Nat P… 9 "" 1760… 2014 <NA>
#> 4 0015023cc06b5… b4 IDBA-UD: a de n… "" "" "" "" 2012 <NA>
#> 5 0015023cc06b5… b6 Basic local ali… J Mol… 215 "" 403-… 1990 <NA>
#> 6 0015023cc06b5… b7 Genetically eng… J 614… 67 "" 5139… 1993 <NA>
#> 7 0015023cc06b5… b9 Both cis and tr… J Vir… 90 "" 6864… 2016 <NA>
#> 8 0015023cc06b5… b10 Mutational anal… J Vir… 620 "" 2027… 1996 <NA>
#> 9 0015023cc06b5… b12 Figure 3. The p… "" "" "" "" NA <NA>
#> 10 0015023cc06b5… b13 A replicon 650 … "" "" "" "" NA <NA>
#> # … with 605,640 more rows
What are the most commonly cited articles?
cord19_paper_citations %>%
count(title, sort = TRUE)
#> # A tibble: 417,863 x 2
#> title n
#> <chr> <int>
#> 1 Isolation of a novel coronavirus from a man with pneumonia in Saudi A… 397
#> 2 Submit your next manuscript to BioMed Central and take full advantage… 295
#> 3 Identification of a novel coronavirus in patients with severe acute r… 236
#> 4 A novel coronavirus associated with severe acute respiratory syndrome 226
#> 5 Global trends in emerging infectious diseases 193
#> 6 Bats are natural reservoirs of SARS-like coronaviruses 177
#> 7 Coronavirus as a possible cause of severe acute respiratory syndrome 164
#> 8 Characterization of a novel coronavirus associated with severe acute … 149
#> 9 Severe acute respiratory syndrome coronavirus-like virus in Chinese h… 140
#> 10 Identification of a new human coronavirus 137
#> # … with 417,853 more rows
We could use the widyr package to find which papers are often cited by the same paper.
library(widyr)
filtered_citations <- cord19_paper_citations %>%
add_count(title) %>%
filter(n >= 25)
# What papers are often cited by the same paper?
filtered_citations %>%
pairwise_cor(title, paper_id, sort = TRUE)
#> # A tibble: 244,530 x 3
#> item1 item2 correlation
#> <chr> <chr> <dbl>
#> 1 Small molecule inhibitors revea… Ebola virus entry requires the… 0.776
#> 2 Ebola virus entry requires the … Small molecule inhibitors reve… 0.776
#> 3 VISA is an adapter protein requ… IPS-1, an adaptor triggering R… 0.765
#> 4 IPS-1, an adaptor triggering RI… VISA is an adapter protein req… 0.765
#> 5 Identification of a novel polyo… Identification of a third huma… 0.735
#> 6 Identification of a third human… Identification of a novel poly… 0.735
#> 7 The IFITM proteins mediate cell… Distinct patterns of IFITM-med… 0.727
#> 8 Distinct patterns of IFITM-medi… The IFITM proteins mediate cel… 0.727
#> 9 Cardif is an adaptor protein in… VISA is an adapter protein req… 0.698
#> 10 VISA is an adapter protein requ… Cardif is an adaptor protein i… 0.698
#> # … with 244,520 more rows
dgrtwo/cord19 documentation built on March 20, 2020, 12:44 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
cord19
The cord19 package shares the COVID-19 Open Research Dataset (CORD-19) in a tidy form that is easily analyzed within R.
Installation
Install the package from GitHub as follows:
remotes::install_github("dgrtwo/cord19")
Papers
The package turns the CORD-19 dataset into a set of tidy tables.
For example, the paper metadata is stored in cord19_papers.
library(dplyr)
library(cord19)
cord19_papers
#> # A tibble: 12,503 x 14
#> paper_id source title doi pmcid pubmed_id license abstract publish_time
#> <chr> <chr> <chr> <chr> <lgl> <dbl> <chr> <chr> <dbl>
#> 1 210a892… CZI Incu… 10.3… NA NA cc-by The geo… 2020
#> 2 e3b40cc… CZI Char… 10.3… NA 32093211 cc-by In Dece… 2020
#> 3 0df0d52… CZI An u… 10.1… NA NA cc-by-… The bas… 2020
#> 4 f242425… CZI Real… 10.1… NA NA cc-by-… The ini… 2020
#> 5 e1b336d… CZI COVI… 10.1… NA NA cc-by-… Cruise … 2020
#> 6 e923910… CZI Dist… 10.1… NA NA cc-by Coronav… 2020
#> 7 469ed0f… CZI Firs… 10.1… NA NA cc-by Similar… 2020
#> 8 4e550e0… CZI Effe… 10.2… NA NA cc-by We simu… 2020
#> 9 4bbb0c5… CZI Geno… 10.1… NA 32108862 cc-by-… SUMMARY… 2020
#> 10 c821803… CZI Case… 10.3… NA NA cc-by-… Since m… 2020
#> # … with 12,493 more rows, and 5 more variables: authors <chr>, journal <chr>,
#> # microsoft_academic_paper_id <dbl>, who_number_covidence <chr>,
#> # has_full_text <lgl>
# Learn how many papers came from each journal
cord19_papers %>%
count(journal, sort = TRUE)
#> # A tibble: 1,300 x 2
#> journal n
#> <chr> <int>
#> 1 PLoS One 1560
#> 2 Emerg Infect Dis 726
#> 3 Viruses 545
#> 4 <NA> 503
#> 5 Sci Rep 485
#> 6 PLoS Pathog 357
#> 7 Virol J 357
#> 8 BMC Infect Dis 246
#> 9 Front Immunol 210
#> 10 Front Microbiol 202
#> # … with 1,290 more rows
Full text
Most usefully, cord19_paragraphs has the full text of the papers, with one observation for each paragraph.
cord19_paragraphs
#> # A tibble: 364,755 x 4
#> paper_id paragraph section text
#> <chr> <int> <chr> <chr>
#> 1 0015023cc06b5362d332b… 1 <NA> VP3, and VP0 (which is further pro…
#> 2 0015023cc06b5362d332b… 2 70 The FMDV 5′ UTR is the largest kno…
#> 3 0015023cc06b5362d332b… 3 120 To introduce mutations into the PK…
#> 4 0015023cc06b5362d332b… 4 120 132 133 author/funder. All rights …
#> 5 0015023cc06b5362d332b… 5 120 The copyright holder for this prep…
#> 6 0015023cc06b5362d332b… 6 135 Mutations were then introduced int…
#> 7 0015023cc06b5362d332b… 7 136 To assess the effects of truncatio…
#> 8 0015023cc06b5362d332b… 8 144 Transcription reactions to produce…
#> 9 0015023cc06b5362d332b… 9 144 The copyright holder for this prep…
#> 10 0015023cc06b5362d332b… 10 144 The copyright holder for this prep…
#> # … with 364,745 more rows
# What are common sections
cord19_paragraphs %>%
count(section, sort = TRUE)
#> # A tibble: 79,531 x 2
#> section n
#> <chr> <int>
#> 1 Discussion 41868
#> 2 Introduction 24128
#> 3 <NA> 12503
#> 4 Results 11317
#> 5 Background 6709
#> 6 Conclusions 5328
#> 7 Methods 4167
#> 8 Materials And Methods 3677
#> 9 Conclusion 2872
#> 10 Statistical Analysis 2689
#> # … with 79,521 more rows
This allows for some analysis with a package like tidytext.
library(tidytext)
set.seed(2020)
# Sample 100 random papers
paper_words <- cord19_paragraphs %>%
filter(paper_id %in% sample(unique(paper_id), 100)) %>%
unnest_tokens(word, text) %>%
anti_join(stop_words, by = "word")
paper_words %>%
count(word, sort = TRUE)
#> # A tibble: 21,612 x 2
#> word n
#> <chr> <int>
#> 1 1 1556
#> 2 2 1366
#> 3 cells 1300
#> 4 virus 1184
#> 5 infection 1033
#> 6 3 920
#> 7 cell 854
#> 8 study 848
#> 9 viral 830
#> 10 data 773
#> # … with 21,602 more rows
Citations
This also includes the articles cited by each paper.
cord19_paper_citations
#> # A tibble: 605,650 x 9
#> paper_id ref_id title venue volume issn pages year doi
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 0015023cc06b5… b0 Genetic economy… PLOS … 13 "" "" 2017 <NA>
#> 2 0015023cc06b5… b2 A universal pro… BMC G… 604 "" "" 2014 <NA>
#> 3 0015023cc06b5… b3 Library prepara… Nat P… 9 "" 1760… 2014 <NA>
#> 4 0015023cc06b5… b4 IDBA-UD: a de n… "" "" "" "" 2012 <NA>
#> 5 0015023cc06b5… b6 Basic local ali… J Mol… 215 "" 403-… 1990 <NA>
#> 6 0015023cc06b5… b7 Genetically eng… J 614… 67 "" 5139… 1993 <NA>
#> 7 0015023cc06b5… b9 Both cis and tr… J Vir… 90 "" 6864… 2016 <NA>
#> 8 0015023cc06b5… b10 Mutational anal… J Vir… 620 "" 2027… 1996 <NA>
#> 9 0015023cc06b5… b12 Figure 3. The p… "" "" "" "" NA <NA>
#> 10 0015023cc06b5… b13 A replicon 650 … "" "" "" "" NA <NA>
#> # … with 605,640 more rows
What are the most commonly cited articles?
cord19_paper_citations %>%
count(title, sort = TRUE)
#> # A tibble: 417,863 x 2
#> title n
#> <chr> <int>
#> 1 Isolation of a novel coronavirus from a man with pneumonia in Saudi A… 397
#> 2 Submit your next manuscript to BioMed Central and take full advantage… 295
#> 3 Identification of a novel coronavirus in patients with severe acute r… 236
#> 4 A novel coronavirus associated with severe acute respiratory syndrome 226
#> 5 Global trends in emerging infectious diseases 193
#> 6 Bats are natural reservoirs of SARS-like coronaviruses 177
#> 7 Coronavirus as a possible cause of severe acute respiratory syndrome 164
#> 8 Characterization of a novel coronavirus associated with severe acute … 149
#> 9 Severe acute respiratory syndrome coronavirus-like virus in Chinese h… 140
#> 10 Identification of a new human coronavirus 137
#> # … with 417,853 more rows
We could use the widyr package to find which papers are often cited by the same paper.
library(widyr)
filtered_citations <- cord19_paper_citations %>%
add_count(title) %>%
filter(n >= 25)
# What papers are often cited by the same paper?
filtered_citations %>%
pairwise_cor(title, paper_id, sort = TRUE)
#> # A tibble: 244,530 x 3
#> item1 item2 correlation
#> <chr> <chr> <dbl>
#> 1 Small molecule inhibitors revea… Ebola virus entry requires the… 0.776
#> 2 Ebola virus entry requires the … Small molecule inhibitors reve… 0.776
#> 3 VISA is an adapter protein requ… IPS-1, an adaptor triggering R… 0.765
#> 4 IPS-1, an adaptor triggering RI… VISA is an adapter protein req… 0.765
#> 5 Identification of a novel polyo… Identification of a third huma… 0.735
#> 6 Identification of a third human… Identification of a novel poly… 0.735
#> 7 The IFITM proteins mediate cell… Distinct patterns of IFITM-med… 0.727
#> 8 Distinct patterns of IFITM-medi… The IFITM proteins mediate cel… 0.727
#> 9 Cardif is an adaptor protein in… VISA is an adapter protein req… 0.698
#> 10 VISA is an adapter protein requ… Cardif is an adaptor protein i… 0.698
#> # … with 244,520 more rows
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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