Parameter Value
hashtag #gi2018
start_day 2018-09-17
end_day 2018-09-20
timezone Europe/London
theme theme_light
accent skyblue3
accent2 #B5D2E6
kcore 2
topics_k 6
bigram_filter 3
fixed TRUE
seed 1

Introduction

An analysis of tweets from the #gi2018 hashtag for the Genome Informatics conference, 17-20 September 2018, at the Wellcome Genome Campus, Hinxton UK.

A total of 2516 tweets from 533 users were collected using the rtweet R package.

1 Timeline

1.1 Tweets by day

1.2 Tweets by day and time

Filtered for dates 2018-09-17 - 2018-09-20 in the Europe/London timezone.

2 Users

2.1 Top tweeters

Overall

Original

Retweets

2.2 Retweet proportion

2.3 Top tweeters timeline

2.4 Top tweeters by day

Overall

Day 1

Day 2

Day 3

Day 4

Original

Day 1

Day 2

Day 3

Day 4

Retweets

Day 1

Day 2

Day 3

Day 4

3 Sources

Users

Tweets

4 Networks

4.1 Replies

The “replies network”, composed from users who reply directly to one another, coloured by PageRank.

4.2 Mentions

The “mentions network”, where users mention other users in their tweets. Filtered for a k-core of 2. Node colour and size adjusted according to PageRank score.

5 Tweet types

5.1 Retweets

Proportion

Count

Top 10

screen_name text retweet_count
aaronquinlan RI’s Key points: 1. Be skeptical of thy data 2. Plot thy data 3. Plot it different ways: by data processed, by source, by batch, etc. #gi2018 57
lazappi Second #GI2018 keynote @rafalab “Variability in high throughput data” https://t.co/yukgjoHvSZ 33
lazappi If you prefer your science in meme form here is one of the key points from @rafalab #GI2018 keynote this morning https://t.co/Xy9ra15Cd3 22
sexchrlab Genome Informatics #GI2019 November 6 - 9, 2019 Abstract Deadline: August 16, 2019 https://t.co/w7wLlPt6Uh #GI2018 21
ksamocha Laura Huerta #GI2018: Expression Atlas (https://t.co/vBdOlZP3Bn) is an open-access resource for gene expression data that has >3300 datasets currently. 18
lauhuema Slides from my presentation about @emblebi @ExpressionAtlas at #GI2018 are available at @F1000Research in case anyone wants to have a look! https://t.co/hbCI1dPvyn https://t.co/cXUAPHq39w 17
AliciaOshlack

I’ll be tweeting from #GI2018 which is getting underway in a few hours and I’m super excited!

Program: https://t.co/gUIz4y6CgY

Poster channel: https://t.co/xrktvzLo6G 		16
JavierHerrero7 .@GreeneScientist — After the controversy on ‘research parasites’ (see https://t.co/aBGwIlZAS5 and https://t.co/57AsQAWCvj), new awards for researchers re-using data at https://t.co/0UTHhiIG77. Deadline approaching fast (30 Sep) #gi2018 16
imarmean Sarah Teichmann on the exponential growth of single cell methods #GI2018 https://t.co/ByqAS2XT3P https://t.co/803Rj2Bv8k 16
sexchrlab

Next at #GI2018 @srikosuri Reminding us that every person has 4-5 million deviations from the reference, pointing us to a preprint from the lab:

Many rare genetic variants have unrecognized large-effect disruptions to exon recognition https://t.co/DACPefBheE 		15

Most retweeted

5.2 Likes

Proportion

Count

Top 10

screen_name text favorite_count
aaronquinlan RI’s Key points: 1. Be skeptical of thy data 2. Plot thy data 3. Plot it different ways: by data processed, by source, by batch, etc. #gi2018 169
lazappi Second #GI2018 keynote @rafalab “Variability in high throughput data” https://t.co/yukgjoHvSZ 110
lazappi If you prefer your science in meme form here is one of the key points from @rafalab #GI2018 keynote this morning https://t.co/Xy9ra15Cd3 57
AliciaOshlack

I’ll be tweeting from #GI2018 which is getting underway in a few hours and I’m super excited!

Program: https://t.co/gUIz4y6CgY

Poster channel: https://t.co/xrktvzLo6G 		46
aaronquinlan Thank you to everyone that attended and presented at this year’s #gi2018 meeting. The quality of the work was exceptionally high, and a sign of great things to come. Genome Informatics is in great hands with @sexchrlab and @AliciaOshlack. See you at #gi2019! 46
sexchrlab

On my way to Genome Informatics 2018 #GI2018

The difference is gonna be like night and day. :) https://t.co/15B4de1wFB 		41
ZaminIqbal My awesome student Rachel Colquhoun @rmcolq talking about pan genome SNP/indel and variation calling in bacteria! (Primarily for @nanopore ) #GI2018 https://t.co/MAYfDbgwpX 37
AliciaOshlack You might have noticed that the #GI2018 conference logo is actually a clustering tree by @_lazappi_ https://t.co/QhIFyFHnzU 36
imarmean Barbara Englehardt (@BeEngelhardt) : Machine learning: biggest open question: could we have used logistic regression instead? Always ask yourself that! #gi2018 36
AliciaOshlack Hanging out in Cambridge with ⁦@sexchrlab⁩ after #GI2018 https://t.co/KlyidQQOAC 32

Most likes

5.3 Quotes

Proportion

Count

Top 10

screen_name text quote_count
deniseOme .@ACSCevents. Looking forward to the 18th edition of #genomeinformatics on this beautiful @wellcomegenome campus. Will be live tweeting my #GI2018 impressions, peppered with some #OpenTargets @targetvalidate flavour. https://t.co/OjIxIbgmwo 3
HKhiabanian Thrilled for the opportunity to present at #GI2018 on the results of our collaboration with Precision Medicine Program @RutgersCancer. Here are the slides for my upcoming talk: https://t.co/JbqHgwO04L https://t.co/ugUgKi9Vcv 3
ConnectingSci Welcome to everyone arriving for #GI2018 today! https://t.co/bHloGWSdk9 3
AliciaOshlack The three commandments #gi2018 https://t.co/1MZv9JBxLD 2
Nicola_Lady The “Our Father” of doing biological data analysis 🙏. Enjoying following the #GI2018 tweets - keep em coming! https://t.co/Gj8HoMoejP 2
imarmean Hagen Tilgner presenting their recent work : preprint below #GI2018 https://t.co/5t7dTepWiY 1
GreeneScientist

Opportunity at Novartis in Basel 👇

Potentially of interest to a number of folks in the #GI2018 audience! https://t.co/LyexAtPS3n 		1
sexchrlab As we watch and discuss all the amazing computational and methodological advances at Genome Informatics, we should be keenly aware of who we are (and are not) training, and how we can do better in the future. https://t.co/cn3YxACUjp #GI2018 1
JesperMaag Shameless plug for #GI2018. gganatogram: VIsualize the human/mouse anatograms from @ExpressionAtlas using ggplot2 https://t.co/BNyFinyZZo 1
imarmean Genome Informatics 2018: soon to start, see below for online program and posters #GI2018 https://t.co/Hsl1sPetRj 1

Most quoted

6 Media

Proportion

Top 10

screen_name text favorite_count
lazappi Second #GI2018 keynote @rafalab “Variability in high throughput data” https://t.co/yukgjoHvSZ 110
sexchrlab

On my way to Genome Informatics 2018 #GI2018

The difference is gonna be like night and day. :) https://t.co/15B4de1wFB 		41
ZaminIqbal My awesome student Rachel Colquhoun @rmcolq talking about pan genome SNP/indel and variation calling in bacteria! (Primarily for @nanopore ) #GI2018 https://t.co/MAYfDbgwpX 37
AliciaOshlack You might have noticed that the #GI2018 conference logo is actually a clustering tree by @_lazappi_ https://t.co/QhIFyFHnzU 36
AliciaOshlack Hanging out in Cambridge with ⁦@sexchrlab⁩ after #GI2018 https://t.co/KlyidQQOAC 32
GreeneScientist

As a heads up, the title of my #GI2018 talk has changed. But feel free to tweet any of the content.

I’ve exported PNGs of the slides in case you want to tweet about one or more of them: https://t.co/GoG64RH9zP https://t.co/FmvYSnWTlr 		27
michaelhoffman Loving the #GI2018 tweets but twitter needs a redundancy filter: https://t.co/J0izh4NktB 27
lauhuema Slides from my presentation about @emblebi @ExpressionAtlas at #GI2018 are available at @F1000Research in case anyone wants to have a look! https://t.co/hbCI1dPvyn https://t.co/cXUAPHq39w 27
GreeneScientist Now @rafalab is judging my #piechart at #GI2018. https://t.co/KWujRuWzuc 25
markrobinsonca Front row at #gi2018 .. ⁦@rafalab⁩ https://t.co/rwu80kEtTA 25

6.1 Most liked image

7 Tweet text

7.1 Word cloud

The top 100 words used 3 or more times.

7.2 Hashtags

Other hashtags used 5 or more times.

7.3 Emojis

7.4 Bigram graph

Words that were tweeted next to each other at least 3 times.

7.5 Topic modelling

Top 10 words associated with 6 topics identified by LDA.

7.5.1 Representative tweets

Most representative tweets for each topic

Topic 1

screen_name text gamma
pmelsted JC: De novo SV in parents can be verified lacking grandparents and transmitted in offsprint. High power to verify true de novo SV. Can visually audit de novo SV calls with SV-Plaudit. Scored using academic mechanical turk (Pizza) #GI2018 0.9930245
pmelsted JB: explanation of 5K deletion, non-allelic homologous recombination, most likely a variant but not de novoe. Estimate of 1SV per 10samples, (compare to 50-70 SNPs per sample de novo), based on 7 calls in 74 parents. Finds lower (possibly) de novo SV in second generation #GI2018 0.9925124
pmelsted IF: scRNA detection rate of variants is a function of gene expression. Highly sparse data. Mutations identify tumor cells, overlays variant assignments with clustering based on expression. Detected TP53 mutant cluster with allele specific expression. #GI2018 0.9925124
pmelsted ST: @humancellatlas project, 22 tissues, 185 projects. Highlighting the maternal-fetal interface. How is fetal co-existence possible wrt immune response? Single cell reveals clusters of maternal and fetal cells (clustered by expression, revealed by dna sequecing). #GI2018 0.9922271
imarmean .@koenvdberge_Be: several papers have noted that bulk RNA-seq DE methods are not worse than fine tuned scRNA-seq tools. BUT zero inflation in scRNA-seq data still has an effect -> lets make bulk RNA-seq DE methods account for zero inflation! #gi2018 0.9922271
pmelsted KP: Sequence motifs fail to explain key aspects of protein-DNA binding. Evidence for high affinity, but no nucleotide sequence motif. Different sequences can have the same DNA shape. Hypothesis: DBPs can detect other things than sequence motifs. #GI2018 0.9912239
imarmean KPollard: nice introduction of the hypotesis that DNA binding proteins (DBPs) recognize shape of DNA and not sequence motifs! one of the many arguments: most DNA binding proteins have high affinity for sequences that lack a nucleotide sequence motif #gi2018 0.9912239
JavierHerrero7 .@nishadi_desilva talking about fungus Z. tritici, a pathogen for wheat. MFS1 gene seems to be the main player in multi-drug resistance (showing an #Ensembl GeneTree, taking me down memory lane). Community annotation allows to give pathogen genomes some extra TLC. #gi2018 0.9912239
pmelsted NDS: Multidrug resistance in Z. tritici fungi, high mutation rates, overexpression of MFS1 confers resistance to drugs, inactivation of MFS1 increases efficiency. PHI-Base: annotations to find orthologs in related fungi #GI2018 0.9908294
imarmean Teichmann: CellPhoneDB: that combines
1. selected and membrane proteins 2. curated protein complexes 3 protein-protein interactions to build cell-cell communication networks from single-cell transcriptomes , paper in press #gi2018 		0.9908294

Topic 2

screen_name text gamma
pmelsted RC: Index reference graph, map to collection of graphs, generate consensus and genotype. Indexed using minimizers rather than k-mers. Sketch reads (seed) compare to index, pick path between hits using maximum likelihood (extend). Genotyping using a poisson model. #GI2018 0.9925124
pmelsted LB: Long read alignment, find anchor k-mers (seed) that are in long and short reads. Find paths between anchors (extend). Heuristics developed for genomic data (e.g. uniform cov) don’t translate to transcriptome data. Coverage varies more, branching nodes for splicing #GI2018 0.9922271
pmelsted LB: IGV plot after correction shows cleaner data with fewer errors and accurate splice junctions. Two methodologies, align short reads to long reads and correct, second is to build a graph structure from short reads, align long reads to graph. #GI2018 0.9919192
pmelsted Prithika Sritharan: using variation graphs to encode yeast diversity #GI2018 National Collection of Yeast Cultures, contains 4K strains from 530 species. Looking at variant discovery, limitations of reference based variant calling. Worse for diverse strains. 0.9915859
AnillaManrique My design team is looking for enterprise data science professionals with >3 years experience as research subjects. Help us create intuitive and painless user experiences for your field! #gi2018 #DataScientist #DataScience #DataGovernance #DataOps #Data 0.9903978
nmensah5 Patrick Brennan at #GI2018 : @nationwidekids plan to build a big data warehouse (using #hadoop and #spark) for querying genomic variants, but currently no data sharing with other hospitals. Would be a v. good future direction for the national #bioinformatics solution @NHSgmc 0.9903978
pmelsted LB: Methods designed for genomic data. Short read alignments suffer from bias towards major isoform, CPU time. Long reads graph alignment faster, but graph complexity is hard #GI2018 0.9903978
ksamocha Guillaume Gautreau #GI2018: The pangenome is the union of sequence entities shared by genomes of interest. Can partition the pangenome to show core elements shared across genomes vs accessory elements. As you add more elements, though, the number of core genes drops. 0.9903978
imarmean Sritharan: comparing the read alignment quality between linear and graph-based reference, the graph based ref alignments lead to 8-41% of reads having increased read alignment quality scores! #GI2018 0.9899234
JavierHerrero7 .@NikkaRyanK — Supernova outputs phased diploid genome. Supernova v2.0 produces higher contig N50. From contigs, generate lines, micro-assemblies, super-scaffolding… several steps to get the final assembly #gi2018 0.9899234

Topic 3

screen_name text gamma
sexchrlab

First @rafalab goes through a toy example that makes a great point:

Summary statistics of height: Average: 6.1 ft Standard deviation: 7.8 ft

EDA shows an outlier - b/c EU student reported in cm, not inches.

Fix this: Average 5.75 ft, StDev 3 inches #GI2018 #LookAtData 		0.9922271
ksamocha .@ellenleffler #GI2018: Testing for host-parasite associations using 1690 SNPs in merozoite surface genes (parasite) and 4055 SNPs in blood group genes (host). Flat QQ plot, but one apparent association signal (parasite: MSP4 and MSP2). 0.9912239
asier_gonzalez_ Nice work of Minerva Trejo Arellano studying the links of methylation and darkness induced senescence in Arabidopsis. Only modest methylation changes were found, but differential gene expression and pathway analyses support the finding of local changes in methylation #gi2018 0.9912239
JavierHerrero7 .@RmMasa — Evolutionary turnover: conserved or diverged if elements is present but not active. Active promoter evolve more slowly than active enhancers and than primed enhancers. Differences between tissue-specific vs tissue shared elements EXCEPT for primed enhancers. #gi2018 0.9908294
MKarimzade #GI2018 Ellen Leffler tested for 7 million pairwise association of 1690 SNPs in 148 malaria merozite surface genes with 4055 SNPs in 39 human blood group genes. Two hits: both close to each other at chromosome 2, with OR of 0.39 for GCNT2 and 2.89 for GBGT1 0.9908294
pmelsted EL: new view: take parasite variation into account. sequence parasite genomes, test for assoc between host and parasite, infected blood samples should yield dna from the parasite at good coverage. Sequenced 900 blood samples #GI2018 0.9908294
ksamocha Gaither #GI2018: Use ViennaRNA package to estimate structural tendencies of all human synonymous SNPs. Focusing on delta minimum free energy (dMFE) measure. Low dMFE = stabilizing; high dMFE = destabilizing. 0.9903978
pmelsted EL: Look for associations between 1.6K snps in parasites vs 4K snps in blood group genes. 7million pairwise tests. Flat QQ plot corresponding to lack of structure between the two sets. Two peaks on the manhattan plot #GI2018 0.9903978
pmelsted First talk of 3rd session, @ellenleffler on host and parasite genomes in malaria. How does genetic variation influence susceptibility to severe malaria? Gwas approach using 5K cases and controls. new assoca with large structural variant #GI2018 0.9903978
aaronquinlan Fabulous, engaging story from @nishadi_desilva about wheat domestication and the consequent many mode of pathogen attack. Weaving in data resources, pathogen-host interactions, and cool biology. A truly enthralling, exceptional talk. #gi2018 0.9903978

Topic 4

screen_name text gamma
pmelsted GG: highlighting Bandage (fantastic software from @rrwick ) which supports GFA. New software GfaViz, interactive and cli mode, supports GFA2, selection of layout algorithms, can save layout/options into the GFA file (I can haz software?) #GI2018 0.9919192
pmelsted NDS: Z. tritici 519bp insert on accessory chromosome in anti-fungal resistance strains, overexpresses MFS1. EnsemblFungi community based projects to spread knowledge, neccessary to standardize gene names (often using manual annotation) #GI2018 0.9915859
pmelsted MWS: mask out PAR, look for evidence of Y, if there is not evidence of Y, remove it from the reference. Depth ratio of chr19 to Y shows that reads map to Y, but lower depth ratio. Read balance of variants, typically two copies ratio of 0.5, for hets. Y is messed up #GI2018 0.9915859
pmelsted SY: plotting metrics from cloud VMs allows users to identify issues. Worklow engine allows “self healing”, detect anomalies and restart/relaunch workers automatically, outputs to email and slack for human consumption #GI2018 0.9908294
pmelsted SY: separating data analysis from workflow execution. Focusing on operations management: tracking and monitoring execution of workflow in the cloud. Using https://t.co/FabIANAAzH for cross cloud operations, https://t.co/VFb2YZeZew for config, workflows using airflow #GI2018 0.9908294
imarmean .@JeffreyMKidd : current genome reference is a boxer-derived genome BUT not perfect 19k gaps, 3.2k unplaced contigs, missing gene models … -> genome assembly of a new individual will capture more genetic diversity #gi2018 0.9908294
MKarimzade #GI2018 Ever wonder how large scale data processing is managed in #PCAWG? Sergei Yakneen discusses 4 main steps: Provisioning with Terraform, configuration with Saltstack, workflow with Airflow, and an in-house developed self-healing operation management. 0.9903978
pmelsted GG: GfaViz can vizualize scaffolding between contigs, can visualize read to read alignments, pipeline taking minimap2 PAF output to GFA2, also for read to contigs (sam to GFA2). GfaViz coming out later this year #GI2018 0.9903978
pmelsted JK: Breed structure enables trait mapping. Existing genome, canfam3, 19K gaps, incomplete or missing gene models. From the old days of yore done with sanger sequencing of plasmids and fosmids #GI2018 0.9903978
pmelsted Sarah Carey: Genome analysis in a polymorphic moss with large ancient sex chromosomes. Haploid genome, with UV sex chromosomes. U (female) and (V) male. Third of the genome is sex chromosomes. Assembled using PacBio #GI2018 0.9899234

Topic 5

screen_name text gamma
pmelsted SK: rare variants seem to play a role in underexpression. inserted synthetic library into exons to report splicing. Took 28K variants from ExAC in exons. Splice disrupting variants, occur at exon intron boundaries, but SNP density is lower at boundary #GI2018 0.9925124
pmelsted RI: Batch effect from sequencer removed, turns out to be similar. Showing earlier results from microarray with similar results (species signal stronger than tissues). What are the “probe effects” in RNA-Seq. Number of transcripts in genes explains variability, GC bias too #GI2018 0.9919192
pmelsted BE: tGPLVM, Q factors (latent vars), noiseless obs drawn from Gaussian process, composite kernel and observations with heavy tail residuals (I’m pretty sure that explains the acronym), trust me the slides were comprehensible as well as the delivery, not this tweet though #GI2018 0.9912239
pmelsted .@rmcolq: Pangenome variation inference from nanopore of illumina data #GI2018. Looking for variation within genes accross species/strains as well as inclusion of genes. 3K core genes, 90K total number of genes, normal E. coli has 5K genes 0.9912239
pmelsted KP: in autism patients, we don’t see depletion of deletions at TAD boundaries, not enough time for evolution to act. Keynote message: Variant effect predictor tools should model the TAD explicitly. First results of tools look like Hi-C generated maps #GI2018 0.9912239
imarmean .@rafalab Comparing species (mouse/human) RNA-seq from microarrays and RNA-seq: initially no correlation BUT there are probe effects, difference in no of transcripts per species, GC content effects that interfere .. #gi2018 0.9899234
pmelsted SY: Pan Cancer Analysis of Whole Genomes, analysing genomes from 2834 donors. Data processing timeline, across 2 years, 16K compute cores, processing issues cause delays, hard to keep sustained throughput #GI2018 0.9899234
JavierHerrero7 .@HKhiabanian —LOHGIC: LOH-Germline Inference Calculator. Tested on 64 patients. Generally correct. One curious case where a BRCA2 mutation was inferred to be somatic instead of germline. In that patient, mutation was indeed germline but reverted in the tumour #gi2018 0.9893998
MKarimzade #GI2018 @HKhiabanian: 1/ Often in clinic you have tumour only data. Your expected allele frequency for each depends on tumour purity, estimated by a pathologist without estimate of variance. Solution? Use binomial likelihood of each possible genotype in an interval of purity. 0.9893998
pmelsted KP: deleterious deletions will be depleted over time, looking at rare variants in patients and healthy people, fixed differences between primates. Deletions are depleted at the BE, 10-fold reduction compared to background #GI2018 0.9893998

Topic 6

screen_name text gamma
MKarimzade #GI2018 1/ @nameluem used 733 DNase-seq data from 439 different human cell types, covering 21% of the genome in total and 3.5 million DNase hypersensitive sites. They used NMF with k=16 and labeled each DHS based on its assigned cell type. This provides a new vocabulary .. 0.9908294
pmelsted KP: enters minefield territory, “challenges and limitations of ML in genomics”. Training data: pairs of enhancer promoter sites, annotated by strength of interaction (actually binary). Predictions are better than “closest gene”. #GI2018 0.9903978
pmelsted RI: Technical vs biological variability. Need technical replicates to distinguish variability that is natural vs variability due to technologies. NGS does not eliminate biological variability. Moves into DNA methylation examples #GI2018 0.9899234
pmelsted KP: cryptic errors made: most ML models assume IID, methods use this aggressively. Genomic data is not IID, dependent observations can appear in training and test set, they “bleeding over” overestimating performance #GI2018 0.9899234
ACSCevents 17 years after the first #Genome #Informatics conference (in 2001), today participants arrive for #GI2018. Thanks to advances in technology this continues to be one of our most popular topics, as the human #genetics community strives to progress in this fast moving field. #ACSC30 https://t.co/2LSRvQ2XcY 0.9899234
sexchrlab Patrick Brennan: Nationwide Children’s has both clinical genetics (CLIA) and research genetics to interplay and improve diagnosis and treatment of childhood cancers (typically fresh-frozen, but sometimes fixed samples). #GI2018 0.9899234
ksamocha Jordana Bell (@jordanatbell) #GI2018: Studied methylation variability in 400 twins from the TwinsUK cohort. Looked at 772k CpGs after QC. Correlation in twin methylation profiles were consistent with heritability. 0.9893998
JavierHerrero7 .@nameluem — Uses DNAse I to look at accessible chromatin. Current maps of the human regulatory regions have scope for improvements. Unclear what the landscape is for any particular gene. Still unclear how to interpret non-coding regulatory elements. #gi2018 0.9888188
deniseOme .@jon_belyeu Great to see a talk at #gi2018 using the CEPH Human Genome Diversity Cell Line Panel. The precursors of 1000G, 10K, 100K genome resources for human genetics/genomics. https://t.co/lUyZnoACac 0.9888188
pmelsted KP: Results revealed distinct genomic signatures of looping DNA. Common ML errors: “always no” predictors can give good performance in unbalanced sets (most E-P sites are not interacting) #GI2018 0.9881705

Session info

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.0 (2020-04-24)
##  os       macOS Catalina 10.15.6      
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Europe/Berlin               
##  date     2020-09-01                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                          
##  askpass        1.1        2019-01-13 [1] CRAN (R 4.0.0)                  
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.0)                  
##  backports      1.1.8      2020-06-17 [1] CRAN (R 4.0.0)                  
##  bitops         1.0-6      2013-08-17 [1] CRAN (R 4.0.0)                  
##  callr          3.4.3      2020-03-28 [1] CRAN (R 4.0.0)                  
##  clamour      * 0.1.0      2020-09-01 [1] Github (lazappi/clamour@c8ea1c7)
##  cli            2.0.2      2020-02-28 [1] CRAN (R 4.0.0)                  
##  colorspace     1.4-1      2019-03-18 [1] CRAN (R 4.0.0)                  
##  crayon         1.3.4      2017-09-16 [1] CRAN (R 4.0.0)                  
##  curl           4.3        2019-12-02 [1] CRAN (R 4.0.0)                  
##  digest         0.6.25     2020-02-23 [1] CRAN (R 4.0.0)                  
##  dplyr        * 1.0.1      2020-07-31 [1] CRAN (R 4.0.2)                  
##  ellipsis       0.3.1      2020-05-15 [1] CRAN (R 4.0.0)                  
##  emo          * 0.0.0.9000 2020-08-17 [1] Github (hadley/emo@3f03b11)     
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.0.0)                  
##  fansi          0.4.1      2020-01-08 [1] CRAN (R 4.0.0)                  
##  farver         2.0.3      2020-01-16 [1] CRAN (R 4.0.0)                  
##  forcats      * 0.5.0      2020-03-01 [1] CRAN (R 4.0.0)                  
##  fs           * 1.5.0      2020-07-31 [1] CRAN (R 4.0.2)                  
##  generics       0.0.2      2018-11-29 [1] CRAN (R 4.0.0)                  
##  ggforce        0.3.2      2020-06-23 [1] CRAN (R 4.0.2)                  
##  ggplot2      * 3.3.2      2020-06-19 [1] CRAN (R 4.0.2)                  
##  ggraph       * 2.0.3      2020-05-20 [1] CRAN (R 4.0.0)                  
##  ggrepel      * 0.8.2      2020-03-08 [1] CRAN (R 4.0.0)                  
##  ggtext       * 0.1.0      2020-06-04 [1] CRAN (R 4.0.2)                  
##  glue           1.4.1      2020-05-13 [1] CRAN (R 4.0.0)                  
##  graphlayouts   0.7.0      2020-04-25 [1] CRAN (R 4.0.0)                  
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.0.0)                  
##  gridtext       0.1.1      2020-02-24 [1] CRAN (R 4.0.2)                  
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.0.0)                  
##  here         * 0.1        2017-05-28 [1] CRAN (R 4.0.0)                  
##  highr          0.8        2019-03-20 [1] CRAN (R 4.0.0)                  
##  htmltools      0.5.0      2020-06-16 [1] CRAN (R 4.0.0)                  
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                  
##  igraph       * 1.2.5      2020-03-19 [1] CRAN (R 4.0.0)                  
##  janeaustenr    0.1.5      2017-06-10 [1] CRAN (R 4.0.0)                  
##  jsonlite       1.7.0      2020-06-25 [1] CRAN (R 4.0.0)                  
##  kableExtra   * 1.2.1      2020-08-27 [1] CRAN (R 4.0.2)                  
##  knitr        * 1.29       2020-06-23 [1] CRAN (R 4.0.0)                  
##  labeling       0.3        2014-08-23 [1] CRAN (R 4.0.0)                  
##  lattice        0.20-41    2020-04-02 [1] CRAN (R 4.0.0)                  
##  lifecycle      0.2.0      2020-03-06 [1] CRAN (R 4.0.0)                  
##  lubridate    * 1.7.9      2020-06-08 [1] CRAN (R 4.0.0)                  
##  magick       * 2.4.0      2020-06-23 [1] CRAN (R 4.0.0)                  
##  magrittr       1.5        2014-11-22 [1] CRAN (R 4.0.0)                  
##  markdown       1.1        2019-08-07 [1] CRAN (R 4.0.0)                  
##  MASS           7.3-51.6   2020-04-26 [1] CRAN (R 4.0.0)                  
##  Matrix         1.2-18     2019-11-27 [1] CRAN (R 4.0.0)                  
##  modeltools     0.2-23     2020-03-05 [1] CRAN (R 4.0.0)                  
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.0.0)                  
##  NLP            0.2-0      2018-10-18 [1] CRAN (R 4.0.0)                  
##  openssl        1.4.2      2020-06-27 [1] CRAN (R 4.0.0)                  
##  pillar         1.4.6      2020-07-10 [1] CRAN (R 4.0.2)                  
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.0.0)                  
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.0.0)                  
##  png            0.1-7      2013-12-03 [1] CRAN (R 4.0.0)                  
##  polyclip       1.10-0     2019-03-14 [1] CRAN (R 4.0.0)                  
##  processx       3.4.3      2020-07-05 [1] CRAN (R 4.0.2)                  
##  ps             1.3.3      2020-05-08 [1] CRAN (R 4.0.0)                  
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.0.0)                  
##  R6             2.4.1      2019-11-12 [1] CRAN (R 4.0.0)                  
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.0.0)                  
##  Rcpp           1.0.5      2020-07-06 [1] CRAN (R 4.0.0)                  
##  RCurl          1.98-1.2   2020-04-18 [1] CRAN (R 4.0.0)                  
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.0.0)                  
##  rlang          0.4.7      2020-07-09 [1] CRAN (R 4.0.2)                  
##  rmarkdown      2.3        2020-06-18 [1] CRAN (R 4.0.0)                  
##  rprojroot      1.3-2      2018-01-03 [1] CRAN (R 4.0.0)                  
##  rstudioapi     0.11       2020-02-07 [1] CRAN (R 4.0.0)                  
##  rtweet       * 0.7.0      2020-01-08 [1] CRAN (R 4.0.0)                  
##  rvest        * 0.3.6      2020-07-25 [1] CRAN (R 4.0.2)                  
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.0.0)                  
##  selectr        0.4-2      2019-11-20 [1] CRAN (R 4.0.0)                  
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.0.0)                  
##  slam           0.1-47     2019-12-21 [1] CRAN (R 4.0.0)                  
##  SnowballC      0.7.0      2020-04-01 [1] CRAN (R 4.0.0)                  
##  stringi        1.4.6      2020-02-17 [1] CRAN (R 4.0.0)                  
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.0.0)                  
##  tibble         3.0.3      2020-07-10 [1] CRAN (R 4.0.2)                  
##  tidygraph      1.2.0      2020-05-12 [1] CRAN (R 4.0.0)                  
##  tidyr        * 1.1.1      2020-07-31 [1] CRAN (R 4.0.2)                  
##  tidyselect     1.1.0      2020-05-11 [1] CRAN (R 4.0.0)                  
##  tidytext     * 0.2.5      2020-07-11 [1] CRAN (R 4.0.2)                  
##  tm             0.7-7      2019-12-12 [1] CRAN (R 4.0.0)                  
##  tokenizers     0.2.1      2018-03-29 [1] CRAN (R 4.0.0)                  
##  topicmodels  * 0.2-11     2020-04-19 [1] CRAN (R 4.0.0)                  
##  tweenr         1.0.1      2018-12-14 [1] CRAN (R 4.0.0)                  
##  usethis        1.6.1      2020-04-29 [1] CRAN (R 4.0.0)                  
##  utf8           1.1.4      2018-05-24 [1] CRAN (R 4.0.0)                  
##  vctrs          0.3.2      2020-07-15 [1] CRAN (R 4.0.2)                  
##  viridis      * 0.5.1      2018-03-29 [1] CRAN (R 4.0.0)                  
##  viridisLite  * 0.3.0      2018-02-01 [1] CRAN (R 4.0.0)                  
##  webshot      * 0.5.2      2019-11-22 [1] CRAN (R 4.0.0)                  
##  withr          2.2.0      2020-04-20 [1] CRAN (R 4.0.0)                  
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.0.0)                  
##  xfun           0.16       2020-07-24 [1] CRAN (R 4.0.2)                  
##  xml2         * 1.3.2      2020-04-23 [1] CRAN (R 4.0.0)                  
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.0.0)                  
## 
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library