From a4714179841a079d7d2a93ffe89e0d0c6c9de948 Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Thu, 22 Sep 2022 16:58:25 -0700 Subject: [PATCH 01/10] adds this to the repo --- job_descriptions/research_intern_jd.txt | 46 +++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 job_descriptions/research_intern_jd.txt diff --git a/job_descriptions/research_intern_jd.txt b/job_descriptions/research_intern_jd.txt new file mode 100644 index 0000000..7fbcfcf --- /dev/null +++ b/job_descriptions/research_intern_jd.txt @@ -0,0 +1,46 @@ +Community Data Science Collective Research Intern Job Description + + +Intern Name: Divya Sikka +Internship Period: 9/19/2022 - 5/31/2023 +Primary Supervisor: Kaylea Champion +Faculty Supervisor: Benjamin Mako Hill + + +Student interns in the Community Data Science Collective join a multi-disciplinary, multi-institutional team of social scientists applying a range of quantitative and qualitative methods to the study of online communities. + + +Interns assist on research projects focused on how the design of communication and information technologies shape fundamental social outcomes with broad theoretical and practical implications—like an individual’s decision to join a community, contribute to a public good, or a group’s ability to make decisions democratically. + + +Interns in the CDSC learn research skills hands-on, helping with active investigations and learning to define their own research projects. Active topics of research in the lab include collaborative production communities, information-seeking, and online anonymity, vulnerability, and privacy. + + +Intern Duties: +* Identify and review literature +* Attend lab meetings +* Attend area seminars +* Prepare data for analysis +* Keep accurate records of research processes +* Participate in data analysis +* Prepare and maintain public dissemination materials +* Participate in composing and editing research reports and publications + + +Intended Learning Outcomes: +* Development of research questions +* Research procedures +* Data collection +* Data analysis + + +Intern Obligations: +* Timely communication, especially with respect to difficulties +* 15 hours per week, of which 3 hours are expected to be completed on-site +* Self motivation and self organization + + +Supervisor Obligations: +* Regular feedback +* Training in the performance of key tasks +* Mentorship in accomplishing goals \ No newline at end of file From aefbee45bad6061bc7992638053fb52b09b3bf0b Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Mon, 21 Nov 2022 19:58:04 -0800 Subject: [PATCH 02/10] adds a git ignore for some types, adds an RMD and an RData under annex control. --- R_examples/.gitignore | 3 + R_examples/EDA.Rmd | 420 ++++++++++++++++++++++++++++++++++++++ R_examples/data/EDA.RData | 1 + 3 files changed, 424 insertions(+) create mode 100644 R_examples/.gitignore create mode 100644 R_examples/EDA.Rmd create mode 120000 R_examples/data/EDA.RData diff --git a/R_examples/.gitignore b/R_examples/.gitignore new file mode 100644 index 0000000..367b636 --- /dev/null +++ b/R_examples/.gitignore @@ -0,0 +1,3 @@ +## Ignore anything ending in these types +*.Rproj +*.tex diff --git a/R_examples/EDA.Rmd b/R_examples/EDA.Rmd new file mode 100644 index 0000000..28f89d0 --- /dev/null +++ b/R_examples/EDA.Rmd @@ -0,0 +1,420 @@ +--- +title: "Taboo EDA" +author: "Kaylea Champion" +date: "2/3/2022" +output: pdf_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +library('ggplot2') +library('scales') +setwd("~/Research/cdsc_examples_repository/R_examples") +#load('../processed_data/artDF.RData') +load('~/Research/taboo/processed_data/EDA.RData') +#need vDF, qDF, revDF.clean + +reverselog_trans <- function(base = exp(1)) { + trans <- function(x) -log(x, base) + inv <- function(x) base^(-x) + trans_new(paste0("reverselog-", format(base)), trans, inv, + log_breaks(base = base), + domain = c(1e-100, Inf)) +} + +options(scipen=999) +``` + +## Overview + +The purpose of this study is to understand the production and development of taboo topics. + +### Independent Variables +- taboo (article) + +### Dependent Variables +- article readership (h1) +- article quality (h2) +- contribution quality (h3) +- contributor identifiability (h4) + +### Important Controls + +- article age (quality tends to grow over time as people have the opportunity to contribute) +- time (general traffic trends wax and wane) + +### Relationships to be Tested + +- H1 - taboo topics have higher readership than random articles +- H2 - taboo topics are lower quality than random articles +- H3 - taboo topics receive lower quality contributions than random articles +- H4 - taboo topics are more likely to receive contributions from less identifiable contributors than random articles + +### Unit(s) of Analysis + - The article (h1, h2) + - The revision of the article (h3, h4) + +## H1 Descriptives: Viewership + +```{r, H1descriptives, echo=FALSE,results='hide',fig.keep='all'} +##article readership -- vDF + +hist(vDF$viewSum) +hist(log(vDF$viewSum)) +``` + +We don't use sum, however -- we use view rank. View Rank behaves reasonably well if logged: + +```{r} + + +hist(vDF$viewRankInMyMonth) +hist(log(vDF$viewRankInMyMonth)) + +## still a model w/time - loess +g <- ggplot(vDF, aes(x=date, y=viewRankInMyMonth, color=source)) + + geom_smooth() + + #scale_y_continuous(trans='reverse') + + theme_bw() + + scale_y_continuous(trans=reverselog_trans(10), + breaks = trans_breaks("log10", function(x) 10^x), + labels = trans_format("log10", math_format(10^.x))) + + scale_x_date() + + scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + + theme(legend.position = 'bottom', legend.title = element_blank()) + + labs(x="Time", y="View Rank") + +g + +``` + +```{r H2descriptivesp1} + +## absolute quality +g <- ggplot(qDF, aes(x=date, y=weighted_sum, group=encodedTitle, color=source)) + + #geom_smooth() + + #scale_y_continuous(trans='reverse') + + ## change this to show trajectories + #geom_line(alpha=.05) + #angry R!! + #geom_point(alpha=.05) + + theme_bw() + + geom_rug(alpha=.1) + +# scale_y_continuous(trans=reverselog_trans(10), +# breaks = trans_breaks("log10", function(x) 10^x), +# labels = trans_format("log10", math_format(10^.x))) + + scale_x_date() + + scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + + theme(legend.position = 'bottom', legend.title = element_blank()) + + labs(x="Time", y="Quality") + +g + +## facet grid +g <- ggplot(qDF, aes(x=source, y=weighted_sum)) + + geom_boxplot() + +g + +table(qDF$source) + +g <- ggplot(qDF, aes(x=weighted_sum)) + + geom_histogram() + + facet_grid(source~., scales='free_y') + +g + +``` + +This graph shows that taboo articles originated much earlier than those obtained with a random sample approach. + +Another key variable is quality --- which again is ranked and logged. + +```{r H2descriptivesp2} +## relative quality + +hist(qDF$weighted_sum) +``` + +```{r} +hist(log(qDF$weighted_sum)) +``` + +```{r} +hist(qDF$qualityRankInMyMonth) +``` + +```{r} +hist(log(qDF$qualityRankInMyMonth)) +``` + +```{r} + +qt <- subset(qDF, qDF$source=='taboo') +qr <- subset(qDF, qDF$source=='ngram') + +mean(qDF$weighted_sum) +mean(qr$weighted_sum) +mean(qt$weighted_sum) +mean(qr$qualityRankInMyMonth) +mean(qt$qualityRankInMyMonth) + +g <- ggplot(qDF, aes(x=qualityRankInMyMonth, color=source)) + + geom_boxplot() + +g + +g <- ggplot(qDF, aes(x=weighted_sum, color=source)) + + geom_boxplot() + +g + +g <- ggplot(qDF, aes(x=date, y=qualityRankInMyMonth, color=source)) + + geom_smooth() + + #scale_y_continuous(trans='reverse') + + theme_bw() + + geom_rug(alpha=.1) + + #scale_y_continuous(trans=reverselog_trans(10), + # breaks = trans_breaks("log10", function(x) 10^x), + # labels = trans_format("log10", math_format(10^.x))) + + scale_x_date() + + #scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + + theme(legend.position = 'bottom', legend.title = element_blank()) + + labs(x="Time", y="Quality Rank") + +g + +summary(qt$qualityRankInMyMonth) +summary(qr$qualityRankInMyMonth) +summary(vDF$viewRankInMyMonth) +summary(qDF$qualityRankInMyMonth) + + +``` + +This examines the same data but with a relative ranking approach for quality. Each article's relative rank is in terms of quality that month, compared to all other articles that were present in Wikipedia at that time. Lower numbers are better (at the top of the graph). + +## H3 - Contribution Quality + +```{r H3descriptives} +##revision quality +artDF$pct_revert <- (artDF$got_reverted / artDF$revid) * 100 +table(artDF$taboo) +hist(artDF$got_reverted) +hist(log(artDF$got_reverted)) +hist(artDF$revid) +hist(log(artDF$revid)) +## facet these hists by source!! +#cor.test(artDF$got_reverted, as.numeric(artDF$taboo)) +#cor.test(artDF$revid, as.numeric(artDF$taboo)) +#hist(log(artDF$got_reverted)) +#hist(log(artDF$revid)) + +## try a wilcoxen/mann-whitney u test +###both are skewed, need to do a rank correlation. +cor.test(artDF$pct_revert, as.numeric(artDF$taboo)) +cor.test(artDF$revid, artDF$pct_revert) +##build the R plots of all the visualizations and distributions +## do the same type of EDA for article-level dataset +## every time there is skew think through tests/models to match +## log-log, hists and boxplots, colored by taboo, etc. + +#drop Barack Obama +artDF <- subset(artDF, artDF$taboo!='0.5') +table(artDF$taboo) +artDF$taboo <- as.logical(artDF$taboo) + +g <- ggplot(artDF, aes(x=revid, y=pct_revert, color=taboo)) + + geom_point() + + labs(x='Number of Revisions', y='Percent Reverted') + +g + +ggplot(artDF, aes(x=got_reverted, color=taboo)) + + geom_boxplot() + +g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=got_reverted, color=taboo)) + + geom_point(alpha=.2) + + geom_smooth() + + geom_rug(alpha=.2)+ + scale_x_continuous(trans='log') + + scale_y_continuous(trans='log') + + labs(x='Number of Revisions', y='Number Reverted') + +g + +g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=pct_revert, color=taboo)) + + geom_point(alpha=.2) + + geom_smooth() + + geom_rug(alpha=.2)+ + scale_x_continuous() + + scale_y_continuous() + + labs(x='Number of Revisions', y='Percent Reverted') + +g + +## pull edits that were later reverted, since they therefore didn't really contribute per se +revDF.clean.norev <- subset(revDF.clean, revDF.clean$got_reverted==FALSE) +revDF.clean.norev <- subset(revDF.clean.norev, revDF.clean.norev$revert != '') +table(revDF.clean.norev$anon) + +print('proportion of reverts by source') +cleanTable <- table(revDF.clean.norev$source, revDF.clean.norev$revert) +ftable(cleanTable) +prop.table(cleanTable, 1) +prop.table(cleanTable, 2) + +``` + +These results preliminarily suggest a log-log linear model. However, we observe that we do not have support in both samples across the full range of the data. The random sample contains a large number of zeroes. + +## H4: Anonymity + +H4 tests three limits on identifiability: +* contributing without an account +* contributing with a new account (or, being a relative newcomer) +* having a short user page. + +We want to make sure we have a reasonable amount of variation and handle zeroes correctly for these measures. +### Contributing without an account + +Proportion of Edits that are made without an account: +```{r} + +anonTable <- table(revDF.clean.norev$source, revDF.clean.norev$revert, revDF.clean.norev$anon) +anonTable +ftable(anonTable) +prop.table(ftable(anonTable),1) + +xTabAnon <- xtabs(~source+revert+anon,data=revDF.clean.norev) +ftable(xTabAnon) +prop.table(ftable(xTabAnon), 1) + +ggplot(revDF.clean.norev, aes(x=anon, color=taboo)) + + geom_boxplot() +``` + + +### Contributing with a new account +```{r H4descriptives} + +g <- ggplot(revDF.clean.norev, aes(x=editor_nth_edit_nocollapse)) + + geom_histogram() + + facet_grid(source~., scales='free_y') + +g + +g <- ggplot(revDF.clean.norev, aes(x=log(editor_nth_edit_nocollapse))) + + geom_histogram() + + facet_grid(source~., scales='free_y') + +g + +g <- ggplot(revDF.clean.norev, aes(x=editor_nth_edit_nocollapse)) + + geom_boxplot() + + facet_grid(source~., scales='free_y') + +g + +t <- subset(revDF.clean.norev, as.logical(revDF.clean.norev$anon)==TRUE) +mean(t$editor_nth_edit_nocollapse) + +t <- subset(revDF.clean.norev, as.logical(revDF.clean.norev$anon)==FALSE) +mean(t$editor_nth_edit_nocollapse) + +g <- ggplot(revDF.clean, aes(x=log(editor_nth_edit_nocollapse))) + + geom_boxplot() + + facet_grid(source~., scales='free_y') + +g + +``` + +### Having a short user page + +```{r} +summary(revDF.clean$userpage_text_chars) + +g <- ggplot(revDF.clean, aes(x=log1p(userpage_text_chars), group=taboo)) + + geom_histogram(binwidth = .5) + + facet_grid(source~., scales='free_y') + +g + +g <- ggplot(subset(revDF.clean, revDF.clean$userpage_text_chars < exp(4)), aes(x=log1p(userpage_text_chars), group=taboo)) + + geom_histogram(binwidth = .5) + + facet_grid(source~., scales='free_y') + +g + + +# look at last edit, what's the 'final' quality? +library(dplyr) +finalQDF <- qDF %>% group_by(encodedTitle) %>% filter(timestamp == max(timestamp)) + +g <- ggplot(finalQDF, aes(x=weighted_sum, y=source)) + + geom_boxplot() + + labs(x='Final Quality') + +g + +summary(artDF$got_reverted/artDF$revid) +``` + + +### Gender + +I used the API to find out a bit of user information -- are they emailable, whether gender was specified, and if it was specified, whether the person was female. + +```{r gender user data} + +table(revDF.clean$gender) +userDF <- data.frame('editor'=revDF.clean$editor, 'gender' = revDF.clean$gender, 'emailable' = revDF.clean$emailable, 'anon'=revDF.clean$anon) + +userDF <- subset(userDF, userDF$anon == 'false') +userDF <- unique(userDF) +table(userDF$gender) + +prop.table(table(revDF.clean$source, revDF.clean$gender), margin = 1) +gaveGenderDF <- subset(revDF.clean, revDF.clean$gender != 'unknown') +prop.table(table(gaveGenderDF$source, gaveGenderDF$gender), margin = 1) + +``` + + +### Being Emailable + + +```{r emailable user data} + +table(revDF.clean$emailable) + +table(userDF$emailable) + +prop.table(table(revDF.clean$source, revDF.clean$emailable), margin = 1) + +``` + +### Protections + +```{r protection} + +artDF.prot.only <- subset(artDF.prot, artDF.prot$pct.prot > 0) +artDF.prot.only <- subset(artDF.prot.only, artDF.prot.only$taboo != 0.5) + + +g <- ggplot(artDF.prot.only, aes(x=pct.prot, group=taboo)) + + geom_boxplot() + + labs(x='Protection Proportion') + +g + +t.artDF <- subset(artDF.prot.only, artDF.prot.only$pct.prot > 0) + +g <- ggplot(t.artDF, aes(x=pct.prot, group=taboo)) + + geom_boxplot() + + labs(x='Protection Proportion (non-zero only)') + +g + + +``` \ No newline at end of file diff --git a/R_examples/data/EDA.RData b/R_examples/data/EDA.RData new file mode 120000 index 0000000..68d11c9 --- /dev/null +++ b/R_examples/data/EDA.RData @@ -0,0 +1 @@ +../../.git/annex/objects/4k/Vg/SHA256E-s123897041--12ef75a841fc616f23c23942f253072318266bf0d399863ccd2cc84430753f57/SHA256E-s123897041--12ef75a841fc616f23c23942f253072318266bf0d399863ccd2cc84430753f57 \ No newline at end of file From 81b1b6c3ee80116fe775541558575cd53e78a59c Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Mon, 21 Nov 2022 19:58:55 -0800 Subject: [PATCH 03/10] early version --- R_examples/EDA.Rmd | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/R_examples/EDA.Rmd b/R_examples/EDA.Rmd index 28f89d0..ec597ab 100644 --- a/R_examples/EDA.Rmd +++ b/R_examples/EDA.Rmd @@ -10,9 +10,7 @@ knitr::opts_chunk$set(echo = TRUE) library('ggplot2') library('scales') setwd("~/Research/cdsc_examples_repository/R_examples") -#load('../processed_data/artDF.RData') -load('~/Research/taboo/processed_data/EDA.RData') -#need vDF, qDF, revDF.clean +load('~/Research/cdsc_examples_repository/R_examples/data/EDA.RData') reverselog_trans <- function(base = exp(1)) { trans <- function(x) -log(x, base) @@ -209,14 +207,8 @@ hist(log(artDF$revid)) #hist(log(artDF$got_reverted)) #hist(log(artDF$revid)) -## try a wilcoxen/mann-whitney u test -###both are skewed, need to do a rank correlation. cor.test(artDF$pct_revert, as.numeric(artDF$taboo)) cor.test(artDF$revid, artDF$pct_revert) -##build the R plots of all the visualizations and distributions -## do the same type of EDA for article-level dataset -## every time there is skew think through tests/models to match -## log-log, hists and boxplots, colored by taboo, etc. #drop Barack Obama artDF <- subset(artDF, artDF$taboo!='0.5') From ea7db249643244ffd2ab58b265faeb02a02ca341 Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Mon, 21 Nov 2022 20:23:25 -0800 Subject: [PATCH 04/10] a few more tweaks --- R_examples/EDA.Rmd | 34 ++++++++++++---------------------- 1 file changed, 12 insertions(+), 22 deletions(-) diff --git a/R_examples/EDA.Rmd b/R_examples/EDA.Rmd index ec597ab..0fae152 100644 --- a/R_examples/EDA.Rmd +++ b/R_examples/EDA.Rmd @@ -196,18 +196,13 @@ This examines the same data but with a relative ranking approach for quality. Ea ```{r H3descriptives} ##revision quality artDF$pct_revert <- (artDF$got_reverted / artDF$revid) * 100 -table(artDF$taboo) +table(artDF$source) hist(artDF$got_reverted) hist(log(artDF$got_reverted)) hist(artDF$revid) hist(log(artDF$revid)) -## facet these hists by source!! -#cor.test(artDF$got_reverted, as.numeric(artDF$taboo)) -#cor.test(artDF$revid, as.numeric(artDF$taboo)) -#hist(log(artDF$got_reverted)) -#hist(log(artDF$revid)) -cor.test(artDF$pct_revert, as.numeric(artDF$taboo)) +cor.test(artDF$pct_revert, as.numeric(artDF$source)) cor.test(artDF$revid, artDF$pct_revert) #drop Barack Obama @@ -215,16 +210,16 @@ artDF <- subset(artDF, artDF$taboo!='0.5') table(artDF$taboo) artDF$taboo <- as.logical(artDF$taboo) -g <- ggplot(artDF, aes(x=revid, y=pct_revert, color=taboo)) + +g <- ggplot(artDF, aes(x=revid, y=pct_revert, color=source)) + geom_point() + labs(x='Number of Revisions', y='Percent Reverted') g -ggplot(artDF, aes(x=got_reverted, color=taboo)) + +ggplot(artDF, aes(x=got_reverted, color=source)) + geom_boxplot() -g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=got_reverted, color=taboo)) + +g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=got_reverted, color=source)) + geom_point(alpha=.2) + geom_smooth() + geom_rug(alpha=.2)+ @@ -234,7 +229,7 @@ g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=got_reverted, color=ta g -g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=pct_revert, color=taboo)) + +g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=pct_revert, color=source)) + geom_point(alpha=.2) + geom_smooth() + geom_rug(alpha=.2)+ @@ -281,7 +276,7 @@ xTabAnon <- xtabs(~source+revert+anon,data=revDF.clean.norev) ftable(xTabAnon) prop.table(ftable(xTabAnon), 1) -ggplot(revDF.clean.norev, aes(x=anon, color=taboo)) + +ggplot(revDF.clean.norev, aes(x=anon, color=source)) + geom_boxplot() ``` @@ -326,13 +321,13 @@ g ```{r} summary(revDF.clean$userpage_text_chars) -g <- ggplot(revDF.clean, aes(x=log1p(userpage_text_chars), group=taboo)) + +g <- ggplot(revDF.clean, aes(x=log1p(userpage_text_chars), group=source)) + geom_histogram(binwidth = .5) + facet_grid(source~., scales='free_y') g -g <- ggplot(subset(revDF.clean, revDF.clean$userpage_text_chars < exp(4)), aes(x=log1p(userpage_text_chars), group=taboo)) + +g <- ggplot(subset(revDF.clean, revDF.clean$userpage_text_chars < exp(4)), aes(x=log1p(userpage_text_chars), group=source)) + geom_histogram(binwidth = .5) + facet_grid(source~., scales='free_y') @@ -380,8 +375,6 @@ prop.table(table(gaveGenderDF$source, gaveGenderDF$gender), margin = 1) table(revDF.clean$emailable) -table(userDF$emailable) - prop.table(table(revDF.clean$source, revDF.clean$emailable), margin = 1) ``` @@ -390,19 +383,16 @@ prop.table(table(revDF.clean$source, revDF.clean$emailable), margin = 1) ```{r protection} -artDF.prot.only <- subset(artDF.prot, artDF.prot$pct.prot > 0) -artDF.prot.only <- subset(artDF.prot.only, artDF.prot.only$taboo != 0.5) - -g <- ggplot(artDF.prot.only, aes(x=pct.prot, group=taboo)) + +g <- ggplot(artDF, aes(x=pct.prot, group=source)) + geom_boxplot() + labs(x='Protection Proportion') g -t.artDF <- subset(artDF.prot.only, artDF.prot.only$pct.prot > 0) +artDF.prot <- subset(artDF, artDF$pct.prot > 0) -g <- ggplot(t.artDF, aes(x=pct.prot, group=taboo)) + +g <- ggplot(artDF.prot, aes(x=pct.prot, group=source)) + geom_boxplot() + labs(x='Protection Proportion (non-zero only)') From a03c08a93202812dd58406c93661af01fa4e9a45 Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Mon, 21 Nov 2022 21:28:05 -0800 Subject: [PATCH 05/10] places these under annex control for use in demos --- R_examples/data/botList.tsv | 1 + R_examples/data/taboo-part1.csv | 1 + R_examples/data/taboo-part2.csv | 1 + 3 files changed, 3 insertions(+) create mode 120000 R_examples/data/botList.tsv create mode 120000 R_examples/data/taboo-part1.csv create mode 120000 R_examples/data/taboo-part2.csv diff --git a/R_examples/data/botList.tsv b/R_examples/data/botList.tsv new file mode 120000 index 0000000..f19e1bd --- /dev/null +++ b/R_examples/data/botList.tsv @@ -0,0 +1 @@ +../../.git/annex/objects/13/jQ/SHA256E-s108950--b2123f4f6a71796bc29ff6b2e8185dd11474872b1a06a770349a145e0f08d466.tsv/SHA256E-s108950--b2123f4f6a71796bc29ff6b2e8185dd11474872b1a06a770349a145e0f08d466.tsv \ No newline at end of file diff --git a/R_examples/data/taboo-part1.csv b/R_examples/data/taboo-part1.csv new file mode 120000 index 0000000..180324d --- /dev/null +++ b/R_examples/data/taboo-part1.csv @@ -0,0 +1 @@ +../../.git/annex/objects/qx/Vw/SHA256E-s2359876--fae0b0d010f85f8c81df5621ccf87c7cdd02e298e547b8827f696fd52d7132cd.csv/SHA256E-s2359876--fae0b0d010f85f8c81df5621ccf87c7cdd02e298e547b8827f696fd52d7132cd.csv \ No newline at end of file diff --git a/R_examples/data/taboo-part2.csv b/R_examples/data/taboo-part2.csv new file mode 120000 index 0000000..c109267 --- /dev/null +++ b/R_examples/data/taboo-part2.csv @@ -0,0 +1 @@ +../../.git/annex/objects/wf/2g/SHA256E-s2362719--0b41859824a65fdf5315687fdefcf286d28cd03d6387697e1dd274e91cdf8a45.csv/SHA256E-s2362719--0b41859824a65fdf5315687fdefcf286d28cd03d6387697e1dd274e91cdf8a45.csv \ No newline at end of file From 97e83d0f09ac7b5c6aef02b5d8138c1cfc88748b Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Tue, 22 Nov 2022 12:00:22 -0800 Subject: [PATCH 06/10] adds this under git control for a demo today --- R_examples/prepDF.R | 148 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 R_examples/prepDF.R diff --git a/R_examples/prepDF.R b/R_examples/prepDF.R new file mode 100644 index 0000000..edd862f --- /dev/null +++ b/R_examples/prepDF.R @@ -0,0 +1,148 @@ + +rm(list=ls()) + +##################### +#The purpose of this file is to load up the datasets and clean them for processing. +# +##################### + +####set globals and make helpers +#basePath = '/home/kaylea/Research/taboo/' +basePath = '/gscratch/comdata/users/kaylea/taboo/' +dataPath = paste0(basePath, 'data/') +rawPath = paste0(basePath, 'raw_data/') +botsFile <- paste0(rawPath, 'botList.tsv') +endOfRecords= '2022-06-02 20:15:46' #derived from end of the action logs +endOfRecords = strptime(endOfRecords, "%Y-%m-%d %H:%M:%S") +startOfRecords= '2008-09-20 05:23:14' +startOfRecords = strptime(startOfRecords, "%Y-%m-%d %H:%M:%S") + + + +library(dplyr) +library(sqldf) +library(lubridate) +library(data.table) +library(urltools) + + +#recipe from https://www.r-bloggers.com/2011/06/merge-all-files-in-a-directory-using-r-into-a-single-dataframe/ +readPileToDF <- function(path) { + file_list <- list.files(path) + print(file_list) + for (my_file in file_list){ + if (my_file == '_SUCCESS') { #spark metadata file, ignore + next + } + # if the merged dataset doesn't exist, create it + if (!exists("dataset")) { + print(paste0('Now Reading: ', path, my_file)) + dataset <- read.table(paste0(path, my_file), quote="\"", header=TRUE, sep="\t", stringsAsFactors=FALSE) + } + # if the merged dataset does exist, append to it + if (exists("dataset")){ + temp_dataset <-read.table(paste0(path, my_file), quote="\"", header=TRUE, sep="\t", stringsAsFactors=FALSE) + dataset<-rbind(dataset, temp_dataset) + rm(temp_dataset) + } + } + dataset <- unique(dataset) + return(dataset) +} + +# Part 1 - load and clean revisions data + + +revDF = readPileToDF(paste0(coefPath, 'revDataPlusUPL/')) +revDF$source <- "taboo" +#revDF.CTab$taboo <- 1 +revDF$userpage_text_chars[is.na(revDF$userpage_text_chars)] <- 0 +head(revDF.CTab) + +##drop unneeded fields +revDF.CTab$prediction <- NULL +revDF.CTab$filtered_title <- NULL +revDF.CTab$target <- NULL + +colnames(revDF.CTab) + +revDF <- revDF[!is.na(revDF$revid),] #drop any where revid is NA + +revDF <- merge(x=revDF, y=userDF, by='editor', all.x=TRUE) #left (outer) join: all of revDF, plus any matches in userDF + +## eliminate any articles in both: + +### prepare bot filter +botDF <- read.table(botsFile, sep='\t', quote='"', header=TRUE, stringsAsFactors=FALSE) +botDF <- unique(botDF) #strip out any repetitions +#botRoleDF <- read.table(botsRoleFile, sep='\t', quote='"', header=TRUE, stringsAsFactors=FALSE) +head(revDF) +head(botDF) +botDF$editor_id <- as.character(botDF$BotUserID) #just to make sure +revDF$editor_id <- as.character(revDF$editor_id) +head(revDF) +head(botDF) +revDF <- setDT(revDF) +botDF <- setDT(botDF) +revDF <- revDF[,isBot :=FALSE][botDF, isBot := TRUE, on= .(editor_id)] # this means: set column isBot to False. then, set the isBot to TRUE if a join could happen '.' means list. +isABot.tab <- table(revDF$isBot) + +## drop all bots here +revDF.clean <- subset(revDF, revDF$isBot==FALSE) +revDF <- NULL #so we don't use it accidentally +revDF.clean$loggedIn <- !(as.logical(revDF.clean$anon)) + +##### filtering done, now to do some summing-up + +### Weighting +##for each article, the weight of each revision for that article is (N_rev_total/N_total articles)/N_art_revnum +#two criteria this meets: +#sum(weights) = total_revs +#sum(weights for given article) = sum(weights for all other articles) + +numEdits <- revDF.clean %>% group_by(encodedTitle) %>% dplyr::summarize(numEdits=length(revid)) ##articlewise revisions count +numEditors <- revDF.clean %>% group_by(encodedTitle) %>% dplyr::summarize(numEditors=length(unique(editor))) ###articlewise editors count, including IP addresses +n.revs <- length(revDF.clean$revid) ## total number of revisions +n.arts <- length(numEdits$encodedTitle) ## total number of articles +revDF.clean <- merge(revDF.clean, numEdits, by="encodedTitle") +revDF.clean <- merge(revDF.clean, numEditors, by="encodedTitle") +revDF.clean$weight <- (n.revs/n.arts)/revDF.clean$numEdits + + +revDF.clean$ngramWeight <- revDF.clean$count #wasn't very descriptive +revDF.clean <- revDF.clean %>% mutate(got_reverted = + case_when(is.na(reverted_by) ~ FALSE, TRUE ~ TRUE)) + +table(revDF.clean$anon) +revDF.clean <- rbind(subset(revDF.clean, revDF.clean$anon=='true'), subset(revDF.clean, revDF.clean$anon=='false')) ##small number of NAs (187), look like parse problems +table(revDF.clean$anon) + + +##### dropping items with missing revids; if this happens, find out why +###revDF.clean <- revDF.clean[!is.na(revDF.clean$revid)] + + + + +artDF <- revDF.clean %>% dplyr::group_by(encodedTitle) %>% dplyr::summarize( + across(revid, length), + across(got_reverted, sum), + across(date_time, min) +) + +titleSampleDF <- data.frame('encodedTitle' = revDF.clean$encodedTitle, 'source'=revDF.clean$source) +titleSampleDF <- unique(titleSampleDF) + +artDF <- merge(artDF, titleSampleDF, by='encodedTitle', all.x=TRUE) #which sample is it from +artDF$min.birthday <- strptime(artDF$date_time, "%Y-%m-%d %H:%M:%S") +artDF$startOfRecords <- startOfRecords +artDF$birthOrLog <- pmax(artDF$min.birthday, artDF$startOfRecords) #birthday or beginning of records, whichever comes later +artDF$secondsOldLog <- as.numeric(difftime(strptime(endOfRecords, "%Y-%m-%d %H:%M:%S"),strptime(artDF$birthOrLog, "%Y-%m-%d %H:%M:%S"), units="secs")) +## how many seconds old is each article inside the logged scope? + +artDF.prot$pct.prot <- artDF.prot$duration/artDF.prot$secondsOldLog ## what proportion of its observed life was the article protected? + +artDF$pct.prot <- artDF.prot$pct.prot + +print("saving full image") +save.image(paste0(dataPath, "dataset1.RData"), version=2) From 8e20edaddd8d7380ea75f03fa04629af574e0321 Mon Sep 17 00:00:00 2001 From: Kaylea Champion Date: Tue, 22 Nov 2022 12:08:16 -0800 Subject: [PATCH 07/10] git-annex in kaylea@wumpus:~/Research/cdsc_examples_repository --- R_examples/EDA.Rmd | 403 +------------------------------------------- R_examples/EDA.pdf | 1 + R_examples/prepDF.R | 149 +--------------- 3 files changed, 3 insertions(+), 550 deletions(-) create mode 100644 R_examples/EDA.pdf diff --git a/R_examples/EDA.Rmd b/R_examples/EDA.Rmd index 0fae152..8d16901 100644 --- a/R_examples/EDA.Rmd +++ b/R_examples/EDA.Rmd @@ -1,402 +1 @@ ---- -title: "Taboo EDA" -author: "Kaylea Champion" -date: "2/3/2022" -output: pdf_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -library('ggplot2') -library('scales') -setwd("~/Research/cdsc_examples_repository/R_examples") -load('~/Research/cdsc_examples_repository/R_examples/data/EDA.RData') - -reverselog_trans <- function(base = exp(1)) { - trans <- function(x) -log(x, base) - inv <- function(x) base^(-x) - trans_new(paste0("reverselog-", format(base)), trans, inv, - log_breaks(base = base), - domain = c(1e-100, Inf)) -} - -options(scipen=999) -``` - -## Overview - -The purpose of this study is to understand the production and development of taboo topics. - -### Independent Variables -- taboo (article) - -### Dependent Variables -- article readership (h1) -- article quality (h2) -- contribution quality (h3) -- contributor identifiability (h4) - -### Important Controls - -- article age (quality tends to grow over time as people have the opportunity to contribute) -- time (general traffic trends wax and wane) - -### Relationships to be Tested - -- H1 - taboo topics have higher readership than random articles -- H2 - taboo topics are lower quality than random articles -- H3 - taboo topics receive lower quality contributions than random articles -- H4 - taboo topics are more likely to receive contributions from less identifiable contributors than random articles - -### Unit(s) of Analysis - - The article (h1, h2) - - The revision of the article (h3, h4) - -## H1 Descriptives: Viewership - -```{r, H1descriptives, echo=FALSE,results='hide',fig.keep='all'} -##article readership -- vDF - -hist(vDF$viewSum) -hist(log(vDF$viewSum)) -``` - -We don't use sum, however -- we use view rank. View Rank behaves reasonably well if logged: - -```{r} - - -hist(vDF$viewRankInMyMonth) -hist(log(vDF$viewRankInMyMonth)) - -## still a model w/time - loess -g <- ggplot(vDF, aes(x=date, y=viewRankInMyMonth, color=source)) + - geom_smooth() + - #scale_y_continuous(trans='reverse') + - theme_bw() + - scale_y_continuous(trans=reverselog_trans(10), - breaks = trans_breaks("log10", function(x) 10^x), - labels = trans_format("log10", math_format(10^.x))) + - scale_x_date() + - scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + - theme(legend.position = 'bottom', legend.title = element_blank()) + - labs(x="Time", y="View Rank") - -g - -``` - -```{r H2descriptivesp1} - -## absolute quality -g <- ggplot(qDF, aes(x=date, y=weighted_sum, group=encodedTitle, color=source)) + - #geom_smooth() + - #scale_y_continuous(trans='reverse') + - ## change this to show trajectories - #geom_line(alpha=.05) + #angry R!! - #geom_point(alpha=.05) + - theme_bw() + - geom_rug(alpha=.1) + -# scale_y_continuous(trans=reverselog_trans(10), -# breaks = trans_breaks("log10", function(x) 10^x), -# labels = trans_format("log10", math_format(10^.x))) + - scale_x_date() + - scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + - theme(legend.position = 'bottom', legend.title = element_blank()) + - labs(x="Time", y="Quality") - -g - -## facet grid -g <- ggplot(qDF, aes(x=source, y=weighted_sum)) + - geom_boxplot() - -g - -table(qDF$source) - -g <- ggplot(qDF, aes(x=weighted_sum)) + - geom_histogram() + - facet_grid(source~., scales='free_y') - -g - -``` - -This graph shows that taboo articles originated much earlier than those obtained with a random sample approach. - -Another key variable is quality --- which again is ranked and logged. - -```{r H2descriptivesp2} -## relative quality - -hist(qDF$weighted_sum) -``` - -```{r} -hist(log(qDF$weighted_sum)) -``` - -```{r} -hist(qDF$qualityRankInMyMonth) -``` - -```{r} -hist(log(qDF$qualityRankInMyMonth)) -``` - -```{r} - -qt <- subset(qDF, qDF$source=='taboo') -qr <- subset(qDF, qDF$source=='ngram') - -mean(qDF$weighted_sum) -mean(qr$weighted_sum) -mean(qt$weighted_sum) -mean(qr$qualityRankInMyMonth) -mean(qt$qualityRankInMyMonth) - -g <- ggplot(qDF, aes(x=qualityRankInMyMonth, color=source)) + - geom_boxplot() - -g - -g <- ggplot(qDF, aes(x=weighted_sum, color=source)) + - geom_boxplot() - -g - -g <- ggplot(qDF, aes(x=date, y=qualityRankInMyMonth, color=source)) + - geom_smooth() + - #scale_y_continuous(trans='reverse') + - theme_bw() + - geom_rug(alpha=.1) + - #scale_y_continuous(trans=reverselog_trans(10), - # breaks = trans_breaks("log10", function(x) 10^x), - # labels = trans_format("log10", math_format(10^.x))) + - scale_x_date() + - #scale_color_discrete(name="Source", labels=c("Random", "Taboo")) + - theme(legend.position = 'bottom', legend.title = element_blank()) + - labs(x="Time", y="Quality Rank") - -g - -summary(qt$qualityRankInMyMonth) -summary(qr$qualityRankInMyMonth) -summary(vDF$viewRankInMyMonth) -summary(qDF$qualityRankInMyMonth) - - -``` - -This examines the same data but with a relative ranking approach for quality. Each article's relative rank is in terms of quality that month, compared to all other articles that were present in Wikipedia at that time. Lower numbers are better (at the top of the graph). - -## H3 - Contribution Quality - -```{r H3descriptives} -##revision quality -artDF$pct_revert <- (artDF$got_reverted / artDF$revid) * 100 -table(artDF$source) -hist(artDF$got_reverted) -hist(log(artDF$got_reverted)) -hist(artDF$revid) -hist(log(artDF$revid)) - -cor.test(artDF$pct_revert, as.numeric(artDF$source)) -cor.test(artDF$revid, artDF$pct_revert) - -#drop Barack Obama -artDF <- subset(artDF, artDF$taboo!='0.5') -table(artDF$taboo) -artDF$taboo <- as.logical(artDF$taboo) - -g <- ggplot(artDF, aes(x=revid, y=pct_revert, color=source)) + - geom_point() + - labs(x='Number of Revisions', y='Percent Reverted') - -g - -ggplot(artDF, aes(x=got_reverted, color=source)) + - geom_boxplot() - -g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=got_reverted, color=source)) + - geom_point(alpha=.2) + - geom_smooth() + - geom_rug(alpha=.2)+ - scale_x_continuous(trans='log') + - scale_y_continuous(trans='log') + - labs(x='Number of Revisions', y='Number Reverted') - -g - -g <- ggplot(artDF, aes(group=as.factor(taboo), x=revid, y=pct_revert, color=source)) + - geom_point(alpha=.2) + - geom_smooth() + - geom_rug(alpha=.2)+ - scale_x_continuous() + - scale_y_continuous() + - labs(x='Number of Revisions', y='Percent Reverted') - -g - -## pull edits that were later reverted, since they therefore didn't really contribute per se -revDF.clean.norev <- subset(revDF.clean, revDF.clean$got_reverted==FALSE) -revDF.clean.norev <- subset(revDF.clean.norev, revDF.clean.norev$revert != '') -table(revDF.clean.norev$anon) - -print('proportion of reverts by source') -cleanTable <- table(revDF.clean.norev$source, revDF.clean.norev$revert) -ftable(cleanTable) -prop.table(cleanTable, 1) -prop.table(cleanTable, 2) - -``` - -These results preliminarily suggest a log-log linear model. However, we observe that we do not have support in both samples across the full range of the data. The random sample contains a large number of zeroes. - -## H4: Anonymity - -H4 tests three limits on identifiability: -* contributing without an account -* contributing with a new account (or, being a relative newcomer) -* having a short user page. - -We want to make sure we have a reasonable amount of variation and handle zeroes correctly for these measures. -### Contributing without an account - -Proportion of Edits that are made without an account: -```{r} - -anonTable <- table(revDF.clean.norev$source, revDF.clean.norev$revert, revDF.clean.norev$anon) -anonTable -ftable(anonTable) -prop.table(ftable(anonTable),1) - -xTabAnon <- xtabs(~source+revert+anon,data=revDF.clean.norev) -ftable(xTabAnon) -prop.table(ftable(xTabAnon), 1) - -ggplot(revDF.clean.norev, aes(x=anon, color=source)) + - geom_boxplot() -``` - - -### Contributing with a new account -```{r H4descriptives} - -g <- ggplot(revDF.clean.norev, aes(x=editor_nth_edit_nocollapse)) + - geom_histogram() + - facet_grid(source~., scales='free_y') - -g - -g <- ggplot(revDF.clean.norev, aes(x=log(editor_nth_edit_nocollapse))) + - geom_histogram() + - facet_grid(source~., scales='free_y') - -g - -g <- ggplot(revDF.clean.norev, aes(x=editor_nth_edit_nocollapse)) + - geom_boxplot() + - facet_grid(source~., scales='free_y') - -g - -t <- subset(revDF.clean.norev, as.logical(revDF.clean.norev$anon)==TRUE) -mean(t$editor_nth_edit_nocollapse) - -t <- subset(revDF.clean.norev, as.logical(revDF.clean.norev$anon)==FALSE) -mean(t$editor_nth_edit_nocollapse) - -g <- ggplot(revDF.clean, aes(x=log(editor_nth_edit_nocollapse))) + - geom_boxplot() + - facet_grid(source~., scales='free_y') - -g - -``` - -### Having a short user page - -```{r} -summary(revDF.clean$userpage_text_chars) - -g <- ggplot(revDF.clean, aes(x=log1p(userpage_text_chars), group=source)) + - geom_histogram(binwidth = .5) + - facet_grid(source~., scales='free_y') - -g - -g <- ggplot(subset(revDF.clean, revDF.clean$userpage_text_chars < exp(4)), aes(x=log1p(userpage_text_chars), group=source)) + - geom_histogram(binwidth = .5) + - facet_grid(source~., scales='free_y') - -g - - -# look at last edit, what's the 'final' quality? -library(dplyr) -finalQDF <- qDF %>% group_by(encodedTitle) %>% filter(timestamp == max(timestamp)) - -g <- ggplot(finalQDF, aes(x=weighted_sum, y=source)) + - geom_boxplot() + - labs(x='Final Quality') - -g - -summary(artDF$got_reverted/artDF$revid) -``` - - -### Gender - -I used the API to find out a bit of user information -- are they emailable, whether gender was specified, and if it was specified, whether the person was female. - -```{r gender user data} - -table(revDF.clean$gender) -userDF <- data.frame('editor'=revDF.clean$editor, 'gender' = revDF.clean$gender, 'emailable' = revDF.clean$emailable, 'anon'=revDF.clean$anon) - -userDF <- subset(userDF, userDF$anon == 'false') -userDF <- unique(userDF) -table(userDF$gender) - -prop.table(table(revDF.clean$source, revDF.clean$gender), margin = 1) -gaveGenderDF <- subset(revDF.clean, revDF.clean$gender != 'unknown') -prop.table(table(gaveGenderDF$source, gaveGenderDF$gender), margin = 1) - -``` - - -### Being Emailable - - -```{r emailable user data} - -table(revDF.clean$emailable) - -prop.table(table(revDF.clean$source, revDF.clean$emailable), margin = 1) - -``` - -### Protections - -```{r protection} - - -g <- ggplot(artDF, aes(x=pct.prot, group=source)) + - geom_boxplot() + - labs(x='Protection Proportion') - -g - -artDF.prot <- subset(artDF, artDF$pct.prot > 0) - -g <- ggplot(artDF.prot, aes(x=pct.prot, group=source)) + - geom_boxplot() + - labs(x='Protection Proportion (non-zero only)') - -g - - -``` \ No newline at end of file +/annex/objects/SHA256E-s10456--a2f961c3e0484f5f253aefec4e79c1ede34a64c6a6e56c6f90647dc98809eabc.Rmd diff --git a/R_examples/EDA.pdf b/R_examples/EDA.pdf new file mode 100644 index 0000000..5eafb7b --- /dev/null +++ b/R_examples/EDA.pdf @@ -0,0 +1 @@ +/annex/objects/SHA256E-s14875097--de4c18a17632e4b432b8689afd182156bafac24a985616c5a5ccc1fa8bfc53bc.pdf diff --git a/R_examples/prepDF.R b/R_examples/prepDF.R index edd862f..c5c4d40 100644 --- a/R_examples/prepDF.R +++ b/R_examples/prepDF.R @@ -1,148 +1 @@ - -rm(list=ls()) - -##################### -#The purpose of this file is to load up the datasets and clean them for processing. -# -##################### - -####set globals and make helpers -#basePath = '/home/kaylea/Research/taboo/' -basePath = '/gscratch/comdata/users/kaylea/taboo/' -dataPath = paste0(basePath, 'data/') -rawPath = paste0(basePath, 'raw_data/') -botsFile <- paste0(rawPath, 'botList.tsv') -endOfRecords= '2022-06-02 20:15:46' #derived from end of the action logs -endOfRecords = strptime(endOfRecords, "%Y-%m-%d %H:%M:%S") -startOfRecords= '2008-09-20 05:23:14' -startOfRecords = strptime(startOfRecords, "%Y-%m-%d %H:%M:%S") - - - -library(dplyr) -library(sqldf) -library(lubridate) -library(data.table) -library(urltools) - - -#recipe from https://www.r-bloggers.com/2011/06/merge-all-files-in-a-directory-using-r-into-a-single-dataframe/ -readPileToDF <- function(path) { - file_list <- list.files(path) - print(file_list) - for (my_file in file_list){ - if (my_file == '_SUCCESS') { #spark metadata file, ignore - next - } - # if the merged dataset doesn't exist, create it - if (!exists("dataset")) { - print(paste0('Now Reading: ', path, my_file)) - dataset <- read.table(paste0(path, my_file), quote="\"", header=TRUE, sep="\t", stringsAsFactors=FALSE) - } - # if the merged dataset does exist, append to it - if (exists("dataset")){ - temp_dataset <-read.table(paste0(path, my_file), quote="\"", header=TRUE, sep="\t", stringsAsFactors=FALSE) - dataset<-rbind(dataset, temp_dataset) - rm(temp_dataset) - } - } - dataset <- unique(dataset) - return(dataset) -} - -# Part 1 - load and clean revisions data - - -revDF = readPileToDF(paste0(coefPath, 'revDataPlusUPL/')) -revDF$source <- "taboo" -#revDF.CTab$taboo <- 1 -revDF$userpage_text_chars[is.na(revDF$userpage_text_chars)] <- 0 -head(revDF.CTab) - -##drop unneeded fields -revDF.CTab$prediction <- NULL -revDF.CTab$filtered_title <- NULL -revDF.CTab$target <- NULL - -colnames(revDF.CTab) - -revDF <- revDF[!is.na(revDF$revid),] #drop any where revid is NA - -revDF <- merge(x=revDF, y=userDF, by='editor', all.x=TRUE) #left (outer) join: all of revDF, plus any matches in userDF - -## eliminate any articles in both: - -### prepare bot filter -botDF <- read.table(botsFile, sep='\t', quote='"', header=TRUE, stringsAsFactors=FALSE) -botDF <- unique(botDF) #strip out any repetitions -#botRoleDF <- read.table(botsRoleFile, sep='\t', quote='"', header=TRUE, stringsAsFactors=FALSE) -head(revDF) -head(botDF) -botDF$editor_id <- as.character(botDF$BotUserID) #just to make sure -revDF$editor_id <- as.character(revDF$editor_id) -head(revDF) -head(botDF) -revDF <- setDT(revDF) -botDF <- setDT(botDF) -revDF <- revDF[,isBot :=FALSE][botDF, isBot := TRUE, on= .(editor_id)] # this means: set column isBot to False. then, set the isBot to TRUE if a join could happen '.' means list. -isABot.tab <- table(revDF$isBot) - -## drop all bots here -revDF.clean <- subset(revDF, revDF$isBot==FALSE) -revDF <- NULL #so we don't use it accidentally -revDF.clean$loggedIn <- !(as.logical(revDF.clean$anon)) - -##### filtering done, now to do some summing-up - -### Weighting -##for each article, the weight of each revision for that article is (N_rev_total/N_total articles)/N_art_revnum -#two criteria this meets: -#sum(weights) = total_revs -#sum(weights for given article) = sum(weights for all other articles) - -numEdits <- revDF.clean %>% group_by(encodedTitle) %>% dplyr::summarize(numEdits=length(revid)) ##articlewise revisions count -numEditors <- revDF.clean %>% group_by(encodedTitle) %>% dplyr::summarize(numEditors=length(unique(editor))) ###articlewise editors count, including IP addresses -n.revs <- length(revDF.clean$revid) ## total number of revisions -n.arts <- length(numEdits$encodedTitle) ## total number of articles -revDF.clean <- merge(revDF.clean, numEdits, by="encodedTitle") -revDF.clean <- merge(revDF.clean, numEditors, by="encodedTitle") -revDF.clean$weight <- (n.revs/n.arts)/revDF.clean$numEdits - - -revDF.clean$ngramWeight <- revDF.clean$count #wasn't very descriptive -revDF.clean <- revDF.clean %>% mutate(got_reverted = - case_when(is.na(reverted_by) ~ FALSE, TRUE ~ TRUE)) - -table(revDF.clean$anon) -revDF.clean <- rbind(subset(revDF.clean, revDF.clean$anon=='true'), subset(revDF.clean, revDF.clean$anon=='false')) ##small number of NAs (187), look like parse problems -table(revDF.clean$anon) - - -##### dropping items with missing revids; if this happens, find out why -###revDF.clean <- revDF.clean[!is.na(revDF.clean$revid)] - - - - -artDF <- revDF.clean %>% dplyr::group_by(encodedTitle) %>% dplyr::summarize( - across(revid, length), - across(got_reverted, sum), - across(date_time, min) -) - -titleSampleDF <- data.frame('encodedTitle' = revDF.clean$encodedTitle, 'source'=revDF.clean$source) -titleSampleDF <- unique(titleSampleDF) - -artDF <- merge(artDF, titleSampleDF, by='encodedTitle', all.x=TRUE) #which sample is it from -artDF$min.birthday <- strptime(artDF$date_time, "%Y-%m-%d %H:%M:%S") -artDF$startOfRecords <- startOfRecords -artDF$birthOrLog <- pmax(artDF$min.birthday, artDF$startOfRecords) #birthday or beginning of records, whichever comes later -artDF$secondsOldLog <- as.numeric(difftime(strptime(endOfRecords, "%Y-%m-%d %H:%M:%S"),strptime(artDF$birthOrLog, "%Y-%m-%d %H:%M:%S"), units="secs")) -## how many seconds old is each article inside the logged scope? - -artDF.prot$pct.prot <- artDF.prot$duration/artDF.prot$secondsOldLog ## what proportion of its observed life was the article protected? - -artDF$pct.prot <- artDF.prot$pct.prot - -print("saving full image") -save.image(paste0(dataPath, "dataset1.RData"), version=2) 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