Threshhold_analysis_R.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.0 (2019-04-26)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 9 (stretch)\n",
       "\n",
       "Matrix products: default\n",
       "BLAS:   /srv/home/nathante/.local/lib/R/lib/libRblas.so\n",
       "LAPACK: /srv/home/nathante/.local/lib/R/lib/libRlapack.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] Rcpp_1.0.1           digest_0.6.18        zeallot_0.1.0       \n",
       " [4] crayon_1.3.4         IRdisplay_0.7.0.9000 repr_1.0.0          \n",
       " [7] jsonlite_1.6         backports_1.1.4      evaluate_0.13       \n",
       "[10] pillar_1.4.0         rlang_0.4.1          uuid_0.1-2          \n",
       "[13] vctrs_0.2.0          IRkernel_1.0.2       tools_3.6.0         \n",
       "[16] compiler_3.6.0       base64enc_0.1-3      pbdZMQ_0.3-3        \n",
       "[19] htmltools_0.3.6     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 method overwritten by 'xts':\n",
      "  method     from\n",
      "  as.zoo.xts zoo \n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "Registered S3 methods overwritten by 'forecast':\n",
      "  method             from    \n",
      "  fitted.fracdiff    fracdiff\n",
      "  residuals.fracdiff fracdiff\n",
      "Loading required package: greybox\n",
      "Package \"greybox\", v0.5.6 loaded.\n",
      "This is package \"smooth\", v2.5.4\n",
      "Loading required package: Matrix\n",
      "Loading required package: Rcpp\n",
      "Registered S3 method overwritten by 'rstanarm':\n",
      "  method         from   \n",
      "  nobs.stanmvreg insight\n",
      "rstanarm (Version 2.19.2, packaged: 2019-10-01 20:20:33 UTC)\n",
      "- Do not expect the default priors to remain the same in future rstanarm versions.\n",
      "Thus, R scripts should specify priors explicitly, even if they are just the defaults.\n",
      "- For execution on a local, multicore CPU with excess RAM we recommend calling\n",
      "options(mc.cores = parallel::detectCores())\n",
      "- bayesplot theme set to bayesplot::theme_default()\n",
      "   * Does _not_ affect other ggplot2 plots\n",
      "   * See ?bayesplot_theme_set for details on theme setting\n",
      "Warning message in `[.data.table`(cutoffs, (wiki.db == wiki) & (deploy.dt == deploy), :\n",
      "“0.998000 (type 'double') at RHS position 1 taken as TRUE when assigning to type 'logical' (column 44 named 'goodfaith.verylikelybad.max.value')”Warning message in `[.data.table`(cutoffs, (wiki.db == wiki) & (deploy.dt == deploy), :\n",
      "“0.998000 (type 'double') at RHS position 1 taken as TRUE when assigning to type 'logical' (column 44 named 'goodfaith.verylikelybad.max.value')”Warning message in eval(family$initialize):\n",
      "“non-integer #successes in a binomial glm!”Warning message in eval(family$initialize):\n",
      "“non-integer #successes in a binomial glm!”Warning message in eval(family$initialize):\n",
      "“non-integer #successes in a binomial glm!”"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(gridExtra)\n",
    "library(grid)\n",
    "library(data.table)\n",
    "library(sjPlot)\n",
    "source(\"helper.R\")\n",
    "source(\"ob_util.R\")\n",
    "source(\"threshhold_analysis.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(bayesplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cairo_pdf(\"model_1_slopes_rank.pdf\",height=90,width=12)\n",
    "mcmc_rank_hist(model.1.slopes)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cairo_pdf(\"model_1_kinks_rank.pdf\",height=90,width=12)\n",
    "mcmc_rank_hist(model.1.kinks)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cairo_pdf(\"model_1_morekinks_rank.pdf\",height=90,width=12)\n",
    "mcmc_rank_hist(model.1.more.kinks)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(model.1.slopes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.1.pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## make synthetic data \n",
    "prob.damaging <- 0:1000/1000\n",
    "syn.likelybad.t <- df[wiki.db=='enwiki']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_model(model.1,type='eff',terms=c('prob.damaging','pred.damaging.likelybad','pred.damaging.verylikelybad'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoffs <- load.cutoffs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff.vars <- c(\"damaging.maybebad.min.value\",\n",
    "                 \"damaging.maybebad.max.value\", \n",
    "                \"damaging.likelybad.min.value\",\n",
    "                \"damaging.likelybad.max.value\",\n",
    "                \"damaging.verylikelybad.min.value\",\n",
    "                \"damaging.verylikelybad.max.value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syn.cutoffs <- list()\n",
    "for(var in cutoff.vars){\n",
    "    syn.cutoffs[[var]] <- first(cutoffs[!is.na(as.numeric(cutoffs[[var]])), var, with=F][[var]])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata <- data.table(prob.damaging = 0:1000/1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.1.post$formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata[,\":=\"(pred.damaging.likelybad=as.factor(prob.damaging >= syn.cutoffs$damaging.likelybad.min.value),\n",
    "              pred.damaging.verylikelybad=as.factor(prob.damaging >= syn.cutoffs$damaging.verylikelybad.min.value), \n",
    "              pred.damaging.maybebad=as.factor(prob.damaging >= syn.cutoffs$damaging.maybebad.min.value), \n",
    "              has.rcfilters.watchlist=FALSE,\n",
    "              wiki.db='enwiki'\n",
    "             )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata2 <- copy(syndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata2[,has.rcfilters.watchlist:=TRUE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata <- rbindlist(list(syndata,syndata2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syndata <- syndata[,\":=\"(has.rcfilters.watchlist = as.factor(has.rcfilters.watchlist),\n",
    "                        post.cutoff = has.rcfilters.watchlist,\n",
    "                        post.cutoff.fact = as.factor(has.rcfilters.watchlist))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted <- posterior_predict(model.1.kinks,newdata=syndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted.interval <- predictive_interval(model.1.kinks,newdata=syndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>1</dt>\n",
       "\t\t<dd>0.000126145922513434</dd>\n",
       "\t<dt>2</dt>\n",
       "\t\t<dd>0.000126426970443346</dd>\n",
       "\t<dt>3</dt>\n",
       "\t\t<dd>0.000126708201240898</dd>\n",
       "\t<dt>4</dt>\n",
       "\t\t<dd>0.000126989621491148</dd>\n",
       "\t<dt>5</dt>\n",
       "\t\t<dd>0.000127271237925565</dd>\n",
       "\t<dt>6</dt>\n",
       "\t\t<dd>0.000127553057423266</dd>\n",
       "</dl>\n"
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      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1] 0.000126145922513434\n",
       "\\item[2] 0.000126426970443346\n",
       "\\item[3] 0.000126708201240898\n",
       "\\item[4] 0.000126989621491148\n",
       "\\item[5] 0.000127271237925565\n",
       "\\item[6] 0.000127553057423266\n",
       "\\end{description*}\n"
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      "text/markdown": [
       "1\n",
       ":   0.0001261459225134342\n",
       ":   0.0001264269704433463\n",
       ":   0.0001267082012408984\n",
       ":   0.0001269896214911485\n",
       ":   0.0001272712379255656\n",
       ":   0.000127553057423266\n",
       "\n"
      ],
      "text/plain": [
       "           1            2            3            4            5            6 \n",
       "0.0001261459 0.0001264270 0.0001267082 0.0001269896 0.0001272712 0.0001275531 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(predicted$se.fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "syn.z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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45Lba2xcLFy5Ltc2J/8gVf6/C6bcVtr86RqpiAGUEgEOwDI16mLwG7W3d2q3mss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\nequrq88777xvf/vbfr8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gYIEzKTm7nz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eubOVnduE1Wq69S7jhRdtrbPfvX7zxtq6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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p <- ggplot(syndata) \n",
    "p <- p + geom_ribbon(aes(ymax=prob.reverted.upper,ymin=prob.reverted.lower,x=prob.damaging, group=pre.cutoff.fact,fill=pre.cutoff.fact),alpha=0.5)\n",
    "p <- p + geom_line(aes(y=prob.reverted, x=prob.damaging,color=pre.cutoff.fact,group=pre.cutoff.fact))\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:  glm(formula = formula.1, family = binomial(link = \"probit\"), \n",
       "    data = df.cutoff, weights = df.cutoff$weight)\n",
       "\n",
       "Coefficients:\n",
       "                                        (Intercept)  \n",
       "                                           -1.75018  \n",
       "                                      prob.damaging  \n",
       "                                            2.43882  \n",
       "                                pre.cutoff.factTRUE  \n",
       "                                           -0.01289  \n",
       "                        pred.damaging.likelybadTRUE  \n",
       "                                            0.18753  \n",
       "                    pred.damaging.verylikelybadTRUE  \n",
       "                                            0.85734  \n",
       "                  prob.damaging:pre.cutoff.factTRUE  \n",
       "                                           -0.05629  \n",
       "    pre.cutoff.factTRUE:pred.damaging.likelybadTRUE  \n",
       "                                            0.02294  \n",
       "pre.cutoff.factTRUE:pred.damaging.verylikelybadTRUE  \n",
       "                                           -0.05450  \n",
       "\n",
       "Degrees of Freedom: 627258 Total (i.e. Null);  627251 Residual\n",
       "  (16841 observations deleted due to missingness)\n",
       "Null Deviance:\t    4143000 \n",
       "Residual Deviance: 3150000 \tAIC: 3121000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>damaging.likelybad.max</th><th scope=col>damaging.likelybad.min</th><th scope=col>damaging.likelybad.min.value</th><th scope=col>damaging.likelybad.max.value</th><th scope=col>deploy.dt</th><th scope=col>has.ores</th><th scope=col>has.rcfilters.watchlist</th><th scope=col>has.rcfilters.watchlist</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                            </td><td>maximum recall @ prevision &gt;= 0.45        </td><td><span style=white-space:pre-wrap>     </span></td><td>1                                            </td><td>2018-05-09 18:04:00                          </td><td>TRUE                                         </td><td>FALSE                                        </td><td>FALSE                                        </td></tr>\n",
       "\t<tr><td>1                                    </td><td>maximum recall @ precision &gt;= 0.45</td><td>0.443                                </td><td>1                                    </td><td>2018-07-17 22:24:00                  </td><td>TRUE                                 </td><td> TRUE                                </td><td> TRUE                                </td></tr>\n",
       "\t<tr><td>1                                    </td><td>maximum recall @ precision &gt;= 0.45</td><td>0.492                                </td><td>1                                    </td><td>2019-09-03 11:07:00                  </td><td>TRUE                                 </td><td> TRUE                                </td><td> TRUE                                </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " damaging.likelybad.max & damaging.likelybad.min & damaging.likelybad.min.value & damaging.likelybad.max.value & deploy.dt & has.ores & has.rcfilters.watchlist & has.rcfilters.watchlist\\\\\n",
       "\\hline\n",
       "\t 1                                  & maximum recall @ prevision >= 0.45 &                                    & 1                                  & 2018-05-09 18:04:00                & TRUE                               & FALSE                              & FALSE                             \\\\\n",
       "\t 1                                  & maximum recall @ precision >= 0.45 & 0.443                              & 1                                  & 2018-07-17 22:24:00                & TRUE                               &  TRUE                              &  TRUE                             \\\\\n",
       "\t 1                                  & maximum recall @ precision >= 0.45 & 0.492                              & 1                                  & 2019-09-03 11:07:00                & TRUE                               &  TRUE                              &  TRUE                             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "damaging.likelybad.max | damaging.likelybad.min | damaging.likelybad.min.value | damaging.likelybad.max.value | deploy.dt | has.ores | has.rcfilters.watchlist | has.rcfilters.watchlist | \n",
       "|---|---|---|\n",
       "| 1                                  | maximum recall @ prevision >= 0.45 |                                    | 1                                  | 2018-05-09 18:04:00                | TRUE                               | FALSE                              | FALSE                              | \n",
       "| 1                                  | maximum recall @ precision >= 0.45 | 0.443                              | 1                                  | 2018-07-17 22:24:00                | TRUE                               |  TRUE                              |  TRUE                              | \n",
       "| 1                                  | maximum recall @ precision >= 0.45 | 0.492                              | 1                                  | 2019-09-03 11:07:00                | TRUE                               |  TRUE                              |  TRUE                              | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  damaging.likelybad.max damaging.likelybad.min            \n",
       "1 1                      maximum recall @ prevision >= 0.45\n",
       "2 1                      maximum recall @ precision >= 0.45\n",
       "3 1                      maximum recall @ precision >= 0.45\n",
       "  damaging.likelybad.min.value damaging.likelybad.max.value deploy.dt          \n",
       "1                              1                            2018-05-09 18:04:00\n",
       "2 0.443                        1                            2018-07-17 22:24:00\n",
       "3 0.492                        1                            2019-09-03 11:07:00\n",
       "  has.ores has.rcfilters.watchlist has.rcfilters.watchlist\n",
       "1 TRUE     FALSE                   FALSE                  \n",
       "2 TRUE      TRUE                    TRUE                  \n",
       "3 TRUE      TRUE                    TRUE                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoffs[wiki.db=='arwiki',c(\"damaging.likelybad.max\",\"damaging.likelybad.min\",\"damaging.likelybad.min.value\",\"damaging.likelybad.max.value\",\"deploy.dt\",\"has.ores\",\"has.rcfilters.watchlist\",'has.rcfilters.watchlist'),with=F]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "old.cutoffs <- fread(file.path(data.dir,'ores_rcfilters_cutoffs.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### old.cutoffs[wiki_db=='arwiki']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.enwiki <- dt.all[ (date==parse.date.iso(\"2018-07-17 22:24:00\")) & (wiki.db=='enwiki')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>date</th><th scope=col>wiki.db</th><th scope=col>N</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2018-05-09 18:04:00</td><td>arwiki             </td><td> 106332            </td></tr>\n",
       "\t<tr><td>2018-05-09 18:04:00</td><td>cawiki             </td><td>  52928            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>cswiki             </td><td> 189924            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>enwiki             </td><td>2871512            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>etwiki             </td><td> 153958            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>fiwiki             </td><td> 186830            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>hewiki             </td><td> 327992            </td></tr>\n",
       "\t<tr><td>2018-05-09 18:04:00</td><td>huwiki             </td><td>  56900            </td></tr>\n",
       "\t<tr><td>2018-05-09 18:04:00</td><td>lvwiki             </td><td>  42504            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>nlwiki             </td><td> 361116            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>plwiki             </td><td> 286356            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>ptwiki             </td><td> 478632            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>rowiki             </td><td> 182350            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>sqwiki             </td><td>  95018            </td></tr>\n",
       "\t<tr><td>2018-03-14 17:18:00</td><td>trwiki             </td><td> 171472            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " date & wiki.db & N\\\\\n",
       "\\hline\n",
       "\t 2018-05-09 18:04:00 & arwiki              &  106332            \\\\\n",
       "\t 2018-05-09 18:04:00 & cawiki              &   52928            \\\\\n",
       "\t 2018-03-14 17:18:00 & cswiki              &  189924            \\\\\n",
       "\t 2018-03-14 17:18:00 & enwiki              & 2871512            \\\\\n",
       "\t 2018-03-14 17:18:00 & etwiki              &  153958            \\\\\n",
       "\t 2018-03-14 17:18:00 & fiwiki              &  186830            \\\\\n",
       "\t 2018-03-14 17:18:00 & hewiki              &  327992            \\\\\n",
       "\t 2018-05-09 18:04:00 & huwiki              &   56900            \\\\\n",
       "\t 2018-05-09 18:04:00 & lvwiki              &   42504            \\\\\n",
       "\t 2018-03-14 17:18:00 & nlwiki              &  361116            \\\\\n",
       "\t 2018-03-14 17:18:00 & plwiki              &  286356            \\\\\n",
       "\t 2018-03-14 17:18:00 & ptwiki              &  478632            \\\\\n",
       "\t 2018-03-14 17:18:00 & rowiki              &  182350            \\\\\n",
       "\t 2018-03-14 17:18:00 & sqwiki              &   95018            \\\\\n",
       "\t 2018-03-14 17:18:00 & trwiki              &  171472            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "date | wiki.db | N | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2018-05-09 18:04:00 | arwiki              |  106332             | \n",
       "| 2018-05-09 18:04:00 | cawiki              |   52928             | \n",
       "| 2018-03-14 17:18:00 | cswiki              |  189924             | \n",
       "| 2018-03-14 17:18:00 | enwiki              | 2871512             | \n",
       "| 2018-03-14 17:18:00 | etwiki              |  153958             | \n",
       "| 2018-03-14 17:18:00 | fiwiki              |  186830             | \n",
       "| 2018-03-14 17:18:00 | hewiki              |  327992             | \n",
       "| 2018-05-09 18:04:00 | huwiki              |   56900             | \n",
       "| 2018-05-09 18:04:00 | lvwiki              |   42504             | \n",
       "| 2018-03-14 17:18:00 | nlwiki              |  361116             | \n",
       "| 2018-03-14 17:18:00 | plwiki              |  286356             | \n",
       "| 2018-03-14 17:18:00 | ptwiki              |  478632             | \n",
       "| 2018-03-14 17:18:00 | rowiki              |  182350             | \n",
       "| 2018-03-14 17:18:00 | sqwiki              |   95018             | \n",
       "| 2018-03-14 17:18:00 | trwiki              |  171472             | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   date                wiki.db N      \n",
       "1  2018-05-09 18:04:00 arwiki   106332\n",
       "2  2018-05-09 18:04:00 cawiki    52928\n",
       "3  2018-03-14 17:18:00 cswiki   189924\n",
       "4  2018-03-14 17:18:00 enwiki  2871512\n",
       "5  2018-03-14 17:18:00 etwiki   153958\n",
       "6  2018-03-14 17:18:00 fiwiki   186830\n",
       "7  2018-03-14 17:18:00 hewiki   327992\n",
       "8  2018-05-09 18:04:00 huwiki    56900\n",
       "9  2018-05-09 18:04:00 lvwiki    42504\n",
       "10 2018-03-14 17:18:00 nlwiki   361116\n",
       "11 2018-03-14 17:18:00 plwiki   286356\n",
       "12 2018-03-14 17:18:00 ptwiki   478632\n",
       "13 2018-03-14 17:18:00 rowiki   182350\n",
       "14 2018-03-14 17:18:00 sqwiki    95018\n",
       "15 2018-03-14 17:18:00 trwiki   171472"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.all[,.(.N),by=.(date,wiki.db)][N>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki.db</th><th scope=col>event.timestamp</th><th scope=col>page.id</th><th scope=col>page.title</th><th scope=col>user.id</th><th scope=col>user.text</th><th scope=col>event.user.isbot1</th><th scope=col>event.user.isbot2</th><th scope=col>revision.id</th><th scope=col>revision.is.identity.reverted</th><th scope=col>⋯</th><th scope=col>damaging.verylikelybad.max.value</th><th scope=col>goodfaith.good.max.value</th><th scope=col>goodfaith.bad.min.value</th><th scope=col>damaging.likelybad.max.value</th><th scope=col>goodfaith.likelygood.max.value</th><th scope=col>goodfaith.likelybad.max.value</th><th scope=col>damaging.likelygood.max.value</th><th scope=col>goodfaith.maybebad.min.value</th><th scope=col>goodfaith.bad.max.value</th><th scope=col>next.deploy.dt</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>enwiki             </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>⋯                  </td><td>NA                 </td><td>NA                 </td><td>NA                 </td><td>1                  </td><td>1                  </td><td>                   </td><td>0.949              </td><td>0                  </td><td>NA                 </td><td>2019-09-03 11:07:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " wiki.db & event.timestamp & page.id & page.title & user.id & user.text & event.user.isbot1 & event.user.isbot2 & revision.id & revision.is.identity.reverted & ⋯ & damaging.verylikelybad.max.value & goodfaith.good.max.value & goodfaith.bad.min.value & damaging.likelybad.max.value & goodfaith.likelygood.max.value & goodfaith.likelybad.max.value & damaging.likelygood.max.value & goodfaith.maybebad.min.value & goodfaith.bad.max.value & next.deploy.dt\\\\\n",
       "\\hline\n",
       "\t enwiki              & NA                  & NA                  & NA                  & NA                  & NA                  & NA                  & NA                  & NA                  & NA                  & ⋯                   & NA                  & NA                  & NA                  & 1                   & 1                   &                     & 0.949               & 0                   & NA                  & 2019-09-03 11:07:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki.db | event.timestamp | page.id | page.title | user.id | user.text | event.user.isbot1 | event.user.isbot2 | revision.id | revision.is.identity.reverted | ⋯ | damaging.verylikelybad.max.value | goodfaith.good.max.value | goodfaith.bad.min.value | damaging.likelybad.max.value | goodfaith.likelygood.max.value | goodfaith.likelybad.max.value | damaging.likelygood.max.value | goodfaith.maybebad.min.value | goodfaith.bad.max.value | next.deploy.dt | \n",
       "|---|\n",
       "| enwiki              | NA                  | NA                  | NA                  | NA                  | NA                  | NA                  | NA                  | NA                  | NA                  | ⋯                   | NA                  | NA                  | NA                  | 1                   | 1                   |                     | 0.949               | 0                   | NA                  | 2019-09-03 11:07:00 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  wiki.db event.timestamp page.id page.title user.id user.text\n",
       "1 enwiki  NA              NA      NA         NA      NA       \n",
       "  event.user.isbot1 event.user.isbot2 revision.id revision.is.identity.reverted\n",
       "1 NA                NA                NA          NA                           \n",
       "  ⋯ damaging.verylikelybad.max.value goodfaith.good.max.value\n",
       "1 ⋯ NA                               NA                      \n",
       "  goodfaith.bad.min.value damaging.likelybad.max.value\n",
       "1 NA                      1                           \n",
       "  goodfaith.likelygood.max.value goodfaith.likelybad.max.value\n",
       "1 1                                                           \n",
       "  damaging.likelygood.max.value goodfaith.maybebad.min.value\n",
       "1 0.949                         0                           \n",
       "  goodfaith.bad.max.value next.deploy.dt     \n",
       "1 NA                      2019-09-03 11:07:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.enwiki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki.db</th><th scope=col>V1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>mediawikiwiki      </td><td>2018-06-16 00:15:00</td></tr>\n",
       "\t<tr><td>test2wiki          </td><td>2018-06-16 00:15:00</td></tr>\n",
       "\t<tr><td>testwiki           </td><td>2018-06-16 00:15:00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " wiki.db & V1\\\\\n",
       "\\hline\n",
       "\t mediawikiwiki       & 2018-06-16 00:15:00\\\\\n",
       "\t test2wiki           & 2018-06-16 00:15:00\\\\\n",
       "\t testwiki            & 2018-06-16 00:15:00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki.db | V1 | \n",
       "|---|---|---|\n",
       "| mediawikiwiki       | 2018-06-16 00:15:00 | \n",
       "| test2wiki           | 2018-06-16 00:15:00 | \n",
       "| testwiki            | 2018-06-16 00:15:00 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  wiki.db       V1                 \n",
       "1 mediawikiwiki 2018-06-16 00:15:00\n",
       "2 test2wiki     2018-06-16 00:15:00\n",
       "3 testwiki      2018-06-16 00:15:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.all[rcfilters.watchlist.enabled == T, max(date),by=.(wiki.db)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in .prepareFastSubset(isub = isub, x = x, enclos = parent.frame(), : RHS of == is length 0 which is not 1 or nrow (5563898). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.\n",
     "output_type": "error",
     "traceback": [
      "Error in .prepareFastSubset(isub = isub, x = x, enclos = parent.frame(), : RHS of == is length 0 which is not 1 or nrow (5563898). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.\nTraceback:\n",
      "1. dt.all[date == dt.all[rcfilters.watchlist.enabled == T, max(date), \n .     by = .(wiki.db)]$date]",
      "2. `[.data.table`(dt.all, date == dt.all[rcfilters.watchlist.enabled == \n .     T, max(date), by = .(wiki.db)]$date)",
      "3. .prepareFastSubset(isub = isub, x = x, enclos = parent.frame(), \n .     notjoin = notjoin, verbose = verbose)",
      "4. stop(\"RHS of \", operator, \" is length \", length(RHS), \" which is not 1 or nrow (\", \n .     nrow(x), \"). For robustness, no recycling is allowed (other than of length 1 RHS). Consider %in% instead.\")"
     ]
    }
   ],
   "source": [
    "# look just at the cutoff that enabled RCFilters on watchlist\n",
    "dt <- dt.all[date=="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt <- dt[, pre_cutoff_1 := factor(pre_cutoff_1, levels = c(\"Before design change\", \"After design change\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt [ (event_user_isbot1 | event_user_isbot2) & revision_is_identity_reverted, revert_tool := 'bot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "ores_cutoffs <- cutoffs[,.(date=first(.SD[has_ores==T]$deploy_dt)),by=c(\"wiki_db\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "ores_sample <- dt[ores_cutoffs,.SD,on=c('date','wiki_db')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_x_names <- c('1 second','5 seconds','1 minute','5 minutes','1 hour','24 hours','1 week','1 month')\n",
    "duration_x_values <- c(1,5,60,60*5,60*60,60*60*24, 60*60*24*7, 60*60*24*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "p <- ggplot(ores_sample, aes(x=time_to_revert,group=revert_tool,color=revert_tool, y=..count..)) + geom_density() + facet_grid(wiki_db~pre_cutoff_1, scales='free_y') + scale_x_log10(breaks=duration_x_values,labels=duration_x_names) + theme(axis.text.x = element_text(angle=90),legend.position='top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 31 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”"
     ]
    }
   ],
   "source": [
    "ggsave('../ores_bias_plots/time_to_revert_by_tool_ores_cuttoff.pdf', p,width=14, height=300, units='cm',limitsize=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "enwiki_ores_cutoff <- first(cutoffs[(wiki_db == 'enwiki') & (has_ores=T)]$deploy_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "enwiki_ores_sample <- dt[ (wiki_db == 'enwiki') & (date == enwiki_ores_cutoff)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "enwiki_ores_sample_reverts <- enwiki_ores_sample[revision_is_identity_reverted == T]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 11 rows containing non-finite values (stat_density).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AsKAAAAwDwgjwMAAACYCQQ7AAAAADOB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6X+3pfXX4+TqP0zXX5ZzjdMx2OufwPI/7WGVG9ETkcogAACAASURBVO1q4dvVwqEo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\ndmC7agAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(enwiki_ores_sample_reverts, aes(x=time_to_revert,group=revert_tool,color=revert_tool, y=..count..)) + geom_density() + facet_grid(pre_cutoff_1~wiki_db) + scale_x_log10(breaks=duration_x_values,labels=duration_x_names) + theme(axis.text.x = element_text(angle=90), legend.position='top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "watchlist_cutoffs <- cutoffs[,.(date=first(.SD[has_rcfilters_watchlist==T]$deploy_dt)),by=c(\"wiki_db\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "watchlist_sample <- dt[watchlist_cutoffs,.SD,on=c('date','wiki_db')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki_db</th><th scope=col>date</th></tr></thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " wiki\\_db & date\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki_db | date | \n",
       "||\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     wiki_db date"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "watchlist_cutoffs[wiki_db =='fawiki']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
      "“number of rows of result is not a multiple of vector length (arg 2)”Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
      "“number of rows of result is not a multiple of vector length (arg 2)”Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
      "“number of rows of result is not a multiple of vector length (arg 2)”Warning message in cbind(parts$left, ellip_h, parts$right, deparse.level = 0L):\n",
      "“number of rows of result is not a multiple of vector length (arg 2)”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki_db</th><th scope=col>date</th><th scope=col>event_timestamp</th><th scope=col>page_id</th><th scope=col>page_title</th><th scope=col>user_id</th><th scope=col>user_text</th><th scope=col>event_user_isbot1</th><th scope=col>event_user_isbot2</th><th scope=col>revision_id</th><th scope=col>⋯</th><th scope=col>period_start</th><th scope=col>period_end</th><th scope=col>pre_cutoff</th><th scope=col>commitsha</th><th scope=col>has_ores</th><th scope=col>has_rcfilters</th><th scope=col>has_rcfilters_watchlist</th><th scope=col>commit_dt</th><th scope=col>deploy_gap</th><th scope=col>pre_cutoff_1</th></tr></thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllll}\n",
       " wiki\\_db & date & event\\_timestamp & page\\_id & page\\_title & user\\_id & user\\_text & event\\_user\\_isbot1 & event\\_user\\_isbot2 & revision\\_id & ⋯ & period\\_start & period\\_end & pre\\_cutoff & commitsha & has\\_ores & has\\_rcfilters & has\\_rcfilters\\_watchlist & commit\\_dt & deploy\\_gap & pre\\_cutoff\\_1\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki_db | date | event_timestamp | page_id | page_title | user_id | user_text | event_user_isbot1 | event_user_isbot2 | revision_id | ⋯ | period_start | period_end | pre_cutoff | commitsha | has_ores | has_rcfilters | has_rcfilters_watchlist | commit_dt | deploy_gap | pre_cutoff_1 | \n",
       "||\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     wiki_db date event_timestamp page_id page_title user_id user_text\n",
       "     event_user_isbot1 event_user_isbot2 revision_id ⋯ period_start period_end\n",
       "     pre_cutoff commitsha has_ores has_rcfilters has_rcfilters_watchlist\n",
       "     commit_dt deploy_gap pre_cutoff_1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt[ (wiki_db=='fawiki') & (event_timestamp > watchlist_cutoffs[wiki_db=='fawiki']$date)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "p <- ggplot(watchlist_sample, aes(x=time_to_revert,group=revert_tool,color=revert_tool,y=..count..)) + geom_density() + facet_grid(wiki_db~pre_cutoff_1, scales='free_y') + scale_x_log10(breaks=duration_x_values,labels=duration_x_names) + theme(axis.text.x = element_text(angle=90), legend.position='top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 26 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”Warning message:\n",
      "“Groups with fewer than two data points have been dropped.”"
     ]
    }
   ],
   "source": [
    "ggsave(\"../ores_bias_plots/time_to_revert_by_tool_watchlist_cuttoff.pdf\",p,width=14,height=300,units='cm',limitsize=F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "enwiki_watchlist_cutoff <- first(cutoffs[(wiki_db == 'enwiki') & (has_rcfilters_watchlist == T)]$deploy_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "enwiki_watchlist_sample <- dt[ (wiki_db == 'enwiki') & (date == enwiki_watchlist_cutoff)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 9 rows containing non-finite values (stat_density).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MMqbjR3OqtbUIADw9u7Ze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\nUNBNGilrQsULkc1m83g8wcHBNfyxIKS6hrsAb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o4ZEHVfW/uFmtqd/GZzByywr1YdmV+0HQDWRed8GDsNqzrErReixgLA29XH+A6C+uJI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\nAAAAQCL+B3uNhWQ/wCasAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(enwiki_watchlist_sample, aes(x=time_to_revert,group=revert_tool,color=revert_tool,y=..count..)) + geom_density() + facet_grid(pre_cutoff_1~wiki_db)  + scale_x_log10(breaks=duration_x_values,labels=duration_x_names) + theme(axis.text.x = element_text(angle=90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample <- watchlist_sample\n",
    "wiki <- 'enwiki'\n",
    "x <- 'time_to_revert'\n",
    "group <- 'revert_tool'\n",
    "y <- \"..count..\"\n",
    "breaks <- duration_x_values\n",
    "labels <- duration_x_names\n",
    "facet_row <- 'pre_cutoff_1'\n",
    "facet_col <- 'revert_tool'\n",
    "bins <- 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "wikis <- unique(sample$wiki_db)\n",
    "tools <- unique(sample$revert_tool)[2:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z5rol2k5SM6dBixPOxFKmpjWC+hSFVVYSqSnGf/g+0BGt6ym9WEKBEk0vNTTfMz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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tool <- 'undo'\n",
    "wiki <- 'cswiki'\n",
    "ttr_grid_plot(sample,wiki,tool,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aliasing might be misleading so we can do it again using kernels: ..density.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 2 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 2 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 2 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 2 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 6 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 6 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 6 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 6 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 10 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 11 rows containing non-finite values (stat_density).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 11 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Transformation introduced infinite values in continuous x-axis”Warning message:\n",
      "“Removed 11 rows containing non-finite values (stat_density).”"
     ]
    }
   ],
   "source": [
    "hist_plots <- list()\n",
    "density_plots <- list()\n",
    "combined.plots <- list()\n",
    "i <- 1\n",
    "for(wiki in wikis){\n",
    "    for(tool in tools){\n",
    "        hist_plots[[i]] <- ttr_histogram_plot(sample,wiki,tool)\n",
    "        density_plots[[i]] <- ttr_density_plot(sample,wiki,tool)\n",
    "        combined.plots[[i]] <- ttr_grid_plot(sample,wiki,tool)\n",
    "        i <- i + 1\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_p <- marrangeGrob(grobs=hist_plots,nrow=1,ncol=length(tools))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "dens_p <- marrangeGrob(grobs=density_plots,nrow=1,ncol=length(tools))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "combin_p <- marrangeGrob(grobs=combined.plots, nrow=1, ncol=length(tools))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave('../ores_bias_plots/time_to_revert_hist_grid.pdf',width=12,height=8,plot=hist_p)\n",
    "ggsave('../ores_bias_plots/time_to_revert_density_grid.pdf',width=12,height=8,plot=dens_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave('../ores_bias_plots/time_to_revert_grid.pdf',width=12,height=8,plot=combin_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "wikis <- unique(dt$wiki_db)\n",
    "tools <- unique(dt$revert_tool)\n",
    "n.wikis <- length(wikis)\n",
    "n.tools <- length(tools)\n",
    "\n",
    "watchlist.utest.grid <- data.table(expand.grid(wiki=wikis,tool=tools),median.pre=NaN, median.post = NaN,U.p.value =NaN,mean.pre=NaN, mean.post=NaN, T.p.value=NaN)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in wilcox.test.default(data.pre, data.post, correct = T, alternative = alt):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(data.pre, data.post, correct = T, alternative = alt):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(data.pre, data.post, correct = T, alternative = alt):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(data.pre, data.post, correct = T, alternative = alt):\n",
      "“cannot compute exact p-value with ties”Warning message in wilcox.test.default(data.pre, data.post, correct = T, alternative = alt):\n",
      "“cannot compute exact p-value with ties”"
     ]
    }
   ],
   "source": [
    "i <- 1\n",
    "for(iwiki in wikis){\n",
    "    for(itool in tools){\n",
    "        data.pre <- watchlist_sample[(wiki_db==iwiki) & (revert_tool==itool) & (pre_cutoff==TRUE)]$time_to_revert\n",
    "        data.post <- watchlist_sample[(wiki_db==iwiki) & (revert_tool==itool) & (pre_cutoff==FALSE)]$time_to_revert\n",
    "\n",
    "        # the hypothesis is that time to reverts will decrease for undo, but may increase or decrease for rollback\n",
    "        alt='two.sided'\n",
    "\n",
    "        if(tool == 'undo'){\n",
    "            alt = 'greater'\n",
    "        }\n",
    "        if(tool == 'rollback'){\n",
    "            alt= 'less'\n",
    "        }\n",
    "        if( (length(data.pre) > 1) & (length(data.post) > 1)){\n",
    "            UT <- wilcox.test(data.pre, data.post, correct=T, alternative=alt)\n",
    "            TT <- t.test(data.pre, data.post, alternative=alt)\n",
    "            watchlist.utest.grid[(wiki==iwiki) & (tool==itool),\n",
    "                                 \":=\"(\n",
    "                                     median.pre=median(data.pre),\n",
    "                                     median.post=median(data.post),\n",
    "                                     U.p.value = UT$p.value,\n",
    "                                     mean.pre = mean(data.pre),\n",
    "                                     mean.post = mean(data.post),\n",
    "                                     T.p.value = TT$p.value\n",
    "                                 )\n",
    "                                ]\n",
    "        } \n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki</th><th scope=col>tool</th><th scope=col>median.pre</th><th scope=col>median.post</th><th scope=col>U.p.value</th><th scope=col>mean.pre</th><th scope=col>mean.post</th><th scope=col>T.p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>arwiki       </td><td>rollback     </td><td>   3955.0    </td><td> 7577.0      </td><td>2.633523e-08 </td><td>  74844.015  </td><td> 74181.610   </td><td>9.365032e-01 </td></tr>\n",
       "\t<tr><td>bswiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>cawiki       </td><td>rollback     </td><td>   1359.0    </td><td> 1306.0      </td><td>6.020255e-01 </td><td>  17210.634  </td><td> 11664.374   </td><td>2.123735e-01 </td></tr>\n",
       "\t<tr><td>cswiki       </td><td>rollback     </td><td>    124.0    </td><td>  108.0      </td><td>4.467042e-01 </td><td>   9794.161  </td><td> 11208.164   </td><td>7.802427e-01 </td></tr>\n",
       "\t<tr><td>enwiki       </td><td>rollback     </td><td>   1297.0    </td><td> 1258.0      </td><td>6.106265e-02 </td><td> 105420.049  </td><td> 66020.954   </td><td>7.848536e-47 </td></tr>\n",
       "\t<tr><td>eswiki       </td><td>rollback     </td><td>     39.0    </td><td>   36.0      </td><td>1.411656e-10 </td><td>  16125.340  </td><td> 17514.010   </td><td>1.970185e-01 </td></tr>\n",
       "\t<tr><td>eswikibooks  </td><td>rollback     </td><td>   3631.0    </td><td> 9558.0      </td><td>6.355431e-03 </td><td>  12483.863  </td><td> 28405.133   </td><td>2.185825e-02 </td></tr>\n",
       "\t<tr><td>eswikiquote  </td><td>rollback     </td><td>  10818.0    </td><td> 9145.5      </td><td>9.331779e-01 </td><td>  16443.500  </td><td> 11554.050   </td><td>3.212021e-01 </td></tr>\n",
       "\t<tr><td>etwiki       </td><td>rollback     </td><td>    776.0    </td><td>  682.5      </td><td>5.293096e-02 </td><td>  77737.715  </td><td>  5303.789   </td><td>1.206638e-03 </td></tr>\n",
       "\t<tr><td>euwiki       </td><td>rollback     </td><td>  10486.5    </td><td> 2347.0      </td><td>6.725371e-02 </td><td>   9765.643  </td><td>  2347.000   </td><td>1.505360e-01 </td></tr>\n",
       "\t<tr><td>fawiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>fiwiki       </td><td>rollback     </td><td>    110.5    </td><td>   60.5      </td><td>4.002253e-05 </td><td>   5206.132  </td><td>  2478.107   </td><td>1.687670e-01 </td></tr>\n",
       "\t<tr><td>frwiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>hewiki       </td><td>rollback     </td><td>    123.0    </td><td>  274.0      </td><td>3.506762e-17 </td><td>  16329.859  </td><td> 19947.196   </td><td>3.385056e-01 </td></tr>\n",
       "\t<tr><td>huwiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>itwiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>kowiki       </td><td>rollback     </td><td>    725.0    </td><td> 5306.0      </td><td>6.198356e-08 </td><td>  41725.657  </td><td>121213.852   </td><td>1.813976e-05 </td></tr>\n",
       "\t<tr><td>lvwiki       </td><td>rollback     </td><td>    377.0    </td><td>  419.0      </td><td>1.171219e-01 </td><td>   2425.194  </td><td>  5969.706   </td><td>1.580550e-01 </td></tr>\n",
       "\t<tr><td>mediawikiwiki</td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>nlwiki       </td><td>rollback     </td><td> 867925.0    </td><td>   93.0      </td><td>2.688331e-11 </td><td> 925000.800  </td><td>  9967.341   </td><td>1.089362e-06 </td></tr>\n",
       "\t<tr><td>plwiki       </td><td>rollback     </td><td>1798302.0    </td><td>  488.0      </td><td>3.643896e-09 </td><td>1777437.750  </td><td> 38458.183   </td><td>5.085595e-07 </td></tr>\n",
       "\t<tr><td>ptwiki       </td><td>rollback     </td><td>   2287.0    </td><td>  973.0      </td><td>6.168982e-06 </td><td>  62232.987  </td><td> 39969.698   </td><td>2.329110e-05 </td></tr>\n",
       "\t<tr><td>rowiki       </td><td>rollback     </td><td>    395.0    </td><td>  643.0      </td><td>1.027233e-01 </td><td>  26466.847  </td><td>137804.085   </td><td>1.143525e-02 </td></tr>\n",
       "\t<tr><td>ruwiki       </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>simplewiki   </td><td>rollback     </td><td>    339.0    </td><td>  115.0      </td><td>1.519125e-23 </td><td>  34924.969  </td><td> 23572.065   </td><td>1.124561e-01 </td></tr>\n",
       "\t<tr><td>sqwiki       </td><td>rollback     </td><td>  16851.0    </td><td> 7492.0      </td><td>5.245358e-01 </td><td>  21169.820  </td><td> 45426.104   </td><td>9.207144e-02 </td></tr>\n",
       "\t<tr><td>srwiki       </td><td>rollback     </td><td>   7608.0    </td><td> 3850.0      </td><td>1.186087e-05 </td><td>  70161.830  </td><td> 33650.603   </td><td>3.006276e-04 </td></tr>\n",
       "\t<tr><td>svwiki       </td><td>rollback     </td><td>     85.0    </td><td>   74.0      </td><td>1.221987e-01 </td><td>  20166.998  </td><td> 14331.602   </td><td>3.140135e-01 </td></tr>\n",
       "\t<tr><td>test2wiki    </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>testwiki     </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "\t<tr><td>trwiki       </td><td>rollback     </td><td>  30497.5    </td><td>12710.0      </td><td>1.205085e-06 </td><td> 156685.731  </td><td> 17613.770   </td><td>5.007558e-08 </td></tr>\n",
       "\t<tr><td>wikidatawiki </td><td>rollback     </td><td>      NaN    </td><td>    NaN      </td><td>         NaN </td><td>        NaN  </td><td>       NaN   </td><td>         NaN </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " wiki & tool & median.pre & median.post & U.p.value & mean.pre & mean.post & T.p.value\\\\\n",
       "\\hline\n",
       "\t arwiki        & rollback      &    3955.0     &  7577.0       & 2.633523e-08  &   74844.015   &  74181.610    & 9.365032e-01 \\\\\n",
       "\t bswiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t cawiki        & rollback      &    1359.0     &  1306.0       & 6.020255e-01  &   17210.634   &  11664.374    & 2.123735e-01 \\\\\n",
       "\t cswiki        & rollback      &     124.0     &   108.0       & 4.467042e-01  &    9794.161   &  11208.164    & 7.802427e-01 \\\\\n",
       "\t enwiki        & rollback      &    1297.0     &  1258.0       & 6.106265e-02  &  105420.049   &  66020.954    & 7.848536e-47 \\\\\n",
       "\t eswiki        & rollback      &      39.0     &    36.0       & 1.411656e-10  &   16125.340   &  17514.010    & 1.970185e-01 \\\\\n",
       "\t eswikibooks   & rollback      &    3631.0     &  9558.0       & 6.355431e-03  &   12483.863   &  28405.133    & 2.185825e-02 \\\\\n",
       "\t eswikiquote   & rollback      &   10818.0     &  9145.5       & 9.331779e-01  &   16443.500   &  11554.050    & 3.212021e-01 \\\\\n",
       "\t etwiki        & rollback      &     776.0     &   682.5       & 5.293096e-02  &   77737.715   &   5303.789    & 1.206638e-03 \\\\\n",
       "\t euwiki        & rollback      &   10486.5     &  2347.0       & 6.725371e-02  &    9765.643   &   2347.000    & 1.505360e-01 \\\\\n",
       "\t fawiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t fiwiki        & rollback      &     110.5     &    60.5       & 4.002253e-05  &    5206.132   &   2478.107    & 1.687670e-01 \\\\\n",
       "\t frwiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t hewiki        & rollback      &     123.0     &   274.0       & 3.506762e-17  &   16329.859   &  19947.196    & 3.385056e-01 \\\\\n",
       "\t huwiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t itwiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t kowiki        & rollback      &     725.0     &  5306.0       & 6.198356e-08  &   41725.657   & 121213.852    & 1.813976e-05 \\\\\n",
       "\t lvwiki        & rollback      &     377.0     &   419.0       & 1.171219e-01  &    2425.194   &   5969.706    & 1.580550e-01 \\\\\n",
       "\t mediawikiwiki & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t nlwiki        & rollback      &  867925.0     &    93.0       & 2.688331e-11  &  925000.800   &   9967.341    & 1.089362e-06 \\\\\n",
       "\t plwiki        & rollback      & 1798302.0     &   488.0       & 3.643896e-09  & 1777437.750   &  38458.183    & 5.085595e-07 \\\\\n",
       "\t ptwiki        & rollback      &    2287.0     &   973.0       & 6.168982e-06  &   62232.987   &  39969.698    & 2.329110e-05 \\\\\n",
       "\t rowiki        & rollback      &     395.0     &   643.0       & 1.027233e-01  &   26466.847   & 137804.085    & 1.143525e-02 \\\\\n",
       "\t ruwiki        & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t simplewiki    & rollback      &     339.0     &   115.0       & 1.519125e-23  &   34924.969   &  23572.065    & 1.124561e-01 \\\\\n",
       "\t sqwiki        & rollback      &   16851.0     &  7492.0       & 5.245358e-01  &   21169.820   &  45426.104    & 9.207144e-02 \\\\\n",
       "\t srwiki        & rollback      &    7608.0     &  3850.0       & 1.186087e-05  &   70161.830   &  33650.603    & 3.006276e-04 \\\\\n",
       "\t svwiki        & rollback      &      85.0     &    74.0       & 1.221987e-01  &   20166.998   &  14331.602    & 3.140135e-01 \\\\\n",
       "\t test2wiki     & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t testwiki      & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\t trwiki        & rollback      &   30497.5     & 12710.0       & 1.205085e-06  &  156685.731   &  17613.770    & 5.007558e-08 \\\\\n",
       "\t wikidatawiki  & rollback      &       NaN     &     NaN       &          NaN  &         NaN   &        NaN    &          NaN \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki | tool | median.pre | median.post | U.p.value | mean.pre | mean.post | T.p.value | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| arwiki        | rollback      |    3955.0     |  7577.0       | 2.633523e-08  |   74844.015   |  74181.610    | 9.365032e-01  | \n",
       "| bswiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| cawiki        | rollback      |    1359.0     |  1306.0       | 6.020255e-01  |   17210.634   |  11664.374    | 2.123735e-01  | \n",
       "| cswiki        | rollback      |     124.0     |   108.0       | 4.467042e-01  |    9794.161   |  11208.164    | 7.802427e-01  | \n",
       "| enwiki        | rollback      |    1297.0     |  1258.0       | 6.106265e-02  |  105420.049   |  66020.954    | 7.848536e-47  | \n",
       "| eswiki        | rollback      |      39.0     |    36.0       | 1.411656e-10  |   16125.340   |  17514.010    | 1.970185e-01  | \n",
       "| eswikibooks   | rollback      |    3631.0     |  9558.0       | 6.355431e-03  |   12483.863   |  28405.133    | 2.185825e-02  | \n",
       "| eswikiquote   | rollback      |   10818.0     |  9145.5       | 9.331779e-01  |   16443.500   |  11554.050    | 3.212021e-01  | \n",
       "| etwiki        | rollback      |     776.0     |   682.5       | 5.293096e-02  |   77737.715   |   5303.789    | 1.206638e-03  | \n",
       "| euwiki        | rollback      |   10486.5     |  2347.0       | 6.725371e-02  |    9765.643   |   2347.000    | 1.505360e-01  | \n",
       "| fawiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| fiwiki        | rollback      |     110.5     |    60.5       | 4.002253e-05  |    5206.132   |   2478.107    | 1.687670e-01  | \n",
       "| frwiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| hewiki        | rollback      |     123.0     |   274.0       | 3.506762e-17  |   16329.859   |  19947.196    | 3.385056e-01  | \n",
       "| huwiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| itwiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| kowiki        | rollback      |     725.0     |  5306.0       | 6.198356e-08  |   41725.657   | 121213.852    | 1.813976e-05  | \n",
       "| lvwiki        | rollback      |     377.0     |   419.0       | 1.171219e-01  |    2425.194   |   5969.706    | 1.580550e-01  | \n",
       "| mediawikiwiki | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| nlwiki        | rollback      |  867925.0     |    93.0       | 2.688331e-11  |  925000.800   |   9967.341    | 1.089362e-06  | \n",
       "| plwiki        | rollback      | 1798302.0     |   488.0       | 3.643896e-09  | 1777437.750   |  38458.183    | 5.085595e-07  | \n",
       "| ptwiki        | rollback      |    2287.0     |   973.0       | 6.168982e-06  |   62232.987   |  39969.698    | 2.329110e-05  | \n",
       "| rowiki        | rollback      |     395.0     |   643.0       | 1.027233e-01  |   26466.847   | 137804.085    | 1.143525e-02  | \n",
       "| ruwiki        | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| simplewiki    | rollback      |     339.0     |   115.0       | 1.519125e-23  |   34924.969   |  23572.065    | 1.124561e-01  | \n",
       "| sqwiki        | rollback      |   16851.0     |  7492.0       | 5.245358e-01  |   21169.820   |  45426.104    | 9.207144e-02  | \n",
       "| srwiki        | rollback      |    7608.0     |  3850.0       | 1.186087e-05  |   70161.830   |  33650.603    | 3.006276e-04  | \n",
       "| svwiki        | rollback      |      85.0     |    74.0       | 1.221987e-01  |   20166.998   |  14331.602    | 3.140135e-01  | \n",
       "| test2wiki     | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| testwiki      | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "| trwiki        | rollback      |   30497.5     | 12710.0       | 1.205085e-06  |  156685.731   |  17613.770    | 5.007558e-08  | \n",
       "| wikidatawiki  | rollback      |       NaN     |     NaN       |          NaN  |         NaN   |        NaN    |          NaN  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   wiki          tool     median.pre median.post U.p.value    mean.pre   \n",
       "1  arwiki        rollback    3955.0   7577.0     2.633523e-08   74844.015\n",
       "2  bswiki        rollback       NaN      NaN              NaN         NaN\n",
       "3  cawiki        rollback    1359.0   1306.0     6.020255e-01   17210.634\n",
       "4  cswiki        rollback     124.0    108.0     4.467042e-01    9794.161\n",
       "5  enwiki        rollback    1297.0   1258.0     6.106265e-02  105420.049\n",
       "6  eswiki        rollback      39.0     36.0     1.411656e-10   16125.340\n",
       "7  eswikibooks   rollback    3631.0   9558.0     6.355431e-03   12483.863\n",
       "8  eswikiquote   rollback   10818.0   9145.5     9.331779e-01   16443.500\n",
       "9  etwiki        rollback     776.0    682.5     5.293096e-02   77737.715\n",
       "10 euwiki        rollback   10486.5   2347.0     6.725371e-02    9765.643\n",
       "11 fawiki        rollback       NaN      NaN              NaN         NaN\n",
       "12 fiwiki        rollback     110.5     60.5     4.002253e-05    5206.132\n",
       "13 frwiki        rollback       NaN      NaN              NaN         NaN\n",
       "14 hewiki        rollback     123.0    274.0     3.506762e-17   16329.859\n",
       "15 huwiki        rollback       NaN      NaN              NaN         NaN\n",
       "16 itwiki        rollback       NaN      NaN              NaN         NaN\n",
       "17 kowiki        rollback     725.0   5306.0     6.198356e-08   41725.657\n",
       "18 lvwiki        rollback     377.0    419.0     1.171219e-01    2425.194\n",
       "19 mediawikiwiki rollback       NaN      NaN              NaN         NaN\n",
       "20 nlwiki        rollback  867925.0     93.0     2.688331e-11  925000.800\n",
       "21 plwiki        rollback 1798302.0    488.0     3.643896e-09 1777437.750\n",
       "22 ptwiki        rollback    2287.0    973.0     6.168982e-06   62232.987\n",
       "23 rowiki        rollback     395.0    643.0     1.027233e-01   26466.847\n",
       "24 ruwiki        rollback       NaN      NaN              NaN         NaN\n",
       "25 simplewiki    rollback     339.0    115.0     1.519125e-23   34924.969\n",
       "26 sqwiki        rollback   16851.0   7492.0     5.245358e-01   21169.820\n",
       "27 srwiki        rollback    7608.0   3850.0     1.186087e-05   70161.830\n",
       "28 svwiki        rollback      85.0     74.0     1.221987e-01   20166.998\n",
       "29 test2wiki     rollback       NaN      NaN              NaN         NaN\n",
       "30 testwiki      rollback       NaN      NaN              NaN         NaN\n",
       "31 trwiki        rollback   30497.5  12710.0     1.205085e-06  156685.731\n",
       "32 wikidatawiki  rollback       NaN      NaN              NaN         NaN\n",
       "   mean.post  T.p.value   \n",
       "1   74181.610 9.365032e-01\n",
       "2         NaN          NaN\n",
       "3   11664.374 2.123735e-01\n",
       "4   11208.164 7.802427e-01\n",
       "5   66020.954 7.848536e-47\n",
       "6   17514.010 1.970185e-01\n",
       "7   28405.133 2.185825e-02\n",
       "8   11554.050 3.212021e-01\n",
       "9    5303.789 1.206638e-03\n",
       "10   2347.000 1.505360e-01\n",
       "11        NaN          NaN\n",
       "12   2478.107 1.687670e-01\n",
       "13        NaN          NaN\n",
       "14  19947.196 3.385056e-01\n",
       "15        NaN          NaN\n",
       "16        NaN          NaN\n",
       "17 121213.852 1.813976e-05\n",
       "18   5969.706 1.580550e-01\n",
       "19        NaN          NaN\n",
       "20   9967.341 1.089362e-06\n",
       "21  38458.183 5.085595e-07\n",
       "22  39969.698 2.329110e-05\n",
       "23 137804.085 1.143525e-02\n",
       "24        NaN          NaN\n",
       "25  23572.065 1.124561e-01\n",
       "26  45426.104 9.207144e-02\n",
       "27  33650.603 3.006276e-04\n",
       "28  14331.602 3.140135e-01\n",
       "29        NaN          NaN\n",
       "30        NaN          NaN\n",
       "31  17613.770 5.007558e-08\n",
       "32        NaN          NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "watchlist.utest.grid[tool=='rollback']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki</th><th scope=col>tool</th><th scope=col>median.pre</th><th scope=col>median.post</th><th scope=col>U.p.value</th><th scope=col>mean.pre</th><th scope=col>mean.post</th><th scope=col>T.p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>arwiki       </td><td>undo         </td><td> 1251.0      </td><td>  1549.0     </td><td>6.802405e-01 </td><td> 83421.46    </td><td>138363.6610  </td><td>3.537932e-01 </td></tr>\n",
       "\t<tr><td>bswiki       </td><td>undo         </td><td>11195.0      </td><td>  5735.0     </td><td>6.811144e-01 </td><td> 38102.42    </td><td>151030.4731  </td><td>1.649548e-02 </td></tr>\n",
       "\t<tr><td>cawiki       </td><td>undo         </td><td> 2446.5      </td><td>  8982.0     </td><td>1.750102e-03 </td><td> 83472.21    </td><td>158726.1238  </td><td>5.283574e-02 </td></tr>\n",
       "\t<tr><td>cswiki       </td><td>undo         </td><td>  668.0      </td><td>   678.0     </td><td>5.572224e-01 </td><td> 91801.71    </td><td> 65358.3237  </td><td>3.711108e-01 </td></tr>\n",
       "\t<tr><td>enwiki       </td><td>undo         </td><td> 6221.0      </td><td>  7160.0     </td><td>7.055017e-07 </td><td> 91240.67    </td><td>106049.9882  </td><td>3.473833e-06 </td></tr>\n",
       "\t<tr><td>eswiki       </td><td>undo         </td><td> 6450.0      </td><td>  6540.0     </td><td>5.581020e-01 </td><td>126190.52    </td><td>128887.9283  </td><td>7.661086e-01 </td></tr>\n",
       "\t<tr><td>eswikibooks  </td><td>undo         </td><td>  349.0      </td><td> 10767.0     </td><td>1.904762e-01 </td><td>  1989.00    </td><td> 57392.6000  </td><td>3.328591e-01 </td></tr>\n",
       "\t<tr><td>eswikiquote  </td><td>undo         </td><td> 5983.0      </td><td>  9205.5     </td><td>1.962799e-01 </td><td> 45621.07    </td><td> 53159.4000  </td><td>8.516291e-01 </td></tr>\n",
       "\t<tr><td>etwiki       </td><td>undo         </td><td> 1342.0      </td><td>  2309.0     </td><td>4.294128e-01 </td><td> 67988.35    </td><td>126873.5806  </td><td>5.315485e-01 </td></tr>\n",
       "\t<tr><td>euwiki       </td><td>undo         </td><td>12899.0      </td><td>  5355.0     </td><td>9.147826e-01 </td><td> 15358.75    </td><td>  5840.6667  </td><td>3.666807e-01 </td></tr>\n",
       "\t<tr><td>fawiki       </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>fiwiki       </td><td>undo         </td><td>  730.0      </td><td>   324.0     </td><td>2.820775e-01 </td><td> 39600.20    </td><td> 58180.7176  </td><td>4.979602e-01 </td></tr>\n",
       "\t<tr><td>frwiki       </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>hewiki       </td><td>undo         </td><td> 1677.0      </td><td>  1838.0     </td><td>4.858311e-01 </td><td>211020.80    </td><td> 40905.5512  </td><td>7.716015e-07 </td></tr>\n",
       "\t<tr><td>huwiki       </td><td>undo         </td><td> 1260.0      </td><td>   580.0     </td><td>3.982066e-02 </td><td> 87546.82    </td><td> 97129.6413  </td><td>8.029728e-01 </td></tr>\n",
       "\t<tr><td>itwiki       </td><td>undo         </td><td> 1642.0      </td><td>  2572.0     </td><td>1.394893e-05 </td><td> 58729.48    </td><td> 62771.8787  </td><td>4.962447e-01 </td></tr>\n",
       "\t<tr><td>kowiki       </td><td>undo         </td><td>14906.0      </td><td>  8017.0     </td><td>7.573458e-02 </td><td>157822.15    </td><td>139934.9368  </td><td>4.938398e-01 </td></tr>\n",
       "\t<tr><td>lvwiki       </td><td>undo         </td><td>  797.0      </td><td>   555.5     </td><td>4.451236e-01 </td><td> 35416.31    </td><td>   936.3571  </td><td>2.768888e-01 </td></tr>\n",
       "\t<tr><td>mediawikiwiki</td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>nlwiki       </td><td>undo         </td><td>  856.5      </td><td>  2204.5     </td><td>1.015178e-09 </td><td> 34760.54    </td><td> 65781.3409  </td><td>3.027967e-04 </td></tr>\n",
       "\t<tr><td>plwiki       </td><td>undo         </td><td> 1149.0      </td><td>  1977.5     </td><td>1.656479e-02 </td><td> 75320.62    </td><td> 80223.0189  </td><td>7.651280e-01 </td></tr>\n",
       "\t<tr><td>ptwiki       </td><td>undo         </td><td> 5355.5      </td><td> 20965.5     </td><td>1.566351e-20 </td><td>124560.90    </td><td>100998.3653  </td><td>1.065914e-01 </td></tr>\n",
       "\t<tr><td>rowiki       </td><td>undo         </td><td>  435.0      </td><td>  1629.5     </td><td>1.363779e-02 </td><td>  8644.00    </td><td> 56644.6875  </td><td>1.501026e-01 </td></tr>\n",
       "\t<tr><td>ruwiki       </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>simplewiki   </td><td>undo         </td><td>  620.0      </td><td>  1842.0     </td><td>1.025501e-01 </td><td>105743.14    </td><td>217679.5536  </td><td>5.015706e-02 </td></tr>\n",
       "\t<tr><td>sqwiki       </td><td>undo         </td><td> 1372.0      </td><td>193180.0     </td><td>4.906618e-03 </td><td> 15936.24    </td><td>274785.8000  </td><td>9.124283e-03 </td></tr>\n",
       "\t<tr><td>srwiki       </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>svwiki       </td><td>undo         </td><td>  455.0      </td><td>   302.0     </td><td>2.038142e-02 </td><td> 50108.62    </td><td> 36844.5163  </td><td>3.667080e-01 </td></tr>\n",
       "\t<tr><td>test2wiki    </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>testwiki     </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "\t<tr><td>trwiki       </td><td>undo         </td><td> 4074.0      </td><td>   461.0     </td><td>6.968022e-06 </td><td> 90625.06    </td><td>  9687.0000  </td><td>4.122149e-02 </td></tr>\n",
       "\t<tr><td>wikidatawiki </td><td>undo         </td><td>    NaN      </td><td>     NaN     </td><td>         NaN </td><td>      NaN    </td><td>        NaN  </td><td>         NaN </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " wiki & tool & median.pre & median.post & U.p.value & mean.pre & mean.post & T.p.value\\\\\n",
       "\\hline\n",
       "\t arwiki        & undo          &  1251.0       &   1549.0      & 6.802405e-01  &  83421.46     & 138363.6610   & 3.537932e-01 \\\\\n",
       "\t bswiki        & undo          & 11195.0       &   5735.0      & 6.811144e-01  &  38102.42     & 151030.4731   & 1.649548e-02 \\\\\n",
       "\t cawiki        & undo          &  2446.5       &   8982.0      & 1.750102e-03  &  83472.21     & 158726.1238   & 5.283574e-02 \\\\\n",
       "\t cswiki        & undo          &   668.0       &    678.0      & 5.572224e-01  &  91801.71     &  65358.3237   & 3.711108e-01 \\\\\n",
       "\t enwiki        & undo          &  6221.0       &   7160.0      & 7.055017e-07  &  91240.67     & 106049.9882   & 3.473833e-06 \\\\\n",
       "\t eswiki        & undo          &  6450.0       &   6540.0      & 5.581020e-01  & 126190.52     & 128887.9283   & 7.661086e-01 \\\\\n",
       "\t eswikibooks   & undo          &   349.0       &  10767.0      & 1.904762e-01  &   1989.00     &  57392.6000   & 3.328591e-01 \\\\\n",
       "\t eswikiquote   & undo          &  5983.0       &   9205.5      & 1.962799e-01  &  45621.07     &  53159.4000   & 8.516291e-01 \\\\\n",
       "\t etwiki        & undo          &  1342.0       &   2309.0      & 4.294128e-01  &  67988.35     & 126873.5806   & 5.315485e-01 \\\\\n",
       "\t euwiki        & undo          & 12899.0       &   5355.0      & 9.147826e-01  &  15358.75     &   5840.6667   & 3.666807e-01 \\\\\n",
       "\t fawiki        & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t fiwiki        & undo          &   730.0       &    324.0      & 2.820775e-01  &  39600.20     &  58180.7176   & 4.979602e-01 \\\\\n",
       "\t frwiki        & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t hewiki        & undo          &  1677.0       &   1838.0      & 4.858311e-01  & 211020.80     &  40905.5512   & 7.716015e-07 \\\\\n",
       "\t huwiki        & undo          &  1260.0       &    580.0      & 3.982066e-02  &  87546.82     &  97129.6413   & 8.029728e-01 \\\\\n",
       "\t itwiki        & undo          &  1642.0       &   2572.0      & 1.394893e-05  &  58729.48     &  62771.8787   & 4.962447e-01 \\\\\n",
       "\t kowiki        & undo          & 14906.0       &   8017.0      & 7.573458e-02  & 157822.15     & 139934.9368   & 4.938398e-01 \\\\\n",
       "\t lvwiki        & undo          &   797.0       &    555.5      & 4.451236e-01  &  35416.31     &    936.3571   & 2.768888e-01 \\\\\n",
       "\t mediawikiwiki & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t nlwiki        & undo          &   856.5       &   2204.5      & 1.015178e-09  &  34760.54     &  65781.3409   & 3.027967e-04 \\\\\n",
       "\t plwiki        & undo          &  1149.0       &   1977.5      & 1.656479e-02  &  75320.62     &  80223.0189   & 7.651280e-01 \\\\\n",
       "\t ptwiki        & undo          &  5355.5       &  20965.5      & 1.566351e-20  & 124560.90     & 100998.3653   & 1.065914e-01 \\\\\n",
       "\t rowiki        & undo          &   435.0       &   1629.5      & 1.363779e-02  &   8644.00     &  56644.6875   & 1.501026e-01 \\\\\n",
       "\t ruwiki        & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t simplewiki    & undo          &   620.0       &   1842.0      & 1.025501e-01  & 105743.14     & 217679.5536   & 5.015706e-02 \\\\\n",
       "\t sqwiki        & undo          &  1372.0       & 193180.0      & 4.906618e-03  &  15936.24     & 274785.8000   & 9.124283e-03 \\\\\n",
       "\t srwiki        & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t svwiki        & undo          &   455.0       &    302.0      & 2.038142e-02  &  50108.62     &  36844.5163   & 3.667080e-01 \\\\\n",
       "\t test2wiki     & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t testwiki      & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\t trwiki        & undo          &  4074.0       &    461.0      & 6.968022e-06  &  90625.06     &   9687.0000   & 4.122149e-02 \\\\\n",
       "\t wikidatawiki  & undo          &     NaN       &      NaN      &          NaN  &       NaN     &         NaN   &          NaN \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki | tool | median.pre | median.post | U.p.value | mean.pre | mean.post | T.p.value | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| arwiki        | undo          |  1251.0       |   1549.0      | 6.802405e-01  |  83421.46     | 138363.6610   | 3.537932e-01  | \n",
       "| bswiki        | undo          | 11195.0       |   5735.0      | 6.811144e-01  |  38102.42     | 151030.4731   | 1.649548e-02  | \n",
       "| cawiki        | undo          |  2446.5       |   8982.0      | 1.750102e-03  |  83472.21     | 158726.1238   | 5.283574e-02  | \n",
       "| cswiki        | undo          |   668.0       |    678.0      | 5.572224e-01  |  91801.71     |  65358.3237   | 3.711108e-01  | \n",
       "| enwiki        | undo          |  6221.0       |   7160.0      | 7.055017e-07  |  91240.67     | 106049.9882   | 3.473833e-06  | \n",
       "| eswiki        | undo          |  6450.0       |   6540.0      | 5.581020e-01  | 126190.52     | 128887.9283   | 7.661086e-01  | \n",
       "| eswikibooks   | undo          |   349.0       |  10767.0      | 1.904762e-01  |   1989.00     |  57392.6000   | 3.328591e-01  | \n",
       "| eswikiquote   | undo          |  5983.0       |   9205.5      | 1.962799e-01  |  45621.07     |  53159.4000   | 8.516291e-01  | \n",
       "| etwiki        | undo          |  1342.0       |   2309.0      | 4.294128e-01  |  67988.35     | 126873.5806   | 5.315485e-01  | \n",
       "| euwiki        | undo          | 12899.0       |   5355.0      | 9.147826e-01  |  15358.75     |   5840.6667   | 3.666807e-01  | \n",
       "| fawiki        | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| fiwiki        | undo          |   730.0       |    324.0      | 2.820775e-01  |  39600.20     |  58180.7176   | 4.979602e-01  | \n",
       "| frwiki        | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| hewiki        | undo          |  1677.0       |   1838.0      | 4.858311e-01  | 211020.80     |  40905.5512   | 7.716015e-07  | \n",
       "| huwiki        | undo          |  1260.0       |    580.0      | 3.982066e-02  |  87546.82     |  97129.6413   | 8.029728e-01  | \n",
       "| itwiki        | undo          |  1642.0       |   2572.0      | 1.394893e-05  |  58729.48     |  62771.8787   | 4.962447e-01  | \n",
       "| kowiki        | undo          | 14906.0       |   8017.0      | 7.573458e-02  | 157822.15     | 139934.9368   | 4.938398e-01  | \n",
       "| lvwiki        | undo          |   797.0       |    555.5      | 4.451236e-01  |  35416.31     |    936.3571   | 2.768888e-01  | \n",
       "| mediawikiwiki | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| nlwiki        | undo          |   856.5       |   2204.5      | 1.015178e-09  |  34760.54     |  65781.3409   | 3.027967e-04  | \n",
       "| plwiki        | undo          |  1149.0       |   1977.5      | 1.656479e-02  |  75320.62     |  80223.0189   | 7.651280e-01  | \n",
       "| ptwiki        | undo          |  5355.5       |  20965.5      | 1.566351e-20  | 124560.90     | 100998.3653   | 1.065914e-01  | \n",
       "| rowiki        | undo          |   435.0       |   1629.5      | 1.363779e-02  |   8644.00     |  56644.6875   | 1.501026e-01  | \n",
       "| ruwiki        | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| simplewiki    | undo          |   620.0       |   1842.0      | 1.025501e-01  | 105743.14     | 217679.5536   | 5.015706e-02  | \n",
       "| sqwiki        | undo          |  1372.0       | 193180.0      | 4.906618e-03  |  15936.24     | 274785.8000   | 9.124283e-03  | \n",
       "| srwiki        | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| svwiki        | undo          |   455.0       |    302.0      | 2.038142e-02  |  50108.62     |  36844.5163   | 3.667080e-01  | \n",
       "| test2wiki     | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| testwiki      | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "| trwiki        | undo          |  4074.0       |    461.0      | 6.968022e-06  |  90625.06     |   9687.0000   | 4.122149e-02  | \n",
       "| wikidatawiki  | undo          |     NaN       |      NaN      |          NaN  |       NaN     |         NaN   |          NaN  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   wiki          tool median.pre median.post U.p.value    mean.pre  mean.post  \n",
       "1  arwiki        undo  1251.0      1549.0    6.802405e-01  83421.46 138363.6610\n",
       "2  bswiki        undo 11195.0      5735.0    6.811144e-01  38102.42 151030.4731\n",
       "3  cawiki        undo  2446.5      8982.0    1.750102e-03  83472.21 158726.1238\n",
       "4  cswiki        undo   668.0       678.0    5.572224e-01  91801.71  65358.3237\n",
       "5  enwiki        undo  6221.0      7160.0    7.055017e-07  91240.67 106049.9882\n",
       "6  eswiki        undo  6450.0      6540.0    5.581020e-01 126190.52 128887.9283\n",
       "7  eswikibooks   undo   349.0     10767.0    1.904762e-01   1989.00  57392.6000\n",
       "8  eswikiquote   undo  5983.0      9205.5    1.962799e-01  45621.07  53159.4000\n",
       "9  etwiki        undo  1342.0      2309.0    4.294128e-01  67988.35 126873.5806\n",
       "10 euwiki        undo 12899.0      5355.0    9.147826e-01  15358.75   5840.6667\n",
       "11 fawiki        undo     NaN         NaN             NaN       NaN         NaN\n",
       "12 fiwiki        undo   730.0       324.0    2.820775e-01  39600.20  58180.7176\n",
       "13 frwiki        undo     NaN         NaN             NaN       NaN         NaN\n",
       "14 hewiki        undo  1677.0      1838.0    4.858311e-01 211020.80  40905.5512\n",
       "15 huwiki        undo  1260.0       580.0    3.982066e-02  87546.82  97129.6413\n",
       "16 itwiki        undo  1642.0      2572.0    1.394893e-05  58729.48  62771.8787\n",
       "17 kowiki        undo 14906.0      8017.0    7.573458e-02 157822.15 139934.9368\n",
       "18 lvwiki        undo   797.0       555.5    4.451236e-01  35416.31    936.3571\n",
       "19 mediawikiwiki undo     NaN         NaN             NaN       NaN         NaN\n",
       "20 nlwiki        undo   856.5      2204.5    1.015178e-09  34760.54  65781.3409\n",
       "21 plwiki        undo  1149.0      1977.5    1.656479e-02  75320.62  80223.0189\n",
       "22 ptwiki        undo  5355.5     20965.5    1.566351e-20 124560.90 100998.3653\n",
       "23 rowiki        undo   435.0      1629.5    1.363779e-02   8644.00  56644.6875\n",
       "24 ruwiki        undo     NaN         NaN             NaN       NaN         NaN\n",
       "25 simplewiki    undo   620.0      1842.0    1.025501e-01 105743.14 217679.5536\n",
       "26 sqwiki        undo  1372.0    193180.0    4.906618e-03  15936.24 274785.8000\n",
       "27 srwiki        undo     NaN         NaN             NaN       NaN         NaN\n",
       "28 svwiki        undo   455.0       302.0    2.038142e-02  50108.62  36844.5163\n",
       "29 test2wiki     undo     NaN         NaN             NaN       NaN         NaN\n",
       "30 testwiki      undo     NaN         NaN             NaN       NaN         NaN\n",
       "31 trwiki        undo  4074.0       461.0    6.968022e-06  90625.06   9687.0000\n",
       "32 wikidatawiki  undo     NaN         NaN             NaN       NaN         NaN\n",
       "   T.p.value   \n",
       "1  3.537932e-01\n",
       "2  1.649548e-02\n",
       "3  5.283574e-02\n",
       "4  3.711108e-01\n",
       "5  3.473833e-06\n",
       "6  7.661086e-01\n",
       "7  3.328591e-01\n",
       "8  8.516291e-01\n",
       "9  5.315485e-01\n",
       "10 3.666807e-01\n",
       "11          NaN\n",
       "12 4.979602e-01\n",
       "13          NaN\n",
       "14 7.716015e-07\n",
       "15 8.029728e-01\n",
       "16 4.962447e-01\n",
       "17 4.938398e-01\n",
       "18 2.768888e-01\n",
       "19          NaN\n",
       "20 3.027967e-04\n",
       "21 7.651280e-01\n",
       "22 1.065914e-01\n",
       "23 1.501026e-01\n",
       "24          NaN\n",
       "25 5.015706e-02\n",
       "26 9.124283e-03\n",
       "27          NaN\n",
       "28 3.667080e-01\n",
       "29          NaN\n",
       "30          NaN\n",
       "31 4.122149e-02\n",
       "32          NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "watchlist.utest.grid[tool=='undo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>wiki</th><th scope=col>tool</th><th scope=col>median.pre</th><th scope=col>median.post</th><th scope=col>U.p.value</th><th scope=col>mean.pre</th><th scope=col>mean.post</th><th scope=col>T.p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>enwiki      </td><td>undo        </td><td>  6221      </td><td>  7160      </td><td>7.055017e-07</td><td> 91240.675  </td><td>106049.988  </td><td>3.473833e-06</td></tr>\n",
       "\t<tr><td>enwiki      </td><td>otherTool   </td><td>  4428      </td><td>  5321      </td><td>5.746767e-27</td><td>154987.888  </td><td>154857.072  </td><td>9.433415e-01</td></tr>\n",
       "\t<tr><td>enwiki      </td><td>rollback    </td><td>  1297      </td><td>  1258      </td><td>6.106265e-02</td><td>105420.049  </td><td> 66020.954  </td><td>7.848536e-47</td></tr>\n",
       "\t<tr><td>enwiki      </td><td>bot         </td><td>167213      </td><td>128575      </td><td>2.204091e-02</td><td>516785.337  </td><td>464721.398  </td><td>6.939289e-02</td></tr>\n",
       "\t<tr><td>enwiki      </td><td>huggle      </td><td>    38      </td><td>    43      </td><td>8.484045e-06</td><td>  4569.639  </td><td>  6586.097  </td><td>1.129462e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " wiki & tool & median.pre & median.post & U.p.value & mean.pre & mean.post & T.p.value\\\\\n",
       "\\hline\n",
       "\t enwiki       & undo         &   6221       &   7160       & 7.055017e-07 &  91240.675   & 106049.988   & 3.473833e-06\\\\\n",
       "\t enwiki       & otherTool    &   4428       &   5321       & 5.746767e-27 & 154987.888   & 154857.072   & 9.433415e-01\\\\\n",
       "\t enwiki       & rollback     &   1297       &   1258       & 6.106265e-02 & 105420.049   &  66020.954   & 7.848536e-47\\\\\n",
       "\t enwiki       & bot          & 167213       & 128575       & 2.204091e-02 & 516785.337   & 464721.398   & 6.939289e-02\\\\\n",
       "\t enwiki       & huggle       &     38       &     43       & 8.484045e-06 &   4569.639   &   6586.097   & 1.129462e-02\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "wiki | tool | median.pre | median.post | U.p.value | mean.pre | mean.post | T.p.value | \n",
       "|---|---|---|---|---|\n",
       "| enwiki       | undo         |   6221       |   7160       | 7.055017e-07 |  91240.675   | 106049.988   | 3.473833e-06 | \n",
       "| enwiki       | otherTool    |   4428       |   5321       | 5.746767e-27 | 154987.888   | 154857.072   | 9.433415e-01 | \n",
       "| enwiki       | rollback     |   1297       |   1258       | 6.106265e-02 | 105420.049   |  66020.954   | 7.848536e-47 | \n",
       "| enwiki       | bot          | 167213       | 128575       | 2.204091e-02 | 516785.337   | 464721.398   | 6.939289e-02 | \n",
       "| enwiki       | huggle       |     38       |     43       | 8.484045e-06 |   4569.639   |   6586.097   | 1.129462e-02 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  wiki   tool      median.pre median.post U.p.value    mean.pre   mean.post \n",
       "1 enwiki undo        6221       7160      7.055017e-07  91240.675 106049.988\n",
       "2 enwiki otherTool   4428       5321      5.746767e-27 154987.888 154857.072\n",
       "3 enwiki rollback    1297       1258      6.106265e-02 105420.049  66020.954\n",
       "4 enwiki bot       167213     128575      2.204091e-02 516785.337 464721.398\n",
       "5 enwiki huggle        38         43      8.484045e-06   4569.639   6586.097\n",
       "  T.p.value   \n",
       "1 3.473833e-06\n",
       "2 9.433415e-01\n",
       "3 7.848536e-47\n",
       "4 6.939289e-02\n",
       "5 1.129462e-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "watchlist.utest.grid[wiki == 'enwiki']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in .checkTypos(e, names(x)): Object 'has_rcfilters_watchlist.y' not found. Perhaps you intended has_rcfilters_watchlist\n",
     "output_type": "error",
     "traceback": [
      "Error in .checkTypos(e, names(x)): Object 'has_rcfilters_watchlist.y' not found. Perhaps you intended has_rcfilters_watchlist\nTraceback:\n",
      "1. dt[has_rcfilters_watchlist.y == T, .(p_reverted = mean(revision_is_identity_reverted), \n .     p_undo = mean(revert_tool == \"undo\"), p_rollback = mean(revert_tool == \n .         \"rollback\"), p_bot = mean(revert_tool == \"bot\"), p_huggle = mean(revert_tool == \n .         \"huggle\"), p_other = mean(revert_tool == \"other\"), n_edits_in_subperiod = .N), \n .     by = .(wiki_db, pre_cutoff, date)]",
      "2. `[.data.table`(dt, has_rcfilters_watchlist.y == T, .(p_reverted = mean(revision_is_identity_reverted), \n .     p_undo = mean(revert_tool == \"undo\"), p_rollback = mean(revert_tool == \n .         \"rollback\"), p_bot = mean(revert_tool == \"bot\"), p_huggle = mean(revert_tool == \n .         \"huggle\"), p_other = mean(revert_tool == \"other\"), n_edits_in_subperiod = .N), \n .     by = .(wiki_db, pre_cutoff, date))",
      "3. tryCatch(eval(.massagei(isub), x, parent.frame()), error = function(e) .checkTypos(e, \n .     names(x)))",
      "4. tryCatchList(expr, classes, parentenv, handlers)",
      "5. tryCatchOne(expr, names, parentenv, handlers[[1L]])",
      "6. value[[3L]](cond)",
      "7. .checkTypos(e, names(x))",
      "8. stop(sprintf(\"Object '%s' not found. Perhaps you intended %s%s\", \n .     used, paste(head(found, 5L), collapse = \", \"), if (length(found) <= \n .         5L) \"\" else paste(\" or\", length(found) - 5L, \"more\")))"
     ]
    }
   ],
   "source": [
    "watchlist.deltas <- dt[has_rcfilters_watchlist.y == T,.(p_reverted = mean(revision_is_identity_reverted),\n",
    "                                                        p_undo = mean(revert_tool == 'undo'),\n",
    "                                                        p_rollback = mean(revert_tool == 'rollback'),\n",
    "                                                        p_bot = mean(revert_tool == 'bot'), \n",
    "                                                        p_huggle = mean(revert_tool == 'huggle'), \n",
    "                                                        p_other = mean(revert_tool == 'other'),\n",
    "                                                        n_edits_in_subperiod = .N\n",
    "                                                       ), by=.(wiki_db, pre_cutoff, date)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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