visualize-history.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The history of publishing delays by Daniel Himmelstein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "library(dplyr, warn=F)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read history dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>journal_nlm_id</th><th scope=col>pubmed_id</th><th scope=col>delay_type</th><th scope=col>date</th><th scope=col>delay</th><th scope=col>year</th><th scope=col>date_decimal</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0001027</td><td>22221113</td><td>Acceptance</td><td>2011-11-15</td><td>111</td><td>2011</td><td>2011.871</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0001027</td><td>22221113</td><td>Publication</td><td>2012-01-05</td><td>51</td><td>2012</td><td>2012.011</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & journal_nlm_id & pubmed_id & delay_type & date & delay & year & date_decimal\\\\\n",
       "\\hline\n",
       "\t1 & 0001027 & 22221113 & Acceptance & 2011-11-15 & 111 & 2011 & 2011.871\\\\\n",
       "\t2 & 0001027 & 22221113 & Publication & 2012-01-05 & 51 & 2012 & 2012.011\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 7]\n",
       "\n",
       "  journal_nlm_id pubmed_id  delay_type       date delay  year date_decimal\n",
       "           (chr)     (int)       (chr)     (date) (int) (dbl)        (dbl)\n",
       "1        0001027  22221113  Acceptance 2011-11-15   111  2011     2011.871\n",
       "2        0001027  22221113 Publication 2012-01-05    51  2012     2012.011"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read history dates for all articles\n",
    "delay_df = file.path('data', 'delays.tsv.gz') %>%\n",
    "  readr::read_tsv(col_types = list(date = readr::col_date())) %>%\n",
    "  dplyr::mutate(year = lubridate::year(date)) %>%\n",
    "  dplyr::mutate(date_decimal = lubridate::decimal_date(date))\n",
    "\n",
    "head(delay_df, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare journal dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining by: \"journal_nlm_id\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>journal_nlm_id</th><th scope=col>journal_abbrev</th><th scope=col>journal</th><th scope=col>delay_type</th><th scope=col>n_articles</th><th scope=col>median_delay</th><th scope=col>mean_delay</th><th scope=col>n_unique_dates</th><th scope=col>n_unique_years</th><th scope=col>n_unique_delays</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0001027</td><td>Aust N Z J Obstet Gynaecol</td><td>The Australian & New Zealand journal of obstetrics & gynaecology</td><td>Acceptance</td><td>397</td><td>111</td><td>122.453</td><td>258</td><td>5</td><td>183</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0001027</td><td>Aust N Z J Obstet Gynaecol</td><td>The Australian & New Zealand journal of obstetrics & gynaecology</td><td>Publication</td><td>325</td><td>56</td><td>68.3015</td><td>138</td><td>4</td><td>107</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       "  & journal_nlm_id & journal_abbrev & journal & delay_type & n_articles & median_delay & mean_delay & n_unique_dates & n_unique_years & n_unique_delays\\\\\n",
       "\\hline\n",
       "\t1 & 0001027 & Aust N Z J Obstet Gynaecol & The Australian & New Zealand journal of obstetrics & gynaecology & Acceptance & 397 & 111 & 122.453 & 258 & 5 & 183\\\\\n",
       "\t2 & 0001027 & Aust N Z J Obstet Gynaecol & The Australian & New Zealand journal of obstetrics & gynaecology & Publication & 325 & 56 & 68.3015 & 138 & 4 & 107\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 10]\n",
       "\n",
       "  journal_nlm_id             journal_abbrev\n",
       "           (chr)                      (chr)\n",
       "1        0001027 Aust N Z J Obstet Gynaecol\n",
       "2        0001027 Aust N Z J Obstet Gynaecol\n",
       "Variables not shown: journal (chr), delay_type (chr), n_articles (int),\n",
       "  median_delay (dbl), mean_delay (dbl), n_unique_dates (int), n_unique_years\n",
       "  (int), n_unique_delays (int)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "journal_df = file.path('data', 'pubmed-journals.tsv') %>%\n",
    "  readr::read_tsv() %>%\n",
    "  dplyr::transmute(journal_nlm_id = NlmId, journal_abbrev = MedAbbr, journal = JournalTitle) %>%\n",
    "  dplyr::inner_join(\n",
    "    delay_df %>%\n",
    "      dplyr::group_by(journal_nlm_id, delay_type) %>%\n",
    "      dplyr::summarize(\n",
    "        n_articles = n(),\n",
    "        median_delay = median(delay),\n",
    "        mean_delay = signif(mean(delay), 6),\n",
    "        n_unique_dates = length(unique(date)),\n",
    "        n_unique_years = length(unique(year)),\n",
    "        n_unique_delays = length(unique(delay))\n",
    "       )\n",
    "  )\n",
    "\n",
    "head(journal_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "journal_df %>%\n",
    "  readr::write_tsv(file.path('data', 'journal-summaries.tsv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>delay_type</th><th scope=col>year</th><th scope=col>n_journals</th><th scope=col>n_articles</th><th scope=col>median_delay</th><th scope=col>mean_delay</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Acceptance</td><td>1965</td><td>1</td><td>85</td><td>91</td><td>114.788</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Acceptance</td><td>1966</td><td>1</td><td>191</td><td>60</td><td>87.0785</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & delay_type & year & n_journals & n_articles & median_delay & mean_delay\\\\\n",
       "\\hline\n",
       "\t1 & Acceptance & 1965 & 1 & 85 & 91 & 114.788\\\\\n",
       "\t2 & Acceptance & 1966 & 1 & 191 & 60 & 87.0785\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 6]\n",
       "Groups: delay_type [1]\n",
       "\n",
       "  delay_type  year n_journals n_articles median_delay mean_delay\n",
       "       (chr) (dbl)      (int)      (int)        (dbl)      (dbl)\n",
       "1 Acceptance  1965          1         85           91   114.7880\n",
       "2 Acceptance  1966          1        191           60    87.0785"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_summary_df = delay_df %>%\n",
    "  dplyr::group_by(delay_type, year) %>%\n",
    "  dplyr::summarize(\n",
    "    n_journals = n_distinct(journal_nlm_id),\n",
    "    n_articles = n(),\n",
    "    median_delay = median(delay),\n",
    "    mean_delay = signif(mean(delay), 6)\n",
    "  )\n",
    "\n",
    "year_summary_df %>%\n",
    "  readr::write_tsv(file.path('data', 'yearly-summaries.tsv'))\n",
    "\n",
    "head(year_summary_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>delay_type</th><th scope=col>year</th><th scope=col>percentile</th><th scope=col>delay</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Acceptance</td><td>1965</td><td>0</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Acceptance</td><td>1965</td><td>2.5</td><td>23.3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & delay_type & year & percentile & delay\\\\\n",
       "\\hline\n",
       "\t1 & Acceptance & 1965 & 0 & 16\\\\\n",
       "\t2 & Acceptance & 1965 & 2.5 & 23.3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 4]\n",
       "Groups: delay_type, year [1]\n",
       "\n",
       "  delay_type  year percentile delay\n",
       "       (chr) (dbl)      (dbl) (dbl)\n",
       "1 Acceptance  1965        0.0  16.0\n",
       "2 Acceptance  1965        2.5  23.3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarize_year <- function(df) {\n",
    "  # Return delay percentiles from df\n",
    "  probs = seq(0, 1, 0.025)\n",
    "  dplyr::data_frame(\n",
    "    percentile = 100 * probs,\n",
    "    delay = quantile(df$delay, probs=probs)\n",
    "  )\n",
    "}\n",
    "\n",
    "year_percentile_df = delay_df %>%\n",
    "  dplyr::group_by(delay_type, year) %>%\n",
    "  dplyr::do(summarize_year(.))\n",
    "\n",
    "year_percentile_df %>%\n",
    "  format(digits=3) %>%\n",
    "  readr::write_tsv(file.path('data', 'yearly-percentiles.tsv'))  \n",
    "\n",
    "head(year_percentile_df, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global plotting settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xlab_accept = 'Date accepted'\n",
    "xlab_publish = 'Date published online'\n",
    "ylab_accept = 'Acceptance delay (days)'\n",
    "ylab_publish = 'Publication delay (days)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot delays by year for all articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot_years <- function(percentile_df, delay_df, min_year, max_delay, xlab, ylab) {\n",
    "  pctl_col = '#00693E'\n",
    "  gg = percentile_df %>%\n",
    "    ggplot2::ggplot(aes(x = year, y = delay, group = percentile)) +\n",
    "    ggplot2::geom_smooth(aes(group=NULL), data=delay_df, color='#B4B3B3', fill = '#B4B3B3', alpha=1) +\n",
    "    ggplot2::geom_line(size=0.1, color = pctl_col) +\n",
    "    ggplot2::geom_line(data = dplyr::filter(percentile_df, percentile == 50), size=0.8, color = pctl_col) +\n",
    "    ggplot2::geom_line(data = dplyr::filter(percentile_df, percentile %in% c(25, 75)), size=0.5, color = pctl_col) +\n",
    "    ggplot2::coord_cartesian(xlim = c(min_year, 2015), ylim = c(0, max_delay), expand = FALSE) +\n",
    "    ggplot2::theme_bw() +\n",
    "    ggplot2::xlab(xlab) +\n",
    "    ggplot2::ylab(ylab) +\n",
    "    ggplot2::theme(plot.margin=grid::unit(c(2, 10, 2, 2), 'points'))\n",
    "  return(gg)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Acceptance delay plot\n",
    "gg = plot_years(\n",
    "  percentile_df = year_percentile_df %>% dplyr::filter(delay_type == 'Acceptance'),\n",
    "  delay_df = delay_df %>% dplyr::filter(delay_type == 'Acceptance'),\n",
    "  min_year = 1981,\n",
    "  max_delay = 205,\n",
    "  xlab = xlab_accept,\n",
    "  ylab = ylab_accept\n",
    ")\n",
    "\n",
    "file.path('viz', 'acceptance-by-article.png') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 0.75 * 5.5)\n",
    "file.path('viz', 'acceptance-by-article.pdf') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 0.75 * 5.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Publication delay plot\n",
    "gg = plot_years(\n",
    "  percentile_df = year_percentile_df %>% dplyr::filter(delay_type == 'Publication'),\n",
    "  delay_df = delay_df %>% dplyr::filter(delay_type == 'Publication'),\n",
    "  min_year = 2000,\n",
    "  max_delay = 105,\n",
    "  xlab = xlab_publish,\n",
    "  ylab = ylab_publish\n",
    ")\n",
    "\n",
    "file.path('viz', 'publication-by-article.png') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 0.75 * 5.5)\n",
    "file.path('viz', 'publication-by-article.pdf') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 0.75 * 5.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Journal-specific publication delay histories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " Acceptance Publication \n",
       "       3086        2756 "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "journal_subset_df = journal_df %>%\n",
    "  dplyr::filter(n_articles >= 100) %>%\n",
    "  dplyr::filter(n_unique_dates >= 5) %>%\n",
    "  dplyr::filter(n_unique_delays >= 5) %>%\n",
    "  dplyr::select(journal_nlm_id:delay_type)\n",
    "\n",
    "table(journal_subset_df$delay_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plot_journal_history <- function(delay_df, journal_abbrev, xlab, ylab) {\n",
    "  # Plot all delays for a single journal and delay_type\n",
    "  x_box = boxplot.stats(delay_df$date_decimal, coef = 1.5)\n",
    "  xlimits = x_box$stats[c(1, 5)]\n",
    "  if(length(x_box$out) >= 35) {\n",
    "    xlimits = boxplot.stats(delay_df$date_decimal, coef = 2.75)$stats[c(1, 5)]\n",
    "  }\n",
    "  ylimits = c(0, boxplot.stats(delay_df$delay, coef = 2.8)$stats[5])\n",
    "  alpha = log(nrow(delay_df)) ^ -1.03 - 0.02\n",
    "  gg = delay_df %>%\n",
    "    ggplot2::ggplot(aes(x = date_decimal, y = delay)) +\n",
    "    ggplot2::geom_point(alpha=alpha, color='#e60000', size=0.8) +\n",
    "    ggplot2::geom_smooth(linetype=0, fill='#454343', alpha=0.26) +\n",
    "    ggplot2::coord_cartesian(xlim = xlimits, ylim = ylimits) +\n",
    "    ggplot2::theme_bw() +\n",
    "    ggplot2::xlab(xlab) +\n",
    "    ggplot2::ylab(ylab) +\n",
    "    ggplot2::theme(plot.margin=grid::unit(c(5, 12, 2, 2), 'points')) +\n",
    "    ggplot2::annotate('text', x = Inf, y = Inf, label = journal_abbrev, fontface='italic', hjust=1.12, vjust=2)\n",
    "  return(gg)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "property_map = list(\n",
    "  Acceptance = list(directory = 'accept', xlab = xlab_accept, ylab = ylab_accept),\n",
    "  Publication = list(directory = 'publish', xlab = xlab_publish, ylab = ylab_publish)\n",
    ")\n",
    "\n",
    "for (i in 1:nrow(journal_subset_df)) {\n",
    "  # Iterate through journal-delay_type pairs\n",
    "  delay_type_ = journal_subset_df$delay_type[i]\n",
    "  props = property_map[[delay_type_]]\n",
    "  nlm_id = journal_subset_df$journal_nlm_id[i]\n",
    "  journal_abbrev = journal_subset_df$journal_abbrev[i]\n",
    "    \n",
    "  gg = delay_df %>%\n",
    "    dplyr::filter(delay_type == delay_type_) %>%\n",
    "    dplyr::filter(journal_nlm_id == nlm_id) %>%\n",
    "    plot_journal_history(\n",
    "      journal_abbrev = journal_abbrev,\n",
    "      xlab = props$xlab,\n",
    "      ylab = props$ylab\n",
    "    )\n",
    "  \n",
    "  path = file.path('viz', 'journal', props$directory, sprintf('%s.png', nlm_id))\n",
    "  ggplot2::ggsave(filename = path, plot = gg, width = 5.5, height = 3.8, dpi = 150)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Journal-specific delay slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "get_beta = function(df) {\n",
    "  # Regress delay against date and return model information\n",
    "  model = lm(delay ~ date_decimal, data = df)\n",
    "  model_df = broom::tidy(model)\n",
    "  row_df = dplyr::data_frame(\n",
    "    slope = model_df$estimate[2],\n",
    "    slope_as_percent = 100 * model_df$estimate[2] / mean(df$delay),\n",
    "    p_value = model_df$p.value[2]\n",
    "  )\n",
    "  return(row_df)\n",
    "}\n",
    "\n",
    "slope_df = journal_subset_df %>%\n",
    "  dplyr::group_by(journal_nlm_id, delay_type) %>%\n",
    "  dplyr::inner_join(delay_df) %>%\n",
    "  dplyr::do(get_beta(.))\n",
    "\n",
    "slope_df %>%\n",
    "  format(digits=3, scientific=3) %>%\n",
    "  readr::write_tsv(file.path('data', 'slopes.tsv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>journal_nlm_id</th><th scope=col>delay_type</th><th scope=col>slope</th><th scope=col>slope_as_percent</th><th scope=col>p_value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0001027</td><td>Acceptance</td><td>2.38085</td><td>1.94429</td><td>0.385</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0001027</td><td>Publication</td><td>14.75634</td><td>21.60469</td><td>4.01e-20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & journal_nlm_id & delay_type & slope & slope_as_percent & p_value\\\\\n",
       "\\hline\n",
       "\t1 & 0001027 & Acceptance & 2.38085 & 1.94429 & 0.385\\\\\n",
       "\t2 & 0001027 & Publication & 14.75634 & 21.60469 & 4.01e-20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 5]\n",
       "\n",
       "  journal_nlm_id  delay_type    slope slope_as_percent  p_value\n",
       "           (chr)       (chr)    (dbl)            (dbl)    (dbl)\n",
       "1        0001027  Acceptance  2.38085          1.94429 3.85e-01\n",
       "2        0001027 Publication 14.75634         21.60469 4.01e-20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head(slope_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Violin plots of per-journal change in delays\n",
    "gg = slope_df %>%\n",
    "  ggplot2::ggplot(aes(x = delay_type, y = slope)) +\n",
    "  ggplot2::geom_violin(linetype=0, fill='#80a5f9') +\n",
    "  ggplot2::stat_summary(fun.y=mean, geom='point', size=6, color=\"white\", shape='+') +\n",
    "  ggplot2::stat_summary(fun.y=quantile, geom='point', size=6, color=\"white\", shape='|') +\n",
    "  ggplot2::geom_hline(yintercept = 0, linetype = 'dashed') + \n",
    "  ggplot2::theme_bw() +\n",
    "  ggplot2::ylab('Δ days of delay per year') +\n",
    "  ggplot2::scale_x_discrete(name = NULL) +\n",
    "  ggplot2::coord_flip(ylim = c(-20, 20)) +\n",
    "  ggplot2::theme(plot.margin=grid::unit(c(2, 2, 2, 2), 'points'))\n",
    "\n",
    "file.path('viz', 'slope-distributions.png') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 2.3)\n",
    "file.path('viz', 'slope-distributions.pdf') %>%\n",
    "  ggplot2::ggsave(plot = gg, width = 5.5, height = 2.3, device = cairo_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>delay_type</th><th scope=col>percent_decreasing</th><th scope=col>lower</th><th scope=col>upper</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Acceptance</td><td>0.4656513</td><td>0.02669577</td><td>1.379158</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Publication</td><td>0.6901306</td><td>-5.369522</td><td>-3.420653</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & delay_type & percent_decreasing & lower & upper\\\\\n",
       "\\hline\n",
       "\t1 & Acceptance & 0.4656513 & 0.02669577 & 1.379158\\\\\n",
       "\t2 & Publication & 0.6901306 & -5.369522 & -3.420653\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Source: local data frame [2 x 4]\n",
       "\n",
       "   delay_type percent_decreasing       lower     upper\n",
       "        (chr)              (dbl)       (dbl)     (dbl)\n",
       "1  Acceptance          0.4656513  0.02669577  1.379158\n",
       "2 Publication          0.6901306 -5.36952170 -3.420653"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Medians of violins\n",
    "slope_df %>%\n",
    "  dplyr::group_by(delay_type) %>%\n",
    "  dplyr::summarize(\n",
    "    percent_decreasing = mean(slope < 0),\n",
    "    lower = t.test(slope, conf.level = 0.99)$conf.int[1],\n",
    "    upper = t.test(slope, conf.level = 0.99)$conf.int[2]\n",
    "  )"
   ]
  }
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