solution/3_analysis_and_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis and visualisation\n",
    "\n",
    "\n",
    "### Step 1: load packages\n",
    "\n",
    "* **dplyr** for data manipulation\n",
    "* **ggplot2** for data visualization\n",
    "* **forcats** for working with categorical variables\n",
    "\n",
    "If necessary, install the packages before loading them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 4.1.0\"\n",
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 4.1.0\"\n",
      "Warning message:\n",
      "\"package 'forcats' was built under R version 4.1.1\"\n"
     ]
    }
   ],
   "source": [
    "#install.packages(\"dplyr\")\n",
    "#install.packages(\"ggplot2\")\n",
    "#install.packages(\"forcats\")\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(forcats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: loading and preparing the data\n",
    "\n",
    "First, we import the metadata we retrieved from crossref and Unpaywall. We use the function *read.csv*, specify that the colum *X* should be interpreted as row names, and store the results in the objects *DF_crossref* and *DF_Unpaywall*.\n",
    "\n",
    "We can combine both data sets by joining them. Join operations combine two data frames by a shared column, following specific rules. The package *dplyr* includes several join operations.\n",
    "\n",
    "In our case, both data frames share the DOIs of the documents we are analysing, and they hold information on the same set of documents. Therefore, we can use the function *left_join*, and specify that we want to join the two data frames by the column *DOIs*.\n",
    "\n",
    "**Question:**\n",
    "What do you think, why are DOIs great for joining metadata on publications?\n",
    "* DOIs are great for joining metadata on publications, because they uniquely and persitently identify a document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the data\n",
    "DF_crossref <- read.csv(\"../data/crossref_metadata.csv\", row.names = \"X\")\n",
    "DF_Unpaywall <- read.csv(\"../data/Unpaywall_metadata.csv\", row.names = \"X\")\n",
    "\n",
    "# join the two data frames by the column DOIs\n",
    "DF <- left_join(DF_crossref, DF_Unpaywall, by = \"DOIs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: inspecting the data\n",
    "\n",
    "Next, we inspect the results. The functions *str*, *head*, and *table* are very useful to get an overview of a data frame:\n",
    "* *str* returns a description of the structure of an object, and\n",
    "* *table* returns a frequency distribution of the variables in one or more column(s).\n",
    "\n",
    "**Question:** What does *str* tell you about the structure of the data frame?\n",
    "* *DF* has 51334 rows (\"observations\" in R) and 5 columns (\"variables\" in R). You can see the data types of each column (integer, character or logical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t51334 obs. of  5 variables:\n",
      " $ DOIs            : chr  \"10.21203/rs.3.rs-38496/v1\" \"10.3892/or.2019.7247\" \"10.1145/2983323.2983807\" \"10.21203/rs.3.rs-128597/v1\" ...\n",
      " $ publication_year: int  2020 2019 2016 2020 2021 2021 2016 2019 2018 2020 ...\n",
      " $ resource_type   : chr  \"posted-content\" \"journal-article\" \"proceedings-article\" \"posted-content\" ...\n",
      " $ is_oa           : logi  TRUE TRUE FALSE TRUE TRUE TRUE ...\n",
      " $ oa_status       : chr  \"green\" \"bronze\" \"closed\" \"green\" ...\n"
     ]
    }
   ],
   "source": [
    "# ispect the structure\n",
    "str(DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Use *table* to look at frequency distribution tables of *publication_year* and *resource_type*. Do you notice any issues with the data we might have to adress before we proceed?\n",
    "* There is one publication with the publicaton year 2022. The publication count for 2021 is incomplete. These cases should be excluded from the analysis.\n",
    "* There are various resource types. We should restrict the analysis to certain resource types to get more reliable results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "2015 2016 2017 2018 2019 2020 2021 2022 \n",
       "5082 6064 7547 8365 8784 9490 6001    1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "               book        book-chapter             dataset     journal-article \n",
       "                 46                 463                 242               44994 \n",
       "      journal-issue           monograph               other         peer-review \n",
       "                  3                  16                   5                 209 \n",
       "     posted-content proceedings-article     reference-entry              report \n",
       "                766                4438                 134                  18 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency distribution of the columns publication_year and resource_type\n",
    "table(DF$publication_year)\n",
    "table(DF$resource_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: cleaning the data\n",
    "\n",
    "The package *dplyr* includes several functions for manipulating data. For example, we can filter out rows where the column *publication_year* contains a value that are larger than *202* (*publication_year <= 2020*), and overwrite *DF* with the new result. If we look at the frequency distribution table of *publication_year*, we see that we have sucessfully removed documents with the publication years 2021 and 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "2015 2016 2017 2018 2019 2020 \n",
       "5082 6064 7547 8365 8784 9490 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove rows where publication_year is larger than 2020\n",
    "DF <- filter(DF, publication_year <= 2020)\n",
    "# new frequency distribution of the column publication_year\n",
    "table(DF$publication_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to restrict our analysis to journal articles and articles published in proceedings. We can do this by specifying two conditions in *filter*, and combine them with the or-operator \"|\". This will return all rows where *resource_type* is either *journal-article* or *proceedings-article*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    journal-article proceedings-article \n",
       "              39611                4168 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filter rows where resouce_type is either journal-article or proceedings-article\n",
    "DF <- filter(DF, resource_type == \"journal-article\" | resource_type == \"proceedings-article\")\n",
    "# new frequency distribution of the column publication_year\n",
    "table(DF$resource_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have cleaned the data, we can check for duplicates. We can do this by passing the DOIs to *duplicated* and the result to *sum*. *duplicated* returns a Boolean vector with the same length as the input, where *TRUE* means that a value is duplicated and *FALSE* means it is not. Most R functions interpret *TRUE* as 1 and *FALSE* as 0. Therefore, we can count the number of *TRUE* values in the vector (= the number of duplicated DOIs) by calling *sum*. In this example, there are no duplicate DOIs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# check if there are duplicates\n",
    "sum(duplicated(DF$DOIs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: analyse the results\n",
    "\n",
    "*dplyr* functions are also very useful for analysing data.\n",
    "\n",
    "For example, you can use *count* to count the frequency of variables in a column, exactly like *table*. The advantage of *count* is that you can pass the output directly to a new *dplyr* function using the pipe operator \"%>%\". Below, we pass the output of *count* to *mutate*, a function that lets you create a new column based on other columns. We create a new column *percent* by dividing *n* (the output column of *count*) by the *sum* of n, multiplied by 100.\n",
    "\n",
    "**Question:** How many publications are (not) Open Access?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 2 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>is_oa</th><th scope=col>n</th><th scope=col>percent</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FALSE</td><td>22519</td><td>51.4379</td></tr>\n",
       "\t<tr><td> TRUE</td><td>21260</td><td>48.5621</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 2 × 3\n",
       "\\begin{tabular}{lll}\n",
       " is\\_oa & n & percent\\\\\n",
       " <lgl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FALSE & 22519 & 51.4379\\\\\n",
       "\t  TRUE & 21260 & 48.5621\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 2 × 3\n",
       "\n",
       "| is_oa &lt;lgl&gt; | n &lt;int&gt; | percent &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| FALSE | 22519 | 51.4379 |\n",
       "|  TRUE | 21260 | 48.5621 |\n",
       "\n"
      ],
      "text/plain": [
       "  is_oa n     percent\n",
       "1 FALSE 22519 51.4379\n",
       "2  TRUE 21260 48.5621"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency distribution of is_oa and calculating percentages\n",
    "DF %>%\n",
    "    count(is_oa) %>%\n",
    "    mutate(percent = n / sum(n) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building on this, we can calculate frequency distributions and percentages by publication year. All we have to do is pass the data through *group_by* first. *group_by* groups a column by its values and lets you treat each group separately.\n",
    "\n",
    "**Question:** From the results, do you suspect a trend in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A grouped_df: 12 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>publication_year</th><th scope=col>is_oa</th><th scope=col>n</th><th scope=col>percent</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2015</td><td>FALSE</td><td>2872</td><td>57.61284</td></tr>\n",
       "\t<tr><td>2015</td><td> TRUE</td><td>2113</td><td>42.38716</td></tr>\n",
       "\t<tr><td>2016</td><td>FALSE</td><td>3313</td><td>57.32826</td></tr>\n",
       "\t<tr><td>2016</td><td> TRUE</td><td>2466</td><td>42.67174</td></tr>\n",
       "\t<tr><td>2017</td><td>FALSE</td><td>4108</td><td>56.02073</td></tr>\n",
       "\t<tr><td>2017</td><td> TRUE</td><td>3225</td><td>43.97927</td></tr>\n",
       "\t<tr><td>2018</td><td>FALSE</td><td>4235</td><td>52.15517</td></tr>\n",
       "\t<tr><td>2018</td><td> TRUE</td><td>3885</td><td>47.84483</td></tr>\n",
       "\t<tr><td>2019</td><td>FALSE</td><td>4185</td><td>49.03914</td></tr>\n",
       "\t<tr><td>2019</td><td> TRUE</td><td>4349</td><td>50.96086</td></tr>\n",
       "\t<tr><td>2020</td><td>FALSE</td><td>3806</td><td>42.15773</td></tr>\n",
       "\t<tr><td>2020</td><td> TRUE</td><td>5222</td><td>57.84227</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A grouped\\_df: 12 × 4\n",
       "\\begin{tabular}{llll}\n",
       " publication\\_year & is\\_oa & n & percent\\\\\n",
       " <int> & <lgl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2015 & FALSE & 2872 & 57.61284\\\\\n",
       "\t 2015 &  TRUE & 2113 & 42.38716\\\\\n",
       "\t 2016 & FALSE & 3313 & 57.32826\\\\\n",
       "\t 2016 &  TRUE & 2466 & 42.67174\\\\\n",
       "\t 2017 & FALSE & 4108 & 56.02073\\\\\n",
       "\t 2017 &  TRUE & 3225 & 43.97927\\\\\n",
       "\t 2018 & FALSE & 4235 & 52.15517\\\\\n",
       "\t 2018 &  TRUE & 3885 & 47.84483\\\\\n",
       "\t 2019 & FALSE & 4185 & 49.03914\\\\\n",
       "\t 2019 &  TRUE & 4349 & 50.96086\\\\\n",
       "\t 2020 & FALSE & 3806 & 42.15773\\\\\n",
       "\t 2020 &  TRUE & 5222 & 57.84227\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A grouped_df: 12 × 4\n",
       "\n",
       "| publication_year &lt;int&gt; | is_oa &lt;lgl&gt; | n &lt;int&gt; | percent &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 2015 | FALSE | 2872 | 57.61284 |\n",
       "| 2015 |  TRUE | 2113 | 42.38716 |\n",
       "| 2016 | FALSE | 3313 | 57.32826 |\n",
       "| 2016 |  TRUE | 2466 | 42.67174 |\n",
       "| 2017 | FALSE | 4108 | 56.02073 |\n",
       "| 2017 |  TRUE | 3225 | 43.97927 |\n",
       "| 2018 | FALSE | 4235 | 52.15517 |\n",
       "| 2018 |  TRUE | 3885 | 47.84483 |\n",
       "| 2019 | FALSE | 4185 | 49.03914 |\n",
       "| 2019 |  TRUE | 4349 | 50.96086 |\n",
       "| 2020 | FALSE | 3806 | 42.15773 |\n",
       "| 2020 |  TRUE | 5222 | 57.84227 |\n",
       "\n"
      ],
      "text/plain": [
       "   publication_year is_oa n    percent \n",
       "1  2015             FALSE 2872 57.61284\n",
       "2  2015              TRUE 2113 42.38716\n",
       "3  2016             FALSE 3313 57.32826\n",
       "4  2016              TRUE 2466 42.67174\n",
       "5  2017             FALSE 4108 56.02073\n",
       "6  2017              TRUE 3225 43.97927\n",
       "7  2018             FALSE 4235 52.15517\n",
       "8  2018              TRUE 3885 47.84483\n",
       "9  2019             FALSE 4185 49.03914\n",
       "10 2019              TRUE 4349 50.96086\n",
       "11 2020             FALSE 3806 42.15773\n",
       "12 2020              TRUE 5222 57.84227"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency distribution of is_oa and calculating percentages, by publication year\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa) %>%\n",
    "    mutate(percent = n / sum(n) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to quantitify the trends in we observed by fitting linear regression models. Here, we fit two models using the funtion *lm*: one for publication rates of Open Access publications, and one for non-OA publication rates. (*lm* uses the leas-squares method)\n",
    "\n",
    "First, we prepare our data by filtering for *\"is_oa == FALSE\"*, grouping by *publication_year* and counting the cases. We then pass this data and the formula. We also specify the *formula*, which follows the pattern *\"dependent variable ~ independent variable\"*. Here, *n* is our dependent variable, which we expect to change depending on *publication_year*. We store the results in *lm_not_oa*.\n",
    "\n",
    "Using *summary*, we can access some summary statistics of *lm_not_oa*:\n",
    "* *coef*: The coefficient indicated the slope of the linear model.\n",
    "* *r.squared*: R squared is a goodness-of-fit measure that indicates how much of the variance in the dependent variable can be explained by the independent variable.\n",
    "\n",
    "Below, we can see that the coefficient for *lm_ot_oa* is ca. 212 non-OA publications per year. R squared indicates that the linear model does not fit the data very well. This makes sense, considering non-OA publication rates increased until 2018 and decreased since."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Estimate</th><th scope=col>Std. Error</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-423553.3</td><td>206381.2848</td><td>-2.052286</td><td>0.1094060</td></tr>\n",
       "\t<tr><th scope=row>publication_year</th><td>    211.8</td><td>   102.2955</td><td> 2.070472</td><td>0.1071728</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 2 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -423553.3 & 206381.2848 & -2.052286 & 0.1094060\\\\\n",
       "\tpublication\\_year &     211.8 &    102.2955 &  2.070472 & 0.1071728\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | Estimate | Std. Error | t value | Pr(&gt;|t|) |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept) | -423553.3 | 206381.2848 | -2.052286 | 0.1094060 |\n",
       "| publication_year |     211.8 |    102.2955 |  2.070472 | 0.1071728 |\n",
       "\n"
      ],
      "text/plain": [
       "                 Estimate  Std. Error  t value   Pr(>|t|) \n",
       "(Intercept)      -423553.3 206381.2848 -2.052286 0.1094060\n",
       "publication_year     211.8    102.2955  2.070472 0.1071728"
      ]
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   "source": [
    "# prepare the data for fittint a linear model\n",
    "lm_not_oa <- DF %>%\n",
    "    filter(is_oa == FALSE) %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa)\n",
    "# fit the linear model\n",
    "lm_not_oa <- lm(data = lm_not_oa, formula = n ~ publication_year)\n",
    "\n",
    "# look at the coefficient\n",
    "summary(lm_not_oa)$coef\n",
    "\n",
    "# look at goodness-of-fit measure\n",
    "summary(lm_not_oa)$r.squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** Repeat the procedure for Open Access publications (setting *\"is_oa == TRUE\"*) and compare the results. What do you notice?\n",
    "* The coefficient for *lm_oa* is much higher (ca. 625 publications per year).\n",
    "* R squared indicates that the linear model is a much better fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 2 × 4 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Estimate</th><th scope=col>Std. Error</th><th scope=col>t value</th><th scope=col>Pr(&gt;|t|)</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-1256183.7</td><td>63238.05516</td><td>-19.86436</td><td>3.789228e-05</td></tr>\n",
       "\t<tr><th scope=row>publication_year</th><td>     624.4</td><td>   31.34475</td><td> 19.92040</td><td>3.747121e-05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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      "text/latex": [
       "A matrix: 2 × 4 of type dbl\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -1256183.7 & 63238.05516 & -19.86436 & 3.789228e-05\\\\\n",
       "\tpublication\\_year &      624.4 &    31.34475 &  19.92040 & 3.747121e-05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 2 × 4 of type dbl\n",
       "\n",
       "| <!--/--> | Estimate | Std. Error | t value | Pr(&gt;|t|) |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept) | -1256183.7 | 63238.05516 | -19.86436 | 3.789228e-05 |\n",
       "| publication_year |      624.4 |    31.34475 |  19.92040 | 3.747121e-05 |\n",
       "\n"
      ],
      "text/plain": [
       "                 Estimate   Std. Error  t value   Pr(>|t|)    \n",
       "(Intercept)      -1256183.7 63238.05516 -19.86436 3.789228e-05\n",
       "publication_year      624.4    31.34475  19.92040 3.747121e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.99002051775161"
      ],
      "text/latex": [
       "0.99002051775161"
      ],
      "text/markdown": [
       "0.99002051775161"
      ],
      "text/plain": [
       "[1] 0.9900205"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the data for fittint a linear model\n",
    "lm_oa <- DF %>%\n",
    "    filter(is_oa == TRUE) %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa)\n",
    "# fit the linear model\n",
    "lm_oa <- lm(data = lm_oa, formula = n ~ publication_year)\n",
    "\n",
    "# look at the coefficient\n",
    "summary(lm_oa)$coef\n",
    "\n",
    "# look at goodness-of-fit measure\n",
    "summary(lm_oa)$r.squared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: visualise the results\n",
    "\n",
    "*dplyr* works very well with *ggplot2*, an R package for data visualisation. You can manipulate your data with *dplyr* functions and pipe the results directly to the *ggplot* function.\n",
    "\n",
    "Within the *ggplot* function, you can assign your data frame columns to common aesthetic properties of graphs (within the *aes* argument), for example the x and y axis (required in most cases), color, or size. Then, you add a geometry (with *+ geom_...*), which determines the type of plot that will be generated.\n",
    "\n",
    "We can use our previous example and create a bar chart (*geom_col*), where *is_oa* is shown on the x axis, and the frequency on the y axis.\n",
    "\n",
    "**Question:** Do you like this plot? Why (not)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yagu68Z0u2dIV2NVBuC7r7qimRIlyPV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\nMCR4AEOCBzAkeABDggf4//sUQCMkb+9UAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart of is_oa and n\n",
    "DF %>%\n",
    "    count(is_oa) %>%\n",
    "    ggplot(aes(x = is_oa, y = n)) +\n",
    "    geom_col()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding information on publication years, we can look at the development of Open Access over the years. We map *publication_year* to the x axis, *n* to the y axis, and *is_oa* to the *fill* aesthetic (determining the color of the bar). Note that *ggplot2* automatically generates a legend! This example creates a stacked bar chart where, the segments are stacked on top of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ui9mVX/5lBm3QtviIQQQpF6M6v+zaHM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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart of publication_year and n, filled by is_oa\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa) %>%\n",
    "    ggplot(aes(x = publication_year, y = n, fill = is_oa)) +\n",
    "    geom_col()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to establish a common baseline for all bars, we can adjust the position in *geom_col* and set it to *dodge*. This will plot the bars side by side, but still grouped together by publication year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g/lWV81cgMKKW0QkpBKDGvNvN4gpN6f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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adjusting the position to \"dodge\"\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa) %>%\n",
    "    ggplot(aes(x = publication_year, y = n, fill = is_oa)) +\n",
    "    geom_col(position = \"dodge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*position* is also very useful if you want to show proportions. If you set *position* to *fill*, the bars will be plotted stacked on top of each other, but the height of the bars will be normalised. As a result, the full length of the stacked bars will correspond to 100 %, and each segment will be plotted proportionally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y2A1FwAqe4a7LcAUnMBpLprsN8CSM0F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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adjusting the position to \"fill\"\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(is_oa) %>%\n",
    "    ggplot(aes(x = publication_year, y = n, fill = is_oa)) +\n",
    "    geom_col(position = \"fill\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: going beyond...\n",
    "\n",
    "* You can stack multipe geometries (here: geom_point and geom_line; geom_area and geom_hline)\n",
    "* You can vary how variables are displayed (here: total or cumulative)\n",
    "* You can change the color palette and the order of the segments\n",
    "* You can change how axis are displayed\n",
    "* You can change labels (here: axis and legend)\n",
    "* You can change the theme (use predefined themes or build your own)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C95/hDJCZ41evP69Rsb5Ri/baJ7doVGjs9zVKKCsnp5/hDJAZ47o0IjKyrNevtR\ne4Lvbm4ql25WVP1hlH8M0fW4rF+eP0Syjfcx3ZuWlQXNfRtfN8gF724+WOacRAVV9fL8IZIN\nDjME7xrl+evGo9frylnydtg2yJVJviVCpKhoJ9sCi4qB3X5YV7GqS1r4tnInIplKVMDQLhba\nV/RoZOOn7n0/9Op1y6sVI7zF2zN8uLE7tJsjUQkiRcJNrVH3f8jua20nRqciLZkxrdjm5KY6\n+3UdUlXU5OrcGOXz5flDpLV8OATpxotFb4553Zr06vWbIQyKXLXrlqX/fSySqGrAfiwgzx8i\nreXmw/EcwWFVg54cuqRXw78/79PK3evWX5BdLlHTAnn+EGklx5NtR5VMGlKsMlTfTB42LtT6\nXSDXaLRWogp5/hBpDeXk6J1Lj0y06F+QnemTcDdVOxIVyPOHSMvprhoVF/fXXNk/ZU7PMnhf\ng5FPb2Qf2mJPogJ5/kwaUB2T7Rn6al5OVBym2DfNLTK2OqQ5Cq0u7v9WWGq6IXYlKpDnz6AB\ntTf1P/2v5uVExPHS7Y21u58tK2RQdu2RlRrMsC9RgTx/5xuQ5Yh0zOkVkPUaOTRoqqa+R46F\nciNRgTx/xkM7RKoYuo64Cn8KnVY76pGD2t1JVCDPnwWRsux4uhQxg5fjFyNS6ECxyDDpka22\nuJWoQJ4/eiRjLFokV6iku15nINNEw6beDeVeogJ5/hDJEEsWhaFQyeC69xKhxt9Y6EeiAnn+\nEMkEG51RQAqVTF8/muHTyDn4k6hAnj9EOstai+b9790ThrczmOj06vWbX/7t337pvrHQr0QF\n8vwh0hmMLBqZIwSpUMnc24ImTuL1qz/98Y9/2v/3S/U38C9RgTx/3NkwhVlfNDBHCFehkqW3\n1w12SH9qefWLRKICef64124U0yHdsR6BK1Sy+jbVI5H+WFn017/+n//9S4F3iQrk+UOkYWZM\njJp3L5x5P1AQCpVYu021PK0//bdCooL/9ddfGrxKVCDPHyINMWd54U05rpt4h2o4BlVYvt37\n+9/+/tdapH9tRfJ+2vL8IdIJM1fpRvdMCE+hEpseFR3P979X7E3611Yi738Aef4Q6Zh5FpVz\nhEGRvDR2CdY8aqZBT9///re//b347/vTL28GWF+VAfL8IVKHWRY1OelvPuKnqUux4tHxqtz3\np6dCo6cfT+UvNaNbef4QqWWGRUchOXRJnhq6gvUenS5vF5/z1Vuj8y+TPH+IVGHeGZ0E5HW7\n+4i31i5lpUeD11p/5D+eyg8W7y3T+ZVJnj9EymdYNBwOa58G4Zo1Ho1IVP/yRKMKfzLJ84dI\npkO6qVBsQqPlHk1KdA4/Msnzl7pIZhb5Haa4YeHtDGscanH/95PnL2mRjIZ0MUiUL/PIikQN\nbv+O8vylK5KJRZFIlC/wyKpEDe7+nvL8pSrSeYvikSif6dGqKdFZ3Pxd5flLUqSznVFUEuVz\nlhncStRg/+8rz196Ip2zKDaJcmOP/EjUYPfvLM9fYiKdsaBVQfgAAAt0SURBVChCiXIzj/xK\n1GDv7y3PX1IiTVoUp0S5gUcaiRrs/N3l+UtHpKnOKFqJ8nMeSR1qWf/3l+cvEZEmLIpZonzS\nozAkalj3Osjzl4RIoxZFLlE+6pF2NDfK8tdDnr/4RRqzKH6J8mGPApWoYdnrIs9f5CKNDOmS\nkCgf8ChwiRrmvz7y/MUs0rBFqUiU9z3aiEQN814nef7iFWnIooQkOr4taGMSNZi/XvL8RSrS\nQGeUlER5x6NtOtRi9rrJ8xejSKcWpSZR3nj0+++blqjh3Ov35rU8f3GI1P0M175FCUqUlx79\nXrB9iRrGX8fi/cmIZIEPh08V71mUikT9PRN+/P77gEaq1tlj8PUsQaT1tN7cHFn0cyIS5b1d\nfAplTj3SttAmJ69psR0aIllg78/PP/30283Nu4NEQxbpWuiWji31155H6gbap/uyVht0ItJq\nPtz89uuvvxX/vbupJBqwSNY6D9S7Mz49fR/wSN04d3Q7JERaRdX7vPut5d3PPw9oJGiZT4ot\ngyu+Px17pG6ZexBpFccrCu9+rSz6y1/++/84schjq/xyGM+1HtVdUmmRvU9tCR6GdrP5MMTP\nf/nzX2r+/FPsEg1dWu2JVEhk61NbtkG5zS0iGdBX55h/+udKo59++sOrKCUasqfDU3doZ/tT\nxLZA+YIj0gRj6vRE2itU8yoeic7YM9QlFR+rkqBHXJAd5Zw6Dc3y509/+MNPxX+vtrAB9yQz\n9Bli4a7E24dbhFoKJYzsOV2W2082C41ev9nA5xMNsciZowLqC7LJepSrb1Gz2IBsdmdwMGGh\nQM1saJPLC8u6nLHSfv+P//h96S75cRCPSAYfbNJ3YKFAp+XKP59o7POBTo6zaU/L4/394+P9\n5/uEPYpIpOGIDzJboLO16zWaUMmNPQcqf+7vC50S1SgykYo5yrA6swWy1CovDKZ/kT3L7kW4\nbzz6nK5HEYn0U/Op3ssEat7HukGeGg2enHc+QzzeVxrtRbq3cj6bJB6RinWzV3MEOnoL64b5\n8VR9sPdTc6+bD3s6fL4/YK/UrRGPSIcLoqMC9e/usVS1mP69bn70qSgGdQeRHhFp+w0oRPrz\nv5xTJyaBSn6c3jTq3J6aZrX7/nPrUbpTpIhEKm4a/Z9j6kQnUEHtyVP/bQwu7al57FKL9Jhy\nhxSRSOX7gZIQqKDb4xxE8vIOoMc+9cJ3wmvfeUwi/frrbzfvoheooD90K28Y9dAN5QMSlfpU\nF2RTHtjFJFKxZ8JN1AKVDM2BzlyQtcSwRPXvEtcoJpFu9lgqK1TGVxJcazQlERTEI1LkXZHH\n5bgeSGRCPCJ19jqNDSQKn3hEilQjJNoG8YhkqZygkFmERHOR5w+RRkCiTSHPHyINgESbQ54/\nROqBRJtEnj9E6oJEW0WeP0RqUHVFSGQDef4QqQCJto48f4iERDEgz1/iIiFRJMjzl7BIoq4I\niVwgz1+iIiFRZMjzl6BISBQh8vylJhISxYk8fymJpOmKkMgH8vylIhISxY08fymIhETxI89f\n7CL5kai/+QgS+Uaev6hF8tQVHW+HhUQK5PmLViR/47mOLkikQp6/OETqbYfld1JUb3Nafj4R\nEolAJAscbdDofWXh8b7+eKL2wyeRyD+ItJ6OMP6X57ofq9LpkhzXCn0QaT31xttDn6ritN7a\nmRORnFYKwyDSan48/Rj4WBWnEh2N4NrPJyqGdg4rhSkQaSXDH/Tlrr6BVbnDB325qxbOgEiL\naaTpi+SswsG17XK5rllkcFY1nAORlnA0Dzr6xDw39Y0pVMvD5xPpQaR5DC1uNyL9eHJQ4RmH\nmqPQSAwimTKkULNc1ywy2K3RSCEIA0QyYNyhSh7rn5hn1g1BQCDSJOcUao+zphEObRNEGsPU\nIXug0IZBpFOCUgiHtgEiHeFdIRyKBESq8d8NMZSLCUQKTCEc2iZpi4RDYIlURRIoxFAuZhIU\niW4I7JOUSIF1QzgUEamI5FihwZtGUSgh4hfJQzd08jYGuqHkiEekUyl8jeSOJMGhNIlHpKPb\nRr3Ohpr3pw7tK4dCiRCPSLUiXhUqebzvbD6CQ4kSk0gD22G5dagyZXBfORxKjHhEOtkOy5lC\nvS5nSCTrdULgRCVSv0uyVHTDyPznaF85HEqUWEWyVGrB1Dpcr0uyWCtsi6hEKod2lsozEKjt\nkpgUQVQiWdkOy9ifgzfsKwfxiLRyQLfAn+6z0Shx4hFp4T4+6wQCqIhHpHka4Q9YJR6RzA5D\nIHBCPCJNBh9/wC3xiDQ43ccf8EM8InVtoAMCz0Ql0tDN1/gDPohHpLG3MSAQeCAqkcbfWWep\nEoARIhfJUuEAZ4hKpM98KDGIiEskS2UBzCUekeiJQEg8InH/NQiJRyQ0AiHxiGSpHIAlyPOH\nSBAD8vwhEsSAPH+IBDEgzx8iQQzI84dIEAPy/CESxIA8f4gEMSDPHyJBDMjzh0gQA/L8IRLE\ngDx/iAQxIM8fIkEMyPOHSBAD8vwhEsSAPH+IBDEgzx8iQQzI84dIEAPy/CESxIA8f4gEMSDP\nHyJBDMjzh0gQA/L8IRLEgDx/iAQxIM8fIkEMyPOHSBAD8vwhEsSAPH+IBDEgzx8iQQzI82dN\nJAAllnK8XIDAyqF+6t9i/YhE/dQfUgPUJ0L91J92AwBiAJEALIBIABZAJAALIBKABRAJwALL\nRWouJx8uK2ftz33oOVW/h+rH6vd2nT3Q85fVf9oeryyuM6v/yQ4PsnUl2qn/8I+m/sMvNfWr\nz19V/2l7/GJPpKz5P5KNZi2vXxak7FCrlw5ppH71+SPSwmd3Gu7/HAKs32sbxs7fVwPCqj8y\nkXyOUQdfSG39uc+XMawgq+vfrkhZrv1DDtbf/lBTv7/aR+r3+D+ysdffVwN69Z889ol1kdaX\nuq5+tcj+ag/0/AuPEWn+c4UiDdbv8Q85cv5ePQrw/LX1b1Ckfut9n0iY9XudIAzUrz5/Xf0n\n7fHK8iq7semPkVc2am39XhowUr/XcdVA/erzl9V/2h6vLK6yvYIvurIdbP2+xjWhnr+o/oH2\neEVRJ0B0IBKABRAJwAKIBGABRAKwACIBWACRACyASAAWQCQACyCSSw7X2IvvRq64f7rIR38H\nW4HXzyVGItU3tvhqEziB188lxyKdPQi2Cy+iLfY+XGdXz3ljRt0HdX5W/vz5Oru4K37/9Tor\nv6tusqx+9z7L3ldHt4fl+Ut22X55KY546T59f+y3iyvfJwt9EMkW+4Tvlbh4ORbpuvOz4r+X\ni0Kc6zx/qG5XvuuIVP2uOvqi+mXJXfaw//dL9jHPyx8XXrVP3x97lb3XnTZUIJIt9oF+ya+q\naFeP+z8r/rvbh/5r8c1l9iXPv7WdUfW7fc9SH71/3qfsoir5W/Hzfdf2Nf9Y/PIu+3T89Dvd\nSUMDItliP8TaD86K7qIr0nPnZ5U/L80Tnh8+Xh2JdNk5+jBEzAuH9kUXWl1WJV8fP/3Z85nC\nAIhki6OZ0eB3xyt3V81b0VqRhp5X8m2vzkMxfsuy9v1rx08HNbwKtpgp0vvs8tPDs5lIRV9V\nTpQOIvWeDmp4FWxRD+OumhW4M0O78qCX0aFde0jJQ3Z3UTy4PF5Of0GkYOBVsEW2d+jlqlpa\n+1J8l3V/dlhsuGvWCL62B+Wniw15fqTIZVYuOBTPzr9Ush49HdTwKtiiWIbOyoW2u2L09XF4\n+fu5WcC+yw6TnIvT5e+qxLbwh6xYpauPKJYeuk8XnTB04VWwxX4Yd1VdTs3347CPhwuy10cX\nZL9d1Rdd3+/NKxfCPzUidS/I5vmxIvXa3HP5rLz7dEQKAl4FWzgN9Nfq7gYIFkSyhVORrsqb\nGyBcEMkWDkXKqqUGCBhEsoVDkS6qexkgYBAJwAKIBGABRAKwACIBWACRACyASAAWQCQACyAS\ngAX+P3Y3zTaZgET+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# publication output per year, total, by OA status\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(oa_status) %>%\n",
    "    ggplot(aes(x = publication_year, y = n, color = oa_status)) +\n",
    "    geom_point(size = 3, alpha = 0.7) +\n",
    "    geom_line(size = 1.5, alpha = 0.7) +\n",
    "    scale_color_manual(values = c(\"#917013\", \"grey50\", \"#e3ac10\", \"#308a17\", \"#c4273f\")) +\n",
    "    scale_x_continuous(expand = c(0,0)) +\n",
    "    labs(x = \"publication year\", y = \"publication output\", color = \"OA status\") +\n",
    "    theme_linedraw() +\n",
    "    theme(panel.grid = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EcSiOcOkQbTPD4qPco6d+o+HnlGPHeINJTG+brao4wUj/wjnjtEGsa20SMlUqnR\n6w6PBBDPHSINosWjtG6Osj4eHgkgnjtEGkLl0dl2hvS59ChrkPBIAPHcIdIAWj2qOnZq6g6P\nJBDPHSLp07m9bvf8vFOLsngkgnjuEEmbvm2qf//nP9/p1wkhnjtE0qVnm+r3v5b398vbJR6J\nIJ47RNKkz6NcoGUGHkkgnjtE0qPvtIll5dEtHkkgnjtE0qLPo/tl6VHWuTM7Eh6NQjx3iKRD\n72l8tUcZRkfCo3GI5w6RNOg/HXZZe3RvJBIejUQ8d4jUj8Zp5XVzlPXxDI6ER2MRzx0i9dLm\n0evhaeW3pUdGDRIejUY8d4jUh5ZH95VHJnN2eDQe8dwhUg/t2+uOL3OyvL3N+nUGHbs1Hhkg\nnjtE6kbXo2LWzrw5wqNxiOcOkbro2O5da2R1ex0ejUQ8d4jUAR7NBvHcIVI7Jx7tn3DlkVkh\nUSOeO0RqBY9mhHjuEKkNjWXYSiM8Ekc8d4jUgt7yER5NBPHcIVIzeDQvxHOHSI3g0cwQzx0i\nNTHAI8Mj4ZEdxHOHSA30eJTfxqNJIZ47RDoHj+aHeO4Q6QyN7Qx2hkd4ZA/x3CHSKXg0R8Rz\nh0gn4NEsEc8dIh2DR/NEPHeIdETL9jr70954ZBfx3CHSIXg0V8Rzh0h72rZ749H0Ec8dItXg\n0YwRzx0iVfR4pG5ammbAI/uI5w6RSvBo1ojnDpEKNLYF4dGEEc8dIuXg0cwRzx0iKfQ9Mj4U\nHjlBPHeIlA7ZzmB8KDxyg3juEKnVIwfT3njkCvHcIZKGR7aGR3jkDPHcIRIehYB47qIXqX87\nAx7NAPHcxS6Sv2VYPHKJeO4iF0nbI/ND4ZFLxHMXt0h4FAriuYtaJDwKBvHcxSwSHoWDeO4i\nFqnZIxfTdXjkHPHcRStSy2kTeDRPxHMXq0h4FBbiuYtUJDwKDPHcxSlSqdHHZo/UTWsa4ZEX\nxHMXpUjd2+vUTTyaGeK5i1EkPAoP8dxFKFKvR/aGR3jkC/HcxSdS7zIsHs0Q8dxFJxIeBYl4\n7mITyeO0Nx55RDx3kYmk65GNY+GRR8RzF5dIeBQq4rmLSiQ8Chbx3MUkUqdHqdXhER55Rjx3\nEYmERwEjnrt4RMKjkBHPXTQi9W1TxaNZI567SETq3e5tUSM8EkA8d3GI1OyRm+k6PJJAPHdR\niIRHwSOeuxhEOvaoehSPQkI8dxGI1LcMa2uaYb1arfFICPHchS+Sp2lvpdF6dbfCIxHEcxe8\nSL48ylmtlE545B/x3IUukq9l2FXl0R0eSSCeu8BF0vTI+DjrVelR1rkzLgyGI567sEXy5VFa\ne5RhXhoMRjx3QYvkzaNMpEqjNSJJIJ67kEVq9Mj+tLdifVd7xBBJAvHcBSxSz3YGmx7VIq1p\nkGQQz124Inn1aF15xJydDOK5C1Ykjx4VS0h3d+t8bwNIIJ67UEXq8ih1srsOjSQRz12gInVt\nU03deGSlMDYGJ1oAAAxvSURBVBiJeO6CFKlzu3fq4mJBeCSMeO5CFEnAIxtlgQHiuQtQpB6P\nHFycAY/EEc9deCL584hu3XQQz11IIm03m63H7QxoNCEQyRpKo+3mcYNHMYJItigaos1G6dTn\nkY3j4dGkQCRbbCqPHn14hEYTA5Essd2UHm0e/+1+OwMeTQ1EskTt0ebfvzZ7xPAoZBDJEptM\noy+fP3/Z/Pu3Xx17xKz3BEEkS2wflUZfPv//d7/+5scj85LAHohkie2fXxT/8+7dL26XYfFo\nkiCSFbIB0ufSo3f/cDldR7duoiCSDdSOoD++/Pln1hz98u5iUT7q0CPzksAuiGQBtYL0x8eP\nH5VGi4ziUTyKCUQyptjS8MfHPxYF14VIeBQViGRKtdn7ovYoHyLZn2ZAoykzC5GK1yQZXT/1\ny7PJttrtXYp0XTRIeBQZcxCp9KX8q+2nfnkWqTT6mPlTepTP2eFRbMxApCSdrEh1c6RYXFwo\nnZx4xKz31JmBSOlkRTryKHepeXxkfCA0mjwhiZQkx8Mlxxx067rPKjc+Eh5Nn5BE0i7PCqfN\nUfbQ62736mDaG49mACKN5NwjpdHr7nlXemR7eGSj0uAMRBpFQ7eu8Ge32+3wKEIQaQwN3bp0\nV3n0jEcRgkgjaPLodVd6lHXuLH9VOR7NgPmINJmdDc2zdbVHGbamGdBoPsxCJMnyzmhqjlIl\nUqXR6w6P4gORBtLiUfr6XHv0L7p18YFIg2hdhM1F+vs///n79fW/rXpkWAx4ApGG0NYcqbnv\nXKO///p/v/8LjyIEkfTZdnn0+v3v//3f79//+c/fl3gUIYikTc/eur++f//+d6bR8tbYIzSa\nH4ikS2dzlDVIf32/v18qbpd4FB+IpEd3t041SPelRxlmh8KjOYJIWpydeVQ9Ue31fl3WGt0b\nicSs9zxBJB36miN1zsRt7ZFJzw6NZgoi9dOxeHRw7lEp0r1Rg4RHcwWReunv1uWbVCuPDKbs\n6NbNF0TqQ6NbV2xSXd7eKp3MPbJQafANInXTd2GGw0sFmWmER7MGkTrpbY7sXXILj2YNInWh\n3a1jM0PsIFI7g7p1ZofCo7mDSK34a47waP4gUhs6i7B2myM8mjGI1AzdOhgEIjVCtw6GgUhN\ntF6YwXpzhEeBgEjnDOnWGR4KjUIBkc7wN8uAR+GASKfQrYMRINIxvd26lOsRQwOIdATdOhgH\nIh0yoFtnWk88CgtE2kO3DkaDSDV062A8iFRBtw4MQKQCjW6dreYIj0IEkXJYPAIzEElBtw4M\nQSS6dWABRJJojvAoOBCpzyOaI9AgdpHo1oEVIhfJY7cOj4ImbpHo1oElYhaprVtHcwSDiVgk\nunVgj3hFEujW4VG4xCoS3TqwSqQieWyO8CgK4hSJbh1YJkaR6NaBdSIUiW4d2Cc+kfQ9Mq8M\nHkVDbCL1d+tojmAEkYlEtw7cEJVI2xaPXMwy4FFcxCSSRLcOjyIhIpGYZQB3RCMS3TpwSSwi\nnWp06pGLWQY8iohIRPLTrVuvVmuaoziJQiQ/swxKo+zPCo9iJAaR/HTrSn8qkwxKghkSgUie\nZhlWpUarOzSKkOBF8rV4tF6VHq3uVngUH6GL5G3xaFVqpEwyLApmSOAi9XbrrE16r2qPVivT\nsmB+BC1SX7fO5uJR1qMrWNOzi5GQRfK7J6gUKevhmZcGsyNgkfx169Jqwo6Z72gJViT9bp35\nMcsVpLu7dbG3AeIjVJE8blHdb61Do3gJVCTP3Tp6dNETnkhbr906NIKc8ETabDY0R+Cb8ETa\nVia5XzxCI6gIT6TMo0e6deCZMEXaeOjWoREcEKRIm0eaI/BLeCJ92Wz+/WuLRzRH4IjwRPr8\n+d2vv9GtA7+EJ9LHd+9+cdutQyM4IzyR3r1794+9RzRH4IXwRPrll3cXi/ZunWn5aARNhCfS\nu8VicVHeoVsHnghPpIUiv2m/W4dG0EKIIl0XPTuaI/BHeCJdX1/nDRLNEXgkSJGu6daBZ8IT\nabG4dtCtQyPoJDyR/ovmCPwTnki73c724hEaQR/hifRamURzBP4IT6TMo2ebHqERaBCmSDtm\nGcAvQYq0e6Y5Ar+EKdKO5gj8Ep5If+9ed/+iOQK/hCfSX3/9/t94BJ4JT6Tvv//+OxqBZ8IT\n6Z///P0Wj8Az4Yn0+3J5uzT9ZmU0gmGEJ9Iy49asDDyCoYQp0tKkBDSC4YQo0v3SpGOHRzCC\n8ES6v783aJDQCEYRpEjjGyQ8gnGEJ9JyfMcOjWAs4YlEcwQChCfS2F9EIzAAkUrwCExApBw0\nAjMQSYFHYAgioRFYAJHwCCwQvUhoBDaIXSQ8AivELRIagSWiFgmPwBYRi4RGYI94RcIjsEis\nIqERWCVSkfAI7BKlSGgEtolRJDwC68QnEhqBA6ITCY/ABZGJhEbghrhEwiNwREwioRE4IyKR\n8AjcEY1IaAQuiUUkPAKnxCESGoFjohAJj8A1EYiERuCe8EXCI/BA6CKhEXghcJHwCPwQtEho\nBL4IWSQ8Am+EKxIagUeCFQmPwCeBioRG4JfwRFLu4BF4JjyRVqsVGoFvwhNpXZtkuWSAdsIT\nKfPoDo3AM2GKtMIj8EuQIq3uLBcK0EOYIq0sFwrQQ4girVd07MAz4Ym0Xq9pkMA3QYpEgwS+\nCU+kFR078E94IqERCBCeSJbLA9BBPHfmFUgybJYHMBzx3BlXIDkuRfwNQZSI5w6RIATEc4dI\nEALiubMoUpIcD5cAvCGeO1okCAHx3CEShIB47hAJQkA8d4gEISCeO0SCEBDPHTsbIATEc8de\nOwgB8dwhEoSAeO4QCUJAPHeIBCEgnjtEghAQzx0iQQiI5w6RIATEc4dIEALiuUMkCAHx3CES\nhIB47hAJQkA8d4gEISCeO0SCEBDPHSJBCIjnDpEgBMRzh0gQAuK5QyQIAfHcWRcJQALLOR4e\n/ImXN6ejR/3mo/6nd1ABPk2OHuHhESmgw8d8dOnDT6ACACGASAAWQCQACyASgAUQCcACiARg\nAXvX/t4vLyf14+417Tq684O3Ht7TYrvsm5/YP/15dbxi7dso9t9KUb4PL++m7egn35Hh+/D7\nJyWO7ufNT+yf/rw6frEvUlL9x2RassnRpURK9geVO7qQSLL/9LMXqSrl4A34fi+yR286vMcq\ntL15P4eXPfqZOEGK5K+v2vhp+uspN4skeHREmqtISSr579l49PpBkcN7O3jz0b39H9b2wcsc\n/uy+T5yJZK/0MUf3+5/yxESS/T8s7wsg0ugyxERqPLrfLDW8eY8eyb152Q9e+pNvqIyNIqTe\nkOzRWw7vszXkn76lDn4xP+Rhck67ysaFmxxd8PA+/0sWe/MT+6c/r45XjA9ZL+KLLHDLHr39\n8H7+S+affn+4hup4ReKYAMGBSAAWQCQACyASgAUQCcACiARgAUQCsAAiAVgAkQAsgEiu2a+z\nq1stq+5fL9PW52AO8Nm5RkukcnOLrzqBdfjsXHMsUu+LYJ7wAdok8+F9cvOSVmaUbdDBY/nj\nL++Tywf1/I/3SX6r2GhZPPchST4Ur65flqZvyVX940294u3w17PX/ry88f1m4RBEskmW8EyJ\ny7djkd4fPKb+vF0qcd6n6VOxZfnhQKTiueLVl8WTOQ/JU/b3t+RTmuYPK6/qX89ee5N8kHvb\ngEh2yQL9lt4U0S7unz6m/jxkof+hblwl39L0Z90YFc9lLUv56uz3viaXRck/1eNZ0/Yj/aSe\nfEi+Hv/6g9ybBgUi2STrYmWdM9VcHIr0cvBY4c9b9QsvT59ujkS6Onj1vouYKoeyopVWV0XJ\n749//cXzO4UTEMkmRyOjxlvHM3c31elotUhNv5fzM1PnSfXfkqQ+h+3410ESPgGbDBTpQ3L1\n9elFTyTVVuUDpb1IJ78OkvAJ2KTsxt1UM3A9Xbv8RW+tXbv6JTlPycOlunN1PJ3+hkiTgE/A\nJknm0NtNMbX2Td1KDh/bTzY8VHMEP+oXpeeTDWl6pMhVkk84qN9OvxWyHv06SMInYBM1DZ3k\nE20Pqvf1qXn6+6WawH5I9oOcy/Pp76LEuvCnRM3Sla9QUw+Hvy70hqGCT8AmWTfuplhOTbN+\n2Kf9guz7owXZnzflouuHzLx8IvxrJdLhgmyaHitSzs295L+VHv46IonDJ2ATp4H+UexugEmC\nSDZxKtJNvrkBpgki2cShSEkx1QATBZFs4lCky2IvA0wURAKwACIBWACRACyASAAWQCQACyAS\ngAUQCcACiARggf8DxgO4eY/qRoUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# publication output per year, cumulative, by OA status\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(oa_status) %>%\n",
    "    ggplot(aes(x = publication_year, y = cumsum(n), color = oa_status)) +\n",
    "    geom_point(size = 3, alpha = 0.7) +\n",
    "    geom_line(size = 1.5, alpha = 0.7) +\n",
    "    scale_color_manual(values = c(\"#917013\", \"grey50\", \"#e3ac10\", \"#308a17\", \"#c4273f\")) +\n",
    "    scale_x_continuous(expand = c(0,0)) +\n",
    "    labs(x = \"publication year\", y = \"publication output\", color = \"OA status\") +\n",
    "    theme_linedraw() +\n",
    "    theme(panel.grid = element_blank())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAP1BMVEUAAABNTU1ZoUVoaGh8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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# publication output per year, proportional, by OA status\n",
    "DF %>%\n",
    "    group_by(publication_year) %>%\n",
    "    count(oa_status) %>%\n",
    "    mutate(oa_status = fct_relevel(oa_status, \"closed\", \"hybrid\", \"bronze\", \"gold\", \"green\")) %>%\n",
    "    ggplot(aes(x = publication_year, y = n, fill = oa_status)) +\n",
    "    geom_area(position = \"fill\", alpha = 0.8) +\n",
    "    geom_hline(yintercept = 0.6, linetype = \"dashed\", color = \"white\", size = 2) +\n",
    "    scale_fill_manual(values = c( \"grey50\", \"#c4273f\", \"#917013\", \"#e3ac10\", \"#308a17\")) +\n",
    "    scale_x_continuous(expand = c(0,0)) +\n",
    "    scale_y_continuous(expand = c(0,0), labels = scales::percent) +\n",
    "    labs(x = \"publication year\", y = \"publication output\", color = \"OA status\") +\n",
    "    theme_linedraw() +\n",
    "    theme(panel.grid = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EXTRA: credit where credit is due\n",
    "\n",
    "Software has become an essential form of research output. Developers of research software carry out valuable work for the research community, and their contribution should be acknowledged in publications.\n",
    "\n",
    "If you want to know how to cite an R package, try the function *citation*: it returns a suggestion for citing a given package, as well as a BibTex entry you can copy and paste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite R in publications use:\n",
       "\n",
       "  R Core Team (2021). R: A language and environment for statistical\n",
       "  computing. R Foundation for Statistical Computing, Vienna, Austria.\n",
       "  URL https://www.R-project.org/.\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Manual{,\n",
       "    title = {R: A Language and Environment for Statistical Computing},\n",
       "    author = {{R Core Team}},\n",
       "    organization = {R Foundation for Statistical Computing},\n",
       "    address = {Vienna, Austria},\n",
       "    year = {2021},\n",
       "    url = {https://www.R-project.org/},\n",
       "  }\n",
       "\n",
       "We have invested a lot of time and effort in creating R, please cite it\n",
       "when using it for data analysis. See also 'citation(\"pkgname\")' for\n",
       "citing R packages.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite jsonlite in publications use:\n",
       "\n",
       "  Jeroen Ooms (2014). The jsonlite Package: A Practical and Consistent\n",
       "  Mapping Between JSON Data and R Objects. arXiv:1403.2805 [stat.CO]\n",
       "  URL https://arxiv.org/abs/1403.2805.\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Article{,\n",
       "    title = {The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects},\n",
       "    author = {Jeroen Ooms},\n",
       "    journal = {arXiv:1403.2805 [stat.CO]},\n",
       "    year = {2014},\n",
       "    url = {https://arxiv.org/abs/1403.2805},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite package 'roadoi' in publications use:\n",
       "\n",
       "  Najko Jahn (2021). roadoi: Find Free Versions of Scholarly\n",
       "  Publications via Unpaywall. R package version 0.7.1.\n",
       "  https://CRAN.R-project.org/package=roadoi\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Manual{,\n",
       "    title = {roadoi: Find Free Versions of Scholarly Publications via Unpaywall},\n",
       "    author = {Najko Jahn},\n",
       "    year = {2021},\n",
       "    note = {R package version 0.7.1},\n",
       "    url = {https://CRAN.R-project.org/package=roadoi},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite package 'rcrossref' in publications use:\n",
       "\n",
       "  Scott Chamberlain, Hao Zhu, Najko Jahn, Carl Boettiger and Karthik\n",
       "  Ram (2020). rcrossref: Client for Various 'CrossRef' 'APIs'. R\n",
       "  package version 1.1.0. https://CRAN.R-project.org/package=rcrossref\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Manual{,\n",
       "    title = {rcrossref: Client for Various 'CrossRef' 'APIs'},\n",
       "    author = {Scott Chamberlain and Hao Zhu and Najko Jahn and Carl Boettiger and Karthik Ram},\n",
       "    year = {2020},\n",
       "    note = {R package version 1.1.0},\n",
       "    url = {https://CRAN.R-project.org/package=rcrossref},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite package 'dplyr' in publications use:\n",
       "\n",
       "  Hadley Wickham, Romain François, Lionel Henry and Kirill Müller\n",
       "  (2021). dplyr: A Grammar of Data Manipulation. R package version\n",
       "  1.0.7. https://CRAN.R-project.org/package=dplyr\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Manual{,\n",
       "    title = {dplyr: A Grammar of Data Manipulation},\n",
       "    author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},\n",
       "    year = {2021},\n",
       "    note = {R package version 1.0.7},\n",
       "    url = {https://CRAN.R-project.org/package=dplyr},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite ggplot2 in publications, please use:\n",
       "\n",
       "  H. Wickham. ggplot2: Elegant Graphics for Data Analysis.\n",
       "  Springer-Verlag New York, 2016.\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Book{,\n",
       "    author = {Hadley Wickham},\n",
       "    title = {ggplot2: Elegant Graphics for Data Analysis},\n",
       "    publisher = {Springer-Verlag New York},\n",
       "    year = {2016},\n",
       "    isbn = {978-3-319-24277-4},\n",
       "    url = {https://ggplot2.tidyverse.org},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "To cite package 'forcats' in publications use:\n",
       "\n",
       "  Hadley Wickham (2021). forcats: Tools for Working with Categorical\n",
       "  Variables (Factors). R package version 0.5.1.\n",
       "  https://CRAN.R-project.org/package=forcats\n",
       "\n",
       "A BibTeX entry for LaTeX users is\n",
       "\n",
       "  @Manual{,\n",
       "    title = {forcats: Tools for Working with Categorical Variables (Factors)},\n",
       "    author = {Hadley Wickham},\n",
       "    year = {2021},\n",
       "    note = {R package version 0.5.1},\n",
       "    url = {https://CRAN.R-project.org/package=forcats},\n",
       "  }\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# citing base R\n",
    "citation(\"base\")\n",
    "# citing the packages we used\n",
    "citation(\"jsonlite\")\n",
    "citation(\"roadoi\")\n",
    "citation(\"rcrossref\")\n",
    "citation(\"dplyr\")\n",
    "citation(\"ggplot2\")\n",
    "citation(\"forcats\")"
   ]
  }
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