metrics_report.Rmd

---
title: "QDR Metrics Report"
author: "Qualitative Data Repository"
date: "Generated `r Sys.time()`"
output:
  pdf_document: 
    includes:
      in_header: header.tex
  html_document: default
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE) 
opts <- options(knitr.kable.NA = "0")
library(httr)
library(readr)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggfortify)
library(stringi)

# load other functions
source("R/im.R") # instutitonal members stats
source("R/deposits.R") # deposits on dataverse
source("R/mdc.R") # make data count stats

### Configurable variables

# Custom start and end dates: use YYYY-MM-DD format
# These are specified from the workflow
# If these aren't provided, we'll: default to today's date as end date and to the beginning of the year as the start date
period_begin <- as.Date(Sys.getenv("period_begin"))
period_end <- as.Date(Sys.getenv("period_end"))

# Calculate dates: We use YTD if none are given
if (is.na(period_begin)) {
  period_begin <- as.Date(paste(as.integer(format(Sys.Date(), "%Y")),
                              "01-01", sep = "-"))}
if (is.na(period_end)){
  period_end <- Sys.Date()
}

# Set tokens and file location settings
dataverse_host <- "data.qdr.syr.edu"
dataverse_key <- Sys.getenv("DATAVERSE_TOKEN") # Github secret
drupal_folder <- paste0(Sys.getenv("EXTERNAL_GH_FOLDER"), "/drupal") # Github secret

# How many logins before we assume a user is QDR staff?
QDR_staff_logins <- 10

# Institutional members
im <- read_csv(paste0(Sys.getenv("EXTERNAL_GH_FOLDER"), "/institutional_members.csv"))
if (file.exists(paste0(Sys.getenv("EXTERNAL_GH_FOLDER"), "")))
# Add the prospective institutional member (input from github actions workflow)
extend <- Sys.getenv("extend_im")
if (extend!="") {
  extend <- unlist(strsplit(extend, ","))
  im <- im %>% bind_rows(tibble(
    canonical=extend, drupal=extend, domain=NA, regexp=extend))
}

# Drupal data, format as necessary
use <- read_csv(paste0(drupal_folder, "/use-stats.csv"))
login <- read_csv(paste0(drupal_folder, "/login-history.csv"))
login$Created <- as.Date(login$Created, format = "%m/%d/%y")
login$`Last login` <- as.Date(login$`Last login`, format = "%a, %m/%d/%Y - %R")
login$Institution <- toupper(login$Institution)
login$Institution <- trimws(login$Institution)
login$full_name <- with(login, paste(`Last Name`, `First Name`, sep=", "))

# Use number of logins specified above for QDR membership
login$frequent_flyer <- login$`Logins during time period` > `QDR_staff_logins`

login$recent <- as.Date(login$`Last login`) > period_begin

# This runs the helper function
login$member <- checkif_member(login, im)

# Calculate basic drupal summary stats
drupal_users <- nrow(login)
drupal_new_users <- sum(login$Created < period_end & login$Created > period_begin)
drupal_unique_logins <- as.integer(sum(login$recent))
drupal_logins_without_ff <- sum(login$`Logins during time period`[!login$frequent_flyer])

# Download and format dataverse data
dataverse <- paste0('http://', dataverse_host, "/api/info/metrics")
# Locally cache dataverse data as RDS files
# This is mainly useful because it reduces API calls when compiling to multiple formats (i.e. PDF+HTML)
if (file.exists("deposits.rds")) {
  deposits <- readRDS("deposits.rds")
  projects <- readRDS("projects.rds")
  mdc <- readRDS("mdc.rds")
} else {
  # This implements the search API to get all available data for all datasets
  deposits <- deposits_get(dataverse_host, dataverse_key)
  deposits$dvurl <- paste0("http://data.qdr.syr.edu/api/datasets/:persistentId/?persistentId=", deposits$global_id)

  institutions  <-  lapply(deposits$contacts, function(contact_group) {
      # For each contact in the group, extract just the affiliation
      lapply(contact_group, function(contact) {
        contact$affiliation
      })
    })
    
  
  # We query individual deposit to get *depositor* affiliation
  # This extra API call would be unnecessary to only get contact affiliation
  # institutions <- lapply(deposits$dvurl, deposit_affiliations, dataverse_key)

print(deposits)
  # Run all the depositor affiliations against a regex of institutional members
print(member_regexp(institutions, im)) 
deposits$member <- member_regexp(institutions, im)

  
  saveRDS(deposits, "deposits.rds")
  
  # These are aggregate stats, only require one API call
  projects <- content(GET(paste0(dataverse, "/uniquedownloads/monthly")))
  projects <- bind_rows(projects$data)
  projects$date <- as.Date(paste0(projects$date, "-01"))
  saveRDS(projects, "projects.rds")
  
  mdc <- mdc_get(dataverse_host)
  saveRDS(mdc, "mdc.rds")
}
```

## Parameters

- Reporting period: `r period_begin` to `r period_end`.
- Non-member institutions considered: `r extend`

## Drupal

- QDR currently has `r drupal_users` users registered in Drupal. 
- `r drupal_new_users` of those users were created between `r period_begin` and `r period_end`.
- `r drupal_unique_logins` unique users have logged in since `r period_begin`.
- There have been at least `r drupal_unique_logins` unique logins since `r period_begin`. 

<!--Based on the time period used to generate the drupal login csv file:-->

<!--- If we subtract presumed QDR staff members (> `r QDR_staff_logins` logins/user), this number drops to `r drupal_logins_without_ff`. --> 

```{r drupal}
login %>% group_by(Institution) %>% summarise(users=length(`User status`)) %>% 
  arrange(desc(users)) %>% slice(1:25) %>%
  knitr::kable(row.names = FALSE, col.names = c("Institution", "Affiliated users"),
               caption="Top institutional affiliations by number of users")

use %>% select(-`Views today`) %>% arrange(desc(`Total views`)) %>% 
  slice(1:10) %>%
  mutate(Title = paste0("[", Title, "](", 
                      "https://qdr.syr.edu", `Link to Content`, ")")) %>%
  select(!`Link to Content`) %>%
  knitr::kable(row.names = FALSE, col.names = c("Title", "Views"),
               caption="Most viewed QDR pages")
```

```{r members_drupal}
# Get total n per member
im_drupal <- count(group_by(login, member, .drop=FALSE))

# Get recent logins since period begin
im_drupal <- login %>% group_by(member, .drop=FALSE) %>% summarize(recent=sum(recent)) %>% full_join(im_drupal)

# Get created between period_begin and period_end
im_drupal <- login %>% group_by(member, .drop=FALSE) %>% filter(Created < period_end & Created > period_begin) %>% 
  summarize(created_since=length(`Email`)) %>% full_join(im_drupal) %>%
  arrange(desc(n)) %>% replace_na(list(created_since=0))

# Here we write drupal member data to an external file
# This was by request, in order to have an easier parsed version than the table
im_drupal %>% filter(!is.na(member)) %>%
  replace_na(replace = list(created_since=0, recent=FALSE, n=0)) %>%
  write_csv(paste0(Sys.getenv("EXTERNAL_GH_FOLDER"), "/output/im_drupal.csv"))

# Now display the same data as a table in the PDF/HTML
opts <- options(knitr.kable.NA = "N/A")
im_drupal %>% 
  knitr::kable(col.names = c("Institution", 
                             paste("Created between", period_begin, "and", period_end),
                             paste("Min unique logins since", period_begin),
                             "Total users"),
               caption="QDR Accounts and Logins by Institutional Members")
```

## Dataverse

```{r total_projects}
dataverse_total <- nrow(deposits)

deposits_new <- deposits %>% filter(as.Date(createdAt) < period_end & as.Date(createdAt) > period_begin) %>%
  summarize(n=n()) %>% .$n

published_new <- deposits %>% filter(as.Date(published_at) < period_end & as.Date(published_at) > period_begin) %>%
  summarize(n=n()) %>% .$n
```

- There are currently `r dataverse_total` projects. 
- `r deposits_new` new deposits were initiated between `r period_begin` and `r period_end`.
- `r published_new` projects were published between `r period_begin` and `r period_end`.

```{r subject}
opts <- options(knitr.kable.NA = 0)

deposits %>% group_by(versionState) %>% summarize(n=n()) %>%
  knitr::kable(caption="Total deposits by publication status", col.names=c("Status", "Deposits"))

# Summarize published projects by subject
deposits_subj <- deposits %>% 
  filter(versionState=="Published") %>% select(c("subjects")) %>%
  unnest_longer(subjects) %>% group_by(subjects) %>% summarize(published=n())
  
# any of those published projects created during the specified period?
if  (nrow(subset(deposits, as.Date(createdAt) < period_end &
               as.Date(createdAt) > period_begin))==0) {
  
  deposits_subj <- deposits %>% 
    filter(as.Date(published_at) < period_end & as.Date(published_at) > period_begin & 
             versionState=="Published") %>%
    select(c("subjects")) %>% unnest_longer(subjects) %>% group_by(subjects) %>%
    summarize(period=n()) %>% full_join(deposits_subj)

} else {
  deposits_subj$period <- 0
}

# Any unpublished projects?
if (any(deposits$versionState=="Unpublished")) {
  # join with the published projects table
  deposits_subj <- deposits %>% filter(versionState=="Unpublished") %>% select(c("subjects")) %>%
    unnest_longer(subjects) %>% group_by(subjects) %>%
    summarize(unpublished=n()) %>% full_join(deposits_subj)
} else {
  deposits_subj$unpublished <- 0
}

# Any unpublished in the time period specified?
if (any(subset(deposits, as.Date(createdAt) < period_end &
               as.Date(createdAt) > period_begin)$versionState=="Unpublished")) {
  deposits_subj <- deposits %>% filter(versionState == "Unpublished" & 
                                      as.Date(createdAt) < period_end &
                                      as.Date(createdAt) > period_begin) %>%
    select(c("subjects")) %>% unnest_longer(subjects) %>% group_by(subjects) %>%
    summarize(unpublished_period=n()) %>% full_join(deposits_subj)
} else {
  deposits_subj$unpublished_period <- 0
}

# make the table on publications by subject
# published and unpublished, total and within-period
deposits_subj[, c("subjects", "published", 
                  "unpublished", "period", "unpublished_period")] %>% 
  knitr::kable(caption="Deposits by subject",
               col.names=c("Subject", "Total published", "Total unpublished",
                           "Published in reporting period", 
                           "Unpublished in reporting period"))
```

```{r top}
# Downloaded projects

# All time downloaded projects, sort from the scraped dataframe and add clickable links
projects %>% group_by(pid) %>% summarize(sum(count)) %>% 
  arrange(desc(`sum(count)`)) %>% slice(1:5) %>%
  mutate(pid = paste0("[", deposits$name[deposits$global_id %in% pid], "](", 
                      "https://data.qdr.syr.edu/dataset.xhtml?persistentId=", 
                      pid, ")")) %>%
  knitr::kable(caption="Top 5 all time downloaded projects",
             row.names = FALSE, col.names = c("Project", "Downloads"))

# same as above, but only for the specified period
projects %>% group_by(pid) %>% filter(date < period_end &
                      date > period_begin) %>% 
  summarize(sum(count)) %>% arrange(desc(`sum(count)`)) %>% slice(1:5) %>%
  mutate(pid = paste0("[", deposits$name[deposits$global_id %in% pid], "](", 
                      "https://data.qdr.syr.edu/dataset.xhtml?persistentId=", 
                      pid, ")")) %>%
  knitr::kable(caption=paste("Top 5 downloaded projects between ", period_begin,
                             "and", period_end),
             row.names = FALSE, col.names = c("Project", "Downloads"))
```


### Institutional members

```{r member_projects}
# projects deposited by users associated with institutional members
im_deposit <- deposits %>% filter(!is.na(member)) %>%
  group_by(member, versionState, .drop=FALSE) %>% 
  tally()

# collect stats of published and unpublished projects per member
# both total and within period
im_deposit <- 
  deposits %>% filter(!is.na(member) & as.Date(createdAt) < period_end & as.Date(createdAt) > period_begin) %>% 
  group_by(member, versionState, .drop=FALSE) %>% 
  summarize(period=length(global_id)) %>% full_join(im_deposit) %>%
  pivot_wider(names_from=versionState, names_sep=".", values_from=c(n, period), values_fill=0) %>%
  select(n.Published, n.Unpublished, period.Published, period.Unpublished)

# generate the table
im_deposit %>% knitr::kable(caption="Deposits associated with institutional members",
               col.names = c("Institutional member name", 
                             "Total published", "Total unpublished",
                             "Published in reporting period", "Unpublished in reporting period"))

# Write the institutional member dataverse data to an external file as well
# Same as the table included immediately above
# This is easier to parse than the table that is included in the PDF/HTML
im_deposit %>% 
  replace_na(list(n.Published=0, n.Unpublished=0, period.Published=0, period.Unpublished=0)) %>%
  write_csv(paste0(Sys.getenv("EXTERNAL_GH_FOLDER"), "/output/im_deposits.csv"))

# Sort the top projects per member and create clickable links
projects %>% group_by(global_id=pid) %>% summarize(downloads=sum(count)) %>%
  inner_join(deposits) %>% select("name", "global_id", "member", "downloads") %>%
  filter(!is.na(member)) %>% arrange(desc(downloads)) %>%
  group_by(member, .drop=FALSE) %>% slice_head(n=3) %>%
  mutate(name = paste0("[", name, "](", 
                      "https://data.qdr.syr.edu/dataset.xhtml?persistentId=", 
                      global_id, ")")) %>% select(!global_id) %>%
  # and output the table
  knitr::kable(caption="Top 3 downloads from each member institution",
               col.names = c("Deposit", "Member institution", "Downloads"),
               linesep = "\\addlinespace")
```

## Make Data Count (MDC)

### Understanding MDC data

MDC reports statistics as "total" and "unique". 

- Unique" views/downloads correspond to the number of unique sessions in which a data project has been viewed/downloaded. Roughly translates to: "X people have viewed/downloaded this project or its files."
- Total views/downloads correspond to the number of times a project has been viewed/downloaded, including, e.g., multiple counts for multiple views/downloads in the same session or by the same user.


```{r mdc}
# not all dataverse instances use MDC data (QDR does). In case they don't, we use the regular downloads stats
if (exists("mdc")) {
  stats_ts <- ts(mdc[, -1], frequency=12, start=strsplit(mdc$date[1], split="-")[[1]])
} else {
  stats_ts <- ts(downloads[, -1], frequency=12, 
                start=strsplit(downloads$date[1], split="-")[[1]])
}

# plot the available stats
autoplot(stats_ts, facets = FALSE) +  labs(title="Statistics", color="Statistic") +
    scale_y_continuous(labels=scales::comma)

```