statusUpdate.R

# ACTIVITY: Status update for all pools
# 1. Download the Articles sheet. Extract data.
# 2. Get article ids present in Coding sheet.
statusUpdate <- function(){
  
  # load packages and custom function
  library(googlesheets)
  library(tidyverse)
  library(utils)
  pull <- function(x,y) {x[,if(is.name(substitute(y))) deparse(substitute(y)) else y, drop = FALSE][[1]]}
  
  # 1. Download the Articles sheet. Extract data.
  articlesSheet <- gs_title("Articles")
  articlesData <- articlesSheet %>% gs_read()
  
  # 2. Download the coding form. Extract data.
  codingSheet <- gs_title("Coding Form (Responses)")
  codingDataRaw <- codingSheet %>% gs_read()
  
  # format columns
  codingData <- codingDataRaw %>%
    rename(date = `What date was the article received by the journal?`,
           availabilityStatement = `Does the article state whether or not the data are available?`,
           availabilitySuccess = `Were you able to successfully download and open the data file(s)?`,
           understandable = `Are the data understandable after brief review?`) %>%
    mutate(date = as.Date(date, format='%m/%d/%Y'),
           month = format(date, '%m'),
           year = format(date, '%Y'),
           period = ifelse(as.numeric(month) <= 6, 1, 2),
           policy = ifelse(date >= "2015-01-03", "POST-", "PRE-"),
           policy = factor(policy, levels = c("PRE-", "POST-")),
           availabilityStatement = recode_factor(availabilityStatement, "Yes - the statement says that the data (or some of the data) are available" = "Yes",
                                                 "No - there is no data availability statement" = "No"),
           availabilitySuccess = factor(availabilitySuccess, levels = c("Yes", "Some files only", "No")),
           understandable = factor(understandable, levels = c("Yes", "Partly", "No"))) 
  
  # summary stats
  graph_availableStatement <- codingData %>%
    group_by(policy, availabilityStatement) %>%
    summarise(n = n()) %>%
    complete(availabilityStatement, fill = list(n = 0)) %>%
    ggplot() + geom_bar(aes(x = policy, y = n, fill = availabilityStatement), stat = "identity", position=position_dodge()) +
    ylim(0,300) +
    theme_minimal() +
    scale_fill_manual(values=c("#009E73", "#E69F00", "#CC6666"))
  
  ## Timeline by year half
  tmp <- codingData %>%
    mutate(period = factor(period),
           year = factor(year),
           policy = factor(policy)) %>%
    unite(time_period, year, period) %>%
    group_by(availabilityStatement, time_period) %>%
    summarise(n = n()) %>%
    complete(time_period, fill = list(n = 0))
  
  graph_availableStatement_timeline <- tmp %>%
    group_by(time_period) %>%
    mutate(prop = n / sum(n)) %>%
    filter(availabilityStatement == 'Yes') %>%
    ungroup() %>%
    select(time_period, prop) %>%
    ggplot() + geom_point(aes(x = time_period, y = prop)) + 
    geom_line(aes(x = time_period, y = prop, group = 1)) +
    theme_minimal() +
    geom_vline(xintercept = which(tmp$time_period == '2015_1'), color = 'red', linetype = 'dashed')
    
  graph_availableStatement_areaPlot <- tmp %>%
    select(availabilityStatement, time_period, n) %>%
    ggplot() + geom_area(aes(x = time_period, y = n, colour = availabilityStatement, fill= availabilityStatement), position = 'stack', size = 3) +
    theme_minimal() +
    geom_vline(xintercept = which(tmp$time_period == '2015_1'), color = 'red', linetype = 'dashed') +
    scale_color_manual(values=c("#009E73", "#CC6666")) +
    scale_fill_manual(values=c("#009E73", "#CC6666"))
  
  ### timeline relative to policy introduction
  relTime <- codingData %>%
    mutate(daysSincePolicy = as.numeric(date - as.Date("2015-01-03")),
           timeBlock = ifelse(daysSincePolicy >= 0 & daysSincePolicy <250, 1, 
                              ifelse(daysSincePolicy >= 250 & daysSincePolicy <500, 2,
                                    ifelse(daysSincePolicy >= 500 & daysSincePolicy <750, 3,
                                           ifelse(daysSincePolicy >= 750 & daysSincePolicy <1000, 4,
                                                  ifelse(daysSincePolicy >= 1000 & daysSincePolicy <1250, 5,
                                                         ifelse(daysSincePolicy < 0 & daysSincePolicy >=-250, -1,
                                                                ifelse(daysSincePolicy < -250 & daysSincePolicy >= -500, -2,
                                                                       ifelse(daysSincePolicy <500 & daysSincePolicy >= -750, -3,
                                                                              ifelse(daysSincePolicy < 750 & daysSincePolicy >= 1000, -4,
                                                                                     ifelse(daysSincePolicy < 1000 & daysSincePolicy >= 1250, -5, "NA")))))))))))

    
  availableStatementN <- table(codingData$availabilityStatement)[['Yes']]
  
  graph_availableSuccess <- codingData %>%
    filter(availabilitySuccess != "NA") %>%
    group_by(policy, availabilitySuccess) %>%
    summarise(n = n()) %>%
    complete(availabilitySuccess, fill = list(n = 0)) %>%
    mutate(prop = n / sum(n)) %>%
    ggplot() + geom_bar(aes(x = policy, y = prop, fill = availabilitySuccess), stat = "identity", position=position_dodge()) +
    ylim(0,1) +
    theme_minimal() +
    ylab("proportion") +
    ggtitle(paste0("For articles (", availableStatementN  ,") with availability statements..")) +
    scale_fill_manual(values=c("#009E73", "#E69F00", "#CC6666"))
  
  availableSuccessN <- table(codingData$availabilitySuccess)[['Yes']]
  
  graph_understand <- codingData %>%
    filter(understandable != "NA") %>%
    group_by(policy, understandable) %>%
    summarise(n = n()) %>%
    mutate(prop = n / sum(n)) %>%
      ggplot() + geom_bar(aes(x = policy, y = prop, fill = understandable), stat = "identity", position=position_dodge()) +
      ylim(0,1) +
      theme_minimal() +
      ylab("proportion") +
      ggtitle(paste0("For articles (", availableSuccessN  ,") with availability success..")) +
      scale_fill_manual(values=c("#009E73", "#E69F00", "#CC6666"))
  
  # individual coders
  coders <- articlesData %>%
    group_by(coder) %>%
    count()
  # individual pilots
  pilots <- articlesData %>%
    filter(triageStatus == 'accepted') %>%
    group_by(pilot) %>%
    count()
  
  # individual copilots
  copilots <- articlesData %>%
    filter(triageStatus == 'accepted') %>%
    group_by(copilot) %>%
    count()
  
  
  print(graph_availableStatement)
  print(graph_availableStatement_timeline)
  print(graph_availableSuccess)
  print(graph_understand)

  print("CODING POOL:")
  print(table(articlesData$codingStatus))
  
  print("TRIAGE POOL:")
  print(table(articlesData$passToTriage))
  
  print("REPRODUCIBILITY POOL:")
  print(table(articlesData$triageStatus))
  
  print("REPRODUCIBILITY CHECK STATUS:")
  print(table(articlesData$reproducibilityStatus))
}