`r params$set_title`"
In wpa: Tools for Analysing and Visualising Viva Insights Data

start_time <- Sys.time()

# 1. Load Library
library(wpa)
library(dplyr)
library(tidyr)

# 1a. Load auxiliary functions
source("auxiliary.R")

# Glossary
gloss_tb <-
  readr::read_csv("glossary.csv")

# 2. Load Data
my_sq_data <- params$wbq
my_em_data <- params$hcq
hrvar <- params$hrvar
mingroup <- params$mingroup
start_hour <- params$start_hour
end_hour <- params$end_hour

intro_toc <- readLines("toc.md")

# Add col checker ----------------------------------------------------------

# If `replacement` is unspecified, it uses `col`

col_checker <- function(x, col, replacement = col){

  if(!(col %in% names(x)) & !(replacement %in% names(x))){

    # column does not exist at all
    # ACTION: DUMMY COLUMN WITH REPLACEMENT
    dplyr::mutate(x, !!sym(replacement) := NA) # create dummy column

  } else if(col %in% names(x) & !(replacement %in% names(x))){

    # column exists in x but not replacement
    # ACTION: RENAME
    dplyr::rename(x, !!sym(replacement) := col)

  } else if(!(col %in% names(x)) & replacement %in% names(x)){  

    # column does not exists in x but in replacement
    # ACTION: RETURN
    x

  } else if(col %in% names(x) & replacement %in% names(x)){  

    # column exists in both x and replacement
    # ACTION: RETURN
    x    

  } else {

    stop("Internal error with conditional.")

  }
}

# 3a. Simulate variables for custom Wellbeing query
my_sq_data <-
  my_sq_data %>%

  # Column checks ----------------------------------------------------------
  col_checker(col = "Instant_Message_hours", replacement = "Instant_message_hours") %>%
  col_checker(col = "Collaboration_hrs", replacement = "Collaboration_hours") %>%
  col_checker(col = "Unscheduled_call_hours", replacement = "Unscheduled_Call_hours") %>%
  col_checker(col = "Urgent_email_hours") %>%
  col_checker(col = "Urgent_meeting_hours") %>%
  col_checker(col = "Weekend_Email", replacement = "Weekend_emails_sent") %>%
  col_checker(col = "Weekend_IMs_sent") %>%
  col_checker(col = "IMs_sent_same_level") %>%
  col_checker(col = "IMs_sent_other_level") %>%
  col_checker(col = "Meeting_hours_1on1", replacement = "Intimate_1_on_1_meeting_hours") %>%
  col_checker(col = "Meeting_hours_intimate_group",
              replacement = "Intimate_group_meeting_hours") %>%
  col_checker(col = "IMs_sent", replacement = "Instant_messages_sent") %>%
  col_checker(col = "Workweek_span") %>%

  # Daily estimates --------------------------------------------------------

  mutate(Work_time = Workweek_span / 5) %>% # Average day span
  mutate(dailyCollabHours = Collaboration_hours / 5)  %>% # Average daily CH
  mutate(dailyAfterHours = After_hours_collaboration_hours / 5) %>%

  # Urgent collaboration ---------------------------------------------------

  # mutate(Urgent_collaboration_hours = Urgent_email_hours) %>%
  mutate(Urgent_collaboration_hours = Urgent_meeting_hours + Urgent_email_hours) %>%
  mutate(IsUrgent = ifelse(Urgent_collaboration_hours > 0, TRUE, FALSE)) %>%

  # Weekend work ------------------------------------------------------------

  # mutate(Weekend_work = ifelse(Weekend_Email > 0 | Weekend_Meeting > 0, TRUE, FALSE)) %>%
  mutate(Weekend_work = ifelse(Weekend_emails_sent > 0 | Weekend_IMs_sent > 0, TRUE, FALSE)) %>%
  group_by(PersonId)  %>%
  mutate(Frequency_of_weekend_work = 1 / (sum(Weekend_work) / n())) %>% #TODO: Verify logic
  mutate(monthly_weekend_work = Frequency_of_weekend_work < (52/12)) %>%
  ungroup() %>%

  # Two Hour Focus Blocks ---------------------------------------------------

  mutate(focus_per = (Total_focus_hours/ Workweek_span)) %>%
  mutate(focus_per = ifelse(is.infinite(focus_per), 0, focus_per)) %>%
  rename(`% of 2-hour focus block` = "focus_per") %>% 
  mutate(Total_focus_hours_daily = Total_focus_hours / 5) %>%

  # Unscheduled collaboration -----------------------------------------------

  mutate(Unscheduled_collaboration_hours_daily = (Unscheduled_Call_hours + Instant_message_hours + Email_hours)/5)

# Values containing groups over minimum group size
mingroup_str <-
  my_sq_data %>%
  hrvar_count(hrvar = hrvar,
              return = "table") %>%
  filter(n >= mingroup) %>%
  pull(!!sym(hrvar))

# 4. Compute working patterns list and flexibility index data
wp_list <-
  my_em_data %>%
  workpatterns_classify(
    start_hour = start_hour,
    end_hour = end_hour,
    signals = c("email", "IM"),
    return = "list",
    hrvar = hrvar)

flex_index_data <- 
  my_em_data %>%
  flex_index(
    return = "data",
    hrvar = hrvar,
    signals = c("email", "IM"),
    start_hour = start_hour,
    end_hour = end_hour
    )

```{js, echo=FALSE} var scale = 'scale(1)'; document.body.style.webkitTransform = scale; // Chrome, Opera, Safari document.body.style.msTransform = scale; // IE 9 document.body.style.transform = scale; // General