In wpa: Tools for Analysing and Visualising Viva Insights Data

Collaboration

Is collaboration load reducing personal time?

Column {data-width=30%}

Average length of working day based on collaboration

if(sum(my_sq_data$Work_time, na.rm = TRUE) == 0){

  KPI_1 <- "0 "

} else {

  KPI_1 <- 
    my_sq_data %>%
    create_bar(metric = "Work_time",
               hrvar = NULL,
               return = "table") %>%
    select(Work_time) %>%
    pull(1) %>%
    round(1) 

}

paste(KPI_1, "hr work days") %>%
  flexdashboard::valueBox(icon = "fa-calendar", color = "#34b1e2")

Are employees able to balance work and personal time?
Employees should strive for an equal balance between work and personal time, assuming 8 hours a day is reserved for sleep. Research shows that employees who protect their personal time are more motivated and struggle less with mental health.

if(sum(my_sq_data$Work_time, na.rm = TRUE) == 0){

  md2html("## Note: there is insufficient urgent collaboration data to display.")

} else {


  balance1_w1_plot1 <- 
    my_sq_data %>%
    mutate(Personal_time = 16 - Work_time) %>%
    create_stacked(metric = c("Work_time", "Personal_time"),
                   hrvar = hrvar,
                   rank = NULL) +
    labs(title = "Employee workday",
         subtitle = "Average hours per person per day")

  balance1_w1_plot1 


}

Column {data-width=30%}

Average collaboration time per day (meetings and emails)

KPI_2 <- my_sq_data %>%
  create_bar(metric = "dailyCollabHours",
             hrvar = NULL,
             return = "table") %>%
  select(dailyCollabHours) %>%
  pull(1) %>%
  round(1)

paste(KPI_2, "hrs in collaboration") %>%
  flexdashboard::valueBox(icon = "fa-user-friends", color = "#00508F")

Is collaboration load impacting personal time?
High levels of daily collaboration can become disruptive to an employee’s workday, forcing them to use personal time to complete tasks and meet deadlines.

balance1_w2_plot1 <- 
  my_sq_data %>%
  rename(`Collaboration Hours` = "dailyCollabHours") %>%
  create_bar(metric = "Collaboration Hours",
             hrvar = hrvar,
             bar_colour = "#00508F",
             rank = NULL)

balance1_w2_plot1 +
  labs(title = "Collaboration hours",
       subtitle= "Average hours per person per day")

Column {data-width=30%}

Percentage of employees that collaborate 6 hrs + per day

KPI_3 <- 
  my_sq_data %>%
  create_dist(metric="dailyCollabHours",
              hrvar=NULL,
              cut = c(3, 6),
             return="table")


if(!("6+ hours" %in% names(KPI_3))){

  KPI_3 <- 0

} else {

  KPI_3 <- 
    KPI_3 %>%
    select("6+ hours") %>%
    pull(1) %>%
    round(2)
}

paste(KPI_3 * 100, "% over-collaborators")  %>%
  flexdashboard::valueBox(icon = "fa-thermometer-full",
                          color = "#f59b76")

Which employees are most impacted by collaboration load?
Identify critical groups in the company that may be stretched with collaboration overload. Encourage these teams to streamline collaboration and preserve personal time.

balance1_w3_plot1 <- 
  my_sq_data %>%
  create_dist(metric="dailyCollabHours",
              hrvar=hrvar, 
              cut = c(3, 6),
              unit = "hours",
              dist_colours = c("#f59b76", "#fcf0eb", "#bfe5ee")) 

balance1_w3_plot1 <- balance1_w3_plot1 +
  labs(title = "Collaboration distribution",
       subtitle ="% of employees by daily collaboration")

balance1_w3_plot1 

wpa documentation built on Aug. 21, 2023, 5:11 p.m.