In wpa: Tools for Analysing and Visualising Viva Insights Data

Flexibility

Are employees embracing flexible work schedules?

Column {data-width=30%}

Percentage of weeks employees define their own start times

KPI_1 <- 
  flex_index_data %>%
  create_bar(
    metric = "ChangeHours",
    hrvar = NULL,
    return="table") %>%
  select(ChangeHours) %>%
  pull(1) %>%
  round(3)*100 


paste(KPI_1, "% define start time")  %>%
  flexdashboard::valueBox(icon = "fa-hourglass-start",
                          color = "#34b1e2")

Are employees freely defining their start time?
Teams that embrace flexibility allow members of the team to start their workday at different times. Employees should be able to follow a schedule that suits their needs, than those imposed by an employer.

# Convert `start_hour` into an hour slot string
hr_slot <- function(x){

  h_str <- as.numeric(substr(x, start = 1, stop = 2))
  m_str <- substr(x, start = 3, stop = 4) # keep as string

  apm <- ifelse(h_str < 12, "AM", "PM")

  slot1 <- paste0(h_str)
  slot2 <- paste0(h_str + 1)

  paste0(slot1, "-", slot2, apm)
}

flex2_w1_plot <- 
  flex_index_data %>%
  mutate(Percentage_of_weeks = ChangeHours)  %>%
  create_bar(metric = "Percentage_of_weeks",
             hrvar = hrvar,
             rank = NULL,
             xlim = 1,
             bar_colour = "#34b1e2",
             percent = TRUE)

flex2_w1_plot +
  labs(title = "Define start times",
       subtitle = paste("% of weeks employees start outside", hr_slot(start_hour), "window"))

Column {data-width=30%}

Percentage of weeks employees have recurring disconnection time

KPI_3 <-  flex_index_data %>%
  create_bar(
    metric = "TakeBreaks",
    hrvar = NULL,
    return="table") %>%
  select(TakeBreaks) %>%
  pull(1) %>%
  round(3)*100 

paste(KPI_3, "% define quiet hours") %>%
  flexdashboard::valueBox(icon = "fa-stopwatch",
                          color = rgb2hex(1, 147, 157))

Are employees disconnecting at a recurring time during the week?
Employees should have the freedom to define their weekday by taking recurring disconnection time to suit their needs. In teams that embrace flexibility, members will choose to organize / split their day in different ways (e.g. take a long lunch-break, disconnect in the afternoon and reconnect in the evening, etc.)

flex2_w3_plot <-
  flex_index_data %>%
  mutate(Percentage_of_weeks = TakeBreaks) %>%
  create_bar(
    metric = "Percentage_of_weeks",
    hrvar = hrvar,
    xlim = 1,
    rank = NULL,
    bar_colour = rgb2hex(1, 147, 157),
    percent = TRUE
  )

flex2_w3_plot +
  labs(title = "Recurring disconnection time",
       subtitle = paste("% of weeks employees have recurring disconnection"))

Column {data-width=30%}

Percentage of weeks employees limit their activity to 8 or less distinct hours

KPI_2 <- flex_index_data %>%
  create_bar(
    metric = "ControlHours",
    hrvar = NULL,
    return="table") %>%
  select(ControlHours) %>%
  pull(1) %>%
  round(3)*100 


paste(KPI_2, "% control active hours") %>%
  flexdashboard::valueBox(icon = "fa-balance-scale",
                          color = "#00508F")

Are employees able to control their active hours?
Employees who choose alternative work schedules should be able to maintain a workload that is broadly equivalent to the standard 8 hours. Flexible working should not result in longer hours spread across the day.

flex2_w2_plot <- flex_index_data %>%
  mutate(Percentage_of_weeks = ControlHours) %>%
  create_bar(
    metric = "Percentage_of_weeks",
    hrvar = hrvar,
    xlim = 1,
    rank = NULL,
    bar_colour = "#00508F",
    percent = TRUE,
    text_just = 2.5,
    text_colour = "#00508F"
    )

flex2_w2_plot +
  labs(title = "Control active hours",
       subtitle = paste("% of weeks employees control active hours"))

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