In wpa: Tools for Analysing and Visualising Viva Insights Data

Balance

Can employees balance collaboration and personal time?

Column {data-width=30%}

Average daily collaboration taking place after-hours

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

paste(KPI_1, "hrs after work-hours")  %>%
  flexdashboard::valueBox(icon = "fa-moon",
                          color = rgb2hex(49, 97, 124))

Is after-hours work common practice?
Although sometimes it may be necessary, habitually extending work into evenings is particularly detrimental to workers’ mental health.

balance2_w1_plot1 <- my_sq_data  %>%
  rename(`After Hours Collaboration` = "dailyAfterHours") %>%
  create_bar(metric = "After Hours Collaboration",
             hrvar = hrvar,
             return = "plot",
             rank = NULL,
             bar_colour =  rgb2hex(49, 97, 124)) # Need to add mode of communication

balance2_w1_plot1 + labs(title = "After hours", subtitle = "Average hours per person per day")

Column {data-width=30%}

Percentage of employees engaging in 2+ after-hours per day

KPI_2 <-
  my_sq_data %>%
    create_dist(metric = "dailyAfterHours",
                hrvar = NULL,
                return = "table",
                cut = c(1,2))

if(!("2+ hours" %in% names(KPI_2))){

  KPI_2 <- 0

} else {

  KPI_2 <- 
    KPI_2 %>%
    pull("2+ hours") %>%
    .[[1]] %>%
    round(3) * 100
}

paste(KPI_2, "% long after-hours") %>%
  flexdashboard::valueBox(icon = "fa-calendar-plus",
                          color = "#f59b76")

Which employees are engaging in the most after hours work?
Identify groups most at risk of losing personal time and take steps to intervene and help reduce regular after-hours.

balance2_w2_plot1 <- 
  my_sq_data %>%
  rename(`After Hours Collaboration` = "dailyAfterHours")  %>% 
  create_dist(metric = "After Hours Collaboration",
              hrvar = hrvar,
              return = "plot",
              cut = c(1,2),
              dist_colours = c("#f59b76", "#fcf0eb", "#bfe5ee"))


balance2_w2_plot1 +
  labs(title = "After hours distribution",
       subtitle = "% of employees by daily after hours collaboration")

Column {data-width=30%}

Percentage of employees active on weekends at least once per month

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

  KPI_3 <- 0

} else {

  KPI_3 <- 
    my_sq_data %>%
    create_bar(metric = "monthly_weekend_work", hrvar = NULL, return="table") %>%
    pull("monthly_weekend_work") %>%
    .[[1]] %>%
    round(3)* 100

}

# Need to calculate right KPI
paste(KPI_3, "% weekend workers") %>%
  flexdashboard::valueBox(icon = "fa-calendar-times",
                          color = "#5B0F66")

Are employees engaging in regular weekend work?
Switching on for even a short period of time on weekends can siginifcantly harm employee wellbeing.

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

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

} else {

  my_sq_data %>%
  mutate(
    across(.cols = Frequency_of_weekend_work,
           .fns = ~ifelse(!is.finite(.), 52, .))) %>% # Assume once a year = Never
  group_by(!!sym(hrvar), PersonId) %>%
  summarise(
    across(
      .cols = Frequency_of_weekend_work,
      .fns = ~median(., na.rm = TRUE)
    ),
    .groups = "drop"
  ) %>%
  group_by(!!sym(hrvar)) %>%
  summarise(
    Frequency_of_weekend_work = median(Frequency_of_weekend_work, na.rm = TRUE),
    n = n_distinct(PersonId)
  ) %>%
  filter(n >= 5) %>%
  ggplot(aes(x = Frequency_of_weekend_work,
             y = !!sym(hrvar),
             colour = "Frequency of weekend work")) +
  geom_point(size = 3) +
  scale_colour_manual(
    name="",
    values = "#5B0F66",
    # guide = "Frequency of weekend work"
    ) +
  scale_x_reverse(
    limits = c(15, 1),
    breaks = c(1, 52/12, 52/6, 13),
    labels = c("Weekly", "Monthly", "Bi-Monthly", "Quarterly"),
    position = "top"
  ) +
  labs(title = "Weekend work", 
       subtitle = "Median frequency of weekend work", 
       caption = extract_date_range(my_sq_data, return = "text")) +
  theme_wpa_basic() + 
  theme(axis.line.y = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        panel.grid.major.y = element_line(color="gray"),
        panel.grid.major.x = element_line(colour = "#D9E7F7", size = 5)) 

}

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