In wpa: Tools for Analysing and Visualising Viva Insights Data

Urgency

Can teams respond to urgent needs without risking employee wellbeing?

Column {data-width=30%}

% of work weeks included urgent collaboration

## Pre-calculation
## Summary table for urgency
urg_sum_tb <-
  list(
  my_sq_data %>%
    group_by(!!sym(hrvar), PersonId) %>%
    summarise(across(IsUrgent, ~any(., na.rm = TRUE)), .groups = "drop") %>% # TRUE if any
    group_by(!!sym(hrvar)) %>%
    summarise(across(IsUrgent, ~mean(., na.rm = TRUE))) %>%
    rename(PercentageAffected = "IsUrgent"),
  my_sq_data %>%
    group_by(!!sym(hrvar), PersonId) %>%
    summarise(across(IsUrgent, ~sum(., na.rm = TRUE)), .groups = "drop") %>%
    group_by(!!sym(hrvar)) %>%
    summarise(across(IsUrgent, ~mean(., na.rm = TRUE))) %>%
    rename(NumberUrgentWeeks = "IsUrgent"),
  my_sq_data %>%
    hrvar_count(hrvar,
                return = "table")
) %>%
  purrr::reduce(full_join, by = hrvar) %>%
  filter(n >= 5)

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

  urg_percent <- "0 %"

} else {

  urg_percent <- scales::percent(mean(my_sq_data$IsUrgent))  

}

## KPI box

paste(urg_percent, "urgent collaboration") %>%
  flexdashboard::valueBox(icon = "fa-exclamation")

Which teams are affected by urgent needs more?
Work can be unpreditcable and there will be urgent requests, but an overwhelming amount will prevent employees from being able to complete business-as-usual activities.

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

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

} else {

  urg_sum_tb %>%
  ggplot(aes(x = PercentageAffected,
             y = NumberUrgentWeeks,
             colour = !!sym(hrvar),
             size = n)) +
  geom_point(alpha = 0.8) +
  theme_wpa_basic() +
  labs(
    title = "Urgent Collaboration",
    subtitle = "Impact and Frequency of Emails with 'Urgent' keywords",
    x = "% of employees affected",
    y = "Urgent weeks per employee (average)",
    caption = paste("Bubble size measures number of people in organization.",
                    extract_date_range(my_sq_data, return = "text"))) +
  scale_size(range = c(1, 20)) +
  scale_x_continuous(labels = scales::percent) +
  ggrepel::geom_text_repel(aes(label = !!sym(hrvar)),
                           size = 3,
                           force_pull = -0.02,
                           colour = "grey40") +
  theme(
    legend.position = "none"
  )

}

Column {data-width=30%}

% change in after-hours when urgent collaboration is present

urg_calc <- my_sq_data %>%
  group_by(IsUrgent) %>%
  summarise(across(After_hours_collaboration_hours, ~mean(.)))

any_urg <-
  urg_calc %>%
  filter(IsUrgent == TRUE) %>%
  nrow()

afch_urg <-
  urg_calc %>%
  filter(IsUrgent == TRUE) %>%
  pull(After_hours_collaboration_hours)

afch_nonurg <-
  urg_calc %>%
  filter(IsUrgent == FALSE) %>%
  pull(After_hours_collaboration_hours)


afch_display <- (afch_urg - afch_nonurg) / afch_nonurg

if(any_urg == 0){

  afch_display <- "0 %"

} else {

  afch_display <- scales::percent(afch_display)

}

afch_display %>%
  paste("change in after hours") %>%
  flexdashboard::valueBox(icon = "fa-moon",
                          color = "#f59b76")

Is the organization able to respond to urgent needs without risking employee wellbeing?
Companies need to cope with unexpected demands without impacting work-life balance. Protect groups with high levels of urgency and after-hours work by shifting towards more planned activities.

groups_above_mingroup <-
  my_sq_data %>%
  hrvar_count(hrvar = hrvar, return = "table") %>%
  filter(n >= 5) %>%
  pull(!!sym(hrvar))

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

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

} else {

my_sq_data %>%
  filter(!!sym(hrvar) %in% groups_above_mingroup) %>%
  group_by(IsUrgent, !!sym(hrvar)) %>%
  summarise(across(After_hours_collaboration_hours, ~mean(.)), .groups = "drop") %>%
  mutate(Urgent_collaboration =
           case_when(IsUrgent == TRUE ~ "Urgent weeks",
                     IsUrgent == FALSE ~ "Non-urgent weeks")) %>%
  ggplot(aes(x = After_hours_collaboration_hours,
             y = !!sym(hrvar),
             colour = Urgent_collaboration)) +
  geom_point(size = 3) +
  scale_x_continuous(position = "top") +
  scale_colour_manual(
    name = "Urgent collaboration",
    values = c(
      "Non-urgent weeks" = "#00508F",
      "Urgent weeks" = "#f59b76"
    )
  ) +
  theme_wpa_basic() +
  labs(title = "After hours collaboration",
       subtitle = "Average hours per person per week",
       caption = extract_date_range(my_sq_data, return = "text"),
       x = us_to_space("After_hours_collaboration_hours")) +
  theme(
    axis.line=element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(colour = "#D9E7F7", size = 3), # lightblue bar
    panel.grid.minor.x = element_line(color="gray"),
    strip.placement = "outside",
    strip.background = element_blank(),
    strip.text = element_blank(),
    axis.title = element_blank()
  ) +
  geom_vline(xintercept =
               my_sq_data %>%
               afterhours_sum(hrvar = NULL,
                              return = "table") %>%
               pull(After_hours_collaboration_hours),
             colour = "red")

}

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