In wpa: Tools for Analysing and Visualising Viva Insights Data

Activity patterns

Are employees able to switch off?

Column {data-width=30%}

Distinct hours with email / chat in the most frequent activity pattern

KPI_1 <- 
  my_em_data %>% workpatterns_rank(return="table") %>%  arrange( desc(WeekCount)) %>%  filter(row_number()==1)  %>% 
  mutate(Signals_Total = select(., starts_with("Signals_sent_")) %>%
           apply(1, sum, na.rm = TRUE)) %>% select(Signals_Total) %>% pull(1)

paste(KPI_1 ,"hours with activity") %>%
  flexdashboard::valueBox(icon = "fa-arrows-alt", color = "#f59b76")

When are employees engaging with work?
Individuals may work outside regular working hours to deal with excessive workload, or to take advantage of flexible working arragements. Digital activity patterns reveals if groups are able to disconnect regularly.

flexibility1_w3_plot1 <- my_em_data %>% workpatterns_rank()


flexibility1_w3_plot1 

Column {data-width=30%}

Percentage of "always on" weeks based on collaboration patterns

KPI_1 <- 
  wp_list$data %>%
  count(Personas) %>%
  mutate(prop = n / sum(n)) %>%
  select(Personas, Total = "prop") %>%
  filter(Personas == "6 Always on (13h+)") %>%
  select(Total) %>%
  pull(1) %>%
  round(3)*100


paste(KPI_1, "% always-on") %>%
  flexdashboard::valueBox(icon = "fa-lightbulb", color = "#325369" )

Are employees able to switch off?
Individuals that work long, continuous hours without breaks or remain actively engaged with work after hours can experience a deterioration of their wellbeing.

flexibility1_w1_plot1 <- wp_list$plot

flexibility1_w1_plot1 +
  labs(title = "Classification of activity patterns", subtitle = "Frequency of common archetypes")

Column {data-width=30%}

is the group where employees have the highest % of "always on" weeks

KPI_2 <-  
  wp_list$table %>%
  filter(Personas =="6 Always on (13h+)") %>%
  pivot_longer(cols = -Personas, names_to = "group", values_to = "alwaysOn") %>%
  filter(group %in% mingroup_str) %>%
  arrange(desc(alwaysOn)) %>%
  pull(group) %>%
  .[[1]]

KPI_2  %>% flexdashboard::valueBox(icon = "fa-search")  

Which teams are most affected?
Identify groups with highest prevalence the negative activity patterns and encourage them to disconnect and build regular breaks in their work day.

round_percent <- function(x){
  scales::percent(x, accuracy = 1)
}

wp_list$data %>%
  mutate(Personas = case_when(Personas == "0 < 3 hours on" ~ "Low\nactivity",
                              Personas == "1 Standard with breaks workday" ~ "Flexible",
                              Personas == "2 Standard continuous workday" ~ "Standard",
                              Personas == "3 Standard flexible workday" ~ "Flexible",
                              Personas == "4 Long flexible workday" ~ "Long\nflexible",
                              Personas == "5 Long continuous workday" ~ "Long\ncontinuous",
                              Personas == "6 Always on (13h+)" ~ "Always\non"
                              ) %>%
           factor(levels = 
                    c("Low\nactivity",
                      "Standard",
                      "Flexible",
                      "Long\nflexible",
                      "Long\ncontinuous",
                      "Always\non"))) %>%
  count(!!sym(hrvar), Personas) %>%
  filter(!!sym(hrvar) %in% mingroup_str) %>%
  group_by(!!sym(hrvar)) %>%
  mutate(percent = n / sum(n)) %>%
  ungroup() %>%
  ggplot(aes(x = Personas,
             y = !!sym(hrvar),
             fill = percent)) +
  geom_tile() +
  geom_text(aes(label = scales::percent(percent, accuracy = 1)),
            size = 3) +
  scale_fill_gradientn(name = "Row percentages",
                       colours = c("steelblue4",
                                   "aliceblue",
                                   "white",
                                   "mistyrose1",
                                   "tomato1"),
                       labels = round_percent) + 
  # viridis::scale_fill_viridis(labels = scales::percent) +
  scale_x_discrete(position = "top") +
  theme_wpa_basic() +
  theme(axis.text.x = element_text(angle = 50,
                                   vjust = 0.5,
                                   hjust = 0),
        axis.title = element_blank(),
        plot.margin=unit(c(0, 0, 0, 0),"cm"),
        legend.text=element_text(size = 8)) +
  labs(title = "Activity pattern distribution",
       subtitle = "Distribution of archetypes",
       caption = extract_date_range(wp_list$data, return = "text"))

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