---
title: "Evaluating a Workplace Intervention with Viva Insights in R"
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: true
    toc_float: true
    theme: "lumen"
---

```{r setup, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
library(tidyverse)
library(vivainsights)
```

# Evaluating a Workplace Intervention

## Introduction

Organisations frequently run interventions aimed at improving how people work: a
protected "focus day", a meeting-reduction push, or a Microsoft 365 Copilot
enablement wave for one team. The natural question is *did it actually work?* -
and, just as importantly, *did anything simply move somewhere else?*

Answering this credibly needs more than a before-and-after comparison for the
group that took part. A company-wide trend, a seasonal effect, or a change in how
a metric is calculated can all masquerade as a programme effect. The antidote is a
simple **quasi-experiment**:

- Compare a **treated** group (who received the intervention) against a **control**
  group (who did not).
- Split time into **Before**, **During**, and **After** windows.
- Use a **difference-in-differences** read: the treated group's change minus the
  control group's change.
- **Discount any signal that moves the same way in the control group**, because a
  change that shows up where there was no intervention cannot have been caused by it.

We demonstrate this on the built-in `pq_data` sample Person Query.

## Data preparation

`pq_data` is a weekly Person Query. We assign a treated group (here, one
organisation that we imagine received the intervention) and a control group
(everyone else), then split the weeks into three equal windows.

```{r}
data("pq_data", package = "vivainsights")

pq <- pq_data %>% mutate(MetricDate = as.Date(MetricDate))

# Treated vs control. In practice you would flag the population that actually
# received the intervention; here we use one organisation for illustration.
TREATED_ORG <- "IT"
pq <- pq %>%
  mutate(group = ifelse(Organization == TREATED_ORG, "Treated", "Control"))

# Before / During / After as three equal windows across the date range.
rng  <- range(pq$MetricDate)
cuts <- rng[1] + diff(rng) * c(1/3, 2/3)
pq <- pq %>%
  mutate(period = factor(
    case_when(
      MetricDate <  cuts[1] ~ "Before",
      MetricDate <  cuts[2] ~ "During",
      TRUE                  ~ "After"
    ),
    levels = c("Before", "During", "After")
  ))

count(pq, group, period)
```

The sample data contains no real intervention, so purely for demonstration we
inject a modest, clearly labelled reduction in multitasking for the treated group
during and after the programme. **Delete this block when using your own data**; it
exists only so the method has a real effect to detect.

```{r}
pq <- pq %>%
  mutate(Multitasking_hours = case_when(
    group == "Treated" & period == "During" ~ Multitasking_hours * 0.92,
    group == "Treated" & period == "After"  ~ Multitasking_hours * 0.80,
    TRUE                                     ~ Multitasking_hours
  ))
```

## Two-stage aggregation

A robust group summary aggregates in **two stages**: first to a typical value per
person (so a few very heavy or very light weeks do not dominate), then to a mean
across people within each group and period. We wrap this in a small reusable
function.

```{r}
two_stage_summary <- function(data, metric,
                              id = "PersonId", group = "group",
                              period = "period") {
  # Stage 1: person-level mean within each period
  s1 <- data %>%
    group_by(.data[[id]], .data[[group]], .data[[period]]) %>%
    summarise(person_mean = mean(.data[[metric]], na.rm = TRUE), .groups = "drop")

  # Stage 2: group mean across persons within each period
  s1 %>%
    group_by(.data[[group]], .data[[period]]) %>%
    summarise(value = mean(person_mean, na.rm = TRUE),
              n_persons = n(), .groups = "drop")
}

summ <- two_stage_summary(pq, metric = "Multitasking_hours")
summ
```

## Difference-in-differences

Now the key comparison. We look at each group's change from Before to After, and
take the difference between them. The control group's change captures whatever was
happening anyway; subtracting it isolates the treated-specific effect.

```{r}
wide <- summ %>%
  select(group, period, value) %>%
  pivot_wider(names_from = period, values_from = value) %>%
  mutate(change_before_after = After - Before)

wide

did <- wide$change_before_after[wide$group == "Treated"] -
       wide$change_before_after[wide$group == "Control"]

did  # difference-in-differences (treated change minus control change)
```

A picture makes the story immediate: the treated line should step down while the
control line stays broadly flat.

```{r}
ggplot(summ, aes(x = period, y = value, colour = group, group = group)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2) +
  labs(
    title = "Weekly multitasking hours by period",
    subtitle = "Treated vs control, two-stage person-then-group means",
    x = NULL, y = "Multitasking hours / person / week", colour = NULL
  ) +
  theme_minimal(base_size = 12)
```

## Reading the result honestly

The **difference-in-differences** is the number to trust, not the treated group's
before/after change on its own. If the control group had moved by a similar amount,
we would conclude the shift was an organisation-wide or seasonal effect (or even a
change in how the metric is computed) and **discount it**, regardless of how good
the treated group's raw change looked. A change that appears equally in a group
that received no intervention cannot have been caused by the intervention.

Two further checks are worth building in as a habit:

- **Look for displacement.** Re-run the same summary on adjacent behaviours (for a
  meeting-reduction programme: after-hours and weekend collaboration hours) to
  confirm the load did not simply move elsewhere. "It went down and did not come
  back somewhere else" is the claim a leader actually needs.
- **Prefer two-stage aggregation.** Person-then-group means stop a handful of
  extreme individuals or unequal week counts from skewing the comparison.

Transferable practices: always include a control population; structure the data as
Before / During / After from the weekly person query; aggregate in two stages; and
discount any signal that also moves in the control.

This design is reusable for any workplace intervention, including an AI-adoption
programme. Framing a Copilot enablement wave as the "treated" group and measuring
the difference-in-differences against a comparable control is a clean way to show
whether adoption is associated with real shifts in collaboration or wellbeing
signals, rather than relying on a simple before-and-after that a company-wide trend
could easily confound.