Template.qmd

---
title: "Weekly Pass Use Comparison"
subtitle: "First Full Week of January"
theme: _brand.yml
execute: 
  echo: false
format: html
---

```{r}
#| label: libraries
#| warning: false

library(DT)
library(tidyverse)
# library(rsconnect)

```

```{r eval=FALSE}
# rsconnect::writeManifest()
```

```{r}
#| label: data

ay_2024 <- read_rds("2024.rds")
ay_2025 <- read_rds("2025.rds")
ay_2026 <- read_rds("2026.rds")
full_data <- rbind(ay_2024, ay_2025, ay_2026)

```

```{r}

# create new columns to make it easier to select specific academic years
full_data <- full_data |>
    mutate(visit_24 = ifelse(year(Date) == 2024, 1, 0),
           visit_25 = ifelse(year(Date) == 2025, 1, 0),
           visit_26 = ifelse(year(Date) == 2026, 1, 0))

```

```{r}
#| label: sort facility data

south_rec <- full_data |> 
    filter(Facility == "1")

north_rec <- full_data |> 
    filter(Facility == "2")

bd_rec <- full_data |> 
    filter(Facility == "3")

s91 <- full_data |> 
    filter(Facility == "4")

unassigned <- full_data |> 
  filter(Facility == "5")

```

```{r}
#| label: build functions for datasets

# Function to create pass comparison with totals
create_pass_comparison <- function(data) {
  
  # Calculate totals
  totals <- data |>
    summarize(
      Pass_Type = "Total",
      Visits24 = sum(visit_24, na.rm = TRUE),
      Visits25 = sum(visit_25, na.rm = TRUE),
      Visits26 = sum(visit_26, na.rm = TRUE)
    )
  
  # Calculate by pass type
  by_pass <- data |>
    group_by(Pass_Type) |>
    summarize(
      Visits24 = sum(visit_24, na.rm = TRUE),
      Visits25 = sum(visit_25, na.rm = TRUE),
      Visits26 = sum(visit_26, na.rm = TRUE),
      .groups = "drop"
    )
  
  # Combine and return
  bind_rows(totals, by_pass)
}

# function to create comparison plots
create_plot <- function(data, title, y_limit = NULL, subtitle = "First Week of January", padding = 1.15) {
  
  # Prepare data
  plot_data <- data |> 
    mutate(Day = wday(Date, label = TRUE, abbr = FALSE),
           Year = year(Date)) |> 
    group_by(Day, Year) |> 
    summarize(TotalVisits = n(), .groups = "drop") |> 
    complete(Day = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"),
      Year,
      fill = list(TotalVisits = 0)) |> 
    mutate(Day = factor(Day, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")))

  if (is.null(y_limit)) {
    y_limit <- max(plot_data$TotalVisits, na.rm = TRUE) * padding
  }
  
  # Calculate averages
  mean_24 <- plot_data |>
    filter(Year == 2024) |>
    summarize(mean.visits = mean(TotalVisits)) |> 
    pull(mean.visits)
  
  mean_25 <- plot_data |>
    filter(Year == 2025) |>
    summarize(mean.visits = mean(TotalVisits)) |> 
    pull(mean.visits)

  mean_26 <- plot_data |> 
    filter(Year == 2026) |> 
    summarize(mean.visits = mean(TotalVisits)) |> 
    pull(mean.visits)
  
  # Create plot
  ggplot(plot_data, aes(x = Day, y = TotalVisits, color = factor(Year), group = Year)) +
    geom_line(linewidth = 0.8) +
    geom_point(aes(fill = factor(Year)), size = 2.3, shape = 21, color = "white", stroke = 1) +
    scale_color_manual(
      values = c("2024" = "gray70", "2025" = "gray40", "2026" = "#AB0520"),
      name = "Year") +
    scale_fill_manual(
      values = c("2024" = "gray70", "2025" = "gray40", "2026" = "#AB0520"),
      name = "Year") +
    scale_y_continuous(expand = c(0, 0), limits = c(0, y_limit)) +
    labs(
      title = title,
      subtitle = subtitle,
      x = "Day of Week",
      y = "Total Visits") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
    annotate("label", x = Inf, y = Inf, 
             label = paste("2024 Avg:", round(mean_24), "\n2025 Avg:", round(mean_25), "\n2026 Avg:", round(mean_26)),
             hjust = 1, vjust = 1,
             fill = "white", alpha = 0.8, size = 3,
             color = "black")
}

# function for data tables

comparison_table <- function(data, 
                                page_length = 15,
                                total_col = "Pass_Type") {
  
  datatable(data, options = list(pageLength = page_length)) |>
    formatStyle(        
      columns = 1:ncol(data),
      target = "row",
      fontWeight = styleEqual("Total", "bold"),
      valueColumns = total_col) 
}

```

```{r}
#| label: create data to visualize

# apply functions to create dataset
TotalPassCompare <- create_pass_comparison(full_data)
SRECPassCompare <- create_pass_comparison(south_rec)
NRECPassCompare <- create_pass_comparison(north_rec)
bd_recPassCompare <- create_pass_comparison(bd_rec)
s91PassCompare <- create_pass_comparison(s91)
unassignedCompare <- create_pass_comparison(unassigned)

TotalPlotFinal <- create_plot(full_data, "Total Pass Usage by Day of the Week")
SouthPlot <- create_plot(south_rec, "South Rec Pass Usage by Day of the Week")
NorthPlot <- create_plot(north_rec, "North Rec Visits by Day of the Week")
BDPlot <- create_plot(bd_rec, "Bear Down Rec Pass Usage by Day of the Week")
s91Plot <- create_plot(s91, "Studio 91 Pass Usage by Day of the Week")
unassignedPlot <- create_plot(unassigned, "Unassigned Pass Usage by Day of the Week") 

SenSource <- tibble(Facility = "Bear Down Building", Visits24 = "6373", Visits25 = "1688", Visits26 = "1486")

facility_overview <- bind_rows(
  TotalPassCompare |> filter(Pass_Type == "Total") |> mutate(Facility = "Total Campus"),
  SRECPassCompare |> filter(Pass_Type == "Total") |> mutate(Facility = "South Rec"),
  NRECPassCompare |> filter(Pass_Type == "Total") |> mutate(Facility = "North Rec"),
  bd_recPassCompare |> filter(Pass_Type == "Total") |> mutate(Facility = "Bear Down Rec"),
  s91PassCompare |> filter(Pass_Type == "Total") |> mutate(Facility = "Studio 91"),
  
) |>
  select(Facility, Visits24, Visits25, Visits26) |> 
  mutate(across(c(Visits24, Visits25, Visits26), as.character)) |> 
  bind_rows(SenSource)

```

### Overview

```{r}

datatable(
  facility_overview,
  options = list(
    pageLength = 10,
    dom = 't'
  )
) |>
  formatStyle(        
    columns = 1:ncol(facility_overview),
    target = "row",
    fontWeight = styleEqual("Total Campus", "bay_2024"),
    valueColumns = "Facility"
  )

```

<br>

Some notes about last week:

* In 2024, classes began a week earlier, explaining the spike in traffic for 2024.
* Bear Down Rec doesn't show any activity from 1.6.2024 to 1.21.2024. If this doesn't seem right, let me know and I can dig in to see if the numbers are elsewhere. If there was a closure or gap in staff, you can expect to see the numbers for Bear Down Rec again beginning the last week in January.
* Same situation may be true for Studio 91 this January.

Percent change when comparing 2024 visits to 2026:  `r round(((TotalPassCompare[1, 4] / TotalPassCompare[1, 2]) - 1) * 100, 1)`%  
Percent change when comparing 2025 visits to 2026:  `r round(((TotalPassCompare[1, 4] / TotalPassCompare[1, 3]) - 1) * 100, 1)`%

### Weekly Review

::: {.panel-tabset}

### Total Campus

```{r}
#| label: Plot total visits

print(TotalPlotFinal)
```

```{r}
#| label: build datatable for all visits

comparison_table(TotalPassCompare)
```

### South Rec

```{r}
#| label: Plot SREC

print(SouthPlot)
```

```{r}
#| label: build datatable for SREC

comparison_table(SRECPassCompare)
```

### North Rec

```{r}
#| label: Plot NREC

print(NorthPlot)
```

```{r}
#| label: build NREC datatable

comparison_table(NRECPassCompare)
```

### Bear Down Rec

```{r}
#| label: Plot bd_rec

print(BDPlot)
```

```{r}
#| label: build datatable for bd_rec

comparison_table(bd_recPassCompare)
```

### Studio 91

```{r}
#| label: Studio 91 plot

print(s91Plot)
```

```{r}
#| label: build datatable for s91

comparison_table(s91PassCompare)
```

### Unassigned 

```{r}
#| label: unassigned plot

print(unassignedPlot)
```

```{r}
#| label: build datatable for unassigned

comparison_table(unassignedCompare)
```

:::

<br>

**Why is there an "unassigned" tab?**
Mostly, these are passes counted for specific classes that aren't tied to a facility. Sometimes it'll catch student passes. They're here to ensure everything is complete.

**Why is the total number of visits different from the Pass Visit Log summary in RecTrac?**
To capture all pass usage, I've included additional passes, like Group Fitness passes, and excluded CREC Employee passes. So there is some overlap between students swiping in and then checking-in to their program. I've also hard-coded a two-minute coay_2024own for swipes, which doesn't appear to be applied to the Pass Visit Log in RecTrac.

**Why isn't my program or pass represented in this report?**
Great question. [Email me](mailto:jcbetts@arizona.edu?subject=Weekly%20Pass%20Report%20Question(s)) and we'll work to get it added!