Focus to Collaboration Ratio Across Industries

How industries balance individual work vs. teamwork (centered at 1.5:1 ratio)

MakeoverMonday
2025
This visualization explores how different industries balance focus time versus collaboration time in their workday, using a 1.5:1 ratio as a benchmark. The chart reveals that most industries favor individual work, with Financial Services showing the strongest focus orientation, while Healthcare leans more toward collaboration. Insurance demonstrates a perfect balance at exactly 1.5:1.
Author

Steven Ponce

Published

March 24, 2025

Original

The original visualization Work Productivity comes from Work Productivity

Original visualization

Makeover

Figure 1: Bar chart showing Focus to Collaboration Ratio across industries. Financial Services has the highest ratio (1.73:1), followed by Professional Services (1.60:1) and Cross-Industry (1.52:1). Insurance is balanced exactly at 1.50:1, while Healthcare is the most collaboration-oriented at 1.43:1. Bars represent deviation from the balanced 1.5:1 ratio, with hour differences noted for each industry.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    camcorder       # Record Your Plot History 
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
#' The raw data for the week MakeoverMonday challenge can be downloaded 
#' https://data.world/makeovermonday/2025w12-precipitation-anomalies
#' 
#' Article
#' https://ourworldindata.org/grapher/global-precipitation-anomaly

productivity_raw <- read_csv(
  here::here('data/Work productivy Activtrak Report.csv')) |> 
    clean_names()

3. Examine the Data

Show code
glimpse(productivity_raw)
skim(productivity_raw)

4. Tidy Data

Show code
### |-  tidy data ----

productivity_data <- productivity_raw |>         
  # Convert total time hours string to numeric for basic metrics
  mutate(
    total_time_numeric = case_when(
      industry == "Cross-Industry" ~ 8 + 54/60,
      industry == "Fin Serv" ~ 9,
      industry == "Healthcare" ~ 8 + 42/60,
      industry == "Insurance" ~ 8 + 41/60,
      industry == "Prof. Servs." ~ 8 + 52/60
    ),
    # basic metrics
    non_productive_time = total_time_numeric - productive_time_hrs,
    work_activities_time = focus_time_hrs + collaboration_time_hrs,
    focus_collaboration_ratio = focus_time_hrs / collaboration_time_hrs,
    productive_time_percent = (productive_time_hrs / total_time_numeric) * 100,
    
    # derived metrics
    ratio_deviation = focus_collaboration_ratio - 1.5,
    industry_label = paste0(industry, " (", format(round(focus_collaboration_ratio, 2), nsmall=2), ":1)"),
    dominance = case_when(
      abs(ratio_deviation) < 0.001 ~ "Balanced",
      ratio_deviation > 0 ~ "More Focused",
      TRUE ~ "More Collaborative"
    ),
    hours_diff = focus_time_hrs - collaboration_time_hrs,
    hours_diff_label = paste0("Difference: ", format(round(abs(hours_diff), 1), nsmall=1), " hrs"),
    annotation = paste0(
      focus_time_hrs, " hrs focus / ", 
      collaboration_time_hrs, " hrs collaboration"
    ),
    industry_order = rank(-focus_collaboration_ratio)
  )

# Annotation for Insurance
insurance_annotation <- tibble(
  x = 0.15,
  y = which(productivity_data$industry == "Insurance"),
  label = "No bar shown because ratio is exactly 1.5:1\n(6.9 hrs focus ÷ 4.6 hrs collaboration),\nplacing it precisely at the baseline"
)

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
"More Collaborative" = "#fc8d62",  
  "Balanced" = "#8da0cb",            
  "More Focused" = "#66c2a5"  
))
  
### |-  titles and caption ----
title_text <- str_wrap("Focus to Collaboration Ratio Across Industries", width = 80)
subtitle_text <- str_glue("How industries balance individual work vs. teamwork (centered at 1.5:1 ratio)<br><br>",
                          "A ratio > 1.5:1 indicates more focus-oriented work culture<br>",
                          "A ratio < 1.5:1 indicates more collaboration-oriented work culture")

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 13,
    source_text = "Activtrak"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Legend formatting
    legend.position = "top",
    legend.title = element_text(face = "bold"),

    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 

    # Axis formatting
    axis.title.x = element_text(face = "bold", size = rel(1.14)),
    axis.title.y = element_blank(),
    axis.text.y = element_text(face = "bold", size = rel(1)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 30, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----

p <- ggplot(
  productivity_data,
  aes(
    x = reorder(industry_label, -industry_order),
    y = ratio_deviation
  )
) +
  # Geoms
  geom_rect(
    aes(
      xmin = as.numeric(factor(reorder(industry_label, -industry_order))) - 0.5,
      xmax = as.numeric(factor(reorder(industry_label, -industry_order))) + 0.5,
      ymin = -Inf,
      ymax = Inf
    ),
    fill = "gray95",
    color = NA,
    alpha = 0.3
  ) +
  geom_rect(
    data = productivity_data |> filter(dominance == "Balanced"),
    aes(
      xmin = as.numeric(factor(reorder(industry_label, -industry_order))) - 0.5,
      xmax = as.numeric(factor(reorder(industry_label, -industry_order))) + 0.5,
      ymin = -Inf,
      ymax = Inf
    ),
    fill = "gray90",
    color = NA,
    alpha = 0.5
  ) +
  geom_hline(
    yintercept = 0,
    color = alpha("black", 0.5),
    linewidth = 0.2,
    linetype = "solid"
  ) +
  geom_col(
    aes(fill = dominance),
    width = 0.7
  ) +
  geom_point(
    data = productivity_data |> filter(dominance == "Balanced"),
    aes(x = reorder(industry_label, -industry_order), y = 0),
    color = colors$palette["Balanced"],
    size = 4,
    shape = 18 # Diamond shape
  ) +
  geom_text(
    aes(
      label = sprintf("%.2f:1", focus_collaboration_ratio),
      y = ifelse(ratio_deviation >= 0,
        pmax(ratio_deviation / 2, 0.02),
        pmin(ratio_deviation / 2, -0.02)
      )
    ),
    color = "black",
    fontface = "bold",
    size = 4
  ) +
  geom_text(
    aes(
      label = hours_diff_label,
      y = 0
    ),
    hjust = -0.1,
    vjust = 2.7,
    size = 3,
    color = "black",
    alpha = 0.7
  ) +
  geom_text(
    data = insurance_annotation,
    aes(x = x, y = y, label = label),
    hjust = 0,
    vjust = 0.5,
    size = 2.8,
    fontface = "italic",
    color = "black",
    alpha = 0.8
  ) +
  geom_vline(
    xintercept = 0,
    color = "black",
    linewidth = 0.7,
    linetype = "dashed"
  ) +
  annotate(
    "text",
    x = 0,
    y = 5.7,
    label = "Balanced (1.5:1)",
    hjust = 0.5,
    vjust = 1,
    size = 3,
    fontface = "italic",
    color = "black",
    alpha = 0.7
  ) +
  # Scales
  scale_x_discrete(position = "top") +
  scale_y_continuous(
    limits = c(-0.1, 0.3),
    breaks = seq(-0.1, 0.3, by = 0.05),
    labels = function(x) sprintf("%+.2f", x)
  ) +
  scale_fill_manual(
    values = colors$palette,
    name = "Work Style"
  ) +
  coord_flip() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Deviation from balanced ratio (1.5:1)"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(2.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(1),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size   = rel(0.75),
      family = fonts$caption,
      color  = colors$caption,
      hjust  = 0.5,
      margin = margin(t = 10)
    )
  )

7. Save

Show code
### |-  plot image ----  
save_plot(
  p, 
  type = "makeovermonday", 
  year = 2025,
  week = 13,
  width = 10, 
  height = 10
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3      pillar_1.9.0     
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3   
[37] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       bit64_4.5.2      
[41] timechange_0.3.0  rmarkdown_2.29    bit_4.5.0         ragg_1.3.3       
[45] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[49] rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[53] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5      
[57] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_13.qmd.

For the full repository, click here.

10. References

  1. Data:
  1. Article
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