Introduction

How Many Hours Are Tracked During a Work Day?

What you quickly realize when tracking your own effective time use, is how little of it you actually have during an average day. To illustrate this point, let’s plot the effective lengths of all my work days as a histogram grouped into half hour intervals.

days <- toggl %>%
  group_by(date) %>%
  summarize(study_hours = sum(study_session_duration)) %>%
  add_column(rolling_mean = rollmean(.$study_hours, 30, na.pad=TRUE))

# Set the theme for all future plots
theme_set(theme_minimal() + theme(legend.position = "bottom"))
colors <- list(
  green = "#536D3D",
  light_green = "#9AB73C",
  orange = "#E5D017",
  dark_orange = "#E59E15",
  red = "#DB4801",
  dark_red = "#FF0000"
)

# Helper function for x-axis representing hours
scale_hours <- function(..., y = FALSE) {
  fun <- ifelse(y, scale_y_continuous, scale_x_continuous)
  fun(
    ...,
    labels = function(x) sprintf("%.0f h", x)
  )
}

binwidth <- 0.5
days %>%
  ggplot() +
  aes(x = study_hours, y = binwidth * ..density..) +
  geom_histogram(
    aes(fill = ..density..),
    binwidth = binwidth,
    color = "white",
    show.legend = FALSE
  ) +
  geom_vline(
    aes(xintercept = mean(study_hours)),
    color = colors$red,
    size = 3
  ) +
  geom_text(
    aes(x = mean(study_hours) + 0.36, y = 0.112),
    label = "Mean",
    color = colors$red
  ) +
  scale_y_continuous(labels = percent_format()) +
  scale_hours(breaks = 0:12) +
  scale_color_discrete("") +
  ylab("Relative frequency") +
  xlab("Length of work day")

I consider myself an above average student when it comes to the time I invest in my studies, but still I only track on average 4.75 hours for the subset of days I study. The data also portrays a large standard deviation of 2.18.

This might by an example of the superiority bias or it may be true that most people actually overestimate their effective work hours during an average day. Based on data gathered by other students at my university I lean towards the latter.

What Kind of Work Is the Time Spent On?

The “nature of the work” is categorized into seven different types of work:

• Lecture - Listening and/or taking notes in lectures.
• Theory - Reading books/articles on the curriculum.
• Hand-in - Work on obligatory homework and projects.
• Exercise - Work on non-obligatory, but recommended homework.
• Repetition - Going over earlier theory and exercises that has been read/done before.
• Organization - Non-learning work, such as planning, delivering hand-ins, and printing material.
• Exam - Taking the final exam.

So how does the work distribute across these categories?

toggl %>%
  group_by(description) %>%
  summarize(study_hours = sum(study_session_duration)) %>%
  ggplot() +
  aes(x = reorder(description, study_hours), y = study_hours) +
  geom_bar(stat = "identity", aes(fill = description)) +
  coord_flip() +
  scale_fill_brewer(palette = "Spectral", direction = -1) +
  ylab("Total Study Hours") +
  xlab("") +
  scale_hours(
    breaks = seq(0, 1500, by = 500),
    y = TRUE
  ) +
  theme(legend.position = "none")

The work is dominated by the Hand-in category. This comes as no surprise to me. Most of the semester is spent handing in one project after another, often with little time in between. I will come back to how this often adversely affects my final grade further down.

Do Certain Type of Work Result in Longer Days?

We can try to visualize what kind of work dominates the workdays of different length.

hours_per_day <- toggl %>%
  group_by(date) %>%
  summarize(study_hours = sum(study_session_duration))

hours_per_category_per_day <- toggl %>%
  group_by(date, description) %>%
  summarize(category_hours = sum(study_session_duration)) %>%
  ungroup()

categories_per_day <- hours_per_day %>%
  inner_join(hours_per_category_per_day) %>%
  group_by(date) %>%
  spread(description, category_hours, fill = 0)

binwidth <- 0.5
categories_per_day %>%
  gather(category, hours, colnames(.)[-c(1:2)]) %>%
  group_by(group = cut(study_hours, breaks = seq(0, 12, by=0.5))) %>%
  ungroup() %>%
  mutate(group = (as.numeric(group) - 1) / 2) %>%
  mutate(category = ordered(category, levels = levels(toggl$description))) %>%
  select(-date, -study_hours) %>%
  group_by(group, category) %>%
  summarize(hours = sum(hours)) %>%
  ggplot() +
  aes(x = group + 0.25) +
  aes(y = hours) +
  aes(fill = category) +
  geom_col() +
  scale_hours(breaks = seq(0, 12)) +
  scale_hours(y = TRUE) +
  scale_fill_brewer("", palette = "Spectral", direction = -1) +
  xlab("Length of work day") +
  ylab("Total hours spent") +
  theme(legend.position = "right")

The conclusion that can be drawn from this plot is that the longer the days, the more probable it is that most of it has been spent on obligatory hand-ins. Most projects last for two/three weeks, and in order to get a good grade it often requires full time work on at least half of those days (from personal experience). Taking into account that I have to attend 3 other courses at the same times, some of them even with their own projects, it often leads to quite long work days.

Trend of Work over Time

Until now we have only looked at aggregate statistics, but has the amount of work changed over time?

rsi_start <- date("2016-01-15")
sick_leave <- tibble(
  interval = c(date("2017-12-14"), date("2018-08-03"))
)

days %>%
  ggplot() +
  geom_ribbon(
    data = sick_leave,
    aes(
      x = interval,
      ymin = 0,
      ymax = Inf,
      y = 1
    ),
    alpha = 0.5,
    fill = colors$red
  ) +
  annotate(
    "text",
    x = mean(sick_leave$interval),
    y = 9,
    label = "RSI\n Sick Leave",
    color = colors$red
  ) +
  geom_vline(
    aes(xintercept = rsi_start),
    color = colors$red
  ) +
  annotate(
    "text",
    x = rsi_start + 10,
    y = 9,
    label = "First RSI\nSymptom",
    color = colors$red,
    hjust = 0
  ) +
  aes(x = date, y = study_hours) +
  geom_point(aes(color = study_hours), show.legend = FALSE) +
  geom_smooth(linetype = "dashed") +
  xlab("Day") +
  ylab("Work hours") +
  scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
  scale_fill_manual("", values = colors$red) +
  scale_linetype_manual("", values = c(1)) +
  theme(legend.position = "bottom")