# “Playing the whole game”: A data collection and analysis exercise with
Google Calendar

Albert Y. Kim
Program in Statistical & Data Sciences, Smith College
and
Johanna Hardin
Department of Mathematics, Pomona College
Albert Y. Kim is Assistant Professor, Statistical & Data Sciences, Smith
College, Northampton, MA 01063 (e-mail: akim04@smith.edu). This work was not
supported by any grant. The authors thank numerous colleagues and students for
their support.

###### Abstract

We provide a computational exercise suitable for early introduction in an
undergraduate statistics or data science course that allows students to ‘play
the whole game’ of data science: performing both data collection and data
analysis. While many teaching resources exist for data analysis, such
resources are not as abundant for data collection given the inherent
difficulty of the task. Our proposed exercise centers around student use of
Google Calendar to collect data with the goal of answering the question ‘How
do I spend my time?’ On the one hand, the exercise involves answering a
question with near universal appeal, but on the other hand, the data
collection mechanism is not beyond the reach of a typical undergraduate
student. A further benefit of the exercise is that it provides an opportunity
for discussions on ethical questions and considerations that data providers
and data analysts face in today’s age of large-scale internet-based data
collection.

Keywords: data science, statistics, education, data collection, Google
Calendar, ethics.

##  1 Introduction

The title of our paper refers to the reality that in order to master a
subject, one needs to do more than practice the individual, elemental, and
necessary parts. For example, no matter how good you are at running, dribbling
the ball, or shooting the ball into the upper corner of the net, you cannot
excel at soccer unless you practice the whole game (Perkins, 2010). While
Wickham & Bryan (2019) use the phrase to describe creating an entire R Package
from beginning all the way to distribution on GitHub, we use the term to
describe the entire process by which data science is performed.

Many statistics and data science educators are likely quite familiar with
Wickham & Grolemund (2017)’s model of the tools needed in a typical data
science project seen in Figure 1. While one should not interpret any
simplifying diagram in a overly literal fashion, we appreciate two aspects of
this diagram. First, the cyclical nature of the “Understand” portion
emphasizes that in many substantive data analysis projects, original models
and visualizations need updating, necessitating many iterations through this
cycle until a desired outcome can be communicated. Second, it encourages a
holistic view of the elements of a typical data science project.

Figure 1: Data science workflow diagram (Wickham & Grolemund, 2017)

Previously many undergraduate statistics courses have not taught such a
holistic approach, instead focusing only on individual components at the
expense of others, thereby leaving gaps in the process. In this paper, we view
these gaps in terms of what Not So Standard Deviations podcast hosts Roger
Peng and Hilary Parker call differences between “what data analysis is” and
“what data analysts do” (Peng & Parker, 2018b). In other words, there are many
differences between the idealized view of the data analysis process and what
is actually done in practice.

We argue that is important to adjust curricula to “bridge the gaps” between
“what data analysis is” and “what data analysts do” (McNamara, 2015). For
example, a substantial bridge of the gap has come through great pedagogical
strides to expose students in statistics and data science courses to the
entirety of the process in Figure 1 (Baumer, 2015, Hardin et al. (2015), Loy
et al. (2019), Yan & Davis (2019)).

Another example of an existing gap in some statistics and data science courses
is sparse treatment of data wrangling. A familiar refrain from working data
scientists is that 80% of their time is spent wrangling data leaving only 20%
for actual analysis (see Figure 2). Given the prospect of the 80-20 rule, Kim
et al. (2018) argue that to completely shield students in statistics courses
from performing meaningful data wrangling is to do them a disservice. Horton
et al. (2015) propose five key data wrangling elements that deserve greater
emphasis in the undergraduate curriculum: creative & constructive thinking,
facility with different types of data, statistical computing skills,
experience wrangling, and an ethos of responsibility. Two of the most seminal
contributions to modernizing the statistics and data science curriculum,
National Academies of Sciences Engineering and Medicine (2018) and Nolan &
Lang (2010), both emphasize the importance of teaching and practicing data
wrangling.

(a) Image credit: (Ruiz, 2017)

(b) Image credit: (Robinson, 2018)

Figure 2: The 80-20 rule of data wrangling

In this manuscript, we argue that another such gap in curricula is inadequate
treatment of the data collection component of the cycle. Many working data
scientists do not spend a large proportion of their time working on one static
data set as suggested by the single “Import” step in Figure 1 (Ruiz, 2017,
Lohr (Aug 17, 2014)). Instead, the data used to address questions are often
dynamic; the data change based on differences in time, sampling strategies,
questions asked, variables collected, etc. Furthermore, only after a first
iteration of data has been collected and analyzed can research questions be
updated and fine tuned, often necessitating another round (or more) of data
collection.

While we are not the first to argue that data collection and acquisition is an
important topic for the statistics classroom (Zhu et al., 2013) or the
computer science classroom (Blitzstein, 2013, Protopapas et al. (2020)), we
point out that Wickham & Grolemund (2017)’s model of data analysis from Figure
1 is not a complete representation of a typical “data science project” as it
neglects a critical phase: (repeated) data collection. We present what we term
“playing the whole game” in Figure 3, which augments the earlier “data
analysis” diagram in Figure 1 with an additional “data collection” block. The
new block comprises of key elements to consider when collecting data,
including ethical considerations, the experimental design (if any), the
sampling methodology, questionnaire design in the case of surveys, the data
input and logging methods used, and most critically, identifying the research
question. While the new block is by no means exhaustive and thus should not be
interpreted in an overly literal fashion either, we again emphasize the
cyclical and iterative nature of data collection and data analysis.

Figure 3: “Playing the whole game”: Data collection as well as data analysis
in data science.

Note that some of the tasks in the “Data Collection” box may not be seen
immediately as collecting data. However, we believe that they are important
considerations at the data collection step. For example as it relates to
“Ethical Considerations”, questions like “Should you collect someone’s
location data?” or “Should you require explicit permission to collect a
particular variable?” would be considered. As it relates to “Question
Prompts”, studies on the psychological effect of “priming” have shown that you
can steer survey responses to a particular direction depending on how and when
questions are asked (Hjortskov, 2017).

In the context of undergraduate statistics and data science classrooms, a
large amount of data analysis conducted by students uses data that they
themselves or the class as a whole had no part in collecting. For example,
many students seek data for assignments and projects from repositories like
Kaggle.com or data.gov. Thus in many cases, students get no exposure to the
“data collection” component of Figure 3 and take the data for granted. Lack of
exposure to data collection has potential pitfalls including potentially
erroneous conclusions based on mistaken assumptions of the data collection
method, a lack of a sense of ownership of the work, and most importantly, no
experience going through the iterative process of “playing the whole game.”

Previous literature has focused on slightly different ways of incorporating
data collection into the classroom: research projects (e.g., Halvorsen & Moore
(2000), Halvorsen (2010), Sole & Weinberg (2017)); simulating realistic data
through games (e.g., Kuiper & Sturdivant (2015)); and collecting classroom
activity data (e.g., Scheaffer et al. (2004)).

Building toward the goal of having students experience the entire data
analysis process, we propose a data collection activity that allows students
to mimic the real-life data collection conducted by numerous internet-age
organizations in industry, media, government, and academia. At the same time
the technological background and sophistication necessary for the activity is
kept at a level suitable for undergraduate classroom settings. The classroom
activity touches many components of playing the whole game, demonstrates to
the students the difficulty of data science, and provides an interesting
research question with which the students can engage. A visual summary of the
activity is presented in Figure 4.

Figure 4: Graphical representation of playing the “Playing the Whole Game”
with Google Calendar.

### Muse

The idea for our “data collection” exercise for students came out of an
episode of the earlier mentioned Not So Standard Deviations podcast titled
“Compromised Shoe Situation” (Peng & Parker, 2018a) (as well as described in a
corresponding blog post by Roger Peng titled “How Data Scientists Think - A
Mini Case Study” (Peng, 2019)).

Hosts Hilary Parker and Roger Peng gave each other a data science challenge
whereby they had to solve a problem using data science. They contrasted it to
other common data science challenges (such as the American Statistical
Association’s DataFest (Bialik, 2014) or prediction and classification
competitions available from Kaggle.com) where the data have already been
collected and cleaned.

The challenge that Parker and Peng proposed centers on identifying what
factors influence the time it takes each of them to get to work. In one of our
favorite discussions of their podcast, they break down the iterative process
of gathering data, learning what information it provides, and gathering more
data. They also discuss the difficulty of gathering precise information and
the balance of “low-touch” and “high-touch” data collection (collection
methods that require _few_ active user actions versus _many_ , respectively).
In Parker’s data collection, she implemented a low-touch system of recording
when her commute started by automatically recording when her phone disconnects
from her home WiFi; a higher-touch part of her analysis was when she manually
logged the route she took to work.

In the rest of the manuscript we describe how we have taken the ideas from
Parker and Peng’s podcast and infused them into a class assignment previewed
in Figure 4 which allows students to practice “playing the whole game.” In
Section 2 we provide the details of the assignment and the considerations that
went into why we made some of the choices we did. Section 3 provides a
reflection on what we learned and how the assignment succeeded in
accomplishing the goals for “playing the whole game.” In Subsection 3.3, we
include quotes from reflection pieces written by students summarizing their
experiences. As a vital aspect of our work, in Section 4 we discuss the
ethical considerations of the assignment and how they can be generalized to
the larger scale data collection done by internet-age organizations in
industry, media, government, and academia. As part of our ethics discussion,
we emphasize the importance of bringing up data ethics all along the semester
with every data analysis, and not just in a data ethics course.

##  2 Materials & Methods

###  2.1 Context

In Fall 2019 both authors were each teaching classes in a context where the
whole game would be important to the learning outcomes of the course. Albert’s
class was “Introduction to Data Science” at Smith College, a no-prerequisite
course designed to appeal to a broad audience and act as an entry-point to the
Statistical and Data Sciences major. He gave the assignment in the fifth week
of the semester as the first “mini-project.” Jo’s class was “Computational
Statistics” at Pomona College with primarily junior and senior math/statistics
majors; she used the assignment as the first homework of the semester.111The
work has been verified by the Smith College Institutional Review Board as
”Exempt” according to 45CFR46.101(b)(1): (1) Educational Practices.

In what follows, we provide the details of the assignment. To engage the
students maximally, we tried to find a question with universal appeal and
landed on “How do I spend my time?” In the assignment, the student is required
to collect, wrangle, visualize, and analyze data based on entries they make in
their own electronic calendar/planner application, such as Google Calendar,
macOS, or Microsoft Outlook.

To assess prior student use of a calendar/planner, Albert polled his class at
Smith College on their preferred method of keeping track of their schedule
(See Figure 5). In Albert’s case, most students were already keeping an
electronic calendar (44 of 74), while some were keeping a hand-written
calendar (21 of 74).222Both authors were surprised to see that any college
student would be able to make it through the semester without any type of
calendar at all (3 of 74)!

Figure 5: Poll: Do you keep a calendar/schedule/planner?

###  2.2 Learning Outcomes

The learning outcomes for the exercise can be broken down into three
categories. The first category is “Data collection” which focuses on the
technical aspects of the activity. The second category is “Data ethics” which
focuses on the larger context and conclusions resulting from the exercise. The
last category encompasses the learning goals related to “Playing the whole
game.”

  * •

Data collection

    1. 1.

Experience creating measurable data observations (e.g., how is “one day”
measured, or what defines “studying”).

    2. 2.

Address data collection constraints due to limits in technological capacity
and human behavior.

  * •

Data ethics

    1. 3.

Practice the ethical and legal responsibilities of those collecting, storing,
and analyzing data.

    2. 4.

Decide limits for personal privacy.

    3. 5.

Deliberate on the trade-offs between research results and privacy.

  * •

“Playing the whole game”

    1. 6.

Tie together data collection, analysis, ethics, and communication components.

    2. 7.

Iterate between and within the components of the “whole game.”

Throughout the manuscript, we touch on all of the learning outcomes. Section
3.1 describes many of the benefits and learning outcomes related to “Data
collection”; Section 3.2 describes many of the benefits and learning outcomes
related to “Playing the whole game”.

As a specific example of learning outcome 5, consider ethics training for
animal studies where there is typically a discussion on the minimum number of
samples needed for “ethical research.” The idea is that an under-powered study
would indicate that the animals had been needlessly sacrificed with no
possibility of moving scientific research forward. As with minimum number of
samples, we ask the students what types of research connect with which types
of privacy violations, and where is the correct balance for pushing forward
knowledge. A more complete examination of data ethics in the classroom is
given in Section 4.

###  2.3 The Assignment

We have provided the complete assignments used by both authors at the
following website https://smithcollege-sds.github.io/sds-
www/JSE_calendar.html/. While the two versions of the assignment vary slightly
in format and reflection, both still require the students to go through the
entire process of collecting, wrangling, visualizing, analyzing, and
communicating about the data, with an important additional step of reflecting
on the information gathered. The assignment was scaffolded so that a student
with minimal computational and coding experience could still directly work
with calendar data.

Before we go into the details of working with calendar data, we first point
out some differences in approach between the two assignments. One unique
aspect of Albert’s class is that he had the students work in pairs. A
particular student would make their calendar entries and then export the data.
However, instead of analyzing their own calendar, they would send their data
to their partner who then wrangled, visualized, and analyzed the data (as well
as vice versa). The motivation was to encourage students to think about what
data they could or should share with their partner, what data they couldn’t
nor shouldn’t, and any particular responsibilities that the individual
analyzing the data had.

In Jo’s class, she spent half of one class period discussing the podcast in
great detail. Because it was early on in the semester, there were lots of
unknowns about what is data science and what are the possible ways one can use
data to make decisions. Additionally, the assignment and podcast continued to
come up in class sessions throughout the semester as a way to ground
conversations with respect to data collection & analysis: what we can do, what
we can’t do, what we should do, and what we shouldn’t do.

###  2.4 Details of Working With Calendar Data

Students were instructed to track how they spent their time on their calendar
application for approximately 10-14 days. More specifically, students were
instructed to fill in blocks of time and mark the entry with the activity they
were performing: sleeping, studying, eating, exercising, socializing, etc. The
students chose their own categories to fit their own schedules. Students were
able to have overlapping blocks of activities in situations where two
activities were done simultaneously. Students were also informed that they
should feel comfortable leaving out any details they did not want to share;
indeed, if they wanted to, they were free to make up all the information in
the calendar.

Note that our example centers around the use of Google Calendar. To ensure as
much consistency as possible we encouraged students who did not have an
electronic calendar to use Google Calendar to record their activities.
However, the assignment can equally be done using macOS calendar or Microsoft
Outlook.

As an example, we filled a sample Google Calendar with entries between
September 2nd and 7th, 2019, which you can view using the Google Calendar
interface at http://bit.ly/dummy_calendar. We suggest that after scrolling to
the week of September 1st, 2019, you click the “Week” tab on the top right for
a week-based overview of the calendar entries.

Figure 6: Sample Google Calendar presented to students.

After looking at the sample calendar, students exported their own calendar
data to .ics file format, a universal calendar format used by several email
and calendar programs, including Google Calendar, macOS Calendar, and
Microsoft Outlook. Their file was then imported into R as a data frame using
the ical_parse_df() function from the ical package (Meissner, 2019).

In order to help the students focus on the larger data science paradigm, the
instructors scaffolded the assignment in template R Markdown files (Allaire et
al., 2019) which served as the foundation of the students’ submissions; we
provide these scaffolded assignments at https://smithcollege-
sds.github.io/sds-www/JSE_calendar.html/. Continuing our earlier example
involving a sample Google Calendar, we exported its contents to a 192.ics
file. Subsequently, we imported the .ics file into R as a tibble data frame
(Müller & Wickham, 2019), and then performed some data wrangling using dplyr
(Wickham, François, Henry & Müller, 2019) and the lubridate package for
parsing and wrangling dates and times (Grolemund & Wickham, 2011).

Below, we give an extract of the code representing the crux of the process
previously described. (Note that to change the "America/New_York" timezone
setting above, run the OlsonNames() function in R to output a list of all time
zones.)

library(ggplot2)

library(dplyr)

library(lubridate)

library(ical)

calendar_data <- "192.ics" %>%

# Use ical package to import into R:

ical_parse_df() %>%

# Convert to "tibble" data frame format:

as_tibble() %>%

# Use lubridate packge to wrangle dates and times:

mutate(

start_datetime = with_tz(start, tzone = "America/New_York"),

end_datetime = with_tz(end, tzone = "America/New_York"),

length = end_datetime - start_datetime,

date = floor_date(start_datetime, unit = "day")

) %>%

# Make calendar entry summary all lowercase:

mutate(summary = tolower(summary)) %>%

# Compute total daily time spent on each activity:

group_by(date, summary) %>%

summarize(

length = sum(length),

length = as.numeric(length)

) %>%

# Set correct time interval length units. Here, hours:

mutate(hours = length/60) %>%

# Filter out only rows where date is later than 2019-09-01:

filter(date > "2019-09-01")

The resulting calendar_data data frame output is presented in Table 1. The
calendar_data data frame can then be used to make plots like the time-series
linegraph in Figure 7 and the side-by-side boxplot in Figure 8.

Table 1: Example calendar data frame. date | summary | length | hours
---|---|---|---
2019-09-02 | sleep | 480 | 8.0
2019-09-02 | study | 60 | 1.0
2019-09-03 | exercise | 60 | 1.0
2019-09-04 | sleep | 960 | 16.0
2019-09-04 | study | 180 | 3.0
2019-09-05 | sleep | 540 | 9.0
2019-09-06 | exercise | 30 | 0.5
2019-09-06 | study | 90 | 1.5
2019-09-07 | exercise | 30 | 0.5
2019-09-07 | sleep | 540 | 9.0
Figure 7: Time series plot of calendar data. Figure 8: Distribution of number
of hours spent split by activity type.

##  3 Results

The assignments we used in our classes did not take a substantial amount of
time to create, execute, or discuss. Albert engaged with the assignment for
about half a class period at three different times: (1) to motivate and
introduce the assignment including an end-to-end demonstration; (2) a check-in
to make sure everyone was on track, and (3) an in-class discussion after the
assignment had been submitted. Jo spent only a few minutes discussing the
technical aspects of the assignment and 30 min during one class period
discussing Peng & Parker (2018a). We believe that including the “play the
whole game” activity in the course allowed for a much deeper understanding and
reflection on the entire data science pipeline, without needing to substitute
any of our previous course learning goals. That is to say, the assignment we
have shared is in keeping with the current structure of many statistics and
data science courses. In fact, while our manuscript was under review, Prof.
Katharine F. Correia at Amherst College extended our original ideas and
modified them so that her students could create “data diaries” during the
social isolation period of Spring 2020. Some examples of her students’
remarkable work can be found here: https://stat231-01-s20.github.io/data-
science-diaries/.

We now discuss what we view as the successes and lessons of the activity (for
student perspectives on these successes and lessons see Section 3.3).

###  3.1 Benefits Inherent to the Activity

First, the focus on “self” and an engaging question (“How do I spend my
time?”) easily captured our students’ interest. Universally, they found the
assignment compelling, with one student remarking informally afterwards that
they planned on pursuing further analyses on their own private Google
Calendar. Another student came up with a clever idea of documenting their
intended time studying versus their actual time studying. Some students seemed
concerned that their question on how they used their time wasn’t interesting
enough, but we emphasized that the project should be more about process than
outcome. Additionally, we prompted students with follow-up queries such as:
“Do the amounts of time spent on certain activities have an inverse
relationship with each other?” or “Do you spend as much time on a particular
activity as you expect to?” (where the latter question would necessitate
recording both expected and actual time for each event).

Second, having the data collection involve time intervals puts a nice
cap/standardization on the scope on the type of data collected. As can be seen
in Table 1 of the example calendar_data data frame, all resulting data frames
consist of three variables: date (numerical), length of time spent
(numerical), and activity type (categorical). Having such a combination of
variables is an excellent starting point for data science courses.
Furthermore, many students encountered very interesting computational and data
wrangling questions quite organically, some along the lines of:

  * •

How do I combine two types of activities in R?

  * •

How do I only select events from days past 2019-09-02?

  * •

My ‘hours spent’ variable is actually in minutes rather than hours. How do I
convert it to hours? (We noticed operating system level variation in the
default units of the recorded length variable in the calendar_data dataframe.)

  * •

How would I make a scatterplot of daily time spent studying versus time spent
sleeping?

The latter question is particular interesting as it necessitates converting
data frames from tall/long format (like that of the calendar_data example in
1) to wide format using the pivot_wider() function from the tidyr package
(Wickham & Henry, 2019). In our opinions, this question serves as an ideal
method to teach the subtle concept of “tidy” data (Wickham, 2014). Rather than
starting with the somewhat technical definition of “tidy” data, there is a
benefit to having students gently “hit the wall” whereby they cannot create a
certain visualization or wrangle data in a certain way unless the data is in
the correct format.

Lastly, both instructors found that the exercise served as an excellent
opportunity to teach students about file paths and including the dataset (in
this case the .ics file) in an appropriate directory. Students were required
to compile .Rmd files which connected to a unique .ics file, and a second
individual (either a partner or a student grader) also needed to be able to
compile the .Rmd file. Due to the typical structure of a homework assignment
(e.g., professor providing a link to a dataset), our experience is that
working with file paths is both difficult for novice data science students as
well as difficult to provide examples for practicing.

###  3.2 Benefits from “Playing Whole Game”

Importantly, there were benefits engendered by having students “play the whole
game,” both (1) of a technical, coding, and computational nature and (2) of a
scientific and research nature. Many lessons were only learned by students
after having gone through an entire iteration of “data collection” and “data
analysis” cycle as seen in Figure 3.

For example, one student made calendar entries recording the start times of
events, whereas the assignment assumed time intervals were being recorded. For
example, “I went to sleep at time X.” versus “I slept between times X and Y,
and thus slept for Z hours.” The student thus needed to revise their data
collection method.

Other students found out that unless their calendar entry titles matched
exactly, they are recorded as two different activity types. Consider the
following extremely nuanced difference due to trailing spaces: "Sleep" versus
"Sleep ", such data collection methods were also revised in order to ensure
entries were standardized. These students thus learned about the differences
in how humans and computers process text data.

Another issue that surprised the instructors was that calendar entries based
on a “repeat” schedule only appeared once in their resulting data in R: the
entry for the first day. The instructors thus had to consider whether to
update the assignment instructions for students in future classes, or to let
them encounter this issue on their own.

On top of more technical, coding, and computational issues, sometimes students
realized mid-way that their research question itself needed revising and thus
had to retroactively fill in their calendars using their best guesses. These
students thus realized the importance of iterating through the entire process
sooner rather than later in order to prevent errors from accumulating.

An unforeseen issue that combined both technical and scientific queries was
how to allocate sleep hours. For example, if a student went to sleep at 10pm
and woke up at 6am, then 2 hours of sleep would be logged for one day and 6
hours for the next, whereas most people would call it a single night of sleep.

###  3.3 Student quotes

On top of the earlier mentioned student “data diaries” from Prof. Katharine F.
Correia’s course available at https://stat231-01-s20.github.io/data-science-
diaries/, we now provide excerpts from the reflection pieces students wrote in
Albert’s course at Smith College. Students were asked to write a joint
reflection piece on their experiences, keeping the podcast in mind (Peng &
Parker, 2018a).333Per Smith College Institutional Review Board guidelines,
explicit consent was obtained directly from all students who are
confidentially quoted in the manuscript. In particular:

  * •

As someone who provides data: What expectations do you have when you give your
data?

  * •

As someone who analyzes other’s data: What legal and ethical responsibilities
do you have?

The student quotes have been grouped into categories which relate back
(roughly) to the previously described learning outcomes (LO) in Subsection
2.2.

Relating to iterating between “data collection” and “data analysis” (LO1):

  * •

“I fell ill and was unable to log in more data points for her to visualize.
Student X was still able to visualize my activity, incorporating my sick days
as another variable”

  * •

“One of the technical issues we ran into, and probably the defining experience
of this project, was the difficulty in creating consistent error free
information to export to our partner. It’s kind of ridiculous how small manual
entry errors, like whether or not I used spaces (in the calendar entries),
made such a difference at the end of the line when mystery variables started
showing up”

  * •

“But that was not the biggest issue we encountered, our issue was the fact
that we inputted repeated events on our calendar which affected how many
observations came up onto the data set (since only the first of the multiple
events would show up)”

  * •

“I, for example, learned that data is fickle and needs consideration of how
variables interact with each other within a data visualization before
unnecessary data collection. Her original question was inadequate as one of
her variables wasn’t a function of time; thus, she had to retroactively enter
the data for her new question using her best estimate, meaning her data should
be recognized as imprecise and that it could affect the outcome of any
analysis.”

Relating to “low” and “high touch” data collection methods (LO2):

  * •

“Specifically for Student X’s data, it was interesting to work with real-time
data recorded by her phone. Because it was automatically collected by her
mobile device, there was less room for human errors when calculating the time
spent on her phone screen.”

  * •

“The time is hard to pin down because sometimes there will be interruptions or
potentially interleaving studying to do other things. We think that the study
time could be off between an hour and a half an hour for regular weekdays and
maybe two during weekends because sometimes it is harder to take down the
exact start and stop time on the spot. Comparatively, recording sleeping time
is much easier because we could always check the time when the alarm goes off
and take a screenshot when setting the alarm for the other day.”

Relating to analyst ethical responsibilities (LO3):

  * •

“For example, in our group, one member accidentally shared their entire
calendar data with the other member when only a small portion of this data was
needed for analysis. The other member realized the first member’s mistake,
deleted the data, and showed the first member how to separate the events she
wanted to be analyzed from the rest of her calendar.”

Relating to prior analyst biases (LO3):

  * •

“It might be worthwhile as the analyzer to be completely transparent and
address any previous biases that may affect the reliability of a given data
analysis.”

  * •

“Keep to the original data, don’t change the data to get a favorable result”

Relating to data provider expectations (LO4):

  * •

“Additionally, the individual should have the right to ask about the research
project that the data is going to and how their data may be used.”

Relating to analyst empathy towards data providers (LO5):

  * •

“The question was straightforward but it also made us uneasy as we had to
consider both what we were comfortable sharing and how to handle our partner’s
data in an ethical manner”

  * •

“On the other hand, because of my discomfort, I was also able to handle
Student X’s data with the same caution that I would expect her to do with my
dataset”

  * •

“This in turn motivated me to handle her data with care and sensitivity. For
this reason, I appreciated this project as it forced us to simultaneously
consider our roles both as people who share our data in numerous scenarios
throughout our daily lives and as data science students with a new
responsibility to handle another person’s data ethically.”

  * •

“If we oppose Facebook tracking our personal information and profiting off it
without our consent, we owe it to our customers/providers of data to not do
the same.”

  * •

“Personally I did question how comfortable I was sharing information about
XXX, but decided to push my boundaries, because though it made me nervous to
share information about XXX I was also very curious about how it affected YYY,
and the actual risk of sharing this information was little to none. I wanted
to gain some insight from this project that would help me lead a healthier,
more stable life.”

Relating to the value of data (LO5):

  * •

“There would be no incentives for us to share our data if we are not getting
anything back from it.”

  * •

“A grey area of data analysis would be targeted ads. On the one hand, one
could argue that getting targeted ads bring convenience to only getting
advertised products we would enjoy. On the other hand, one could also argue it
pushes capitalism on us and thus only disadvantages the public by heavily
influencing us to buy products we might not need.”

Relating to the balancing act many analysts face (LO5):

  * •

“Information that puts our security or identity at risk should be left out.
However, if too little information is shared, it may affect the ability to
accurately represent any phenomena occurring within the data.”

  * •

“In the podcast we listened to, Hilary Parker specifically mentioned how in
collecting employees’ commute time, she wanted to protect privacy by
withholding the exact minute they left work. An alternative was releasing the
general length of time employees spend commuting to work would, but this would
remove the impact of confounding variables and externatlities. The exact times
an employee commuted were important because part of the data collection
included factoring in traffic patterns. Because of this consideration, Parker
decided it would be more effective to round the times an employee arrived of
left work to the nearest hour to maximize privacy and accuracy of the data.”

Relating to actionable insight (LO6):

  * •

“Since then I have been more cautious about granting apps access to my
information.”

  * •

“Visualization 1 made me realize that I should also spend more time during the
weekdays sleeping and hanging out with friends to take care of myself.”

Relating to revised research questions (LO7):

  * •

“For example, while analyzing Student X’s data, we had to decide whether or
not Friday would be considered a weekday or a weekend.”

  * •

“As for improvements in our next projects, for consistency and accuracy, it
might be important to keep the data collection dates fixed so that there are
the same number of the week days considered in the analysis.”

##  4 Discussion

As noted by Baumer et al. (2020), many major professional societies, including
the American Statistical Association (ASA) (Committee on Professional Ethics,
2018b), the Association for Computing Machinery (ACM) (Committee on
Professional Ethics, 2018a), and the National Academy of Sciences (NAS)
(Committee on Science, Engineering, and Public Policy, 2009), have long
published guidelines for conducting ethical research. However, only more
recently has there been growing literature on the weaving of data ethics
topics early and often all throughout undergraduate curricula in data science,
statistics, and computer science (Baumer et al., 2020, Ott (2019), Burton et
al. (2018), Elliott et al. (2018), Heggeseth (2019)).

The class activity we present provides a myriad of ways to start a classroom
conversation about data ethics. Albert motivated the idea of the project by
discussing the pros and cons of how he logs his daily calorie consumption
using the MyFitnessPal app. On the one hand, the students could appreciate the
convenience of logging calories using the barcode scanner, on the other hand,
they could also understand that Albert is essentially telling the Under Armour
corporation everything he eats and when. In the age of Fitbit, 23andMe, and
innumerable different health and fitness apps, the example presents the
students with an opportunity to think about what information they are sharing
with large corporations.

Instructors may choose a different type of data collection, or let each
student decide on their own collection method. Some possible extensions
include the above mentioned fitness apps (outputting all entries in a .csv
file); every interaction on Instagram (outputting a .json file); Google trends
or Google search terms (output as a function of time); or personal Facebook
information (as .html or .json). Each data type will present a unique setting
in which to discuss privacy and data ethics considerations.

In Jo’s class conversation about the podcast, one student appreciated that
Hilary and Roger discussed whether they _should_ do the data collection. The
student expressed that maybe Hilary should just leave earlier for work so as
to not make anyone wait. That would be the more kind thing to do. Indeed, as
Hilary herself says “… it’s probably better to just give yourself a cushion so
you don’t have to worry.” The student comment led to a larger conversation
about what types of questions we should answer with data science and what
types of questions should not be data driven.

In the last few minutes of the podcast, Parker says (Peng & Parker, 2018a):

> … setting up, thinking through what could you collect, what makes sense to
> collect? What are the assumptions in the data that you’re collecting? Like
> going through that, I would encourage people to go through that process,
> even if you end up like not doing it long-term or something, but it’s still,
> I think that’s the skill set that’s important to develop if you want to work
> in data science.

Why does Hilary Parker implore people to “play the whole game?” We believe
that there is a disconnect in the narrative surrounding “what is data
analysis” when instead it should be more closely connected to “what do data
analysts do.” The only way to inculcate lessons pertaining to “what data
analysts do” is by having students “play the whole game.” Furthermore, just as
in real-life, it is critical that students go through the cycle several times.
In other words, “iterate early and iterate often.”

##  5 Acknowledgements

Most importantly, we’d like to acknowledge the Smith College and Pomona
College students with whom we work. It is from their ideas and energy that we
discovered the fun and beauty of working in statistics and data science.
Hilary Parker & Roger Peng provided not only the inspiration for the activity
but also helpful feedback on the pre-print. We appreciate the thoughtful
suggestions of three anonymous referees and the associate editor. On top of
the packages cited in the manuscript, the authors also used the ggplot2
(Wickham, Chang, Henry, Pedersen, Takahashi, Wilke, Woo & Yutani, 2019),
kableExtra (Zhu, 2019), knitr (Xie, 2019), and viridis packages (Garnier,
2018). We also thank Hadley Wickham for providing the direct insipiration for
the “playing the whole game” wording in our paper.

##  6 Declaration of Interest Statement

Nothing to declare.

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