Analyzing iPhone Usage Data in R

I’m constantly thinking about how to capture and analyze data from day-to-day life. One data source I’ve written about previously is Moment, an iPhone app that tracks screen time and phone pickups. Under the advanced settings, the app offers data export (via JSON file) for nerds like me.

Here we’ll step through a basic analysis of my usage data using R. To replicate this analysis with your own data, fork this code and point the directory to your ‘moment.json’ file.

Cleaning + Feature Engineering

We’ll start by calling the “rjson” library and bringing in the JSON file.

library("rjson")
json_file = "/Users/erikgregorywebb/Downloads/moment.json"
json_data <- fromJSON(file=json_file)

Because of the structure of the file, we need to “unlist” each day and then combine them into a single data frame. We’ll then add column names and ensure the variables are of the correct data type and format.

df <- lapply(json_data, function(days) # Loop through each "day"
{data.frame(matrix(unlist(days), ncol=3, byrow=T))})

# Connect the list of dataframes together in one single dataframe
moment <- do.call(rbind, df)

# Add column names, remove row names
colnames(moment) <- c("minuteCount", "pickupCount", "Date")
rownames(moment) <- NULL

# Correctly format variables
moment$minuteCount <- as.numeric(as.character(moment$minuteCount))
moment$pickupCount <- as.numeric(as.character(moment$pickupCount))
moment$Date <- substr(moment$Date, 0, 10)
moment$Date <- as.Date(moment$Date, "%Y-%m-%d")

Let’s create a feature to enrich our analysis later on. A base function in R called “weekdays” quickly extracts the weekday, month or quarter of a date object.

moment$DOW <- weekdays(moment$Date)
moment$DOW <- as.factor(moment$DOW)

With the data cleaning and feature engineering complete, the data frame looks like this:

Minute CountPickup CountDateDOW
131542018-06-16Saturday
53462018-06-15Friday
195642018-06-14Thursday
91522018-06-13Wednesday

For clarity, the minute count refers to the number of minutes of “screen time.” If the screen is off, Moment doesn’t count listening to music or talking on the phone. What about a pickup? Moment’s FAQs define a pickup as each separate time you turn on your phone screen. For example, if you pull your phone out of your pocket, respond to a text, then put it back, that counts as one pickup.

With those feature definitions clarified, let’s move to the fun part: visualization and modeling!

Visualization

I think good questions bring out the best visualizations so let’s start by thinking of some questions we can answer about my iPhone usage:

  1. What do the distributions of minutes and pickups look like?
  2. How does the number of minutes and pickups trend over time?
  3. What’s the relationship between minutes and pickups?
  4. Does the average number of minutes and pickups vary by weekday?

Let’s start with the first question, arranging the two distributions side by side.

g1 <- ggplot(moment, aes(x = minuteCount)) +
  geom_density(alpha=.2, fill="blue") +
  labs(title = "Screen Time Minutes",
       x = "Minutes",
       y = "Density") +
  theme_minimal() + 
  theme(plot.title = element_text(hjust = 0.5))

g2 <- ggplot(moment, aes(x = pickupCount)) +
  geom_density(alpha=.2, fill="red") +
  labs(title = "Phone Pickups",
       x = "Pickups",
       y = "Density") +
  theme_minimal() + 
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(g1, g2, ncol=2)

On average, it looks like I spend about 120 minutes (2 hours) on my phone with about 50 pickups. Check out that screen time minutes outlier; I can’t remember spending 500+ minutes (8 hours) on my phone!

Next, how does my usage trend over time?

g4 <- ggplot(moment, aes(x = Date, y = minuteCount)) +
  geom_line() +
  geom_smooth(se = FALSE) +
  labs(title = "Screen Minutes Over Time ",
       x = "Date",
       y = "Minutes") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

g5 <- ggplot(moment, aes(x = Date, y = pickupCount)) +
  geom_line() +
  geom_smooth(se = FALSE) +
  labs(title = "Phone Pickups Over Time ",
       x = "Date",
       y = "Pickups") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(g4, g5, nrow=2)

Screen time appears fairly constant over time but there’s an upward trend in the number of pickups starting in late March. Let’s remove some of the noise and plot these two metrics by month.

moment$monyr <- as.factor(paste(format(moment$Date, "%Y"), format(moment$Date, "%m"), "01", sep = "-"))

bymonth <- moment %>%
  group_by(monyr) %>%
  summarise(avg_minute = mean(minuteCount),
            avg_pickup = mean(pickupCount)) %>%
  filter(avg_minute > 50) %>% # used to remove the outlier for July 2017
  arrange(monyr)

bymonth$monyr <- as.Date(as.character(bymonth$monyr), "%Y-%m-%d")
g7 <- ggplot(bymonth, aes(x = monyr, y = avg_minute)) + 
  geom_line(col = "grey") + 
  geom_smooth(se = FALSE) + 
  ylim(90, 170) + 
  labs(title = "Average Screen Time by Month",
       x = "Date",
       y = "Minutes") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

g8 <- ggplot(bymonth, aes(x = monyr, y = avg_pickup)) + 
  geom_line(col = "grey") + 
  geom_smooth(se = FALSE) + 
  ylim(30, 70) + 
  labs(title = "Average Phone Pickups by Month",
       x = "Date",
       y = "Pickups") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(g7, g8, nrow=2)

This helps the true pattern emerge. The average values are plotted in light grey and overlayed with a blue, smoothed line. Here we see a clear decline in both screen-time minutes and pickups from August until January and then a clear increase from January until June.

Finally, let’s see how our usage metrics vary by day of the week. We might suspect some variation since my weekday and weekend schedules are different.

byDOW <- moment %>%
  group_by(DOW) %>%
  summarise(avg_minute = mean(minuteCount),
            avg_pickup = mean(pickupCount)) %>%
  arrange(desc(avg_minute))

g10 <- ggplot(byDOW, aes(x = reorder(DOW, -avg_minute), y = avg_minute)) + 
  geom_bar(stat = "identity", alpha = .4, fill = "blue", colour="black") +
  labs(title = "Average Screen Time by DOW",
       x = "",
       y = "Minutes") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

g11 <- ggplot(byDOW, aes(x = reorder(DOW, -avg_pickup), y = avg_pickup)) + 
  geom_bar(stat = "identity", alpha = .4, fill = "red", colour="black") +
  labs(title = "Average Phone Pickups by DOW",
       x = "",
       y = " Pickups") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(g10, g11, ncol=2)

Looks like self-control slips in preparation for the weekend! Friday is the day with the greatest average screen time and average phone pickups.

Modeling

To finish, let’s fit a basic linear model to explore the relationship between phone pickups and screen-time minutes.

fit <- lm(minuteCount ~ pickupCount, data = moment)
summary(fit)

Below is the output:

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  39.9676     9.4060   4.249 2.82e-05 ***
pickupCount   1.7252     0.1824   9.457  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 50.07 on 320 degrees of freedom
Multiple R-squared:  0.2184,	Adjusted R-squared:  0.216 
F-statistic: 89.43 on 1 and 320 DF,  p-value: < 2.2e-16

This means that, on average, each additional phone pickup results in 1.7 minutes of screen time. Let’s visualize the model fit.

g13 <- ggplot(moment, aes(x = pickupCount, y = minuteCount)) + 
  geom_point(alpha = .6) + 
  geom_smooth(method = 'lm', formula = y ~ x, se = FALSE) +
  #geom_bar(stat = "identity", alpha = .4, fill = "blue", colour="black") +
  labs(title = "Minutes of Screen Time vs Phone Pickups",
       x = "Phone Pickups",
       y = "Minutes of Screen Time") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

You can find all the code used in this post here. Download your own Moment data, point the R script towards the file, and Voila, two dashboard-type images like the one below will be produced for your personal enjoyment.

What other questions would you answer?

5 Essential iPhone Apps

I’m always looking for apps that enhance my life and productivity. A family member recently purchased a new iPhone and asked for my top app recommendations. Here are five, in no particular order:

1. LastPass [Link]

One master password (or touch ID) unlocks all other passwords, eliminating the need for frequent password resetting. Available both as a mobile app or Chrome extension, LastPass seamlessly and securely syncs across devices. My LastPass ‘vault’ contains overs 125 of my passwords, and with features like autofill and auto-login, my work is never slowed by manual password entry.

2. Pocket [Link]

Everyday I see dozens of things online I don’t have time to read or view in the moment. With Pocket I can save those news articles, blog posts, talks, or tutorials for later viewing, even if offline. Pocket allows me to organize things I’ve saved with tags and eliminates the need for me to send links to myself via email or bookmark web pages. Pocket’s recommendation algorithm (optimized based on the content saved) is spot-on and delivers interesting and relevant articles for me to consume on demand.

3. Moment [Link]

As useful as having a computer in your pocket can be, it’s important to be aware of your device usage and ‘screen time.’ Moment allows me to track how much I use my phone or tablet each day automatically. The ‘insight’ tab provides useful metrics like minutes of screen time, percent of waking life on phone, and the number of pickups by day and week. Moment helps ensure my device usage remains within a healthful limit.

4. Mint [Link]

The king of the personal finance apps, Mint aggregates all of your financial accounts and allows you to manage your money from one place. Rather than check my bank, credit card, and investment accounts separately, Mint displays my nine account balances in near real-time. I’m also able to view my credit score, receive bill reminders, create budgets by category, and set financial goals. Mint is an essential for the financially-savvy.

5. Google Keep [Link]

Google Keep is hand’s down my go-to productivity and note-keeping app, allowing me to capture and record any thought or plan tasks. It’s really an extension of my brain. With features like check-markable boxes for to-do lists and searchable note archives, Keep is an app I interact with dozens of times a day across my devices.

What apps do you love?