Using a mobile
phone as a therapist

Introduction

📱+ 📊 + 🤔 = 💡?
Using a mobile phone as a therapist
Can I learn something about myself,
by taking data from “activity apps”,
and having a look at it?

Introduction
In this presentation I will, using R, take a look at data from “Activity Tracker”
apps installed on my mobile phone.
So both you and I, will learn a bit about my personal activity level, in regards
to active check-ins and automatically tracked physical activity.
My point is also to show how relatively easy it really is, to import different
datasets into R, and work further with them there.

About me
Peter Meyer
• In digital since 1997
• Analytics Developer
• Implementing TMS, Analytics and A/B testing
• Tealium IQ and Ensighten Manage certified
• .NET and Sitecore CMS background
New position
June 1st 2017, I left IIH Nordic, and started in a
position at Nordea, one of the biggest banks in
Europe.

Roots dating back to 1820
Danish author and fairy tale writer Hans
Christian Andersen (1805–1875) is one of
our first customers.

The Apps

Vendors & Apps
To name but a few…

My “Activity” Apps
Manual check-ins
at visited locations
Automatic tracking of locations,
activity types, and visited places

Swarm (by Foursquare)
• Check-ins
• Gamification
• Points
• Mayorships
• Streaks
• Stickers
• Leaderboards

Downloaded ICS file

Word Cloud

Places

Word Cloud - R script
library(dplyr)
library(tidyr)
library(wordcloud)
filename <- "./foursquare_export/qwerty123456.ics"
checkins <- read.delim(filename, header = FALSE, sep = ":")
names(checkins) <- c("key", "value")
checkinsCount <- checkins %>%
filter(key == "LOCATION") %>%
add_count(value, sort = TRUE) %>%
unique()
wordcloud(words = checkinsCount$value, freq = checkinsCount$n,
scale = c(8,.3), min.freq = 1, max.words = 60,
random.order = FALSE, rot.per = 0.35,
colors = brewer.pal(8, "Dark2"))

Top 10

Top 10 - R Script
library("dplyr")
library("tidyr")
library("ggplot2")
filename <- "./foursquare_export/qwerty123456.ics"
checkins <- read.delim(filename, header = FALSE, sep = ":")
names(checkins) <- c("key", "value")
checkinsCount <- checkins %>%
filter(key == "LOCATION") %>%
add_count(value, sort = TRUE) %>%
unique() %>%
filter(rank(desc(n)) <=10)
ggplot(checkinsCount,
aes(x = reorder(value, -n), y = n, fill = value)
) +
geom_bar(stat="identity") +
theme(legend.position="none", axis.title = element_blank(),
axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.text.x = element_text(size = 18, angle=65, vjust=0.6))

Maps

Swarm - CopenhagenCopenhagen

Swarm - Hungary
Somewhere
in Hungary

Maps - R Script #1/2
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggmap)
filename <- "./foursquare_export/qwerty12345.ics"
checkins <- read.delim(filename, header = FALSE, sep = ":")
names(checkins) <- c("key", "value")
checkinsCount <- checkins %>%
filter(key == "GEO") %>%
add_count(value, sort = TRUE) %>%
unique() %>%
separate(value, c("Latitude", "Longitude"), sep = ";", convert = TRUE)
# Plot Copenhagen
map <- get_map(location = '2300 Amagerbro, Denmark', zoom = 12)

Maps - R Script #2/2
ggmap(map, extent = "device") +
# Outline
geom_density2d(data = checkinsCount,
aes(x = Longitude, y = Latitude),
size = 0.3) +
# Heatmap
stat_density2d(data = checkinsCount,
aes(x = Longitude, y = Latitude,
fill = ..level .., alpha = ..level ..),
size = 0.01, show.legend = FALSE,
bins = 16, geom = "polygon") +
scale_fill_gradient(low = "green", high = "red") +
scale_alpha(range = c(0.0, 0.3), guide = FALSE)

Moves (my activity tracker)
• Automatic tracking of
• Places visited (approx.)
• Work/Home time
• Activity type and duration
• Categorizes by activity type

Downloaded CSV files

Activity
Types

Activity Types
(All records)

Activity (All records) - R Script
library("dplyr")
library(“tidyr")
library(“lubridate")
library("ggplot2")
filename <- "./moves_export/csv/full/activities.csv"
activities <- read.csv(filename, stringsAsFactors = FALSE)
activities$Date <- mdy(activities$Date)
activitiesGrouped <- activities %>%
select(Date, Activity, Duration) %>%
group_by(Date, Activity) %>%
summarise(Duration = sum(Duration / 3600))
ggplot(activitiesGrouped,
aes(x = Date, y = Duration, color = Activity)
) +
geom_point() +
theme(axis.title = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank(), axis.text.x = element_text(size = 18, angle = 0))

Activity Types
(Summarized)

Activity (Sum.) - R Script
library("dplyr")
library("tidyr")
library("ggplot2")
filename <- "./moves_export/csv/full/activities.csv"
activities <- read.csv(filename, stringsAsFactors = FALSE)
activitiesGrouped <- activities %>%
select(Activity, Duration) %>%
group_by(Activity) %>%
summarise(Duration = sum(Duration))
ggplot(activitiesGrouped,
aes(x = Activity, y = Duration, fill = Activity)
) +
geom_bar(stat="identity") +
theme(legend.position="none", axis.title = element_blank(),
axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.text.x = element_text(size = 18, angle = 0))

Maps

Copenhagen

Check-insAuto

Somewhere
in Hungary

Check-insAuto

library("dplyr")
library("tidyr")
library("ggmap")
filename <- "./moves_export/csv/full/places.csv"
places <- read.csv(filename, stringsAsFactors = FALSE)
placesGrouped <- places %>%
select(Latitude, Longitude, Duration) %>%
group_by(Latitude, Longitude) %>%
summarise(Duration = sum(Duration / 60))
# Plot travels in Hungary
map <- get_map(location = 'Hungary', zoom = 7) # Nice illustration
Maps - R script #1/2

ggmap(map, extent = "device") +
# Outline
geom_density2d(data = placesGrouped,
aes(x = Longitude, y = Latitude),
size = 0.3) +
# Heatmap
stat_density2d(data = placesGrouped,
aes(x = Longitude, y = Latitude,
fill = ..level .., alpha = ..level ..),
size = 0.01,
show.legend = FALSE,
bins = 16, geom = "polygon") +
scale_fill_gradient(low = "green", high = "red") +
scale_alpha(range = c(0.0, 0.3), guide = FALSE)
Maps - R script #2/2

Airplane
Destinations

Count

Duration

Destinations - R script #1/3
library("dplyr")
library("tidyr")
library("stringr")
library("ggplot2")
filename <- "./moves_export/csv/full/storyline.csv"
storyline <- read.csv(filename, stringsAsFactors = FALSE)

Destinations - R script #2/3
destinations <- storyline %>%
# Get needed columns
select(Name, Duration) %>%
# Only include non-walking activity
filter(Name != 'walking') %>%
# Define column as following rows Name value
mutate(Destination = dplyr ::lead(Name, n = 1, default = NA)) %>%
# Remove 'Place in' text from Destination value
mutate(Destination = str_replace(Destination, 'Place in ', '')) %>%
# Only include airplane trips to destinations != Kastrup (Home airport)
filter(Name == 'airplane' & Destination != 'Kastrup') %>%
# Merge Budapest rows
mutate(Destination = replace(Destination, Destination %in%
c('Pestlőrinc, Budapest', 'Vecsés'), 'Budapest')) %>%
# Group by Destination names
group_by(Destination) %>%
# Summarise duration lenght in hours, and count occurences
summarise(Duration = sum(Duration / 3600), Count = n()) %>%
# Only include Top 10 trip durations
filter(rank(desc(Duration)) <= 10)

Destinations - R script #3/3
# Plot Duration
ggplot(destinations,
aes(x = reorder(Destination, -Duration), y = Duration, fill = Destination)
) +
geom_bar(stat="identity") +
theme(legend.position="none", axis.title.x = element_blank(),
axis.text.x = element_text(size = 18))
# Plot Count
ggplot(destinations,
aes(x = reorder(Destination, -Count), y = Count, fill = Destination)
) +
geom_bar(stat="identity") +
theme(legend.position="none", axis.title.x = element_blank(),
axis.text.x = element_text(size = 18))

What I learned
from my therapist

🚴My bicycle is not overused
More exercise probably wouldn’t hurt

I value my privacy
I go places, but check-in less
🗺
🚗

R is really, really powerful
You can do a lot, with very little code
📊
💾
🔎
💪

🤔
Check your phone’s data
Learn about yourself, from your apps
📱
💡

Ressources

Ressource links - Apps
Swarm
• Website
https://www.swarmapp.com/
• Feeds
https://foursquare.com/feeds/
• API
http://developer.foursquare.com/
Moves
• Website
https://moves-app.com/
• Export
https://accounts.moves-
app.com/export
• API
https://dev.moves-app.com/

Ressource links - R stuff
• http://dplyr.tidyverse.org/
R package used for data manipulation
• http://stringr.tidyverse.org/
R package for working with strings
• http://tidyr.tidyverse.org/
R package used for creating tidy data
• http://lubridate.tidyverse.org/
R package for working with dates
• http://ggplot2.org/
R package used for plotting the data
• https://github.com/dkahle/ggmap
R package for plotting maps with ggplot2
• http://www.dartistics.com/
A must-use site, if you want to learn
about using R for analytics purposes.
Developed and maintained by Mark
Edmondson and Tim Wilson.

Questions?

Peter Meyer
Senior Web Analytics Developer
Nordea
@ peter.meyer@nordea.com
My contact details
@pmeyerdk
https://www.linkedin.com/in/pmeyerdk