Data stats

Before I get to actually plotting maps, I want to explore a few basic statistics of the data.
1. How many data points did Google record over what period of time?

nrow(locations)

## [1] 268248

2. From when till when is this data recorded

min(locations$time)

## [1] "2017-11-22 13:20:07 IST"

max(locations$time)

## [1] "2018-12-05 15:14:54 IST"

3. How many days were recorded?

length(unique(date(locations$time)))

## [1] 379

Integrating with attendance data set

The data set contains the time when I swiped into office and the time when I swiped out of office. Data from 4th October 2017 to 29th November 2018 is considered. For more details on attendance data set, please read here

Finding how many working days are captured in Google Maps.

working_days <- locations %>% 
  filter((date(time) %in% date(attendance$Attendance.Date)))
length(unique(date(working_days$time)))

## [1] 246

A total of 246 working days were captured of 266 working days.

library(ggmap)
qmplot(lon, lat, data = working_days,zoom = 5)

Filtering for data for Bangalore

# Bangalore North Eastern border is 13.071393, 77.787015 (Hoskote)
# Bangalore South Western border is 12.889629, 77.454102 (Kumbalgodu)
bangalore <- locations %>% filter(lon < 77.787015, lon > 77.454102, lat < 13.071393,lat > 12.889629 )
qmplot(lon, lat, data = bangalore, geom = c("point","density2d"))

Considering only the time when I will be leaving the house
out.of.house.time = Clustering the longitudes and latitudes near my house. Then finding the maximum time (before the office in time) when I was in this cluster.
in.time = office in time taken from attendance data set

travelling.time = out.of.house.time – in.time

##           date travelling.time
## 124 2018-06-04   2823.197 secs
## 168 2018-08-09   1347.491 secs
## 70  2018-03-13   1266.811 secs
## 194 2018-09-24   1038.792 secs
## 74  2018-03-19    791.593 secs

Plotting histogram of travel time

ggplot(travel,aes(x = as.numeric(travelling.time))) +
  geom_histogram(aes(y=..density..), bins = 50, fill = "cornflowerblue", alpha = 0.8) +
  labs(x = 'Travel Time (seconds)', y='Density', title = "Travel time") +
  theme_minimal()

Mode of transport

Google also guesses my activity based on distance traveled per time.
In the Bangalore data frame, the column activity has activities data.
Removing null values and creating a data frame for activities (in Bangalore)
Capturing the activity in main_activity

activities <- bangalore$activity

list.condition <- sapply(activities, function(x) !is.null(x[[1]]))

activities  <- activities[list.condition]

activities.df <- do.call("rbind", activities)

activities.df <- activities.df %>%
  mutate(main_activity = sapply(activity, function(x) x[[1]][1][[1]][1]),
         time = as.POSIXct(as.numeric(timestampMs)/1000, origin = "1970-01-01")) %>% 
  dplyr::select(time, main_activity)
dplyr::sample_n(activities.df, 5)

##                      time main_activity
## 31039 2018-08-17 11:57:32    IN_VEHICLE
## 13259 2018-10-09 09:35:16       TILTING
## 40059 2018-07-17 11:41:28       TILTING
## 90643 2018-01-14 23:39:23       ON_FOOT
## 79169 2018-03-08 20:15:39         STILL

Plotting the frequency of Main Activities in Bangalore.

ggplot(activities.df, aes(x = main_activity, group = main_activity, fill = main_activity)) + 
  geom_bar()  + 
  guides(fill = FALSE) +
  theme_minimal() +
  labs(
    x = "",
    y = "Count",
    title = "Main activities in Bangalore",
    caption = "Associated activity for recorded positions in Bangalore. 
    Because Google records activity probabilities for each position, 
    only the activity with highest likelihood were chosen for each position."
  )

Filtering for transportation during coming to office only
1. Filtering for activities during working days only
2. Merging the travel data set(contains travel time)
3. Filtering for time between out.of.house.time and in.time (office in time)
4. On days where more than one means of transport is used, considering the one which was observed maximum number of times or using some logic.

transport <- activities.df %>% 
  filter((date(time) %in% date(attendance$Attendance.Date))) %>% 
  mutate(date = date(time))
transport <- merge(transport, travel, by = 'date') %>% 
  filter(time > out.of.house.time, time < in.time.dmy.hm) %>% 
  group_by(date, main_activity) %>% 
  summarise(count = n()) %>% 
  group_by(date) %>%
  filter(main_activity %in% c("ON_BICYCLE", "EXITING_VEHICLE", 'IN_VEHICLE',"ON_FOOT")) %>%
  top_n(1, count) %>% 
  top_n(1, match(main_activity, c("ON_BICYCLE", 'IN_VEHICLE', "EXITING_VEHICLE", "ON_FOOT"))) %>%
  arrange(date)

The final data set will look as follows:

##           date main_activity travelling.time
## 168 2018-08-17    IN_VEHICLE    731.287 secs
## 35  2018-01-18       ON_FOOT   1029.621 secs
## 30  2018-01-10       ON_FOOT    308.578 secs
## 190 2018-09-25    IN_VEHICLE    616.699 secs
## 198 2018-10-10    IN_VEHICLE   1501.639 secs

Plotting the frequencies of the means of transportation.

ggplot(transport, aes(x = main_activity, group = main_activity, fill = main_activity)) + 
  geom_bar()  + 
  guides(fill = FALSE) +
  theme_minimal() +
  labs(
    x = "",
    y = "Count",
    title = "Mode of transport"
  )