Objective

The Quantified Self (QS) is a movement motivated to leverage the synergy of wearables, analytics, and “Big Data”. This movement exploits the ease and convenience of data acquisition through the internet of things (IoT) to feed the growing obsession of personal informatics and quotidian data. The website http://quantifiedself.com/ is a great place to start to understand more about the QS movement. The value of the QS for our class is that its core mandate is to visualize and generate questions and insights about a topic that is of immense importance to most people - themselves. It also produces a wealth of data in a variety of forms. Therefore, designing this project around the QS movement makes perfect sense because it offers you the opportunity to be both the data and question provider, the data analyst, the vis designer, and the end user. This means you will be in the unique position of being capable of providing feedback and direction at all points along the data visualization/analysis life cycle.

The Google location history can be downloaded from my Google account under https://takeout.google.com/settings/takeout.I only tick “location history” for download.

The data Google provides for download is a .json file and can be loaded with the jsonlite package. Loading this file into R might take a few minutes because it can be quite big, depending on how many location points Google had saved.

## 
## Attaching package: 'shiny'
## The following object is masked from 'package:jsonlite':
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##     validate
## 
## Attaching package: 'shinydashboard'
## The following object is masked from 'package:graphics':
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##     box
## -- Attaching packages ------------------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.3.4     v dplyr   0.7.4
## v tidyr   0.7.2     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.2.0
## -- Conflicts ---------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter()   masks stats::filter()
## x purrr::flatten()  masks jsonlite::flatten()
## x dplyr::lag()      masks stats::lag()
## x shiny::validate() masks jsonlite::validate()
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## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
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##     date
## [1] 183531

How are they distributed over days, months and years?

## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric
## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%Y/%m/%d'

How many days were recorded?

## [1] 397

How many months?

## [1] 17

Span over years?

## [1] 4

Investigation of Google Location History data collection

How accurate is the data? Smaller the value better the accuracy

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Plotting the data points on to a map

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=USA&zoom=5&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=USA&sensor=false
## Warning: Removed 8018 rows containing missing values (geom_point).

Map shows 2D plot of accuracy measured for all data points recorded in Dallas.

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Dallas&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Dallas&sensor=false
## Warning: Removed 183179 rows containing non-finite values (stat_summary2d).
## Warning: Removed 3 rows containing missing values (geom_tile).

Conclusion:

In this project I have explored What are my travelling patters over the time with in the continental united states of america. In this process I have tried to answer the following questions

1.How many data points did Google record over what period of time?

2.What is the distribution of days, months and years

3.How many days, months and years of data have been collected by google?

4.How accurate is the data?

5.What is my travelling pattern?