Analyzing Google Search History

About this Notebook

The google search data on this notebook comes from a google account archive
The steps outlined here to collect and analyze the data may change at any time
Below are the steps to claim your google account data

Analytics Toolkit: Require Packages

Package: tidyverse, lubridate, rvest, tm, wordcloud

# Here we are checking if the package is installed
if(!require(rvest, quietly = TRUE)) 
  # If the package is not in the system then it will be install
  install.packages(rvest, dependencies = TRUE, quietly = TRUE)
  # Here we are loading the package
  library(rvest, quietly = TRUE)

if(!require(lubridate, quietly = TRUE)) 
  install.packages(lubridate, dependencies = TRUE, quietly = TRUE)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

  library(lubridate, quietly = TRUE)

if(!require(wordcloud, quietly = TRUE)) 
  install.packages(wordcloud, dependencies = TRUE, quietly = TRUE)
  library(wordcloud, quietly = TRUE)

if(!require(tm, quietly = TRUE)) 
  install.packages(tm, dependencies = TRUE, quietly = TRUE)
  library(tm, quietly = TRUE)

if(!require(tidyverse, quietly = TRUE))
  install.packages(tidyverse, dependencies = TRUE, quietly = TRUE)

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
## ✔ tibble  1.4.2     ✔ dplyr   0.7.4
## ✔ tidyr   0.7.2     ✔ stringr 1.2.0
## ✔ readr   1.1.1     ✔ forcats 0.2.0

## ── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::annotate()      masks NLP::annotate()
## ✖ lubridate::as.difftime() masks base::as.difftime()
## ✖ lubridate::date()        masks base::date()
## ✖ dplyr::filter()          masks stats::filter()
## ✖ readr::guess_encoding()  masks rvest::guess_encoding()
## ✖ lubridate::intersect()   masks base::intersect()
## ✖ dplyr::lag()             masks stats::lag()
## ✖ purrr::pluck()           masks rvest::pluck()
## ✖ lubridate::setdiff()     masks base::setdiff()
## ✖ lubridate::union()       masks base::union()

  library(tidyverse, quietly = TRUE)

Data Collection: Claming your Google Search Data

Data Preparation: Extracting Google Search Information

Locate the Google archive, then find the search data. For this case, it is an html file located in “My Activity” folder" then find the “Search” folder and the html file “MyActivity.html” should be there:

Takeout -> My Activity -> Search -> MyActivity.html

Using the rvest package we can read the html document that contains the related google search data

doc <- "My Activity/Search/MyActivity.html"
search_archive <- read_html(doc)

Laveraging regular expression we can search the html document to extract:

Extract Search Time

date_search <- search_archive %>% 
  html_nodes(xpath = '//div[@class="mdl-grid"]/div/div') %>% 
  str_extract(pattern = "(?<=<br>)(.*)(?<=PM|AM)") %>%
  mdy_hms()

Extract Search Text

text_search <- search_archive %>% 
  html_nodes(xpath = '//div[@class="mdl-grid"]/div/div') %>%
  str_extract(pattern = '(?<=<a)(.*)(?=</a>)') %>% 
  str_extract(pattern = '(?<=\">)(.*)')

Extract Search Type

type_search <- search_archive %>% 
  html_nodes(xpath = '//div[@class="mdl-grid"]/div/div') %>% 
  str_extract(pattern = "(?<=mdl-typography--body-1\">)(.*)(?=<a)") %>% 
  str_extract(pattern = "(\\w+)(?=\\s)")

Create a data frame using the data extracted from the html file

search_data <- tibble(timestamp = date_search,
                      date = as_date(date_search),
                      year = year(date_search),
                      month = month(date_search, label = TRUE),
                      day = weekdays(date_search),
                      hour = hour(date_search),
                      type = type_search,
                      search = text_search)

search_data$day <- factor(search_data$day, 
                          levels = c("Sunday", "Monday", "Tuesday",
                                     "Wednesday","Thursday", "Friday",
                                     "Saturday"))

search_data <- na.omit(search_data)

head(search_data)

## # A tibble: 6 x 8
##   timestamp           date        year month day      hour type     search
##   <dttm>              <date>     <dbl> <ord> <fct>   <int> <chr>    <chr> 
## 1 2018-01-30 11:59:56 2018-01-30  2018 Jan   Tuesday    11 Visited  https…
## 2 2018-01-30 11:59:52 2018-01-30  2018 Jan   Tuesday    11 Searched aligh…
## 3 2018-01-30 11:54:50 2018-01-30  2018 Jan   Tuesday    11 Searched rambl…
## 4 2018-01-30 11:13:48 2018-01-30  2018 Jan   Tuesday    11 Visited  https…
## 5 2018-01-30 11:13:46 2018-01-30  2018 Jan   Tuesday    11 Searched sakai 
## 6 2018-01-30 10:57:06 2018-01-30  2018 Jan   Tuesday    10 Visited  https…

Data Analysis: Visualizing Google Searches

To get an overall idea of the search volume, we can plot searches by year

p <- ggplot(search_data, aes(year))
p + geom_bar()

After determine the years with the largest search volume we can plot monthly searches

monthly <- search_data[(search_data$year > 2014 & search_data$year< 2018), ]

ggplot(monthly) + geom_bar(aes(x = month, group = year)) +
  theme(axis.text.x = element_text(angle=90)) +
  facet_grid(.~year, scales="free")

Another interesting metrict is searches by Hour

p <- ggplot(search_data, aes(hour))
p + geom_bar()

We can also plot the search data by day of the week to determine day are the most active

p <- ggplot(search_data, aes(day))
p + geom_bar()

We can take it an step further and group search time with day of the week.

ggplot(search_data) + 
  geom_bar(aes(x = hour, group = day) ) +
  facet_grid(.~day, scales = "free")

We can group the search data by year and day of the week, to visualize the overall trend

wkday <- group_by(search_data, year, day) %>% summarize(count = n())
p <- ggplot(wkday, aes(day, count, fill = year)) 
p + geom_bar(stat = "identity") + labs(x = "", y = "Search Volume")

Reporting: A wordcloud from Google Search Data

First we need to extract the text and clean it using regular expressions

search <- tolower(search_data$search)
search <- iconv(search, "ASCII", "UTF-8", " ")
search <- gsub('(http|https)\\S+\\s*|(#|@)\\S+\\s*|\\n|\\"', " ", search)
search <- gsub("(.*.)\\.com(.*.)\\S+\\s|[^[:alnum:]]", " ", search)
search <- trimws(search)

After cleaning the text we can create a Text Corpus (a large and structured set of texts) and remove some words

search_corpus <-  Corpus(VectorSource(search))
search_corpus <- tm_map(search_corpus, content_transformer(removePunctuation))
search_corpus <- tm_map(search_corpus, content_transformer(removeNumbers))
stopwords <- c(stopwords("english"), "chrome", "chicago", "jlroo", "google")
search_corpus <- tm_map(search_corpus, removeWords, stopwords)

search_tdm <- TermDocumentMatrix(search_corpus)
search_matrix <- as.matrix(search_tdm)

Using the Term Document matrix we can create a data frame with the words and its related frequencies

v <- sort(rowSums(search_matrix), decreasing = TRUE)
tw_names <- names(v)
d <- data.frame(word = tw_names, freq = v)

Set a threshold for the min frequency of the words to display as well as max frequency

wordcloud(d$word, d$freq, min.freq = 50, scale = c(3 , 0.5), max.words = 200)

Analyzing Google Search History

CME Group Foundation Business Analytics Lab

Cassie Cumberland

Februrary 1, 2018

About this Notebook

Analytics Toolkit: Require Packages

Data Collection: Claming your Google Search Data

Data Preparation: Extracting Google Search Information

Locate the Google archive, then find the search data. For this case, it is an html file located in “My Activity” folder" then find the “Search” folder and the html file “MyActivity.html” should be there:

Laveraging regular expression we can search the html document to extract:

Extract Search Time

Extract Search Text

Extract Search Type

Create a data frame using the data extracted from the html file

Data Analysis: Visualizing Google Searches

To get an overall idea of the search volume, we can plot searches by year

After determine the years with the largest search volume we can plot monthly searches

Another interesting metrict is searches by Hour

We can also plot the search data by day of the week to determine day are the most active

We can take it an step further and group search time with day of the week.

We can group the search data by year and day of the week, to visualize the overall trend

Reporting: A wordcloud from Google Search Data

First we need to extract the text and clean it using regular expressions

After cleaning the text we can create a Text Corpus (a large and structured set of texts) and remove some words

Set a threshold for the min frequency of the words to display as well as max frequency