# install package --------------------------------------------------------------
# current cran version
install.packages("globaltrends")
# current dev version
::install_github("ha-pu/globaltrends", build_vignettes = TRUE)
devtools
# load package -----------------------------------------------------------------
library(globaltrends)
# package version --------------------------------------------------------------
packageVersion("globaltrends")
#> [1] '0.0.14'
Google offers public access to global search volumes from its search
engine through the Google
Trends portal. Users select keywords for which they want to obtain
search volumes and specify the time period and location (global,
country, state, community) of interest. For these combinations of
keywords, periods, and locations Google Trends provides search volumes
that indicate the number of search queries submitted to the Google
search engine. The globaltrends
package downloads these
search volumes from Google Trends and uses them to measure and analyze
the distribution of search scores across or within locations. The
package allows researchers and analysts to use these search scores to
investigate global trends based on patterns within these scores. This
offers insights such as degree of internationalization of firms and
organizations or dissemination of political, social, or technological
trends across the globe or within single countries.
With the help of the globaltrends
package, researches
and analysts can compute and investigate three measures based on Google
search volumes for objects of interest. Local search scores provide
insights into the local relevance of objects and the exposure of these
objects to the respective locations. Global search scores track the
worldwide relevance of objects of interest and approximate their volume
of internationalization. The across-country distribution of search
scores relates to the degree of internationalization of these objects of
interest.
The globaltrends
package computes two conceptually
distinct measures of internationalization capturing volume of
internationalization (VOI) and degree
of internationalization (DOI). We do so since
absolute search volumes are conceptually different from their
international distributions—see Puhr and Müllner (2021) for a discussion
and Puhr and Müllner (2022) for an application in the International
Business context. In the figure below, we illustrate this
differentiation and analyze the internationalization of the fidget
spinner toy, a product with a very distinctive, fad-like
internationalization history. The fidget spinner was invented
and commercialized in late 2016 and became the world’s most sold toy
within only two months of commercialization. After 2017, interest in the
fidget spinner somewhat resided, but remained highly
globalized. In other words, the fidget spinner
internationalized at a very fast pace and interest in the product is
still evenly distributed across the globe, but this interest remains at
a much lower overall volume. By creating two separate measures, we can
capture both global search volumes as wells as the distribution of these
search volumes across the globe, allowing for more fine-grained analysis
and interaction between these distinguishable concepts of
internationalization.
In the globaltrends
package, we provide a tailor-made
operationalization of internationalization that relies on the global
recognition of objects of interest. This complements traditional
approaches in international business research that approximate the
configuration of a firm’s international operations. Our package allows
users to download time series of Google search volumes from 2004
onwards. Because Google Trends organizes its data output as
single-country keyword batches, the package uses batched downloads.
Within these batches, Google Trends normalizes search volumes to values
between 0 and 100. We devise a mapping algorithm to transform Google
Trends output to a more general data structure. For each country,
globaltrends
downloads a set of control keywords that
captures “standard” search volumes in the country. Then, the package
downloads batches of company names, including synonyms and alternative
spellings, for each country. We follow Castelnuovo and Tran (2017) to
implement a mapping of search volumes for firms to search volumes for
control keywords in each country. The package then uses these time
series to compute search scores as the ratio between search volumes for
a firm in comparison to search volumes for control keywords in each
country. We offer a detailed outline of this approach in the Appendix B.
Search scores are interpretable as the proportion of search volumes for an object of interest compared to search volumes for control keywords within a country. Search scores therefore allow comparison across different types of objects (e.g., firms, persons, products…), time periods, and countries and provide insights into the local relevance of objects and their exposure to the respective countries.
Country search scores focus on search volumes for objects in single
countries. To compute an object’s volume of internationalization, we
focus on global search volumes instead of country search volumes. Using
the same approach as outlined above, globaltrends
first
downloads global search volumes for each control keyword and firm. Next,
the package runs the control-object mapping and computes global search
scores as the ratio of global search volumes for each object and global
control search volumes.
Like country search scores, we can interpret global search scores as the proportion of global search volumes for an object of interest compared to global search volumes for control keywords. This allows researchers to track changes in global interest in objects and highlights phases of fast or receding internationalization. Since the computation of country and global search scores follows the same procedures, the two measures have the same properties. This provides for a direct comparison between country search scores and volume of internationalization in terms of global search scores.
As the main measure for degree of internationalization, the
globaltrends
package uses the global dispersion of country
search scores. The more uniform the distribution of search scores across
countries, the higher an object’s degree of internationalization. When
the distribution of country search scores is highly skewed, with high
search scores in the object’s country of origin and low search scores in
other countries, the object has a low degree of internationalization. To
compute an object’s degree of internationalization, the packages by
default uses as the inverted Gini coefficient.
We demonstrate the functionality of the globaltrends
package based on a sample of six large U.S. firms. Measuring degree of
internationalization for firms is an essential empirical task in
international business research. Yet the proposed methodology can be
generalized to other applications. In this brief case study, we analyze
the degree of internationalization of Alaska Air Group Inc.,
Coca-Cola Company, Facebook Inc., Illinois Tool
Works Inc., J.M. Smucker Company, and Microsoft
Corporation. The workflow proceeds in four major steps:
Research projects that use Google Trends generate a substantial
amount of data. To optimally handle this data, the
globaltrends
package uses an SQLite database to store
and handle all data. This ensures efficiency and portability on the one
hand and seamless integration with functions implemented in the
DBI
and dplyr
packages on the other hand.
Users create the underlying database through the
initialize_db
command. The command creates a folder named
db within the current working directory and creates an SQLite
database file named globaltrends_db.sqlite within this folder.
The command also creates all necessary tables within the database. For
more information on database tables, please refer to their built-in
documentation e.g., ?globaltrends::data_score
. The database
initialization is necessary only for the first usage of the
globaltrends
package.
# initialize_db ----------------------------------------------------------------
setwd("your/globaltrends/folder")
initialize_db()
#> Database has been created.
#> Table 'batch_keywords' has been created.
#> ...
#> Table 'data_global' has been created.
#> Successfully disconnected.
After initialization or when resuming work on an existing database it
is sufficient to call start_db
from the respective working
directory. This command connects to the globaltrends_db.sqlite
database in the folder db and creates connections to all tables
in the database.
# start_db ---------------------------------------------------------------------
setwd("your/globaltrends/folder")
start_db()
#> Successfully connected to database.
#> Successfully exported all objects to .GlobalEnv.
print(ls())
#> [1] "batch_keywords" "batch_time" "countries" "data_control"
#> [5] "data_doi" "data_global" "data_locations" "data_mapping"
#> [9] "data_object" "data_score" "dir_current" "dir_wd"
#> [13] "globaltrends_db" "keyword_synonyms" "keywords_control" "keywords_object"
#> [17] "time_control" "time_object" "us_states"
After work with the globaltrends
package is complete,
the user disconnects from the database with the command
disconnect_db
.
# disconnect_db ----------------------------------------------------------------
disconnect_db()
#> Successfully disconnected.
The next step in the globaltrends
workflow is data
download from Google Trends. The globaltrends
package
includes four types of download functions that we explain in detail
below. Each of these functions uses the gtrendsR::gtrends
function to access the Google Trends API. The Google Trends API allows
inputs of up to five keywords for a given location and period.
Therefore, the globaltrends
package works with “keyword
batches” that combine up to five keywords (see Appendix A for a discussion of data manipulation
applied by Google). The respective batch numbers are an input to all
functions – either as list
or as single
integer
objects. In the package, we distinguish two types
of batches: control batches that include keywords
indicating baseline search activity and object batches
that include keywords relating to the objects of interest (e.g., firms,
persons, products…). Currently, globaltrends
only includes
two sets of locations. The countries set, which covers all
countries that generated at least 0.1% of world GDP in 2018 and the
us_states set, covering all US states and Washington DC, see below for further details.
The download for a single keyword batch for a single location takes
about 10 seconds. This includes a randomized waiting period of 5-10
seconds between downloads. Depending on download frequency, Google
Trends might block users for some time. Unfortunately, the exact
download limits are unknown (Issue
#140, Issue
#255). In this case, globaltrends
waits 1 minute before
it retries the download.
First, we add a batch of control keywords to the database using
add_control_keyword
. Since gmail, maps,
translate, wikipedia, and youtube allow an
approximation of “standard” search volumes on Google, we propose them as
control keywords for global trend analysis. The
globaltrends
package also allows the usage of search topics instead of
individual search terms as keywords. These keywords approximate the
baseline search traffic on Google. For specific research settings, we
suggest adapting keywords to the respective setting and testing them on
the Google Trends portal
beforehand. The output of add_control_keyword
is a
list
object of new control batch numbers that can serve as
input for other functions.
# add_control_keyword ----------------------------------------------------------
<- add_control_keyword(
new_control keyword = c("gmail", "maps", "translate", "wikipedia", "youtube"),
time = "2010-01-01 2020-12-31"
)#> Successfully created new control batch 1 (gmail ... youtube, 2010-01-01 2020-12-31).
The function add_control_keyword
also updates the object
keywords_control
in the global environment. This
tibble
can be used for batch lookup.
# keywords_control and dplyr interaction ---------------------------------------
::filter(keywords_control, keyword == "gmail")
dplyr#> # A tibble: 1 x 2
#> batch keyword
#> <int> <chr>
#> 1 1 gmail
As a second step, we download the control data with
download_control
, using the output from
add_control_keyword
as control
input, the
numbers of control batches for which we want to download data. The input
defaults to countries
, see below for further details.
# download_control -------------------------------------------------------------
download_control(control = new_control, locations = countries)
#> Successfully downloaded control data | control: 1 | location: US [1/66]
#> ...
#> Successfully downloaded control data | control: 1 | location: DO [66/66]
A message indicates each successful download of search volumes for
control keywords. The data is written directly to the table
data_control in the database. The function
download_control_global
follows the same approach and
downloads control data on a global level.
# download_control_global ------------------------------------------------------
download_control_global(control = new_control)
#> Successfully downloaded control data | control: 1 | location: world [1/1]
For object data, as for control data, the first step is to add
keywords that correspond to the objects of interest. While we use a
single control batch for the entire analysis, there are more than one
object batch. To each object batch, a control keyword is added to allow
mapping between control and object search volumes (further details below).
Therefore, the number of keywords is limited to four, rather than five
as for control batches. Before we add the object keywords, we clean
them, deleting punctuation and form of incorporation: alaska air
group, coca cola, facebook, illinois tool
works, jm smucker, and microsoft. Since this
affects search results, the transformation requires substantial
consideration and depends on the respective research setting. The
globaltrends
package also allows the usage of search topics instead of
individual search terms as keywords. To ensure the expected results, we
propose testing keyword transformations on the Google Trends portal
beforehand.
# add_object_keyword -----------------------------------------------------------
<- add_object_keyword(
new_object keyword = list(
c("coca cola", "facebook", "microsoft"),
c("alaska air group", "illinois tool works", "jm smucker")
),time = "2010-01-01 2020-12-31"
)#> Successfully created new object batch 1 (coca cola ... microsoft, 2010-01-01 2020-12-31).
#> Successfully created new object batch 2 (alaska air group ... jm smucker, 2010-01-01 2020-12-31).
As for control keywords, the function add_object_keyword
also updates the object keywords_object
in the global
environment. This tibble
can be used for batch lookup.
# keywords_object and dplyr interaction ----------------------------------------
::filter(keywords_object, keyword == "coca cola")
dplyr#> # A tibble: 1 x 2
#> batch keyword
#> <int> <chr>
#> 1 1 coca cola
Again, the second step is to download the object data with
download_object
, using the output from
add_object_keyword
as object
input, the
numbers of object batches for which we want to download data. As above,
the input locations
defaults to countries
. The
package automatically adds a control keyword to each batch of four
object keywords. This control keyword then allows a mapping between
control batches and object batches.
# download_object --------------------------------------------------------------
download_object(object = new_object, locations = countries)
#> Successfully downloaded object data | object: 1 | location: US [1/66]
#> ...
#> Successfully downloaded object data | object: 2 | location: DO [66/66]
A message indicates each successful download of search volumes for
object keywords. The data is written directly to the table
data_object in the database. The function
download_object_global
follows the same approach and
downloads object data on a global level.
# download_object_global -------------------------------------------------------
download_object_global(object = new_object)
#> Successfully downloaded object data | object: 1 | location: world [1/1]
#> Successfully downloaded object data | object: 2 | location: world [1/1]
Once the user has completed all control and object downloads,
globaltrends
computes search scores for each
keyword-time-location combination and at a global level (volume of
internationalization). Next, the package uses the across-country
distribution of these search scores to measure the degree of
internationalization of an object keyword.
The function compute_score
divides the search volumes
for an object keyword by the sum of search volumes for the keywords in
the respective control batch. The search score computation proceeds in
four steps. First, the function aggregates all search volumes to monthly
data. Then, it applies some optional time series adjustments that we
outline in greater detail below.
Next, it follows the procedure proposed by Castelnuovo and Tran (2017,
pp. A1-A2) and outlined in the Appendix B to
map control and object data. After the mapping, object search volumes
are divided by the sum of control search volumes in the respective
control batch. We use the sum of search volumes for a set of control
keywords, rather than the search volumes for a single control keyword,
to smooth-out variation in the underlying control data. Because of this
division, it is essential to define a set of control keywords that
mirrors “standard” Google usage for the given research setting.
# compute_score ----------------------------------------------------------------
compute_score(control = new_control[[1]], object = new_object, locations = countries)
#> Successfully computed search score | control: 1 | object: 1 | location: US [1/66]
#> ...
#> Successfully computed search score | control: 1 | object: 2 | location: DO [66/66]
A message indicates each successful computation of search scores. The
data is written directly to table data_score in the database.
The computation of the volume of internationalization follows the same
principles. Instead of search volumes of control and object keywords at
the country level, the function compute_voi
compares
control and object search volumes at the global level.
# compute_voi ------------------------------------------------------------------
compute_voi(control = new_control[[1]], object = new_object)
#> Successfully computed search score | control: 1 | object: 1 | location: world [1/1]
#> Successfully computed search score | control: 1 | object: 2 | location: world [1/1]
The globaltrends
package uses the distribution of search
scores across countries to compute degree of internationalization for
objects of interest. The function compute_doi
uses an
inverted Gini-coefficient as measure for degree of internationalization.
The more uniform the distribution of search scores across all countries,
the higher the inverted Gini-coefficient and the greater the degree of
internationalization. In addition to the Gini-coefficient, the package
uses inverted Herfindahl index and inverted Entropy as measures for
internationalization (details
below).
# compute_doi ------------------------------------------------------------------
compute_doi(control = new_control[[1]], object = new_object, locations = "countries")
#> Successfully computed DOI | control: 1 | object: 1 [1/2]
#> Successfully computed DOI | control: 1 | object: 2 [2/2]
A message indicates each successful computation. The data is written directly to table data_doi in the database.
Functions in globaltrends
write all data directly to
tables in the database. With the help of functions from the
dplyr
package and connections exported from
start_db
, users can access database tables and prepare
their own analysis.
# manual exports ---------------------------------------------------------------
library(dplyr)
%>%
data_score filter(keyword == "coca cola") %>%
collect()
#> # A tibble: 8,040 x 8
#> location keyword date score_obs score_sad score_trd batch_c batch_o
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <int> <int>
#> 1 US coca cola 14610 0.00362 0.00381 0.00548 1 1
#> ...
#> 10 US coca cola 14883 0.00347 0.00365 0.00389 1 1
#> # ... with 8,030 more rows
To enhance usability, the globaltrends
package includes
a set of export functions that offer filters and return data as
tibble
. The default value for the batch/keyword, for which
export_xxx
exports data is NULL
. In this case,
all values from the database are exported. Alternatively, users can
specify filters (e.g., keywords, batches, locations) individually, as
vector or as list.
# export_control ---------------------------------------------------------------
export_control(control = 1)
#> # A tibble: 39,600 x 5
#> location keyword date hits control
#> <chr> <chr> <date> <dbl> <int>
#> 1 US gmail 2010-01-01 22 1
#> ...
#> 10 US gmail 2010-10-01 27 1
#> # ... with 39,590 more rows
# export_score -----------------------------------------------------------------
export_score(object = 1, control = 1)
#> # A tibble: 23,760 x 8
#> location keyword date score_obs score_sad score_trd control object
#> <chr> <chr> <date> <dbl> <dbl> <dbl> <int> <int>
#> 1 US coca cola 2010-01-01 0.00362 0.00381 0.00548 1 1
#> ...
#> 10 US coca cola 2010-10-01 0.00347 0.00365 0.00389 1 1
#> # ... with 23,750 more rows
# export_doi and purrr interaction ---------------------------------------------
::map_dfr(c("coca cola", "microsoft"), export_doi, control = 1, type = "obs")
purrr#> # A tibble: 240 x 9
#> keyword date type gini hhi entropy control object locations
#> <chr> <date> <chr> <dbl> <dbl> <dbl> <int> <int> <chr>
#> 1 coca cola 2010-01-01 score_obs 0.397 0.874 -0.938 1 1 countries
#> ...
#> 10 coca cola 2010-10-01 score_obs 0.574 0.968 -0.303 1 1 countries
#> # ... with 230 more rows
The export functions from globaltrends
also allow direct
interaction with dplyr
or other packages for further
analysis.
# export and dplyr interaction -------------------------------------------------
library(dplyr)
export_doi(object = 1, control = 1, type = "obs") %>%
filter(lubridate::year(date) == 2019) %>%
group_by(keyword) %>%
summarise(gini = mean(gini), .groups = "drop")
#> # A tibble: 3 x 2
#> keyword gini
#> <chr> <dbl>
#> 1 coca cola 0.615
#> 2 facebook 0.707
#> 3 microsoft 0.682
Exports from globaltrends
also serve as input for plot
functions and the computation of abnormal changes in
internationalization implemented in the package. Except for
plot_voi_doi
, plot functions have methods for classes of
outputs from export_score
, export_voi
, and
export_doi
. Alternatively, all plot-functions provide
options to work without the respective class e.g., for cases where the
class gets lost in a join
. The function
plot_bar
uses the output from export_score
as
input and shows the locations with the highest search scores for a given
object keyword. The function uses only the first keyword in the dataset
and averages the search scores for the input dataset – we therefore
suggest filtering the output from export_score
to a
specific period. The plot shows that Coca-Cola has high search scores
across Latin America and India.
# plot_score -------------------------------------------------------------------
library(dplyr)
export_score(keyword = "coca cola", control = 1) %>%
filter(lubridate::year(date) == 2019) %>%
plot_bar()
The functions plot_box
and plot_ts
have
methods for classes of output from export_score
,
export_voi
, and export_doi
. The time series
plot function plot_ts
shows how search scores and volume or
degree of internationalization for objects of interest develops over
time. The function plot_box
generates boxplots of search
score and volume or degree of internationalization distributions. The
four plots below compare volume and degree of internationalization for
the six companies in our sample. At first glance, we see that Coca-Cola,
Facebook, and Microsoft have higher degrees of internationalization than
Alaska Air Group, Illinois Tool Works, and J.M. Smucker. It seems as if
the degree of internationalization of Facebook and Microsoft increased
slightly from 2010 to 2015. Although the overall trend remains stable,
Coca-Cola shows greater variation than the other companies.
# plot_doi_ts and plot_doi_box -------------------------------------------------
<- purrr::map_dfr(1:2, export_doi, keyword = NULL, control = 1, type = "obs")
data plot_ts(data)
plot_box(data)
With the function plot_voi_doi
, users can compare the
volume of internationalization for an object of interest to its degree
of internationalization. Like plot_bar
, the function uses
only the first keyword in a dataset, filtering might be necessary. In
the plot below, we compare Facebook’s volume of internationalization to
its degree of internationalization. While volume of internationalization
indicates the level of global search scores, degree of
internationalization relates to the global distribution of search
scores. We see that Facebook’s volume of internationalization constantly
decreased after its peak in 2013. At the same time, we observe that its
degree of internationalization grew from 2010 before peaking in
2013.
# plot_voi_doi -----------------------------------------------------------------
<- export_voi(keyword = "facebook", type = "obs")
out_voi <- export_doi(keyword = "facebook", object = 1, type = "obs")
out_doi plot_voi_doi(data_voi = out_voi, data_doi = out_doi)
A unique feature of internationalization data from
globaltrends
is that it allows time series analysis. For a
better understanding of changes in the data, the function provides the
get_abnorm_hist
function that implements functionality used
in financial event studies (MacKinlay, 1997; McWilliams & Siegel,
1997). The function compares search scores and volume or degree of
internationalization to a historic baseline. By default, the historic
baseline is the average from the preceding twelve months. Users can
specify the window of the baseline period (train_win
) and a
can use a break between baseline and date of interest
(train_break
). Since they are used as baseline, the first
train_win + train_break
abnormal changes are
NA
. The get_abnorm_hist
function has methods
for classes of outputs from export_score
,
export_voi
, and export_doi
. For each month in
the dataset, the deviation from the historic baseline is computed. To
identify abnormal changes, the function provides the percentile rank for
each change within the distribution of changes.
<- export_score(keyword = "facebook", locations = countries)
data <- get_abnorm_hist(data)
out na.omit(out) # to drop baseline NA values
#> # A tibble: 7,590 x 8
#> keyword location date control object score score_abnorm quantile
#> <chr> <chr> <date> <int> <int> <dbl> <dbl> <dbl>
#> 1 facebook US 2011-01-01 1 1 1.19 0.0220 0.728
#> ...
#> 10 facebook US 2011-10-01 1 1 1.32 -0.0669 0.456
#> # ... with 7,580 more rows
<- export_voi(object = 1)
data <- get_abnorm_hist(data)
out na.omit(out) # to drop baseline NA values
#> # A tibble: 345 x 7
#> keyword date control object voi voi_abnorm quantile
#> <chr> <date> <int> <int> <dbl> <dbl> <dbl>
#> 1 coca cola 2011-01-01 1 1 0.00320 -0.000299 0.316
#> ...
#> 10 coca cola 2011-10-01 1 1 0.00274 -0.000458 0.193
#> # ... with 335 more rows
<- export_doi(keyword = "microsoft", locations = "us_states")
data <- get_abnorm_hist(data)
out na.omit(out) # to drop baseline NA values
#> # A tibble: 345 x 9
#> keyword date type control object locations doi doi_abnorm quantile
#> <chr> <date> <chr> <int> <int> <chr> <dbl> <dbl> <dbl>
#> 1 microsoft 2011-01-01 score_obs 1 1 us_states 0.919 0.0330 0.991
#> ...
#> 10 microsoft 2011-04-01 score_obs 1 1 us_states 0.909 0.0171 0.886
#> # ... with 335 more rows
The functions plot_bar
, plot_box
, and
plot_ts
have methods for classes of output from
get_abnorm_hist
. This allows seamless plotting of changes
in internationalization. The function plot_bar
shows the
five locations with the highest and lowest changes in search scores for
a given object keyword. The function uses only the first keyword in the
dataset and averages changes in search scores for the input dataset – we
therefore suggest filtering the output from get_abnorm_hist
to a specific period. The plot shows that while positive abnormal
changes in search scores for Facebook were greatest in Ecuador and
Myanmar, negative abnormal changes were greatest in Italy and
Argentina.
<- export_score(object = 1, locations = countries)
data <- dplyr::filter(data, keyword == "facebook" & lubridate::year(date) >= 2018)
data # use 2018 as baseline to compute abnormal changes in 2019
<- get_abnorm_hist(data)
out plot_bar(out)
The time series plot function plot_ts
shows how search
scores and volume or degree of internationalization for objects of
interest changed over time. The function plot_box
generates
boxplots of changes in search score and volume or degree of
internationalization distributions. The input ci
allows
users to set a confidence interval for plotting. Changes with percentile
ranks outside this two-tailed confidence interval are highlighted with
red dots. The left-hand plot shows abnormal changes in Facebook’s search
score for Germany. Search scores increased “abnormally” (i.e., compared
to the historic average) in 2012 and decreased abnormally in 2014. The
right-hand plot shows the distribution for Coca Cola’s degree of
internationalization and indicates abnormal changes.
<- export_score(keyword = "facebook", locations = "DE")
data <- get_abnorm_hist(data)
out plot_ts(out)
<- export_doi(keyword = "coca cola", locations = "countries")
data <- get_abnorm_hist(data)
out plot_box(out)
The globaltrends
package offers several options that
allow robustness checks and adjustments for default computations. Users
can compute global trend dispersion based on different types of time
series, use other measures than the inverted Gini-coefficient, or change
the set of locations.
The computation of search scores in the globaltrends
package compares a time series of search volumes for object keywords to
the time series of search volumes for control keywords. Noise and
seasonality in search volume time series could affect the resulting
search scores. The globaltrends
package offers two time
series adjustments as robustness checks. In the data_score
table, column score_obs
refers to values without
adjustment. Column score_trd
uses the underlying time
series’ trend for computation.
# computation seasonally adjusted ----------------------------------------------
<- ts(data$hits, frequency = 12)
search_score <- stl(search_score, s.window = "period")
fit <- fit$time.series[, "trend"]
trend # computation trend only -------------------------------------------------------
<- ts(data$hits, frequency = 12)
search_score <- stl(search_score, s.window = "period")
fit <- forecast::seasadj(fit) seasad
Column score_sad
corrects the time series for seasonal
patterns. In general, outcomes for all three types of time series are
similar. Column score_trd
applies the greatest smoothing,
while score_sad
reduces some noise.
The export_doi
, get_abnorm_hist
,
plot_bar
, plot_ts
, plot_box
, and
plot_voi_doi
functions allow filtering for the type of time
series through the type
input.
# adapt export and plot options ------------------------------------------------
<- export_score(keyword)
data_score <- export_voi(keyword)
data_voi <- export_doi(keyword, type = "obs")
data_doi
plot_bar(data_score, type = "obs")
plot_ts(data_voi, type = "sad")
plot_box(data_doi, type = "trd")
plot_voi_doi(data_voi, data_doi, type = "obs")
get_abnorm_hist(data_voi, type = "obs")
The globaltrends
package computes degree of
internationalization based on the across-location distribution of search
scores. By default, the package uses an inverted Gini-coefficient. In
addition, the package provides inverted Herfindahl index and inverted
Entropy as robustness checks. In general, outcomes for all three
dispersion measures are similar.
The export_doi
, get_abnorm_hist
,
plot_ts
, plot_box
, and
plot_voi_doi
functions allow filtering for the type of
dispersion measures through the measure
input.
# adapt export and plot options ------------------------------------------------
<- export_voi(keyword)
data_voi <- export_doi(keyword, measure = "gini")
data_doi
plot_ts(data_doi, measure = "gini")
plot_box(data_doi, measure = "hhi")
plot_voi_doi(data_voi, data_doi, measure = "entropy")
get_abnorm_hist(data_doi, measure = "hhi")
By default, globaltrends
makes all downloads and
computations for the countries set of locations. The
countries set covers all countries that generated at least 0.1%
of world GDP in 2018. By changing the input locations
to
us_states, the package uses US states and Washington DC as
basis for downloads and computations instead. Apart from
compute_doi
, all functions use the name of the variable
that contains the location vector as inputs for locations
(e.g., countries, us_states). The function
start_db
exports these vectors of ISO2 codes to the global
environment. Function compute_doi
, however does not
directly refer to these objects, but to their names (e.g., “countries”,
“us_states”). Using state or district level locations allows users to
analyze within-country dispersion of firms.
# change locations -------------------------------------------------------------
download_control(control = 1, locations = us_states)
download_object(object = list(1, 2), locations = us_states)
download_mapping(control = 1, object = 2, locations = us_states)
compute_score(control = 1, object = 2, locations = us_states)
compute_doi(control = 1, object = list(1, 2), locations = "us_states")
Users can add individual sets of locations through the function
add_locations
. In the variable locations
,
users specify the location codes (e.g., “AT”, “CH”, “DE”) and
type
takes the name of the location set (e.g., “DACH”). The
new location set can be used in all functions. Since all functions check
whether data on a location already exists, globaltrends
does not duplicate data for new location sets.
add_locations(c("AT", "CH", "DE"), type = "dach")
#> Successfully created new location set dach (AT, CH, DE).
<- export_score(keyword = "coca cola", locations = dach)
data ::count(data, location)
dplyr#> # A tibble: 3 x 2
#> location n
#> <chr> <int>
#> 1 AT 127
#> 2 CH 127
#> 3 DE 127
Results for individual keywords as search terms (e.g., weather, apple, coca cola) might be distorted by translation issues (i.e., keywords are search for in different languages), keyword contamination (i.e., keywords relate to different queries: apple vs. Apple Inc.), and keyword dilution (i.e., multiple keywords relate to the same query: election, vote). Search topics allow users to partly overcome these issues. Google defines a search topic as “a group of terms that share the same concept in any language.” Thereby, queries that use search topics are language-independent, cover queries for different terms, and differentiate between queries.
Users can identify the codes of search topics on the Google Trends portal, by selecting the respective topic, rather than a search term (see the screenshot below).
After selecting the relevant search topics, users can identify the topic codes in the query’s URL. For example, based on the URL https://trends.google.com/trends/explore?q=%2Fm%2F03phgz&geo=AT the topic The Coca-Cola Company is %2Fm%2F03phgz. Users can use these topic codes as keywords instead of single search terms. We point users to Kupfer and Zorn (2020, pp. 1169-1170) for a detailed comparison of search topics and search terms.
Important: We recommend that search topics for control keywords are used in combination with search topics for object keywords and vice versa.
To measure degree of internationalization, globaltrends
offers a wide array of empirical possibilities (Puhr & Müllner,
2021). It allows researchers to compare degree of internationalization
for various organizations on a unified scale (e.g., Coca-Cola
Company, Facebook Inc., Real Madrid, and
Manchester United). In addition, the time-series nature of
Google Trends allows for historical analysis of internationalization
patterns and speed within organizations.