Add batches of control or object keywords

Description

The function adds one or more batches of keywords with a time period for downloads to the database. The batches serve as input for all download and computation functions.

Usage

add_control_keyword(keyword, time = "2010-01-01 2020-12-31")

add_object_keyword(keyword, time = "2010-01-01 2020-12-31")

Arguments

Details

Since Google Trends allows a maximum of five keywords for each query, batches of control keywords can consist of up to five keywords. Since one control keyword is added to batches of object keywords for mapping, object batch length is limited to four keywords. When a character vector contains more than four (five) keywords, the vector is split into four-keyword (five-keyword) batches. A list must contain character vectors of length four (five) or less. Each batch of keywords is combined with a time period for which data will be downloaded. To change the time period for an existing batch, all downloads and computations must be rerun.

Value

Message that the batch has been created successfully. Batch data is written to tables batch_keywords and batch_time. Numeric vector containing the newly added batch numbers are returned.

Warning

If you use search topics for object keywords, make sure to use search topics for control keywords and vice versa. See Google's FAQ for additional information on search topics.

Note

To avoid trailing spaces stringr::str_squish is automatically applied to all keywords.

See Also

• example_keywords()

• example_time()

• stringr::str_squish()

Examples

## Not run: 
add_control_keyword(
  keyword = c("gmail", "maps", "translate", "wikipedia", "youtube"),
  time = "2016-01-01 2019-12-31"
)
add_object_keyword(
  keyword = c("apple", "facebook", "google", "microsoft"),
  time = "2016-01-01 2019-12-31"
)

add_control_keyword(
  keyword = c("gmail", "maps", "news", "translate", "weather", "wikipedia", "youtube"),
  time = "2016-01-01 2019-12-31"
)
add_control_keyword(
  keyword = c("amazon", "apple", "facebook", "google", "microsoft", "netflix", "twitter"),
  time = "2016-01-01 2019-12-31"
)

add_control_keyword(
  keyword = list(
    c("gmail", "maps", "news"),
    c("translate", "weather", "wikipedia", "youtube")
  ),
  time = "2016-01-01 2019-12-31"
)
add_control_keyword(
  keyword = list(
    c("amazon", "apple", "facebook", "google"),
    c("microsoft", "netflix", "twitter")
  ),
  time = "2016-01-01 2019-12-31"
)

# search topics
add_control_keyword(
  keyword = c("%2Fm%2F02q_bk", "%2Fm%2F055t58", "%2Fm%2F025sndk", "%2Fm%2F0d07ph", "%2Fm%2F09jcvs"),
  time = "2016-01-01 2019-12-31"
)
# This adds the following topics: Gmail, Google Maps, Google Translate, Wikipedia, YouTube

## End(Not run)

Add sets of locations

Description

The function adds a new set of locations for downloads and computations to the database. The location set serves as input for all download and computation functions.

Usage

add_locations(locations, type, export = TRUE)

Arguments

Details

Location sets control the locations for which data is downloaded or to which computations are applied. By adding new location sets, the default sets countries and us_states can be expanded by additional sets. Thereby, users can compute DOI within a region (e.g., adding EU countries as a set) or single countries (e.g., adding regions of France as a set). Download and computation functions check whether data for a location already exists. Therefore, data will not be duplicated when location data already exists from another set.

Value

Message that the location set has been created successfully. Location data is written to table data_locations.

Warning

Unfortunately, the Google Trends API cannot handle the location "NA - Namibia". Therefore, the location will be dropped automatically.

Examples

## Not run: 
add_locations(locations = c("AT", "CH", "DE"), type = "DACH")

## End(Not run)

Add synonyms for object keywords

Description

The function allows to add synonyms for object keywords. Sometimes, objects of interest can be searched with different keywords on Google e.g., FC Bayern for Bayern Munich. Search scores for keywords that are added as synonyms are aggregated when running compute_score. The function allows to add synonyms for a single keyword at a time.

Usage

add_synonym(keyword, synonym)

## S3 method for class 'character'
add_synonym(keyword, synonym)

## S3 method for class 'list'
add_synonym(keyword, synonym)

Arguments

Value

Message that the synonym has been added successfully. Synonym data is written to table keyword_synonyms.

Note

To avoid trailing spaces stringr::str_squish is automatically applied to all keywords and synonyms.

See Also

• compute_score()

• stringr::str_squish()

Examples

## Not run: 
add_synonym(
  keyword = "fc bayern",
  synonym = "bayern munich"
)

## End(Not run)

batch_keywords

Description

The table batch_keywords contains the keywords for each batch. Each line contains one keyword, the type of the batch (i.e., control or object) and the id of the batch to which the keyword is assigned. Keywords can be added with the function add_keywords. The function start_db exports the table batch_keywords as objects keywords_control and keywords_object to the package environment gt.env.

Example data for the table batch_keywords is available as R object example_keywords.

Usage

example_keywords

Format

A tibble with 19 rows and 3 variables:

type

Column of type character showing the type of each batch, takes "control" for control batches and "object" for object batches.

batch

Column of type integer showing the number of each batch.

keyword

Column of type character showing the keywords included in each batch.

See Also

• add_control_keyword()

batch_time

Description

The table batch_time contains the time period for which data is downloaded for each batch. Each line contains one time period, the type of the batch (i.e., control or object) and the id of the batch to which the time period is assigned. Time frames take the form "YYYY-MM-DD YYYY-MM-DD". Time periods are added automatically through the function add_keywords. The function start_db exports the table batch_time as objects time_control and time_object to .GlobalEnv.

Example data for the table batch_time is available as R object example_time.

Usage

example_time

Format

A tibble with 5 rows and 3 variables:

type

Column of type character showing the type of each batch, takes "control" for control batches and "object" for object batches.

batch

Column of type integer showing number of each batch.

time

Column of type character showing the time period for each batch as "YYYY-MM-DD YYYY-MM-DD".

See Also

• add_control_keyword()

Aggregate keyword-country data and compute DOI

Description

The function computes degree of internationalization (DOI) for object keywords. Degree of internationalization is measured based on the distribution of country search scores.

Usage

compute_doi(object, control = 1, locations = "countries")

## S3 method for class 'numeric'
compute_doi(object, control = 1, locations = "countries")

## S3 method for class 'list'
compute_doi(object, control = 1, locations = "countries")

Arguments

Details

The function uses an inverted Gini-coefficient as measure for the 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.

Value

Message that data was aggregated successfully. Data is written to table data_doi.

See Also

• example_doi()

Examples

## Not run: 
compute_doi(
  object = 1,
  control = 1,
  locations = "countries"
)
compute_doi(
  object = as.list(1:5),
  control = 1,
  locations = "countries"
)

## End(Not run)

Compute keyword-country search score

Description

The function computes search scores for object keywords. Search volumes for control and object batches are mapped to the same base. Next, search volumes for object batches are divided by the sum of search volumes for the respective control batch. compute_voi computes volume of internationalization (VOI) as global search scores.

Usage

compute_score(object, control = 1, locations = gt.env$countries)

## S3 method for class 'numeric'
compute_score(object, control = 1, locations = gt.env$countries)

## S3 method for class 'list'
compute_score(object, control = 1, locations = gt.env$countries)

compute_voi(object, control = 1)

Arguments

Details

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: seasonally adjusted forecast::seasadj and trend only stats::stl. Next, it follows the procedure outlined by Castelnuovo and Tran (2017, pp. A1-A2) 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.

Castelnuovo, E. & Tran, T. D. 2017. Google It Up! A Google Trends-based Uncertainty index for the United States and Australia. Economics Letters, 161: 149-153.

Value

Message that data has been computed successfully. Data is written to table data_score.

Note

When synonyms were specified through add_synonym, search scores for synonyms are added to the main keyword.

See Also

• example_score()

• add_synonym()

• stats::stl()

• forecast::seasadj()

Examples

## Not run: 
compute_score(
  object = 1,
  control = 1,
  locations = countries
)
compute_voi(
  object = 1,
  control = 1
)
compute_score(
  object = as.list(1:5),
  control = 1,
  locations = countries
)

## End(Not run)

countries

Description

A character vector that includes ISO2 codes for all countries with a share in global GDP >= 0.1\ World Development Indicators database. The data includes:

• AE

• AO

• AR

• AT

• AU

• BD

• BE

• BR

• CA

• CH

• CL

• CN

• CO

• CU

• CZ

• DE

• DK

• DO

• DZ

• EC

• EG

• ES

• ET

• FI

• FR

• GB

• GR

• HK

• HU

• ID

• IE

• IL

• IN

• IQ

• IR

• IT

• JP

• KR

• KW

• KZ

• LK

• MA

• MX

• MY

• NG

• NL

• NO

• NZ

• OM

• PE

• PH

• PK

• PL

• PR

• PT

• QA

• RO

• RU

• SA

• SD

• SE

• SG

• SK

• TH

• TR

• TW

• UA

• US

• UZ

• VN

• ZA

Usage

countries

Format

An object of class character of length 71.

countries_wdi

Description

A data.frame that includes ISO2 codes and country names for all countries and locations in the World Bank's World Development Indicators database.

Usage

countries_wdi

Format

An object of class data.frame with 299 rows and 2 columns.

data_control

Description

The table data_control contains the downloaded data for each control batch. Each line contains the search hits for each keyword in a control batch for a given location and date. Global data gets the value world as location. Data is downloaded and automatically written to the table through the function download_control. The function start_db exports the table data_control as database connection tbl_control to the package environment gt.env. Users can access the database table through dplyr::tbl. The sample data included in data_control was simulated based on actual Google Trends data.

Example data for the table data_control is available as R object example_control.

Usage

example_control

Format

A tibble with 2,400 rows and 5 variables:

location

Column of type character showing the ISO2 code of the country or region for which the data was downloaded.

keyword

Column of type character showing the keyword for which the data was downloaded.

date

Column of type integer showing the date for which the data was downloaded. Can be transformed into date format with lubridate::as_date.⁠\item{hits}{Column of type⁠double⁠showing search volumes for the respective location-keyword-date combination.} \item{batch}{Column of type⁠integer' showing the number of each batch.

Source

https://trends.google.com/trends/

See Also

• download_control()

• dplyr::tbl()

data_doi

Description

The table data_doi contains the degree of internationalization (DOI) for each object batch. Each line contains the DOI computed as inverted gini coefficient, as inverted hhi, or inverted entropy for each keyword in an object batch_o for a given date and type of search score. The column batch_c indicates the control batch that has been used as baseline for mapping. Column locations indicates which set of locations was used to compute the distribution of search scores. DOI is computed and automatically written to the table with the function compute_doi. The function start_db exports the table data_doi as database connection tbl_doi to the package environment gt.env. Users can access the database table through dplyr::tbl. The sample data included in data_doi was simulated based on actual Google Trends data.

Usage

example_doi

Format

A tibble with 4,320 rows and 9 variables:

keyword

Column of type character showing the keyword for which the data was computed.

date

Column of type integer showing the date for which the data was computed Can be transformed into date format with lubridate::as_date.

type

Column of type character indicating the type of time series-column from data_score that is used for DOI computation, takes either "score_obs", "score_sad", or "score_trd".

gini

Column of type double showing the DOI computed as inverted Gini coefficient of the search score distribution from data_score.

hhi

Column of type double showing the DOI computed as inverted Herfindahl-Hirschman index of the search score distribution from data_score.

entropy

Column of type double showing the DOI computed as inverted Entropy measure for the search score distribution from data_score.

batch_c

Column of type integer showing the number of each control batch.

batch_o

Column of type integer showing the number of each object batch.

locations

Column of type character showing the list of locations for which the search score distribution is used.

See Also

• compute_doi()

• dplyr::tbl()

data_object

Description

The table data_object contains the downloaded data for each object batch. Each line contains the search hits for each keyword in an object batch_o for a given location and date. The column batch_c indicates the control batch to which the data will be mapped. Global data takes the value world as location. Data is downloaded and automatically written to the table through the function download_object. The function start_db exports the table data_object as database connection tbl_object to the package environment gt.env. Users can access the database table through dplyr::tbl. The sample data included in data_object was simulated based on actual Google Trends data.

Example data for the table data_object is available as R object example_object.

Usage

example_object

Format

A tibble with 8,640 rows and 6 variables:

location

Column of type character showing the ISO2 code of the country or region for which the data was downloaded.

keyword

Column of type character showing the keyword for which the data was downloaded.

date

Column of type integer showing the date for which the data was downloaded. Can be transformed into date format with lubridate::as_date.

hits

Column of type double showing search volumes for the respective location-keyword-date combination.

batch_c

Column of type integer showing the number of each control batch.

batch_o

Column of type integer showing the number of each object batch.

Source

https://trends.google.com/trends/

See Also

• download_object()

• dplyr::tbl()

data_score

Description

The table data_score contains the search scores for each object batch. Each line contains the observed search score (score_obs), the seasonally adjusted search score (score_sad), and the trend only search score (score_trd) for each keyword in an object batch_o for a given location and date. The column batch_c indicates the control batch that has been used as baseline for mapping. Global data takes the value world as location. Search scores are computed and automatically written to the table with the function compute_score. The function start_db exports the table data_score as database connection tbl_score to the package environment gt.env. Users can access the database table through dplyr::tbl. The sample data included in data_score was simulated based on actual Google Trends data.

Example data for the table data_score is available as R object example_score.

Usage

example_score

Format

A tibble with 6,000 rows and 8 variables:

location

Column of type character showing the ISO2 code of the country or region for which the data was computed.

keyword

Column of type character showing the keyword for which the data was downloaded.

date

Column of type integer showing the date for which the data was computed Can be transformed into date format with lubridate::as_date.

score_obs

Column of type double showing search score for the respective location-keyword-date combination - no time series adjustment.

score_sad

Column of type double showing the search score for the respective location-keyword-date combination - seasonally adjusted time series.

score_trd

Column of type double showing the search score for the respective location-keyword-date combination - trend-only time series.

batch_c

Column of type integer showing the number of each control batch.

batch_o

Column of type integer showing the number of each object batch.

synonym

Column of type integer showing whether the line will be aggregated as synonym.

See Also

• compute_score()

• compute_voi()

• dplyr::tbl()

Disconnect from database

Description

The function closes the connection to the database file db/globaltrends_db.sqlite in the working directory.

Usage

disconnect_db()

Value

Message that disconnection was successful.

Warning

SQLite databases only allow one writer at any instant in time. To run parallel downloads use one database for each download client and merge them once all downloads are complete.

See Also

• initialize_db()

• start_db()

Examples

## Not run: 
disconnect_db()

## End(Not run)

Download data for control keywords

Description

The function downloads search volumes from Google Trends for a control batch in a set of locations. Data is automatically written to table data_control. For download_control_global the input location is automatically set to world.

Usage

download_control(control, locations = gt.env$countries, ...)

## S3 method for class 'numeric'
download_control(control, locations = gt.env$countries, ...)

## S3 method for class 'list'
download_control(control, locations = gt.env$countries, ...)

download_control_global(control, ...)

Arguments

Details

Downloads through the Google Trends API are made through gtrendsR::gtrends. Each control batch can consist of up to five keywords and is predefined in tables batch_keywords and batch_time through add_keywords. The download for a single keyword batch for a single location takes about 30 seconds. This includes a randomized waiting period of 5-10 seconds between downloads. Depending on the frequency of downloads, Google Trends might block users for some time. In this case, download_control waits 60 minutes before it retries the download.

Value

Message that data has been downloaded successfully. Data is written to table data_control.

Warning

We advise against the usage of category codes in downloads. If you use categories to narrow the context of keyword usage, these categories are applied to ALL keywords in the batch. This applies to control keywords as well as object keywords and can result in unintended behavior.

See Also

• example_control()

• gtrendsR::gtrends()

Examples

## Not run: 
download_control(
  control = 1,
  locations = countries
)
download_control(
  control = as.list(1:5),
  locations = countries
)

## End(Not run)

Download data for object batch

Description

The function downloads search volumes from Google Trends for an object batch (batch_o) and one keyword from a control batch (batch_c) in a set of locations. Data is automatically written to table data_object. For download_object_global the input location is automatically set to world.

Usage

download_object(object, control = 1, locations = gt.env$countries, ...)

## S3 method for class 'numeric'
download_object(object, control = 1, locations = gt.env$countries, ...)

## S3 method for class 'list'
download_object(object, control = 1, locations = gt.env$countries, ...)

download_object_global(object, control = 1, ...)

Arguments

Details

Downloads through the Google Trends API are made through gtrendsR::gtrends. Each object batch can consist of up to four keywords and is predefined in tables batch_keywords and batch_time through add_keywords. In addition, one control keyword is added to each object batch. The control keyword then allows a mapping between search volumes for control keywords stored in data_control and search volumes for object keywords. The download for a single keyword batch for a single location takes about 30 seconds. This includes a randomized waiting period of 5-10 seconds between downloads. Depending on the frequency of downloads, Google Trends might block users for some time. In this case, download_object waits 60 minutes before it retries the download.

Value

Message that data was downloaded successfully. Data is written to table data_object.

Warning

We advise against the usage of category codes in downloads. If you use categories to narrow the context of keyword usage, these categories are applied to ALL keywords in the batch. This applies to control keywords as well as object keywords and can result in unintended behavior.

See Also

• example_object()

• gtrendsR::gtrends()

Examples

## Not run: 
download_object(
  object = 1,
  locations = countries
)
download_object(
  object = as.list(1:5),
  locations = countries
)

## End(Not run)

Export data from database table

Description

The function allows to export data from database tables. In combination with various write functions in R, the functions allow exports from the database to local files.

Usage

export_control(control = NULL, location = NULL)

export_control_global(control = NULL)

export_object(keyword = NULL, object = NULL, control = NULL, location = NULL)

export_object_global(keyword = NULL, object = NULL, control = NULL)

export_score(keyword = NULL, object = NULL, control = NULL, location = NULL)

export_voi(keyword = NULL, object = NULL, control = NULL)

export_doi(
  keyword = NULL,
  object = NULL,
  control = NULL,
  locations = NULL,
  type = c("obs", "sad", "trd")
)

Arguments

Details

Exports can be filtered by keyword, object, control, location, locations, or type. Not all filters are applicable for all functions. When filter keyword and object are used together, keyword overrules object. When supplying NULL as input, no filter is applied to the variable.

Value

The functions export and filter the respective database tables.

• export_control and export_control_global export data from table *data_control⁠with columns location, keyword, date, hits, control. Object of class⁠"data.frame"⁠. Methods are applied based on input *control*. \item ⁠export_objectandexport_object_global⁠ export data from table *data_object⁠ with columns location, keyword, date, hits, object. Object of class "data.frame". Methods are applied based on input keyword.

• export_score exports data from table *data_score⁠with columns location, keyword, date, score_obs, score_sad, score_trd, control, object. Object of class⁠c("exp_score", "data.frame")⁠. Methods are applied based on input *keyword*. \item ⁠export_voi⁠ exports data from table *data_score⁠ with columns keyword, date, hits, control, filters for location == "world". Object of class c("exp_voi", "data.frame"). Methods are applied based on input keyword.

• export_doi exports data from table *data_doi⁠with columns keyword, date, type, gini, hhi, entropy, control, object, locations. Object of class⁠c("exp_doi", "data.frame")'. Methods are applied based on input keyword.

See Also

• example_control()

• example_object()

• example_score()

• example_doi()

Examples

## Not run: 
export_control(control = 2)

export_object(
  keyword = "manchester united",
  locations = countries
)

export_object(
  keyword = c("manchester united", "real madrid")
)

export_object(
  keyword = list("manchester united", "real madrid")
)

export_score(
  object = 3,
  control = 1,
  location = us_states
) %>%
  readr::write_csv("data_score.csv")

export_doi(
  keyword = "manchester united",
  control = 2,
  type = "sad",
  locations = "us_states"
) %>%
  writexl::write_xlsx("data_doi.xlsx")

## End(Not run)

Compute abnormal changes in data - historic baseline

Description

The function allows to compute changes in search scores, voi, and doi and shows percentile of changes to identify abnormal changes. In combination with various write functions in R, the functions allow exports from the database to local files.

Usage

get_abnorm_hist(data, ...)

## S3 method for class 'exp_score'
get_abnorm_hist(
  data,
  train_win = 12,
  train_break = 0,
  type = c("obs", "sad", "trd"),
  ...
)

## S3 method for class 'exp_voi'
get_abnorm_hist(
  data,
  train_win = 12,
  train_break = 0,
  type = c("obs", "sad", "trd"),
  ...
)

## S3 method for class 'exp_doi'
get_abnorm_hist(
  data,
  train_win = 12,
  train_break = 0,
  measure = c("gini", "hhi", "entropy"),
  ...
)

Arguments

Details

The function computes abnormal changes in search scores, VOI, or DOI for each date. We define "abnormal" in terms of deviation from a historic baseline value. To compute the historic baseline value, the function computes a moving average. Users can specify the window for moving average training train_win and a break between training and the given date train_break. Abnormal changes are the difference between the moving average and the respective search score, VOI, or DOI. To highlight abnormal changes, the function computes a historic percentile rank for each abnormal change within the distribution of abnormal changes. Low percentile ranks signify abnormally high negative changes. High percentile ranks signify abnormally high positive changes. The function uses the output from export_... functions as input. As get_abnorm_hist offers no additional filters, users are advised to use filters in the export_... functions or to pre-process data before using get_abnorm_hist.

Value

The functions export and filter the respective database tables and return objects of class ⁠"tbl_df", "tbl", "data.frame"⁠.

• Input class exp_score computes abnormal changes in search scores with columns keyword, location, date, control, object, score, score_abnorm, quantile. Object of class c("abnorm_score", "data.frame").

• Input class exp_voi computes abnormal changes in VOI with columns keyword, date, control, object, voi, voi_abnorm, quantile. Object of class c("abnorm_voi", "data.frame").

• Input class exp_doi computes abnormal changes in DOI with columns keyword, locations, date, control, object, doi, doi_abnorm, quantile. Object of class c("abnorm_doi", "data.frame").

See Also

• export_score()

• export_voi()

• export_doi()

• dplyr::filter()

Examples

## Not run: 
data <- export_score(keyword = "amazon")
get_abnorm_hist(data, train_win = 12, train_break = 0, type = "obs")

data <- export_voi(keyword = "amazon")
get_abnorm_hist(data, train_win = 12, train_break = 0, type = "obs")

data <- export_score(keyword = "amazon")
get_abnorm_hist(data, train_win = 12, train_break = 0, measure = "gini")

## End(Not run)

globaltrends package environment

Description

The environment gt.env contains all package-related data objects, such as the handle for the SQLite database file or connections to tables. The object contains:

• globaltrends_db: Handle for the SQLite database file.

• tbl_locations: Connection to table that contains the lists of locations saved in the database.

• tbl_keywords: Connection to table that contains the lists of keywords saved in the database.

• tbl_time: Connection to table that contains the lists of batch times saved in the database.

• tbl_synonyms: Connection to table that contains the lists of keyword synonyms saved in the database.

• tbl_doi: Connection to table that contains the DOI data saved in the database.

• tbl_control: Connection to table that contains data on search volume for control terms saved in the database.

• tbl_object: Connection to table that contains data on search volume for object terms saved in the database.

• tbl_score: Connection to table that contains data on search scores saved in the database.

• keywords_control: Tibble that contains all keywords per control batch.

• time_control: Tibble that contains all batch times per control batch.

• keywords_object: Tibble that contains all keywords per object batch.

• time_object: Tibble that contains all batch times per object batch.

• keyword_synonyms: Tibble that contains all keyword/synonym combinations.

Usage

gt.env

Format

An object of class environment of length 14.

See Also

• example_control()

• example_object()

• example_score()

• example_doi()

Initialize database

Description

The function creates a new database for the globaltrends package and creates all necessary tables within the database.

Usage

initialize_db()

Details

The function creates a new SQLite database for the globaltrends package. The database is saved as file db/globaltrends_db.sqlite in the working directory. If the folder db does not exists in the working directory, the folder is created. If the database already exists in the working directory, the function exits with an error. Within the database all tables are created and the default location sets are added to the respective table:

• countries - all countries with a share in global GDP >= 0.1\ in 2018.

• us_states - all US federal states and Washington DC.

After creating the database, the function disconnects from the database.

Value

Database is created.

Warning

SQLite databases only allow one writer at any instant in time. To run parallel downloads use one database for each download client and merge them once all downloads are complete.

See Also

• start_db()

• disconnect_db()

• countries()

• us_states()

• example_keywords()

• example_time()

• example_control()

• example_object()

• example_score()

• example_doi()

• https://www.sqlite.org/index.html

Examples

## Not run: 
initialize_db()

## End(Not run)

Create barplot for cross-sectional globaltrends data

Description

The function creates barplots for cross-sectional search score data. It uses the output of export_score to prepare a bar plot of search scores for the top 10 countries. For output from get_abnorm_hist the plot shows five locations with the highest and lowest abnormal changes each. When the output includes more than one keyword, only the first keyword is used.

Usage

plot_bar(data, ...)

## S3 method for class 'exp_score'
plot_bar(data, type = c("obs", "sad", "trd"), ...)

## S3 method for class 'abnorm_score'
plot_bar(data, ...)

Arguments

Value

Barplot of cross-sectional data as ggplot2 object.

Examples

## Not run: 
data <- export_score(keyword = "amazon")
plot_bar(data, type = "obs")

data <- export_score(keyword = "amazon")
data <- get_abnorm_hist(data, train_win = 12, train_break = 0, type = "obs")
plot_bar(data)

## End(Not run)

Create boxplot for time series of globaltrends data

Description

The function creates boxplots for time series globaltrends data. It uses the output of export_... or get_abnorm_hist to prepare boxplots for up to nine keywords.

Usage

plot_box(data, ...)

## S3 method for class 'exp_score'
plot_box(data, type = c("obs", "sad", "trd"), ...)

## S3 method for class 'abnorm_score'
plot_box(data, ci = 0.95, ...)

## S3 method for class 'exp_voi'
plot_box(data, type = c("obs", "sad", "trd"), ...)

## S3 method for class 'abnorm_voi'
plot_box(data, ci = 0.95, ...)

## S3 method for class 'exp_doi'
plot_box(
  data,
  type = c("obs", "sad", "trd"),
  measure = c("gini", "hhi", "entropy"),
  locations = "countries",
  ...
)

## S3 method for class 'abnorm_doi'
plot_box(
  data,
  type = c("obs", "sad", "trd"),
  locations = "countries",
  ci = 0.95,
  ...
)

Arguments

Details

For output of export_score, only data for a single location is shown. When date for more than one location is provided, the function selects only the first location. For output of get_abnorm_hist, users can specify confidence intervals to highlight abnormal changes in the data.

Value

Boxplot of time series as ggplot2 object. For plots for output from get_abnorm_hist the provided confidence interval is indicated by red dots.

Examples

## Not run: 
data <- export_score(keyword = "amazon")
plot_box(data, type = "obs")

data <- export_voi(keyword = "amazon")
data <- get_abnorm_hist(data, train_win = 12, train_break = 0, type = "obs")
plot_box(data)

data <- export_doi(keyword = "amazon")
data <- get_abnorm_hist(data, train_win = 12, train_break = 0, measure = "gini")
plot_box(data, ci = 0.9)

## End(Not run)

Plot time series of globaltrends data

Description

The function creates line plots for time series globaltrends data. It uses the output of export_... or get_abnorm_hist to prepare line plots for up to nine keywords.

Usage

plot_ts(data, ...)

## S3 method for class 'exp_score'
plot_ts(data, type = c("obs", "sad", "trd"), smooth = TRUE, ...)

## S3 method for class 'abnorm_score'
plot_ts(data, ci = 0.95, ...)

## S3 method for class 'exp_voi'
plot_ts(data, type = c("obs", "sad", "trd"), smooth = TRUE, ...)

## S3 method for class 'abnorm_voi'
plot_ts(data, ci = 0.95, ...)

## S3 method for class 'exp_doi'
plot_ts(
  data,
  type = c("obs", "sad", "trd"),
  measure = c("gini", "hhi", "entropy"),
  locations = "countries",
  smooth = TRUE,
  ...
)

## S3 method for class 'abnorm_doi'
plot_ts(
  data,
  type = c("obs", "sad", "trd"),
  locations = "countries",
  ci = 0.95,
  ...
)

Arguments

Details

For output of export_score, only data for a single location is shown. When date for more than one location is provided, the function selects only the first location. For output of get_abnorm_hist, users can specify confidence intervals to highlight abnormal changes in the data.

Value

Line plot of time series as ggplot2 object. For plots for output from get_abnorm_hist the provided confidence interval is indicated by red dots.

Examples

## Not run: 
data <- export_score(keyword = "amazon")
plot_ts(data, type = "obs")

data <- export_voi(keyword = "amazon")
data <- get_abnorm_hist(data, train_win = 12, train_break = 0, type = "obs")
plot_ts(data)

data <- export_doi(keyword = "amazon")
data <- get_abnorm_hist(data, train_win = 12, train_break = 0, measure = "gini")
plot_ts(data, ci = 0.9)

## End(Not run)

Line plots of VOI and DOI time series

Description

The function uses the outputs of export_voi and export_doi to prepare a parallel time series plot of volume and degree of internationalization values. When the output includes more than one keyword, only the first keyword is used.

Usage

plot_voi_doi(
  data_voi,
  data_doi,
  type = c("obs", "sad", "trd"),
  measure = c("gini", "hhi", "entropy"),
  locations = "countries",
  smooth = TRUE
)

Arguments

Value

Line plot of VOI and DOI time series as ggplot2 object.

See Also

• export_voi()

• export_doi()

• ggplot2::ggplot()

Examples

## Not run: 
data1 <- export_voi(keyword = "manchester united")
data2 <- export_doi(
  keyword = "manchester united",
  locations = "countries"
)
plot_voi_doi(
  data_voi = data1,
  data_doi = data2,
  type = "obs",
  measure = "gini",
  smooth = TRUE
)
plot_voi_doi(
  data_voi = data1,
  data_doi = data2,
  type = "sad",
  measure = "hhi",
  smooth = FALSE
)
plot_voi_doi(
  data_voi = data1,
  data_doi = data2,
  type = "trd",
  measure = "entropy",
  smooth = TRUE
)

## End(Not run)

Remove data from database tables

Description

The function removes data from database tables for control or object batches.

Usage

remove_data(table, control = NULL, object = NULL)

vacuum_data()

Arguments

Details

The function removes data "greedily": all data that builds on the deleted data is removed. For example, when data from data_control is removed data in data_object that maps to this control batch is also removed. The dependency structure works as follows: batch_keyword / batch_time -> data_control -> data_object -> data_score -> data_doi.

After using remove_data, run vacuum_data to free-up unused memory in the database file. Depending on the database size, vacuum_data might take some minutes for execution.

Value

Message that data has been removed successfully. Data is removed from database tables.

See Also

• example_keywords()

• example_time()

• example_control()

• example_object()

• example_score()

• example_doi()

Examples

## Not run: 
remove_data(
  table = "batch_keywords",
  control = 1
)
remove_data(
  table = "data_score",
  control = 1,
  object = 1
)
vacuum_data()

## End(Not run)

Load globaltrends database and tables

Description

The function connects to the database file db/globaltrends_db.sqlite in the working directory. After connecting to the database connections to the database tables (through dplyr::tbl) are created. Data from the tables batch_keywords and batch_time are exported to the tibble objects keywords_control, keywords_object, time_control, and time_object.

Usage

start_db()

Value

The function exports the following objects to the package environment globaltrends_db:

• globaltrends_db A DBIConnection object, as returned by DBI::dbConnect(), connecting to the SQLite database in the working directory

• tbl_doi A remote data source pointing to the table data_doi in the connected SQLite database

• tbl_control A remote data source pointing to the table data_control in the connected SQLite database

• tbl_mapping A remote data source pointing to the table data_mapping in the connected SQLite database

• tbl_object A remote data source pointing to the table data_object in the connected SQLite database

• tbl_score A remote data source pointing to the table data_score in the connected SQLite database

• countries A character vector containing ISO2 country codes of countries that add at least 0.1\

• us_states A character vector containing ISO2 regional codes of US states

• keywords_control A tibble containing keywords of control batches

• time_control A tibble containing times of control batches

• keywords_object A tibble containing keywords of object batches

• time_object A tibble containing times of control batches

• keyword_synonyms A tibble containing synonymous keywords

Warning

SQLite databases only allow one writer at any instant in time. To run parallel downloads use one database for each download client and merge them once all downloads are complete.

See Also

• initialize_db()

• disconnect_db()

• dplyr::tbl()

Examples

## Not run: 
start_db()

## End(Not run)

us_states

Description

A character vector that includes ISO2 codes for all US federal states and Washington DC. The data includes:

• US-AL

• US-AK

• US-AZ

• US-AR

• US-CA

• US-CO

• US-CT

• US-DE

• US-FL

• US-GA

• US-HI

• US-ID

• US-IL

• US-IN

• US-IA

• US-KS

• US-KY

• US-LA

• US-ME

• US-MD

• US-MA

• US-MI

• US-MN

• US-MS

• US-MO

• US-MT

• US-NE

• US-NV

• US-NH

• US-NJ

• US-NM

• US-NY

• US-NC

• US-ND

• US-OH

• US-OK

• US-OR

• US-PA

• US-RI

• US-SC

• US-SD

• US-TN

• US-TX

• US-UT

• US-VT

• US-VA

• US-WA

• US-WV

• US-WI

• US-WY

• US-DC

Usage

us_states

Format

An object of class character of length 51.