account_count is used to count mentions in a BrandsEye account matching
matching a particular filter, and to produce aggreagate data related to them.
It's possible to group mentions, order the
results, and to include various other bits of useful information. It's also
possible to perform count operations across multiple accounts, or to count
things other than the number of mentions received, such as the number of unique
authors, sites, and so on.
1 2 3 4 5 6 7 8 9 10 11 12 13 14
account_count(account, ...) ## S3 method for class 'character' account_count(accounts, filter = NULL, groupby = NULL, include = NULL, count = NULL, authentication = pkg.env$defaultAuthentication, showProgress = length(accounts) > 10, .process = TRUE) ## S3 method for class 'brandseye.account' account_count(account, filter = NULL, groupby = NULL, include = NULL) ## S3 method for class 'factor' account_count(account, ...)
An account to be queried.
A vector of account codes. If this is a single account code, this function will return a data frame of results just from that account. If it contains multiple accounts, this will return a data frame containing all the results across accounts, and a column indicating the account that the particular result is from.
A filter string describing the mentions that should be counted by this query.
A vector of items that should be grouped by. For example,
A vector of items naming values that should be included.
A vector items that should be counted instead of mentions themselves. By default,
Set to true if you would like a progress bar to be shown when querying multiple accounts.
Indicates whether the types should be cleaned. For instance, date values transformed from strings to POSIXct objects, NA values properly handled, etc.
Filters are described in the api documentation https://api.brandseye.com/docs
It's possible to parallelise this call. This is only useful if you're querying multiple accounts:
there will be no benefit when querying only a single account. Any parallel backend for
foreach package can be used to enable parallel functioning. For example, on
a Linux or OX X based system, the following will work well:
account_count(list_account_codes(), "published inthelast day")
character: For querying accounts encoded as character strings, or as a vector
of character strings.
brandseye.account: For querying objects returned by
factor: Useful for querying accounts encoded as
factors, such as account
codes given in
account_count function will by default return only a count of the mentions
matching the given filter. If you would like more information, you should
group by particular values. The following (possibly incomplete) list
of fields can be grouped by:
action, alexaRank, assignee, author, authorName, brand, city, country, credibility, extract, feed, gender, language, link, linked, media, pageRank, phrase, phraseMatches, pickedUp, process, published, region, relevancy, relevancyVerified, sentiment, sentimentVerified, tag, title, updated, uri, replycount, resharecount, responsetime
account_count will by default count the number of mentions matching
the filter (or the group that the mentions are being grouped by). It is also
possible to count other items. These include:
id (the default), credibility, media, action, site, authorName, language, country, region, city, assignee, author, gender
Grouping is the first step to include extra data. However, some data
cannot be grouped by, and are instead extra information added on to
each of the returned buckets. This might include information as simple
as a new format for the country code (
countryISO3 being an example),
or aggregate data for the group (
OTS being examples).
An incomplete list of data that can be included are:
ave, ots, percentages, engagement, sentiment-reach, sentiment-count, countryISO3, latlon, scale, yaw, pitch, roll
The canonical documentation for the filter language, and what fields may be grouped and included, is the BrandsEye API documentation https://api.brandseye.com/docs.
account for information on querying account information, including
seeing the brands and phrases associated with an account.
account_mentions for querying raw mention data.
sentiment for comparing sentiment values.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
## Not run: account_count("QUIR01BA", "published inthelast month") # Uses default authentication, # if that has been set up. account_count("QUIR01BA", "published inthelast month", authentication = authentication(key = "<my key>")) # Return results for multiple accounts account_count(c("QUIR01BA", "BEAD33AA"), "published inthelast month") # Return results for all accounts account_count(list_account_codes(), "published inthelast month") # Return results grouped by publication date account_count("QUIR01BA", "published inthelast month", groupby = "published) # Include Ad Value Equivalent (AVE) and Opportunity to See account_count("QUIR01BA", "published inthelast month", groupby = "published, include = c("ave", "ots")) # Count the number of unique authors account_count("QUIR01BA", "published inthelast month", count="author") # Count the number of unique authors in each country that we have received # mentions from account_count("QUIR01BA", "published inthelast month", count="author", groupby="country") ## End(Not run) ## Not run: # Not using global authentication, but authenticating directly in the call # itself. ac <- account("QUIR01BA", key="<my key>") # A single number counting the mentions published in the last week. account_count(ac, "published inthelast week") # The number of relevant mentions published in the last month account_count(ac, "published inthelast month and relevancy isnt irrelevant") # As above, but grouped by publication day account_count(ac, "published inthelast month and relevancy isnt irrelevant", groupby="published") ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.