Convert word counts to total reading time
get_reading_time(
series = 2,
jurisdiction,
year,
document_type = 1,
summary = TRUE,
date_is_range = TRUE,
country = FALSE,
agency = NULL,
cluster = NULL,
label = NULL,
industry = NULL,
filtered = TRUE,
label_level = 3,
industry_level = NULL,
label_source = "NAICS",
version = NULL,
download = FALSE,
page = NULL,
date = NULL,
verbose = 0
)
Returns pandas dataframe with the metadata
Series ID (s), default value is 2 here
ID for the jurisdiction
Year(s) of data
ID for type of document, default value of 1
Return summary instead of document level data (only one year of data is allowed for document level data), default value is TRUE
Indicating whether the time parameter is range or should be treated as single data points, default value is TRUE
Get values for all subjurisdictions, default value is FALSE
Agency ID, default value is NULL
Cluster ID
Industry code using the jurisdiction-specific coding system (returns all 3-digit industries by default), default value is NULL
industry is deprecated; use label
Exclude poorly-performing industry results (use of unfiltered results is NOT recommended), default value is TRUE
Level of NAICS industries to include, default value is 3
industry_level is deprecated; use label_level
classification standard (NAICS, BEA, SOC), default value of "NAICS"
Version ID for datasets with multiple versions (if no ID is given, returns most recent version), default value is NULL
If not False, a path location for a downloaded csv of the results, default value is FALSE
Page Number of the Response, default value is NULL
date is deprecated, use year now
Print out the url of the API call (useful for debugging), default value is 0
if (FALSE) get_reading_time(
jurisdiction = 45,
year = array(c(2022, 2023))
)
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