ipumsr (version 0.5.2)

get_recent_extracts_info: Get information on recent extracts

Description

Get information on recent extracts for a given IPUMS collection via the IPUMS API, returned either as a list or tibble. For an overview of ipumsr microdata API functionality, see vignette("ipums-api", package = "ipumsr").

Usage

get_recent_extracts_info_list(
  collection,
  how_many = 10,
  api_key = Sys.getenv("IPUMS_API_KEY")
)
get_recent_extracts_info_tbl(
  collection,
  how_many = 10,
  api_key = Sys.getenv("IPUMS_API_KEY")
)

Value

For get_recent_extracts_info_list(), a list of extract objects. For get_recent_extracts_info_tbl(), a tibble with information on one extract in each row.

Arguments

collection

The code for an IPUMS data collection. For a list of the codes used to refer to the data collections, see ipums_data_collections().

how_many

Number of recent extracts for which you'd like information. Defaults to 10 extracts.

api_key

API key associated with your user account. Defaults to the value of environment variable "IPUMS_API_KEY".

See Also

Other ipums_api: add_to_extract(), define_extract_cps(), define_extract_from_json(), define_extract_usa(), download_extract(), extract_list_to_tbl(), extract_tbl_to_list(), get_extract_info(), get_last_extract_info(), ipums_data_collections(), is_extract_ready(), remove_from_extract(), save_extract_as_json(), set_ipums_api_key(), submit_extract(), wait_for_extract()

Examples

if (FALSE) {
# Get list of recent extracts
list_of_last_10_extracts <- get_recent_extracts_info_list("usa")

# Print the extract number for extracts that are downloadable:
for (extract in list_of_last_10_extracts) {
  if (is_extract_ready(extract)) print(extract$number)
}

# Get tibble of recent extracts
tbl_of_last_10_extracts <- get_recent_extracts_info_tbl("usa")

# Filter down to extracts with "income" in the description
description_mentions_income <- grepl(
  "[Ii]ncome",
  tbl_of_last_10_extracts$description
)
income_extracts <- tbl_of_last_10_extracts[description_mentions_income, ]

# Convert tibble of extracts to list of extracts
income_extracts <- extract_tbl_to_list(income_extracts)

# Now it's easier to operate on those elements as extract objects:
revised_income_extract <- add_to_extract(
  income_extracts[[1]],
  samples = "us2018a"
)

submitted_revised_income_extract <- submit_extract(revised_income_extract)
}