View source: R/ipc_get_population.R
ipc_get_population | R Documentation |
Accesses the population resources on the IPC API. Contains detailed
population data. If country
and/or start
and end
parameters are passed,
accesses the population advanced API endpoint and pulls in all data.
filtered by those parameters. To get the population data for a specific
analysis, available on the types/{id} advanced API endpoint,
pass in id
. You cannot pass in both sets of parameters.
ipc_get_population(
country = NULL,
start = NULL,
end = NULL,
id = NULL,
api_key = NULL,
tidy_df = TRUE
)
country |
ISO2 country code. |
start |
Start year. |
end |
End year. |
id |
Analysis ID. |
api_key |
IPC API key. If |
tidy_df |
If |
Unlike the other ipc_get_..()
family of functions, this returns a list of
datasets, corresponding to country
, areas
, and groups
data. The benefit of
ipc_get_population()
is that the
returned data for each level of analysis contains all periods of analysis.
Groups data, where available, are geographies within a country that comprise multiple areas and/or points. Areas and points data is the lowest level of IPC analysis where population estimates for each phase are provided and a general area-level classification is made. There is no phase classification at the group level, but populations in each phase are provided. The same applies to country-level data.
These datasets are available elsewhere through:
Country data: ipc_get_country()
Areas data: ipc_get_areas()
Groups data: Not available through other functions
See the respective function documentation for more details on what each dataset comprises or the IPC website and API documentation for more detailed and comprehensive information on the data and analysis.
A list of 3 data frames:
Country data frame.
Areas data frame.
Groups data frame.
Refer to the IPC-CH Public API documentation for details on the returned values, with variables described in full in the extended documentation.
When tidy_df
is TRUE
, the data returned from the population end point is
transformed into a list of 3 data frames to ensure that each row represents a
single analysis, and all estimates and values are stored as columns, while
data at different levels of aggregation are in completely separate data
frames. The steps are:
analysis_period_start
and analysis_period_end
created as Date
columns
from the period_dates
column respectively, allocating the day of the
start and end periods to be the 15th of the month.
analysis_date
converted to a date column, using the 15th day of the month.
phases
is unnested from a list column to bring the phase data into
the main data frame.
The population estimates are pivoted to a wider format with names phase#_num
and phase#_pct
.
id
column renamed to analysis_id
.
Data frames are split out so multiple aggregations not present in a single single data frame.
# get all populations from the simplified API
ipc_get_population()
# get populations for specific analysis ID from advanced API
ipc_get_population(id = 12856213) # analysis with areas data frame
ipc_get_population(id = 65508276) # analysis with groups data frame
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