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Get data from a specific table

Source: R/data_tables.R
get_data_table.Rd

Get data from a specific table

Usage

get_data_table(
  idTable = NULL,
  filter = NULL,
  nlast = NULL,
  det = NULL,
  tip = NULL,
  lang = "ES",
  validate = TRUE,
  verbose = FALSE,
  unnest = FALSE,
  metanames = FALSE,
  metacodes = FALSE
)

Arguments

idTable

(int): id of the table. For further information about ids click this link.

filter

(list): list of variables and values.

Filtering data from tables

When we request data from tables there is the possibility of filtering data on the fly using metadata information about the variables and their values that define the series. See get_metadata_table_varval() to get all the values at once. There are different approaches to build the filter depending on the table type.

Case one: tempus table

URL example. For a tempus table the filter is based on ids. The format is list(id_variable1 = id_value1, id_variable2 = id_value2). Besides:

  • A variable can take more than one value: list(id_variable1 = c(id_value11, id_value12), id_variable2 = id_value2).

  • A variable can take a empty character "" to get all its possible values: list(id_variable1 = id_value1, id_variable2 = "").

Case two: px tables

URL example. For a px table the filter is based on codes. The format is list(cod_variable1 = cod_value1, cod_variable2 = cod_value2). Besides:

  • A variable can take more than one value: list(cod_variable1 = c(cod_value11, cod_value12), id_variable2 = cod_value2).

  • A variable can take a empty character "" to get all its possible values: list(cod_variable1 = cod_value1, cod_variable2 = "").

Case three: tpx table

URL example. For a tpx table the filter is based on codes. The format is list(cod_variable1 = cod_value1, cod_variable2 = cod_value2). Besides:

  • A variable can take more than one value: list(cod_variable1 = c(cod_value11, cod_value12), id_variable2 = cod_value2).

  • A variable can take a empty character "" to get all its possible values: list(cod_variable1 = cod_value1, cod_variable2 = "").

URL example. There are tpx tables that contain variable ids and value ids. In this case, we can use the ids instead of the codes to build the filter. To do this we add the alias ~id at the end of each id: list(id_variable1~id = id_value1~id, id_variable2~id = id_value2~id).

Using shortcuts

Additionally, shortcuts can be used to filter. They simplify the filtering approach by using standardized names for variable IDs and therefore simplify their use. The format for a tempus table is: list(shortcut_variable1 = name1, shortcut_variable2 = name2). However, for px and tpx tables the format is: list(values = c(name1, name2). The values wrapper can also be used with tempus tables. To see a list of all available shortcuts, see get_filter_shortcuts() function. Let’s also remark that for better performance is recommended to use numeric ids for tempus tables and alphanumeric codes for px and tpx tables.

nlast

(int): number of periods to retrieve. By default it retrieves all available periods.

det

(int): level of detail. Valid values: 0, 1 or 2.

tip

(string): set to 'A' for friendly output (e.g. readable dates), set to 'M' to include metadata or set to 'AM' for both.

lang

(string): language of the retrieved data. Set to 'ES' for Spanish or set to 'EN' for English.

validate

(logical): validate the input parameters. A FALSE value means fewer API calls. Therefore, it is recommended to set it to FALSE when there is no doubt about the validity of the input parameters, including the filter.

verbose

(logical): print additional information, including the URL to call the API service.

unnest

(logical): set to TRUE to obtain a single data frame of data.

metanames

(logical): set to TRUE to extract the name of the values that defined the table. The names are extracted from the metadata information (it is mandatory to include 'M' in the tip parameter). Several columns are created corresponding to the values of the different variables.

metacodes

(logical): set to TRUE to extract the codes and ids of the values that defined the table. The codes and ids are extracted from the metadata information (it is mandatory to include 'M' in the tip parameter). Several columns are created corresponding to the values of the different variables.

Value

Data frame with data of a table according to the id specified in the function

Examples

if (FALSE) {
get_data_table(idTable = 50902)
get_data_table(idTable = 50902, nlast = 2, unnest = TRUE, metanames = TRUE,
               metacodes = TRUE, tip = "M")
get_data_table(idTable = 8105, filter = list("18"="454"), verbose = TRUE)
get_data_table(idTable = 33387,
               filter = list(tipodematerial = c("extraccionnacional",
                                                "2mineralesmetalicosmineralenbruto")))
get_data_table(idTable = "t20/e245/p08/l0/01001.px",
               filter = list(edad3gruposdeedad = "015anos",
                             sexo = c("mujeres", "hombres")))
}