Family Basket Cost — costo_canasta

Downloads and returns the monthly cost of the Dominican Republic's family basket (Canasta Familiar) published by the Central Bank of the Dominican Republic. Data are available either by income quintile or by geographic region.

Usage

costo_canasta_familiar(
  by = c("regiones", "quintiles"),
  format = c("long", "wide")
)

Arguments

by

Character. Level of disaggregation. One of:

"regiones": Family basket cost by geographic region.
"quintiles": Family basket cost by household income quintile.

format

Character. Output format:

"long": Returns a tidy data frame with one observation per date and group.
"wide": Returns the original table in wide format.

Value

A tibble containing the monthly family basket cost.

When format = "long", the returned data frame contains:

date: Date corresponding to the first day of each month.
year: Year.
mes: Month.
regiones or quintiles: Grouping variable specified by by.
costo: Family basket cost in Dominican pesos (DOP).

When format = "wide", the original publication layout is returned with cleaned column names.

Details

The data are downloaded directly from the Central Bank of the Dominican Republic and correspond to the 2019–2020 reference base.

Examples

# Family basket cost by income quintile
costo_canasta_familiar(by = "quintiles")
#> # A tibble: 618 × 5
#>    date        year   mes quintiles  costo
#>    <date>     <dbl> <dbl> <chr>      <dbl>
#>  1 2018-01-01  2018     1 Quintil 1 19402.
#>  2 2018-01-01  2018     1 Quintil 2 25439.
#>  3 2018-01-01  2018     1 Quintil 3 30445.
#>  4 2018-01-01  2018     1 Quintil 4 35856.
#>  5 2018-01-01  2018     1 Quintil 5 56196.
#>  6 2018-01-01  2018     1 Nacional  33464.
#>  7 2018-02-01  2018     2 Quintil 1 19345.
#>  8 2018-02-01  2018     2 Quintil 2 25384.
#>  9 2018-02-01  2018     2 Quintil 3 30393.
#> 10 2018-02-01  2018     2 Quintil 4 35820.
#> # ℹ 608 more rows

# Family basket cost by region
costo_canasta_familiar(by = "regiones")
#> # A tibble: 280 × 5
#>    date        year   mes regiones              costo
#>    <date>     <dbl> <dbl> <chr>                 <dbl>
#>  1 2020-10-01  2020    10 Región Ozama         42976.
#>  2 2020-10-01  2020    10 Región Norte o Cibao 34392.
#>  3 2020-10-01  2020    10 Región Este          33583.
#>  4 2020-10-01  2020    10 Región Sur           28750.
#>  5 2020-11-01  2020    11 Región Ozama         43229.
#>  6 2020-11-01  2020    11 Región Norte o Cibao 34598.
#>  7 2020-11-01  2020    11 Región Este          33719.
#>  8 2020-11-01  2020    11 Región Sur           28979.
#>  9 2020-12-01  2020    12 Región Ozama         43424.
#> 10 2020-12-01  2020    12 Región Norte o Cibao 34774.
#> # ℹ 270 more rows

# Wide format
costo_canasta_familiar(
  by = "quintiles",
  format = "wide"
)
#> # A tibble: 103 × 9
#>    date        year   mes quintil_1 quintil_2 quintil_3 quintil_4 quintil_5
#>    <date>     <dbl> <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#>  1 2018-01-01  2018     1    19402.    25439.    30445.    35856.    56196.
#>  2 2018-02-01  2018     2    19345.    25384.    30393.    35820.    56200.
#>  3 2018-03-01  2018     3    19439.    25497.    30515.    35951.    56379.
#>  4 2018-04-01  2018     4    19492.    25571.    30619.    36087.    56650.
#>  5 2018-05-01  2018     5    19515.    25620.    30694.    36188.    56837.
#>  6 2018-06-01  2018     6    19571.    25692.    30769.    36257.    56922.
#>  7 2018-07-01  2018     7    19532.    25663.    30748.    36257.    56976.
#>  8 2018-08-01  2018     8    19476.    25614.    30724.    36271.    57105.
#>  9 2018-09-01  2018     9    19426.    25579.    30720.    36302.    57265.
#> 10 2018-10-01  2018    10    19492.    25657.    30799.    36380.    57350.
#> # ℹ 93 more rows
#> # ℹ 1 more variable: nacional <dbl>