The goal of mapping is to provide callable key-value tables that can be manipulated as {vctrs}.
install.packages("jameelalsalam/mapping")
This is a basic example which shows you how to solve a common problem:
library(mapping)
suppressPackageStartupMessages(library(tidyverse))
## basic example code
countries <- c("FR", "GR", "IT")
capitals <- c("Paris", "Berlin", "Rome")
eu_caps <- mapping(countries, capitals)
Display:
eu_caps
#> <mapping[3]>
#> [1] FR->Paris GR->Berlin IT->Rome
Apply the mapping:
eu_caps(countries)
#> [1] "Paris" "Berlin" "Rome"
Use in tidyverse pipelines:
tibble(
countries = c("FR", "FR", "IT", "GR")
) %>%
mutate(capitals = eu_caps(countries))
#> # A tibble: 4 x 2
#> countries capitals
#> <chr> <chr>
#> 1 FR Paris
#> 2 FR Paris
#> 3 IT Rome
#> 4 GR Berlin
The keys, values, and keyvalues can be retrieved, queried, and modified:
In future, it will go inside a tibble:
# data.frame(
# kv = eu_caps
# )
Compose mappings:
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