lookupr

Lookup pulls common lookup tables from the lookup repository and joins them to your data.

This is a port of Python's agate-lookup.

This version does not do caching. If you don't want to redownload lookup tables, save a local copy of your results.

Install

From Github:

install.packages("devtools")
devtools::install_github("wireservice/lookupr")

Usage

Load libraries:

library(dplyr)
library(lookupr)

Lookup the consumer price index for yearly data:

data <- data.frame(year = c("2004", "2005", "2006", "2007"))

data %>%
  lookup("year", "cpi")
#>   year     cpi
#> 1 2004 188.908
#> 2 2005 195.267
#> 3 2006 201.558
#> 4 2007 207.344

If you're column name is different from year:

data <- data.frame(anum = c("2004", "2005", "2006", "2007"))

data %>%
  lookup("anum", "cpi", lookup_keys = "year")
#>   anum     cpi
#> 1 2004 188.908
#> 2 2005 195.267
#> 3 2006 201.558
#> 4 2007 207.344

Monthly CPI:

data <- data.frame(
  year = c("2004", "2004", "2005", "2005"),
  month = c("11", "12", "1", "2")
)

data %>%
  lookup(c("year", "month"), "cpi")
#>   year month   cpi
#> 1 2004    11 191.7
#> 2 2004    12 191.7
#> 3 2005     1 191.6
#> 4 2005     2 192.4

Inflation adjustment

As it's such a common use-case, lookup includes shortcut functions for doing CPI adjustment:

data <- data.frame(
  year = c("2005", "2006", "2007", "2008", "2009", "2010"),
  price_a = c(100, 100, 100, 100, 100, 100),
  price_b = c(200, 200, 200, 200, 200, 200)
)

data %>%
  lookup_cpi("year", c("price_a", "price_b"), base = "2010")
#>   year   price_a  price_b
#> 1 2005  89.54080 179.0816
#> 2 2006  92.42558 184.8512
#> 3 2007  95.07878 190.1576
#> 4 2008  98.70596 197.4119
#> 5 2009  98.39001 196.7800
#> 6 2010 100.00000 200.0000