Example 01 – Inflation in the US

Let’s download and plot information about inflation in the US:

library(GetQuandlData)
library(tidyverse)

my_id <- c('Inflation USA' = 'RATEINF/INFLATION_USA')
my_api <- readLines('~/Dropbox/.quandl_api.txt') # you need your own API (get it at https://www.quandl.com/sign-up-modal?defaultModal=showSignUp>)
first_date <- '2000-01-01'
last_date <- Sys.Date()

df <- get_Quandl_series(id_in = my_id, 
                        api_key = my_api, 
                        first_date = first_date,
                        last_date = last_date, 
                        cache_folder = tempdir())

glimpse(df)
## Observations: 236
## Variables: 4
## $ series_name <chr> "Inflation USA", "Inflation USA", "Inflation USA", "…
## $ ref_date    <date> 2019-08-31, 2019-07-31, 2019-06-30, 2019-05-31, 201…
## $ value       <dbl> 1.750, 1.811, 1.648, 1.790, 1.996, 1.863, 1.520, 1.5…
## $ id_quandl   <chr> "RATEINF/INFLATION_USA", "RATEINF/INFLATION_USA", "R…

As you can see, the data is in the long format. Let’s plot it:

p <- ggplot(df, aes(x = ref_date, y = value/100)) + 
  geom_col() + 
  labs(y = 'Inflation (%)', 
       x = '',
       title = 'Inflation in the US') + 
  scale_y_continuous(labels = scales::percent)

p

Beautiful!

Example 02 - Inflation for many countries

Next, lets have a look into a more realistic case, where we need inflation data for several countries:

First, we need to see what are the available datasets from database RATEINF:

library(GetQuandlData)
library(tidyverse)

db_id <- 'RATEINF'
my_api <- readLines('~/Dropbox/.quandl_api.txt') # you need your own API

df <- get_database_info(db_id, my_api)

knitr::kable(df)

Nice. Now we only need to filter the series with YOY inflation:

idx <- stringr::str_detect(df$name, 'Inflation YOY')
df_series <- df[idx, ]

and grab the data:

my_id <- df_series$quandl_code
names(my_id) <- df_series$name
first_date <- '2010-01-01'
last_date <- Sys.Date()

df_inflation <- get_Quandl_series(id_in = my_id, 
                                  api_key = my_api,
                                  first_date = first_date,
                                  last_date = last_date)

glimpse(df_inflation)
## Observations: 897
## Variables: 4
## $ series_name <chr> "Inflation YOY - Argentina", "Inflation YOY - Argent…
## $ ref_date    <date> 2013-12-31, 2013-11-30, 2013-10-31, 2013-09-30, 201…
## $ value       <dbl> 10.95, 10.54, 10.55, 10.49, 10.55, 10.61, 10.46, 10.…
## $ id_quandl   <chr> "RATEINF/INFLATION_ARG", "RATEINF/INFLATION_ARG", "R…

And, finally, an elegant plot:

p <- ggplot(df_inflation, aes(x = ref_date, y = value/100)) + 
  geom_col() + 
  labs(y = 'Inflation (%)', 
       x = '',
       title = 'Inflation in the World',
       subtitle = paste0(first_date, ' to ', last_date)) + 
  scale_y_continuous(labels = scales::percent) + 
  facet_wrap(~series_name)

p