Using GetQuandlData to study inflation rates around the world

Marcelo Perlin

2019-10-16

Quandl is one of the best platforms for finding and downloading financial and economic time series. The collection of free databases is comprehensive and I’ve used it intensively in my research and class material.

But, a couple of things from the native package Quandl always bothered me:

• Multiple data is always returned in the wide (column oriented) format (why??);
• No local caching of data;
• No control for importing error and status;

As you suspect, I decided to tackle the problem over the weekend. The result is package GetQuandlData. This is what it does differently:

• It uses the json api (and not the Quandl native function);
• The data always returns in the long format, even for multiple series;
• Users can set custom names for series. This is very useful when using along ggplot or making tables;
• Uses package memoise to set a local caching system. This means that the second time you ask for a particular time series, it will grab it from your hard drive (and not the internet);
• Always compares the requested dates against dates available in the platform.

# not in CRAN yet (need to test it further)
#install.packages('GetQuandlData')

# from github
devtools::install_github('msperlin/GetQuandlData')

library(GetQuandlData)
library(tidyverse)

my_id <- c('Inflation USA' = 'RATEINF/INFLATION_USA')
my_api <- readLines('YOURAPIHERE') # 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)

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

library(GetQuandlData)
library(tidyverse)

db_id <- 'RATEINF'
my_api <- readLines('YOURAPIHERE') # you need your own API

df <- get_database_info(db_id, my_api)

head(df)

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

df_series <- df[idx, ]

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,
                                  cache_folder = tempdir())

glimpse(df_inflation)

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