Package refreshr

What is refreshr?

refreshr allows you to create dataframes/tables that are refreshable. That means they have information about their (online) data source baked into them (as attributes) and can be updated from that source using a simple call of the refresh() function. The dataframe can then be shared with coworkers (e.g. as an RData file) and the recipient does not need to care about how he can update the data. If he wants the data updated from the original source refreshr will do the job for him.

How to make a dataframe/table refreshable?

The function make_refreshable() converts a conventional dataframe/table into a refreshable dataframe/table. This is done by specifying a load_code that is essentially the code you would call to download the data from the original data source.

Sometimes, you want to process the raw data that is downloaded from the remote data source. This can be achieved using the prep_code argument of the make_refreshable(). prep_code stores a code that is automatically applied to the raw data from the the data source after the data has been refreshed.

Let us take as an example US labor market data provided by the U.S. Bureau of Labor Statistics (BLS). We want to download this data from BLS’ public website and filter it for the overall unemployment rate (data series LNS14000000); the overall dataset contains many more data series beyond the overall unemployment rate.

First, we load the data:

library(refreshr)
library(data.table)
library(dplyr)

## 
## Attache Paket: 'dplyr'

## Die folgenden Objekte sind maskiert von 'package:data.table':
## 
##     between, first, last

## Die folgenden Objekte sind maskiert von 'package:stats':
## 
##     filter, lag

## Die folgenden Objekte sind maskiert von 'package:base':
## 
##     intersect, setdiff, setequal, union

data <- fread("https://download.bls.gov/pub/time.series/ln/ln.data.1.AllData", sep="\t")
data <- filter(data, series_id=="LNS14000000")

Then we make data refreshable:

data_refresh <- make_refreshable(data,
                    load_code = "fread(\"https://download.bls.gov/pub/time.series/ln/ln.data.1.AllData\", 
                              sep=\"\t\")",
          prep_code = "filter(#, series_id==\"LNS14000000\")")

The # in the data preparation code prep_code is not an R comment but a reference to the refreshable dataframe.

We have now a refreshable dataframe:

class(data_refresh)

## [1] "refreshr"   "data.table" "data.frame"

is.refreshr(data_refresh)

## [1] TRUE

We could now save our dataframe, e.g. with

save(data_refresh, file = "refresh.RData")

and share it with other people.

If we want to refresh the data we just need to call

data_refresh <- refresh(data_refresh)

## Origina data set had 901 rows, updated dataset has 901.

The function uptodate() confirms, that the data in our dataframe is up-to-date:

uptodate(data_refresh)

## [1] TRUE

If we have a look at the properties of the refreshable dataframe

properties(data_refresh)

## Last refresh: 2023-02-19 13:28:15
## Data source: https://download.bls.gov/pub/time.series/ln/ln.data.1.AllData
## Structure: 901 rows | 5 columns
## Load code: fread("https://download.bls.gov/pub/time.series/ln/ln.data.1.AllData", 
##                            sep=" ")
## Preparation code: filter(#, series_id=="LNS14000000")

we see that confirmed by the date/timestamp of the last update.