[R] challenging data merging/joining problem

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Sun Jul 5 23:52:42 CEST 2020

*Just my opinion* : --> feel free to disregard

I would suggest that you stop thinking in terms of tidyverse functionality
and instead think of what kind of data structure you need for your ongoing
work and where you will source data to populate that structure both now --
including legacy data -- and in future. *Then* you can decide what
functionality you need and whether/how tidyverse functionality meets those
needs. It sounds like you are tying yourself in knots by restricting
yourself to what you know of one limited paradigm. R has the richness and
flexibility to create general purpose data structures (e.g. via lists) --
tidyverse functionality may or may not be sufficient or convenient for your
needs **once you have fully defined them (which only you can do).**

Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Sun, Jul 5, 2020 at 11:51 AM Christopher W. Ryan <cryan using binghamton.edu>

> I've been conducting relatively simple COVID-19 surveillance for our
> jurisdiction. We get data on lab test results automatically, and then
> interview patients to obtain other information, like clinical details.
> We had been recording all data in our long-time data system (call it
> dataSystemA). But as of a particular date, there was a major change in
> the data system we were compelled to use. Call the new one dataSystemB.
> dataSystemA and dataSystemB contain very similar information,
> conceptually, but the variable names are all different, and there are
> some variables in one that do not appear in the other. Total number of
> variables in each is about 50-70.
> Furthermore, for about 2 weeks prior to the transition, lab test results
> started being deposited into dataSystemB while dataSystemA was still
> being used to record the full information from the interviews.
> Subsequent to the transition, lab test results and interview information
> are being recorded in dataSystemB, while the lab test results alone are
> still being automatically deposited into dataSystemA.
> Diagrammatically:
> dataSystemA usage: ____________________ ............>>
> dataSystemB usage:               ......._____________>>
> where ________ represents full data and ..... represents partial data,
> and >> represents the progress of time.
> The following will create MWE of the data wrangling problem, with the
> change in data systems made to occur overnight on 2020-07-07:
> library(dplyr)
> dataSystemA <- tibble(lastName = c("POTTER", "WEASLEY", "GRAINGER",
>                       firstName = c("harry", "ron", "hermione", "neville"),
>                       dob = as.Date(Sys.Date() + c(sample(-3650:-3000,
> size = 2), -3500, -3450)),
>                       onsetDate = as.Date(Sys.Date() + 1:4),
>                       symptomatic = c(TRUE, FALSE, NA, NA) )
> dataSystemB <- tibble(last_name = c("GRAINGER", "LONGBOTTOM", "MALFOY",
>                       first_name = c("hermione", "neville", "draco",
> "luna", "cedric"),
>                       birthdate = as.Date(Sys.Date() + c(-3500, -3450,
> sample(-3650:-3000, size = 3))),
>                       date_of_onset = as.Date(Sys.Date() + 3:7),
>                       symptoms_present = c(TRUE, TRUE, FALSE, FALSE, TRUE))
> Obviously, this is all the same public health problem, so I don't want a
> big uninterpretable gap in my reports. I am looking for advice on the
> best strategy for combining two different tibbles with some overlap in
> observations (some patients appear in both data systems, with varying
> degrees of completeness of data) and with some of the same things being
> mesaured and recorded in the two data systems, but with different
> variable names.
> I've thought of two different strategies, neither of which seems ideal
> but either of which might work:
> 1. change the variable names in dataSystemB to match their
> conceptually-identical variables in dataSystemA, and then use some
> version of bind_rows()
> 2. Create a unique identifier from last names, first names, and dates of
> birth, use some type of full_join(), matching on that identifier,
> obtaining all columns from both tibbles, and then "collapse"
> conceptually-identical variables like onsetDate and date_of_onset using
> coalesce()
> Sorry for my long-windedness. Grateful for any advice.
> --Chris Ryan
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