He creado un ejemplo reproducible para ilustrar el problema que estoy teniendo con la evaluación no estándar en R (dplyr). Me gustaría usar nombres de variables dinámicas en el escenario a continuación:

# Given a data frame of patient data, I need to find records containing date logic errors.
# My datasets are enormous but here is a tiny example

patientData <- data.frame(
      patientID = 1:20,
      birth_d = seq(as.Date("2010-01-01"),by = 90, length.out = 20),
      treat_d = seq(as.Date("2011-01-01"),by = 90, length.out = 20),
      death_d = seq(as.Date("2012-01-01"),by = 90, length.out = 20)
 )

# To create some random records that will be in error (death_d before birth_d, birth_d after treat_d, etc):

patientData$birth_d[5] <- as.Date("2017-01-01")
patientData$death_d[7] <- as.Date("2001-01-01")
patientData$treat_d[10] <- as.Date("2018-01-01")
patientData$birth_d[12] <- as.Date("2018-05-05")

# To determine which records have birth_d after death_d I could do the following:

badRecords <- patientData %>% filter(death_d < birth_d)

OR

badRecords <- patientData %>% mutate(dateDiff = death_d - birth_d) %>% filter(dateDiff < 0)

# But in my large application (with lots and lots of date variables) 
# I want to be able to use the date field names as *variables* and, using one date pair at a time,
# determine which records have dates out of sequence. For example,

firstDateName <- "birth_d"
secondDateName <- "death_d"

# I would like to do this, but it doesn't work
badRecords <- patientData %>% filter(!!firstDateName > !!secondDateName)

# This doesn't work... 
badRecords <- patientData %>% mutate(dateDiff = !!secondDateName - !!firstDateName) %>% filter(dateDiff < 0)

# Neither does this... it creates a dateDiff data frame.. with 20 duplicate records
badRecords <- patientData %>% mutate(dateDiff = .[secondDateName] - .[firstDateName]) %>% filter(dateDiff < 0)

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