The R package seplyr supplies improved standard evaluation interfaces for some common dplyr data plying tasks.

To get started we suggest visiting the seplyr site, and checking out some examples.

One quick example:

# Assume this is set elsewhere,
# supplied by a user, function argument, or control file.
orderTerms <- c('cyl', 'desc(gear)')

library("seplyr")

# where we are actually working (perhaps in a re-usable
# script or function)
datasets::mtcars %.>%
arrange_se(., orderTerms) %.>%
#     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#  1 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#  2 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#  3 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#  4 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#  5 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#  6 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1

The concept is: in writing re-usable code or scripts you pretend you do not know the actual column names you will be asked to work with (that these will be supplied as values later at analysis time). This forces you to write scripts that can be used even if data changes, and are re-usable on new data you did not know about when writing the script.

To install this package please either install from CRAN with:

   install.packages('seplyr')

Please see help("%.>%", package="wrapr") for details on “dot pipe.”

In addition to standard interface adapters seplyr supplies some non-trivial statement transforms:

## Note

Note: seplyr is meant only for “tame names”, that is: variables and column names that are also valid simple (without quotes) R variables names.