A Value Replacement Utility.
replacer
replacer, a value replacement utility currently based on package data.table, is intended for outside-database update of data. It requires the preparation of a lookup file which is a list of replacement requests with and, in special circumstances, without an index column.
This utility is accessible to beginners to R and facilitates complex dataset updates with minimal prompt time by employing User-friendly functions which automatically follow a decision tree rooted in User’s input.
Data processing such as data cleanup, file format conversion and the appendage of new data to data files are outside the scope.
Installation
You can install the released version of replacer from CRAN with:
install.packages("replacer")
Example
Below is a basic example which shows the screen result of data replacement using a lookup file without index:
require(replacer)
#> Loading required package: replacer
## basic example code
dir = system.file('extdata', package = 'replacer')
## update the 'data' dataset with new replacement values
replaceVals(dir, save = FALSE)
#>
#> reading data from: C:/R/R-4.1.2/library/replacer/extdata...
#> data reading complete ...
#> checking standard columns in lookup ...
#> starting replacements ...
#> found duplicates and simple replacements but no index in lookup :
#>
#> would recommend User-made index
#> proceeding any way ...
#> subsetting lookup and creating index ...
#> found request for 1:1 replacements: proceeding ...
#> completed 1:1 replacements ...
#> rejoining columns uninvolved in simple replacements ...
#> searching for 1:many replacements ...
#> found request for multiple duplicated value replacments: creating index ...
#> replacing multiple duplicated values ...
#> found request for multiple missing value replacements: replacing ...
#> processed 1:many replacements ...
#> there are still missing values in some involved columns!
#>
#> helper function has completed!
#> $` updated_data_using_lookup`
#> a b c d
#> 1: aa 7.174 0.259 11
#> 2: NOT PRESENT 17.572 0.478 5555
#> 3: DUP VALUE_1 8.888 0.707 NA
#> 4: dd 0.794 0.737 5555
#> 5: UU -13.964 999.000 5555
#> 6: DUP VALUE_1 0.127 0.737 8
#> 7: ff 8.836 0.476 5555
#> 8: NOT PRESENT 6.397 -0.001 8
#> 9: hh -4.979 0.121 9
#> 10: NOT PRESENT -2.755 -0.227 8
#> 11: EE 1.111 -0.127 6
#> 12: NOT PRESENT 1.121 0.333 7
#> 13: NOT PRESENT -4.979 0.737 5555
#> 14: NOT PRESENT 999.000 0.476 5555
#> 15: NOT PRESENT 1.111 999.000 8
#> 16: JJ 1.121 999.000 5555
#>
#> $` multiple_dups_repl_counts`
#> vars oldVals newVals a b d
#> 1: a cc DUP VALUE_1 2 NA NA
#> 2: b 2.142 1.111 NA 2 NA
#> 3: d 15 5555 NA NA 7
#>
#> $` NAs_remaining`
#> a b c d
#> 0 0 0 1
Familiarity with basic R commands is necessary. Familiarity with data.table is not, yet strongly recommended!
Users are encouraged to start by reading the vignette.