Hi David,
Hi Petr,
many thanks for your help. With your hints I got the idea how I could do
it and I came up with this solution:
-- cut --
#-------------------------------------------------------------------------------
# Module : t_merge_variables.R
# Author : Georg Maubach
# Date : 2016-06-06
# Update : 2016-06-06
# Description : Merge two variables
# Source System : R 3.2.5 (64 Bit)
# Target System : R 3.2.5 (64 Bit)
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#--------1---------2---------3---------4---------5---------6---------7---------8
t_module_name = "t_merge_variables.R"
t_version = "2016-06-06"
cat(
paste0("\n",
t_module_name, " (Version: ", t_version, ")", "\n", "\n",
"This software comes with ABSOLUTELY NO WARRANTY.",
"\n", "\n"))
# If do_test is not defined globally define it here locally by
un-commenting it
# Switch t_do_test to TRUE to run test
t_do_test <- FALSE
# [ Function Defintion
]--------------------------------------------------------
t_merge_variables <-
function(dataset,
var1,
var2,
merged_var) {
# Merges two variables with identical, different or missing values
#
# Args:
# dataset (data frame, data table):
# Object with dimnames, e.g. data frame, data table.
# var1 (character):
# Variable 1 to be merged.
# var2 (character):
# Variable 2 to be merged.
# merged_var (class based on input variable, coercion done if
possible):
# Variable with the merged variables var1 and var2.
#
# Operation:
# Var1 and var2 are merged like follows:
# if var1 == var2: merged_var <- var1
# if var1 != var2: merged_var <- -900 (-900 = indicating mismatch)
# if var1 is filled & var2 is missing: merged_var <- var1
# if var1 is missing & var2 is filled: merged_var <- var2
# if var1 is missing & var2 is filled: merged_var <- -999
# (-999 = indicating NA)
#
# Returns:
# Original dataset and variable given in "merged_var" will be added.
#
# Error handling:
# None.
#
# Credits:
# https://www.mail-archive.com/r-help@r-project.org/msg236012.html
# Initialize
dataset[merged_var] = rep(NA, nrow(dataset))
dataset[merged_var] <-
# Check 1: var1 missing, var2 missing
ifelse(is.na(dataset[, var1]) & is.na(dataset[, var2]),
# then
dataset[[merged_var]] <- 0,
# Check 2: var1 filled, var2 missing
ifelse(!is.na(dataset[, var1]) & is.na(dataset[, var2]),
# then
dataset[[merged_var]] <- dataset[, var1],
# Check 3: var1 missing, var2 filled
ifelse(is.na(dataset[ , var1]) & !is.na(dataset[, var2]),
# then
dataset[[merged_var]] <- dataset[ , var2],
# Check 4: var1 == var2
ifelse(dataset[, var1] == dataset[, var2],
# then: use var1
dataset[[merged_var]] <- dataset[, var1],
#Leftover: var1 != var2
dataset[merged_var] <- 1))))
return(dataset)
}
# [ Test Defintion
]------------------------------------------------------------
t_test <- function(do_test = FALSE) {
if (do_test == TRUE) {
cat("\n", "\n", "Test function t_count_na()", "\n", "\n")
# Example dataset
customer.x <- c("Miller", "Smith", NA, "Bird", NA)
customer.y <- c("Miller", NA, "Doe", "Fish", NA)
ds_test <-
data.frame(customer.x, customer.y, stringsAsFactors = FALSE)
# Call function
ds_merge <- t_merge_variables(
dataset = ds_test,
var1 = "customer.x",
var2 = "customer.y",
merged_var = "customer"
)
# Dataset after function call
ds_merge
}
}
# [ Test Run
]------------------------------------------------------------------
t_test(do_test = t_do_test)
# [ Clean up
]------------------------------------------------------------------
rm("t_do_test", "t_module_name", "t_version", "t_test")
# EOF
-- cut --
It delivers the customer name if there is one or they match. If they don't
match it delivers 1. If both are missing it delivers 0.
This solution is for my applications sufficient.
Many thanks again for your help and giving me the ideas to solve my data
transformation task.
Kind regards
Georg
Von: PIKAL Petr <petr.pi...@precheza.cz>
An: "g.maub...@weinwolf.de" <g.maub...@weinwolf.de>,
"r-help@r-project.org" <r-help@r-project.org>,
Datum: 06.06.2016 15:04
Betreff: RE: [R] Merging variables
Hi
Not sure if this is the most effective or general solution but
Here you get 2 if the value is same in both columns, 1 if it is only in
one column and the other is NA and 0 if there is mismatch of values.
temp <- (ds_test[,2] %in% ds_test[,1])+(ds_test[,1] %in% ds_test[,2])
here you get 0 if the value is same or if there is mismatch, 1 if NA is in
first column, 2 if it is in second and 3 if in both.
temp2 <- (is.na(ds_test[,2])+2*is.na(ds_test[,1]))
and with combination you get 1 if you want value from first column, 2 if
from second, 4 if they are both NA, and -1 if there is mismatch.
temp2 + temp - 1
You could then construct ifelse command to select proper value.
Regards
Petr
> ds_test
customer.x customer.y
1 Miller Miller
2 Smith <NA>
3 <NA> Doe
4 Bird Fish
5 <NA> <NA>
> ds_test+temp
Error in FUN(left, right) : non-numeric argument to binary operator
> (is.na(ds_test[,1])+2*is.na(ds_test[,2]))+temp
[1] 2 3 2 0 5
> (is.na(ds_test[,1])+2*is.na(ds_test[,2]))+temp-2
[1] 0 1 0 -2 3
> (is.na(ds_test[,1])+2*is.na(ds_test[,2]))+temp-1
[1] 1 2 1 -1 4
> is.na(ds_test[,2])+2*is.na(ds_test[,1])
[1] 0 1 2 0 3
> (is.na(ds_test[,2])+2*is.na(ds_test[,1]))+temp-1
[1] 1 1 2 -1 4
> (is.na(ds_test[,2])+2*is.na(ds_test[,1]))+temp-1
> -----Original Message-----
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of
> g.maub...@weinwolf.de
> Sent: Monday, June 6, 2016 2:30 PM
> To: r-help@r-project.org
> Subject: [R] Merging variables
>
> Hi All,
>
> I merged two datasets:
>
> ds_merge1 <- merge(x = ds_bw_customer_4_match, y =
> ds_zww_customer_4_match,
> by.x = "customer", by.y = "customer",
> all.x = TRUE, all.y = FALSE)
>
> R created a new dataset with the variables customer.x and customer.y. I
> would like to merge these two variable back together. I wrote a little
function
> (code can be run) for it:
>
> -- cut --
>
> customer.x <- c("Miller", "Smith", NA, "Bird", NA)
> customer.y <- c("Miller", NA, "Doe", "Fish", NA)
> ds_test <- data.frame(customer.x, customer.y, stringsAsFactors = FALSE)
>
> t_merge_variables <-
> function(dataset,
> var1,
> var2,
> merged_var) {
>
> # Initialize
> dataset[[merged_var]] = rep(NA, nrow(dataset))
> dataset[["mismatch"]] = rep(NA, nrow(dataset))
>
> for (i in 1:nrow(dataset)) {
>
> # Check 1: var1 missing, var2 missing
> if (is.na(dataset[[i, var1]]) &
> is.na(dataset[[i, var2]])) {
> dataset[["mismatch"]] <- 1 # var1 & var2 are missing
>
> # Check 2: var1 filled, var2 missing
> } else if (!is.na(dataset[[i, var1]]) &
> is.na(dataset[[i, var2]])) {
> dataset[[i, merged_var]] <- dataset[[i, var1]]
> dataset[["mismatch"]] <- 0
>
> # Check 3: var1 missing, var2 filled
> } else if (is.na(dataset[[i, var1]]) &
> !is.na(dataset[i, var2])) {
> dataset[[i, merged_var]] <- dataset[[i, var2]]
> dataset[["mismatch"]] <- 0
>
> # Check 4: var1 == var2
> } else if (dataset[[i, var1]] == dataset[[i, var2]]) {
> dataset[[i, merged_var]] <- dataset[[i, var1]]
> dataset[["mismatch"]] <- 0
>
> # Leftover: var1 != var2
> } else {
> dataset[[i, merged_var]] <- NA
> dataset[["mismatch"]] <- 2 # var1 != var2
> } # end if
> } # end for
> return(dataset)
> }
>
> ds_var_merge1 <- t_merge_variables(dataset = ds_test,
> var1 = "customer.x",
> var2 = "customer.y",
> merged_var = "customer")
>
> ds_var_merge1
>
> -- cut --
>
> It is executed without error but delivers the wrong values in the
variable
> "mismatch". This variable is always 1 although it should be NA, 1 or 2
> respectively.
>
> Can you tell me why the variable is not correctly set?
>
> Kind regards
>
> Georg
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.
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