library(tidyr)
library(dplyr)
library(tidyverse)
Below is the code for a simple dataframe. I have some messy data that was exported with column fac
We can use unite
library(tidyverse)
DF %>%
unite(Sat, matches("^Sat"))
For multiple cases, perhaps
gather(DF, Var, Val, -Client, na.rm = TRUE) %>%
separate(Var, into = c("Var1", "Var2")) %>%
group_by(Client, Var1) %>%
summarise(Val = paste(Val[!(is.na(Val)|Val=="")], collapse="_")) %>%
spread(Var1, Val)
# Client CommunicationType Satisfaction Sex
#* <chr> <chr> <chr> <chr>
#1 Client1 Email Satisfied Male
#2 Client2 Phone VerySatisfied Female
#3 Client3 Phone VerySatisfied Male
#4 Client4 Email Satisfied Female
#5 Client5 Email Satisfied Male
Something like this? If you have loads of columns.
result<-with(new.env(),{
Client<-c("Client1","Client2","Client3","Client4","Client5")
Sex_M<-c("Male","NA","Male","NA","Male")
Sex_F<-c(" ","Female"," ","Female"," ")
Satisfaction_Satisfied<-c("Satisfied"," "," ","Satisfied","Satisfied")
Satisfaction_VerySatisfied<-c(" ","VerySatisfied","VerySatisfied"," "," ")
CommunicationType_Email<-c("Email"," "," ","Email","Email")
CommunicationType_Phone<-c(" ","Phone ","Phone "," "," ")
x<-ls()
categories<-unique(sub("(.*)_(.*)", "\\1", x))
df<-setNames(data.frame( lapply(x, function(y) get(y))), x)
for(nm in categories){
df<-unite_(df, nm, x[contains(vars = x, match = nm)])
}
return(df)
})
Client CommunicationType Satisfaction Sex
1 Client1 Email_ Satisfied_ _Male
2 Client2 _Phone _VerySatisfied Female_NA
3 Client3 _Phone _VerySatisfied _Male
4 Client4 Email_ Satisfied_ Female_NA
5 Client5 Email_ Satisfied_ _Male