Predominant calculation for Character fields - r

Question

I'm trying to loop through my column names where type = character and return one Data frame which contains all the predominant values of each character column, grouped by an ID field.
Is there a way to replicate the following code in some kind of loop?:
DF_Characters <- DF_Characters[,sapply(dfr,is.character)]
##Predominance Column1##
Predom <- select(DF_Characters, Group_ID, Column_1)
Predom <- group_by(Predom,Group_ID, Column_1)
Predom <- summarise(Predom,
CountPredom = n()
)
Predom <- arrange(Predom,Group_ID, desc(CountPredom) )
Predom <- data.table(Predom, key="Group_ID")
Predominant_Column_1 <- Predom[,head(.SD,1),by=Group_ID]
##Predominant Column_2##
Predom <- select(DF_Characters, Group_ID, Column_2)
Predom <- group_by(Predom,Group_ID, Column_2)
Predom <- summarise(Predom,
CountPredom = n()
)
Predom <- arrange(Predom,Group_ID, desc(CountPredom) )
Predom <- data.table(Predom, key="Group_ID")
Predominant_Column_2 <- Predom[,head(.SD,1),by=Group_ID]
##Merge final table##
Merged <- merge(Predominant_Column_1 ,Predominant_Column_2 ,by="Group_ID")
Also to clarify my question I added a dummy table:
DF_Character_table
Result shoul look like this
Result Table
So for Group 1 Petre was the predominant name in Column 1 and Car was the predominant mode of travel. Column 1 and Column 2 predominance should be calculated respectively.
Thank you

Answer 1

This is probably not the best solution but it works.
##########Predominant Calculations
#Character fields
DF_Characters <- as.data.frame(dfr)
DF_Characters <- DF_Characters[,sapply(dfr,is.character)]
# Field names without the Group by id
CharactersToMerge <- c(names(DF_Characters))
#Add Groupby ID to Character fields
Character_Field_List <- c("Groupby_ID", names(DF_Characters))
DF_Characters <- subset(dfr,select = Character_Field_List)
#Column Names to loop through
DF_FieldsToMerge <- subset(dfr,select = CharactersToMerge)
# Predominant Table
fin_table <- DF_Characters %>% group_by(Groupby_ID) %>%
tally(sort = TRUE) #Count observations
# Loop and merge tables to Predominant Table
for(i in names(DF_FieldsToMerge)){
temp_table <- DF_Characters %>% group_by_("Groupby_ID", i ) %>%
tally(sort = TRUE)
temp_table <- temp_table[,head(.SD,1),by=Groupby_ID] #Remove ties
temp_table <- subset(temp_table,select = c("Groupby_ID", i)) #remove counts
fin_table <- merge(fin_table, temp_table, by="Groupby_ID")
}