R Assign (or copy) column classes from a data frame to another

I have created a large data frame (1700 + obs, 159 variables) with a function that collects information from a website. Usually the function finds numeric values ​​for some columns, and therefore they are numeric. Sometimes, however, it finds some text and converts the entire column to text. I have one df whose column classes are correct and I would like to "insert" those classes into a new, incorrect df. Let's say for example:

dfCorrect<-data.frame(x=c(1,2,3,4),y=as.factor(c("a","b","c","d")),z=c("bar","foo","dat","dot"),stringsAsFactors = F)
str(dfCorrect)
'data.frame':   4 obs. of  3 variables:
 $ x: num  1 2 3 4
 $ y: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
 $ z: chr  "bar" "foo" "dat" "dot"

## now I have my "wrong" data frame:
dfWrong<-as.data.frame(sapply(dfCorrect,paste,sep=""))
str(dfWrong)
'data.frame':   4 obs. of  3 variables:
 $ x: Factor w/ 4 levels "1","2","3","4": 1 2 3 4
 $ y: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
 $ z: Factor w/ 4 levels "bar","dat","dot",..: 1 4 2 3

      

        
        
        
      

    

I wanted to copy the classes of each column dfCorrect

in dfWrong

but couldn't find how to do it correctly. I've tested:

dfWrong1<-dfWrong
dfWrong1[0,]<-dfCorrect[0,]
str(dfWrong1) ## bad result
'data.frame':   4 obs. of  3 variables:
 $ x: Factor w/ 4 levels "1","2","3","4": 1 2 3 4
 $ y: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
 $ z: Factor w/ 4 levels "bar","dat","dot",..: 1 4 2 3

dfWrong1<-dfWrong
str(dfWrong1)<-str(dfCorrect)
'data.frame':   4 obs. of  3 variables:
 $ x: num  1 2 3 4
 $ y: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
 $ z: chr  "bar" "foo" "dat" "dot"
Error in str(dfWrong1) <- str(dfCorrect) : 
  could not find function "str<-"

      

        
        
        
      

    

With this small matrix I could go freehand, but what about the larger ones? Is there a way to "copy" classes from one df to another without having to know the individual classes (and indexes) of each column?

Expected end result (after correct "glueing" of classes):

all.equal(sapply(dfCorrect,class),sapply(dfWrong,class))
[1] TRUE

      

        
        
        
      

    

Thank,