Merging Two Data Frames with Numbers and Characters in the Same Column in R

In this article, we will delve into merging two data frames that contain numbers and characters in the same column using R. This is a common problem when working with datasets that have mixed data types.

Introduction

When working with datasets, it’s not uncommon to encounter columns that contain both numerical values and character strings. In such cases, merging these columns can be challenging. The question arises how to merge two data frames in R that have columns containing numbers and characters in the same column.

In this article, we’ll explore a solution using both traditional for loops and the tidyverse package. We’ll also discuss the implications of each approach on the resulting output.

Traditional Approach

The traditional approach involves using a for loop to iterate over each column in the data frames and merge them manually. Here’s an example code snippet that demonstrates this approach:

words1<-c(1,2,3,"How","did","Quebec")
words2<-c(.24,.25,.66,"Why","does","volicty")
words3<-c("How","do","I","clean","a","car")
library<-c(1,3,.25,.66,"How","did","does","do","I","wash","a","Quebec","car","is")
embedding1<-c(.48,.68,.52,.39,.5,.6,.7,.8,.9,.3,.46,.48,.53,.42)
df <- data.frame(words1,words2,words3)
names(df)<-c("words1","words2","words3")

words1<-c(.48,NA,.68,.5,.6,.48)
words2<-c(NA,.52,.39,NA,.7,NA)
words3<-c(.5,.8,.9,NA,.46,.53)
output<-data.frame(words1,words2,words3)

df2 <- data.frame(library,embedding1)
names(df2)<-c("library","embedding1")

l=ncol(df)
l
mynames<-colnames(df)
head(mynames)

List = list()
for(name in mynames){
  df1<-df[,name]
  df1<-as.data.frame(df1)
  x_train2<-merge(x= df1, y = df2, 
                  by.x = "df1", by.y = 'library',all.x=T, sort=F)
  new_x_train2<-x_train2[duplicated(x_train2[,2]),]
  x_train2<-x_train2[,-1]
  x_train2<-as.data.frame(x_train2)
  names(x_train2) <- name
  List[[length(List)+1]] = x_train2
}
list<-List

DF  <-  as.data.frame(matrix(unlist(list), nrow=length(unlist(list[1]))))

However, this traditional approach has several drawbacks. For one, it’s time-consuming and error-prone, especially when dealing with large datasets. Moreover, it may not preserve the order of the characters in the resulting output.

Using tidyverse

A more efficient and elegant solution is to use the tidyverse package. Here’s an example code snippet that demonstrates this approach:

library(tidyverse)
library(reshape2)

df %>% melt(id = NULL) %>% 
  inner_join(.,df2,  by = c("value" = "library")) %>% 
  spread(variable, embedding1) %>% 
  select(-value)

This approach leaves more NAs in the columns than the traditional for loop method but produces a more predictable and consistent output.

Conclusion

Merging two data frames that contain numbers and characters in the same column can be challenging. While traditional approaches using for loops may seem like an easy solution, they often result in less-than-ideal outputs. The tidyverse package provides a more efficient and elegant solution, but it may require some practice to master.

In conclusion, when working with datasets that contain mixed data types, it’s essential to consider the implications of each approach on the resulting output. By choosing the right method, you can ensure that your dataset is accurately merged and presented in a consistent manner.

Last modified on 2024-11-16