CODEWITHSUNDEEP
rdplyr
Aashay Mehta
Aashay Mehta

Reputation: 128

Creating an additional dataframe using two existing dataframe

Input Data Frame

Data Frame 1 (example - nrow = 100)

Col A | Col B | Col C
  a       1       2    
  a       3       4    
  b       5       6    
  c       9      10

Data frame 2 (example - nrow = 200)

Col A | Col B | Col E
  a       1       22    
  a       31      41    
  a       3       63    
  b       5       6
  b       11      13   
  c       9       20

I want to create a third data set which contains each of the additional row found in the Data Frame 2 for the Col A entry.

Output File (nrow = 200-100 = 100)

Col A | Col B | Col E
  a       31      41    
  b       11      13

Upvotes: 2

Views: 61

Answers (2)

akrun
akrun

Reputation: 887118

If we need it in a loop, create an empty dataset with the column names of the second dataset. Loop over the unique values of 'ColA' from second dataset, subset the 'df2', get the difference in number of rows between the subset and the corresponding row of 'df1' ('cnt'), rbind the 'out' with the tail of the subset of dataset

#// Create an empty dataset structure
out <- data.frame(ColA = character(), ColD = numeric(), ColE = numeric()) 
# // Get the unique values of the column 
un1 <- unique(df2$ColA)
# // Loop over the unique values
for(un in un1)  {
        # // subset the dataset df2
        tmp <- subset(df2, ColA == un)
        # // get a difference in row count
        cnt <- nrow(tmp) - sum(df1$ColA == un)
        # // use the count to subset the subset of df2
        # // rbind and assign back to the original out
        out <- rbind(out, tail(tmp, cnt))
}

row.names(out) <- NULL
out
#  ColA ColD ColE
#1    a   51   63
#2    b   11   13

For multiple columns, we could paste to create a single column

df1 <- data.frame(ColA = c('a', 'a', 'b', 'c'), ColB = c(1, 3, 5, 9), 
      ColC = c(2, 4, 6, 10))
df2 <- data.frame(ColA = c('a', 'a', 'a', 'b', 'b', 'c'), 
     ColB = c(1, 31, 3, 5, 11, 9), ColE = c(22, 41, 63, 6, 13, 20))

create the function

f1 <- function(data1, data2, by_cols) {
           # // Create an empty dataset structure
           
           # // Get the unique value by pasteing the by_cols
           data2$new <- do.call(paste, data2[by_cols])
           data1$new <- do.call(paste, data1[by_cols])
           out <- data2[0,]
           un1 <- unique(data2$new)
          

           # // Loop over the unique values
           for(un in un1) {
                # // subset the second dataset              
                tmp <- subset(data2, new == un)
              
                # // get the difference in row count
                cnt <- nrow(tmp) - sum(data1$new == un)
               
                # // use the count to subet the subset of data2
                # // rbind and assign back to the original out
                out <- rbind(out, tail(tmp, cnt))
              }
            out$new <- NULL
            row.names(out) <- NULL
            out
         }
f1(df1, df2, c("ColA", "ColB"))
# ColA ColB ColE
#1    a   31   41
#2    b   11   13

data

df1 <- structure(list(ColA = c("a", "a", "b", "c"), ColB = c(1, 3, 5, 
9), ColC = c(2, 4, 6, 10)), class = "data.frame", row.names = c(NA, 
-4L))

df2 <- structure(list(ColA = c("a", "a", "a", "b", "b", "c"), ColD = c(12, 
31, 51, 71, 11, 93), ColE = c(22, 41, 63, 86, 13, 20)), class = "data.frame",
row.names = c(NA, 
-6L))

Upvotes: 1

Ben
Ben

Reputation: 30474

You could add row numbers to each data frame, and then do an anti_join:

library(tidyverse)

df2 %>%
  group_by(colA) %>%
  mutate(rn = row_number()) %>%
  anti_join(df1 %>% group_by(colA) %>% mutate(rn = row_number())) %>%
  select(-rn)

Output

# A tibble: 2 x 3
# Groups:   colA [2]
  colA   colD  colE
  <chr> <dbl> <dbl>
1 a        51    63
2 b        11    13

Upvotes: 2

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