Add columns to a table of varied rows

Question

[radnay]

Hi, I am trying to find a way to add the columns to the existing table, where the number of rows in the "adding column" is different from "existing table." I was trying dplyr or should I look for something altogether different.

Thank you.

Good Day

[zx8754]

Please provide example input and expected output.

[radnay]

The example input

Gender_1  Age_1
1            23
1             35     
2             47  
2             58

The table where it should be inserted

LungCap Age Height  Smoke   Gender  Caesarean
6.475   6   62.1    no  male    no
10.125  18  74.7    yes female  no
9.55    16  69.7    no  female  yes
11.125  14  71  no  male    no
4.8 5   56.9    no  male    no
6.225   11  58.7    no  female  no
4.95    8   63.3    no  male    yes
7.325   11  70.4    no  male    no
8.875   15  70.5    no  male    no

I want to add the input column to the side of existing table and number of rows differs between and I don`t want the data to be recycled

[zx8754]

Looks like a job for merge, what is the expected output for this example data?

[radnay]

LungCap Age Height  Smoke   Gender  Caesarean Gender_1  Age_1
6.475   6   62.1    no  male    no 1 23
10.125  18  74.7    yes female  no 1 35
9.55    16  69.7    no  female  yes 2 47
11.125  14  71  no  male    no 2 58
4.8 5   56.9    no  male    no
6.225   11  58.7    no  female  no
4.95    8   63.3    no  male    yes
7.325   11  70.4    no  male    no
8.875   15  70.5    no  male    no

This is what I want.

Answer 1

[zx8754]

Add id column then use merge:

df1$id <- seq(nrow(df1)) 
df2$id <- seq(nrow(df2)) 

merge(df1, df2, by = "id", all.x = TRUE)
#   id LungCap Age Height Smoke Gender Caesarean Gender_1 Age_1
# 1  1   6.475   6   62.1    no   male        no        1    23
# 2  2  10.125  18   74.7   yes female        no        1    35
# 3  3   9.550  16   69.7    no female       yes        2    47
# 4  4  11.125  14   71.0    no   male        no        2    58
# 5  5   4.800   5   56.9    no   male        no       NA    NA
# 6  6   6.225  11   58.7    no female        no       NA    NA
# 7  7   4.950   8   63.3    no   male       yes       NA    NA
# 8  8   7.325  11   70.4    no   male        no       NA    NA
# 9  9   8.875  15   70.5    no   male        no       NA    NA

Or Using custom function from StackOverflow post - Column bind with fill:

cbind.fill <- function(...){
  nm <- list(...) 
  nm <- lapply(nm, as.matrix)
  n <- max(sapply(nm, nrow)) 
  do.call(cbind, lapply(nm, function (x) 
    rbind(x, matrix(, n-nrow(x), ncol(x))))) 
}

result <- data.frame(cbind.fill(df1, df2))
result
#   LungCap Age Height Smoke Gender Caesarean Gender_1 Age_1
# 1   6.475   6   62.1    no   male        no        1    23
# 2  10.125  18   74.7   yes female        no        1    35
# 3   9.550  16   69.7    no female       yes        2    47
# 4  11.125  14   71.0    no   male        no        2    58
# 5   4.800   5   56.9    no   male        no     <NA>  <NA>
# 6   6.225  11   58.7    no female        no     <NA>  <NA>
# 7   4.950   8   63.3    no   male       yes     <NA>  <NA>
# 8   7.325  11   70.4    no   male        no     <NA>  <NA>
# 9   8.875  15   70.5    no   male        no     <NA>  <NA>

Example data input:

df1 <- read.table(text = "LungCap Age Height  Smoke   Gender  Caesarean
6.475   6   62.1    no  male    no
10.125  18  74.7    yes female  no
9.55    16  69.7    no  female  yes
11.125  14  71  no  male    no
4.8 5   56.9    no  male    no
6.225   11  58.7    no  female  no
4.95    8   63.3    no  male    yes
7.325   11  70.4    no  male    no
8.875   15  70.5    no  male    no", header = TRUE)

df2 <- read.table(text = "Gender_1  Age_1
1            23
1             35     
2             47  
2             58", header = TRUE)

[radnay]

Thanks a lot. My problem was solved.

Good Day

[GenoMax]

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