Mutating multiple columns in a data frame using dplyr

We can use base R instead of using any extra packages like dplyr or data.table

We can use mapply to vectorize the operation for multiple vectors at the same time

n <- ncol(df)/2
mapply(`*`, df[1:n], df[(n + 1):ncol(df)])

#     v1 v2
#[1,]  7 20
#[2,] 20 18

We can merge (cbind) this dataframe to your original one then.

---

If you are interested in tidyverse solution the equivalent in purrr would be variants of map2

purrr::map2_df(df[1:n], df[(n + 1):ncol(df)], `*`)

# A tibble: 2 x 2
#     v1    v2
#  <dbl> <dbl>
#1     7    20
#2    20    18

You are really close.

df2 <- 
    df %>% 
    mutate(v1v3 = v1 * v3,
           v2v4 = v2 * v4)

such a beautifully simple language, right?

For more great tricks please see here.

EDIT: Thanks to @Facottons pointer to this answer: https://stackoverflow.com/a/34377242/5088194, here is a tidy approach to resolving this issue. It keeps one from having to write a line to hard code in each new column desired. While it is a bit more verbose than the Base R approach, the logic is at least more immediately transparent/readable. It is also worth noting that there must be at least half as many rows as there are columns for this approach to work.

# prep the product column names (also acting as row numbers)
df <- 
    df %>%
    mutate(prod_grp = paste0("v", row_number(), "v", row_number() + 2)) 

# converting data to tidy format and pairing columns to be multiplied together.
tidy_df <- 
    df %>%
    gather(column, value, -prod_grp) %>% 
    mutate(column = as.numeric(sub("v", "", column)),
           pair = column - 2) %>% 
    mutate(pair = if_else(pair < 1, pair + 2, pair))

# summarize the products for each column
prod_df <- 
    tidy_df %>% 
    group_by(prod_grp, pair) %>% 
    summarize(val = prod(value)) %>% 
    spread(prod_grp, val) %>% 
    mutate(pair = paste0("v", pair, "v", pair + 2)) %>% 
    rename(prod_grp = pair)

# put the original frame and summary frames together
final_df <- 
    df %>% 
    left_join(prod_df) %>% 
    select(-prod_grp)

Just use mutate as is with a comma to separate new columns mutate(df,"v1v3"=v1*v3,"v2v4"= v2*v4)

I think I found a solution:

df %>%
  mutate(n = df[1:(ncol(df)/2)] * df[(1+ncol(df)/2):(ncol(df))]) %>% head()

The result is valid for any number of variables. It only remains a problem with the name of the new variables. This is the result:

  v1 v2 v3 v4 n.v1 n.v2
1  1  5  7  4    7   20
2  2  6 10  3   20   18