如何在R中添加一个具有一个值和所有其他NA值的新行

Question

I have a vector of sample IDs that are required to be in my dataframe (otherwise the function I am applying to them doesn't work) but are missing (called missing ).

For each of the elements in the I want to add a row to the end of my dataframe for missing data, where I include the ID, but the rest of the data (for all the other columns) in the row is all NAs.

What I am currently trying, based on some other Stack Overflow posts I saw that talk only about adding empty rows, is as follows:

for (element in missing) {
    df[nrow(df)+1,] <- NA
    df[nrow(df),1] <- element
}

Is there a simpler and faster way to do this, since it takes some time for even 1000 missing elements, whereas I might later have to deal with a lot more.

Answer 1

Sample data:

samp <- data.frame(id = 1:10, val1 = 11:20, val2 = 21:30)
missing <- c(11, 13, 15)

1. Merge :

    merge(samp, data.frame(id = missing), by = "id", all = TRUE) # id val1 val2 # 1 1 11 21 # 2 2 12 22 # 3 3 13 23 # 4 4 14 24 # 5 5 15 25 # 6 6 16 26 # 7 7 17 27 # 8 8 18 28 # 9 9 19 29 # 10 10 20 30 # 11 11 NA NA # 12 13 NA NA # 13 15 NA NA

2. Row-bind with an external package:

    data.table::rbindlist(list(samp, data.frame(id = missing)), use.names = TRUE, fill = TRUE) dplyr::bind_rows(samp, data.frame(id = missing))

3. Row-bind with base R, a little more work:

    samp0 <- samp[rep(1, length(missing)),,drop = FALSE][NA,] samp0$id <- missing rownames(samp0) <- NULL rbind(samp, samp0)

Answer 2

1) Using the built-in anscombe data frame, this inserts two rows putting -1 and -3 in the x1 column.

library(janitor)
new <- c(-1, -3)
add_row(anscombe, x1 = new)

giving:

   x1 x2 x3 x4    y1   y2    y3    y4
1  10 10 10  8  8.04 9.14  7.46  6.58
2   8  8  8  8  6.95 8.14  6.77  5.76
3  13 13 13  8  7.58 8.74 12.74  7.71
4   9  9  9  8  8.81 8.77  7.11  8.84
5  11 11 11  8  8.33 9.26  7.81  8.47
6  14 14 14  8  9.96 8.10  8.84  7.04
7   6  6  6  8  7.24 6.13  6.08  5.25
8   4  4  4 19  4.26 3.10  5.39 12.50
9  12 12 12  8 10.84 9.13  8.15  5.56
10  7  7  7  8  4.82 7.26  6.42  7.91
11  5  5  5  8  5.68 4.74  5.73  6.89
12 -1 NA NA NA    NA   NA    NA    NA
13 -3 NA NA NA    NA   NA    NA    NA

2) Here is a base solution. new is from (1)

(If overwriting anscombe is ok, but typically this would make it harder to debug, then omit the first line and replace anscombe2 with anscombe .)

anscombe2 <- anscombe
anscombe2[nrow(anscombe2) + seq_along(new), "x1"] <- new

3) Using the tibble package (or dplyr which imports this) we can use rows_insert. new is from (1).

library(dplyr)
rows_insert(anscombe, tibble(x1 = new))