How do I generate a count in R within mutate()?

[Qomisa]

Good day, everyone

I'm new in using R and have learnt some useful tricks. I'm trying to get a count of number in column 'P_ID' and create a new column 'count' where I look at what number appears in P_ID and count same numbers in the same column and enter the retuned vaclue in the corresponding cell in 'count' column. I can do this in excel using the 'countif' function (e.g. =COUNTIF($A$2:$A$20,A2) ) and here are results:
P_ID Count
18 1
1 1
4 3
5 2
10 2
12 1
2 2
5 2
2 2
10 2
4 3
4 3
8 1
9 2
7 2
17 1
7 2
19 1
9 2
I've tried the following in R without getting the desired results:
library(dplyr)
dt<-tibble(P_ID=c(18,1,4,5,10,12,2,5,2,10,4,4,8,9,7,17,7,19,9))
dt%>%
mutate(count=for (x in 1:length(P_ID)) {sum(P_ID==P_ID)})%>%
View()
and only get:
P_ID
1 18
2 1
3 4
4 5
5 10
6 12
7 2
8 5
9 2
10 10
11 4
12 4
13 8
14 9
15 7
16 17
17 7
18 19
19 9
Showing 1 to 11 of 19 entries, 1 total columns

I'll appreciate it if somebody can advise me what to do and where I'm going wrong.
Thank you.

[mfherman]

Hi @Qomisa and welcome! Thanks for sharing your sample data and your expected output.

You were on the right track with using mutate() to create a new column, but there is actually a built in function in dplyr to count the number of rows per group -- it is called n(). In your example, you wanted to get the number of rows per P_ID, so you need to group by that variable and then create a new count variable.

library(dplyr)

df <- structure(list(P_ID = c(18, 1, 4, 5, 10, 12, 2, 5, 2, 10, 4, 
4, 8, 9, 7, 17, 7, 19, 9)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -19L))

df %>% 
  group_by(P_ID) %>% 
  mutate(count = n())
#> # A tibble: 19 x 2
#> # Groups:   P_ID [12]
#>     P_ID count
#>    <dbl> <int>
#>  1    18     1
#>  2     1     1
#>  3     4     3
#>  4     5     2
#>  5    10     2
#>  6    12     1
#>  7     2     2
#>  8     5     2
#>  9     2     2
#> 10    10     2
#> 11     4     3
#> 12     4     3
#> 13     8     1
#> 14     9     2
#> 15     7     2
#> 16    17     1
#> 17     7     2
#> 18    19     1
#> 19     9     2

^{Created on 2020-05-05 by the reprex package (v0.3.0)}

[mfherman]

If your question's been answered (even by you!), would you mind choosing a solution? It helps other people see which questions still need help, or find solutions if they have similar problems. Here’s how to do it:

FAQ: How do I mark a solution?

If your question has been answered, don't forget to mark the solution! How do I mark a solution? Find the reply you want to mark as the solution and look for the row of small gray icons at the bottom of that reply. Click the one that looks like a box with a checkmark in it: [image] Hovering over the mark solution button shows the label, "Select if this reply solves the problem". If you don't see the mark solution button, try clicking the three dots button ( ••• ) to expand the full set of options. When a solution is chosen, the icon turns green and the hover label changes to: "Unselect if this reply no longer solves the problem". Success! [solution_reply_author] :information_s…

[siddharthprabhu]

Just to add on to mfherman's excellent answer, this is such a common operation that dplyr has a dedicated verb for this task. add_count() is essentially shorthand for group_by() the variables passed to it, add a group-wise count of observations in a new column named n and and then ungroup(). You can control the new column's name with the name parameter.

So df %>% add_count(P_ID, name = "count") will get you the same result.

[Qomisa]

Hi Matt,

Thank you very much - I'm sorted.

Regards,
Mbuso

[Qomisa]

Hi Siddharth,

I appreciate it.

Regards,
Mbuso