How to create conditionally new groups when summarizing group means in R - r

Question

I have data for which I want to summarize group means. I then would like to re-group some of the smaller groups (matching a certain n < x condition) into a group called "others". I found a way to do this. But it feels like there are more efficient solutions out there. I wonder how a data.table approach would solve the problem.
Here is an example using tibble and dyplr.
# preps
library(tibble)
library(dplyr)
set.seed(7)
# generate 4 groups with more observations
tbl_1 <- tibble(group = rep(sample(letters[1:4], 150, TRUE), each = 4),
score = sample(0:10, size = 600, replace = TRUE))
# generate 3 groups with less observations
tbl_2 <- tibble(group = rep(sample(letters[5:7], 50, TRUE), each = 3),
score = sample(0:10, size = 150, replace = TRUE))
# put them into one data frame
tbl <- rbind(tbl_1, tbl_2)
# aggregate the mean scores and count the observations for each group
tbl_agg1 <- tbl %>%
group_by(group) %>%
summarize(MeanScore = mean(score),
n = n())
So far so easy.
Next I want to only show groups with more than 100 observations. All other groups should be merged into one group called "others".
# First, calculate summary stats for groups less then n < 100
tbl_agg2 <- tbl_agg1 %>%
filter(n<100) %>%
summarize(MeanScore = weighted.mean(MeanScore, n),
sumN = sum(n))
Note: There was a mistake in the calculation above which is now corrected (#Frank: thanks for spotting it!)
# Second, delete groups less then n < 100 from the aggregate table and add a row containing the summary statistics calculated above instead
tbl_agg1 <- tbl_agg1 %>%
filter(n>100) %>%
add_row(group = "others", MeanScore = tbl_agg2[["MeanScore"]], n = tbl_agg2[["sumN"]])
tbl_agg1 basically shows what I want it to show, but I wonder if there is a smoother, more efficient way to do this. At the same time I wonder how a data.table approach would deal with the problem at hand.
I welcome any suggestions.

Answer 1

Your calculation for the "other" group is wrong, I guess... should be...
tbl_agg1 %>% {bind_rows(
filter(., n>100),
filter(., n<100) %>%
summarize(group = "other", MeanScore = weighted.mean(MeanScore, n), n = sum(n))
)}
However, you could keep things a lot simpler from the start by using a different grouping variable:
tbl %>%
group_by(group) %>%
group_by(g = replace(group, n() < 100, "other")) %>%
summarise(n = n(), m = mean(score))
# A tibble: 5 x 3
g n m
<chr> <int> <dbl>
1 a 136 4.79
2 b 188 4.49
3 c 160 5.32
4 d 116 4.78
5 other 150 5.42
Or with data.table
library(data.table)
DT = data.table(tbl)
DT[, n := .N, by=group]
DT[, .(.N, m = mean(score)), keyby=.(g = replace(group, n < 100, "other"))]
g N m
1: a 136 4.786765
2: b 188 4.489362
3: c 160 5.325000
4: d 116 4.784483
5: other 150 5.420000