R - dplyr Summarize and Retain Other Columns - r

Question

I am grouping data and then summarizing it, but would also like to retain another column. I do not need to do any evaluations of that column's content as it will always be the same as the group_by column. I can add it to the group_by statement but that does not seem "right". I want to retain State.Full.Name after grouping by State. Thanks
TDAAtest <- data.frame(State=sample(state.abb,1000,replace=TRUE))
TDAAtest$State.Full.Name <- state.name[match(TDAAtest$State,state.abb)]
TDAA.states <- TDAAtest %>%
filter(!is.na(State)) %>%
group_by(State) %>%
summarize(n=n()) %>%
ungroup() %>%
arrange(State)

Answer 1

Perhaps we need
TDAAtest %>%
filter(!is.na(State)) %>%
group_by(State) %>%
summarise(State.Full.Name = first(State.Full.Name), n = n())
Or use mutate to create the column and then do the distinct
TDAAtest %>% f
filter(!is.na(State)) %>%
group_by(State) %>%
mutate(n= n()) %>%
distinct(State, .keep_all=TRUE)

Answer 2

To retain all columns, you can include across() as a summarize argument, as explained in the documentation for dplyr::do().
by_cyl <- head(mtcars) %>%
group_by(cyl)
by_cyl %>%
summarise(m_mpg = mean(mpg), across())
cyl m_mpg mpg disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 4 22.8 22.8 108 93 3.85 2.32 18.6 1 1 4 1
2 6 20.4 21 160 110 3.9 2.62 16.5 0 1 4 4
3 6 20.4 21 160 110 3.9 2.88 17.0 0 1 4 4
4 6 20.4 21.4 258 110 3.08 3.22 19.4 1 0 3 1
5 6 20.4 18.1 225 105 2.76 3.46 20.2 1 0 3 1
6 8 18.7 18.7 360 175 3.15 3.44 17.0 0 0 3 2
To retain only a subset of unaltered columns, you can select them within across using tidyselect semantics.

Answer 3

I believe there are more accurate answers than the accepted answer specially when you don't have unique data for other columns in each group (e.g. max or min or top n items based on one particular column
).
Although the accepted answer works for this question, for instance, you would like to find the county with the max population for each state. (You need to have county and population columns).
We have the following options:
1. dplyr version
From this link, you have three extra operations (mutate, ungroup and filter) to achieve that:
TDAAtest %>%
filter(!is.na(State)) %>%
group_by(State) %>%
mutate(maxPopulation = max(Population)) %>%
ungroup() %>%
filter(maxPopulation == Population)
2. Function version
This one gives you as much flexibility as you want and you can apply any kind of operation to each group:
maxFUN = function(x) {
# order population in a descending order
x = x[with(x, order(-Population)), ]
x[1, ]
}
TDAAtest %>%
filter(!is.na(State)) %>%
group_by(State) %>%
do(maxFUN(.))
This one is highly recommended for more complex operations. For instance, you can return top n (topN) counties per state by having x[1:topN] for the returned dataframe in maxFUN.