Creating a proper table from the table function, when using a continuous variable

I have data as follows:

df <- structure(list(ID = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 
2, 2), year = c(2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 
2003, 2001, 2002, 2003, 2001, 2002, 2003), Type = c("A", "A", 
"A", "B", "B", "B", "A", "A", "A", "B", "B", "B", "C", "C", "C"
), Subtype = c(2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2), 
    Value = c(0.480513615083894, 0.909788893002047, 0.685141970365005, 
    0.138835747632889, 0.899508237239289, 0.535632890739584, 
    0.0712054637209442, 0.655905506366812, 0.694753916517691, 
    0.469249523993816, 0.295044859429007, 0.209906890342936, 
    0.193574644156237, 0.0715219759792846, 0.626529278499682)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -15L))

df <- setDT(df)[,mVal:=mean(Value), by=Type]
table(df$mVal, df$Type)

                    A B C
  0.297208632878401 0 0 3
  0.424696358229587 0 6 0
  0.582884894176066 6 0 0

I really like the information that this table provides, so I was wondering whether there is an easy way to convert this table into a more proper format as below:

Desired output:

  mVal              N Type
  0.297208632878401 3 C
  0.424696358229587 6 B
  0.582884894176066 6 A

>Solution :

We can convert to data.frame directly from table object to return a long data.frame and subset where Freq is not 0

out <- subset(as.data.frame(table(df$mVal, df$Type)), Freq != 0)
names(out) <- c("mVal", "Type", "N")

-output

> out
               mVal Type N
3 0.582884894176065    A 6
5 0.424696358229587    B 6
7 0.297208632878401    C 3

As the object is a tibble, we could also use tidyverse solutions to directly get the long format instead of doing the table and then reshaping

library(dplyr)
df %>% 
  count(mVal, Type, name = "N")
        mVal   Type     N
       <num> <char> <int>
1: 0.2972086      C     3
2: 0.4246964      B     6
3: 0.5828849      A     6