How to Count Unique rows in a data frame?

To the question

How to Count Unique rows in a data frame?

Then use sum and duplicated. E.g.,

df <- data.frame(
  `Fake Name` = c(
    "June", "June", "Television", "Television", "Television", "CRT"),
  `Fake ID` = c("0003", "0003", "202", "202", "202", "12"),
  `Fake Status` = c("Green", "Green", "Blue", "Green", "Green", "Red"),
  `Fake Program` = c("PR1", "PR1", "PR3", "PR3", "PR3", "PR0"), 
  check.names = FALSE)
df
#R    Fake Name Fake ID Fake Status Fake Program
#R 1       June    0003       Green          PR1
#R 2       June    0003       Green          PR1
#R 3 Television     202        Blue          PR3
#R 4 Television     202       Green          PR3
#R 5 Television     202       Green          PR3
#R 6        CRT      12         Red          PR0
sum(!duplicated(df))
#R [1] 4

For the table you request then you can use data.table as follows

library(data.table)
df <- data.table(df)
df[, .(COUNT = .N), by = names(df)]
#R     Fake Name Fake ID Fake Status Fake Program COUNT
#R 1:       June    0003       Green          PR1     2
#R 2: Television     202        Blue          PR3     1
#R 3: Television     202       Green          PR3     2
#R 4:        CRT      12         Red          PR0     1

Use group_by_all then count the number of rows with n:

df %>% group_by_all() %>% summarise(COUNT = n())

# A tibble: 4 x 5
# Groups:   Fake.Name, Fake.ID, Fake.Status [?]
#  Fake.Name  Fake.ID Fake.Status Fake.Program COUNT
#  <fct>        <int> <fct>       <fct>        <int>
#1 CRT             12 Red         PR0              1
#2 June             3 Green       PR1              2
#3 Television     202 Blue        PR3              1
#4 Television     202 Green       PR3              2

Or even better as from @Ryan's comment:

df %>% group_by_all %>% count

The following uses duplicated to get the result data.frame and then rle to get the counts.

res <- dat[!duplicated(dat), ]

d <- duplicated(dat) | duplicated(dat, fromLast = TRUE)
res$COUNT <- rle(d)$lengths

res
#   Fake Name Fake ID Fake Status Fake Program COUNT
#1       June    0003       Green          PR1     2
#3 Television     202        Blue          PR3     1
#4 Television     202       Green          PR3     2
#6        CRT      12         Red          PR0     1