Get frequency and first occurrence for each item of a column in a data frame-CodePudding

In R, I'm trying to get the frequency and the first occurence for each item of a column in a data frame.

I have this:

df_input <- data.frame(observation_source = c("AB","CD","EF","GH","IJ","KL","MN"), observation_value = c(15,17,17,17,21,32,32))

  observation_source observation_value
1                 AB                15
2                 CD                17
3                 EF                17
4                 GH                17
5                 IJ                21
6                 KL                32
7                 MN                32

And I'm trying to get this:

  observation_source observation_value value_frequency value_first_row
1                 AB                15               1               1
2                 CD                17               3               2
3                 EF                17               3               2
4                 GH                17               3               2
5                 IJ                21               1               5
6                 KL                32               2               6
7                 MN                32               2               6

Such that, on row 4 for example, the value 17 occurs 3 times in total and occurs on row 2 for the first time.

I know how to do this with a for loop, but it gets extremely slow as the number of row increases (e.g. 100,000). Any idea how else I can do that? Many thanks!!

CodePudding user response：

You could use add_count and match for the both goals:

library(dplyr)

df_input %>%
  add_count(observation_value, name = "value_frequency") %>%
  mutate(value_first_row = observation_value %>% match(., .))

#   observation_source observation_value value_frequency value_first_row
# 1                 AB                15               1               1
# 2                 CD                17               3               2
# 3                 EF                17               3               2
# 4                 GH                17               3               2
# 5                 IJ                21               1               5
# 6                 KL                32               2               6
# 7                 MN                32               2               6

CodePudding user response：

library(dplyr)
df_input %>%
  mutate(
    value_first_row = row_number()
  ) %>%
  group_by(observation_value) %>%
  mutate(
    value_frequency = n(),
    value_first_row = min(value_first_row)
  )
# # A tibble: 7 × 4
# # Groups:   observation_value [4]
#   observation_source observation_value value_first_row value_frequency
#   <chr>                          <dbl>           <int>           <int>
# 1 AB                                15               1               1
# 2 CD                                17               2               3
# 3 EF                                17               2               3
# 4 GH                                17               2               3
# 5 IJ                                21               5               1
# 6 KL                                32               6               2
# 7 MN                                32               6               2

You can adjust the column order too, if that's important, with ... %>% relocate(value_first_row, .after = last_col())

CodePudding user response：

Using base R

transform(df_input, value_frequency = ave(observation_value, 
    observation_value, FUN = length),
   value_first_row = ave(seq_along(observation_value), 
      observation_value, FUN = min))

-output

  observation_source observation_value value_frequency value_first_row
1                 AB                15               1               1
2                 CD                17               3               2
3                 EF                17               3               2
4                 GH                17               3               2
5                 IJ                21               1               5
6                 KL                32               2               6
7                 MN                32               2               6