How to collect rows if they have several conditions in only one other column in r?

Pegi 0
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I want to collect members of one column in my data frame where it has all the values in another column.

The sample dataset looks like this:

df <- structure(list(ID=c('a','a','a','a','b','b','c','c','d','d','d','d','e','e','f','f','f','g','h','h','h','h','i','i','i','i'),
                     A1=c('g1','g1','g1','g1','g1','g1','g1','g1','g2','g2','g2','g2','g2','g2','g2', 'g2','g2','g3', 'g3','g3','g3','g3','g3','g3', 'g3','g3'),
                     number=c(1, 2,3,4,1,2,1,3,1,2,3,4,2,3,2,3,4,1,1,2,3,4,1,2,3,4)), 
                class = "data.frame", row.names = c(NA,-26L))

As a result I want to know which groups have exactly all of the given values in the column number.

df.result<- structure(list(ID=c('a','b','c','d','e','f','g','h','i'),
                     A1=c('g1','g1','g1','g2','g2','g2','g3','g3','g3'),
                     all.numbers=c('yes','no','no','yes','no','no','no','yes','yes')), 
                class = "data.frame", row.names = c(NA,-9L))

Thanks

case dplyr if-statement r
3 Answers

How about this? It produces your expected output, let me know if there are any more test cases.

df %>%
  group_by(ID, A1) %>%
  summarise(all_numbers = all(number == row_number()) & n() == 4, .groups = "drop")
dcsuka0

Using left_join

df %>% 
  left_join(., df.result, by = c("ID", "A1"))

edit

considering @dcsuka's comment

another way to get your df.result is using a condition when n rows are >= 4.

df %>% 
  group_by(ID, A1) %>% 
  tally()  %>% 
  mutate(n = case_when(n >= 4 ~ "yes",
                       T ~ "no")) %>% 
  rename(all.numbers = n)

## A tibble: 9 x 3
## Groups:   ID [9]
#ID    A1    all.numbers
#<chr> <chr> <chr>      
#  1 a     g1    yes        
#2 b     g1    no         
#3 c     g1    no         
#4 d     g2    yes        
#5 e     g2    no         
#6 f     g2    no         
#7 g     g3    no         
#8 h     g3    yes        
#9 i     g3    yes
cdcarrion0

Here is a base-R solution using with():

with(df, {
  numbers = unique(number)
  IDs = unique(ID)
  df_byID = split(df, ~ ID)
  # next line only works cause there is one unique 'A1' value for each 'ID'
  A1s = sapply(df_byID, function (x) unique(x[['A1']]))
  all.numbers = sapply(df_byID, function (x) all(numbers %in% x[['number']]))
  data.frame(ID = IDs, A1 = A1s, all.numbers = ifelse(all.numbers, 'yes', 'no'))
})

#  ID A1 all.numbers
#a  a g1         yes
#b  b g1          no
#c  c g1          no
#d  d g2         yes
#e  e g2          no
#f  f g2          no
#g  g g3          no
#h  h g3         yes
#i  i g3         yes
Coy0
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