Dealing with grouped dataset in R - r

Question

I have a dataset like:
+----+-------+---------+----------+--+
| id | time | event | timediff | |
+----+-------+---------+----------+--+
| 1 | 15.00 | install | - | |
| 1 | 15.30 | sale | 00.30 | |
| 1 | 16.00 | sale | 00.30 | |
| 2 | 15.00 | sale | - | |
| 2 | 15.30 | sale | 0.30 | |
| 3 | 16.00 | install | - | |
| 4 | 15.00 | install | - | |
| 5 | 13.00 | install | - | |
| 5 | 14.00 | sale | 01.00 | |
+----+-------+---------+----------+--+
I want to clean this dataset:
I want to exclude the ids for which the first (and the next n..) events are sales but not installs.
I want to exclude the ids for which there is an install but no sales (those ids are indeed the unique ones)
Obtaining finally a result like:
+----+-------+---------+----------+
| id | time | event | timediff |
+----+-------+---------+----------+
| 1 | 15.00 | install | - |
| 1 | 15.30 | sale | 0.30 |
| 1 | 16.00 | sale | 0.30 |
| 5 | 13.00 | install | - |
| 5 | 14.00 | sale | 01.00 |
+----+-------+---------+----------+
How can I do that in R? is there any specific package for data manipulation or I can just use if formulas? Should I use tapply?

Answer 1

Based on the example, we can group by 'id' and filter the 'event' column that has first element as 'install' and 2nd as 'sale' to get the expected output.
df1 %>%
group_by(id) %>%
filter(first(event)=='install' & event[2L]=='sale')
id time event timediff
# (int) (dbl) (chr) (dbl)
#1 1 15.0 install NA
#2 1 15.3 sale 0.3
#3 1 16.0 sale 0.3
#4 5 13.0 install NA
#5 5 14.0 sale 1.0
Or if all the elements except first one should be 'sale', we create a logical variable ('ind') by comparing the first element as 'install' and the successive elements as 'sale' (using lead), then filter groups where all the 'ind' are TRUE. If needed, we can remove the 'ind' column using select.
df1 %>%
group_by(id) %>%
mutate(ind= first(event)=='install' & lead(event, default='sale')=='sale') %>%
filter(all(ind)) %>%
ungroup() %>%
select(-ind)
Or we can use data.table., grouped by 'id', if the number of rows is greater than 1 (.N >1), first element is 'install' (event[1L]=='install') and all the rest of the elements are 'sale', then we get the Subset of Data.table (.SD).
library(data.table)
setDT(df1)[, if(.N > 1 & event[1L]=='install' & all(event[2:.N]=='sale')) .SD, by = id]
# id time event timediff
#1: 1 15.0 install NA
#2: 1 15.3 sale 0.3
#3: 1 16.0 sale 0.3
#4: 5 13.0 install NA
#5: 5 14.0 sale 1.0