I am new to R. I am trying to delete prior rows based on conditions set by another column.

I have found solutions with dplyr and data.table which I believe to be close to what I am looking for, as they do the opposite.

Sample data:

```
Cust_ID | Date                 | Value
500219  | 2016-04-11 12:00:00  | 0
500219  | 2016-04-12 16:00:00  | A
500219  | 2016-04-14 11:00:00  | A
500219  | 2016-04-15 12:00:00  | B
500219  | 2016-05-23 09:00:00  | B
500219  | 2016-05-02 19:00:00  | C
500220  | 2016-04-11 12:00:00  | C
500220  | 2016-04-14 11:00:00  | C
500220  | 2016-04-15 12:00:00  | A
500220  | 2016-05-23 09:00:00  | A
500220  | 2016-05-02 19:00:00  | A

```

For each Cust\_ID, I would like to retain only the rows after Value == "A" including that row. This should result in the following data frame:

```
Cust_ID | Date                 | Value
500219  | 2016-04-12 16:00:00  | A
500219  | 2016-04-14 11:00:00  | A
500219  | 2016-04-15 12:00:00  | B
500219  | 2016-05-23 09:00:00  | B
500219  | 2016-05-02 19:00:00  | C
500220  | 2016-04-15 12:00:00  | A
500220  | 2016-05-23 09:00:00  | A
500220  | 2016-05-02 19:00:00  | A

```

These are the solution I have already found ([R delete rows based on values in previous rows](https://stackoverflow.com/questions/38662478/r-delete-rows-based-on-values-in-previous-rows))

```
library(data.table)
setDT(df1)[df1[,  if(any(Value == "A")) .I[seq(max(which(Value == "A")))]
                                 else .I[1:.N] , by = Cust_ID]$V1]

library(dplyr)
df1 %>% 
     group_by(Cust_ID) %>% 
     slice(if(any(Value=="A")) seq(max(which(Value=="A"))) else row_number())

```