Subsetting rows in R - r

Question

I have a huge data set in the following format:
ID Interaction Interaction_number
1 abc 1
1 xyz 2
1 pqr 3
1 ced 0
2 ab 0
2 efg 1
3 asdf 2
3 fgh 3
3 abc 0
4 sql 1
4 ghj 2
5 poi 2
6 pqr 1
Now I want to extract all the ID data where there is interaction_number as 0. for eg:
ID Interaction Interaction_number
1 abc 1
1 xyz 2
1 pqr 3
1 ced 0
2 ab 0
2 efg 1
3 asdf 2
3 fgh 3
3 abc 0
Its a huge dataset. I need to extract it using R.
I tried using the sqldf function.
x<-sqldf("select * from data where data$ID in (select data$ID from data where data$Interaction_number ==0)")
But the function didnt work. I was looking to add a flagging column ( 1 for all IDs where there is interaction_number 0) and then subset those rows. But I cant figure out exactly how to do.
Can we create the data frame of the ID's and then using that data frame, we can use subset to get all the rows?
Please help.
Thank You

Answer 1

I suggest using data.table package. Then you could obtain your result. Say your data is in data.frame df. Then
library(data.table)
dt <- data.table(df, key = 'ID')
tmp <- dt[, list(condition = any(Interaction_number == 0)), by = ID]
res <- dt[tmp[condition == TRUE, list(ID)]]

Answer 2

Use this
sqldf("SELECT * FROM data WHERE ID IN (SELECT ID FROM data WHERE Interaction_number=0)")
You do not need the double equal in your test, and do not use data$ID and such to refer to the data columns in the SQL expression (you can use data.ID but it is unnecessary to use the dataframe name in this case).
It may be helpful to read up on SQL before using this function much. Keep in mind that what it will do is turn all your referenced dataframes into tables using the same name as the dataframe, and all of the columns into fields using the same name as the columns. Thus in this case, we are querying a table named data with fields named ID, Interaction, and Interaction_number.

Answer 3

We can do this with dplyr. Group the 'data' by 'ID', and filter if there is any 0 values in the 'Interaction_number'.
library(dplyr)
df1 %>%
group_by(ID) %>%
filter(any(!Interaction_number))
# ID Interaction Interaction_number
# (int) (chr) (int)
#1 1 abc 1
#2 1 xyz 2
#3 1 pqr 3
#4 1 ced 0
#5 2 ab 0
#6 2 efg 1
#7 3 asdf 2
#8 3 fgh 3
#9 3 abc 0
Or using ave from base R
df1[with(df1, ave(!Interaction_number, ID, FUN=any)),]
Or this can be done without any group by
df1[df1$ID %in%subset(df1, !Interaction_number)$ID,]