Create groups based on percent_rank in dplyr - r

Question

I am trying to create some groups based on the percent rank of some values in dplyr.
The code below creates a data frame and then sapply a function to determine the groups. The downside is that I can't get sapply to work for tbl_postgres, only data frames. So I'm curious if there is another solution for this.
I had considered something with ntile, but the groups I want to create have some arbitrary cut-offs. Also, I have not had much luck getting it to work with dplyr (maybe pure sql might work).
library(dplyr)
n <- 100
df1 <- data.frame(idx = 1:n, x = rnorm(n))
df1 <- df1 %>%
arrange(x) %>%
mutate(pc_x = percent_rank(x))
index <- function(x) {
if (x < 0) {
return(NA)
} else if (x < 0.3) {
return(1)
} else if (x < 0.7) {
return(2)
} else if (x <= 1) {
return(3)
} else {
return(NA)
}
}
df1 <- df1 %>%
mutate(group = sapply(pc_x, index))

Answer 1

Perhaps cut will serve your needs:
library(dplyr)
n <- 100
set.seed(42)
df1 <- data.frame(idx = 1:n, x = rnorm(n))
df1 <- df1 %>%
arrange(x) %>%
mutate(pc_x = percent_rank(x))
I use -1e9 in breaks because cut is "left-open", so if I used breaks <- c(0, ...) then the first row would be NA instead of 1.
breaks <- c(-1e9, 0.3, 0.7, 1)
df1 %>%
mutate(grp = cut(pc_x, breaks=breaks, labels=FALSE)) %>%
group_by(grp)
## Source: local data frame [100 x 4]
## Groups: grp [3]
## idx x pc_x grp
## (int) (dbl) (dbl) (int)
## 1 59 -2.9930901 0.00000000 1
## 2 18 -2.6564554 0.01010101 1
## 3 19 -2.4404669 0.02020202 1
## 4 39 -2.4142076 0.03030303 1
## 5 22 -1.7813084 0.04040404 1
## .. ... ... ... ...

Answer 2

As per suggested by #joranE and #krlmlr in response to the issue you posted on GitHub, you could build your own custom sql query using sql():
library(dplyr)
library(microbenchmark)
n <- 100
set.seed(42)
df <- data.frame(idx = 1:10e5, x = rnorm(n))
copy_to(my_db, df, "df")
mbm <- microbenchmark(
joranE = tbl(my_db, sql("
SELECT x,
CASE
WHEN x > 0 AND x <= 0.3 THEN 1
WHEN x > 0.3 AND x <= 0.6 THEN 2
WHEN x > 0.6 AND x <= 1 THEN 3
ELSE NULL
END
FROM df")),
krlmlr = tbl(my_db, sql("
SELECT x,
CASE
WHEN x <= 0.3 THEN
CASE WHEN x <= 0 THEN NULL
ELSE 1
END
ELSE
CASE WHEN x <= 0.6 THEN 2
WHEN x <= 1 THEN 3
ELSE NULL
END
END
FROM df")),
times = 100
)
Both methods yield similar results:
#Unit: milliseconds
# expr min lq mean median uq max neval cld
# joranE 3.070625 3.118589 3.548202 3.206681 3.307202 30.688142 100 a
# krlmlr 3.058583 3.109567 3.250952 3.205483 3.278453 3.933817 100 a