Thresholding a data frame without removing values - r

Question

I have a data frame that consisting of a non-unique identifier (ID) and measures of some property of the objects within that ID, something like this:
ID Sph
A 1.0
A 1.2
A 1.1
B 0.5
B 1.8
C 2.2
C 1.1
D 2.1
D 3.0
First, I get the number of instances of each ID as X using table(df$ID), i.e. A=3, B=2 ,C=2 and D=2. Next, I would like to apply a threshold in the "Sph" category after getting the number of instances, limiting to rows where the Sph value exceeds the threshold. With threshold 2.0, for instance, I would use thold=df[df$Sph>2.0,]. Finally I would like to replace the ID column with the X value that I computed using table above. For instance, with a threshold of 1.1 in the "Sph" columns I would like the following output:
ID Sph
3 1.0
2 1.8
2 2.2
2 2.1
2 3.0
In other words, after using table() to get an x value corresponding to the number of times an ID has occurred, say 3, I would like to then assign that number to every value in that ID, Y, that is over some threshold.

Answer 1

There are some inconsistencies in your question and you didn't give a reproducible example, however here's my attempt.
I like to use the dplyr library, in this case I had to break out an sapply, maybe someone can improve on my answer.
Here's the short version:
library(dplyr)
#your data
x <- data.frame(ID=c(rep("A",3),rep("B",2),rep("C",2),rep("D",2)),Sph=c(1.0,1.2,1.1,0.5,1.8,2.2,1.1,2.1,3.0),stringsAsFactors = FALSE)
#lookup table
y <- summarise(group_by(x,ID), IDn=n())
#fill in original table
x$IDn <- sapply(x$ID,function(z) as.integer(y[y$ID==z,"IDn"]))
#filter for rows where Sph greater or equal to 1.1
x <- x %>% filter(Sph>=1.1)
#done
x
And here's the longer version with explanatory output:
> library(dplyr)
> #your data
> x <- data.frame(ID=c(rep("A",3),rep("B",2),rep("C",2),rep("D",2)),Sph=c(1.0,1.2,1.1,0.5,1.8,2.2,1.1,2.1,3.0),stringsAsFactors = FALSE)
> x
ID Sph
1 A 1.0
2 A 1.2
3 A 1.1
4 B 0.5
5 B 1.8
6 C 2.2
7 C 1.1
8 D 2.1
9 D 3.0
>
> #lookup table
> y <- summarise(group_by(x,ID), IDn=n())
> y
Source: local data frame [4 x 2]
ID IDn
1 A 3
2 B 2
3 C 2
4 D 2
>
> #fill in original table
> x$IDn <- sapply(x$ID,function(z) as.integer(y[y$ID==z,"IDn"]))
> x
ID Sph IDn
1 A 1.0 3
2 A 1.2 3
3 A 1.1 3
4 B 0.5 2
5 B 1.8 2
6 C 2.2 2
7 C 1.1 2
8 D 2.1 2
9 D 3.0 2
>
> #filter for rows where Sph greater or equal to 1.1
> x <- x %>% filter(Sph>=1.1)
>
> #done
> x
ID Sph IDn
1 A 1.2 3
2 A 1.1 3
3 B 1.8 2
4 C 2.2 2
5 C 1.1 2
6 D 2.1 2
7 D 3.0 2

Answer 2

You can actually do this in one step after computing X and thold as you did in your question:
X <- table(df$ID)
thold <- df[df$Sph > 1.1,]
thold$ID <- X[as.character(thold$ID)]
thold
# ID Sph
# 2 3 1.2
# 5 2 1.8
# 6 2 2.2
# 8 2 2.1
# 9 2 3.0
Basically you look up the frequency of each ID value in the table X that you built.