Find value for each subject and save as new table - r

Question

I have a data frame with long format data as follows
ID Frame.No ROI.No Flux.med
01 1 1 78
01 1 2 76
01 2 1 80
01 2 2 80
01 3 1 89
01 3 2 80
27 1 1 60
27 1 2 68
27 4 1 80
27 4 2 89
For each "ID" I want to get the first and maximum Flux.med for both ROI 1 and 2 and put all these in a new dataframe. If I have a dataframe with just one subject (e.g. ID 01) I am able to identify the Flux.med values I need using the following code:
ROI1.baseline <- mydata %>%
filter(ROI.No == "ROI 1" & Frame.No == min(Frame.No))%>%
select(Flux.Med)
ROI1.max <- mydata%>%
filter(ROI.No == "ROI 1")%>%
filter (Flux.Med == max(Flux.Med))%>%
select(Flux.Med)
ROI2.baseline <- mydata%>%
filter(ROI.No == "ROI 2" & Frame.No == min(Frame.No))%>%
select(Flux.Med)
ROI.max <- mydata%>%
filter(ROI.No == "ROI 2")%>%
filter (Flux.Med == max(Flux.Med))%>%
select(Flux.Med)
But I need to do that for each ID and save the results in a dataframe.
Can I do this with a for loop?

Answer 1

We can get first and max value in each ID and ROI.No.
library(dplyr)
mydata %>%
group_by(ID, ROI.No) %>%
summarise(first_flux = first(Flux.med),
max_flux = max(Flux.med))
# ID ROI.No first_flux max_flux
# <int> <int> <int> <int>
#1 1 1 78 89
#2 1 2 76 80
#3 27 1 60 80
#4 27 2 68 89
Or using aggregate :
aggregate(Flux.med~ID + ROI.No, mydata, function(x) c(first = x[1], max = max(x)))
data
mydata <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 27L, 27L, 27L,
27L), Frame.No = c(1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 4L, 4L), ROI.No = c(1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), Flux.med = c(78L, 76L, 80L,
80L, 89L, 80L, 60L, 68L, 80L, 89L)), class = "data.frame", row.names = c(NA,-10L))

Answer 2

We can use data.table
library(data.table)
setDT(df1)[, .(first_flux = first(Flux.med),
max_flux = max(Flux.med)), .(ID, ROI.No)]
data
df1 <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 27L, 27L, 27L,
27L), Frame.No = c(1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 4L, 4L), ROI.No = c(1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), Flux.med = c(78L, 76L, 80L,
80L, 89L, 80L, 60L, 68L, 80L, 89L)), class = "data.frame",
row.names = c(NA,-10L))

Answer 3

Thanks for the suggestions. So this is how I did it in the end:
ROI1.baseline <- ldi_data %>%
group_by(ID)%>%
filter(ROI.No == "ROI 1" & Frame.No == min(Frame.No))%>% ###uses lowest number frame as baseline (not necessarily frame 1 if it was excluded)
select(Flux.Med)%>%
dplyr::rename(ROI1_baseline = Flux.Med)%>%
as.data.frame(ROI1.baseline)
ROI1.max <- ldi_data%>%
group_by(ID)%>%
filter(ROI.No == "ROI 1")%>%
filter (Flux.Med == max(Flux.Med))%>%
select(Flux.Med)%>%
dplyr::rename(ROI1_max = Flux.Med)%>%
as.data.frame(ROI1.max)
ROI2.baseline <- ldi_data%>%
group_by(ID)%>%
filter(ROI.No == "ROI 2" & Frame.No == min(Frame.No))%>%
select(Flux.Med)%>%
dplyr::rename(ROI2_baseline = Flux.Med)%>%
as.data.frame(ROI2.baseline)
ROI2.max <- ldi_data%>%
group_by(ID)%>%
filter(ROI.No == "ROI 2")%>%
filter (Flux.Med == max(Flux.Med))%>%
select(Flux.Med)%>%
dplyr::rename(ROI2_max = Flux.Med)%>%
as.data.frame(ROI2.max)
summary <- Reduce(merge, list(ROI1.baseline, ROI1.max, ROI2.baseline, ROI2.max))