Loop is not calculating the values of Intervals but for the whole df. Help needed

Heey, I’m trying to creat a loop that can calculate a bunch of statistics for a df I have.

The df contains data where each 110 data points (X and Y) have the same time.

I want the loop to calculate these statistics each for all the data points that have the same time but my current loop calculates the statistics for the whole df.

I think my problem right now is, that all the calculations refer to the df as a whole, like


but when I change it to refere to the Interval it only introduces NAs


Here is my current skript. I asume my mistake has something to do with the Interval but I´m not sure how to fix it, maybe somepone can help.

Thanks already in advance !

SP <- data

for(i in 1:length(unique(SP$time))) #loop beginn; use output format (2)


Intervall <- SP[(as.numeric(SP$time))==i,]


mwx <- mean(SP$X) #Mittelwert

mwy <- mean(SP$Y)

maxx <- max(SP$X) #Maximum

maxy <- max(SP$Y)

minx <- min(SP$X) #Minimum

miny <- min(SP$Y)

varx <- var(SP$X) #Varianz

vary <- var(SP$Y)

sdx <- sd(SP$X) #Standardabweichung

sdy <- sd(SP$Y)

ICVx <- mwx/sdx #Inverser Varianzkoeffizient

ICVy <- mwy/sdy

acx<-acf(SP$X,lag.max=length(SP$X),plot=FALSE) #autocorrelation


psx <-fft(acx$acf) #Fast Fourier Transformation

psy <-fft(acy$acf)

psx <-Mod(psx)

psy <-Mod(psy)

psxhalf <-psx[2:((length(psx))/2)]-0.5

psyhalf <-psy[2:((length(psy))/2)]-0.5

psnx <-psxhalf*100/sum(psxhalf)

psny <-psyhalf*100/sum(psyhalf)

psnx <-c(psnx,0)

psny <-c(psny,0)

p <- kIntDur/(1:length(psnx))

wmx <-weighted.mean(p,psnx) #weighted mean per burst

wmy <-weighted.mean(p,psny)

Kx <- kurtosis(SP$X) #kurtosis per burst

Ky <- kurtosis(SP$Y)

Sx<-skewness(SP$X) #skewness per burst


q<-sqrt(mwx*mwx+mwy*mwy) #square-root-of-the-sum-of-squares per burst


write(lda.var, file=paste("predictors.txt", sep=","),ncolumns=19,sep=",", append=TRUE)

detach(Intervall) #loop end


I think you are making life hard for yourself hand crafting for loops when its not necessary; and there are fairly approachable tools that let one powerfully apply all sorts of functions across columns of a frame very succinctly.

For example consider the mtcars dataset and how one might get min,max,mean values for the columns disp and drat. by cylinder groups


mtcars |> 
  group_by(cyl) |> 
  summarise(across(where(is.numeric) & 

its a few lines of fairly straightforward instructions.
For lessons on how to write code in this vein; see