r - dplyr>Get rows with minimum and maximum of variable -


i have data.frame want return min , max time observations of value.

df<- data.frame(    time=c(24594.55, 29495.45, 24594.55, 39297.27, 24594.55, 34396.36, 19693.64, 14792.73, 29495.45),    mz=c(-0.04729751, -0.50902297, -0.04376393, -0.22218980, -0.36407263, -0.38341534, -0.34597255, -0.01480776, -0.00999671),    set_nbr=c(1, 1,1, 2, 2, 2, 3, 3, 3))            library(dplyr)  min_time <- df %>%    group_by(set_nbr) %>%   slice(which(mz<0))%>%   filter(rank(time,ties.method="min")==1)%>%   distinct   min_time    ##source: local data frame [3 x 3]  ##groups: set_nbr        time          mz set_nbr ## 1 24594.55 -0.04729751       1 ## 2 24594.55 -0.36407263       2 ## 3 14792.73 -0.01480776       3

this works, when try max_time, strange result comes:

max_time <- df %>%    group_by(set_nbr) %>%   slice(which(mz<0))%>%   filter(rank(time,ties.method="max")==1)%>%   distinct  max_time   ##source: local data frame [2 x 3] ##groups: set_nbr          time          mz set_nbr ##1 24594.55 -0.36407263       2 ##2 14792.73 -0.01480776       3

set_nbr 1, , max time values incorrect. don't know why.

expected output 

max_time         time            mz    set_nbr ##1 29495.45 -0.50902297       1 ##2 39297.27 -0.22218980       2 ##3 29495.45 -0.00999671       3

try

df %>%     group_by(set_nbr) %>%     filter(time==max(time)) #     time          mz set_nbr #1 29495.45 -0.50902297       1 #2 39297.27 -0.22218980       2 #3 29495.45 -0.00999671       3

or

 df %>%     group_by(set_nbr) %>%     slice(which.max(time))  #      time          mz set_nbr  #1 29495.45 -0.50902297       1  #2 39297.27 -0.22218980       2  #3 29495.45 -0.00999671       3

regarding why code didn't work

 df %>%      group_by(set_nbr) %>%     slice(which(mz <0)) %>%     mutate(rn = rank(time, ties.method='max'))  #      time          mz set_nbr rn  #1 24594.55 -0.04729751       1  2  #2 29495.45 -0.50902297       1  3  #3 24594.55 -0.04376393       1  2  #4 39297.27 -0.22218980       2  3  #5 24594.55 -0.36407263       2  1  #6 34396.36 -0.38341534       2  2  #7 19693.64 -0.34597255       3  2  #8 14792.73 -0.01480776       3  1  #9 29495.45 -0.00999671       3  3

if @ output, 'set_nbr' group '1', there no '1' 'rn' there ties. do

 df %>%      group_by(set_nbr) %>%     slice(which(mz <0)) %>%     filter(rn = rank(-time, ties.method='first')==1)  #      time          mz set_nbr  #1 29495.45 -0.50902297       1  #2 39297.27 -0.22218980       2  #3 29495.45 -0.00999671       3

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