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We have a data set dat with multiple observations per subject. We want to create a subset of this data such that each subject (with ID giving the unique identifier for the subject) contributes the observation where the variable X takes it’s maximum value for that subject.

## An R solution

Using the excellent R package dplyr, we can do this using windowing functions included in dplyr. The following solution is available on StackOverflow, by junkka, and gets around the real possibility that multiple observations might have the same maximum value of X by choosing one of them.

library(dplyr)
dat_max <- dat %>% group_by(ID) %>% filter(row_number(X)==n())


To be a bit more explicit, row_number is a wrapper around rank, using the option ties.method=“first”. So you can use the rank function explicitly here as well.

A solution using the plyr package might be

library(plyr)
dat_max <- ddply(dat, .(ID), function(u) u[rank(u\$X, ties.method=’first’)==nrow(u),])


## A Python solution

In Python, the excellent pandas package allows us to do similar operations. The following example is from this thread on StackOverflow.

import pandas as pd
df = DataFrame({’Sp’:[‘a’,’b‘,’c’,’d‘,’e’,’f’], ‘Mt’:[‘s1’, ‘s1’, ‘s2’,’s2‘,’s2’,’s3’], ‘Value’:[1,2,3,4,5,6], ‘count’:[3,2,5,10,10,6]})
df.iloc[df.groupby([‘Mt’]).apply(lambda x: x[‘count’].idxmax())]

You could also do (from the same thread)

df.sort(‘count’, ascending=False).groupby(‘Mt’, as_index=False).first()


but it is about 50% slower.