How to use Dplyr's Summarize and which() to lookup min/max values

Question

I have the following data:

Name <- c(\"Sam\", \"Sarah\", \"Jim\", \"Fred\", \"James\", \"Sally\", \"Andrew\", \"John\", \"Mairin\", \"Kate\", \"Sasha\", \         


        

Answer 1

Here are some data.table approaches, the first one borrowed from @akrun:

setDT(data)

# show one, wide format
data[,c(min=.SD[which.min(Age)],max=.SD[which.max(Age)]),by=Group]
   # Group min.Name min.Age max.Name max.Age
# 1:     A      Sam      22      Sam      22
# 2:     B    Sarah      31    James      58
# 3:     C   Andrew      17    Sally      82
# 4:     D   Mairin      12      Ray      67

# show all, long format
data[,{
  mina=min(Age)
  maxa=max(Age)
  rbind(
    data.table(minmax="min",Age=mina,Name=Name[which(Age==mina)]),
    data.table(minmax="max",Age=maxa,Name=Name[which(Age==maxa)])
)},by=Group]
   # Group minmax Age   Name
# 1:     A    min  22    Sam
# 2:     A    max  22    Sam
# 3:     B    min  31  Sarah
# 4:     B    min  31    Jim
# 5:     B    max  58  James
# 6:     C    min  17 Andrew
# 7:     C    max  82  Sally
# 8:     D    min  12 Mairin
# 9:     D    max  67    Ray    

I think the long format is the best, since it allows you to filter with minmax, but the code is tortured and inefficient.

Here are some arguably less good ways:

# show all, wide format (with a list column)
data[,{
  mina=min(Age)
  maxa=max(Age)
  list(
    minAge=mina,
    maxAge=maxa,
    minNames=list(Name[Age==mina]),
    maxNames=list(Name[Age==maxa]))
},by=Group]
   # Group minAge maxAge  minNames maxNames
# 1:     A     22     22       Sam      Sam
# 2:     B     31     58 Sarah,Jim    James
# 3:     C     17     82    Andrew    Sally
# 4:     D     12     67    Mairin      Ray


# show all, wide format (with a string column)
# (just look at @shadow's answer)

Answer 2

I would actually recommend keeping your data in a "long" format. Here's how I would approach this:

library(dplyr)

Keeping all values when there are ties:

data %>%
  group_by(Group) %>%
  arrange(Age) %>%  ## optional
  filter(Age %in% range(Age))
# Source: local data frame [8 x 3]
# Groups: Group
# 
#     Name Age Group
# 1    Sam  22     A
# 2  Sarah  31     B
# 3    Jim  31     B
# 4  James  58     B
# 5 Andrew  17     C
# 6  Sally  82     C
# 7 Mairin  12     D
# 8    Ray  67     D

Keeping only one value when there are ties:

data %>%
  group_by(Group) %>%
  arrange(Age) %>%
  slice(if (length(Age) == 1) 1 else c(1, n())) ## maybe overkill?
# Source: local data frame [7 x 3]
# Groups: Group
# 
#     Name Age Group
# 1    Sam  22     A
# 2  Sarah  31     B
# 3  James  58     B
# 4 Andrew  17     C
# 5  Sally  82     C
# 6 Mairin  12     D
# 7    Ray  67     D

---

If you really want a "wide" dataset, the basic concept would be to gather and spread the data, using "tidyr":

library(dplyr)
library(tidyr)

data %>%
  group_by(Group) %>%
  arrange(Age) %>%
  slice(c(1, n())) %>%
  mutate(minmax = c("min", "max")) %>%
  gather(var, val, Name:Age) %>%
  unite(key, minmax, var) %>%
  spread(key, val)
# Source: local data frame [4 x 5]
# 
#   Group max_Age max_Name min_Age min_Name
# 1     A      22      Sam      22      Sam
# 2     B      58    James      31    Sarah
# 3     C      82    Sally      17   Andrew
# 4     D      67      Ray      12   Mairin

Though what wide form you would want with ties is unclear.

Answer 3

You can use which.min and which.max to get the first value.

data %>% group_by(Group) %>%
  summarize(minAge = min(Age), minAgeName = Name[which.min(Age)], 
            maxAge = max(Age), maxAgeName = Name[which.max(Age)])

To get all values, use e.g. paste with an appropriate collapse argument.

data %>% group_by(Group) %>%
  summarize(minAge = min(Age), minAgeName = paste(Name[which(Age == min(Age))], collapse = ", "), 
            maxAge = max(Age), maxAgeName = paste(Name[which(Age == max(Age))], collapse = ", "))