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

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.