R group by aggregate

Question

In R (which I am relatively new to) I have a data frame consists of many column and a numeric column I need to aggregate according to groups determined by another column.

 SessionID   Price
 '1',       '624.99'
 '1',       '697.99'
 '1',       '649.00'
 '7',       '779.00'
 '7',       '710.00'
 '7',       '2679.50'

I need to group by SessionID and return the Max and Min for each ONTO the original data frame e.g. :

 SessionID   Price     Min     Max
 '1',       '624.99'   624.99  697.99
 '1',       '697.99'   624.99  697.99
 '1',       '649.00'   624.99  697.99
 '7',       '779.00'   710.00  2679.50
 '7',       '710.00'   710.00  2679.50
 '7',       '2679.50'  710.00  2679.50

any ideas how to do this efficiently ?

Answer 1

Using base R:

df <- transform(df, Min = ave(Price, SessionID, FUN = min),
                    Max = ave(Price, SessionID, FUN = max))
df
#  SessionID   Price    Min     Max
#1         1  624.99 624.99  697.99
#2         1  697.99 624.99  697.99
#3         1  649.00 624.99  697.99
#4         7  779.00 710.00 2679.50
#5         7  710.00 710.00 2679.50
#6         7 2679.50 710.00 2679.50

Since your desired result is not aggregated but just the original data with two extra columns, you want to use ave in base R instead of aggregate, which you would typically use if you wanted to aggregate the data by SessionID. (NB: AEBilgrau shows that you could also do it with aggregate with some additional matching.)

Similarly, for dplyr, you want to use mutate instead of summarise because you dont want to aggregate/summarise the data.

Using dplyr:

library(dplyr)
df <- df %>% group_by(SessionID) %>% mutate(Min = min(Price), Max = max(Price))