在两个数据框之间匹配数据的快速方法[R]

Question

I have two dataframes: df_workingFile and df_groupIDs

df_workingFile:

ID | GroupID | Sales | Date
v  | a1      |  1    |  2011
w  | a1      |  3    |  2010
x  | b1      |  8    |  2007
y  | b1      |  3    |  2006
z  | c3      |  2    |  2006

df_groupIDs:

GroupID | numIDs  | MaxSales 
a1      | 2       |  3       
b1      | 2       |  8       
c3      | 1       |  2      

For df_groupIDs, I want to get the ID and Date of the event with the max sales in that group. So group "a1" has 2 events in df_workingFile, "v" and "w". I want to identify that event "w" has the Max sales value and bring it's information into df_groupIDs. The final output should look like this:

GroupID | numIDs  | MaxSales | ID | Date
a1      | 2       |  3       | w  | 2010
b1      | 2       |  8       | x  | 2007
c3      | 1       |  2       | z  | 2006

Now here's the problem . I wrote code that does this, but it's very inefficient and takes forever to process when I deal with datasets of 50-100K rows. I need help figuring out how to rewrite my code to be more efficient. Here's what I currently have:

i = 1
for (groupID in df_groupIDs$groupID) {

    groupEvents <- subset(df_workingFile, df_workingFile$groupID == groupID)
    index <- match(df_groupIDs$maxSales[i], groupEvents$Sales)
    df_groupIDs$ID[i] = groupEvents$ID[index]
    df_groupIDs$Date[i] = groupEvents$Date[index]

    i = i+1
}

Answer 1

Using dplyr :

library(dplyr)

df_workingFile %>% 
  group_by(GroupID) %>%      # for each group id
  arrange(desc(Sales)) %>%   # sort by Sales (descending)
  slice(1) %>%               # keep the top row
  inner_join(df_groupIDs)    # join to df_groupIDs
  select(GroupID, numIDs, MaxSales, ID, Date)
    # keep the columns you want in the order you want

Another simpler method, if the Sales are integers (and can thus be relied on for equality testing with the MaxSales column):

inner_join(df_groupIDs, df_workingFile,
           by = c("GroupID" = "GroupID", "MaxSales" = "Sales"))

Answer 2

This makes use of a feature that SQLite has that if max is used on a line then it automatically brings along the row that the maximum came from.

library(sqldf)

sqldf("select g.GroupID, g.numIDs, max(w.Sales) MaxSales, w.ID, w.Date 
       from df_groupIDs g left join df_workingFile w using(GroupID) 
       group by GroupID")

giving:

  GroupID numIDs MaxSales ID Date
1      a1      2        3  w 2010
2      b1      2        8  x 2007
3      c3      1        2  z 2006

Note: The two input data frames shown reproducibly are:

Lines1 <- "
ID | GroupID | Sales | Date
v  | a1      |  1    |  2011
w  | a1      |  3    |  2010
x  | b1      |  8    |  2007
y  | b1      |  3    |  2006
z  | c3      |  2    |  2006"
df_workingFile <- read.table(text = Lines1, header = TRUE, sep = "|", strip.white = TRUE)

Lines2 <- "
GroupID | numIDs  | MaxSales 
a1      | 2       |  3       
b1      | 2       |  8       
c3      | 1       |  2"      

df_groupIDs <- read.table(text = Lines2, header = TRUE, sep = "|", strip.white = TRUE)