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Asked: 2026-05-26T01:02:20+00:00

I’m a power excel pivot table user who is forcing himself to learn R.

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I’m a power excel pivot table user who is forcing himself to learn R. I know exactly how to do this analysis in excel, but can’t figure out the right way to code this in R.

I’m trying to group user data by 2 different variables, while grouping the variables into ranges (or bins), then summarizing other variables.

Here is what the data looks like:

userid  visits  posts   revenue
1       25      0       25
2       2       2       0
3       86      7       8
4       128     24      94
5       30      5       18
…       …       …        …
280000  80      10      100
280001  42      4       25
280002  31      8       17

Here is what I am trying to get the output to look like:

VisitRange  PostRange   # of Users  Total Revenue   Average Revenue
0           0           X            Y              Z
1-10        0           X   Y   Z
11-20       0           X   Y   Z
21-30       0           X   Y   Z
31-40       0           X   Y   Z
41-50       0           X   Y   Z
> 50        0           X   Y   Z
0           1-10        X            Y              Z
1-10        1-10        X            Y              Z
11-20       1-10        X            Y              Z
21-30       1-10        X            Y              Z
31-40       1-10        X            Y              Z
41-50       1-10        X            Y              Z
> 50        1-10        X            Y              Z

want to group by visits and posts by 10 up to a certain level, then group anything higher than 50 as ‘> 51’

I’ve looked a tapply and ddply as ways to accomplish this, but I don’t think they will work the way I am expecting, but I could be wrong.

Lastly, I know I could do this in SQL using and if/then statement to identify the range of visits and the range of posts (for example – If visits between 1 and 10, then ‘1-10’), then just group by visit range and post range, but my goal here is to start forcing myself to use R. Maybe R isn’t the right tool here, but I think it is…

All help would be appreciated. Thanks in advance.

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1 Answer

  1. Editorial Team

    The idiom in the plyr package and ddply in particular, is very similar to pivot tables in Excel.

    In your example, the only thing you need to do is the cut your grouping variables into the desired breaks, before passing to ddply. Here is an example:

    First, create some sample data:

    set.seed(1)
dat <- data.frame(
  userid = 1:500,
  visits =sample(0:50, 500, replace=TRUE),
  posts = sample(0:50, 500, replace=TRUE),
  revenue = sample(1:100, replace=TRUE)
  )

    Now, use cut to divide your grouping variables into the desired ranges:

    dat$PostRange <- cut(dat$posts, breaks=seq(0, 50, 10), include.lowest=TRUE)
dat$VisitRange <- cut(dat$visits, breaks=seq(0, 50, 10), include.lowest=TRUE)

    Finally, use ddply with summarise:

    library(plyr)
ddply(dat, .(VisitRange, PostRange), 
      summarise, 
      Users=length(userid), 
      `Total Revenue`=sum(revenue), 
      `Average Revenue`=mean(revenue))

    The results:

       VisitRange PostRange Users Total Revenue Average Revenue
1      [0,10]    [0,10]    23          1318        57.30435
2      [0,10]   (10,20]    23          1136        49.39130
3      [0,10]   (20,30]    28          1499        53.53571
4      [0,10]   (30,40]    20           923        46.15000
5      [0,10]   (40,50]    14           826        59.00000
6     (10,20]    [0,10]    23          1227        53.34783
7     (10,20]   (10,20]    17           642        37.76471
8     (10,20]   (20,30]    20           888        44.40000
9     (10,20]   (30,40]    15           622        41.46667
10    (10,20]   (40,50]    21           968        46.09524
11    (20,30]    [0,10]    23          1226        53.30435
12    (20,30]   (10,20]    19          1021        53.73684
13    (20,30]   (20,30]    23          1380        60.00000
14    (20,30]   (30,40]     8           313        39.12500
15    (20,30]   (40,50]    19           710        37.36842
16    (30,40]    [0,10]    18           782        43.44444
17    (30,40]   (10,20]    25          1308        52.32000
18    (30,40]   (20,30]    14           553        39.50000
19    (30,40]   (30,40]    26          1131        43.50000
20    (30,40]   (40,50]    20          1295        64.75000
21    (40,50]    [0,10]    20           958        47.90000
22    (40,50]   (10,20]    21          1168        55.61905
23    (40,50]   (20,30]    20          1118        55.90000
24    (40,50]   (30,40]    20          1009        50.45000
25    (40,50]   (40,50]    20           934        46.70000
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