· r-2 rstats dplyr

R: dplyr - Error: cannot modify grouping variable

I’ve been doing some exploration of the posts made on this blog and I thought I’d start with answering a simple question - on which dates did I write the most posts?

I started with a data frame containing each post and the date it was published:

> library(dplyr)
> df %>% sample_n(5)
                                                title                date
1148 Taiichi Ohno's Workplace Management: Book Review 2008-12-08 14:14:48
158     Rails: Faking a delete method with 'form_for' 2010-09-20 18:52:15
331           Retrospectives: The 4 L's Retrospective 2011-07-25 21:00:30
1035       msbuild - Use OutputPath instead of OutDir 2008-08-14 18:54:03
1181                The danger of commenting out code 2009-01-17 06:02:33

To find the most popular days for blog posts we can write the following aggregation function:

> df %>% mutate(day = as.Date(date)) %>% count(day) %>% arrange(desc(n))

Source: local data frame [1,140 x 2]

          day n
1  2012-12-31 6
2  2014-05-31 6
3  2008-08-08 5
4  2013-01-27 5
5  2009-08-24 4
6  2012-06-24 4
7  2012-09-30 4
8  2012-10-27 4
9  2012-11-24 4
10 2013-02-28 4

So we can see a couple of days with 6 posts, a couple with 5 posts, a few more with 4 posts and then presumably loads of days with 1 post.

I thought it’d be cool if we could blog a histogram which had on the x axis the number of posts and on the y axis how many days that number of posts occurred e.g. for an x value of 6 (posts) we’d have a y value of 2 (occurrences).

My initial attempt was this:

> df %>% mutate(day = as.Date(date)) %>% count(day) %>% count(n)
Error: cannot modify grouping variable

Unfortunately that isn’t allowed. I tried ungrouping and then counting again:

 df %>% mutate(day = as.Date(date)) %>% count(day) %>% ungroup() %>% count(n)
Error: cannot modify grouping variable

Still no luck. I did a bit of googlign around and came across a post which suggested using a combination of group_by + mutate or group_by + summarize.

I tried the mutate approach first:

> df %>% mutate(day = as.Date(date)) %>%
+     group_by(day) %>% mutate(n = n()) %>% ungroup() %>% sample_n(5)
                                                        title                Source: local data frame [5 x 4]

                                    title                date        day n
1 QCon London 2009: DDD & BDD - Dan North 2009-03-13 15:28:04 2009-03-13 2
2        Onboarding: Sketch the landscape 2013-02-15 07:36:06 2013-02-15 1
3                           Ego Depletion 2013-06-04 23:16:29 2013-06-04 1
4                 Clean Code: Book Review 2008-09-15 09:52:33 2008-09-15 1
5            Dreyfus Model: More thoughts 2009-08-10 10:36:51 2009-08-10 1

That keeps around the 'title' which is a bit annoying. We can get rid of it using a distinct on 'day' if we want and if we also implement the second part of the function we end up with the following:

> df %>% mutate(day = as.Date(date)) %>%
    group_by(day) %>% mutate(n = n()) %>% distinct(day) %>% ungroup() %>%
    group_by(n) %>%
    mutate(c = n()) %>%
    distinct(n)

Source: local data frame [6 x 5]
Groups: n

                                                title                date        day n   c
1       Functional C#: Writing a 'partition' function 2010-02-01 23:34:02 2010-02-01 1 852
2                            Willed vs Forced designs 2010-02-08 22:48:05 2010-02-08 2 235
3                            TDD: Testing collections 2010-07-28 06:05:25 2010-07-28 3  41
4  Creating a Samba share between Ubuntu and Mac OS X 2012-06-24 00:40:35 2012-06-24 4   8
5            Gamification and Software: Some thoughts 2012-12-31 10:57:19 2012-12-31 6   2
6 Python/numpy: Selecting specific column in 2D array 2013-01-27 02:10:10 2013-01-27 5   2

Annoyingly we’ve still got the 'title', 'date' and 'day' columns hanging around which we’d need to get rid of with a call to 'select'. The code also feels quite icky, especially the use of distinct in a couple of places.

In fact we can simplify the code if we use summarize instead of mutate:

> df %>% mutate(day = as.Date(date)) %>%
    group_by(day) %>% summarize(n = n()) %>% ungroup() %>%
    group_by(n) %>% summarize(c = n())


Source: local data frame [6 x 2]

  n   c
1 1 852
2 2 235
3 3  41
4 4   8
5 5   2
6 6   2

And we’ve got also rid of the extra columns in the bargain which is great! And now we can plot our histogram:

> library(ggplot2)
> post_frequencies = df %>% mutate(day = as.Date(date)) %>%
    group_by(day) %>% summarize(n = n()) %>% ungroup() %>%
    group_by(n) %>% summarize(c = n())
> ggplot(aes(x = n, y = c), data = post_frequencies) + geom_bar(stat = "identity")
2015 07 09 06 44 47

In this case we don’t actually need to do the second grouping to create the bar chart since ggplot will do it for us if we feed it the following data:

. ggplot(aes(x = n),
         data = df %>% mutate(day = as.Date(date)) %>% group_by(day) %>% summarize(n = n()) %>% ungroup()) +
    geom_bar(binwidth = 1) +
    scale_x_continuous(limits=c(1, 6))
2015 07 09 06 55 12

Still, it’s good to know how!

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