· r-2 rstats

R: Date for given week/year

As I mentioned in my last couple of blog posts I've been looking at the data behind this blog and I wanted to plot a chart showing the number of posts per week since the blog started.

I started out with a data frame with posts and publication date:


> library(dplyr)
> df = read.csv("posts.csv")
> df$date = ymd_hms(df$date)

> df %>% sample_n(10)
                                                                                title                date
538                                    Nygard Big Data Model: The Investigation Stage 2012-10-10 00:00:36
341                                                            The read-only database 2011-08-29 23:32:26
1112                                  CSS in Internet Explorer - Some lessons learned 2008-10-31 15:24:51
143                                                       Coding: Mutating parameters 2010-08-26 07:47:23
433  Scala: Counting number of inversions (via merge sort) for an unsorted collection 2012-03-20 06:53:18
618                                    neo4j/cypher: SQL style GROUP BY functionality 2013-02-17 21:05:27
1111                                 Testing Hibernate mappings: Setting up test data 2008-10-30 13:24:14
462                                       neo4j: What question do you want to answer? 2012-05-05 13:20:41
1399                                       Book Club: Design Sense (Michael Feathers) 2009-09-29 14:42:29
494                                    Bash Shell: Reusing parts of previous commands 2012-07-05 23:42:35

The first step was to add a couple of columns representing the week and year for the publication date. The 'lubridate' library came in handy here:


byWeek = df %>% 
  mutate(year = year(date), week = week(date)) %>% 
  group_by(week, year) %>% summarise(n = n()) %>% 
  ungroup() %>% arrange(desc(n))

> byWeek
Source: local data frame [352 x 3]

   week year  n
1    33 2008 14
2    35 2008 11
3    53 2012 11
4     9 2013 10
5    12 2013  9
6    21 2009  9
7    22 2009  9
8    38 2013  9
9    40 2008  9
10   48 2012  9
..  ...  ... ..

The next step is to calculate the start date of each of those weeks so that we can plot the counts on a continuous date scale. I spent a while searching how to do this before realising that the 'week' function I used before can set the week for a given data as well. Let's get to work:


calculate_start_of_week = function(week, year) {
  date <- ymd(paste(year, 1, 1, sep="-"))
  week(date) = week
  return(date)
}

> calculate_start_of_week(c(1,2,3), c(2015,2014,2013))
[1] "2015-01-01 UTC" "2014-01-08 UTC" "2013-01-15 UTC"

And now let's transform our data frame and plot the counts:


ggplot(aes(x=start_of_week, y=n, group=1), 
       data = byWeek %>% mutate(start_of_week = calculate_start_of_week(week, year))) + 
  geom_line()
2015 07 10 22 43 54

It's a bit erratic as you can see. Some of this can be explained by the fact that I do in fact post in an erratic way while some of it is explained by the fact that some weeks only have a few days if they start on the 29th onwards.

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