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Mark Needham
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Thoughts on Software Development
Updated: 5 hours 45 min ago

Neo4j: Modelling sub types

Tue, 10/21/2014 - 01:08

A question which sometimes comes up when discussing graph data modelling is how you go about modelling sub/super types.

In my experience there are two reasons why we might want to do this:

  • To ensure that certain properties exist on bits of data
  • To write drill down queries based on those types

At the moment the former isn’t built into Neo4j and you’d only be able to achieve it by wiring up some code in a pre commit hook of a transaction event handler so we’ll focus on the latter.

The typical example used for showing how to design sub types is the animal kingdom and I managed to find a data set from Louiseville, Kentucky’s Animal Services which we can use.

In this case the sub types are used to represent the type of animal, breed group and breed. We then also have ‘real data’ in terms of actual dogs under the care of animal services.

We effectively end up with two graphs in one – a model and a meta model:

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The cypher query to create this graph looks like this:

LOAD CSV WITH HEADERS FROM "file:/Users/markneedham/projects/neo4j-subtypes/data/dogs.csv" AS line
MERGE (animalType:AnimalType {name: "Dog"})
MERGE (breedGroup:BreedGroup {name: line.BreedGroup})
MERGE (breed:Breed {name: line.PrimaryBreed})
MERGE (animal:Animal {id: line.TagIdentity, primaryColour: line.PrimaryColour, size: line.Size})
 
MERGE (animalType)<-[:PARENT]-(breedGroup)
MERGE (breedGroup)<-[:PARENT]-(breed)
MERGE (breed)<-[:PARENT]-(animal)

We could then write a simple query to find out how many dogs we have:

MATCH (animalType:AnimalType)<-[:PARENT*]-(animal)
RETURN animalType, COUNT(*) AS animals
ORDER BY animals DESC
==> +--------------------------------+
==> | animalType           | animals |
==> +--------------------------------+
==> | Node[89]{name:"Dog"} | 131     |
==> +--------------------------------+
==> 1 row

Or we could write a slightly more complex query to find the number of animals at each level of our type hierarchy:

MATCH path = (animalType:AnimalType)<-[:PARENT]-(breedGroup)<-[:PARENT*]-(animal)
RETURN [node IN nodes(path) | node.name][..-1] AS breed, COUNT(*) AS animals
ORDER BY animals DESC
LIMIT 5
==> +-----------------------------------------------------+
==> | breed                                     | animals |
==> +-----------------------------------------------------+
==> | ["Dog","SETTER/RETRIEVE","LABRADOR RETR"] | 15      |
==> | ["Dog","SETTER/RETRIEVE","GOLDEN RETR"]   | 13      |
==> | ["Dog","POODLE","POODLE MIN"]             | 10      |
==> | ["Dog","TERRIER","MIN PINSCHER"]          | 9       |
==> | ["Dog","SHEPHERD","WELSH CORGI CAR"]      | 6       |
==> +-----------------------------------------------------+
==> 5 rows

We might then decide to add an exercise sub graph which indicates how much exercise each type of dog requires:

MATCH (breedGroup:BreedGroup)
WHERE breedGroup.name IN ["SETTER/RETRIEVE", "POODLE"]
MERGE (exercise:Exercise {type: "2 hours hard exercise"})
MERGE (exercise)<-[:REQUIRES_EXERCISE]-(breedGroup);
MATCH (breedGroup:BreedGroup)
WHERE breedGroup.name IN ["TERRIER", "SHEPHERD"]
MERGE (exercise:Exercise {type: "1 hour gentle exercise"})
MERGE (exercise)<-[:REQUIRES_EXERCISE]-(breedGroup);

We could then query that to find out which dogs need to come out for 2 hours of hard exercise:

MATCH (exercise:Exercise {type: "2 hours hard exercise"})<-[:REQUIRES_EXERCISE]-()<-[:PARENT*]-(dog)
WHERE NOT (dog)<-[:PARENT]-()
RETURN dog
LIMIT 10
==> +-----------------------------------------------------------+
==> | dog                                                       |
==> +-----------------------------------------------------------+
==> | Node[541]{id:"664427",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[542]{id:"543787",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[543]{id:"584021",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[544]{id:"584022",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[545]{id:"664430",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[546]{id:"535176",primaryColour:"BLACK",size:"SMALL"} |
==> | Node[567]{id:"613557",primaryColour:"WHITE",size:"SMALL"} |
==> | Node[568]{id:"531376",primaryColour:"WHITE",size:"SMALL"} |
==> | Node[569]{id:"613567",primaryColour:"WHITE",size:"SMALL"} |
==> | Node[570]{id:"531379",primaryColour:"WHITE",size:"SMALL"} |
==> +-----------------------------------------------------------+
==> 10 rows

In this query we ensured that we only returned dogs rather than breeds by checking that there was no incoming PARENT relationship. Alternatively we could have filtered on the Animal label…

MATCH (exercise:Exercise {type: "2 hours hard exercise"})<-[:REQUIRES_EXERCISE]-()<-[:PARENT*]-(dog:Animal)
RETURN dog
LIMIT 10

or if we wanted to only take the dogs out for exercise perhaps we’d have Dog label on the appropriate nodes.

People are often curious why labels don’t have super/sub types between them but I tend to use labels for simple categorisation – anything more complicated and we may as well use the built in power of the graph model!

The code is on github should you wish to play with it.

Categories: Blogs

Python: Converting a date string to timestamp

Mon, 10/20/2014 - 17:53

I’ve been playing around with Python over the last few days while cleaning up a data set and one thing I wanted to do was translate date strings into a timestamp.

I started with a date in this format:

date_text = "13SEP2014"

So the first step is to translate that into a Python date – the strftime section of the documentation is useful for figuring out which format code is needed:

import datetime
 
date_text = "13SEP2014"
date = datetime.datetime.strptime(date_text, "%d%b%Y")
 
print(date)
$ python dates.py
2014-09-13 00:00:00

The next step was to translate that to a UNIX timestamp. I thought there might be a method or property on the Date object that I could access but I couldn’t find one and so ended up using calendar to do the transformation:

import datetime
import calendar
 
date_text = "13SEP2014"
date = datetime.datetime.strptime(date_text, "%d%b%Y")
 
print(date)
print(calendar.timegm(date.utctimetuple()))
$ python dates.py
2014-09-13 00:00:00
1410566400

It’s not too tricky so hopefully I shall remember next time.

Categories: Blogs

Neo4j: LOAD CSV – The sneaky null character

Sat, 10/18/2014 - 12:49

I spent some time earlier in the week trying to import a CSV file extracted from Hadoop into Neo4j using Cypher’s LOAD CSV command and initially struggled due to some rogue characters.

The CSV file looked like this:

$ cat foo.csv
foo,bar,baz
1,2,3

I wrote the following LOAD CSV query to extract some of the fields and compare others:

load csv with headers from "file:/Users/markneedham/Downloads/foo.csv" AS line
RETURN line.foo, line.bar, line.bar = "2"
==> +--------------------------------------+
==> | line.foo | line.bar | line.bar = "2" |
==> +--------------------------------------+
==> | <null>   | "2"     | false          |
==> +--------------------------------------+
==> 1 row

I had expect to see a “1” in the first column and a ‘true’ in the third column, neither of which happened.

I initially didn’t have a text editor with hexcode mode available so I tried checking the length of the entry in the ‘bar’ field:

load csv with headers from "file:/Users/markneedham/Downloads/foo.csv" AS line
RETURN line.foo, line.bar, line.bar = "2", length(line.bar)
==> +---------------------------------------------------------+
==> | line.foo | line.bar | line.bar = "2" | length(line.bar) |
==> +---------------------------------------------------------+
==> | <null>   | "2"     | false          | 2                |
==> +---------------------------------------------------------+
==> 1 row

The length of that value is 2 when we’d expect it to be 1 given it’s a single character.

I tried trimming the field to see if that made any difference…

load csv with headers from "file:/Users/markneedham/Downloads/foo.csv" AS line
RETURN line.foo, trim(line.bar), trim(line.bar) = "2", length(line.bar)
==> +---------------------------------------------------------------------+
==> | line.foo | trim(line.bar) | trim(line.bar) = "2" | length(line.bar) |
==> +---------------------------------------------------------------------+
==> | <null>   | "2"            | true                 | 2                |
==> +---------------------------------------------------------------------+
==> 1 row

…and it did! I thought there was probably a trailing whitespace character after the “2” which trim had removed and that ‘foo’ column in the header row had the same issue.

I was able to see that this was the case by extracting the JSON dump of the query via the Neo4j browser:

{  
   "table":{  
      "_response":{  
         "columns":[  
            "line"
         ],
         "data":[  
            {  
               "row":[  
                  {  
                     "foo\u0000":"1\u0000",
                     "bar":"2\u0000",
                     "baz":"3"
                  }
               ],
               "graph":{  
                  "nodes":[  
 
                  ],
                  "relationships":[  
 
                  ]
               }
            }
         ],
      ...
}

It turns out there were null characters scattered around the file so I needed to pre process the file to get rid of them:

$ tr < foo.csv -d '\000' > bar.csv

Now if we process bar.csv it’s a much smoother process:

load csv with headers from "file:/Users/markneedham/Downloads/bar.csv" AS line
RETURN line.foo, line.bar, line.bar = "2", length(line.bar)
==> +---------------------------------------------------------+
==> | line.foo | line.bar | line.bar = "2" | length(line.bar) |
==> +---------------------------------------------------------+
==> | "1"      | "2"      | true           | 1                |
==> +---------------------------------------------------------+
==> 1 row

Note to self: don’t expect data to be clean, inspect it first!

Categories: Blogs

R: Linear models with the lm function, NA values and Collinearity

Sat, 10/18/2014 - 08:35

In my continued playing around with R I’ve sometimes noticed ‘NA’ values in the linear regression models I created but hadn’t really thought about what that meant.

On the advice of Peter Huber I recently started working my way through Coursera’s Regression Models which has a whole slide explaining its meaning:

2014 10 17 06 21 07

So in this case ‘z’ doesn’t help us in predicting Fertility since it doesn’t give us any more information that we can’t already get from ‘Agriculture’ and ‘Education’.

Although in this case we know why ‘z’ doesn’t have a coefficient sometimes it may not be clear which other variable the NA one is highly correlated with.

Multicollinearity (also collinearity) is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy.

In that situation we can make use of the alias function to explain the collinearity as suggested in this StackOverflow post:

library(datasets); data(swiss); require(stats); require(graphics)
z <- swiss$Agriculture + swiss$Education
fit = lm(Fertility ~ . + z, data = swiss)
> alias(fit)
Model :
Fertility ~ Agriculture + Examination + Education + Catholic + 
    Infant.Mortality + z
 
Complete :
  (Intercept) Agriculture Examination Education Catholic Infant.Mortality
z 0           1           0           1         0        0

In this case we can see that ‘z’ is highly correlated with both Agriculture and Education which makes sense given its the sum of those two variables.

When we notice that there’s an NA coefficient in our model we can choose to exclude that variable and the model will still have the same coefficients as before:

> require(dplyr)
> summary(lm(Fertility ~ . + z, data = swiss))$coefficients
                   Estimate  Std. Error   t value     Pr(>|t|)
(Intercept)      66.9151817 10.70603759  6.250229 1.906051e-07
Agriculture      -0.1721140  0.07030392 -2.448142 1.872715e-02
Examination      -0.2580082  0.25387820 -1.016268 3.154617e-01
Education        -0.8709401  0.18302860 -4.758492 2.430605e-05
Catholic          0.1041153  0.03525785  2.952969 5.190079e-03
Infant.Mortality  1.0770481  0.38171965  2.821568 7.335715e-03
> summary(lm(Fertility ~ ., data = swiss))$coefficients
                   Estimate  Std. Error   t value     Pr(>|t|)
(Intercept)      66.9151817 10.70603759  6.250229 1.906051e-07
Agriculture      -0.1721140  0.07030392 -2.448142 1.872715e-02
Examination      -0.2580082  0.25387820 -1.016268 3.154617e-01
Education        -0.8709401  0.18302860 -4.758492 2.430605e-05
Catholic          0.1041153  0.03525785  2.952969 5.190079e-03
Infant.Mortality  1.0770481  0.38171965  2.821568 7.335715e-03

If we call alias now we won’t see any output:

> alias(lm(Fertility ~ ., data = swiss))
Model :
Fertility ~ Agriculture + Examination + Education + Catholic + 
    Infant.Mortality
Categories: Blogs

The Hard Thing About Hard Things – Ben Horowitz: Book Review

Tue, 10/14/2014 - 01:59

I came across ‘The Hard Thing About Hard Things‘ while reading an article about Ben Horowitz’s venture capital firm and it was intriguing enough that I bought it and then read through it over a couple of days.

Although the blurb suggests that it’s a book about about building and running a startup I think a lot of the lessons are applicable for any business.

These were some of the main points that stood out for me:

  • The Positivity Delusion – CEOs should tell it like it is.

    My single biggest improvement as CEO occurred on the day when I stopped being too positive.

    Horowitz suggests that he used to be too positive and would shield bad news from his employees as he thought he’d make the problem worse by transferring the burden onto them.

    He came to the realisation that this was counter productive since he often wasn’t the best placed person to fix a problem e.g. if it was a problem with the product then the engineering team needed to know so they could write the code to fix it.

    He goes on to suggest that…

    A healthy company culture encourages people to share bad news. A company that discusses its problems freely and openly can quickly solve them. A company that covers up its problems frustrated everyone involved.

    I’ve certainly worked on projects in the past where the view projected by the most senior person is overly positive and seems to ignore any problems that seem obvious to everyone else. This eventually leads to people being unsure whether to take them seriously which isn’t a great situation to be in.

  • Lead Bullets – fix the problem, don’t run away from it.

    Horowitz describes a couple of situations where his products have been inferior to their competitors and it’s been tempting to take the easy way out by not fixing the product.

    There comes a time in every company’s life where it must fight for its life. If you find yourself running when you should be fighting, you need to ask yourself, “If our company isn’t good enough to win, then do we need to exist at all?”.

    I can’t think of any examples around this from my experience but I really like the advice – I’m sure it’ll come in handy in future.

  • Give ground grudgingly – dealing with the company increasing in size.

    Horowitz suggests that the following things become more difficult as a company grows in size:

    • Communication
    • Common Knowledge
    • Decision Making

    but…

    If the company doesn’t expand it will never be much…so the challenge is to grow but degrade as slowly as possible.

    He uses the metaphor of an offensive linesman in American football who has to stop onrushing defensive linesman but giving ground to them slowly by backing up a little at a time.

    I’ve worked in a few different companies now and noticed things become more structured (and in my eyes worse!) as the company grew over time but I hadn’t really thought about why that was happening. The chapter on scaling a company does a decent job.

  • The Law of Crappy People – people baseline against the worst person at a grade level.

    For any title level in a large organisation, the talent on that level will eventually converge to the crappiest person with that title.

    This is something that he’s also written about on his blog and certainly seems very recognisable.

    His suggestion for mitigating the problem is to have a “properly constructed and highly disciplined promotion process” in place. He describes this like so:

    When a manager wishes to promote an employee, she will submit that employee for review with an explanation of why she believes her employee satisfies the skill criteria required for the level.

    The committee should compare the employee to both the level’s skill description and the skills of the other employees at that level to determine whether or not to approve the promotion.

  • Hire people with the right kind of ambition

    The wrong kind of ambition is ambition for the executive’s personal success regardless of the company’s outcome.

    This suggestion comes from the chapter in which Horowitz discusses how to minimise politics in an organisation.

    I really like this idea but it seems like a difficult thing to judge/achieve. In my experience people often have their own goals which aren’t necessarily completely aligned with the company’s. Perhaps complete alignment isn’t as important unless you’re right at the top of the company?

    He also has quite a neat definition of politics:

    What do I mean by politics? I mean people advancing their careers or agendas by means other than merit and contribution.

    He goes on to describe a few stories of how political behaviour can subtly creep into a company without the CEO meaning for it to happen. This chapter was definitely eye opening for me.

There are some other interesting chapters on the best types of CEOs for different companies, when to hire Senior external people, product management and much more.

I realise that the things I’ve picked out are mostly a case of confirmation bias so I’m sure everyone will have different things that stand out for them.

Definitely worth a read.

Categories: Blogs

Lessons from running Neo4j based ‘hackathons’

Sat, 10/11/2014 - 12:52

Over the last 6 months my colleagues and I have been running hands on Neo4j based sessions every few weeks and I was recently asked if I could write up the lessons we’ve learned.

So in no particular order here are some of the things that we’ve learnt:

Have a plan but don’t stick to it rigidly

Something we learnt early on is that it’s helpful to have a rough plan of how you’re going to spend the session otherwise it can feel quite chaotic for attendees.

We show people that plan at the beginning of the session so that they know what to expect and can plan their time accordingly if the second part doesn’t interest them as much.

Having said that, we’ve often gone off on a tangent and since people have been very interested in that we’ve just gone with it.

This sometimes means that you don’t cover everything you had in mind but the main thing is that people enjoy themselves so it’s nothing to worry about.

Prepare for people to be unprepared

We try to set expectations in advanced of the sessions with respect to what people should prepare or have installed on their machines but despite that you’ll have people in varying levels of readiness.

Having noticed this trend over a few months we now allot time in the schedule for getting people up and running and if we’re really struggling then we’ll ask people to pair with each other.

There will also be experience level differences so we always factor in some time to go over the basics for those who are new. We also encourage experienced people to help the others out – after all you only really know if you know something when you try to teach someone else!

Don’t try to do too much

Our first ‘hackathon’-esque event involved an attempt to build a Java application based on a British Library dataset.

I thought we’d be able to model the data set, import it and then wire up some queries to an application in a few hours.

This proved to be ever so slightly ambitious!

It took much longer than anticipated to do those first two steps and we didn’t get to build any of the application – teaching people how to model in a graph is probably a session in its own right.

Show the end state

Feedback we got from attendees to the first few versions was that they’d like to see what the end state should have looked like if they’d completed everything.

In our Clojure Hackathon Rohit got the furthest so we shared his code with everyone afterwards.

An even better approach is to have the final solution ready in advance and have it checked in on a different branch that you can point people at afterwards.

Show the intermediate states

Another thing we noticed was that if people got behind in the first part of the session then they’d never be able to catch up.

Nigel therefore came up with the idea of snapshotting intermediate states so that people could reset themselves after each part of the session. This is something that the Polymer tutorial does as well.

We worked out that we have two solid one hour slots before people start getting distracted by their journey home so we came up with two distinct types of tasks for people to do and then created a branch with the solution at the end of those tasks.

No doubt there will be more lessons to come as we run more sessions but this is where we are at the moment. If you fancy joining in our next session is Java based in a couple of weeks time.

Finally, if you want to see a really slick hands on meetup then you’ll want to head over to the London Clojure DojoBruce Durling has even written up some tips on how you run one yourself.

Categories: Blogs

Conceptual Model vs Graph Model

Mon, 10/06/2014 - 09:11

We’ve started running some sessions on graph modelling in London and during the first session it was pointed out that the process I’d described was very similar to that when modelling for a relational database.

I thought I better do some reading on the way relational models are derived and I came across an excellent video by Joe Maguire titled ‘Data Modelers Still Have Jobs: Adjusting For the NoSQL Environment

Joe starts off by showing the following ‘big picture framework’ which describes the steps involved in coming up with a relational model:

2014 10 05 19 04 46

A couple of slides later he points out that we often blur the lines between the different stages and end up designing a model which contains a lot of implementation details:

2014 10 06 23 25 22

If, on the other hand, we compare a conceptual model with a graph model this is less of an issue as the two models map quite closely:

  • Entities -> Nodes / Labels
  • Attributes -> Properties
  • Relationships -> Relationships
  • Identifiers -> Unique Constraints

Unique Constraints don’t quite capture everything that Identifiers do since it’s possible to create a node of a specific label without specifying the property which is uniquely constrained. Other than that though each concept matches one for one.

We often say that graphs are white board friendly by which we mean that that the model you sketch on a white board is the same as that stored in the database.

For example, consider the following sketch of people and their interactions with various books:

IMG 2342

If we were to translate that into a write query using Neo4j’s cypher query language it would look like this:

CREATE (ian:Person {name: "Ian"})
CREATE (alan:Person {name: "Alan"})
CREATE (gg:Person:Author {name: "Graham Greene"})
CREATE (jlc:Person:Author {name: "John Le Carre"})
 
CREATE (omih:Book {name: "Our Man in Havana"})
CREATE (ttsp:Book {name: "Tinker Tailor, Soldier, Spy"})
 
CREATE (gg)-[:WROTE]->(omih)
CREATE (jlc)-[:WROTE]->(ttsp)
CREATE (ian)-[:PURCHASED {date: "05-02-2011"}]->(ttsp)
CREATE (ian)-[:PURCHASED {date: "08-09-2011"}]->(omih)
CREATE (alan)-[:PURCHASED {date: "05-07-2014"}]->(ttsp)

There are a few extra brackets and the ‘CREATE’ key word but we haven’t lost any of the fidelity of the domain and in my experience a non technical / commercial person would be able to understand the query.

By contrast this article shows the steps we might take from a conceptual model describing employees, departments and unions to the eventual relational model.

If you don’t have the time to read through that, we start with this initial model…

2014 10 07 00 13 51

…and by the time we’ve got to a model that can be stored in our relational database:

2014 10 07 00 14 32

You’ll notice we’ve lost the relationship types and they’ve been replaced by 4 foreign keys that allow us to join the different tables/sets together.

In a graph model we’d have been able to stay much closer to the conceptual model and therefore closer to the language of the business.

I’m still exploring the world of data modelling and next up for me is to read Joe’s ‘Mastering Data Modeling‘ book. I’m also curious how normal forms and data redundancy apply to graphs so I’ll be looking into that as well.

Thoughts welcome, as usual!

Categories: Blogs

R: A first attempt at linear regression

Wed, 10/01/2014 - 00:20

I’ve been working through the videos that accompany the Introduction to Statistical Learning with Applications in R book and thought it’d be interesting to try out the linear regression algorithm against my meetup data set.

I wanted to see how well a linear regression algorithm could predict how many people were likely to RSVP to a particular event. I started with the following code to build a data frame containing some potential predictors:

library(RNeo4j)
officeEventsQuery = "MATCH (g:Group {name: \"Neo4j - London User Group\"})-[:HOSTED_EVENT]->(event)<-[:TO]-({response: 'yes'})<-[:RSVPD]-(),
                           (event)-[:HELD_AT]->(venue)
                     WHERE (event.time + event.utc_offset) < timestamp() AND venue.name IN [\"Neo Technology\", \"OpenCredo\"]
                     RETURN event.time + event.utc_offset AS eventTime,event.announced_at AS announcedAt, event.name, COUNT(*) AS rsvps"
 
events = subset(cypher(graph, officeEventsQuery), !is.na(announcedAt))
events$eventTime <- timestampToDate(events$eventTime)
events$day <- format(events$eventTime, "%A")
events$monthYear <- format(events$eventTime, "%m-%Y")
events$month <- format(events$eventTime, "%m")
events$year <- format(events$eventTime, "%Y")
events$announcedAt<- timestampToDate(events$announcedAt)
events$timeDiff = as.numeric(events$eventTime - events$announcedAt, units = "days")

If we preview ‘events’ it contains the following columns:

> head(events)
            eventTime         announcedAt                                        event.name rsvps       day monthYear month year  timeDiff
1 2013-01-29 18:00:00 2012-11-30 11:30:57                                   Intro to Graphs    24   Tuesday   01-2013    01 2013 60.270174
2 2014-06-24 18:30:00 2014-06-18 19:11:19                                   Intro to Graphs    43   Tuesday   06-2014    06 2014  5.971308
3 2014-06-18 18:30:00 2014-06-08 07:03:13                         Neo4j World Cup Hackathon    24 Wednesday   06-2014    06 2014 10.476933
4 2014-05-20 18:30:00 2014-05-14 18:56:06                                   Intro to Graphs    53   Tuesday   05-2014    05 2014  5.981875
5 2014-02-11 18:00:00 2014-02-05 19:11:03                                   Intro to Graphs    35   Tuesday   02-2014    02 2014  5.950660
6 2014-09-04 18:30:00 2014-08-26 06:34:01 Hands On Intro to Cypher - Neo4j's Query Language    20  Thursday   09-2014    09 2014  9.497211

We want to predict ‘rsvps’ from the other columns so I started off by creating a linear model which took all the other columns into account:

> summary(lm(rsvps ~., data = events))
 
Call:
lm(formula = rsvps ~ ., data = events)
 
Residuals:
    Min      1Q  Median      3Q     Max 
-8.2582 -1.1538  0.0000  0.4158 10.5803 
 
Coefficients: (14 not defined because of singularities)
                                                                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                                       -9.365e+03  3.009e+03  -3.113  0.00897 **
eventTime                                                          3.609e-06  2.951e-06   1.223  0.24479   
announcedAt                                                        3.278e-06  2.553e-06   1.284  0.22339   
event.nameGraph Modelling - Do's and Don'ts                        4.884e+01  1.140e+01   4.286  0.00106 **
event.nameHands on build your first Neo4j app for Java developers  3.735e+01  1.048e+01   3.562  0.00391 **
event.nameHands On Intro to Cypher - Neo4j's Query Language        2.560e+01  9.713e+00   2.635  0.02177 * 
event.nameIntro to Graphs                                          2.238e+01  8.726e+00   2.564  0.02480 * 
event.nameIntroduction to Graph Database Modeling                 -1.304e+02  4.835e+01  -2.696  0.01946 * 
event.nameLunch with Neo4j's CEO, Emil Eifrem                      3.920e+01  1.113e+01   3.523  0.00420 **
event.nameNeo4j Clojure Hackathon                                 -3.063e+00  1.195e+01  -0.256  0.80203   
event.nameNeo4j Python Hackathon with py2neo's Nigel Small         2.128e+01  1.070e+01   1.989  0.06998 . 
event.nameNeo4j World Cup Hackathon                                5.004e+00  9.622e+00   0.520  0.61251   
dayTuesday                                                         2.068e+01  5.626e+00   3.676  0.00317 **
dayWednesday                                                       2.300e+01  5.522e+00   4.165  0.00131 **
monthYear01-2014                                                  -2.350e+02  7.377e+01  -3.185  0.00784 **
monthYear02-2013                                                  -2.526e+01  1.376e+01  -1.836  0.09130 . 
monthYear02-2014                                                  -2.325e+02  7.763e+01  -2.995  0.01118 * 
monthYear03-2013                                                  -4.605e+01  1.683e+01  -2.736  0.01805 * 
monthYear03-2014                                                  -2.371e+02  8.324e+01  -2.848  0.01468 * 
monthYear04-2013                                                  -6.570e+01  2.309e+01  -2.845  0.01477 * 
monthYear04-2014                                                  -2.535e+02  8.746e+01  -2.899  0.01336 * 
monthYear05-2013                                                  -8.672e+01  2.845e+01  -3.049  0.01011 * 
monthYear05-2014                                                  -2.802e+02  9.420e+01  -2.975  0.01160 * 
monthYear06-2013                                                  -1.022e+02  3.283e+01  -3.113  0.00897 **
monthYear06-2014                                                  -2.996e+02  1.003e+02  -2.988  0.01132 * 
monthYear07-2014                                                  -3.123e+02  1.054e+02  -2.965  0.01182 * 
monthYear08-2013                                                  -1.326e+02  4.323e+01  -3.067  0.00976 **
monthYear08-2014                                                  -3.060e+02  1.107e+02  -2.763  0.01718 * 
monthYear09-2013                                                          NA         NA      NA       NA   
monthYear09-2014                                                  -3.465e+02  1.164e+02  -2.976  0.01158 * 
monthYear10-2012                                                   2.602e+01  1.959e+01   1.328  0.20886   
monthYear10-2013                                                  -1.728e+02  5.678e+01  -3.044  0.01020 * 
monthYear11-2012                                                   2.717e+01  1.509e+01   1.800  0.09704 . 
month02                                                                   NA         NA      NA       NA   
month03                                                                   NA         NA      NA       NA   
month04                                                                   NA         NA      NA       NA   
month05                                                                   NA         NA      NA       NA   
month06                                                                   NA         NA      NA       NA   
month07                                                                   NA         NA      NA       NA   
month08                                                                   NA         NA      NA       NA   
month09                                                                   NA         NA      NA       NA   
month10                                                                   NA         NA      NA       NA   
month11                                                                   NA         NA      NA       NA   
year2013                                                                  NA         NA      NA       NA   
year2014                                                                  NA         NA      NA       NA   
timeDiff                                                                  NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 5.287 on 12 degrees of freedom
Multiple R-squared:  0.9585,	Adjusted R-squared:  0.8512 
F-statistic: 8.934 on 31 and 12 DF,  p-value: 0.0001399

As I understand it we can look at the R-squared value to understand how much of the variance in the data has been explained by the model – in this case it’s 85%.

A lot of the coefficients seem to be based around specific event names which seems a bit too specific to me so I wanted to see what would happen if I derived a feature which indicated whether a session was practical:

events$practical = grepl("Hackathon|Hands on|Hands On", events$event.name)

We can now run the model again with the new column having excluded ‘event.name’ field:

> summary(lm(rsvps ~., data = subset(events, select = -c(event.name))))
 
Call:
lm(formula = rsvps ~ ., data = subset(events, select = -c(event.name)))
 
Residuals:
    Min      1Q  Median      3Q     Max 
-18.647  -2.311   0.000   2.908  23.218 
 
Coefficients: (13 not defined because of singularities)
                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)      -3.980e+03  4.752e+03  -0.838   0.4127  
eventTime         2.907e-06  3.873e-06   0.751   0.4621  
announcedAt       3.336e-08  3.559e-06   0.009   0.9926  
dayTuesday        7.547e+00  6.080e+00   1.241   0.2296  
dayWednesday      2.442e+00  7.046e+00   0.347   0.7327  
monthYear01-2014 -9.562e+01  1.187e+02  -0.806   0.4303  
monthYear02-2013 -4.230e+00  2.289e+01  -0.185   0.8553  
monthYear02-2014 -9.156e+01  1.254e+02  -0.730   0.4742  
monthYear03-2013 -1.633e+01  2.808e+01  -0.582   0.5676  
monthYear03-2014 -8.094e+01  1.329e+02  -0.609   0.5496  
monthYear04-2013 -2.249e+01  3.785e+01  -0.594   0.5595  
monthYear04-2014 -9.230e+01  1.401e+02  -0.659   0.5180  
monthYear05-2013 -3.237e+01  4.654e+01  -0.696   0.4952  
monthYear05-2014 -1.015e+02  1.509e+02  -0.673   0.5092  
monthYear06-2013 -3.947e+01  5.355e+01  -0.737   0.4701  
monthYear06-2014 -1.081e+02  1.604e+02  -0.674   0.5084  
monthYear07-2014 -1.110e+02  1.678e+02  -0.661   0.5163  
monthYear08-2013 -5.144e+01  6.988e+01  -0.736   0.4706  
monthYear08-2014 -1.023e+02  1.784e+02  -0.573   0.5731  
monthYear09-2013 -6.057e+01  7.893e+01  -0.767   0.4523  
monthYear09-2014 -1.260e+02  1.874e+02  -0.672   0.5094  
monthYear10-2012  9.557e+00  2.873e+01   0.333   0.7430  
monthYear10-2013 -6.450e+01  9.169e+01  -0.703   0.4903  
monthYear11-2012  1.689e+01  2.316e+01   0.729   0.4748  
month02                  NA         NA      NA       NA  
month03                  NA         NA      NA       NA  
month04                  NA         NA      NA       NA  
month05                  NA         NA      NA       NA  
month06                  NA         NA      NA       NA  
month07                  NA         NA      NA       NA  
month08                  NA         NA      NA       NA  
month09                  NA         NA      NA       NA  
month10                  NA         NA      NA       NA  
month11                  NA         NA      NA       NA  
year2013                 NA         NA      NA       NA  
year2014                 NA         NA      NA       NA  
timeDiff                 NA         NA      NA       NA  
practicalTRUE    -9.388e+00  5.289e+00  -1.775   0.0919 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 10.21 on 19 degrees of freedom
Multiple R-squared:  0.7546,	Adjusted R-squared:  0.4446 
F-statistic: 2.434 on 24 and 19 DF,  p-value: 0.02592

Now we’re only accounting for 44% of the variance and none of our coefficients are significant so this wasn’t such a good change.

I also noticed that we’ve got a bit of overlap in the date related features – we’ve got one column for monthYear and then separate ones for month and year. Let’s strip out the combined one:

> summary(lm(rsvps ~., data = subset(events, select = -c(event.name, monthYear))))
 
Call:
lm(formula = rsvps ~ ., data = subset(events, select = -c(event.name, 
    monthYear)))
 
Residuals:
     Min       1Q   Median       3Q      Max 
-16.5745  -4.0507  -0.1042   3.6586  24.4715 
 
Coefficients: (1 not defined because of singularities)
                Estimate Std. Error t value Pr(>|t|)  
(Intercept)   -1.573e+03  4.315e+03  -0.364   0.7185  
eventTime      3.320e-06  3.434e-06   0.967   0.3425  
announcedAt   -2.149e-06  2.201e-06  -0.976   0.3379  
dayTuesday     4.713e+00  5.871e+00   0.803   0.4294  
dayWednesday  -2.253e-01  6.685e+00  -0.034   0.9734  
month02        3.164e+00  1.285e+01   0.246   0.8075  
month03        1.127e+01  1.858e+01   0.607   0.5494  
month04        4.148e+00  2.581e+01   0.161   0.8736  
month05        1.979e+00  3.425e+01   0.058   0.9544  
month06       -1.220e-01  4.271e+01  -0.003   0.9977  
month07        1.671e+00  4.955e+01   0.034   0.9734  
month08        8.849e+00  5.940e+01   0.149   0.8827  
month09       -5.496e+00  6.782e+01  -0.081   0.9360  
month10       -5.066e+00  7.893e+01  -0.064   0.9493  
month11        4.255e+00  8.697e+01   0.049   0.9614  
year2013      -1.799e+01  1.032e+02  -0.174   0.8629  
year2014      -3.281e+01  2.045e+02  -0.160   0.8738  
timeDiff              NA         NA      NA       NA  
practicalTRUE -9.816e+00  5.084e+00  -1.931   0.0645 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 10.19 on 26 degrees of freedom
Multiple R-squared:  0.666,	Adjusted R-squared:  0.4476 
F-statistic: 3.049 on 17 and 26 DF,  p-value: 0.005187

Again none of the coefficients are statistically significant which is disappointing. I think the main problem may be that I have very few data points (only 42) making it difficult to come up with a general model.

I think my next step is to look for some other features that could impact the number of RSVPs e.g. other events on that day, the weather.

I’m a novice at this but trying to learn more so if you have any ideas of what I should do next please let me know.

Categories: Blogs

Neo4j: Generic/Vague relationship names

Tue, 09/30/2014 - 18:47

An approach to modelling that I often see while working with Neo4j users is creating very generic relationships (e.g. HAS, CONTAINS, IS) and filtering on a relationship property or on a property/label at the end node.

Intuitively this doesn’t seem to make best use of the graph model as it means that you have to evaluate many relationships and nodes that you’re not interested in.

However, I’ve never actually tested the performance differences between the approaches so I thought I’d try it out.

I created 4 different databases which had one node with 60,000 outgoing relationships – 10,000 which we wanted to retrieve and 50,000 that were irrelevant.

I modelled the ‘relationship’ in 4 different ways…

  • Using a specific relationship type
    (node)-[:HAS_ADDRESS]->(address)
  • Using a generic relationship type and then filtering by end node label
    (node)-[:HAS]->(address:Address)
  • Using a generic relationship type and then filtering by relationship property
    (node)-[:HAS {type: "address"}]->(address)
  • Using a generic relationship type and then filtering by end node property
    (node)-[:HAS]->(address {type: “address”})

…and then measured how long it took to retrieve the ‘has address’ relationships.

The code is on github if you want to take a look.

Although it’s obviously not as precise as a JMH micro benchmark I think it’s good enough to get a feel for the difference between the approaches.

I ran a query against each database 100 times and then took the 50th, 75th and 99th percentiles (times are in ms):

Using a generic relationship type and then filtering by end node label
50%ile: 6.0    75%ile: 6.0    99%ile: 402.60999999999825
 
Using a generic relationship type and then filtering by relationship property
50%ile: 21.0   75%ile: 22.0   99%ile: 504.85999999999785
 
Using a generic relationship type and then filtering by end node label
50%ile: 4.0    75%ile: 4.0    99%ile: 145.65999999999931
 
Using a specific relationship type
50%ile: 0.0    75%ile: 1.0    99%ile: 25.749999999999872

We can drill further into why there’s a difference in the times for each of the approaches by profiling the equivalent cypher query. We’ll start with the one which uses a specific relationship name

Using a specific relationship type

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS_ADDRESS]->() return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +SimplePatternMatcher
      |
      +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+-----------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                 Other |
+----------------------+-------+--------+-----------------------------+-----------------------+
|         ColumnFilter |     1 |      0 |                             | keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                       |
| SimplePatternMatcher | 10000 |  10000 | n,   UNNAMED53,   UNNAMED35 |                       |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |          {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+-----------------------+
 
Total database accesses: 10001

Here we can see that there were 10,002 database accesses in order to get a count of our 10,000 HAS_ADDRESS relationships. We get a database access each time we load a node, relationship or property.

By contrast the other approaches have to load in a lot more data only to then filter it out:

Using a generic relationship type and then filtering by end node label

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS]->(:Address) return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+----------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                            Other |
+----------------------+-------+--------+-----------------------------+----------------------------------+
|         ColumnFilter |     1 |      0 |                             |            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                  |
|               Filter | 10000 |  10000 |                             | hasLabel(  UNNAMED45:Address(0)) |
| SimplePatternMatcher | 10000 |  60000 | n,   UNNAMED45,   UNNAMED35 |                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+----------------------------------+
 
Total database accesses: 70001

Using a generic relationship type and then filtering by relationship property

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS {type: "address"}]->() return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                                            Other |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|         ColumnFilter |     1 |      0 |                             |                            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                                  |
|               Filter | 10000 |  20000 |                             | Property(  UNNAMED35,type(0)) == {  AUTOSTRING1} |
| SimplePatternMatcher | 10000 | 120000 | n,   UNNAMED63,   UNNAMED35 |                                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
 
Total database accesses: 140001

Using a generic relationship type and then filtering by end node property

neo4j-sh (?)$ profile match (n) where id(n) = 0 match (n)-[:HAS]->({type: "address"}) return count(n);
+----------+
| count(n) |
+----------+
| 10000    |
+----------+
1 row
 
ColumnFilter
  |
  +EagerAggregation
    |
    +Filter
      |
      +SimplePatternMatcher
        |
        +NodeByIdOrEmpty
 
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|             Operator |  Rows | DbHits |                 Identifiers |                                            Other |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
|         ColumnFilter |     1 |      0 |                             |                            keep columns count(n) |
|     EagerAggregation |     1 |      0 |                             |                                                  |
|               Filter | 10000 |  20000 |                             | Property(  UNNAMED45,type(0)) == {  AUTOSTRING1} |
| SimplePatternMatcher | 10000 | 120000 | n,   UNNAMED45,   UNNAMED35 |                                                  |
|      NodeByIdOrEmpty |     1 |      1 |                        n, n |                                     {  AUTOINT0} |
+----------------------+-------+--------+-----------------------------+--------------------------------------------------+
 
Total database accesses: 140001

So in summary…specific relationships #ftw!

Categories: Blogs

PostgreSQL: ERROR: column does not exist

Tue, 09/30/2014 - 00:40

I’ve been playing around with PostgreSQL recently and in particular the Northwind dataset typically used as an introductory data set for relational databases.

Having imported the data I wanted to take a quick look at the employees table:

postgres=# SELECT * FROM employees LIMIT 1;
 EmployeeID | LastName | FirstName |        Title         | TitleOfCourtesy | BirthDate  |  HireDate  |           Address           |  City   | Region | PostalCode | Country |   HomePhone    | Extension | Photo |                                                                                      Notes                                                                                      | ReportsTo |              PhotoPath               
------------+----------+-----------+----------------------+-----------------+------------+------------+-----------------------------+---------+--------+------------+---------+----------------+-----------+-------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+--------------------------------------
          1 | Davolio  | Nancy     | Sales Representative | Ms.             | 1948-12-08 | 1992-05-01 | 507 - 20th Ave. E.\nApt. 2A | Seattle | WA     | 98122      | USA     | (206) 555-9857 | 5467      | \x    | Education includes a BA IN psychology FROM Colorado State University IN 1970.  She also completed "The Art of the Cold Call."  Nancy IS a member OF Toastmasters International. |         2 | http://accweb/emmployees/davolio.bmp
(1 ROW)

That works fine but what if I only want to return the ‘EmployeeID’ field?

postgres=# SELECT EmployeeID FROM employees LIMIT 1;
ERROR:  COLUMN "employeeid" does NOT exist
LINE 1: SELECT EmployeeID FROM employees LIMIT 1;

I hadn’t realised (or had forgotten) that field names get lower cased so we need to quote the name if it’s been stored in mixed case:

postgres=# SELECT "EmployeeID" FROM employees LIMIT 1;
 EmployeeID 
------------
          1
(1 ROW)

From my reading the suggestion seems to be to have your field names lower cased to avoid this problem but since it’s just a dummy data set I guess I’ll just put up with the quoting overhead for now.

Categories: Blogs

R: Deriving a new data frame column based on containing string

Mon, 09/29/2014 - 23:37

I’ve been playing around with R data frames a bit more and one thing I wanted to do was derive a new column based on the text contained in the existing column.

I started with something like this:

> x = data.frame(name = c("Java Hackathon", "Intro to Graphs", "Hands on Cypher"))
> x
             name
1  Java Hackathon
2 Intro to Graphs
3 Hands on Cypher

And I wanted to derive a new column based on whether or not the session was a practical one. The grepl function seemed to be the best tool for the job:

> grepl("Hackathon|Hands on|Hands On", x$name)
[1]  TRUE FALSE  TRUE

We can then add a column to our data frame with that output:

x$practical = grepl("Hackathon|Hands on|Hands On", x$name)

And we end up with the following:

> x
             name practical
1  Java Hackathon      TRUE
2 Intro to Graphs     FALSE
3 Hands on Cypher      TRUE

Not too tricky but it took me a bit too long to figure it out so I thought I’d save future Mark some time!

Categories: Blogs

R: Filtering data frames by column type (‘x’ must be numeric)

Mon, 09/29/2014 - 07:46

I’ve been working through the exercises from An Introduction to Statistical Learning and one of them required you to create a pair wise correlation matrix of variables in a data frame.

The exercise uses the ‘Carseats’ data set which can be imported like so:

> install.packages("ISLR")
> library(ISLR)
> head(Carseats)
  Sales CompPrice Income Advertising Population Price ShelveLoc Age Education Urban  US
1  9.50       138     73          11        276   120       Bad  42        17   Yes Yes
2 11.22       111     48          16        260    83      Good  65        10   Yes Yes
3 10.06       113     35          10        269    80    Medium  59        12   Yes Yes
4  7.40       117    100           4        466    97    Medium  55        14   Yes Yes
5  4.15       141     64           3        340   128       Bad  38        13   Yes  No
6 10.81       124    113          13        501    72       Bad  78        16    No Yes

filter the categorical variables from a data frame and

If we try to run the ‘cor‘ function on the data frame we’ll get the following error:

> cor(Carseats)
Error in cor(Carseats) : 'x' must be numeric

As the error message suggests, we can’t pass non numeric variables to this function so we need to remove the categorical variables from our data frame.

But first we need to work out which columns those are:

> sapply(Carseats, class)
      Sales   CompPrice      Income Advertising  Population       Price   ShelveLoc         Age   Education 
  "numeric"   "numeric"   "numeric"   "numeric"   "numeric"   "numeric"    "factor"   "numeric"   "numeric" 
      Urban          US 
   "factor"    "factor"

We can see a few columns of type ‘factor’ and luckily for us there’s a function which will help us identify those more easily:

> sapply(Carseats, is.factor)
      Sales   CompPrice      Income Advertising  Population       Price   ShelveLoc         Age   Education 
      FALSE       FALSE       FALSE       FALSE       FALSE       FALSE        TRUE       FALSE       FALSE 
      Urban          US 
       TRUE        TRUE

Now we can remove those columns from our data frame and create the correlation matrix:

> cor(Carseats[sapply(Carseats, function(x) !is.factor(x))])
                  Sales   CompPrice       Income  Advertising   Population       Price          Age    Education
Sales        1.00000000  0.06407873  0.151950979  0.269506781  0.050470984 -0.44495073 -0.231815440 -0.051955242
CompPrice    0.06407873  1.00000000 -0.080653423 -0.024198788 -0.094706516  0.58484777 -0.100238817  0.025197050
Income       0.15195098 -0.08065342  1.000000000  0.058994706 -0.007876994 -0.05669820 -0.004670094 -0.056855422
Advertising  0.26950678 -0.02419879  0.058994706  1.000000000  0.265652145  0.04453687 -0.004557497 -0.033594307
Population   0.05047098 -0.09470652 -0.007876994  0.265652145  1.000000000 -0.01214362 -0.042663355 -0.106378231
Price       -0.44495073  0.58484777 -0.056698202  0.044536874 -0.012143620  1.00000000 -0.102176839  0.011746599
Age         -0.23181544 -0.10023882 -0.004670094 -0.004557497 -0.042663355 -0.10217684  1.000000000  0.006488032
Education   -0.05195524  0.02519705 -0.056855422 -0.033594307 -0.106378231  0.01174660  0.006488032  1.000000000
Categories: Blogs

Neo4j: COLLECTing multiple values (Too many parameters for function ‘collect’)

Fri, 09/26/2014 - 22:46

One of my favourite functions in Neo4j’s cypher query language is COLLECT which allows us to group items into an array for later consumption.

However, I’ve noticed that people sometimes have trouble working out how to collect multiple items with COLLECT and struggle to find a way to do so.

Consider the following data set:

create (p:Person {name: "Mark"})
create (e1:Event {name: "Event1", timestamp: 1234})
create (e2:Event {name: "Event2", timestamp: 4567})
 
create (p)-[:EVENT]->(e1)
create (p)-[:EVENT]->(e2)

If we wanted to return each person along with a collection of the event names they’d participated in we could write the following:

$ MATCH (p:Person)-[:EVENT]->(e)
> RETURN p, COLLECT(e.name);
+--------------------------------------------+
| p                    | COLLECT(e.name)     |
+--------------------------------------------+
| Node[0]{name:"Mark"} | ["Event1","Event2"] |
+--------------------------------------------+
1 row

That works nicely, but what about if we want to collect the event name and the timestamp but don’t want to return the entire event node?

An approach I’ve seen a few people try during workshops is the following:

MATCH (p:Person)-[:EVENT]->(e)
RETURN p, COLLECT(e.name, e.timestamp)

Unfortunately this doesn’t compile:

SyntaxException: Too many parameters for function 'collect' (line 2, column 11)
"RETURN p, COLLECT(e.name, e.timestamp)"
           ^

As the error message suggests, the COLLECT function only takes one argument so we need to find another way to solve our problem.

One way is to put the two values into a literal array which will result in an array of arrays as our return result:

$ MATCH (p:Person)-[:EVENT]->(e)
> RETURN p, COLLECT([e.name, e.timestamp]);
+----------------------------------------------------------+
| p                    | COLLECT([e.name, e.timestamp])    |
+----------------------------------------------------------+
| Node[0]{name:"Mark"} | [["Event1",1234],["Event2",4567]] |
+----------------------------------------------------------+
1 row

The annoying thing about this approach is that as you add more items you’ll forget in which position you’ve put each bit of data so I think a preferable approach is to collect a map of items instead:

$ MATCH (p:Person)-[:EVENT]->(e)
> RETURN p, COLLECT({eventName: e.name, eventTimestamp: e.timestamp});
+--------------------------------------------------------------------------------------------------------------------------+
| p                    | COLLECT({eventName: e.name, eventTimestamp: e.timestamp})                                         |
+--------------------------------------------------------------------------------------------------------------------------+
| Node[0]{name:"Mark"} | [{eventName -> "Event1", eventTimestamp -> 1234},{eventName -> "Event2", eventTimestamp -> 4567}] |
+--------------------------------------------------------------------------------------------------------------------------+
1 row

During the Clojure Neo4j Hackathon that we ran earlier this week this proved to be a particularly pleasing approach as we could easily destructure the collection of maps in our Clojure code.

Categories: Blogs

Neo4j: LOAD CSV – Column is null

Wed, 09/24/2014 - 22:21

One problem I’ve seen a few people have recently when using Neo4j’s LOAD CSV function is dealing with CSV files that have dodgy hidden characters at the beginning of the header line.

For example, consider an import of this CSV file:

$ cat ~/Downloads/dodgy.csv
userId,movieId
1,2

We might start by checking which columns it has:

$ load csv with headers from "file:/Users/markneedham/Downloads/dodgy.csv" as line return line;
+----------------------------------+
| line                             |
+----------------------------------+
| {userId -> "1", movieId -> "2"} |
+----------------------------------+
1 row

Looks good so far but what about if we try to return just ‘userId’?

$ load csv with headers from "file:/Users/markneedham/Downloads/dodgy.csv" as line return line.userId;
+-------------+
| line.userId |
+-------------+
| <null>      |
+-------------+
1 row

Hmmm it’s null…what about ‘movieId’?

$ load csv with headers from "file:/Users/markneedham/Downloads/dodgy.csv" as line return line.movieId;
+--------------+
| line.movieId |
+--------------+
| "2"          |
+--------------+
1 row

That works fine so immediately we can suspect there are hidden characters at the beginning of the first line of the file.

The easiest way to check if this is the case is open the file using a Hex Editor – I quite like Hex Fiend for the Mac.

If we look at dodgy.csv we’ll see the following:

2014 09 24 21 20 06

Let’s delete the highlighted characters and try our cypher query again:

$ load csv with headers from "file:/Users/markneedham/Downloads/dodgy.csv" as line return line.userId;
+-------------+
| line.userId |
+-------------+
| "1"         |
+-------------+
1 row

All is well again, but something to keep in mind if you see a LOAD CSV near you behaving badly.

Categories: Blogs

R: ggplot – Plotting multiple variables on a line chart

Tue, 09/16/2014 - 18:59

In my continued playing around with meetup data I wanted to plot the number of members who join the Neo4j group over time.

I started off with the variable ‘byWeek’ which shows how many members joined the group each week:

> head(byWeek)
Source: local data frame [6 x 2]
 
        week n
1 2011-06-02 8
2 2011-06-09 4
3 2011-06-30 2
4 2011-07-14 1
5 2011-07-21 1
6 2011-08-18 1

I wanted to plot the actual count alongside a rolling average for which I created the following data frame:

library(zoo)
joinsByWeek = data.frame(actual = byWeek$n, 
                         week = byWeek$week,
                         rolling = rollmean(byWeek$n, 4, fill = NA, align=c("right")))
> head(joinsByWeek, 10)
   actual       week rolling
1       8 2011-06-02      NA
2       4 2011-06-09      NA
3       2 2011-06-30      NA
4       1 2011-07-14    3.75
5       1 2011-07-21    2.00
6       1 2011-08-18    1.25
7       1 2011-10-13    1.00
8       2 2011-11-24    1.25
9       1 2012-01-05    1.25
10      3 2012-01-12    1.75

The next step was to work out how to plot both ‘rolling’ and ‘actual’ on the same line chart. The easiest way is to make two calls to ‘geom_line’, like so:

ggplot(joinsByWeek, aes(x = week)) + 
  geom_line(aes(y = rolling), colour="blue") + 
  geom_line(aes(y = actual), colour = "grey") + 
  ylab(label="Number of new members") + 
  xlab("Week")
2014 09 16 21 57 14

Alternatively we can make use of the ‘melt’ function from the reshape library…

library(reshape)
meltedJoinsByWeek = melt(joinsByWeek, id = 'week')
> head(meltedJoinsByWeek, 20)
   week variable value
1     1   actual     8
2     2   actual     4
3     3   actual     2
4     4   actual     1
5     5   actual     1
6     6   actual     1
7     7   actual     1
8     8   actual     2
9     9   actual     1
10   10   actual     3
11   11   actual     1
12   12   actual     2
13   13   actual     4
14   14   actual     2
15   15   actual     3
16   16   actual     5
17   17   actual     1
18   18   actual     2
19   19   actual     1
20   20   actual     2

…which then means we can plot the chart with a single call to geom_line:

ggplot(meltedJoinsByWeek, aes(x = week, y = value, colour = variable)) + 
  geom_line() + 
  ylab(label="Number of new members") + 
  xlab("Week Number") + 
  scale_colour_manual(values=c("grey", "blue"))

2014 09 16 22 17 40

Categories: Blogs

R: ggplot – Plotting a single variable line chart (geom_line requires the following missing aesthetics: y)

Sat, 09/13/2014 - 13:41

I’ve been learning how to do moving averages in R and having done that calculation I wanted to plot these variables on a line chart using ggplot.

The vector of rolling averages looked like this:

> rollmean(byWeek$n, 4)
  [1]  3.75  2.00  1.25  1.00  1.25  1.25  1.75  1.75  1.75  2.50  2.25  2.75  3.50  2.75  2.75
 [16]  2.25  1.50  1.50  2.00  2.00  2.00  2.00  1.25  1.50  2.25  2.50  3.00  3.25  2.75  4.00
 [31]  4.25  5.25  7.50  6.50  5.75  5.00  3.50  4.00  5.75  6.25  6.25  6.00  5.25  6.25  7.25
 [46]  7.75  7.00  4.75  2.75  1.75  2.00  4.00  5.25  5.50 11.50 11.50 12.75 14.50 12.50 11.75
 [61] 11.00  9.25  5.25  4.50  3.25  4.75  7.50  8.50  9.25 10.50  9.75 15.25 16.00 15.25 15.00
 [76] 10.00  8.50  6.50  4.25  3.00  4.25  4.75  7.50 11.25 11.00 11.50 10.00  6.75 11.25 12.50
 [91] 12.00 11.50  6.50  8.75  8.50  8.25  9.50  8.50  8.75  9.50  8.00  4.25  4.50  7.50  9.00
[106] 12.00 19.00 19.00 22.25 23.50 22.25 21.75 19.50 20.75 22.75 22.75 24.25 28.00 23.00 26.00
[121] 24.25 21.50 26.00 24.00 28.25 25.50 24.25 31.50 31.50 35.75 35.75 29.00 28.50 27.25 25.50
[136] 27.50 26.00 23.75

I initially tried to plot a line chart like this:

library(ggplot2)
library(zoo)
rollingMean = rollmean(byWeek$n, 4)
qplot(rollingMean) + geom_line()

which resulted in this error:

stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
Error: geom_line requires the following missing aesthetics: y

It turns out we need to provide an x and y value if we want to draw a line chart. In this case we’ll generate the ‘x’ value – we only care that the y values get plotted in order from left to right:

qplot(1:length(rollingMean), rollingMean, xlab ="Week Number") + geom_line()
2014 09 13 16 58 57

If we want to use the ‘ggplot’ function then we need to put everything into a data frame first and then plot it:

ggplot(data.frame(week = 1:length(rollingMean), rolling = rollingMean),
       aes(x = week, y = rolling)) +
  geom_line()

2014 09 13 17 11 13

Categories: Blogs

R: Calculating rolling or moving averages

Sat, 09/13/2014 - 10:15

I’ve been playing around with some time series data in R and since there’s a bit of variation between consecutive points I wanted to smooth the data out by calculating the moving average.

I struggled to find an in built function to do this but came across Didier Ruedin’s blog post which described the following function to do the job:

mav <- function(x,n=5){filter(x,rep(1/n,n), sides=2)}

I tried plugging in some numbers to understand how it works:

> mav(c(4,5,4,6), 3)
Time Series:
Start = 1 
End = 4 
Frequency = 1 
[1]       NA 4.333333 5.000000       NA

Here I was trying to do a rolling average which took into account the last 3 numbers so I expected to get just two numbers back – 4.333333 and 5 – and if there were going to be NA values I thought they’d be at the beginning of the sequence.

In fact it turns out this is what the ‘sides’ parameter controls:

sides	
for convolution filters only. If sides = 1 the filter coefficients are for past values only; if sides = 2 they 
are centred around lag 0. In this case the length of the filter should be odd, but if it is even, more of the 
filter is forward in time than backward.

So in our ‘mav’ function the rolling average looks both sides of the current value rather than just at past values. We can tweak that to get the behaviour we want:

mav <- function(x,n=5){filter(x,rep(1/n,n), sides=1)}
> mav(c(4,5,4,6), 3)
Time Series:
Start = 1 
End = 4 
Frequency = 1 
[1]       NA       NA 4.333333 5.000000

The NA values are annoying for any plotting we want to do so let’s get rid of them:

> na.omit(mav(c(4,5,4,6), 3))
Time Series:
Start = 3 
End = 4 
Frequency = 1 
[1] 4.333333 5.000000

Having got to this point I noticed that Didier had referenced the zoo package in the comments and it has a built in function to take care of all this:

> library(zoo)
> rollmean(c(4,5,4,6), 3)
[1] 4.333333 5.000000

I also realised I can list all the functions in a package with the ‘ls’ function so I’ll be scanning zoo’s list of functions next time I need to do something time series related – there’ll probably already be a function for it!

> ls("package:zoo")
  [1] "as.Date"              "as.Date.numeric"      "as.Date.ts"          
  [4] "as.Date.yearmon"      "as.Date.yearqtr"      "as.yearmon"          
  [7] "as.yearmon.default"   "as.yearqtr"           "as.yearqtr.default"  
 [10] "as.zoo"               "as.zoo.default"       "as.zooreg"           
 [13] "as.zooreg.default"    "autoplot.zoo"         "cbind.zoo"           
 [16] "coredata"             "coredata.default"     "coredata<-"          
 [19] "facet_free"           "format.yearqtr"       "fortify.zoo"         
 [22] "frequency<-"          "ifelse.zoo"           "index"               
 [25] "index<-"              "index2char"           "is.regular"          
 [28] "is.zoo"               "make.par.list"        "MATCH"               
 [31] "MATCH.default"        "MATCH.times"          "median.zoo"          
 [34] "merge.zoo"            "na.aggregate"         "na.aggregate.default"
 [37] "na.approx"            "na.approx.default"    "na.fill"             
 [40] "na.fill.default"      "na.locf"              "na.locf.default"     
 [43] "na.spline"            "na.spline.default"    "na.StructTS"         
 [46] "na.trim"              "na.trim.default"      "na.trim.ts"          
 [49] "ORDER"                "ORDER.default"        "panel.lines.its"     
 [52] "panel.lines.tis"      "panel.lines.ts"       "panel.lines.zoo"     
 [55] "panel.plot.custom"    "panel.plot.default"   "panel.points.its"    
 [58] "panel.points.tis"     "panel.points.ts"      "panel.points.zoo"    
 [61] "panel.polygon.its"    "panel.polygon.tis"    "panel.polygon.ts"    
 [64] "panel.polygon.zoo"    "panel.rect.its"       "panel.rect.tis"      
 [67] "panel.rect.ts"        "panel.rect.zoo"       "panel.segments.its"  
 [70] "panel.segments.tis"   "panel.segments.ts"    "panel.segments.zoo"  
 [73] "panel.text.its"       "panel.text.tis"       "panel.text.ts"       
 [76] "panel.text.zoo"       "plot.zoo"             "quantile.zoo"        
 [79] "rbind.zoo"            "read.zoo"             "rev.zoo"             
 [82] "rollapply"            "rollapplyr"           "rollmax"             
 [85] "rollmax.default"      "rollmaxr"             "rollmean"            
 [88] "rollmean.default"     "rollmeanr"            "rollmedian"          
 [91] "rollmedian.default"   "rollmedianr"          "rollsum"             
 [94] "rollsum.default"      "rollsumr"             "scale_x_yearmon"     
 [97] "scale_x_yearqtr"      "scale_y_yearmon"      "scale_y_yearqtr"     
[100] "Sys.yearmon"          "Sys.yearqtr"          "time<-"              
[103] "write.zoo"            "xblocks"              "xblocks.default"     
[106] "xtfrm.zoo"            "yearmon"              "yearmon_trans"       
[109] "yearqtr"              "yearqtr_trans"        "zoo"                 
[112] "zooreg"
Categories: Blogs

R: ggplot – Cumulative frequency graphs

Mon, 09/01/2014 - 00:10

In my continued playing around with ggplot I wanted to create a chart showing the cumulative growth of the number of members of the Neo4j London meetup group.

My initial data frame looked like this:

> head(meetupMembers)
  joinTimestamp            joinDate  monthYear quarterYear       week dayMonthYear
1  1.376572e+12 2013-08-15 13:13:40 2013-08-01  2013-07-01 2013-08-15   2013-08-15
2  1.379491e+12 2013-09-18 07:55:11 2013-09-01  2013-07-01 2013-09-12   2013-09-18
3  1.349454e+12 2012-10-05 16:28:04 2012-10-01  2012-10-01 2012-10-04   2012-10-05
4  1.383127e+12 2013-10-30 09:59:03 2013-10-01  2013-10-01 2013-10-24   2013-10-30
5  1.372239e+12 2013-06-26 09:27:40 2013-06-01  2013-04-01 2013-06-20   2013-06-26
6  1.330295e+12 2012-02-26 22:27:00 2012-02-01  2012-01-01 2012-02-23   2012-02-26

The first step was to transform the data so that I had a data frame where a row represented a day where a member joined the group. There would then be a count of how many members joined on that date.

We can do this with dplyr like so:

library(dplyr)
> head(meetupMembers %.% group_by(dayMonthYear) %.% summarise(n = n()))
Source: local data frame [6 x 2]
 
  dayMonthYear n
1   2011-06-05 7
2   2011-06-07 1
3   2011-06-10 1
4   2011-06-12 1
5   2011-06-13 1
6   2011-06-15 1

To turn that into a chart we can plug it into ggplot and use the cumsum function to generate a line showing the cumulative total:

ggplot(data = meetupMembers %.% group_by(dayMonthYear) %.% summarise(n = n()), 
       aes(x = dayMonthYear, y = n)) + 
  ylab("Number of members") +
  xlab("Date") +
  geom_line(aes(y = cumsum(n)))
2014 08 31 22 58 42

Alternatively we could bring the call to cumsum forward and generate a data frame which has the cumulative total:

> head(meetupMembers %.% group_by(dayMonthYear) %.% summarise(n = n()) %.% mutate(n = cumsum(n)))
Source: local data frame [6 x 2]
 
  dayMonthYear  n
1   2011-06-05  7
2   2011-06-07  8
3   2011-06-10  9
4   2011-06-12 10
5   2011-06-13 11
6   2011-06-15 12

And if we plug that into ggplot we’ll get the same curve as before:

ggplot(data = meetupMembers %.% group_by(dayMonthYear) %.% summarise(n = n()) %.% mutate(n = cumsum(n)), 
       aes(x = dayMonthYear, y = n)) + 
  ylab("Number of members") +
  xlab("Date") +
  geom_line()

If we want the curve to be a bit smoother we can group it by quarter rather than by day:

> head(meetupMembers %.% group_by(quarterYear) %.% summarise(n = n()) %.% mutate(n = cumsum(n)))
Source: local data frame [6 x 2]
 
  quarterYear   n
1  2011-04-01  13
2  2011-07-01  18
3  2011-10-01  21
4  2012-01-01  43
5  2012-04-01  60
6  2012-07-01 122

Now let’s plug that into ggplot:

ggplot(data = meetupMembers %.% group_by(quarterYear) %.% summarise(n = n()) %.% mutate(n = cumsum(n)), 
       aes(x = quarterYear, y = n)) + 
    ylab("Number of members") +
    xlab("Date") +
    geom_line()
2014 08 31 23 08 24
Categories: Blogs

R: dplyr – group_by dynamic or programmatic field / variable (Error: index out of bounds)

Fri, 08/29/2014 - 11:13

In my last blog post I showed how to group timestamp based data by week, month and quarter and by the end we had the following code samples using dplyr and zoo:

library(RNeo4j)
library(zoo)
 
timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01", tz = "GMT")
 
query = "MATCH (:Person)-[:HAS_MEETUP_PROFILE]->()-[:HAS_MEMBERSHIP]->(membership)-[:OF_GROUP]->(g:Group {name: \"Neo4j - London User Group\"})
         RETURN membership.joined AS joinTimestamp"
meetupMembers = cypher(graph, query)
 
meetupMembers$joinDate <- timestampToDate(meetupMembers$joinTimestamp)
meetupMembers$monthYear <- as.Date(as.yearmon(meetupMembers$joinDate))
meetupMembers$quarterYear <- as.Date(as.yearqtr(meetupMembers$joinDate))
 
meetupMembers %.% group_by(week) %.% summarise(n = n())
meetupMembers %.% group_by(monthYear) %.% summarise(n = n())
meetupMembers %.% group_by(quarterYear) %.% summarise(n = n())

As you can see there’s quite a bit of duplication going on – the only thing that changes in the last 3 lines is the name of the field that we want to group by.

I wanted to pull this code out into a function and my first attempt was this:

groupMembersBy = function(field) {
  meetupMembers %.% group_by(field) %.% summarise(n = n())
}

And now if we try to group by week:

> groupMembersBy("week")
 Show Traceback
 
 Rerun with Debug
 Error: index out of bounds

It turns out if we want to do this then we actually want the regroup function rather than group_by:

groupMembersBy = function(field) {
  meetupMembers %.% regroup(list(field)) %.% summarise(n = n())
}

And now if we group by week:

> head(groupMembersBy("week"), 20)
Source: local data frame [20 x 2]
 
         week n
1  2011-06-02 8
2  2011-06-09 4
3  2011-06-16 1
4  2011-06-30 2
5  2011-07-14 1
6  2011-07-21 1
7  2011-08-18 1
8  2011-10-13 1
9  2011-11-24 2
10 2012-01-05 1
11 2012-01-12 3
12 2012-02-09 1
13 2012-02-16 2
14 2012-02-23 4
15 2012-03-01 2
16 2012-03-08 3
17 2012-03-15 5
18 2012-03-29 1
19 2012-04-05 2
20 2012-04-19 1

Much better!

Categories: Blogs

R: Grouping by week, month, quarter

Fri, 08/29/2014 - 02:25

In my continued playing around with R and meetup data I wanted to have a look at when people joined the London Neo4j group based on week, month or quarter of the year to see when they were most likely to do so.

I started with the following query to get back the join timestamps:

library(RNeo4j)
query = "MATCH (:Person)-[:HAS_MEETUP_PROFILE]->()-[:HAS_MEMBERSHIP]->(membership)-[:OF_GROUP]->(g:Group {name: \"Neo4j - London User Group\"})
         RETURN membership.joined AS joinTimestamp"
meetupMembers = cypher(graph, query)
 
> head(meetupMembers)
      joinTimestamp
1 1.376572e+12
2 1.379491e+12
3 1.349454e+12
4 1.383127e+12
5 1.372239e+12
6 1.330295e+12

The first step was to get joinDate into a nicer format that we can use in R more easily:

timestampToDate <- function(x) as.POSIXct(x / 1000, origin="1970-01-01", tz = "GMT")
meetupMembers$joinDate <- timestampToDate(meetupMembers$joinTimestamp)
 
> head(meetupMembers)
  joinTimestamp            joinDate
1  1.376572e+12 2013-08-15 13:13:40
2  1.379491e+12 2013-09-18 07:55:11
3  1.349454e+12 2012-10-05 16:28:04
4  1.383127e+12 2013-10-30 09:59:03
5  1.372239e+12 2013-06-26 09:27:40
6  1.330295e+12 2012-02-26 22:27:00

Much better!

I started off with grouping by month and quarter and came across the excellent zoo library which makes it really easy to transform dates:

library(zoo)
meetupMembers$monthYear <- as.Date(as.yearmon(meetupMembers$joinDate))
meetupMembers$quarterYear <- as.Date(as.yearqtr(meetupMembers$joinDate))
 
> head(meetupMembers)
  joinTimestamp            joinDate  monthYear quarterYear
1  1.376572e+12 2013-08-15 13:13:40 2013-08-01  2013-07-01
2  1.379491e+12 2013-09-18 07:55:11 2013-09-01  2013-07-01
3  1.349454e+12 2012-10-05 16:28:04 2012-10-01  2012-10-01
4  1.383127e+12 2013-10-30 09:59:03 2013-10-01  2013-10-01
5  1.372239e+12 2013-06-26 09:27:40 2013-06-01  2013-04-01
6  1.330295e+12 2012-02-26 22:27:00 2012-02-01  2012-01-01

The next step was to create a new data frame which grouped the data by those fields. I’ve been learning dplyr as part of Udacity’s EDA course so I thought I’d try and use that:

> head(meetupMembers %.% group_by(monthYear) %.% summarise(n = n()), 20)
 
    monthYear  n
1  2011-06-01 13
2  2011-07-01  4
3  2011-08-01  1
4  2011-10-01  1
5  2011-11-01  2
6  2012-01-01  4
7  2012-02-01  7
8  2012-03-01 11
9  2012-04-01  3
10 2012-05-01  9
11 2012-06-01  5
12 2012-07-01 16
13 2012-08-01 32
14 2012-09-01 14
15 2012-10-01 28
16 2012-11-01 31
17 2012-12-01  7
18 2013-01-01 52
19 2013-02-01 49
20 2013-03-01 22
> head(meetupMembers %.% group_by(quarterYear) %.% summarise(n = n()), 20)
 
   quarterYear   n
1   2011-04-01  13
2   2011-07-01   5
3   2011-10-01   3
4   2012-01-01  22
5   2012-04-01  17
6   2012-07-01  62
7   2012-10-01  66
8   2013-01-01 123
9   2013-04-01 139
10  2013-07-01 117
11  2013-10-01  94
12  2014-01-01 266
13  2014-04-01 359
14  2014-07-01 216

Grouping by week number is a bit trickier but we can do it with a bit of transformation on our initial timestamp:

meetupMembers$week <- as.Date("1970-01-01")+7*trunc((meetupMembers$joinTimestamp / 1000)/(3600*24*7))
 
> head(meetupMembers %.% group_by(week) %.% summarise(n = n()), 20)
 
         week n
1  2011-06-02 8
2  2011-06-09 4
3  2011-06-16 1
4  2011-06-30 2
5  2011-07-14 1
6  2011-07-21 1
7  2011-08-18 1
8  2011-10-13 1
9  2011-11-24 2
10 2012-01-05 1
11 2012-01-12 3
12 2012-02-09 1
13 2012-02-16 2
14 2012-02-23 4
15 2012-03-01 2
16 2012-03-08 3
17 2012-03-15 5
18 2012-03-29 1
19 2012-04-05 2
20 2012-04-19 1

We can then plug that data frame into ggplot if we want to track membership sign up over time at different levels of granularity and create some bar charts of scatter plots depending on what we feel like!

Categories: Blogs