It’s the holiday season, so why not amp you your vizzes’ holiday spirit by adding some bell curves to your histograms? Also, I just recently came across this request in a customer meeting and thereby discovered how easy that is to do. The most difficult part is wrapping your head around what a normal distribution is (please resort to Wikipedia for that), how it’s calculated (I literally stole the equation from Wikipedia) and how to translate that into a Calculated Field in Tableau. The rest is a simple dual-axis chart, a parameter and some rather basic Tableau techniques that need your attention.
With my background of spatial terrorism analysis I’m always very interested in the statistical analysis of crime data. Scott Stoltzman over at stoltzmaniac.com discovered a great data set by the City and County of Denver. It has data about all the criminal offenses in the City and County of Denver for the previous five calendar years plus the current year to date with plenty of attributes, timestamps and even geographic locations. Scott wrote a series of blog posts (starting here, then here, and here) showing some initial exploratory data analysis (ETA) in R and also some in-depth looks into a few topics that sparked his interest along the way. That’s exactly what Tableau wants to enable people to do, so with this first episode of “Let me tableau this for you” I want to show how easy it is to get to the same interesting insights Scott outlined in his write-up, only without all the coding. I’m not sure how long it took him to get from finding the data to generating all the plots in the article, but my guess would be it took longer than this ~20 minute screencast. Enjoy the video below, and please let us know in the comments section if you have anything to add. Please refer to the original post if you want to know more about the idea behind Let me tableau this for you (LMTTFY).
Today I stumbled across something I wouldn’t exactly consider a bug, but at least some rather unintuitive and annoying behavior in QGIS when performing table joins.
I did something very mundane: joining a Postgres table of spatial data to another Postgres table of attribute data. The normal way to do this (for me) is as follows:
- Open the spatial table using
Layer > Add Layer > Add PostGIS Layers...
- Open the attribute table the same way (1 & 2 can be loaded in one go)
- Join the tables in the spatial table’s
For that last step I decided to join the two tables (
plr is the spatial table here, while
mss has the attributes) using the field
plr_id, which exists in both tables and only once on each side (hence a plain vanilla 1:1 join).
That works perfectly fine, except that somehow the order of the joined fields appears to get messed up:
Some research revealed that this seems to be a problem caused by identical field names in the two joined tables other than the join field itself. In my case the aforementioned
plr_id was used to join the two tables, but in addition both tables also had a field
gid, as can be seen in the following screenshot on the left:
Removing this field
gid from the attribute table
mss was no problem, since the 1:1 relation to the spatial data uses the key
plr_id anyways. As can be seen in the screenshot above on the right, the new table
mss2 is identical to
mss, only without the field
gid. And lo-and-behold – joining this attribute table to the spatial table
plr in QGIS works flawlessly now:
This problem had already been identified in QGIS 2.0 in late 2013, and has been marked as fixed in the meantime. Removing fields with identical names in the two tables is one – admittedly rather radical way – to
solve circumvent the issue. Another, more intuitive way would be to choose a meaningful table prefix in the
Add vector join dialog which can be seen in the first image above. As you can see I checked the
Custom field name prefix checkbox but left the field empty. I prefer this, since it keeps my field names nice and tidy, but in cases where homonymous fields exist in the two tables you will run into trouble – hence entering a prefix here would be a nice and easy fix for this issue.
Everything described above was performed on
QGIS 2.8.1-Wien (64bit) on a Windows 7 machine and
PostgreSQL 9.1.16 on a
64bit Ubuntu 4.6.3 server (