Focus of chapter three in Nathan Yau's Visualize this is "Choosing Tools to Visualize data".
There are several options to choose tools from. As for categories of tools, Nathan divides them as out of the box, click and drag, those that require programming, some others more specific for graphics.
The chapter is a survey of the various categories of tools. Trade offs with each of the categories and tools are discussed - that is the highlight of the chapter. It is better the reader knows ahead on the learning curve, what the features are about. Instead of searching Google randomly for tools, techniques and trade offs, it is a relief to find all in one place in a chapter like this. Resources to get to these tools is also provided in at a glance side notes.
Basics that lay the foundations of the features of the tools are quite simple.
Bar charts are about displaying the frequency of values for a specific column. A pie charts shows proportional relationships. Mapping is about overlap and overlay of data with geographical data. Heat maps are special case where the relative data intensity is overlaid on a Map.
Useful Mapping API resources include Google Maps API, Yahoo Maps Web services and such.
Excel, R, Tableau, Adobe Illustrator, Flash/ ActionScript, Processing, SAS are some of the tools discussed
The list is lengthy. Spending time to understand the strength and long term growth of the features is useful knowledge for the learner.
For the learner,
Working with Excel to familiarize with the standard chart types,
then work with Google spreadsheets and Google Gadgets to take advantage of the web based, more advanced applications of charts. Programming would be an option if there is large scale data handling, general/ specific processing involved. Both commercial and open source software are available.
There are several options to choose tools from. As for categories of tools, Nathan divides them as out of the box, click and drag, those that require programming, some others more specific for graphics.
The chapter is a survey of the various categories of tools. Trade offs with each of the categories and tools are discussed - that is the highlight of the chapter. It is better the reader knows ahead on the learning curve, what the features are about. Instead of searching Google randomly for tools, techniques and trade offs, it is a relief to find all in one place in a chapter like this. Resources to get to these tools is also provided in at a glance side notes.
Basics that lay the foundations of the features of the tools are quite simple.
Bar charts are about displaying the frequency of values for a specific column. A pie charts shows proportional relationships. Mapping is about overlap and overlay of data with geographical data. Heat maps are special case where the relative data intensity is overlaid on a Map.
Useful Mapping API resources include Google Maps API, Yahoo Maps Web services and such.
Excel, R, Tableau, Adobe Illustrator, Flash/ ActionScript, Processing, SAS are some of the tools discussed
The list is lengthy. Spending time to understand the strength and long term growth of the features is useful knowledge for the learner.
For the learner,
Working with Excel to familiarize with the standard chart types,
then work with Google spreadsheets and Google Gadgets to take advantage of the web based, more advanced applications of charts. Programming would be an option if there is large scale data handling, general/ specific processing involved. Both commercial and open source software are available.
