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170 Getting Started with Processing
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}
noFill();
stroke(204, 51, 0);
beginShape();
for (int i = 0; i < homeRuns.length; i++) {
float x = map(i, 0, homeRuns.length-1, 20, 460);
float y = map(homeRuns[i], 0, 60, 100, 20);
vertex(x, y);
}
endShape();
This example is so minimal that it’s not necessary to store this
data in arrays, but the idea can be applied to more complex
examples you might want to make in the future. In addition, you
can see how this example could be enhanced with more information—for
instance, information on the vertical axis to state
the number of home runs and on the horizontal to define the
year.
Example 12-3: 29,740 Cities
To get a better idea about the potential of working with data
tables, the next example uses a larger data set and introduces a
convenient feature. This table data is different because the first
row, the first line in the file, is a header. The header defines a
label for each column to clarify the context. This is the first five
lines of our new data file called cities.csv:
zip,state,city,lat,lng
35004,AL,Acmar,33.584132,-86.51557
35005,AL,Adamsville,33.588437,-86.959727
35006,AL,Adger,33.434277,-87.167455
35007,AL,Keystone,33.236868,-86.812861
The header makes it easier to read the code—for example, the
second line of the file states the zip code of Acmar, Alabama, is
35004 and defines the latitude of the city as 33.584132 and the
longitude as -86.51557. In total, the file is 29,741 lines long and it
defines the location and zip codes of 29,740 cities in the United
States.
The next example loads this data within setup() and then draws
it to the screen in a for loop within draw(). The setXY() function
converts the latitude and longitude data from the file into a
point on the screen: