Accelerate Exploratory Programming Using the Live Editor

Open Live Script

The following is an example of how to use the Live Editor to accelerate exploratory programming. This example demonstrates how you can use the Live Editor to:

  • See output together with the code that produced it.

  • Divide your program into sections to evaluate blocks of code individually.

  • Include visualizations.

  • Experiment with parameter values using controls.

  • Summarize and share your findings.

Load Highway Fatality Data

The Live Editor displays output together with the code that produced it. To run a section, go to the Live Editor tab and select the Run Section button. You can also click the blue bar that appears when you move your mouse to the left edge of a section.

In this example, we explore some highway fatality data. Start by loading the data. The variables are shown as the column headers of the table.

load fatalities
fatalities(1:10,:)
ans=10×8 table
    -107.5556    43.0327    164    380.1800      671.5290     9261     54    65.2257
     -77.0269    38.8921     43    349.1220      240.4030     3742     12        100
     -72.5565    44.0435     98    550.4620      551.5160     7855     20    38.1964
     -99.4998    47.4691    100    461.7800      721.8350     7594     35    55.8072
     -99.6790    44.2720    197    563.2980      882.7690     8784     76    51.9228
     -75.4942    39.1071    134    533.9430      728.5240     9301     48    80.0211
    -110.5763    46.8671    229    712.8800    1.0567e+03    11207    100    54.0310
     -71.4337    41.5887     83    741.8410      834.5010     8473     41    90.9356
     -71.5591    43.9080    171    985.7750    1.2446e+03    13216     51    59.1811
     -69.0811    44.8858    194    984.8290    1.1068e+03    14948     58    40.2057

Calculate Fatality Rates

The Live Editor allows you to divide your program into sections containing text, code, and output. To create a new section, go to the Live Editor tab and click the Section Break button. The code in a section can be run independently, which makes it easy to explore ideas as you write your program.

Calculate the fatality rate per one million vehicle miles. From these values we can find the states with the lowest and highest fatality rates.

states = fatalities.Properties.RowNames;
rate = fatalities.deaths./fatalities.vehicleMiles;
[~, minIdx] = min(rate);                  % Minimum accident rate
[~, maxIdx] = max(rate);                  % Maximum accident rate
disp([states{minIdx} ' has the lowest fatality rate at ' num2str(rate(minIdx))])
Massachusetts has the lowest fatality rate at 0.0086907

disp([states{maxIdx} ' has the highest fatality rate at ' num2str(rate(maxIdx))])
Mississippi has the highest fatality rate at 0.022825

Distribution of Fatalities

You can include visualizations in your program. Like output, plots and figures appear together with the code that produced them.

We can use a bar chart to see the distribution of fatality rates among the states. There are 11 states that have a fatality rate greater than 0.02 per million vehicle miles.

histogram(rate,10)
xlabel('Fatalities per Million Vehicle Miles')
ylabel('Number of States')

Figure contains an axes object. The axes object with xlabel Fatalities per Million Vehicle Miles, ylabel Number of States contains an object of type histogram.

Find Correlations in the Data

You can explore your data quickly in the Live Editor by experimenting with parameter values to see how your results will change. Add controls to change parameter values interactively. To add controls, go to the Live Editor tab, click the Control button, and select from the available options.

We can experiment with the data to see if any of the variables in the table are correlated with highway fatalities. For example, it appears that highway fatality rates are lower in states with a higher percentage urban population.

dataToPlot = "urbanPopulation";
close                                      % Close any open figures
scatter(fatalities.(dataToPlot),rate)         % Plot fatalities vs. selected variable
xlabel(dataToPlot)
ylabel('Percent Fatalities per Million Vehicle Miles') 

hold on
xmin = min(fatalities.(dataToPlot));
xmax = max(fatalities.(dataToPlot));
p = polyfit(fatalities.(dataToPlot),rate,1);  % Calculate & plot least squares line
plot([xmin xmax], polyval(p,[xmin xmax]))

Figure contains an axes object. The axes object with xlabel urbanPopulation, ylabel Percent Fatalities per Million Vehicle Miles contains 2 objects of type scatter, line.

Plot Fatalities and Urbanization on a US Map

Summarize your results and share your live script with your colleagues. Using your live script, they can recreate and extend your analysis. You can also save your analysis as HTML, Microsoft® Word, or PDF documents for publication.

Based on this analysis, we can summarize our findings using a plot of fatality rates and urban population on a map of the continental United States.

load usastates.mat
figure
geoplot([usastates.Lat], [usastates.Lon], 'black')
geobasemap darkwater
hold on
geoscatter(fatalities.latitude,fatalities.longitude,2000*rate,fatalities.urbanPopulation,'filled')
c = colorbar;
title(c,'Percent Urban')

Figure contains an axes object with type geoaxes. The geoaxes object contains 2 objects of type line, scatter.

See Also

Topics