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Use the Live Editor to Accelerate Exploratory Programming

The following is an example of how to use the Live Editor to accelerate exploratory programming. This example demonstates 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.

  • 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
                            longitude    latitude    deaths    drivers    vehicles    vehicleMiles    alcoholRelated    urbanPopulation
                            _________    ________    ______    _______    ________    ____________    ______________    _______________

    Wyoming                 -107.56      43.033      164       380.18     671.53       9261            54               65.226         
    District_of_Columbia    -77.027      38.892       43       349.12      240.4       3742            12                  100         
    Vermont                 -72.556      44.043       98       550.46     551.52       7855            20               38.196         
    North_Dakota              -99.5      47.469      100       461.78     721.84       7594            35               55.807         
    South_Dakota            -99.679      44.272      197        563.3     882.77       8784            76               51.923         
    Delaware                -75.494      39.107      134       533.94     728.52       9301            48               80.021         
    Montana                 -110.58      46.867      229       712.88     1056.7      11207           100               54.031         
    Rhode_Island            -71.434      41.589       83       741.84      834.5       8473            41               90.936         
    New_Hampshire           -71.559      43.908      171       985.77     1244.6      13216            51               59.181         
    Maine                   -69.081      44.886      194       984.83     1106.8      14948            58               40.206         

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')

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.

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

varName = 'urbanPopulation';
scatter(fatalities.(varName),rate)         % Plot fatalities vs. selected variable
xlabel(varName)
ylabel('Percent Fatalities per Million Vehicle Miles') 

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

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 or PDF 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
for i = 1:49
    patch(usastates(i).Lon, usastates(i).Lat,'white')
end
daspect([1.4 1 1])
axis tight off
hold on
scatter(fatalities.longitude,fatalities.latitude,2000*rate,fatalities.urbanPopulation,'filled')
c = colorbar;
title(c,'Percent Urban')

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