I have data that have three variables: vehicle id, speed_kmh, and Time_of_Travel.
I am seeking your help with a code that is capable of calculating the mean of all observations at each travel time (or fixed distance based on the x-axis unit) and then creating a line graph that shows the trend of the observation along with the mean of all observations. An example is shown below for speed vs distance along with my data.
I am using the following code:
xtline speed_kmh , i( vehicleid ) t( timeoftravelall ) overlay leg(off)
if possible, a code works for both cases 1- speed vs time and 2- speed vs distance.
Thank you so much.
Array Array
Vehicle ID | Speed_kmh | Time_of_Travel |
1 | 80.99 | 0.00 |
1 | 80.99 | 0.50 |
1 | 80.29 | 1.00 |
1 | 78.92 | 1.50 |
1 | 76.87 | 2.00 |
1 | 75.50 | 2.50 |
1 | 74.10 | 3.00 |
1 | 72.67 | 3.50 |
1 | 70.56 | 4.00 |
1 | 69.17 | 4.50 |
1 | 67.75 | 5.00 |
1 | 66.27 | 5.50 |
1 | 64.06 | 6.00 |
1 | 62.59 | 6.50 |
1 | 61.24 | 7.00 |
1 | 60.02 | 7.50 |
1 | 58.21 | 8.00 |
1 | 57.03 | 8.50 |
1 | 55.63 | 9.00 |
1 | 54.03 | 9.50 |
1 | 51.63 | 10.00 |
1 | 50.06 | 10.50 |
1 | 48.23 | 11.00 |
1 | 46.13 | 11.50 |
1 | 42.99 | 12.00 |
1 | 40.90 | 12.50 |
1 | 39.02 | 13.00 |
1 | 37.38 | 13.50 |
1 | 34.90 | 14.00 |
1 | 33.25 | 14.50 |
1 | 31.56 | 15.00 |
1 | 29.84 | 15.50 |
1 | 27.26 | 16.00 |
1 | 25.53 | 16.50 |
1 | 23.74 | 17.00 |
1 | 21.87 | 17.50 |
1 | 19.07 | 18.00 |
1 | 17.21 | 18.50 |
1 | 15.38 | 19.00 |
1 | 13.58 | 19.50 |
1 | 10.89 | 20.00 |
1 | 9.10 | 20.50 |
1 | 8.04 | 21.00 |
1 | 7.72 | 21.50 |
1 | 7.24 | 22.00 |
1 | 6.92 | 22.50 |
1 | 6.26 | 23.00 |
1 | 5.28 | 23.50 |
1 | 3.81 | 24.00 |
1 | 2.83 | 24.50 |
1 | 2.37 | 25.00 |
1 | 2.41 | 25.50 |
1 | 2.49 | 26.00 |
1 | 2.54 | 26.50 |
1 | 2.31 | 27.00 |
1 | 1.81 | 27.50 |
1 | 1.08 | 28.00 |
1 | 0.59 | 28.50 |
1 | 0.34 | 29.00 |
1 | 0.34 | 29.50 |
1 | 0.34 | 30.00 |
2 | 89.11 | 0.00 |
2 | 89.11 | 0.50 |
2 | 88.32 | 1.00 |
2 | 86.75 | 1.50 |
2 | 84.43 | 2.00 |
2 | 82.90 | 2.50 |
2 | 81.78 | 3.00 |
2 | 81.09 | 3.50 |
2 | 80.08 | 4.00 |
2 | 79.43 | 4.50 |
2 | 78.11 | 5.00 |
2 | 76.14 | 5.50 |
2 | 73.21 | 6.00 |
2 | 71.28 | 6.50 |
2 | 69.13 | 7.00 |
2 | 66.76 | 7.50 |
2 | 63.26 | 8.00 |
2 | 60.94 | 8.50 |
2 | 58.31 | 9.00 |
2 | 55.34 | 9.50 |
2 | 50.88 | 10.00 |
2 | 47.91 | 10.50 |
2 | 44.91 | 11.00 |
2 | 41.89 | 11.50 |
2 | 37.37 | 12.00 |
2 | 34.35 | 12.50 |
2 | 31.19 | 13.00 |
2 | 27.91 | 13.50 |
2 | 22.98 | 14.00 |
2 | 19.69 | 14.50 |
2 | 17.45 | 15.00 |
2 | 16.25 | 15.50 |
2 | 14.46 | 16.00 |
2 | 13.27 | 16.50 |
2 | 12.21 | 17.00 |
2 | 11.27 | 17.50 |
2 | 9.86 | 18.00 |
2 | 8.92 | 18.50 |
2 | 7.94 | 19.00 |
2 | 6.94 | 19.50 |
2 | 5.43 | 20.00 |
2 | 4.43 | 20.50 |
2 | 3.63 | 21.00 |
2 | 3.03 | 21.50 |
2 | 2.14 | 22.00 |
2 | 1.54 | 22.50 |
2 | 1.11 | 23.00 |
2 | 0.84 | 23.50 |
2 | 0.47 | 24.00 |
2 | 0.23 | 24.50 |
2 | 0.12 | 25.00 |
2 | 0.12 | 25.50 |
2 | 0.12 | 26.00 |
3 | 86.50 | 0.00 |
3 | 86.50 | 0.50 |
3 | 85.87 | 1.00 |
3 | 84.65 | 1.50 |
3 | 82.85 | 2.00 |
3 | 81.67 | 2.50 |
3 | 80.90 | 3.00 |
3 | 80.54 | 3.50 |
3 | 80.05 | 4.00 |
3 | 79.74 | 4.50 |
3 | 78.74 | 5.00 |
3 | 77.04 | 5.50 |
3 | 74.51 | 6.00 |
3 | 72.85 | 6.50 |
3 | 70.71 | 7.00 |
3 | 68.09 | 7.50 |
3 | 64.22 | 8.00 |
3 | 61.65 | 8.50 |
3 | 58.48 | 9.00 |
3 | 54.67 | 9.50 |
3 | 48.96 | 10.00 |
3 | 45.16 | 10.50 |
3 | 41.55 | 11.00 |
3 | 38.13 | 11.50 |
3 | 33.01 | 12.00 |
3 | 29.59 | 12.50 |
3 | 26.75 | 13.00 |
3 | 24.49 | 13.50 |
3 | 21.10 | 14.00 |
3 | 18.84 | 14.50 |
3 | 16.55 | 15.00 |
3 | 14.24 | 15.50 |
3 | 10.77 | 16.00 |
3 | 8.46 | 16.50 |
3 | 7.19 | 17.00 |
3 | 6.96 | 17.50 |
3 | 6.61 | 18.00 |
3 | 6.38 | 18.50 |
3 | 5.62 | 19.00 |
3 | 4.33 | 19.50 |
3 | 2.39 | 20.00 |
3 | 1.10 | 20.50 |
3 | 0.45 | 21.00 |
3 | 0.45 | 21.50 |
3 | 0.45 | 22.00 |
0 Response to Line graph of the mean of all observations
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