Apply function to data grouped by cut() - r
I would like some help with summarising data using cut. I have been successful in less complicated situations, but now I am stuck.
The data:
> dput(sumsq)
structure(list(part_no = c(10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), ratperc = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0, 0, 0, 0,
0, 0, 75.6, 0, 89.6, 24.8, -100, -100, 75.6, 100, 100, -100,
-100, -100, -100, -100, -100, 75.6, 98.4, 98.4, -51.2, -51.2,
0.8, 0.8, 0.4, 0.4, 0.4, 0.4, 75.2, -100, -100, -100, 1.2, -0.4,
-0.4, -0.4, -0.4, 100, 100, -1.6, 0, 0, 0, 0, -100, 0.4, 100,
0.4, 0.4, 100, -0.4, -78.4, 0.4, 100, 100, 100, 100, -100, 23.6,
61.2, 61.2, 69.2, 75.6, 75.6, 75.6, 75.6, 75.6, 98, 98, 98, -75.2,
-75.2, 47.2, 47.2, 47.2, 47.2, 76.8, 97.6, -71.6, -71.6, -71.6,
-71.6, 24, 52, 52, 52, 75.2, 75.2, -77.6, 25.2, 47.2, 76.4, 76.4,
76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4,
76.4, -73.2, -73.2, -73.2, -73.2, 0.8, 0.8, 75.2, 75.2, 75.2,
75.2, 75.2, 75.2, 0.4, 0.4, 0.4, 0.4, 0.4, -100, -100, -100,
-100, -100, 73.2, 2, -0.8, -0.8, -0.8, -100, -0.4, -0.4, 50.4,
50.4, 50.4, 50.4, 50.4, 50.4, -76.4, 99.6, 99.6, -76.4, 100,
100, 50.4, 1.2, 28, -1.2, 93.6, 41.2, 1.6, 24.8, -1.6, 0, 0,
24.8, -24, 26, 50.8, 2, 28, 36.4, 24, -43.6, 33.6, 61.2, 81.2,
86.8, 34, -51.6, -2, 28.4, 2, 82, 41.6, 25.6, 82, 0.8, 92, 1.2,
86.4, 54, 96, 0.4, -54.4, 1.2, -93.2, -49.2, -98.4, -2, -77.2,
93.2, 23.6, 78.8, 42.4, 0.4, 2.8, 70.8, 24.4, 2.4, 62, 92.8,
16.4, -61.2, 24.4, -77.2, -0.4, 74.8, 3.6, 82, 82, 18, 54, 9.2,
55.2, 96.4, 96.4, 90, 90, -84.4, -84.4, -2.8, -2, -90.4, 2.4,
34.8, 24, -1.6, -16.8, 2.8, 2.4, -83.2, 22.4, 22.4, -1.6, -1.6,
60, -2.4, 2.4, 2, 0.8, -22.8, 2, -1.6, 25.2, 2, 2, -52.8, -1.2,
-1.2, 3.2, -74.4, 3.2, 3.2, -78.4, 0.4, -2.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, -79.2, -0.8, -0.8, -0.8, -0.8, -0.8, -3.2, 41.2,
-0.8, -0.8, -0.8, -0.8, -83.2, -1.6, -1.6, 0.4, 0.4, 0.4, -90,
-1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6, -1.6,
77.6, -79.6, 80.8, -81.6, -93.2, -100, 8.4, 75.6, 82.8, 67.2,
-27.2, 78.8, 65.6, 84.8, 73.6, 46.8, -62.4, 57.2, 74, 13.6, -0.8,
32.8, -27.2, 6.4, -67.2, 79.2, -64, 58, -40.4, 64, 8, 60, 76.8,
-24.8, -52.4, 56.8, 75.6, 38.4, -50.4, -72.8, -83.6, 24, 34.8,
54.4, -54, 67.6, 78.4, -41.6, -64.4, -83.6, -93.6, 76.8, -2.4,
-19.2, -54, -38, 5.2, 52.4, 64.8, 42.4, 77.6, -46.4, -74.8, -60.4,
-83.2, -56.4, -34.8, -16.8, 21.2, 40, 59.2, 0.4, -17.6, 24.4,
-14.4, 35.2, -26.8, 42, 44, -1.2, -35.6, 10.8, -19.6, -35.2,
22.4, -18.4, 27.6, -9.6, 43.2, -31.2, 45.2, 23.6, -16.4, 28.8,
40.4, 25.6, -8, 15.6, 11.2, -17.2, 15.6, -17.6, 18, 24, -9.6,
-34.8, 12.4, -17.2, 36.4, -9.2, -35.2, -19.6, 10.4, -15.6, -30.4,
30.8, 16.4, -14.8, -26.4, -34.4, 52.8, 34.4, 55.6, 21.2, 41.2,
52, 36.8, 50, 15.6, 36, 53.6, -22.8, 14.8, 25.2, -13.2, -18.8,
32, 20.8, -6.8, -16.4, -27.6, 14.4, 26.8, 38, -28.4, 19.6, -23.6,
18.4, -19.6, 11.6, 0, 0, 0, -26, -52.4, -24.4, 2, 19.6, -10.8,
3.6, 3.6, -25.2, 28.4, 12, -11.2, 3.2, 37.2, 26, 0.8, 47.6, -17.2,
2.4, -12, -52.4, 0.8, 28.4, -12, 36.4, 2.4, 50.4, -16, 24.4,
-2.4, -2.4, 15.2, -1.6, -1.6, -1.6, 24.4, -36, 33.2, 1.2, 1.2,
-48.8, -22.4, -1.2, -100, -1.6, -1.6, -26.4, 28, -47.6, 86, -1.6,
-1.6, -1.6, -1.6, -1.6, 41.6, -16, 29.6, -14.8, 3.2, 3.2, 100,
0.8, 0.8, 0.8, 0.8, 25.6, 24.8, -28, 0.8, -39.2, -97.6, -97.6,
-50, 0, 0, 49.6, 0.8, 54, 25.6, -1.2, -1.2, -90.8, 4.4, 4.4,
41.6, -40.8, -6, -6, 51.6, -8.4, 0, 0, 0, -60, 2.8, -52.4, 1.6,
1.6, 1.6, 18.8, 24.4, -0.4, -0.4, -0.4, -0.4, -51.6, -0.4, -0.4,
-0.4, 26, 0, 18, -42.4, -1.6, -0.4, 60.4, -2.8, -2.8, -2.8, 76,
2.8, 2.8, -29.2, -23.2, 23.6, -26.8, 0.4, 0.4, -40.8, -3.6, -47.6,
27.6, -2.4, -2.4, -76, -2, -2, -2, -30.8, 26.8, -4.4, -4.4, -4.4,
3.6, -0.8, -0.8, 67.2, -1.2, -48.8, 63.2, -42, 50, 30.8, 57.6,
-48.8, -48.8, 41.6, -39.2, -39.2, -35.6, 40, -44, -39.6, -39.6,
-50.8, 0, -48.8, 40, -53.2, 52, -47.2, -47.2, -46, 26.4, -29.2,
0, -46.8, -46.8, 34.8, -43.6, 0, 39.2, 0.4, -48.4, 0, -23.6,
29.2, 29.2, -53.2, -53.2, 19.2, 46.4, 46.4, -2, 36, 2, -25.2,
-50, -1.6, -2, 35.2, -32.8, 31.2, -43.2, 46, -28.8, -0.4, -50.4,
0.8, -43.6, 0.4, 27.6, -37.6, -37.6, 37.6, -50, 40.8, -0.8, -50.4,
-49.6, 45.6, 45.6, -48.8, -0.8, -54, -54, 43.2, -48.8, 46.4,
-42.8, 54, -54.4, 34.8, 0.4, 0.4, 0.4, 0.8, -50.4, -50.8, -50.8,
51.6, -68.8, 0.8, 52, -42, -42, 0, -56.8, -56.8, 0.8, -48, -46.4,
-46.8, -46.8, 0.4, 0.4, 37.2, -36.8, -36.8, -0.4, -0.4, -0.4,
-0.4, -0.4, -48.8, 0.8, 0.8, 58.8, 2, 2, 2, 2, 29.2, -50.4, 49.6,
41.2, -39.2, 38.8, -38.8, 28, -38, 40.8, 0.8, 0.8, 0.8, 0.8,
0.8, -51.2, 27.2, -54.8, 0.8, 0.8, -40.4, -40.4, 0, -46.8, 35.2,
-50.4, 9.6, -0.4, -15.2, 17.6, -26.8, -14.4, 42.8, 18.8, 2.8,
0, -33.2, -36.4, -7.6, 18.8, 34.4, 8.8, -25.6, -16.8, -10, -50.8,
10, -11.2, -7.2, -15.2, -62.8, 27.6, -12.8, -1.2, -24.4, 18.8,
-7.2, 37.2, 8.4, -40, -9.6, 20, -27.2, 27.2, 7.2, -31.6, -31.6,
27.6, -1.6, -20, -20, 34.4, 18, -23.6, 28.4, -16, 15.2, -30.4,
-9.2, -7.6, 12.4, 23.2, 15.6, 23.2, 37.2, -8.8, -21.6, -31.6,
-23.2, 25.2, 33.2, 9.2, 34.4, 18, 5.2, -50.4, 34.8, 12.4, -13.6,
-7.2, 6.4, 15.2, 2, 12.8, -14.4, 32.4, 15.6, 23.2, 30, -11.6,
-34.8, 12, -24, -11.2, -41.2, 34.4, 18.8, 18.8, 12, 37.6, 10,
35.2, -24.4, 24.8, 40.4, 52.4, 14, -41.6, 34, 43.2, -6, -28,
24, 35.2, 26.8, -15.2, 28, 38.8, 11.6, 57.6, 28, 12, -18.8, 35.6,
25.2, 40.4, 59.2, -58.4, 10.4, -23.6, 18, -14, 35.2, 13.6, 48.4,
32.8, 32.8, -17.2, -11.2, 26, -24, 15.2, -66.4, 24.4, -30.4,
39.6, 30, 53.2, 59.6, -40.4, -14, 36, 36, 41.6, 32, 57.6, 8.4,
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1563L, 1564L, 1565L, 1566L, 1567L, 1568L, 1569L, 1570L, 1571L,
1572L, 1573L, 1574L, 1575L, 1576L, 1577L, 1578L, 1579L, 1580L,
1581L, 1582L, 1583L, 1584L, 1585L, 1586L, 1587L, 1588L, 1589L,
1590L, 1591L, 1592L, 1593L, 1594L, 1595L, 1596L, 1597L, 1598L,
1599L, 1600L, 1601L, 1602L, 1603L, 1604L, 1605L, 1606L, 1607L,
1608L, 1609L, 1610L, 1611L, 1612L, 1613L, 1614L, 1615L, 1616L,
1617L, 1618L, 1619L, 1620L, 1621L, 1622L, 1623L, 1624L, 1625L,
1626L, 1627L, 1628L, 1629L, 1630L, 1631L, 1632L, 1633L, 1634L,
1635L, 1636L, 1637L, 1638L, 1639L, 1640L, 1641L, 1642L, 1643L,
1644L, 1645L, 1646L, 1647L, 1648L, 1649L, 1650L, 1801L, 1802L,
1803L, 1804L, 1805L, 1806L, 1807L, 1808L, 1809L, 1810L, 1811L,
1812L, 1813L, 1814L, 1815L, 1816L, 1817L, 1818L, 1819L, 1820L,
1821L, 1822L, 1823L, 1824L, 1825L, 1826L, 1827L, 1828L, 1829L,
1830L, 1831L, 1832L, 1833L, 1834L, 1835L, 1836L, 1837L, 1838L,
1839L, 1840L, 1841L, 1842L, 1843L, 1844L, 1845L, 1846L, 1847L,
1848L, 1849L, 1850L, 1851L, 1852L, 1853L, 1854L, 1855L, 1856L,
1857L, 1858L, 1859L, 1860L, 1861L, 1862L, 1863L, 1864L, 1865L,
1866L, 1867L, 1868L, 1869L, 1870L, 1871L, 1872L, 1873L, 1874L,
1875L, 1876L, 1877L, 1878L, 1879L, 1880L, 1881L, 1882L, 1883L,
1884L, 1885L, 1886L, 1887L, 1888L, 1889L, 1890L, 1891L, 1892L,
1893L, 1894L, 1895L, 1896L, 1897L, 1898L, 1899L, 1900L, 1901L,
1902L, 1903L, 1904L, 1905L, 1906L, 1907L, 1908L, 1909L, 1910L,
1911L, 1912L, 1913L, 1914L, 1915L, 1916L, 1917L, 1918L, 1919L,
1920L, 1921L, 1922L, 1923L, 1924L, 1925L, 1926L, 1927L, 1928L,
1929L, 1930L, 1931L, 1932L, 1933L, 1934L, 1935L, 1936L, 1937L,
1938L, 1939L, 1940L, 1941L, 1942L, 1943L, 1944L, 1945L, 1946L,
1947L, 1948L, 1949L, 1950L, 2101L, 2102L, 2103L, 2104L, 2105L,
2106L, 2107L, 2108L, 2109L, 2110L, 2111L, 2112L, 2113L, 2114L,
2115L, 2116L, 2117L, 2118L, 2119L, 2120L, 2121L, 2122L, 2123L,
2124L, 2125L, 2126L, 2127L, 2128L, 2129L, 2130L, 2131L, 2132L,
2133L, 2134L, 2135L, 2136L, 2137L, 2138L, 2139L, 2140L, 2141L,
2142L, 2143L, 2144L, 2145L, 2146L, 2147L, 2148L, 2149L, 2150L,
2151L, 2152L, 2153L, 2154L, 2155L, 2156L, 2157L, 2158L, 2159L,
2160L, 2161L, 2162L, 2163L, 2164L, 2165L, 2166L, 2167L, 2168L,
2169L, 2170L, 2171L, 2172L, 2173L, 2174L, 2175L, 2176L, 2177L,
2178L, 2179L, 2180L, 2181L, 2182L, 2183L, 2184L, 2185L, 2186L,
2187L, 2188L, 2189L, 2190L, 2191L, 2192L, 2193L, 2194L, 2195L,
2196L, 2197L, 2198L, 2199L, 2200L, 2201L, 2202L, 2203L, 2204L,
2205L, 2206L, 2207L, 2208L, 2209L, 2210L, 2211L, 2212L, 2213L,
2214L, 2215L, 2216L, 2217L, 2218L, 2219L, 2220L, 2221L, 2222L,
2223L, 2224L, 2225L, 2226L, 2227L, 2228L, 2229L, 2230L, 2231L,
2232L, 2233L, 2234L, 2235L, 2236L, 2237L, 2238L, 2239L, 2240L,
2241L, 2242L, 2243L, 2244L, 2245L, 2246L, 2247L, 2248L, 2249L,
2250L), class = "data.frame")
using the vector:
timevec1 = as.vector(ggplot2:::breaks(sumsq$diffdist, "n", n=8))
I normally summarise the data using xtabs and cutusing:
bb1 = data.frame(xtabs(~ratperc +cut(diffdist, timevec1 ), dat=sumsq))
colnames(bb1) = c("rating", "range", "freq", "id")
While this solution is not idea for what I wanted it, I was able to then summarise the values for each cut using ddply.
However now I need to preserve the part_no too, but I can't seem to be able to pass more than one column to cut.
The question is, is there any way to do everything in one step? Basically get for each participant the mean of all the ratings for each cut? In other words, part_no as rows, ranges as columns and the intersection being the mean of ratings for the values that below there.
If you just want the mean rating for each part_no and interval from cut(diffdist, timevec1 ) I would just do something like this:
#Add cut variable as new column
sumsq$range <- cut(sumsq$diffdist,timevec1)
#Summarise using ddply
ddply(sumsq,.(part_no,range),summarise,val = mean(ratperc))
I didn't get if you want the mean for each participant and interval or the cumulative mean along the intervals for each participant.
If you want the normal mean you can get it with
sapply(split(sumsq, cut(sumsq$diffdist, timevec1)), function(ss)
sapply(split(ss$ratperc, ss$part_no), mean))
If you want the cumulative you can rephrase it as
t(sapply(split(sumsq, sumsq$part_no), function(ss){
sapply(timevec1[-1], function(tc) mean(ss$ratperc[ss$diffdist <= tc]))
}))
Related
Merging two files based on dates with missing values
I am trying to merge two files together based on dates:
However, there are two issues:
I cannot use the left join function due to one of the files having the dates set as characters, and the other one set as dates. Changing one of the files values to dates does not fix the issue as it creates a list instead
Using the merge function leads to a lot of missing values, as one of my dataset does not contain data for each date
jointdataset <- merge(group_df, group_tweet, by = 'date', all.x= TRUE)
View(jointdataset)
jointdataset <- dplyr::left_join(group_df, group_tweet)
Here is how my data looks like
> dput(group_tweet)
structure(list(date = structure(c(18628, 18629, 18630, 18631,
18632, 18633, 18634, 18635, 18636, 18637, 18638, 18639, 18640,
18641, 18642, 18643, 18644, 18645, 18646, 18647, 18648, 18649,
18650, 18651, 18652, 18653, 18654, 18655, 18656, 18657, 18658,
18659, 18660, 18661, 18662, 18663, 18664, 18665, 18666, 18667,
18668, 18669, 18670, 18671, 18672, 18673, 18674, 18675, 18676,
18677, 18678, 18679, 18680, 18681, 18682, 18683, 18684, 18685,
18686, 18687, 18688, 18689, 18690, 18691, 18692, 18693, 18694,
18695, 18696, 18697, 18698, 18699, 18700, 18701, 18702, 18703,
18704, 18705, 18706, 18707, 18708, 18709, 18710, 18711, 18712,
18713, 18714, 18715, 18716, 18717, 18718, 18719, 18720, 18721,
18722, 18723, 18724, 18725, 18726, 18727, 18728, 18729, 18730,
18731, 18732, 18733, 18734, 18735, 18736, 18737, 18738, 18739,
18740, 18741, 18742, 18743, 18744, 18745, 18746, 18747, 18748,
18749, 18750, 18751, 18752, 18753, 18754, 18755, 18756, 18757,
18758, 18759, 18760, 18761, 18762, 18763, 18764, 18765, 18766,
18767, 18768, 18769, 18770, 18771, 18772, 18773, 18774, 18775,
18776, 18777, 18778, 18779, 18780, 18781, 18782, 18783, 18784,
18785, 18786, 18787, 18788, 18789, 18790, 18791, 18792, 18793,
18794, 18795, 18796, 18797, 18798, 18799, 18800, 18801, 18802,
18803, 18804, 18805, 18806, 18807, 18808), class = "Date"), `length(text)` = c(1324L,
1548L, 1297L, 1585L, 1636L, 1583L, 1492L, 1676L, 1745L, 1389L,
1718L, 1781L, 1858L, 1798L, 1714L, 1808L, 1315L, 1644L, 1603L,
1820L, 1770L, 1843L, 1885L, 1390L, 1763L, 1875L, 1912L, 1812L,
1764L, 2117L, 1641L, 1914L, 1963L, 2092L, 1968L, 2021L, 2023L,
1331L, 1557L, 1555L, 1904L, 2034L, 1850L, 2067L, 1507L, 1738L,
1915L, 2057L, 1931L, 1859L, 2010L, 1525L, 1835L, 1850L, 1958L,
1848L, 1929L, 2015L, 1449L, 1823L, 1796L, 1902L, 1888L, 1875L,
2078L, 1442L, 1757L, 1877L, 2104L, 1926L, 1949L, 2175L, 1604L,
2030L, 1918L, 2011L, 1978L, 1959L, 2171L, 1528L, 1936L, 1889L,
2132L, 1907L, 2173L, 2233L, 1581L, 1793L, 1986L, 2128L, 2030L,
1805L, 1954L, 1459L, 1691L, 1967L, 2049L, 1835L, 1948L, 2246L,
1581L, 1950L, 1904L, 2245L, 2053L, 1877L, 1913L, 1571L, 1932L,
2004L, 2058L, 2087L, 1989L, 2180L, 1567L, 1865L, 1995L, 2144L,
2169L, 2148L, 2318L, 1606L, 1856L, 1948L, 2036L, 1887L, 2021L,
2132L, 1390L, 1717L, 1872L, 1919L, 1867L, 1994L, 2083L, 1509L,
1786L, 1808L, 1860L, 1854L, 1813L, 2102L, 1513L, 1890L, 1877L,
2063L, 1857L, 1827L, 2059L, 1413L, 1614L, 2153L, 1859L, 1920L,
1877L, 2106L, 1458L, 1822L, 1851L, 2005L, 1984L, 2097L, 2396L,
1607L, 2106L, 2256L, 2398L, 2245L, 2239L, 2287L, 1564L, 1880L,
1991L, 2053L, 2017L, 2012L, 1998L, 1361L, 1663L, 1778L, 1987L
)), row.names = c(NA, -181L), class = c("tbl_df", "tbl", "data.frame"
))
dput(group_df)
structure(list(date = c("01/01/2021", "01/05/2021", "01/06/2021",
"01/07/2021", "01/11/2021", "01/12/2021", "01/14/2021", "01/15/2021",
"01/18/2021", "01/19/2021", "01/20/2021", "01/21/2021", "01/22/2021",
"01/23/2021", "01/26/2021", "01/27/2021", "01/28/2021", "01/29/2021",
"01/31/2021", "02/01/2021", "02/02/2021", "02/04/2021", "02/08/2021",
"02/09/2021", "02/10/2021", "02/11/2021", "02/12/2021", "02/15/2021",
"02/16/2021", "02/18/2021", "02/19/2021", "02/22/2021", "02/23/2021",
"02/24/2021", "02/25/2021", "02/27/2021", "03/01/2021", "03/02/2021",
"03/04/2021", "03/05/2021", "03/06/2021", "03/07/2021", "03/08/2021",
"03/09/2021", "03/11/2021", "03/14/2021", "03/15/2021", "03/16/2021",
"03/17/2021", "03/18/2021", "03/19/2021", "03/20/2021", "03/21/2021",
"03/22/2021", "03/23/2021", "03/24/2021", "03/25/2021", "03/26/2021",
"03/28/2021", "03/29/2021", "03/30/2021", "03/31/2021", "04/01/2021",
"04/02/2021", "04/03/2021", "04/04/2021", "04/05/2021", "04/06/2021",
"04/07/2021", "04/08/2021", "04/09/2021", "04/10/2021", "04/11/2021",
"04/12/2021", "04/13/2021", "04/14/2021", "04/15/2021", "04/16/2021",
"04/17/2021", "04/18/2021", "04/19/2021", "04/20/2021", "04/21/2021",
"04/22/2021", "04/23/2021", "04/24/2021", "04/26/2021", "04/27/2021",
"04/28/2021", "04/29/2021", "04/30/2021", "05/01/2021", "05/02/2021",
"05/03/2021", "05/04/2021", "05/05/2021", "05/06/2021", "05/07/2021",
"05/08/2021", "05/10/2021", "05/11/2021", "05/12/2021", "05/13/2021",
"05/14/2021", "05/15/2021", "05/18/2021", "05/19/2021", "05/20/2021",
"05/21/2021", "05/22/2021", "05/23/2021", "05/25/2021", "05/26/2021",
"05/27/2021", "05/29/2021", "05/31/2021", "06/01/2021", "06/02/2021",
"06/03/2021", "06/04/2021", "06/05/2021", "06/06/2021", "06/07/2021",
"06/08/2021", "06/09/2021", "06/10/2021", "06/11/2021", "06/12/2021",
"06/13/2021", "06/14/2021", "06/15/2021", "06/16/2021", "06/17/2021"
), `length(category)` = c(4L, 8L, 4L, 4L, 4L, 12L, 8L, 8L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 9L, 25L, 10L, 5L, 10L,
5L, 20L, 5L, 5L, 19L, 10L, 20L, 5L, 5L, 5L, 2L, 12L, 22L, 14L,
2L, 4L, 6L, 9L, 5L, 37L, 5L, 4L, 10L, 12L, 21L, 4L, 4L, 6L, 6L,
9L, 16L, 9L, 6L, 13L, 2L, 19L, 24L, 20L, 8L, 25L, 16L, 5L, 24L,
27L, 24L, 6L, 9L, 15L, 8L, 25L, 35L, 26L, 8L, 2L, 5L, 14L, 12L,
20L, 19L, 7L, 9L, 5L, 4L, 17L, 13L, 15L, 8L, 10L, 7L, 5L, 16L,
11L, 16L, 10L, 6L, 3L, 22L, 10L, 10L, 3L, 1L, 7L, 4L, 6L, 4L,
5L, 3L, 6L, 3L, 3L, 1L, 3L, 9L, 6L, 4L, 2L, 1L, 1L, 3L, 5L, 2L,
4L, 2L, 2L, 1L, 5L, 5L, 6L, 3L), `sum(usd_pledged)` = c(50278.64,
366279.590415302, 172073.0471292, 230.537553792, 304353.5676352,
285277.861423738, 931521.92, 62322.104033708, 292739.37663744,
97895.025306156, 20538.4758468, 5716, 69712, 15248.7519561728,
1257502.99126928, 102268, 32151.762183708, 70322.17520884, 27549.00453216,
8371657.96195552, 1420782.4068818, 137818.171860595, 1175436.75496273,
1770825.83285245, 1070, 178761.8127624, 10016.918409372, 146129.2,
610608.234267955, 676175.367448825, 1274147.86429578, 45595.6660422,
1776940, 4198.748196, 17.02446963, 55272.18380506, 490750.12364435,
571800.5, 227.26420984, 619.23335154, 5942.151506976, 1098.48977709,
87300, 1879564.14902818, 57100, 3.628861728, 715993, 228885.860968739,
1005412.33040269, 32978.39955816, 1956.52590528, 5789.84841572,
508.6539266268, 192238.643979976, 278988.70418106, 32470.60072344,
10653.5543364, 333900.00289616, 2467.35065664, 698082.326436438,
581461.49769354, 170032.27513805, 24516.65281874, 11530.3738156855,
20060.6125168384, 154030.2061095, 215919.1704622, 1320696.42919177,
2000470.11990896, 1414.108082664, 12429.5108974052, 80676.567104964,
3211.42501648, 69994.39317561, 827188.797715076, 51349.427891072,
47925.216359587, 42, 28391.25545206, 6199141.75469484, 16078.9170341546,
724307.969231123, 238317.166592813, 243887.2812338, 3856.910710253,
5982.1359855268, 1986.4520326, 180186.734055936, 25818.301703542,
284175.24917946, 27486.5134227176, 51837.3569258, 218101.634171675,
543.2343820104, 299634.97422679, 9200.1639420603, 112660.244016819,
27675.2965010449, 5203.03118806, 75650.25, 327968.549088524,
1002.27102748, 37881.2795828444, 2896.1824198269, 596, 206880.064933601,
1688.14367074, 19176.1722666628, 48811.21823157, 4572.83962465,
375.36475346, 126743.686979946, 489.0998, 4042.91231235, 1343.0098244,
2286.711086, 77485.7681307, 25272.4433349128, 11630.0007860946,
377.22065991, 199.9004139, 161, 2667.93172391, 69404.71338983,
23796.67811208, 17174.64072667, 9950.0416377665, 35443.95356773,
184.603426004, 85934.0997877056, 5603.88739935, 2306.02823117,
13.0064795336), `sum(backers_count)` = c(2880L, 6588L, 3528L,
16L, 4204L, 6632L, 15404L, 1672L, 3588L, 416L, 464L, 364L, 1488L,
228L, 17124L, 2284L, 1348L, 2324L, 744L, 185822L, 35095L, 5980L,
24615L, 32525L, 40L, 4650L, 450L, 2640L, 15952L, 9275L, 16404L,
990L, 35075L, 79L, 6L, 1578L, 10705L, 9302L, 12L, 20L, 36L, 91L,
1505L, 40509L, 1890L, 4L, 9684L, 4196L, 16477L, 754L, 62L, 54L,
36L, 3787L, 3809L, 996L, 132L, 13216L, 50L, 12073L, 13826L, 4353L,
220L, 393L, 628L, 3607L, 6712L, 22403L, 30468L, 106L, 136L, 1854L,
102L, 1623L, 14638L, 1396L, 1923L, 6L, 426L, 161556L, 1020L,
6922L, 3575L, 1627L, 84L, 101L, 56L, 2291L, 450L, 2439L, 678L,
864L, 3195L, 17L, 6072L, 215L, 3119L, 1473L, 85L, 2306L, 7622L,
41L, 973L, 117L, 20L, 3127L, 45L, 424L, 819L, 84L, 12L, 3239L,
18L, 66L, 7L, 35L, 1651L, 651L, 242L, 25L, 13L, 6L, 59L, 1747L,
325L, 244L, 231L, 862L, 6L, 1819L, 74L, 66L, 4L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -133L))
dput(group_df)
structure(list(date = c("01/01/2021", "01/05/2021", "01/06/2021",
"01/07/2021", "01/11/2021", "01/12/2021", "01/14/2021", "01/15/2021",
"01/18/2021", "01/19/2021", "01/20/2021", "01/21/2021", "01/22/2021",
"01/23/2021", "01/26/2021", "01/27/2021", "01/28/2021", "01/29/2021",
"01/31/2021", "02/01/2021", "02/02/2021", "02/04/2021", "02/08/2021",
"02/09/2021", "02/10/2021", "02/11/2021", "02/12/2021", "02/15/2021",
"02/16/2021", "02/18/2021", "02/19/2021", "02/22/2021", "02/23/2021",
"02/24/2021", "02/25/2021", "02/27/2021", "03/01/2021", "03/02/2021",
"03/04/2021", "03/05/2021", "03/06/2021", "03/07/2021", "03/08/2021",
"03/09/2021", "03/11/2021", "03/14/2021", "03/15/2021", "03/16/2021",
"03/17/2021", "03/18/2021", "03/19/2021", "03/20/2021", "03/21/2021",
"03/22/2021", "03/23/2021", "03/24/2021", "03/25/2021", "03/26/2021",
"03/28/2021", "03/29/2021", "03/30/2021", "03/31/2021", "04/01/2021",
"04/02/2021", "04/03/2021", "04/04/2021", "04/05/2021", "04/06/2021",
"04/07/2021", "04/08/2021", "04/09/2021", "04/10/2021", "04/11/2021",
"04/12/2021", "04/13/2021", "04/14/2021", "04/15/2021", "04/16/2021",
"04/17/2021", "04/18/2021", "04/19/2021", "04/20/2021", "04/21/2021",
"04/22/2021", "04/23/2021", "04/24/2021", "04/26/2021", "04/27/2021",
"04/28/2021", "04/29/2021", "04/30/2021", "05/01/2021", "05/02/2021",
"05/03/2021", "05/04/2021", "05/05/2021", "05/06/2021", "05/07/2021",
"05/08/2021", "05/10/2021", "05/11/2021", "05/12/2021", "05/13/2021",
"05/14/2021", "05/15/2021", "05/18/2021", "05/19/2021", "05/20/2021",
"05/21/2021", "05/22/2021", "05/23/2021", "05/25/2021", "05/26/2021",
"05/27/2021", "05/29/2021", "05/31/2021", "06/01/2021", "06/02/2021",
"06/03/2021", "06/04/2021", "06/05/2021", "06/06/2021", "06/07/2021",
"06/08/2021", "06/09/2021", "06/10/2021", "06/11/2021", "06/12/2021",
"06/13/2021", "06/14/2021", "06/15/2021", "06/16/2021", "06/17/2021"
), `length(category)` = c(4L, 8L, 4L, 4L, 4L, 12L, 8L, 8L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 9L, 25L, 10L, 5L, 10L,
5L, 20L, 5L, 5L, 19L, 10L, 20L, 5L, 5L, 5L, 2L, 12L, 22L, 14L,
2L, 4L, 6L, 9L, 5L, 37L, 5L, 4L, 10L, 12L, 21L, 4L, 4L, 6L, 6L,
9L, 16L, 9L, 6L, 13L, 2L, 19L, 24L, 20L, 8L, 25L, 16L, 5L, 24L,
27L, 24L, 6L, 9L, 15L, 8L, 25L, 35L, 26L, 8L, 2L, 5L, 14L, 12L,
20L, 19L, 7L, 9L, 5L, 4L, 17L, 13L, 15L, 8L, 10L, 7L, 5L, 16L,
11L, 16L, 10L, 6L, 3L, 22L, 10L, 10L, 3L, 1L, 7L, 4L, 6L, 4L,
5L, 3L, 6L, 3L, 3L, 1L, 3L, 9L, 6L, 4L, 2L, 1L, 1L, 3L, 5L, 2L,
4L, 2L, 2L, 1L, 5L, 5L, 6L, 3L), `sum(usd_pledged)` = c(50278.64,
366279.590415302, 172073.0471292, 230.537553792, 304353.5676352,
285277.861423738, 931521.92, 62322.104033708, 292739.37663744,
97895.025306156, 20538.4758468, 5716, 69712, 15248.7519561728,
1257502.99126928, 102268, 32151.762183708, 70322.17520884, 27549.00453216,
8371657.96195552, 1420782.4068818, 137818.171860595, 1175436.75496273,
1770825.83285245, 1070, 178761.8127624, 10016.918409372, 146129.2,
610608.234267955, 676175.367448825, 1274147.86429578, 45595.6660422,
1776940, 4198.748196, 17.02446963, 55272.18380506, 490750.12364435,
571800.5, 227.26420984, 619.23335154, 5942.151506976, 1098.48977709,
87300, 1879564.14902818, 57100, 3.628861728, 715993, 228885.860968739,
1005412.33040269, 32978.39955816, 1956.52590528, 5789.84841572,
508.6539266268, 192238.643979976, 278988.70418106, 32470.60072344,
10653.5543364, 333900.00289616, 2467.35065664, 698082.326436438,
581461.49769354, 170032.27513805, 24516.65281874, 11530.3738156855,
20060.6125168384, 154030.2061095, 215919.1704622, 1320696.42919177,
2000470.11990896, 1414.108082664, 12429.5108974052, 80676.567104964,
3211.42501648, 69994.39317561, 827188.797715076, 51349.427891072,
47925.216359587, 42, 28391.25545206, 6199141.75469484, 16078.9170341546,
724307.969231123, 238317.166592813, 243887.2812338, 3856.910710253,
5982.1359855268, 1986.4520326, 180186.734055936, 25818.301703542,
284175.24917946, 27486.5134227176, 51837.3569258, 218101.634171675,
543.2343820104, 299634.97422679, 9200.1639420603, 112660.244016819,
27675.2965010449, 5203.03118806, 75650.25, 327968.549088524,
1002.27102748, 37881.2795828444, 2896.1824198269, 596, 206880.064933601,
1688.14367074, 19176.1722666628, 48811.21823157, 4572.83962465,
375.36475346, 126743.686979946, 489.0998, 4042.91231235, 1343.0098244,
2286.711086, 77485.7681307, 25272.4433349128, 11630.0007860946,
377.22065991, 199.9004139, 161, 2667.93172391, 69404.71338983,
23796.67811208, 17174.64072667, 9950.0416377665, 35443.95356773,
184.603426004, 85934.0997877056, 5603.88739935, 2306.02823117,
13.0064795336), `sum(backers_count)` = c(2880L, 6588L, 3528L,
16L, 4204L, 6632L, 15404L, 1672L, 3588L, 416L, 464L, 364L, 1488L,
228L, 17124L, 2284L, 1348L, 2324L, 744L, 185822L, 35095L, 5980L,
24615L, 32525L, 40L, 4650L, 450L, 2640L, 15952L, 9275L, 16404L,
990L, 35075L, 79L, 6L, 1578L, 10705L, 9302L, 12L, 20L, 36L, 91L,
1505L, 40509L, 1890L, 4L, 9684L, 4196L, 16477L, 754L, 62L, 54L,
36L, 3787L, 3809L, 996L, 132L, 13216L, 50L, 12073L, 13826L, 4353L,
220L, 393L, 628L, 3607L, 6712L, 22403L, 30468L, 106L, 136L, 1854L,
102L, 1623L, 14638L, 1396L, 1923L, 6L, 426L, 161556L, 1020L,
6922L, 3575L, 1627L, 84L, 101L, 56L, 2291L, 450L, 2439L, 678L,
864L, 3195L, 17L, 6072L, 215L, 3119L, 1473L, 85L, 2306L, 7622L,
41L, 973L, 117L, 20L, 3127L, 45L, 424L, 819L, 84L, 12L, 3239L,
18L, 66L, 7L, 35L, 1651L, 651L, 242L, 25L, 13L, 6L, 59L, 1747L,
325L, 244L, 231L, 862L, 6L, 1819L, 74L, 66L, 4L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -133L))
My final objective is to get a dataset, where i can get for each date the data, including for the days where there is missing data in one of the data sets
I assume your key problem is changing eg 01/15/2022 to a date. You can easily use the package lubridates mdy() function for this (install.packages('lubridate').
*adjusted from your comment.
You can run:
library(dplyr)
library(lubridate)
group_df %>%
mutate(date = mdy(date)) %>%
full_join(
group_tweet
) %>%
arrange(date)
Output is:
Joining, by = "date"
# A tibble: 181 × 5
date `length(category)` `sum(usd_pledged)` `sum(backers_count)` `length(text)`
<date> <int> <dbl> <int> <int>
1 2021-01-01 4 50279. 2880 1324
2 2021-01-02 NA NA NA 1548
3 2021-01-03 NA NA NA 1297
4 2021-01-04 NA NA NA 1585
5 2021-01-05 8 366280. 6588 1636
6 2021-01-06 4 172073. 3528 1583
7 2021-01-07 4 231. 16 1492
8 2021-01-08 NA NA NA 1676
9 2021-01-09 NA NA NA 1745
10 2021-01-10 NA NA NA 1389
# … with 171 more rows
How can I add a legend to this graph?
I have a graph that I would like to add a legend to:
So far I have been able to either add a legend which defaults to its own colours, or one which changes the colours to a gradient fill which isn't what I want.
What am I doing wrong and how can I fix it?
Plot code:
ggplot(df) +
geom_line(aes(date, actual, group = 1),
colour = "forestgreen") +
geom_line(aes(date, plan, group = 1),
colour = "red") +
geom_smooth(aes(date, difference),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y") +
facet_grid(sector ~ ., scales = "free")
dput:
structure(list(date = structure(c(17959, 17959, 17959, 17959,
17959, 17960, 17960, 17960, 17960, 17960, 17961, 17961, 17961,
17961, 17961, 17962, 17962, 17962, 17962, 17962, 17963, 17963,
17963, 17963, 17963, 17964, 17964, 17964, 17964, 17964, 17965,
17965, 17965, 17965, 17965, 17966, 17966, 17966, 17966, 17966,
17967, 17967, 17967, 17967, 17967, 17968, 17968, 17968, 17968,
17968, 17969, 17969, 17969, 17969, 17969, 17970, 17970, 17970,
17970, 17970, 17971, 17971, 17971, 17971, 17971, 17972, 17972,
17972, 17972, 17972, 17973, 17973, 17973, 17973, 17973, 17974,
17974, 17974, 17974, 17974, 17975, 17975, 17975, 17975, 17975,
17976, 17976, 17976, 17976, 17976, 17977, 17977, 17977, 17977,
17977, 17978, 17978, 17978, 17978, 17978, 17979, 17979, 17979,
17979, 17979, 17980, 17980, 17980, 17980, 17980, 17981, 17981,
17981, 17981, 17981, 17982, 17982, 17982, 17982, 17982, 17983,
17983, 17983, 17983, 17983, 17984, 17984, 17984, 17984, 17984,
17985, 17985, 17985, 17985, 17985, 17986, 17986, 17986, 17986,
17986, 17987, 17987, 17987, 17987, 17987, 17988, 17988, 17988,
17988, 17988, 17989, 17989, 17989, 17989, 17989, 17990, 17990,
17990, 17990, 17990, 17991, 17991, 17991, 17991, 17991, 17992,
17992, 17992, 17992, 17992, 17993, 17993, 17993, 17993, 17993
), class = "Date"), sector = structure(c(1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("ADVANCE CLINICIAN",
"CALL HANDLERS", "CLINICIANS", "GP", "PHARMACIST"), class = "factor"),
actual = c(16L, 232L, 104L, 40L, 12L, 23L, 231L, 90L, 47L,
12L, 34L, 245L, 109L, 61L, 0L, 32L, 226L, 99L, 50L, 0L, 25L,
247L, 103L, 58L, 7L, 84L, 362L, 89L, 57L, 8L, 50L, 333L,
86L, 44L, 11L, 14L, 276L, 71L, 68L, 7L, 24L, 263L, 93L, 62L,
12L, 42L, 241L, 92L, 42L, 13L, 42L, 242L, 106L, 60L, 8L,
50L, 262L, 101L, 58L, 8L, 38L, 340L, 80L, 42L, 0L, 32L, 312L,
73L, 39L, 8L, 14L, 219L, 81L, 54L, 15L, 19L, 239L, 100L,
50L, 20L, 15L, 245L, 104L, 58L, 13L, 38L, 233L, 90L, 57L,
8L, 50L, 236L, 94L, 41L, 7L, 78L, 370L, 106L, 61L, 8L, 34L,
328L, 91L, 45L, 8L, 16L, 247L, 54L, 57L, 20L, 19L, 263L,
93L, 43L, 15L, 26L, 231L, 88L, 58L, 13L, 50L, 234L, 87L,
61L, 8L, 68L, 245L, 113L, 50L, 15L, 68L, 352L, 89L, 38L,
8L, 22L, 288L, 98L, 18L, 11L, 13L, 201L, 34L, 40L, 8L, 12L,
208L, 41L, 45L, 8L, 5L, 215L, 48L, 45L, 0L, 53L, 210L, 45L,
55L, 0L, 20L, 220L, 71L, 23L, 8L, 12L, 349L, 80L, 54L, 8L,
2L, 287L, 52L, 35L, 0L), plan = c(43L, 291L, 28L, 32L, 11L,
37L, 262L, 25L, 32L, 11L, 39L, 260L, 22L, 32L, 11L, 34L,
266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L, 66L, 422L,
152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L, 291L, 106L,
32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L, 94L, 32L,
11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L,
66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L,
291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L,
94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L,
32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L,
14L, 43L, 291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L,
39L, 260L, 94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L,
269L, 93L, 32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L,
133L, 53L, 14L, 43L, 283L, 73L, 32L, 11L, 37L, 283L, 99L,
32L, 11L, 39L, 264L, 88L, 32L, 11L, 34L, 260L, 90L, 32L,
11L, 39L, 275L, 98L, 32L, 11L, 66L, 424L, 156L, 61L, 14L,
54L, 360L, 130L, 53L, 14L), difference = c(-27L, -59L, 76L,
8L, 1L, -14L, -31L, 65L, 15L, 1L, -5L, -15L, 87L, 29L, -11L,
-2L, -40L, 9L, 18L, -11L, -14L, -22L, 10L, 26L, -4L, 18L,
-60L, -63L, -4L, -6L, -4L, -40L, -47L, -9L, -3L, -29L, -15L,
-35L, 36L, -4L, -13L, 1L, 3L, 30L, 1L, 3L, -19L, -2L, 10L,
2L, 8L, -24L, 16L, 28L, -3L, 11L, -7L, 8L, 26L, -3L, -28L,
-82L, -72L, -19L, -14L, -22L, -61L, -60L, -14L, -6L, -29L,
-72L, -25L, 22L, 4L, -18L, -23L, 10L, 18L, 9L, -24L, -15L,
10L, 26L, 2L, 4L, -33L, 0L, 25L, -3L, 11L, -33L, 1L, 9L,
-4L, 12L, -52L, -46L, 0L, -6L, -20L, -45L, -42L, -8L, -6L,
-27L, -44L, -52L, 25L, 9L, -18L, 1L, 3L, 11L, 4L, -13L, -29L,
-6L, 26L, 2L, 16L, -32L, -3L, 29L, -3L, 29L, -24L, 20L, 18L,
4L, 2L, -70L, -63L, -23L, -6L, -32L, -85L, -35L, -35L, -3L,
-30L, -82L, -39L, 8L, -3L, -25L, -75L, -58L, 13L, -3L, -34L,
-49L, -40L, 13L, -11L, 19L, -50L, -45L, 23L, -11L, -19L,
-55L, -27L, -9L, -3L, -54L, -75L, -76L, -7L, -6L, -52L, -73L,
-78L, -18L, -14L)), row.names = c(NA, -175L), class = c("tbl_df",
"tbl", "data.frame"))
Place the colour argument in aes and just write the label for it there. You can rename the title with +labs(colour="Legend Title"), and manipulate the colours with scale_fill_manual(values)
ggplot(df) +
geom_line(aes(date, actual, group = 1,colour = "actual")
) +
geom_line(aes(date, plan, group = 1,colour ="plan")) +
geom_smooth(aes(date, difference,colour="difference"),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y")+
facet_grid(sector ~ ., scales = "free")+
labs(colour="Method")+
scale_colour_manual(values=c("forestgreen","red","blue"))
Hope this is helpful!
Count UNIQUE points within each grid box
I have the following data:
EDIT: With smaller sample data
dat <- structure(list(SN = c(198305L, 198305L, 198305L, 198305L,
198305L,198305L, 198305L, 198305L, 198305L, 198305L, 198305L,
198305L, 198305L, 198305L, 198305L, 198305L, 198305L, 198305L,
198305L, 198305L, 198305L, 198305L, 198305L, 198305L, 198305L,
198305L, 198305L, 198305L, 198305L, 198305L, 198305L, 198305L,
198305L, 198305L, 198305L, 198306L, 198306L, 198306L, 198306L,
198308L, 198308L, 198308L, 198308L, 198308L, 198308L, 198308L,
198308L, 198308L, 198308L, 198308L, 198308L, 198308L, 198308L,
198308L, 198308L, 198308L, 198308L, 198308L, 198308L, 198308L,
198308L, 198308L, 198308L, 198310L, 198310L, 198310L, 198310L,
198310L, 198310L, 198310L, 198310L, 198310L, 198310L, 198310L,
198310L, 198310L, 198310L, 198310L, 198310L, 198310L, 198310L,
198310L, 198310L, 198310L, 198310L, 198310L, 198310L, 198310L,
198310L), CY = c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L), Year = c(1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L,
1983L, 1983L), Month = c(8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L
), Day = c(9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L,
11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 14L, 14L,
14L, 14L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 21L, 22L,
22L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L), Hour = c(0L, 6L, 12L,
18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L,
6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L,
18L, 0L, 6L, 12L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L,
12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L,
0L, 6L, 12L, 18L, 0L, 6L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L,
0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L, 12L, 18L, 0L, 6L,
12L, 18L), Lat = c(17.8, 18.1, 18.7, 18.9, 19.2, 19.5, 19.9,
20.1, 20.6, 21.2, 21.6, 22, 22.5, 22.9, 23.4, 23.9, 24.6, 24.9,
25.4, 26.1, 26.6, 27.2, 27.6, 28.1, 28.5, 29.1, 29.5, 30, 31.1,
31.8, 32.7, 33.8, 34.6, 35.1, 35.6, 19.8, 19.9, 19.9, 20.2, 15.9,
16.1, 16.3, 16.5, 16.9, 17.4, 18, 18.7, 19.3, 20, 23.8, 24.2,
24.9, 25.4, 25.8, 25.5, 25.1, 25.3, 25.8, 26.2, 26.5, 27.1, 27.9,
29.1, 10.3, 10.2, 9, 9.2, 9.2, 9.5, 10, 10.5, 10.9, 11.3, 12.3,
13, 13.7, 14.4, 15, 15.9, 16.8, 17.2, 17.8, 18.3, 18.7, 19, 19.3,
19.5, 19.7, 20), Lon = c(130.8, 130.7, 130.3, 130.4, 130.4, 130.4,
130.5, 130.5, 130.7, 130.8, 130.7, 130.6, 130.7, 130.8, 131.2,
131.5, 131.8, 132.2, 132.6, 133, 133.3, 133.5, 133.5, 133.5,
133.6, 134.1, 134.3, 134.8, 135.1, 135.8, 136.5, 137, 137.3,
138.1, 139.4, 121.5, 122.7, 124.4, 126.2, 133.8, 133.2, 132.8,
132.4, 132.2, 132.5, 133, 133.7, 134.7, 135.6, 140.1, 141.6,
142.6, 143.1, 143.5, 144, 144.3, 144.7, 144.7, 144.1, 143.4,
142.8, 141.8, 141.3, 151.2, 149.2, 143.4, 141.8, 140.2, 138.9,
137.5, 136, 134.4, 133, 131.7, 130.7, 129.6, 128.8, 128, 126.9,
125.8, 125.1, 124.1, 123.2, 122.2, 121.2, 120.2, 119.2, 118.3,
117.5), VMax = c(145L, 135L, 125L, 120L, 120L, 120L, 115L, 110L,
110L, 115L, 120L, 120L, 120L, 120L, 125L, 120L, 115L, 110L, 105L,
105L, 110L, 100L, 100L, 95L, 90L, 85L, 85L, 80L, 75L, 70L, 70L,
70L, 60L, 55L, 45L, 35L, 40L, 45L, 40L, 35L, 40L, 45L, 50L, 55L,
50L, 45L, 40L, 35L, 35L, 35L, 45L, 50L, 50L, 45L, 40L, 40L, 45L,
50L, 50L, 50L, 45L, 40L, 35L, 35L, 35L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 85L, 90L, 95L, 95L, 95L, 95L, 100L, 120L,
125L, 120L, 100L, 90L, 85L, 85L, 80L), Cat = structure(c(5L,
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
4L, 4L, 4L, 3L, 2L, 2L, 2L, 1L), .Label = c("Cat1", "Cat2", "Cat3",
"Cat4", "Cat5", "TS"), class = "factor")), row.names = c(243L,
244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L,
255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L,
266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L,
277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L,
288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L,
299L, 300L, 301L, 302L, 303L, 304L, 305L, 927L, 928L, 929L, 930L,
931L, 932L, 933L, 934L, 935L, 936L, 937L, 938L, 939L, 940L, 941L,
942L, 943L, 944L, 945L, 946L, 947L, 948L, 949L, 950L, 951L, 952L
), class = "data.frame")
Each lat-lon pair has a unique identifier, the SN column.
I created a grid and I want to count the number of unique lat-lon pairs within each grid.
Here's my script:
latmin=0
latmax=50
lonmin=60
lonmax=180
dlat=2.5
dlon=2.5
latint=dlat*0.5
lonint=dlon*0.5
## derive center lat and lon points
x.Lon<-seq((lonmin+lonint),(lonmax-lonint),lonint)
y.Lat<-seq((latmin+latint),(latmax-latint),latint)
df2<-as.data.frame(expand.grid(x.Lon=x.Lon,y.Lat=y.Lat))
df2$count<-"0"
library(data.table)
library(expss)
setDT(dat)
dummy<-matrix(ncol=1,nrow=nrow(df2))
for (i in 1:nrow(df2)){
df_bounds<-data.frame(north=(df2[i,]$y.Lat+latint),south=(df2[i,]$y.Lat-latint),west=(df2[i,]$x.Lon-lonint),east=(df2[i,]$x.Lon+lonint))
dat[,inBounds := Lat >= (df2[i,]$y.Lat-latint) & Lat <= (df2[i,]$y.Lat+latint) & Lon >= (df2[i,]$x.Lon-lonint) & Lon >= (df2[i,]$x.Lon+lonint)]
dat1<-dat[SN %in% dat[inBounds == TRUE, unique(SN)],passesThroughBox := T]
#dat2<-dat[is.na(passesThroughBox),passesThroughBox := F]
#dat3<-dat1[which(passesThroughBox == TRUE),]
dummy[i,]<-count_if("TRUE",dat1$passesThroughBox)
}
PROBLEMS/QUESTIONS
The dummy matrix only contains 0 values.
I think I am not counting the unique points correctly.
EXPECTED OUTPUTS
The df2 data frame with an additional column containing the unique counts.
A gridded plot of df2, where the color corresponds to the counts.
Any suggestions on how to do this in R?
I'll appreciate any help.
Given the observations data-frame dat and grid data-frame df2. For each observation in dat find the nearest grid center in df2 to obtain the grid it belongs to.
This is based on assumption that an observation belongs to its nearest grid center. Also, For each SN only the first entry reported is taken as the unique observation.
df2<-as.data.frame(expand.grid(x.Lon=x.Lon,y.Lat=y.Lat)) # grid box centers
df2$count<- 0L # keeping count as integer
# function to calculate distance between two points
dist <- function(x, y) {diff <- (y - x) ; sqrt(sum(diff^2))}
# Filtering only the unique observation
dat <- dat[!duplicated(dat$SN), ]
# Finding closest grid center for every observation in dat
closest_grid <- apply(dat[,c('Lon','Lat')],1, function(x){
dist_grid <- apply(df2[,c('x.Lon','y.Lat')], 1, function(y) dist(x,y))
return(which.min(dist_grid))
})
# Summarizing no of of counts for each grid center with non zero counts
df2[names(table(closest_grid)),'count'] <- as.integer(table(closest_grid))
df2[names(table(closest_grid)),] # the non-zero counts
# x.Lon y.Lat count
#738 151.25 10.00 1
#1199 133.75 16.25 1
#1292 131.25 17.50 1
#1474 121.25 20.00 1
Use ggplot2 to plot the the count heat-map at grid centers:
library(ggplot2) # fill = counts data
ggplot(data = df2, aes(x = x.Lon, y = y.Lat)) + geom_tile(aes(fill = count))
The below plot is for your full data-set:
Extracting overlaping pairs of rows from df with two columns
I would like to find out which pairs overlap between these two tables:
> dput(data1)
structure(list(Name.x = c("MDH1", "MDH1", "IDH2", "IDH2", "IDH2",
"IDH2", "IDH2", "IDH2", "IDH2", "SCOALB", "SCOALB", "CSY4", "CSY4",
"CSY4", "CSY4", "CSY4", "FUM1", "FUM1", "IDH6", "IDH6", "IDH6",
"ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC2-1", "ODC2-1",
"ODC2-1", "ACO2", "IDH1", "IDH1", "IDH1", "IDH1", "ODC2-2"),
Name.y = c("SCOALB", "SCOALA-1", "CSY4", "IDH6", "ODC1-1",
"ODC2-1", "IDH1", "ODC2-2", "ODC1-2", "SCOALA-1", "SCOALA-2",
"IDH6", "SDH2-1", "IDH1", "IDH5", "ICDH", "ODC1-1", "ODC1-2",
"ACO2", "IDH1", "IDH5", "ODC2-1", "IDH1", "IDH5", "ODC2-2",
"ODC1-2", "IDH1", "ODC2-2", "ODC1-2", "IDH1", "IDH5", "SCOALA-2",
"ODC2-2", "ODC1-2", "ODC1-2")), .Names = c("Name.x", "Name.y"
), class = "data.frame", row.names = c(NA, -35L))
> dput(data2)
structure(list(Protein1 = structure(c(3L, 7L, 18L, 19L, 7L, 19L,
6L, 18L, 6L, 18L, 18L, 19L, 9L, 8L, 19L, 18L, 9L, 7L, 18L, 12L,
8L, 19L, 5L, 29L, 12L, 29L, 12L, 18L, 7L, 17L, 6L, 5L, 9L, 19L,
12L, 3L, 19L, 16L, 18L, 17L, 16L, 17L, 9L, 29L, 12L, 7L, 29L,
18L, 16L, 18L, 29L, 8L, 17L, 16L, 17L, 12L, 6L, 8L, 17L, 29L,
9L, 17L, 29L, 19L, 8L, 17L, 29L, 9L, 9L, 16L, 29L, 29L, 19L,
19L, 19L, 29L, 12L, 19L, 17L, 29L, 17L, 16L, 16L, 19L, 16L, 4L,
1L, 5L, 17L, 9L, 18L, 18L, 6L, 4L, 8L, 16L, 16L, 29L, 7L, 12L,
8L, 4L, 29L, 12L, 5L), .Label = c("ACO2", "ACO3", "CSY4", "FUM1",
"ICDH", "IDH1", "IDH2", "IDH5", "IDH6", "LPD1", "LPD2", "MDH1",
"MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2", "ODC2-1", "ODC2-2",
"PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1", "PDC2-2", "SCoALa-1",
"SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1", "SDH2-2", "SDH2-3",
"SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a", "SDH7b", "SDH8"), class = "factor"),
Protein2 = structure(c(1L, 6L, 7L, 17L, 1L, 16L, 3L, 9L,
1L, 5L, 17L, 9L, 8L, 7L, 18L, 18L, 5L, 3L, 16L, 3L, 5L, 8L,
4L, 7L, 5L, 3L, 6L, 6L, 5L, 3L, 5L, 3L, 3L, 6L, 7L, 3L, 7L,
9L, 1L, 8L, 5L, 16L, 7L, 6L, 4L, 7L, 4L, 3L, 3L, 12L, 1L,
1L, 9L, 7L, 7L, 9L, 6L, 6L, 5L, 8L, 1L, 17L, 29L, 3L, 8L,
6L, 9L, 9L, 6L, 12L, 5L, 19L, 12L, 5L, 1L, 16L, 1L, 19L,
4L, 18L, 12L, 1L, 4L, 4L, 6L, 3L, 1L, 1L, 1L, 4L, 4L, 8L,
4L, 1L, 3L, 8L, 16L, 12L, 4L, 12L, 4L, 4L, 17L, 8L, 5L), .Label = c("ACO2",
"ACO3", "CSY4", "FUM1", "ICDH", "IDH1", "IDH2", "IDH5", "IDH6",
"LPD1", "LPD2", "MDH1", "MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2",
"ODC2-1", "ODC2-2", "PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1",
"PDC2-2", "SCoALa-1", "SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1",
"SDH2-2", "SDH2-3", "SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a",
"SDH7b", "SDH8"), class = "factor")), .Names = c("Protein1",
"Protein2"), class = "data.frame", row.names = c(1L, 4L, 6L,
12L, 22L, 25L, 28L, 33L, 44L, 48L, 51L, 52L, 53L, 60L, 68L, 70L,
72L, 76L, 86L, 109L, 110L, 119L, 133L, 144L, 146L, 158L, 170L,
197L, 202L, 206L, 211L, 213L, 226L, 227L, 237L, 271L, 272L, 286L,
290L, 297L, 304L, 305L, 306L, 319L, 323L, 327L, 347L, 348L, 351L,
357L, 370L, 372L, 373L, 378L, 379L, 392L, 406L, 410L, 414L, 417L,
419L, 437L, 442L, 445L, 448L, 455L, 457L, 462L, 471L, 479L, 482L,
483L, 488L, 503L, 509L, 522L, 536L, 563L, 618L, 620L, 623L, 628L,
630L, 644L, 647L, 666L, 668L, 673L, 676L, 678L, 679L, 690L, 691L,
694L, 698L, 703L, 709L, 714L, 715L, 722L, 723L, 724L, 727L, 739L,
740L))
In each of df there are two columns which store strings. Strings overlap between table. However, the order between pairs might be different. One string from the pair might be find in first column of data1 and in second column in data2. How to find what pairs and how many of them overlap between datasets ?
> data1$combine = as.character(interaction(data1$Name.x, data1$Name.y)) > data2$combine = as.character(interaction(data2$Protein1, data2$Protein2)) > > dat.overlap = data1[complete.cases(match(data2$combine, data1$combine)),] > dat.overlap Name.x Name.y combine 2 MDH1 SCOALA-1 MDH1.SCOALA-1 4 IDH2 IDH6 IDH2.IDH6 11 SCOALB SCOALA-2 SCOALB.SCOALA-2 13 CSY4 SDH2-1 CSY4.SDH2-1 18 FUM1 ODC1-2 FUM1.ODC1-2 28 ODC2-1 ODC2-2 ODC2-1.ODC2-2 data1[complete.cases(match(data1$combine, data2$combine)),] Name.x Name.y combine 3 IDH2 CSY4 IDH2.CSY4 7 IDH2 IDH1 IDH2.IDH1 19 IDH6 ACO2 IDH6.ACO2 20 IDH6 IDH1 IDH6.IDH1 21 IDH6 IDH5 IDH6.IDH5 23 ODC1-1 IDH1 ODC1-1.IDH1 24 ODC1-1 IDH5 ODC1-1.IDH5 27 ODC2-1 IDH1 ODC2-1.IDH1 29 ODC2-1 ODC1-2 ODC2-1.ODC1-2 35 ODC2-2 ODC1-2 ODC2-2.ODC1-2
Sort row-wise and make a key by pasting, then merge: data1$key <- apply(data1, 1, function(i) paste(sort(i), collapse = "_")) data2$key <- apply(data2, 1, function(i) paste(sort(i), collapse = "_")) res <- merge(data1, data2, by = "key") head(res) # key Name.x Name.y Protein1 Protein2 # 1 ACO2_IDH1 ACO2 IDH1 IDH1 ACO2 # 2 ACO2_IDH6 IDH6 ACO2 IDH6 ACO2 # 3 CSY4_ICDH CSY4 ICDH ICDH CSY4 # 4 CSY4_IDH1 CSY4 IDH1 IDH1 CSY4 # 5 CSY4_IDH2 IDH2 CSY4 IDH2 CSY4 # 6 CSY4_IDH5 CSY4 IDH5 IDH5 CSY4
Error while storing ggplots in list. Impossible to plot multiple ggplot, but possible to plot them separately
I'm currently working on the effects of environmental variables on the toxicity of a shellfish. This toxicity happens only on certain years. I would like to compare time series of 15 different environmental variables between toxic years and non toxic years. My data or on 10 years and 6 locations.
I would like to have 1 window / site, each window containing 10 ggplots representing the 10 annual time series of one parameter
here are the data i give for a reproducible example, on one location, for one parameter (Temperature): (corrected to be reproducible)
structure(list(Date = structure(c(12065, 12065, 12079, 12079,
12088, 12095, 12095, 12104, 12115, 12115, 12123, 12123, 12130,
12130, 12135, 12137, 12137, 12142, 12146, 12146, 12149, 12150,
12150, 12156, 12157, 12157, 12164, 12164, 12165, 12170, 12177,
12177, 12177, 12184, 12185, 12185, 12191, 12192, 12192, 12198,
12199, 12199, 12205, 12206, 12206, 12213, 12215, 12215, 12219,
12219, 12219, 12226, 12233, 12235, 12235, 12240, 12240, 12240,
12240, 12240, 12240, 12248, 12248, 12248, 12254, 12255, 12255,
12261, 12263, 12263, 12268, 12268, 12268, 12275, 12275, 12275,
12282, 12283, 12283, 12289, 12291, 12291, 12296, 12297, 12297,
12303, 12305, 12305, 12311, 12311, 12318, 12318, 12326, 12331,
12338, 12352, 12368, 12381, 12395, 12403, 12424, 12436, 12452,
12464, 12478, 12495, 12507, 12522, 12528, 12534, 12541, 12548,
12562, 12571, 12571, 12576, 12576, 12583, 12583, 12591, 12598,
12613, 12620, 12625, 12633, 12639, 12646, 12653, 12661, 12667,
12676, 12682, 12690, 12696, 12702, 12709, 12716, 12724, 12730,
12744, 12758, 12772, 12795, 12800, 12814, 12828, 12843, 12856,
12871, 12877, 12884, 12898, 12905, 12912, 12926, 12933, 12940,
12954, 12954, 12961, 12961, 12968, 12968, 12982, 12982, 13011,
13011, 13024, 13024, 13038, 13052, 13052, 13067, 13083, 13094,
13111, 13122, 13136, 13151, 13166, 13178, 13192, 13206, 13221,
13236, 13248, 13262, 13270, 13278, 13292, 13298, 13305, 13318,
13318, 13326, 13332, 13332, 13333, 13339, 13346, 13346, 13377,
13390, 13402, 13432, 13466, 13529, 13542, 13585, 13599, 13614,
13626, 13643, 13655, 13669, 13675, 13683, 13698, 13710, 13725,
13731, 13741, 13754, 13760, 13767, 13781, 13789, 13795, 13809,
13823, 13838, 13851, 13867, 13901, 13901, 13907, 13921, 13936,
13936, 13957, 13963, 13963, 13978, 13992, 13992, 14005, 14020,
14020, 14036, 14036, 14041, 14047, 14047, 14047, 14047, 14047,
14053, 14054, 14061, 14061, 14069, 14076, 14076, 14076, 14076,
14077, 14082, 14089, 14089, 14105, 14105, 14105, 14105, 14118,
14118, 14131, 14131, 14145, 14145, 14152, 14160, 14166, 14173,
14180, 14188, 14202, 14216, 14230, 14258, 14271, 14287, 14299,
14312, 14327, 14340, 14354, 14368, 14375, 14382, 14397, 14411,
14411, 14425, 14425, 14440, 14440, 14447, 14453, 14453, 14467,
14467, 14474, 14481, 14481, 14488, 14494, 14502, 14509, 14509,
14516, 14523, 14539, 14565, 14579, 14593, 14607, 14635, 14649,
14663, 14683, 14700, 14706, 14714, 14719, 14727, 14736, 14749,
14763, 14763, 14777, 14777, 14791, 14819, 14819, 14824, 14832,
14832, 14845, 14845, 14861, 14861, 14873, 14873, 14888, 14902,
14929, 14985, 14999, 15015, 15029, 15043, 15057, 15071, 15085,
15097, 15111, 15125, 15141, 15141, 15153, 15153, 15167, 15167,
15181, 15181, 15195, 15195, 15209, 15209, 15223, 15237, 15237,
15251, 15265, 15281, 15293, 15307, 15321, 15335, 15349, 15377,
15391, 15405, 15419, 15433, 15447, 15457, 15463, 15474, 15491,
15503, 15503, 15517, 15517, 15523, 15533, 15545, 15545, 15559,
15559, 15573, 15573, 15589, 15589, 15601, 15601, 15615, 15629,
15643, 15657, 15671, 15685, 15702), class = "Date"), Annee = structure(c(9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L), .Label = c("1995", "1996", "1997", "1998", "1999", "2000",
"2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013"), class = "factor"), Mois = structure(c(1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
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"39901", "32468", "28144", "26261", "27515"))
here is an extract of my program
p<-list()
#Creating the graphs year by year
for(a in 1: 10){
#selecting the year
An<-baie[baie$Annee==unique(baie$Annee)[a],]
moyparam<-ddply(An, .(Date, Annee, Mois, Jourannee), function(x) data.frame(Mesure=mean(x$Mesure)))
p[[a]]<-ggplot(data=moyparam, aes(x=moyparam$Date, y=moyparam$Mesure))+geom_point()+theme_bw()
}
grid.arrange(p)
#or
multiplot(plotlist=p, layout=matrix(c(1:10),nrow=2,ncol=5, byrow=TRUE))
I manage to plot each graphs separately, they are even stored in a list, but when i display the list or when i try to do the multiple plot, i get a message:
Error in data.frame(x = c(15349, 15365, 15377, 15392, 15411, 15412,
15419, : arguments imply differing number of rows
Where am I wrong? Maybe the answer is simple, but i think i could use a new point of view on the problem.
Thank you for any help you can give me.
As an update:
thank you to Roland and noah for pointing my errors and helping me so quickly! but here's a precision:
I did not mention it previously, but my code is a bit more complicated than what is written here. In reality, i add a partially colored background on a "risk period" only on years where toxicity of the shelfish is observed (so that i can compare parameters on toxic years (precisely: on risk period) and non toxic years (on the entire year).
so my code is testing if the year is toxic, and if so, it add a color background on the risk period. I did not put it before because my error occurs even without this test, and i mention it now because it explains why i can't use facets grid (or can i? is there a way i can add partially colored background only on some facets?)
If you correct your misuse of $ in aes() the code works as expected, p[[a]] <- ggplot(data=moyparam, aes(x=Date, y=Mesure)) + geom_point()+theme_bw() And here's a more concise way to do the processing: baie2 = plyr::ddply(baie, .(Date, Annee, Mois, Jourannee), summarise, Mesure = mean(Mesure)) base_plot = ggplot(baie2, aes(x=Date, y=Mesure)) + geom_point()+theme_bw() lp = plyr::dlply(baie2, "Annee", `%+%`, e1 = base_plot) from which you can arrange all plots in a page: gridExtra::grid.arrange(grobs = lp) Now, for the broader question, you have two options: use facetting for the year, and a loop / **ply to open a new page for each site base_plot + facet_wrap(~Annee, scales="free") use gridExtra::marrangeGrob, like grid.arrange above but automatically splits the layout into multiple pages if necessary. It also works with ggsave.