I conducted a psychology experiment and I want to analyze participants clock monitoring frequency. I want look for the mean number of clock checks by time blocks of 5 seconds. Then it would be nice to do a scatter plot to see if the distribution of clock checks differs by valence.
Any suggestions?
Here's part of my data...
tibble::tribble(
~Valence, ~CheckTime, ~Participant,
0, 44.4, 1L,
1, 7, 2L,
2, 15.9, 3L,
0, 35, 1L,
1, 27.4, 2L,
2, 30.4, 3L,
0, 56.9, 1L,
1, 45.7, 2L,
2, 40.4, 3L,
0, 40.8, 1L,
1, 50.9, 2L,
2, 48, 3L,
0, 60, 1L,
1, 55.4, 2L,
2, 20, 3L,
0, 23.4, 1L,
1, 10.9, 2L,
2, 37.6, 3L,
0, 47.4, 1L,
1, 27.4, 2L,
2, 57.4, 3L,
0, 61, 1L,
1, 47.4, 2L,
2, 37, 3L,
0, 35.2, 1L,
1, 55.4, 2L,
2, 53.9, 3L,
0, 54, 1L,
1, 1.5, 2L,
2, 36.1, 3L,
0, 30.4, 1L,
1, 8.9, 2L,
2, 58.6, 3L,
0, 58.7, 1L,
1, 20.6, 2L,
2, 45.9, 3L,
0, 81.5, 1L,
1, 26.9, 2L,
2, 54.6, 3L,
0, 26.2, 1L,
1, 44.5, 2L,
2, 47.4, 3L,
0, 52, 1L,
1, 51.5, 2L,
2, 62.1, 3L,
0, 38.7, 1L,
1, 54.3, 2L,
2, 39.5, 3L,
0, 60, 1L,
1, 24, 2L,
2, 58.4, 3L,
0, 21.1, 1L,
1, 39, 2L,
2, 58.9, 3L,
0, 65.2, 1L,
1, 49.4, 2L,
2, 54.2, 3L,
0, 30.4, 1L,
1, 56.6, 2L,
2, 40.5, 3L,
0, 50.3, 1L,
1, 0.8, 2L,
2, 51.8, 3L,
0, 60.3, 1L,
1, 16.7, 2L,
2, 37.9, 3L,
0, 18.7, 1L,
1, 41.8, 2L,
2, 53.6, 3L,
0, 33.8, 1L,
1, 49.4, 2L,
2, 36.2, 3L,
0, 49, 1L,
1, 53.5, 2L,
2, 59.5, 3L,
0, 61.4, 1L,
1, 1.4, 2L,
2, 49, 3L,
0, 15.6, 1L,
1, 8.3, 2L,
2, 58.7, 3L,
0, 36.5, 1L,
1, 33.4, 2L,
2, 52.2, 3L,
0, 52.4, 1L,
1, 58.6, 2L,
2, 57.4, 3L,
0, 59, 1L,
1, 0.6, 2L,
2, 31.7, 3L,
0, 30.6, 1L,
1, 8.9, 2L,
2, 48.9, 3L,
0, 57.8, 1L,
1, 23.9, 2L,
2, 55.7, 3L,
0, 18.3, 1L,
1, 41, 2L,
2, 27, 3L,
0, 35.9, 1L,
1, 50, 2L,
2, 54.6, 3L,
0, 58.5, 1L,
1, 0.8, 2L,
2, 39.4, 3L,
0, 21.2, 1L,
1, 10.1, 2L,
2, 64.8, 3L,
0, 36.8, 1L,
1, 26.8, 2L,
2, 27.2, 3L,
0, 58.4, 1L,
1, 45.4, 2L,
2, 47, 3L,
0, 25.8, 1L,
1, 54.9, 2L,
2, 56.8, 3L,
0, 41.1, 1L,
1, 56.8, 2L,
2, 32.8, 3L,
0, 60.6, 1L,
1, 80, 2L,
2, 48.2, 3L,
0, 27.8, 1L,
1, 1.1, 2L,
2, 58.9, 3L,
0, 57.5, 1L,
1, 7.8, 2L,
2, 22.5, 3L,
0, 36.9, 1L,
1, 19.5, 2L,
2, 40, 3L,
1, 32.5, 2L,
1, 43.5, 2L,
1, 54.3, 2L,
1, 12.6, 2L,
1, 27.2, 2L,
1, 47.1, 2L,
1, 69.5, 2L,
1, 21.6, 2L,
1, 32.8, 2L,
1, 43.3, 2L,
1, 48.5, 2L,
1, 56.5, 2L,
1, 37.2, 2L,
1, 45.7, 2L,
1, 50.3, 2L,
1, 1.5, 2L,
1, 22.9, 2L,
1, 52, 2L,
1, 0.6, 2L,
1, 19, 2L,
1, 30.2, 2L,
1, 43.9, 2L,
1, 45.2, 2L,
1, 53, 2L,
1, 0.8, 2L,
1, 23.4, 2L,
1, 41.6, 2L,
1, 55, 2L,
1, 0.5, 2L,
1, 12.6, 2L,
1, 26.7, 2L,
1, 40.8, 2L,
1, 53.6, 2L,
1, 0.7, 2L,
1, 16.6, 2L,
1, 33.5, 2L,
1, 49.6, 2L,
1, 59.4, 2L,
1, 12.9, 2L,
1, 23.3, 2L,
1, 36, 2L,
1, 40.9, 2L,
1, 50.4, 2L,
1, 53.6, 2L,
1, 0.8, 2L,
1, 19.9, 2L,
1, 32.6, 2L,
1, 62, 2L,
1, 0.6, 2L,
1, 8.7, 2L,
1, 45.2, 2L,
1, 56.6, 2L,
1, 2.1, 2L
)