Thanks andresrcs,
Here are small portions of the data
The below is CSV1 that I talked about in the first post.
tidedata <- tibble::tribble(
~tideDT, ~reading, ~Tide,
"2015-09-01 00:00:00", 3.75, "High",
"2015-09-01 00:10:00", 3.836, "High",
"2015-09-01 00:20:00", 3.91, "High",
"2015-09-01 00:30:00", 3.97, "High",
"2015-09-01 00:40:00", 4.012, "High",
"2015-09-01 00:50:00", 4.039, "High",
"2015-09-01 01:00:00", 4.045, "High",
"2015-09-01 01:10:00", 4.029, "High",
"2015-09-01 01:20:00", 3.998, "High",
"2015-09-01 01:30:00", 3.95, "High",
"2015-09-01 01:40:00", 3.885, "High",
"2015-09-01 01:50:00", 3.812, "High",
"2015-09-01 02:00:00", 3.729, "High",
"2015-09-01 02:10:00", 3.636, "High",
"2015-09-01 02:20:00", 3.525, "High",
"2015-09-01 02:30:00", 3.405, "High",
"2015-09-01 02:40:00", 3.285, "High",
"2015-09-01 02:50:00", 3.143, "High",
"2015-09-01 03:00:00", 3.009, "High",
"2015-09-01 03:10:00", 2.865, "High",
"2015-09-01 03:20:00", 2.718, "High",
"2015-09-01 03:30:00", 2.568, "High",
"2015-09-01 03:40:00", 2.424, "High",
"2015-09-01 03:50:00", 2.287, "High",
"2015-09-01 04:00:00", 2.139, "High",
"2015-09-01 04:10:00", 1.995, "High",
"2015-09-01 04:20:00", 1.862, "High",
"2015-09-01 04:30:00", 1.737, "High",
"2015-09-01 04:40:00", 1.605, "High",
"2015-09-01 04:50:00", 1.475, "Low",
"2015-09-01 05:00:00", 1.35, "Low",
"2015-09-01 05:10:00", 1.229, "Low",
"2015-09-01 05:20:00", 1.116, "Low",
"2015-09-01 05:30:00", 1.005, "Low",
"2015-09-01 05:40:00", 0.899, "Low",
"2015-09-01 05:50:00", 0.802, "Low",
"2015-09-01 06:00:00", 0.708, "Low",
"2015-09-01 06:10:00", 0.618, "Low",
"2015-09-01 06:20:00", 0.54, "Low",
"2015-09-01 06:30:00", 0.465, "Low",
"2015-09-01 06:40:00", 0.41, "Low",
"2015-09-01 06:50:00", 0.359, "Low",
"2015-09-01 07:00:00", 0.335, "Low",
"2015-09-01 07:10:00", 0.33, "Low",
"2015-09-01 07:20:00", 0.35, "Low",
"2015-09-01 07:30:00", 0.401, "Low"
)
tidedata$tideDT <- as.POSIXct(tidedata$tideDT , format = "%Y-%m-%d %H:%M:%S")
head(tidedata)
#> # A tibble: 6 x 3
#> tideDT reading Tide
#> <dttm> <dbl> <chr>
#> 1 2015-09-01 00:00:00 3.75 High
#> 2 2015-09-01 00:10:00 3.84 High
#> 3 2015-09-01 00:20:00 3.91 High
#> 4 2015-09-01 00:30:00 3.97 High
#> 5 2015-09-01 00:40:00 4.01 High
#> 6 2015-09-01 00:50:00 4.04 High
and the following is CSV2
telemetrydata <- tibble::tribble(
~id, ~DateTime, ~lat, ~lon, ~qi, ~sex, ~ageclass, ~depth.exp, ~depth.HT,
"QA58295", "2015-10-01 00:02:00", -23.7676, 151.2965, 6L, "F", "SA", -3.15134787, -3.23284787,
"QA43123", "2015-10-01 00:10:00", -23.7733, 151.2915, 6L, "F", "A", -3.006766321, -3.124266321,
"QA58206", "2015-10-01 00:40:00", -23.6856, 151.3048, 4L, "F", "SA", -18.72804075, -19.05964075,
"QA58284", "2015-10-01 02:15:00", -23.7637, 151.1582, 8L, "M", "A", -3.187366148, -3.316566148,
"QA58284", "2015-10-01 03:08:00", -23.7639, 151.1579, 8L, "M", "A", -2.772221329, -3.289721329,
"QA58295", "2015-10-01 03:45:00", -23.7693, 151.2944, 5L, "F", "SA", -1.472480488, -3.271480488,
"QA58284", "2015-10-01 04:15:00", -23.764, 151.1579, 8L, "M", "A", -1.84678562, -3.30158562,
"K28651", "2015-10-01 06:55:00", -23.7553, 151.3169, 5L, "M", "A", -3.795818538, -6.773818538,
"QA58291", "2015-10-01 07:27:00", -24.5293, 152.0331, 6L, "M", "A", 1.11056039, -1.76943961,
"QA58295", "2015-10-01 07:45:00", -23.7768, 151.3097, 5L, "F", "SA", -0.857955725, -3.567955725,
"QA58291", "2015-10-01 08:01:00", -24.531, 152.0311, 4L, "M", "A", 0.228332558, -2.224067442,
"K28651", "2015-10-01 10:51:00", -23.7556, 151.3167, 6L, "M", "A", -5.854780024, -6.496980024,
"K28651", "2015-10-01 11:30:00", -23.7557, 151.3169, 6L, "M", "A", -6.165598638, -6.633598638,
"QA58295", "2015-10-01 11:49:00", -23.778, 151.3076, 6L, "F", "SA", -3.701196658, -4.026996658,
"K28651", "2015-10-01 13:03:00", -23.7557, 151.3167, 7L, "M", "A", -6.487376042, -6.493576042,
"K28651", "2015-10-01 13:34:00", -23.7551, 151.317, 8L, "M", "A", -6.83297897, -6.93147897,
"QA58284", "2015-10-01 13:45:00", -23.7656, 151.1546, 6L, "M", "A", -2.520284467, -2.527484467,
"QA58295", "2015-10-01 13:55:00", -23.778, 151.3074, 6L, "F", "SA", -3.62481876, -3.83581876,
"K28651", "2015-10-01 14:16:00", -23.754, 151.3155, 4L, "M", "A", -6.554817435, -6.915717435,
"QA58206", "2015-10-01 14:38:00", -23.7435, 151.3051, 4L, "F", "SA", -7.002649565, -7.531949565,
"K28651", "2015-10-01 14:56:00", -23.7541, 151.3152, 9L, "M", "A", -5.983471077, -6.757471077,
"QA58284", "2015-10-01 14:58:00", -23.7655, 151.1554, 8L, "M", "A", -2.360926318, -2.579626318,
"QA58206", "2015-10-01 15:29:00", -23.7433, 151.3051, 5L, "F", "SA", -6.276333921, -7.385933921,
"QA58295", "2015-10-01 16:20:00", -23.7773, 151.3075, 5L, "F", "SA", -1.696819658, -3.390319658
)
telemetrydata$DateTime <- as.POSIXct(telemetrydata$DateTime , format = "%Y-%m-%d %H:%M:%S")
head(telemetrydata)
#> # A tibble: 6 x 9
#> id DateTime lat lon qi sex ageclass depth.exp depth.HT
#> <chr> <dttm> <dbl> <dbl> <int> <chr> <chr> <dbl> <dbl>
#> 1 QA582~ 2015-10-01 00:02:00 -23.8 151. 6 F SA -3.15 -3.23
#> 2 QA431~ 2015-10-01 00:10:00 -23.8 151. 6 F A -3.01 -3.12
#> 3 QA582~ 2015-10-01 00:40:00 -23.7 151. 4 F SA -18.7 -19.1
#> 4 QA582~ 2015-10-01 02:15:00 -23.8 151. 8 M A -3.19 -3.32
#> 5 QA582~ 2015-10-01 03:08:00 -23.8 151. 8 M A -2.77 -3.29
#> 6 QA582~ 2015-10-01 03:45:00 -23.8 151. 5 F SA -1.47 -3.27
I hope I've done that correctly. I should note, that while these two snapshots of the data don't have overlapping dates, that the entire datasets do in fact overlap.