Interval Summary Statistics
interval_stats(df, type = "diurnal", seas = NULL)
| df | a dataframe containing columns "x", "y", and "date" |
|---|---|
| type | a character string indicating the intervals to calculate, one of "diurnal", "lunar", or "seasonal". |
| seas | a character vector including the start date of each season interval in the format
%Y-%m-%d, e.g. "2015-01-01". Required if |
Note: if your trajectory includes dates before the first date in your seas vector, they
will automatically be considered a separate season.
# diurnal is default interval_stats(AG195)#> # A tibble: 730 x 9 #> # Groups: interval_start [730] #> interval_start TOD mean_dist sd_dist acf_dist mean_ang sd_ang acf_ang #> <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2010-01-01 00:00:00 0:00… 88.0 114. 0.515 -0.0457 1.37 -0.399 #> 2 2010-01-01 12:00:00 12:0… 146. 181. 0.570 -0.290 1.19 0.0298 #> 3 2010-01-02 00:00:00 0:00… 147. 177. 0.591 0.493 1.31 -0.201 #> 4 2010-01-02 12:00:00 12:0… 157. 154. 0.612 -0.0942 0.923 -0.412 #> 5 2010-01-03 00:00:00 0:00… 87.2 125. 0.436 0.0130 0.902 0.0815 #> 6 2010-01-03 12:00:00 12:0… 43.5 64.3 0.452 0.257 1.33 0.0269 #> 7 2010-01-04 00:00:00 0:00… 70.6 96.2 0.595 -0.314 1.54 0.123 #> 8 2010-01-04 12:00:00 12:0… 42.5 33.8 0.143 -0.0764 1.45 -0.0202 #> 9 2010-01-05 00:00:00 0:00… 95.2 111. 0.473 -0.0782 1.37 -0.102 #> 10 2010-01-05 12:00:00 12:0… 63.3 42.6 0.0695 0.0619 1.02 0.0658 #> # … with 720 more rows, and 1 more variable: ccf <dbl>interval_stats(AG195, type = "lunar")#> # A tibble: 25 x 9 #> # Groups: interval_start [25] #> interval_start phase mean_dist sd_dist acf_dist mean_ang sd_ang acf_ang #> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2010-01-01 Full… 99.8 126. 0.571 -5.92e-4 1.31 -0.0351 #> 2 2010-01-12 New-… 118. 122. 0.517 1.52e-2 1.22 -0.0611 #> 3 2010-01-26 Full… 181. 239. 0.686 -3.61e-2 1.32 0.0199 #> 4 2010-02-10 New-… 244. 284. 0.710 6.39e-2 1.30 0.0235 #> 5 2010-02-25 Full… 210. 254. 0.674 6.49e-2 1.37 0.0434 #> 6 2010-03-12 New-… 192. 261. 0.700 1.24e-2 1.37 -0.0126 #> 7 2010-03-27 Full… 105. 152. 0.709 5.35e-2 1.28 -0.00154 #> 8 2010-04-10 New-… 184. 246. 0.751 -2.38e-2 1.21 -0.00632 #> 9 2010-04-25 Full… 119. 161. 0.539 4.81e-2 1.23 -0.119 #> 10 2010-05-10 New-… 115. 169. 0.612 4.37e-2 1.19 -0.0617 #> # … with 15 more rows, and 1 more variable: ccf <dbl># for seasonal, include y-m-d formatted `seas` vector interval_stats(AG195, type = "seasonal", seas = c("2010-03-01", "2010-06-01", "2010-9-01"))#> # A tibble: 4 x 8 #> interval_start mean_dist sd_dist acf_dist mean_ang sd_ang acf_ang ccf #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2009-12-31 174. 226. 0.697 0.0102 1.28 -0.00832 0.00843 #> 2 2010-03-01 144. 207. 0.690 0.0233 1.28 -0.0282 -0.00724 #> 3 2010-06-01 93.6 137. 0.564 -0.000686 1.25 -0.0124 0.0107 #> 4 2010-09-01 109. 172. 0.675 0.0118 1.33 -0.0358 -0.00321