SRF VidSync 2019

Keane Flynn
3/18/2019

SRF 2019 VidSync Analysis

Load Libraries

library(dplyr)
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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library(readr)
library(tidyverse)
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## ✔ tibble  1.4.2     ✔ stringr 1.3.1
## ✔ tidyr   0.8.1     ✔ forcats 0.3.0

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library(ggplot2)
library(lubridate)
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library(geometry)
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library(rgl)
library(hypervolume)
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library(zoo)
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##     as.Date, as.Date.numeric
library(stringr)
library(data.table)
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VidSync Data

This section of the code is devoted to importing, analyzing, and creating correlations with 3-D Videogrammetry data collected using VidSync from the summer of 2018 on Porter Creek. This is my child and I will keep it neat.

Importing Data

Pre-Augmentation

GolfBall_VidSync_Pre <- 
  readr::read_csv(file = "Vidsync-data/PreAugmentation/Porter_BACI_GolfBall_30June2018_Part3.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-06-30")) %>% 
  mutate(BACI_date = as.Date("2018-06-29")) %>% 
  mutate(site = "18.3") %>% 
  mutate(sample_event = "Before")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 168 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
HalfTire_VidSync_Pre <-
  readr::read_csv(file = "Vidsync-data/PreAugmentation/Porter_BACI_HalfTire_29June2018_Part2.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-06-29")) %>% 
  mutate(BACI_date = as.Date("2018-06-29")) %>% 
  mutate(site = "18.8") %>% 
  mutate(sample_event = "Before")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 113 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PreAugmentation/Porter_BA…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
RoachRun_VidSync_Pre <-
  readr::read_csv(file = "Vidsync-data/PreAugmentation/Porter_BACI_RoachRun_29June2018_Part3.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-06-29")) %>% 
  mutate(BACI_date = as.Date("2018-06-29")) %>% 
  mutate(site = "18.5") %>% 
  mutate(sample_event = "Before")

JesusToast_VidSync_Pre <- 
  readr::read_csv(file = "Vidsync-data/PreAugmentation/Porter_BACI_JTPool_30June2018_Part1.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-06-30")) %>% 
  mutate(BACI_date = as.Date("2018-06-29")) %>% 
  mutate(site = "18.2") %>% 
  mutate(sample_event = "Before")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 113 parsing failures.
## row # A tibble: 5 x 5 col     row col       expected actual                   file                   expected   <int> <chr>     <chr>    <chr>                    <chr>                  actual 1     1 screen_c… a double "LC: screen={499.007995… 'Vidsync-data/PreAugm… file 2     2 screen_c… a double "RC: screen={246.945831… 'Vidsync-data/PreAugm… row 3     3 screen_c… a double "LC: screen={448.637695… 'Vidsync-data/PreAugm… col 4     4 screen_c… a double "LC: screen={470.562622… 'Vidsync-data/PreAugm… expected 5     5 screen_c… a double "LC: screen={470.971984… 'Vidsync-data/PreAugm…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.

Post-Augmentation

GolfBall_VidSync_Post <-
  readr::read_csv(file = "Vidsync-data/PostAugmentation/Porter_BACI_Golfball_6July2018_Part1.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>%
  mutate(date = as.Date("2018-07-06")) %>% 
  mutate(BACI_date = as.Date("2018-07-05")) %>% 
  mutate(site = "18.3") %>% 
  mutate(sample_event = "After")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 107 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
RoachRun_VidSync_Post <-
  readr::read_csv(file = "Vidsync-data/PostAugmentation/Porter_BACI_RoachRun_5July2018_Part1.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-07-05")) %>% 
  mutate(BACI_date = as.Date("2018-07-05")) %>% 
  mutate(site = "18.5") %>% 
  mutate(sample_event = "After")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 475 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
HalfTire_VidSync_Post <- 
  readr::read_csv(file = "Vidsync-data/PostAugmentation/Porter_BACI_HalfTire_5July2018_Part3.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-07-05")) %>% 
  mutate(BACI_date = as.Date("2018-07-05")) %>% 
  mutate(site = "18.8") %>% 
  mutate(sample_event = "After")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 692 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
JesusToast_VidSync_Post <-
  readr::read_csv(file = "Vidsync-data/PostAugmentation/Porter_BACI_JesusToast_6July2018_Part1.csv",
           skip = 2,
                    col_names = c("objects", "event", "timecode", "time", "X", "Y", "Z", "pld_error", "projection_error", "nearest_camera_distance", "screen_coordinates"),
                    col_types = "cccdddddddd") %>% 
  mutate(date = as.Date("2018-07-06")) %>% 
  mutate(BACI_date = as.Date("2018-07-05")) %>% 
  mutate(site = "18.2") %>% 
  mutate(sample_event = "After")
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 145 parsing failures.
## row # A tibble: 5 x 5 col     row col   expected   actual   file                                     expected   <int> <chr> <chr>      <chr>    <chr>                                    actual 1     1 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… file 2     2 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… row 3     3 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… col 4     4 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B… expected 5     5 <NA>  11 columns 10 colu… 'Vidsync-data/PostAugmentation/Porter_B…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.

Test Measurement Accuracy

Vidsync_TestMeasurements <- 
  readr::read_csv(file = "Vidsync-data/Test-Data/Vidsync Test Measurments - Sheet1.csv") %>%
  filter(Stream == "Porter") %>%
  mutate(Percent_Accuracy = (1 - (abs(Test_measured_mm - Test_target_mm)/Test_target_mm))*100) %>%
  select(Site, Event, Test_Type, Percent_Accuracy)
## Parsed with column specification:
## cols(
##   Date = col_date(format = ""),
##   Stream = col_character(),
##   Site = col_character(),
##   Event = col_character(),
##   Video_num = col_character(),
##   Test_num = col_integer(),
##   Test_target_mm = col_integer(),
##   Test_measured_mm = col_double(),
##   Test_Type = col_character(),
##   Notes = col_character()
## )
Vidsync_TestMeasurements
## # A tibble: 40 x 4
##    Site     Event  Test_Type    Percent_Accuracy
##    <chr>    <chr>  <chr>                   <dbl>
##  1 RoachRun Before horizontal_1             99.8
##  2 RoachRun Before horizontal_2             99.6
##  3 RoachRun Before vertical_1               99.6
##  4 RoachRun Before vertical_2               98.9
##  5 RoachRun Before depth                    96.6
##  6 RoachRun After  horizontal_1            100  
##  7 RoachRun After  horizontal_2             99.9
##  8 RoachRun After  vertical_1               99.6
##  9 RoachRun After  vertical_2               99.7
## 10 RoachRun After  depth                    95  
## # ... with 30 more rows
ggplot(Vidsync_TestMeasurements, aes(Test_Type, Percent_Accuracy)) + 
  geom_col(width = 0.4, fill = "white", color = "black") + 
  facet_grid(Site ~ Event) +
  xlab("Test Type") +
  ylab("Percent Accuracy")

Analyzation

We will be analyzing functions such as NND, distance travelled per time (cm/s), distance to forage attempt (DFA), proportion of forage types, volume occupied, overlap in forage volumes, etc.

Nearest Neighbor Distance (NND)

This will use the three dimensional distance formula to look at nearest neighbor at any given time during the subsample rather than the average centroid point.

Pre-Augmentation

nnd_GolfBall_Pre <- GolfBall_VidSync_Pre %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-06-30")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.3") %>%
  mutate(sample_event = "Before") 

nnd_HalfTire_Pre <- HalfTire_VidSync_Pre %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-06-29")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.8") %>%
  mutate(sample_event = "Before")   

nnd_RoachRun_Pre <- RoachRun_VidSync_Pre %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-06-29")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.5") %>%
 mutate(sample_event = "Before")   

nnd_JesusToast_Pre <- JesusToast_VidSync_Pre %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-06-30")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.2") %>%
  mutate(sample_event = "Before")

Post-Augmentation

nnd_GolfBall_Post <- GolfBall_VidSync_Post %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-07-06")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.3") %>%
  mutate(sample_event = "After") 

nnd_HalfTire_Post <- HalfTire_VidSync_Post %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-07-05")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.8") %>%
  mutate(sample_event = "After")   

nnd_RoachRun_Post <- RoachRun_VidSync_Post %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-07-05")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.5") %>%
  mutate(sample_event = "After")  

nnd_JesusToast_Post <- JesusToast_VidSync_Post %>%
  ungroup() %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  arrange(time) %>%
  group_by(time) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(nnd = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  group_by(time) %>%
  filter(!nnd == 0) %>%
  filter(nnd == min(nnd)) %>%
  ungroup() %>%
  group_by(subsample, index) %>%
  summarise(mean_nnd = mean(nnd), median_nnd = median(nnd)) %>%
  mutate(date = as.Date("2018-07-06")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.2") %>%
  mutate(sample_event = "After") 

nnd_data <- 
  bind_rows(nnd_RoachRun_Pre, nnd_RoachRun_Post, nnd_GolfBall_Pre, nnd_GolfBall_Post, nnd_HalfTire_Pre, nnd_HalfTire_Post, nnd_JesusToast_Pre, nnd_JesusToast_Post) %>%
  select(date, BACI_date, sample_event, site, subsample, index, mean_nnd, median_nnd)
nnd_data
## # A tibble: 77 x 8
## # Groups:   subsample [6]
##    date       BACI_date  sample_event site  subsample index mean_nnd
##    <date>     <date>     <chr>        <chr>     <dbl> <dbl>    <dbl>
##  1 2018-06-29 2018-06-29 Before       18.5          1     1   0.0725
##  2 2018-06-29 2018-06-29 Before       18.5          1     2  39.1   
##  3 2018-06-29 2018-06-29 Before       18.5          2     6  63.9   
##  4 2018-06-29 2018-06-29 Before       18.5          3     9  43.2   
##  5 2018-06-29 2018-06-29 Before       18.5          3    10  69.5   
##  6 2018-06-29 2018-06-29 Before       18.5          4    12  84.6   
##  7 2018-06-29 2018-06-29 Before       18.5          5    15  37.8   
##  8 2018-06-29 2018-06-29 Before       18.5          6    18  41.4   
##  9 2018-06-29 2018-06-29 Before       18.5          6    19  32.0   
## 10 2018-07-05 2018-07-05 After        18.5          1     3   3.77  
## # ... with 67 more rows, and 1 more variable: median_nnd <dbl>

Distance Travelled per Time (cm/s)

This will use the three dimensional distance formula divided by time.

Pre-Augmentation

GolfBall_DistPerTime_Pre <- GolfBall_VidSync_Pre %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-06-30")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.3") %>%
  mutate(sample_event = "Before")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))


HalfTire_DistPerTime_Pre <- HalfTire_VidSync_Pre %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-06-29")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.8") %>%
  mutate(sample_event = "Before")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
 dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))
  

RoachRun_DistPerTime_Pre <- RoachRun_VidSync_Pre %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-06-29")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.5") %>%
  mutate(sample_event = "Before")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>% 
  distinct() %>%
  arrange(as.numeric(index))

JesusToast_DistPerTime_Pre <- JesusToast_VidSync_Pre %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-06-30")) %>%
  mutate(BACI_date = as.Date("2018-06-29")) %>%
  mutate(site = "18.2") %>%
  mutate(sample_event = "Before")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))

Post-Augmentation

GolfBall_DistPerTime_Post <- GolfBall_VidSync_Post %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-07-06")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.3") %>%
  mutate(sample_event = "After")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))


RoachRun_DistPerTime_Post <- RoachRun_VidSync_Post %>%
  arrange(objects, time) %>% 
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-07-05")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.5") %>%
  mutate(sample_event = "After")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))

HalfTire_DistPerTime_Post <- HalfTire_VidSync_Post %>%
  arrange(objects, time) %>% 
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-07-05")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.8") %>%
  mutate(sample_event = "After")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))

JesusToast_DistPerTime_Post <- JesusToast_VidSync_Post %>%
  arrange(objects, time) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(distance_travelled_X_cm = X - lag(X, default = first(X))) %>%
  mutate(distance_travelled_Y_cm = Y - lag(Y, default = first(Y))) %>%
  mutate(distance_travelled_Z_cm = Z - lag(Z, default = first(Z))) %>%
  mutate(fish_distance_travelled_cm = sqrt((distance_travelled_X_cm)^2
                                        + (distance_travelled_Y_cm)^2
                                        + (distance_travelled_Z_cm)^2)) %>%
  mutate(date = as.Date("2018-07-06")) %>%
  mutate(BACI_date = as.Date("2018-07-05")) %>%
  mutate(site = "18.2") %>%
  mutate(sample_event = "After")  %>%
  group_by(index) %>%
  mutate(distance_cm_per_sec = fish_distance_travelled_cm /(time - lag(time, default = first(time)))) %>%
  filter(!distance_cm_per_sec == Inf) %>%
  mutate(DistPerTime_Median = median(fish_distance_travelled_cm)) %>%
  mutate(DistPerTime_Mean = mean(fish_distance_travelled_cm)) %>%
  dplyr::select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
  distinct() %>%
  arrange(as.numeric(index))

MovementPerTime_Data <-
bind_rows(RoachRun_DistPerTime_Pre, RoachRun_DistPerTime_Post, GolfBall_DistPerTime_Pre, GolfBall_DistPerTime_Post, HalfTire_DistPerTime_Pre, HalfTire_DistPerTime_Post, JesusToast_DistPerTime_Pre, JesusToast_DistPerTime_Post) %>%
  select(date, BACI_date, sample_event, site, species, subsample, index, DistPerTime_Median, DistPerTime_Mean)
MovementPerTime_Data
## # A tibble: 148 x 9
## # Groups:   index [47]
##    date       BACI_date  sample_event site  species subsample index
##    <date>     <date>     <chr>        <chr> <chr>       <dbl> <dbl>
##  1 2018-06-29 2018-06-29 Before       18.5  Omykiss         1     1
##  2 2018-06-29 2018-06-29 Before       18.5  Omykiss         1     2
##  3 2018-06-29 2018-06-29 Before       18.5  Omykiss         1     4
##  4 2018-06-29 2018-06-29 Before       18.5  Omykiss         2     5
##  5 2018-06-29 2018-06-29 Before       18.5  Omykiss         2     6
##  6 2018-06-29 2018-06-29 Before       18.5  Omykiss         2     7
##  7 2018-06-29 2018-06-29 Before       18.5  Omykiss         3     8
##  8 2018-06-29 2018-06-29 Before       18.5  Omykiss         3     9
##  9 2018-06-29 2018-06-29 Before       18.5  Okisut…         3    10
## 10 2018-06-29 2018-06-29 Before       18.5  Omykiss         4    11
## # ... with 138 more rows, and 2 more variables: DistPerTime_Median <dbl>,
## #   DistPerTime_Mean <dbl>

Maximum Number of Fish in Each Subsample

Max_Fish_Tally_Dataset <- 
  bind_rows(RoachRun_VidSync_Pre, RoachRun_VidSync_Post, GolfBall_VidSync_Pre, GolfBall_VidSync_Post, HalfTire_VidSync_Pre, HalfTire_VidSync_Post, JesusToast_VidSync_Pre, JesusToast_VidSync_Post) %>% 
  filter(grepl("^Subsample.*", objects)) %>% 
  filter(!grepl("^Length.*", event)) %>% 
   mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>% 
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>% 
  mutate(one_col = 1) %>% 
  arrange(site, sample_event, subsample, time) %>% 
  group_by(sample_event, site, time) %>% 
  mutate(max_time_tally = sum(one_col)) %>%
  distinct(time, .keep_all = TRUE) %>%
  select(date, BACI_date, site, sample_event, species, subsample, index, time, max_time_tally)
Max_Fish_Tally_Dataset
## # A tibble: 492 x 9
## # Groups:   sample_event, site, time [492]
##    date       BACI_date  site  sample_event species subsample index  time
##    <date>     <date>     <chr> <chr>        <chr>       <dbl> <dbl> <dbl>
##  1 2018-07-06 2018-07-05 18.2  After        Omykiss         1     1   450
##  2 2018-07-06 2018-07-05 18.2  After        Omykiss         1     1   453
##  3 2018-07-06 2018-07-05 18.2  After        Omykiss         1     1   456
##  4 2018-07-06 2018-07-05 18.2  After        Omykiss         1     2   459
##  5 2018-07-06 2018-07-05 18.2  After        Omykiss         1     2   462
##  6 2018-07-06 2018-07-05 18.2  After        Omykiss         1     2   465
##  7 2018-07-06 2018-07-05 18.2  After        Omykiss         1     2   468
##  8 2018-07-06 2018-07-05 18.2  After        Omykiss         1     2   471
##  9 2018-07-06 2018-07-05 18.2  After        Omykiss         1     4   474
## 10 2018-07-06 2018-07-05 18.2  After        Omykiss         1     4   480
## # ... with 482 more rows, and 1 more variable: max_time_tally <dbl>

Proportion of Forage Types

Breaking down the proportion of each type of forage event and how it changes throughout the study period.

Pre-Augmentation

Behavior_Types_Dataset <-
  bind_rows(RoachRun_VidSync_Pre, RoachRun_VidSync_Post, GolfBall_VidSync_Pre, GolfBall_VidSync_Post, HalfTire_VidSync_Pre, HalfTire_VidSync_Post, JesusToast_VidSync_Pre, JesusToast_VidSync_Post) %>%
  filter(grepl("^Subsample.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  arrange(BACI_date, site, time) %>%
  group_by(sample_event, site) %>% 
  mutate(Behaviors = if_else(grepl("^Drift_Forage", event), "Drift Forage", if_else(grepl("^Search_Forage", event), "Search Forage", if_else(grepl("^Search_Forage", event), "Search Forage", if_else(grepl("^Benthic_Forage", event), "Benthic Forage", if_else(grepl("Nip|Attack", event), "Attack", if_else(grepl("^Surface_Strike", event), "Surface Strike", if_else(grepl("^Movement", event), "Movement", "NA")))))))) %>% 
  mutate(subsample = as.numeric(str_extract(objects, "\\d"))) %>%
  mutate(index = as.numeric(str_extract(objects, "\\h\\d{1,2}"))) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  filter(!species == "Omykiss | Okisutch") %>% 
  select(BACI_date, date, site, sample_event, species, subsample, index, time, Behaviors) 

Forage_Types <- Behavior_Types_Dataset %>%
  group_by(site, sample_event) %>% 
  count(Behaviors) %>% 
  filter(!Behaviors == "Attack") %>% 
  filter(!Behaviors == "Movement") %>% 
  filter(!Behaviors == "Surface Strike") %>% 
  setnames(old = "Behaviors", new = "Foraging_Mode")

Normalized_Forage_Types <- Forage_Types %>% 
  group_by(site, sample_event) %>% 
  mutate(forage_mode_percent = n/sum(n))

Forage_Types$sample_event1 <- factor(Forage_Types$sample_event, levels = c("Before", "After"))
Normalized_Forage_Types$sample_event2 <- factor(Normalized_Forage_Types$sample_event, levels = c("Before", "After"))

Forage_Types_Plot <- 
  ggplot(Forage_Types, aes(x = site, y = n, fill = Foraging_Mode)) +
  geom_col(width = 0.4) + 
  facet_grid(.~sample_event1) +
  xlab("Site") +
  ylab("Number of Forage Events") +
  ggtitle("Number and Proportion of Forage Modes") +
  scale_fill_manual(values = c("black", "darkblue", "goldenrod")) +
  guides(fill=guide_legend(title="Foraging Modes")) +
  theme_bw() +
  theme(legend.title = element_text(size = 12, face = "bold"), plot.title = element_text(size = 18, face = "bold", hjust = 0.5), axis.title.x = element_text(size = 12, face = "bold"), axis.title.y = element_text(size = 12, face = "bold"), axis.text.x = element_text(colour = c("black", "black", "red", "red", "black", "black", "red", "red")))
Forage_Types_Plot

Normalized_Forage_Types_Plot <- 
  ggplot(Normalized_Forage_Types, aes(x = site, y = forage_mode_percent, fill = Foraging_Mode)) + 
  geom_col(width = 0.4) + 
  facet_grid(.~sample_event2) +
  xlab("Site") +
  ylab("Proportion of Fish Foraging") +
  scale_fill_discrete(name = "Foraging Mode")

Volume Occupied Dataset

Using Delaunay Triangulation to calculate the volume each fish is occupying during each subsample.

Importing Final Volume CSV

SRF_Volume_Dataset <- 
  readr::read_csv(file = "Vidsync-data/BACI Fish Volume Data - SRF-Volumes.csv",
                  col_names = TRUE,
                  col_types = "ccccDcdddddddddddc") %>% 
  setnames(old = "event", new = "sample_event") %>% 
  setnames(old = "pool", new = "site") %>%
  setnames(old = "fish_species", new = "species") %>%
  select(stream, reach, date, sample_event, site, species, subsample, index, fish_length_mm, num_fish_in_subsample, volume_occupied_all_cm3, volume_occupied_forage_cm3, points_in_sample, repeat_index) %>% 
  filter(!species == "Hsymmetricus")
SRF_Volume_Dataset
## # A tibble: 145 x 14
##    stream reach date       sample_event site  species subsample index
##    <chr>  <chr> <date>     <chr>        <chr> <chr>       <dbl> <dbl>
##  1 Porter BACI  2018-06-29 Before       18.5  Omykiss         1     1
##  2 Porter BACI  2018-06-29 Before       18.5  Omykiss         1     2
##  3 Porter BACI  2018-06-29 Before       18.5  Omykiss         1     4
##  4 Porter BACI  2018-06-29 Before       18.5  Omykiss         2     5
##  5 Porter BACI  2018-06-29 Before       18.5  Omykiss         2     6
##  6 Porter BACI  2018-06-29 Before       18.5  Omykiss         2     7
##  7 Porter BACI  2018-06-29 Before       18.5  Omykiss         3     8
##  8 Porter BACI  2018-06-29 Before       18.5  Omykiss         3     9
##  9 Porter BACI  2018-06-29 Before       18.5  Okisut…         3    10
## 10 Porter BACI  2018-06-29 Before       18.5  Omykiss         4    11
## # ... with 135 more rows, and 6 more variables: fish_length_mm <dbl>,
## #   num_fish_in_subsample <dbl>, volume_occupied_all_cm3 <dbl>,
## #   volume_occupied_forage_cm3 <dbl>, points_in_sample <dbl>,
## #   repeat_index <dbl>

Final Fish Dataset Creation

Fish_Dataset <- Behavior_Types_Dataset %>% 
  left_join(Max_Fish_Tally_Dataset, by = c("date", "BACI_date", "site", "sample_event", "species", "subsample", "index", "time")) %>% 
  left_join(SRF_Volume_Dataset, by = c("date", "site", "sample_event", "species", "subsample", "index")) %>% 
  left_join(MovementPerTime_Data, by = c("date", "BACI_date", "site", "sample_event", "species", "subsample", "index")) %>% 
  left_join(nnd_data, by = c("date", "BACI_date", "site", "sample_event", "subsample", "index")) %>% 
  select(date, BACI_date, sample_event, site, species, subsample, index, time, Behaviors, max_time_tally, DistPerTime_Median, DistPerTime_Mean, mean_nnd, median_nnd, fish_length_mm, num_fish_in_subsample, volume_occupied_all_cm3, volume_occupied_forage_cm3, points_in_sample, repeat_index) %>% 
  arrange(BACI_date, sample_event, site, subsample, index, time) %>% 
  group_by(sample_event, site, subsample) %>% 
  mutate(Total_Volume_Occupied = sum(base::unique(volume_occupied_all_cm3), na.rm = TRUE)) %>% 
  mutate(Total_Forage_Volume_Occupied = sum(base::unique(volume_occupied_forage_cm3), na.rm = TRUE)) %>% 
  ungroup() %>% 
  group_by(sample_event, site) %>% 
  mutate(Mean_Total_Volume_Occupied_cm3 = mean(base::unique(Total_Volume_Occupied))) %>% 
  mutate(Mean_Total_Forage_Volume_Occupied_cm3 = mean(base::unique(Total_Forage_Volume_Occupied))) %>%
  select(date, BACI_date, sample_event, site, species, subsample, index, num_fish_in_subsample, time, Behaviors, max_time_tally, DistPerTime_Median, DistPerTime_Mean, mean_nnd, median_nnd, fish_length_mm, volume_occupied_all_cm3, volume_occupied_forage_cm3, Mean_Total_Volume_Occupied_cm3, Mean_Total_Forage_Volume_Occupied_cm3, points_in_sample, repeat_index)
Fish_Dataset
## # A tibble: 1,205 x 22
## # Groups:   sample_event, site [8]
##    date       BACI_date  sample_event site  species subsample index
##    <date>     <date>     <chr>        <chr> <chr>       <dbl> <dbl>
##  1 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  2 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  3 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  4 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  5 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  6 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  7 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  8 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
##  9 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
## 10 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
## # ... with 1,195 more rows, and 15 more variables:
## #   num_fish_in_subsample <dbl>, time <dbl>, Behaviors <chr>,
## #   max_time_tally <dbl>, DistPerTime_Median <dbl>,
## #   DistPerTime_Mean <dbl>, mean_nnd <dbl>, median_nnd <dbl>,
## #   fish_length_mm <dbl>, volume_occupied_all_cm3 <dbl>,
## #   volume_occupied_forage_cm3 <dbl>,
## #   Mean_Total_Volume_Occupied_cm3 <dbl>,
## #   Mean_Total_Forage_Volume_Occupied_cm3 <dbl>, points_in_sample <dbl>,
## #   repeat_index <dbl>

Fish Graphs

Total Volume Graph

Mean_Total_Volume_Dataset <- Fish_Dataset %>% 
  select(sample_event, site, Mean_Total_Volume_Occupied_cm3, Mean_Total_Forage_Volume_Occupied_cm3, num_fish_in_subsample) %>% 
  distinct(Total_Volume_Occupied, .keep_all = TRUE)
## Warning: Trying to compute distinct() for variables not found in the data:
## - `Total_Volume_Occupied`
## This is an error, but only a warning is raised for compatibility reasons.
## The operation will return the input unchanged.
Mean_Total_Volume_Dataset$sample_event <- factor(Mean_Total_Volume_Dataset$sample_event, levels = c("Before", "After"))
  
Total_Volume_Plot <- 
  ggplot(Mean_Total_Volume_Dataset) +
  geom_bar(aes(x = site, y = Mean_Total_Volume_Occupied_cm3, fill = sample_event, width = 0.5), position = position_dodge(), stat = "identity") +
  theme_bw() +
  guides(fill=guide_legend(title = "Augmentation\nPeriod")) +
  scale_fill_manual(values = c("goldenrod", "darkblue")) +
  xlab("Site") +
  ylab("Volume Occupied (cm^3)") +
  ggtitle("Total Volume Occupied by Present Salmonids in Pool Head Patch") +
  theme(axis.title.x = element_text(face = "bold", size = 14), axis.title.y = element_text(face = "bold", size = 14), legend.title = element_text(face = "plain", size = 14), plot.title = element_text(face = "bold", size = 16, hjust = 0.25), axis.text.x = element_text(colour = c("black", "black", "red", "red")))
## Warning: Ignoring unknown aesthetics: width
Total_Volume_Plot

NND Graph

NND_Graph_Dataset <- Fish_Dataset %>% 
  select(sample_event, site, subsample, mean_nnd, median_nnd) %>% 
  distinct(mean_nnd, .keep_all = TRUE) %>% 
  na.omit() %>% 
  group_by(sample_event, site) %>% 
  mutate(mean_nnd = base::mean(mean_nnd)) %>% 
  mutate(median_nnd = base::mean(median_nnd)) %>% 
  select(sample_event, site, mean_nnd, median_nnd) %>% 
  distinct(.keep_all = TRUE)

NND_Graph <- 
  ggplot(NND_Graph_Dataset) + 
  geom_bar(aes(x = site, y = median_nnd, fill = sample_event), position = position_dodge() ,stat = "identity")

Volume Dataset Calculations

Pre-Augmentation: All Behaviors

GolfBall_Volume_Pre <- GolfBall_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:13) {
  if (i == 3) {
    next
  }
  if (i == 10) {
    next
  }
  GolfBall_Volume_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

HalfTire_Volume_Pre <- HalfTire_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:8) {
  HalfTire_Volume_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

RoachRun_Volume_Pre <- RoachRun_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:19) {
  if (i == 3) {
    next
  }
  if (i == 7) {
    next
  }
  if (i == 14) {
    next
  }
  if (i == 16) {
    next
  }
  RoachRun_Volume_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

JesusToast_Volume_Pre <- JesusToast_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:13) {
  if (i == 2) {
    next
  }
  if (i == 5) {
    next
  }
  if (i == 6) {
    next
  }
  if (i == 10) {
    next
  }
  if (i == 12) {
    next
  }
  JesusToast_Volume_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

Post-Augmentation: All Behaviors

GolfBall_Volume_Post <- GolfBall_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:6) {
  GolfBall_Volume_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

HalfTire_Volume_Post <- HalfTire_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:48) {
  if (i == 6) {
    next
  }
  if (i == 7) {
    next
  }
  if (i == 8) {
    next
  }
  if (i == 14) {
    next
  }
  if (i == 20) {
    next
  }
  if (i == 29) {
    next
  }
  if (i == 36) {
    next
  }
  if (i == 40) {
    next
  }
  if (i == 41) {
    next
  }
  HalfTire_Volume_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

JesusToast_Volume_Post <- JesusToast_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:16) {
  if (i == 1) {
    next
  }
  if (i == 6) {
    next
  }
  if (i == 8) {
    next
  }
  if (i == 9) {
    next
  }
  if (i == 10) {
    next
  }
  if (i == 11) {
    next
  }
  if (i == 16) {
    next
  }
  JesusToast_Volume_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

RoachRun_Volume_Post <- RoachRun_VidSync_Post %>% 
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:33) {
  RoachRun_Volume_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

Pre-Augmentation: Foraging Modes

GolfBall_Volume_Forage_Pre <- GolfBall_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

a <- c(7,13)
for (i in a) {
  GolfBall_Volume_Forage_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

HalfTire_Volume_Forage_Pre <- HalfTire_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:8) {
  HalfTire_Volume_Forage_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

RoachRun_Volume_Forage_Pre <- RoachRun_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:19) {
  if (i == 3) {
    next
  }
  if (i == 7) {
    next
  }
  if (i == 14) {
    next
  }
  if (i == 16) {
    next
  }
  RoachRun_Volume_Forage_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

JesusToast_Volume_Forage_Pre <- JesusToast_VidSync_Pre %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:13) {
  if (i == 2) {
    next
  }
  if (i == 5) {
    next
  }
  if (i == 6) {
    next
  }
  if (i == 10) {
    next
  }
  if (i == 12) {
    next
  }
  JesusToast_Volume_Forage_Pre %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

Post-Augmentation: Foraging Modes

GolfBall_Volume_Forage_Post <- GolfBall_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

HalfTire_Volume_Forage_Post <- HalfTire_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:48) {
  if (i == 6) {
    next
  }
  if (i == 8) {
    next
  }
  if (i == 12) {
    next
  }
  if (i == 23) {
    next
  }
  if (i == 32) {
    next
  }
  if (i == 39) {
    next
  }
  if (i == 40) {
    next
  }
  if (i == 41) {
    next
  }
  HalfTire_Volume_Forage_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

RoachRun_Volume_Forage_Post <- RoachRun_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:33) {
  if (i == 2) {
    next
  }
  if (i == 6) {
    next
  }
  RoachRun_Volume_Forage_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

JesusToast_Volume_Forage_Post <- JesusToast_VidSync_Post %>%
  arrange(objects) %>%
  filter(!grepl("Surface_Shots.*", objects)) %>%
  filter(!grepl("^Length.*", event)) %>%
  filter(!grepl("^Attack.*", event)) %>%
  filter(!grepl("^Nip.*", event)) %>%
  filter(!grepl("Movement", event)) %>%
  filter(!grepl("Surface_Strike", event)) %>%
  mutate(subsample = str_extract(objects, "\\d")) %>%
  mutate(index = str_extract(objects, "\\h\\d{1,2}")) %>%
  mutate(species = str_extract(objects, "Omykiss|Okisutch")) %>%
  transform(index = as.numeric(index),
            subsample = as.numeric(subsample)) %>%
  arrange(subsample, index, time) %>%
  select(subsample, index, X, Y, Z) %>%
  arrange(subsample, index)

for (i in 1:16) {
  if (i == 1) {
    next
  }
  if (i == 3) {
    next
  }
  if (i == 6) {
    next
  }
  if (i == 8) {
    next
  }
  if (i == 9) {
    next
  }
  if (i == 10) {
    next
  }
  if (i == 11) {
    next
  }
  if (i == 12) {
    next
  }
  if (i == 14) {
    next
  }
  if (i == 16) {
    next
  }
  JesusToast_Volume_Forage_Post %>%
    filter(index == i) %>%
    select(X, Y, Z) %>%
    as.matrix() %>%
    convhulln("FA") #%>%
    #
}

Hydrology

Importing Datasets

Flow_Dataset <-
  read_csv(file = "Hydrology-Data/TU_FlowData - Sheet1.csv") %>% 
  filter(Date >= "2018-01-01") %>% 
  group_by(Date) %>% 
  mutate(Streamflow_gpm = mean(Streamflow_cfs)*448.83) %>% 
  distinct(Streamflow_gpm, .keep_all = TRUE) %>% 
  select(Streamflow_cfs, Streamflow_gpm, WaterTemp_C)
## Parsed with column specification:
## cols(
##   Date = col_date(format = ""),
##   WaterTemp_C = col_double(),
##   WaterDepth_ft = col_double(),
##   Streamflow_cfs = col_double()
## )

## Adding missing grouping variables: `Date`
RCT_Dataset <- 
  read_csv(file = "Hydrology-Data/POR_MovementStudyData_Analysis_withER - RCT.csv") %>% 
  select(SampleDate, RCT_RR, RCT_BB, RCT_HT) %>% 
  group_by(SampleDate) %>% 
  summarize(RCT_RR = mean(RCT_RR), RCT_BB = mean(RCT_BB), RCT_HT = mean(RCT_HT))
## Warning: Missing column names filled in: 'X21' [21], 'X32' [32],
## 'X33' [33], 'X34' [34], 'X35' [35], 'X36' [36], 'X37' [37], 'X38' [38],
## 'X39' [39], 'X40' [40], 'X41' [41]

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   SampleDate = col_date(format = ""),
##   `Simple Date` = col_character(),
##   Time = col_time(format = ""),
##   Datetime = col_character(),
##   `PIT Detections 1.59 (18.6 US1)` = col_integer(),
##   `18.6 US1 RCT * # Detections` = col_integer(),
##   `PIT Detections 1.56 (18.7 US1 RCT)` = col_integer(),
##   `18.7 US1 RCT * # Detections` = col_integer(),
##   `PIT Detections 1.55 (18.8 US1 RCT)` = col_integer(),
##   `18.8 US1 RCT * # Detections` = col_integer(),
##   `PIT Detections 1.73 (18.5 US1 RCT)` = col_integer(),
##   `18.5 US1 RCT * # Detections` = col_integer(),
##   `Across Unit Mean RCT at Detection (Excluding 18.7)` = col_integer(),
##   X21 = col_character(),
##   `Detections by Bin 18.6` = col_integer(),
##   `Detections by Bin 18.7` = col_integer(),
##   `Detections by Bin 18.8` = col_integer(),
##   `Detections by Bin 18.5` = col_integer(),
##   `Total Detections Across Units (Excluding 18.7)` = col_integer(),
##   `Number if values in dataset in Bin` = col_integer()
##   # ... with 6 more columns
## )

## See spec(...) for full column specifications.

## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)

## Warning: 487 parsing failures.
## row # A tibble: 5 x 5 col     row col                  expected      actual  file                    expected   <int> <chr>                <chr>         <chr>   <chr>                   actual 1 23861 18.6 US1 RCT * # De… no trailing … .24464… 'Hydrology-Data/POR_Mo… file 2 23861 18.7 US1 RCT * # De… no trailing … .63114… 'Hydrology-Data/POR_Mo… row 3 23861 18.8 US1 RCT * # De… no trailing … .51769… 'Hydrology-Data/POR_Mo… col 4 23861 Across Unit Mean RC… no trailing … .76233… 'Hydrology-Data/POR_Mo… expected 5 23863 18.6 US1 RCT * # De… no trailing … .96865… 'Hydrology-Data/POR_Mo…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.
Augmentation_Dataset <- 
  read_csv(file = "Hydrology-Data/PorterCreek_AugmentationData.csv") %>% 
  select(Date, avg_gpm) %>% 
  filter(!Date == "Grand Total") %>% 
  setnames(old = "avg_gpm", new = "Augmentation_gpm")
## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   X1 = col_integer(),
##   Date = col_character(),
##   avg_gpm = col_double(),
##   Min = col_integer(),
##   Max = col_integer(),
##   avg_clean = col_double(),
##   gpd = col_double(),
##   `ac-ft` = col_double(),
##   cum_ac_ft = col_double()
## )
Augmentation_Dataset$Date <- base::as.Date(Augmentation_Dataset$Date, "%m/%d/%y")

DO_Dataset_Impacted <- 
  read_csv(file = "Hydrology-Data/Porter_BACI_DO_Data.csv") %>% 
  select(Date, Value_Adj) %>% 
  setnames(old = "Value_Adj", new ="DO_mg_L") %>% 
  group_by(Date) %>% 
  na.omit() %>% 
  summarize(DO_Impacted_mg_L = mean(DO_mg_L))
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   Value_Raw = col_double(),
##   Value_Adj = col_double(),
##   FileID = col_integer(),
##   DateTimeID = col_integer(),
##   Year = col_integer(),
##   Time = col_time(format = ""),
##   RiverKm = col_double()
## )
## See spec(...) for full column specifications.
DO_Dataset_Impacted$Date <- base::as.Date(DO_Dataset_Impacted$Date, "%m/%d/%Y")

DO_Dataset_Control <- 
  read_csv(file = "Hydrology-Data/Porter_POFW_DO_Data.csv") %>% 
  select(Date, Value_Adj) %>% 
  setnames(old = "Value_Adj", new ="DO_mg_L") %>% 
  group_by(Date) %>% 
  na.omit() %>% 
  summarize(DO_Control_mg_L = mean(DO_mg_L))
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   Value_Raw = col_double(),
##   Value_Adj = col_double(),
##   FileID = col_integer(),
##   DateTimeID = col_integer(),
##   Year = col_integer(),
##   Time = col_time(format = ""),
##   RiverKm = col_double(),
##   LoggerSN = col_integer()
## )
## See spec(...) for full column specifications.
DO_Dataset_Control$Date <- base::as.Date(DO_Dataset_Control$Date, "%m/%d/%Y") 

Hydrology_Dataset <- RCT_Dataset %>% 
  left_join(Flow_Dataset, by = c("SampleDate" = "Date")) %>% 
  left_join(Augmentation_Dataset, by = c("SampleDate" = "Date")) %>% 
  left_join(DO_Dataset_Control, by = c("SampleDate" = "Date")) %>% 
  left_join(DO_Dataset_Impacted, by = c("SampleDate" = "Date")) %>%
  filter(!Streamflow_gpm == "NA") %>% 
  filter(!Augmentation_gpm == "NA") %>% 
  filter(!DO_Control_mg_L == "NA") %>% 
  filter(!DO_Impacted_mg_L == "NA") %>% 
  mutate(Streamflow_cfs = Streamflow_gpm/448.83) %>% 
  mutate(Augmentation_cfs = Augmentation_gpm/448.83) %>% 
  setnames(old = "SampleDate", new = "Date")
Hydrology_Dataset
## # A tibble: 131 x 11
##    Date       RCT_RR RCT_BB RCT_HT Streamflow_cfs Streamflow_gpm
##    <date>      <dbl>  <dbl>  <dbl>          <dbl>          <dbl>
##  1 2018-04-27   16.6   15.9   17.1           2.69          1208.
##  2 2018-04-28   16.2   15.6   16.8           2.60          1167.
##  3 2018-04-29   15.8   15.3   16.4           2.44          1094.
##  4 2018-04-30   15.3   14.9   16.0           2.30          1033.
##  5 2018-05-01   14.9   14.6   15.6           2.17           975.
##  6 2018-05-02   14.4   14.2   15.2           2.05           919.
##  7 2018-05-03   14.1   13.9   14.9           1.94           871.
##  8 2018-05-04   13.8   13.7   14.6           1.86           835.
##  9 2018-05-05   13.4   13.4   14.3           1.75           784.
## 10 2018-05-06   13.1   13.1   14.0           1.66           744.
## # ... with 121 more rows, and 5 more variables: WaterTemp_C <dbl>,
## #   Augmentation_gpm <dbl>, DO_Control_mg_L <dbl>, DO_Impacted_mg_L <dbl>,
## #   Augmentation_cfs <dbl>

Hydrology Graphs

Natural_PorterCreek_Hydrograph <- 
  ggplot(Hydrology_Dataset) + 
  geom_line(aes(x = Date, y = (Streamflow_cfs - Augmentation_cfs))) +
  xlab("Date") +
  ylab("Discharge in Cubic Feet per Second (CFS)") +
  ggtitle("Porter Creek Natural Hydrograph 2018") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"))
Natural_PorterCreek_Hydrograph

PorterCreek_Augmentation <- 
  ggplot(Hydrology_Dataset) +
  geom_line(aes(x = Date, y = Augmentation_cfs)) +
  xlab("Date") +
  ylab("Cubic Feet per Second (CFS)") +
  ggtitle("Porter Creek Augmentation Discharge (Q) 2018") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"))
PorterCreek_Augmentation

Augmented_PorterCreek_Hydrograph <- 
  ggplot(Hydrology_Dataset) +
  geom_line(aes(x = Date, y = Streamflow_cfs)) +
  xlab("Date") +
  ylab("Discharge in Cubic Feet per Second (CFS)") +
  ggtitle("Porter Creek Total Discharge (Q) 2018") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"))
Augmented_PorterCreek_Hydrograph

RCT_DO_Graph <- 
  ggplot(Hydrology_Dataset) +
  geom_smooth(aes(x = RCT_RR, y = DO_Impacted_mg_L, color = "Site 18.5")) +
  geom_smooth(aes(x = RCT_BB, y = DO_Impacted_mg_L, color = "Site 18.6")) +
  geom_smooth(aes(x = RCT_HT, y = DO_Impacted_mg_L, color = "Site 18.8")) +
  scale_x_reverse() +
  labs(color = "Study Site") +
  xlab("Riffle Crest Thalweg (RCT) Depth in cm") +
  ylab("Dissolve Oxygen (mg/L)") +
  ggtitle("Effect of Upstream RCT Depth on Dissolved Oxygen Content") +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold")) +
  theme_bw()
RCT_DO_Graph
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

WaterTemp_PorterCreek <- 
  ggplot(Hydrology_Dataset) +
  geom_line(aes(x = Date, y = WaterTemp_C))
WaterTemp_PorterCreek

Final SRF Dataset

Final_SRF_Dataset <- Fish_Dataset %>% 
  left_join(Hydrology_Dataset, by = c("date" = "Date"))
Final_SRF_Dataset
## # A tibble: 1,205 x 32
## # Groups:   sample_event, site [?]
##    date       BACI_date  sample_event site  species subsample index
##    <date>     <date>     <chr>        <chr> <chr>       <dbl> <dbl>
##  1 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  2 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  3 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  4 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  5 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  6 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  7 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     1
##  8 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
##  9 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
## 10 2018-06-30 2018-06-29 Before       18.2  Omykiss         1     5
## # ... with 1,195 more rows, and 25 more variables:
## #   num_fish_in_subsample <dbl>, time <dbl>, Behaviors <chr>,
## #   max_time_tally <dbl>, DistPerTime_Median <dbl>,
## #   DistPerTime_Mean <dbl>, mean_nnd <dbl>, median_nnd <dbl>,
## #   fish_length_mm <dbl>, volume_occupied_all_cm3 <dbl>,
## #   volume_occupied_forage_cm3 <dbl>,
## #   Mean_Total_Volume_Occupied_cm3 <dbl>,
## #   Mean_Total_Forage_Volume_Occupied_cm3 <dbl>, points_in_sample <dbl>,
## #   repeat_index <dbl>, RCT_RR <dbl>, RCT_BB <dbl>, RCT_HT <dbl>,
## #   Streamflow_cfs <dbl>, Streamflow_gpm <dbl>, WaterTemp_C <dbl>,
## #   Augmentation_gpm <dbl>, DO_Control_mg_L <dbl>, DO_Impacted_mg_L <dbl>,
## #   Augmentation_cfs <dbl>