Keane Flynn & Weston Slaughter
12/30/2018
library(dplyr)##
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## filter, lag
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## intersect, setdiff, setequal, unionlibrary(readr)
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library(lubridate)##
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library(hypervolume)## Loading required package: Rcpplibrary(zoo)##
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## as.Date, as.Date.numericlibrary(stringr)ChlA_Data_Porter <-
readr::read_csv(file = "ChlA-Data/ChlA-Data.csv",
col_types = "ccccdcddddddddc") %>%
filter(Site == "Porter_Above") %>%
group_by(Date_collected, Site_1)## Warning: Duplicated column names deduplicated: 'Site' => 'Site_1' [4]ChlA_Data_Porter## # A tibble: 72 x 15
## # Groups: Date_collected, Site_1 [20]
## Site Date_collected Date_processed Site_1 Cobble_SID Pool_or_Riffle
## <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 Port… 5/1/18 5/2/18 Frog_… 1 riffle
## 2 Port… 5/1/18 5/2/18 Frog_… 5 riffle
## 3 Port… 5/1/18 5/2/18 Frog_… 7 pool
## 4 Port… 5/1/18 5/2/18 Frog_… 11 pool
## 5 Port… 5/1/18 5/2/18 Jesus… 8 riffle
## 6 Port… 5/1/18 5/2/18 Jesus… 9 riffle
## 7 Port… 5/1/18 5/2/18 Jesus… 15 pool
## 8 Port… 5/1/18 5/2/18 Jesus… 16 pool
## 9 Port… 5/1/18 5/2/18 Golf_… 4 riffle
## 10 Port… 5/1/18 5/2/18 Golf_… 6 riffle
## # ... with 62 more rows, and 9 more variables: Volume_mL <dbl>,
## # Volume_Added_mL <dbl>, Canister_Num <dbl>, Chla_reading <dbl>,
## # Total_Chla_extracted <dbl>, Total_Chla_in_Sample <dbl>,
## # Chla_area <dbl>, Cobble_Area <dbl>, Notes <chr>Summarized_ChlA_Data_Pool <- ChlA_Data_Porter %>%
filter(Pool_or_Riffle == "pool") %>%
group_by(Date_collected, Site_1) %>%
summarise(mean_ChlA_area = mean(Chla_area))
Summarized_ChlA_Data_Riffle <- ChlA_Data_Porter %>%
filter(Pool_or_Riffle == "riffle") %>%
group_by(Date_collected, Site_1) %>%
summarise(mean_ChlA_area = mean(Chla_area))ChlA_Graph_Pool <- Summarized_ChlA_Data_Pool %>%
ggplot(aes(x = Date_collected, y = mean_ChlA_area)) +
geom_line(aes(group = Site_1, color = Site_1)) +
geom_point(aes(shape = Site_1)) +
labs(x = "Date", y = "Average Chlorophyll-A Reading per Unit Area of Cobble") +
ggtitle("Chlorophyll-A Measurements vs Time (Pool)")
ChlA_Graph_Pool
ChlA_Graph_Riffle <- Summarized_ChlA_Data_Riffle %>%
ggplot(aes(x = Date_collected, y = mean_ChlA_area)) +
geom_line(aes(group = Site_1, color = Site_1)) +
geom_point(aes(shape = Site_1)) +
labs(x = "Date", y = "Average Chlorophyll-A Reading per Unit Area of Cobble") +
ggtitle("Chlorophyll-A Measurements vs Time (Riffle)")
ChlA_Graph_Riffle
AFD_Data_Porter <-
readr::read_csv(file = "ChlA-Data/AFD-Data.csv",
col_types = "ccccdcddddddddc") %>%
filter(Site == "Porter_Above") %>%
group_by(Date_collected, Site_1) %>%
select(Date_collected, Site_1, Cobble_SID, Pool_or_Riffle, Dry_Weight_g, AFD_Weight_g, Difference)## Warning: Missing column names filled in: 'X14' [14], 'X15' [15]
## Warning: Duplicated column names deduplicated: 'Site' => 'Site_1' [4]
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 19 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 30 Notes a double Odd donut shape in filter 'ChlA-Data/AFD-D… file 2 33 Notes a double very small amount of algae scrap… 'ChlA-Data/AFD-D… row 3 48 Notes a double in oven at 5:10 PM 'ChlA-Data/AFD-D… col 4 64 Notes a double in oven at 5:45 PM 'ChlA-Data/AFD-D… expected 5 69 Notes a double MELTED IN OVEN REDO 'ChlA-Data/AFD-D…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.AFD_Data_Porter## # A tibble: 72 x 7
## # Groups: Date_collected, Site_1 [20]
## Date_collected Site_1 Cobble_SID Pool_or_Riffle Dry_Weight_g
## <chr> <chr> <dbl> <chr> <dbl>
## 1 5/1/18 Frog_… 1 riffle 1.14
## 2 5/1/18 Frog_… 5 riffle 1.18
## 3 5/1/18 Frog_… 7 pool 1.16
## 4 5/1/18 Frog_… 11 pool 1.15
## 5 5/1/18 Jesus… 8 riffle 1.14
## 6 5/1/18 Jesus… 9 riffle 1.14
## 7 5/1/18 Jesus… 15 pool 1.15
## 8 5/1/18 Jesus… 16 pool 1.15
## 9 5/1/18 Golf_… 4 riffle 1.15
## 10 5/1/18 Golf_… 6 riffle 1.14
## # ... with 62 more rows, and 2 more variables: AFD_Weight_g <dbl>,
## # Difference <dbl>Summarized_AFD_Data_Pool <- AFD_Data_Porter %>%
filter(Pool_or_Riffle == "pool") %>%
filter(!AFD_Weight_g == "NA") %>%
group_by(Date_collected, Site_1) %>%
summarise(mean_diff = mean(Difference))
Summarized_AFD_Data_Riffle <- AFD_Data_Porter %>%
filter(Pool_or_Riffle == "riffle") %>%
filter(!AFD_Weight_g == "NA") %>%
group_by(Date_collected, Site_1) %>%
summarise(mean_diff = mean(Difference))AFD_Graph_Pool <- Summarized_AFD_Data_Pool %>%
ggplot(aes(x = Date_collected, y = mean_diff)) +
geom_line(aes(group = Site_1, color = Site_1)) +
geom_point(aes(shape = Site_1)) +
labs(x = "Date", y = "Average Ash Free Dry Mass (Grams)") +
ggtitle("Ash Free Dry Mass Measurements vs Time (Pool)")
AFD_Graph_Pool
AFD_Graph_Riffle <- Summarized_AFD_Data_Riffle %>%
ggplot(aes(x = Date_collected, y = mean_diff)) +
geom_line(aes(group = Site_1, color = Site_1)) +
geom_point(aes(shape = Site_1)) +
labs(x = "Date", y = "Average Ash Free Dry Mass (Grams)") +
ggtitle("Ash Free Dry Mass Measurements vs Time (Riffle)")
AFD_Graph_Riffle
DO_Data_Porter <-
readr::read_csv(file = "Photosynthesis/2018-Photosynthesis.csv",
col_types = "ccccdcddddcdddcdddcdddcddddddcdddddd") %>%
filter(Stream == "Porter") %>%
mutate(Max_Depth_DO = (MaxDepth_DO1 + MaxDepth_DO2 + MaxDepth_DO3)/3) %>%
select(Stream, Date, Site_Name, Site_Num, Pool_Riffle, Max_Depth_DO, Light_Do_s, Light_DO_e, Dark_Do_s, Dark_DO_e)## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 50 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 1 MaxDepth_… a double n/a (did not mea… 'Photosynthesis/2018-Photos… file 2 1 MaxDepth_… a double n/a 'Photosynthesis/2018-Photos… row 3 1 MaxDepth_… a double n/a 'Photosynthesis/2018-Photos… col 4 2 MaxDepth_… a double n/a 'Photosynthesis/2018-Photos… expected 5 2 MaxDepth_… a double n/a 'Photosynthesis/2018-Photos…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.DO_Data_Porter## # A tibble: 72 x 10
## Stream Date Site_Name Site_Num Pool_Riffle Max_Depth_DO Light_Do_s
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 Porter 5/1/… Frog_Legs 18.1 R 10.4 10.0
## 2 Porter 5/1/… Frog_Legs 18.1 R 10.4 10.6
## 3 Porter 5/1/… Frog_Legs 18.1 P 10.4 10.6
## 4 Porter 5/1/… Frog_Legs 18.1 P 10.4 10.6
## 5 Porter 5/1/… Jesus_To… 18.2 R 10.1 10.4
## 6 Porter 5/1/… Jesus_To… 18.2 R 10.1 10.5
## 7 Porter 5/1/… Jesus_To… 18.2 P 10.1 10.5
## 8 Porter 5/1/… Jesus_To… 18.2 P 10.1 10.5
## 9 Porter 5/1/… Golf_Ball 18.3 R 10.0 10.6
## 10 Porter 5/1/… Golf_Ball 18.3 R 10.0 10.5
## # ... with 62 more rows, and 3 more variables: Light_DO_e <dbl>,
## # Dark_Do_s <dbl>, Dark_DO_e <dbl>Summary_DO_Data_Porter_Pool <- DO_Data_Porter %>%
group_by(Stream, Date, Site_Name, Site_Num) %>%
summarise(Max_Depth_DO = mean(Max_Depth_DO), Light_Do_s = mean(Light_Do_s), Light_DO_e = mean(Light_DO_e), Dark_Do_s = mean(Dark_Do_s), Dark_DO_e = mean(Dark_DO_e)) %>%
arrange(Date, Site_Num)
Summary_DO_Data_Porter_Pool## # A tibble: 20 x 9
## # Groups: Stream, Date, Site_Name [20]
## Stream Date Site_Name Site_Num Max_Depth_DO Light_Do_s Light_DO_e
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Porter 5/1/… Frog_Legs 18.1 10.4 10.5 11.1
## 2 Porter 5/1/… Jesus_To… 18.2 10.1 10.5 11.4
## 3 Porter 5/1/… Golf_Ball 18.3 10.0 10.5 11.3
## 4 Porter 5/1/… Waterfall 18.4 9.90 10.3 11.7
## 5 Porter 5/18… Frog_Legs 18.1 10.2 10.3 10.5
## 6 Porter 5/18… Jesus_To… 18.2 9.92 10 10.4
## 7 Porter 5/18… Golf_Ball 18.3 9.65 9.84 10.3
## 8 Porter 5/18… Waterfall 18.4 9.52 9.62 9.96
## 9 Porter 6/13… Frog_Legs 18.1 10.3 10.0 10.2
## 10 Porter 6/13… Jesus_To… 18.2 10.3 10.0 10.9
## 11 Porter 6/13… Golf_Ball 18.3 10.3 10.8 11.5
## 12 Porter 6/13… Waterfall 18.4 8.31 10.4 10.9
## 13 Porter 7/13… Frog_Legs 18.1 7.84 7.72 8.34
## 14 Porter 7/13… Jesus_To… 18.2 8.51 7.96 9.44
## 15 Porter 7/13… Golf_Ball 18.3 10.8 10.6 11.7
## 16 Porter 7/13… Waterfall 18.4 7.60 6.77 8.12
## 17 Porter 8/8/… Frog_Legs 18.1 3.17 4.34 6.02
## 18 Porter 8/8/… Jesus_To… 18.2 7.90 8.68 9.65
## 19 Porter 8/8/… Golf_Ball 18.3 5.83 6.30 6.96
## 20 Porter 8/8/… Waterfall 18.4 9.33 9.46 10.5
## # ... with 2 more variables: Dark_Do_s <dbl>, Dark_DO_e <dbl>Max_Depth_DO_Graph_Pool <- Summary_DO_Data_Porter_Pool %>%
ggplot(aes(Date, Max_Depth_DO)) +
geom_line(aes(group = Site_Name, color = Site_Name)) +
geom_point(aes(shape = Site_Name))
Max_Depth_DO_Graph_Pool
Light_DO_s_Graph <- Summary_DO_Data_Porter_Pool %>%
ggplot(aes(Date, Light_Do_s)) +
geom_line(aes(group = Site_Name, color = Site_Name)) +
geom_point(aes(shape = Site_Name))
Light_DO_s_Graph
Light_DO_e_Graph <- Summary_DO_Data_Porter_Pool %>%
ggplot(aes(Date, Light_DO_e)) +
geom_line(aes(group = Site_Name, color = Site_Name)) +
geom_point(aes(shape = Site_Name))
Light_DO_e_Graph
Dark_DO_s_Graph <- Summary_DO_Data_Porter_Pool %>%
ggplot(aes(Date, Dark_Do_s)) +
geom_line(aes(group = Site_Name, color = Site_Name)) +
geom_point(aes(shape = Site_Name))
Dark_DO_s_Graph
Dark_DO_e_Graph <- Summary_DO_Data_Porter_Pool %>%
ggplot(aes(Date, Dark_DO_e)) +
geom_line(aes(group = Site_Name, color = Site_Name)) +
geom_point(aes(shape = Site_Name))
Dark_DO_e_Graph
EFishing_Data <-
readr::read_csv(file = "FishPopulation/EFishingData2018.csv",
col_types = "cccddddttddcdccdccdddddddcddddddddd") %>%
filter(Stream == "Porter") %>%
arrange(Date, Unit, Fish_Num, Pass_Num)## Warning: Missing column names filled in: 'X36' [36], 'X37' [37],
## 'X38' [38], 'X39' [39], 'X40' [40]
## Warning: Unnamed `col_types` should have the same length as `col_names`.
## Using smaller of the two.
## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 2006 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 1 Time_Per_Pass_Se… a double N/A 'FishPopulation/EFishingDat… file 2 1 <NA> 35 columns 40 colu… 'FishPopulation/EFishingDat… row 3 2 Time_Per_Pass_Se… a double N/A 'FishPopulation/EFishingDat… col 4 2 <NA> 35 columns 40 colu… 'FishPopulation/EFishingDat… expected 5 3 Time_Per_Pass_Se… a double N/A 'FishPopulation/EFishingDat…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.EFishing_Data## # A tibble: 524 x 35
## Date Stream Unit Fish_Num_Per_Si… Fish_Num Pass_Num Time_Per_Pass_S…
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 5/23… Porter 18.1… 51 120 1 289
## 2 5/23… Porter 18.1… 52 121 1 289
## 3 5/23… Porter 18.1… 53 122 1 289
## 4 5/23… Porter 18.1… 54 123 1 289
## 5 5/23… Porter 18.1… 55 124 1 289
## 6 5/23… Porter 18.1… 56 125 1 289
## 7 5/23… Porter 18.1… 57 126 1 289
## 8 5/23… Porter 18.1… 58 127 1 289
## 9 5/23… Porter 18.1… 59 128 1 289
## 10 5/23… Porter 18.1… 60 129 1 289
## # ... with 514 more rows, and 28 more variables: Start_Time <time>,
## # End_Time <time>, Length_mm <dbl>, Weight_g <dbl>, Species <chr>,
## # Recapture <dbl>, PIT <chr>, Fecal_Num <chr>, Mort <dbl>, Notes <chr>,
## # Fin <chr>, Stage <dbl>, `Pool Length` <dbl>, Width_1 <dbl>,
## # Width_2 <dbl>, Width_3 <dbl>, Max_Depth_cm <dbl>, DS_RCT_cm <dbl>,
## # `SeaGrant HabID` <chr>, Fat1 <dbl>, Fat2 <dbl>, Fat3 <dbl>,
## # Fat4 <dbl>, Fat5 <dbl>, Fat6 <dbl>, Fat7 <dbl>, Fat8 <dbl>,
## # Fat_AVG <dbl>CombiningMovement_Efish <- EFishing_Data %>%
filter(!PIT == "N/A")Porter_FlowData <-
readr::read_csv("Hydrology/PorterCreek_FlowData_TU/Po04-Table 1.csv",
skip = 4) %>%
mutate(Date = str_extract(SampleDate, "\\d......")) %>%
group_by(Date) %>%
summarise(Streamflow_cfs = mean(Streamflow_cfs)) %>%
mutate(Streamflow_gpm = Streamflow_cfs*448) %>%
mutate(Date = str_extract(Date, "[0-9]{1,2}/[0-9]{1,2}/18")) %>%
filter(!Date == "NA") ## Warning: Missing column names filled in: 'X5' [5], 'X6' [6], 'X7' [7],
## 'X8' [8], 'X9' [9], 'X10' [10], 'X11' [11], 'X12' [12], 'X13' [13],
## 'X14' [14], 'X15' [15], 'X16' [16], 'X17' [17]
## Parsed with column specification:
## cols(
## SampleDate = col_character(),
## `WaterTemp, C` = col_double(),
## `Water Depth, ft` = col_double(),
## Streamflow_cfs = col_double(),
## X5 = col_character(),
## X6 = col_character(),
## X7 = col_character(),
## X8 = col_character(),
## X9 = col_character(),
## X10 = col_character(),
## X11 = col_character(),
## X12 = col_character(),
## X13 = col_character(),
## X14 = col_character(),
## X15 = col_character(),
## X16 = col_character(),
## X17 = col_character()
## )Porter_FlowData## # A tibble: 247 x 3
## Date Streamflow_cfs Streamflow_gpm
## <chr> <dbl> <dbl>
## 1 1/1/18 0.470 210.
## 2 1/10/18 13.2 5893.
## 3 1/11/18 7.14 3197.
## 4 1/12/18 4.74 2126.
## 5 1/13/18 3.44 1540.
## 6 1/14/18 2.69 1206.
## 7 1/15/18 2.19 983.
## 8 1/16/18 2.07 925.
## 9 1/17/18 1.75 782.
## 10 1/18/18 1.95 874.
## # ... with 237 more rowsPorter_AugmentationData <-
readr::read_csv("Hydrology/PorterCreek_AugmentationData.csv") %>%
select(X1, Date, avg_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()
## )Porter_AugmentationData## # A tibble: 367 x 3
## X1 Date avg_gpm
## <int> <chr> <dbl>
## 1 1 1/3/18 73.2
## 2 2 1/4/18 25.1
## 3 3 1/5/18 0.0208
## 4 4 1/6/18 0.0625
## 5 5 1/7/18 0
## 6 6 1/8/18 0.167
## 7 7 1/9/18 0.0625
## 8 8 1/10/18 0.125
## 9 9 1/11/18 0.146
## 10 10 1/12/18 0.0417
## # ... with 357 more rowsPIT_Data <-
readr::read_csv(file = "FishPopulation/POR_MovementStudy.csv",
col_types = "cccctccccc") %>%
mutate(PIT = str_extract(PITNumber, "06....")) %>%
select(Species_Lookup, PIT, Date_Lookup, Date, Time, Site, HabitatType, UnitNumber, Survey, Comments)
PIT_Data## # A tibble: 624 x 10
## Species_Lookup PIT Date_Lookup Date Time Site HabitatType
## <chr> <chr> <chr> <chr> <tim> <chr> <chr>
## 1 COHO SALMON 068D… 6/7/18 6/7/… 12:15 POR 1 POOL
## 2 COHO SALMON 068D… 6/7/18 6/8/… 13:28 POR … (blank)
## 3 COHO SALMON 068D… 6/7/18 6/12… 19:57 POR … (blank)
## 4 COHO SALMON 068D… 6/7/18 6/7/… 12:44 POR 1 POOL
## 5 COHO SALMON 068D… 6/7/18 6/8/… 13:42 POR … (blank)
## 6 COHO SALMON 068D… 6/7/18 6/8/… 20:35 POR … (blank)
## 7 COHO SALMON 068D… 6/7/18 6/7/… 12:23 POR 1 POOL
## 8 COHO SALMON 068D… 6/7/18 6/8/… 13:35 POR … (blank)
## 9 COHO SALMON 068D… 6/7/18 6/9/… 05:15 POR … (blank)
## 10 COHO SALMON 06A7… 6/7/18 6/7/… 11:14 POR 1 POOL
## # ... with 614 more rows, and 3 more variables: UnitNumber <chr>,
## # Survey <chr>, Comments <chr>EFish_Movement_Data <- left_join(PIT_Data, CombiningMovement_Efish, by = "PIT") %>%
select(Species, PIT, Date_Lookup, Date.x, Time, Site, UnitNumber, Survey, Unit, Fish_Num_Per_Site, Fish_Num, Length_mm, Weight_g, Recapture, Fat_AVG, Comments) %>%
mutate(Survey_tally = if_else(Survey == "ANT", 1, 0)) %>%
group_by(Date.x) %>%
mutate(Movement_tally = cumsum(Survey_tally)) %>%
mutate(Max_Ping_Tally = max(Movement_tally)) %>%
arrange(Date.x, Time) %>%
filter(!Species == "Cottus_spp")
EFish_Movement_Data## # A tibble: 995 x 19
## # Groups: Date.x [68]
## Species PIT Date_Lookup Date.x Time Site UnitNumber Survey Unit
## <chr> <chr> <chr> <chr> <tim> <chr> <chr> <chr> <chr>
## 1 Omykiss 06A7… 6/7/18 6/10/… 02:00 POR … (blank) ANT 18.6
## 2 Okisut… 06A8… 6/7/18 6/10/… 05:19 POR … (blank) ANT 18.6
## 3 Okisut… 06A8… 6/7/18 6/10/… 05:19 POR … (blank) ANT 18.6
## 4 Omykiss 068D… 6/7/18 6/10/… 05:45 POR … (blank) ANT 18.55
## 5 Okisut… 06A7… 6/7/18 6/10/… 06:02 POR … (blank) ANT 18.6
## 6 Okisut… 06A7… 6/7/18 6/10/… 06:02 POR … (blank) ANT 18.6
## 7 Omykiss 06A8… 6/4/18 6/10/… 09:54 POR … (blank) ANT 18.3
## 8 Omykiss 068D… 6/7/18 6/10/… 15:39 POR … (blank) ANT 18.55
## 9 Omykiss 068D… 6/7/18 6/10/… 17:29 POR … (blank) ANT 18.5
## 10 Okisut… 06A7… 6/7/18 6/10/… 18:26 POR … (blank) ANT 18.6
## # ... with 985 more rows, and 10 more variables: Fish_Num_Per_Site <dbl>,
## # Fish_Num <dbl>, Length_mm <dbl>, Weight_g <dbl>, Recapture <dbl>,
## # Fat_AVG <dbl>, Comments <chr>, Survey_tally <dbl>,
## # Movement_tally <dbl>, Max_Ping_Tally <dbl>Augmentation_MovementData <- EFish_Movement_Data %>%
distinct(Max_Ping_Tally) %>%
left_join(Porter_AugmentationData, EFish_Movement_Data, by = c("Date.x" = "Date")) %>%
arrange(X1) %>%
select(X1, Date.x, Max_Ping_Tally, avg_gpm) %>%
filter(!X1 == "NA")
names(Augmentation_MovementData)[2] <- "Date"
Augmentation_MovementData ## # A tibble: 68 x 4
## # Groups: Date.x [68]
## X1 Date Max_Ping_Tally avg_gpm
## <int> <chr> <dbl> <dbl>
## 1 153 6/4/18 0 0.132
## 2 155 6/6/18 5 0.0417
## 3 156 6/7/18 24 0.194
## 4 157 6/8/18 47 0.0417
## 5 158 6/9/18 29 0
## 6 159 6/10/18 15 0.174
## 7 160 6/11/18 26 0
## 8 161 6/12/18 13 0.0417
## 9 162 6/13/18 6 0.312
## 10 165 6/16/18 2 0.0208
## # ... with 58 more rowsAugmentation_MovementData$Date <- as.Date(Augmentation_MovementData$Date, "%m/%d/%Y")ggplot(Augmentation_MovementData) +
geom_col(aes(x = Date, y = Max_Ping_Tally), width = 0.5, fill = "blue") +
geom_line(aes(x = Date, y = avg_gpm/5), group = 1, color = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(sec.axis = sec_axis(~.*5, name = "Augmentation (GPM)")) +
labs(x = "Date", y = "Salmonid PIT Movement Pings") +
ggtitle("Augmentation vs Interpool Movement Relationship") +
theme(axis.text.y.right = element_text(colour="red"), axis.text.y.left = element_text(colour="blue"), axis.title.y.right = element_text(color = "red"), axis.title.y.left = element_text(color = "blue"))
Flow_MovementData <- EFish_Movement_Data %>%
distinct(Max_Ping_Tally) %>%
left_join(Porter_FlowData, EFish_Movement_Data, by = c("Date.x" = "Date")) %>%
select(Date.x, Max_Ping_Tally, Streamflow_gpm) %>%
filter(!Date.x == "NA")
names(Flow_MovementData)[1] <- "Date"
Flow_MovementData## # A tibble: 68 x 3
## # Groups: Date.x [68]
## Date Max_Ping_Tally Streamflow_gpm
## <chr> <dbl> <dbl>
## 1 6/10/18 15 242.
## 2 6/11/18 26 234.
## 3 6/12/18 13 229.
## 4 6/13/18 6 212.
## 5 6/16/18 2 204.
## 6 6/18/18 1 228.
## 7 6/19/18 3 220.
## 8 6/22/18 2 175
## 9 6/30/18 6 473.
## 10 6/4/18 0 298.
## # ... with 58 more rowsFlow_MovementData$Date <- as.Date(Flow_MovementData$Date, "%m/%d/%y")
Flow_MovementData## # A tibble: 68 x 3
## # Groups: Date.x [68]
## Date Max_Ping_Tally Streamflow_gpm
## <date> <dbl> <dbl>
## 1 2018-06-10 15 242.
## 2 2018-06-11 26 234.
## 3 2018-06-12 13 229.
## 4 2018-06-13 6 212.
## 5 2018-06-16 2 204.
## 6 2018-06-18 1 228.
## 7 2018-06-19 3 220.
## 8 2018-06-22 2 175
## 9 2018-06-30 6 473.
## 10 2018-06-04 0 298.
## # ... with 58 more rowsggplot(Flow_MovementData) +
geom_col(aes(x = Date, y = Max_Ping_Tally), width = 0.5, fill = "darkblue") +
geom_line(aes(x = Date, y = Streamflow_gpm/5), group = 1, color = "goldenrod") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(sec.axis = sec_axis(~.*5, name = "Discharge (GPM)")) +
labs(x = "Date", y = "Salmonid PIT Movement Pings") +
ggtitle("Discharge vs Interpool Movement Relationship") +
theme(axis.text.y.right = element_text(colour="goldenrod", face = "bold"), axis.text.y.left = element_text(colour="darkblue", face = "bold"), axis.title.y.right = element_text(color = "goldenrod", face = "bold"), axis.title.y.left = element_text(color = "darkblue", face = "bold"), plot.title = element_text(hjust = 0.5, size = 22, face = "bold"), axis.title.x = element_text(face = "bold", size = 18), axis.text.x = element_text(size = 12)) +
theme_bw()
BMI_Drift_Flux_Dataset <-
read_csv(file = "Macroinvertebrate/Porter_BACI_Study_Drift - BACI_Sites_2018.csv",
col_types = "cccccttdddddccdddddc") %>%
arrange(desc(Event), Pool) %>%
mutate(Test_Reach = ifelse(Pool == "18.2", "Control", if_else(Pool == "18.3", "Control", if_else(Pool == "18.4", "Control", "Augmented Flow")))) %>%
select(Date, Test_Reach, Event, Pool, Duration_hrs, ID, biomass) %>%
na.omit(TRUE) %>%
group_by(Event, Pool) %>%
mutate(BMI_Flux_mg_hrs = sum(biomass)/Duration_hrs) ## Warning in rbind(names(probs), probs_f): number of columns of result is not
## a multiple of vector length (arg 1)
## Warning: 9 parsing failures.
## row # A tibble: 5 x 5 col row col expected actual file expected <int> <chr> <chr> <chr> <chr> actual 1 24 lna a double #N/A 'Macroinvertebrate/Porter_BACI_Study_Drif… file 2 24 b a double #N/A 'Macroinvertebrate/Porter_BACI_Study_Drif… row 3 24 biomass a double #N/A 'Macroinvertebrate/Porter_BACI_Study_Drif… col 4 44 lna a double #N/A 'Macroinvertebrate/Porter_BACI_Study_Drif… expected 5 44 b a double #N/A 'Macroinvertebrate/Porter_BACI_Study_Drif…
## ... ................. ... .......................................................................... ........ .......................................................................... ...... .......................................................................... .... .......................................................................... ... .......................................................................... ... .......................................................................... ........ ..........................................................................
## See problems(...) for more details.BMI_Drift_Flux_Dataset$Date <- base::as.Date(BMI_Drift_Flux_Dataset$Date, "%m/%d/%Y")
BMI_Drift_Flux_Dataset## # A tibble: 137 x 8
## # Groups: Event, Pool [12]
## Date Test_Reach Event Pool Duration_hrs ID biomass
## <date> <chr> <chr> <chr> <dbl> <chr> <dbl>
## 1 2018-07-01 Control Befo… 18.2 1.53 Chir… 0.0319
## 2 2018-07-01 Control Befo… 18.2 1.53 ter … 0.250
## 3 2018-07-01 Control Befo… 18.2 1.53 chir… 0.0221
## 4 2018-07-01 Control Befo… 18.2 1.53 adul… 0.1
## 5 2018-07-01 Control Befo… 18.2 1.53 ter … 0.0126
## 6 2018-07-01 Control Befo… 18.2 1.53 Hydr… 0.0350
## 7 2018-07-01 Control Befo… 18.2 1.53 adul… 0.297
## 8 2018-07-01 Control Befo… 18.2 1.53 Elmi… 0.801
## 9 2018-07-01 Control Befo… 18.2 1.53 ter … 0.505
## 10 2018-07-01 Control Befo… 18.2 1.53 ter … 0.0830
## # ... with 127 more rows, and 1 more variable: BMI_Flux_mg_hrs <dbl>BMI_Drift_Flux_Dataset$Event <- factor(BMI_Drift_Flux_Dataset$Event, levels = c("Before", "After"))
col_value <- c("Control" = "goldenrod", "Augmented Flow" = "darkblue")
Macroinvertebrate_BACI_Flux <-
ggplot(BMI_Drift_Flux_Dataset, aes(x = Event, y = BMI_Flux_mg_hrs)) +
geom_point(aes(color = Test_Reach)) +
geom_line(aes(group = Pool, color = Test_Reach)) +
geom_text(aes(label = Pool, hjust = -0.2, vjust = -0.5, color = Test_Reach), check_overlap = TRUE) +
scale_color_manual(values = col_value) +
xlab("Before/After Flow Augmentation") +
ylab("BMI Flux (mg/hour)") +
ggtitle("Changes in BMI Flux as a Function of Flow Augmentation") +
theme(plot.title = element_text(face = "bold", size = 15, hjust = 0.5), axis.title.x = element_text(size = 12, face = "bold"), axis.title.y = element_text(size = 12, face = "bold"), legend.title = element_text(size = 15), legend.text = element_text(size = 11)) +
labs(color = "Study Site")
Macroinvertebrate_BACI_Flux
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.
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")## 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")## 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")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")## 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")## 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")## 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.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}"))) %>%
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(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(event = "Before")
nnd_GolfBall_Pre## # A tibble: 8 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 1 29.3 29.4 2018-06-30 2018-06-29 18.3 Before
## 2 2 28.2 28.3 2018-06-30 2018-06-29 18.3 Before
## 3 5 56.7 69.2 2018-06-30 2018-06-29 18.3 Before
## 4 6 36.4 36.4 2018-06-30 2018-06-29 18.3 Before
## 5 7 39.1 39.1 2018-06-30 2018-06-29 18.3 Before
## 6 8 75.3 75.3 2018-06-30 2018-06-29 18.3 Before
## 7 11 28.4 29.4 2018-06-30 2018-06-29 18.3 Before
## 8 12 19.8 23.8 2018-06-30 2018-06-29 18.3 Beforennd_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}"))) %>%
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(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(event = "Before")
nnd_HalfTire_Pre## # A tibble: 2 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 4 67.0 61.8 2018-06-29 2018-06-29 18.8 Before
## 2 6 77.6 86.1 2018-06-29 2018-06-29 18.8 Beforennd_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}"))) %>%
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(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(event = "Before")
nnd_RoachRun_Pre## # A tibble: 9 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 1 0.0725 0.0725 2018-06-29 2018-06-29 18.5 Before
## 2 2 39.1 39.7 2018-06-29 2018-06-29 18.5 Before
## 3 6 63.9 40.2 2018-06-29 2018-06-29 18.5 Before
## 4 9 43.2 40.4 2018-06-29 2018-06-29 18.5 Before
## 5 10 69.5 72.0 2018-06-29 2018-06-29 18.5 Before
## 6 12 84.6 84.9 2018-06-29 2018-06-29 18.5 Before
## 7 15 37.8 39.6 2018-06-29 2018-06-29 18.5 Before
## 8 18 41.4 39.2 2018-06-29 2018-06-29 18.5 Before
## 9 19 32.0 32.0 2018-06-29 2018-06-29 18.5 Beforennd_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}"))) %>%
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(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(event = "After")
nnd_GolfBall_Post## # A tibble: 2 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 5 0.126 0.126 2018-07-06 2018-07-05 18.3 After
## 2 6 0.0969 0.0969 2018-07-06 2018-07-05 18.3 Afternnd_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}"))) %>%
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(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(event = "After")
nnd_HalfTire_Post## # A tibble: 29 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 1 25.2 25.2 2018-07-05 2018-07-05 18.8 After
## 2 4 31.5 31.2 2018-07-05 2018-07-05 18.8 After
## 3 5 60.8 60.8 2018-07-05 2018-07-05 18.8 After
## 4 6 21.7 21.7 2018-07-05 2018-07-05 18.8 After
## 5 9 58.9 58.9 2018-07-05 2018-07-05 18.8 After
## 6 10 25.7 25.7 2018-07-05 2018-07-05 18.8 After
## 7 11 34.2 34.2 2018-07-05 2018-07-05 18.8 After
## 8 12 38.8 39.3 2018-07-05 2018-07-05 18.8 After
## 9 16 55.5 55.5 2018-07-05 2018-07-05 18.8 After
## 10 17 12.1 12.1 2018-07-05 2018-07-05 18.8 After
## # ... with 19 more rowsnnd_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}"))) %>%
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(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(event = "After")
nnd_RoachRun_Post## # A tibble: 17 x 7
## index mean_nnd median_nnd date BACI_date site event
## <dbl> <dbl> <dbl> <date> <date> <chr> <chr>
## 1 3 3.77 3.77 2018-07-05 2018-07-05 18.5 After
## 2 4 107. 110. 2018-07-05 2018-07-05 18.5 After
## 3 5 56.7 43.3 2018-07-05 2018-07-05 18.5 After
## 4 6 19.5 19.5 2018-07-05 2018-07-05 18.5 After
## 5 8 22.6 25.8 2018-07-05 2018-07-05 18.5 After
## 6 9 30.1 30.1 2018-07-05 2018-07-05 18.5 After
## 7 10 25.2 26.2 2018-07-05 2018-07-05 18.5 After
## 8 16 29.3 31.3 2018-07-05 2018-07-05 18.5 After
## 9 17 32.7 32.7 2018-07-05 2018-07-05 18.5 After
## 10 18 0.753 0.753 2018-07-05 2018-07-05 18.5 After
## 11 19 43.5 43.5 2018-07-05 2018-07-05 18.5 After
## 12 24 18.5 19.0 2018-07-05 2018-07-05 18.5 After
## 13 25 22.9 22.9 2018-07-05 2018-07-05 18.5 After
## 14 29 42.3 38.6 2018-07-05 2018-07-05 18.5 After
## 15 30 37.3 37.3 2018-07-05 2018-07-05 18.5 After
## 16 31 47.3 46.6 2018-07-05 2018-07-05 18.5 After
## 17 33 113. 114. 2018-07-05 2018-07-05 18.5 Afternnd_data <-
bind_rows(nnd_RoachRun_Pre, nnd_RoachRun_Post, nnd_GolfBall_Pre, nnd_GolfBall_Post, nnd_HalfTire_Pre, nnd_HalfTire_Post) %>%
select(date, BACI_date, event, site, index, mean_nnd, median_nnd)
nnd_data## # A tibble: 67 x 7
## date BACI_date event site index mean_nnd median_nnd
## <date> <date> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2018-06-29 2018-06-29 Before 18.5 1 0.0725 0.0725
## 2 2018-06-29 2018-06-29 Before 18.5 2 39.1 39.7
## 3 2018-06-29 2018-06-29 Before 18.5 6 63.9 40.2
## 4 2018-06-29 2018-06-29 Before 18.5 9 43.2 40.4
## 5 2018-06-29 2018-06-29 Before 18.5 10 69.5 72.0
## 6 2018-06-29 2018-06-29 Before 18.5 12 84.6 84.9
## 7 2018-06-29 2018-06-29 Before 18.5 15 37.8 39.6
## 8 2018-06-29 2018-06-29 Before 18.5 18 41.4 39.2
## 9 2018-06-29 2018-06-29 Before 18.5 19 32.0 32.0
## 10 2018-07-05 2018-07-05 After 18.5 3 3.77 3.77
## # ... with 57 more rowsThis 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(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, event, site, 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(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, event, site, 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(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, event, site, subsample, index, DistPerTime_Median, DistPerTime_Mean) %>%
distinct() %>%
arrange(as.numeric(index))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(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, event, site, 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(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, event, site, 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(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, event, site, 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) %>%
mutate(ID = 1) %>%
group_by(ID) %>%
mutate(unique_ID = cumsum(ID)) %>%
select(date, BACI_date, event, site, unique_ID, subsample, index, DistPerTime_Median, DistPerTime_Mean)## Adding missing grouping variables: `ID`MovementPerTime_Data## # A tibble: 125 x 10
## # Groups: ID [1]
## ID date BACI_date event site unique_ID subsample index
## <dbl> <date> <date> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 2018-06-29 2018-06-29 Befo… 18.5 1 1 1
## 2 1 2018-06-29 2018-06-29 Befo… 18.5 2 1 2
## 3 1 2018-06-29 2018-06-29 Befo… 18.5 3 1 4
## 4 1 2018-06-29 2018-06-29 Befo… 18.5 4 2 5
## 5 1 2018-06-29 2018-06-29 Befo… 18.5 5 2 6
## 6 1 2018-06-29 2018-06-29 Befo… 18.5 6 2 7
## 7 1 2018-06-29 2018-06-29 Befo… 18.5 7 3 8
## 8 1 2018-06-29 2018-06-29 Befo… 18.5 8 3 9
## 9 1 2018-06-29 2018-06-29 Befo… 18.5 9 3 10
## 10 1 2018-06-29 2018-06-29 Befo… 18.5 10 4 11
## # ... with 115 more rows, and 2 more variables: DistPerTime_Median <dbl>,
## # DistPerTime_Mean <dbl>Finding the centroid point then looking at the average distance to each subsequent point taken in the subsample and taking the average.
Breaking down the proportion of each type of forage event and how it changes throughout the study period.
GolfBall_ForageDist_Pre <- GolfBall_VidSync_Pre %>%
filter(grepl("^Subsample.*", objects)) %>%
filter(!grepl("^Length.*", event)) %>%
select(objects, event, time) %>%
arrange(time) %>%
mutate(drift_forage = if_else(grepl("^Drift_Forage", event), 1, 0)) %>%
mutate(search_forage = if_else(grepl("^Search_Forage", event), 1, 0)) %>%
mutate(benthic_forage = if_else(grepl("^Benthic_Forage", event), 1, 0)) %>%
mutate(other = if_else(grepl("Movement|Surface_Strike|Nip|Attack", event), 1, 0))
GolfBall_ForageDist_Pre## # A tibble: 106 x 7
## objects event time drift_forage search_forage benthic_forage other
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Subsample… Searc… 0 0 1 0 0
## 2 Subsample… Movem… 0 0 0 0 1
## 3 Subsample… Movem… 0 0 0 0 1
## 4 Subsample… Searc… 3 0 1 0 0
## 5 Subsample… Movem… 3 0 0 0 1
## 6 Subsample… Movem… 3 0 0 0 1
## 7 Subsample… Movem… 6 0 0 0 1
## 8 Subsample… Movem… 6 0 0 0 1
## 9 Subsample… Searc… 6 0 1 0 0
## 10 Subsample… Movem… 9 0 0 0 1
## # ... with 96 more rowsUsing Delaunay Triangulation to calculate the volume each fish is occupying during each subsample.
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
}
GB_Pre_Filename <- paste("GB_Vol_Pre_", toString(i), sep = "")
GolfBall_Volume_Pre %>%
filter(index == i) %>%
select(X, Y, Z) %>%
as.matrix() %>%
convhulln("FA") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 1 2 11
## [2,] 1 3 2
## [3,] 4 2 11
## [4,] 4 3 2
## [5,] 5 10 11
## [6,] 5 4 11
## [7,] 9 1 11
## [8,] 9 8 1
## [9,] 9 10 11
## [10,] 9 8 10
## [11,] 7 1 3
## [12,] 7 8 1
## [13,] 6 7 3
## [14,] 6 4 3
## [15,] 6 5 4
## [16,] 6 5 10
## [17,] 6 8 10
## [18,] 6 7 8
##
## $area
## [1] 17.54319
##
## $vol
## [1] 3.572776
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 9 10
## [2,] 3 1 2
## [3,] 3 9 2
## [4,] 8 1 2
## [5,] 5 9 2
## [6,] 5 8 2
## [7,] 5 8 10
## [8,] 4 3 1
## [9,] 4 8 1
## [10,] 4 3 10
## [11,] 4 8 10
## [12,] 7 9 10
## [13,] 7 5 10
## [14,] 7 5 9
##
## $area
## [1] 0.2842032
##
## $vol
## [1] 0.005368735
##
## $hull
## [,1] [,2] [,3]
## [1,] 10 4 9
## [2,] 5 9 1
## [3,] 5 10 9
## [4,] 11 4 9
## [5,] 3 5 1
## [6,] 3 5 10
## [7,] 3 10 4
## [8,] 7 11 9
## [9,] 7 11 4
## [10,] 6 9 1
## [11,] 6 7 1
## [12,] 6 7 9
## [13,] 2 3 4
## [14,] 2 7 4
## [15,] 2 3 1
## [16,] 2 7 1
##
## $area
## [1] 0.4229652
##
## $vol
## [1] 0.01056027
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 4 5
## [2,] 11 4 5
## [3,] 11 4 2
## [4,] 10 4 2
## [5,] 10 7 2
## [6,] 9 11 5
## [7,] 9 7 5
## [8,] 6 7 4
## [9,] 6 10 4
## [10,] 6 10 7
## [11,] 3 11 2
## [12,] 3 9 11
## [13,] 3 7 2
## [14,] 3 9 7
##
## $area
## [1] 0.1019071
##
## $vol
## [1] 0.001129695
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 1 6
## [2,] 3 2 6
## [3,] 3 5 6
## [4,] 4 1 6
## [5,] 4 5 6
## [6,] 4 3 5
## [7,] 4 2 1
## [8,] 4 3 2
##
## $area
## [1] 102.0796
##
## $vol
## [1] 16.1811
##
## $hull
## [,1] [,2] [,3]
## [1,] 9 6 10
## [2,] 8 6 7
## [3,] 8 9 7
## [4,] 8 9 6
## [5,] 1 6 7
## [6,] 1 5 7
## [7,] 1 6 10
## [8,] 1 5 10
## [9,] 4 9 7
## [10,] 4 5 7
## [11,] 4 9 10
## [12,] 4 5 10
##
## $area
## [1] 1222.774
##
## $vol
## [1] 1298.271
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 6 4
## [2,] 3 9 11
## [3,] 3 9 4
## [4,] 10 9 11
## [5,] 10 9 6
## [6,] 5 6 4
## [7,] 5 9 4
## [8,] 5 9 6
## [9,] 1 2 4
## [10,] 1 3 4
## [11,] 1 2 6
## [12,] 7 1 6
## [13,] 7 10 11
## [14,] 7 10 6
## [15,] 7 3 11
## [16,] 7 1 3
##
## $area
## [1] 87.85428
##
## $vol
## [1] 17.89703
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 2 3
## [2,] 1 2 3
## [3,] 1 4 3
## [4,] 1 4 2
##
## $area
## [1] 661.3971
##
## $vol
## [1] 22.31643
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 8 11
## [2,] 2 8 1
## [3,] 2 10 11
## [4,] 2 5 1
## [5,] 2 10 5
## [6,] 9 8 11
## [7,] 9 5 8
## [8,] 9 10 11
## [9,] 9 10 5
## [10,] 3 8 1
## [11,] 3 5 1
## [12,] 7 5 8
## [13,] 7 3 8
## [14,] 7 3 5
##
## $area
## [1] 138.945
##
## $vol
## [1] 34.66847
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 9 2
## [2,] 3 9 4
## [3,] 3 5 2
## [4,] 3 5 4
## [5,] 8 9 2
## [6,] 8 5 2
## [7,] 8 9 4
## [8,] 8 5 4
##
## $area
## [1] 3.899656
##
## $vol
## [1] 0.1942445
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 5 2
## [2,] 4 5 2
## [3,] 4 3 5
## [4,] 1 3 2
## [5,] 1 4 2
## [6,] 1 4 3
##
## $area
## [1] 1400.067
##
## $vol
## [1] 2627.808HalfTire_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") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 6 1 4
## [2,] 3 1 4
## [3,] 5 6 4
## [4,] 5 3 4
## [5,] 5 6 1
## [6,] 5 3 1
##
## $area
## [1] 1284.728
##
## $vol
## [1] 790.8059
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 5 8
## [2,] 1 7 8
## [3,] 2 5 8
## [4,] 2 4 5
## [5,] 2 1 8
## [6,] 6 7 5
## [7,] 3 1 7
## [8,] 3 6 7
## [9,] 3 2 4
## [10,] 3 2 1
## [11,] 3 4 5
## [12,] 3 6 5
##
## $area
## [1] 4376.576
##
## $vol
## [1] 4623.805
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 7 8
## [2,] 2 7 3
## [3,] 9 7 8
## [4,] 9 2 8
## [5,] 1 9 7
## [6,] 6 7 3
## [7,] 6 1 3
## [8,] 6 1 7
## [9,] 4 1 3
## [10,] 4 1 9
## [11,] 4 2 3
## [12,] 4 9 2
##
## $area
## [1] 1787.963
##
## $vol
## [1] 1941.927
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 1 5
## [2,] 4 2 5
## [3,] 3 1 5
## [4,] 3 4 5
## [5,] 3 2 1
## [6,] 3 4 2
##
## $area
## [1] 651.3871
##
## $vol
## [1] 418.8056
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 3 1
## [2,] 7 3 8
## [3,] 6 3 1
## [4,] 6 7 1
## [5,] 6 7 8
## [6,] 4 3 8
## [7,] 4 6 8
## [8,] 4 6 3
##
## $area
## [1] 3964.071
##
## $vol
## [1] 2153.043
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 1 4
## [2,] 2 5 4
## [3,] 2 5 1
## [4,] 3 1 4
## [5,] 3 2 4
## [6,] 3 2 1
##
## $area
## [1] 977.0674
##
## $vol
## [1] 208.3656
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 7 4
## [2,] 1 5 7
## [3,] 3 7 4
## [4,] 3 1 7
## [5,] 6 3 4
## [6,] 6 3 1
## [7,] 6 5 4
## [8,] 6 1 5
##
## $area
## [1] 1715.592
##
## $vol
## [1] 1761.056
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 10 1
## [2,] 5 10 6
## [3,] 3 10 1
## [4,] 7 10 6
## [5,] 7 3 6
## [6,] 7 3 10
## [7,] 4 5 6
## [8,] 4 3 6
## [9,] 4 5 1
## [10,] 4 3 1
##
## $area
## [1] 3735.021
##
## $vol
## [1] 5329.532RoachRun_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") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 3 2 7
## [2,] 1 2 7
## [3,] 1 9 2
## [4,] 10 1 7
## [5,] 10 1 9
## [6,] 10 8 7
## [7,] 10 8 9
## [8,] 6 3 7
## [9,] 6 8 7
## [10,] 6 8 3
## [11,] 4 8 3
## [12,] 4 8 9
## [13,] 4 3 2
## [14,] 4 9 2
##
## $area
## [1] 92.91676
##
## $vol
## [1] 35.39281
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 3 1
## [2,] 4 2 1
## [3,] 9 3 7
## [4,] 9 3 1
## [5,] 11 2 3
## [6,] 11 4 2
## [7,] 11 3 7
## [8,] 6 11 7
## [9,] 6 11 4
## [10,] 5 4 1
## [11,] 5 9 1
## [12,] 5 6 4
## [13,] 5 9 7
## [14,] 5 6 7
##
## $area
## [1] 166.9069
##
## $vol
## [1] 53.06112
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 1 7
## [2,] 8 4 7
## [3,] 9 8 1
## [4,] 9 8 4
## [5,] 6 1 7
## [6,] 6 4 7
## [7,] 3 9 1
## [8,] 3 9 4
## [9,] 3 6 1
## [10,] 3 6 4
##
## $area
## [1] 909.039
##
## $vol
## [1] 1089.977
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 6 10
## [2,] 2 6 10
## [3,] 11 7 1
## [4,] 11 2 1
## [5,] 11 2 10
## [6,] 3 2 1
## [7,] 3 2 6
## [8,] 8 7 10
## [9,] 8 11 10
## [10,] 8 11 7
## [11,] 4 3 1
## [12,] 4 7 1
## [13,] 5 3 6
## [14,] 5 4 3
## [15,] 5 7 6
## [16,] 5 4 7
##
## $area
## [1] 82.60097
##
## $vol
## [1] 33.98795
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 7 2
## [2,] 6 5 7
## [3,] 6 4 7
## [4,] 6 4 5
## [5,] 1 7 2
## [6,] 1 5 7
## [7,] 3 4 5
## [8,] 3 1 5
## [9,] 3 4 2
## [10,] 3 1 2
##
## $area
## [1] 5345.584
##
## $vol
## [1] 6648.572
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 1 4
## [2,] 9 8 4
## [3,] 9 3 4
## [4,] 6 1 4
## [5,] 6 8 4
## [6,] 2 3 1
## [7,] 2 9 3
## [8,] 7 9 8
## [9,] 11 6 1
## [10,] 11 2 1
## [11,] 11 6 8
## [12,] 11 7 8
## [13,] 11 2 9
## [14,] 11 7 9
##
## $area
## [1] 10.56231
##
## $vol
## [1] 1.759631
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 2 8
## [2,] 4 8 5
## [3,] 4 1 8
## [4,] 7 2 8
## [5,] 7 6 2
## [6,] 7 8 5
## [7,] 7 6 5
## [8,] 3 1 2
## [9,] 3 4 1
## [10,] 3 6 2
## [11,] 3 6 5
## [12,] 3 4 5
##
## $area
## [1] 956.7945
##
## $vol
## [1] 1030.59
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 1 3
## [2,] 6 2 3
## [3,] 6 2 1
## [4,] 4 1 3
## [5,] 4 6 3
## [6,] 5 6 1
## [7,] 5 4 1
## [8,] 5 4 6
##
## $area
## [1] 508.412
##
## $vol
## [1] 160.8925
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 2 4
## [2,] 3 8 4
## [3,] 7 2 9
## [4,] 7 2 4
## [5,] 7 8 9
## [6,] 7 8 4
## [7,] 6 2 9
## [8,] 6 8 9
## [9,] 1 3 2
## [10,] 1 3 8
## [11,] 1 6 2
## [12,] 1 6 8
##
## $area
## [1] 2.771477
##
## $vol
## [1] 0.1886601
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 5 1
## [2,] 2 3 1
## [3,] 2 3 5
## [4,] 4 5 1
## [5,] 4 3 1
## [6,] 4 3 5
##
## $area
## [1] 16.44502
##
## $vol
## [1] 1.156468
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 5 1
## [2,] 6 2 1
## [3,] 11 5 10
## [4,] 11 6 10
## [5,] 11 6 1
## [6,] 8 6 2
## [7,] 8 2 5
## [8,] 4 5 1
## [9,] 4 11 1
## [10,] 4 11 5
## [11,] 9 5 10
## [12,] 9 8 5
## [13,] 9 6 10
## [14,] 9 8 6
##
## $area
## [1] 108.2664
##
## $vol
## [1] 69.95656
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 7 5
## [2,] 8 2 7
## [3,] 8 11 7
## [4,] 6 7 5
## [5,] 6 11 7
## [6,] 10 8 2
## [7,] 10 8 11
## [8,] 4 6 5
## [9,] 4 2 5
## [10,] 4 10 2
## [11,] 3 10 11
## [12,] 3 4 10
## [13,] 3 6 11
## [14,] 3 4 6
##
## $area
## [1] 224.6459
##
## $vol
## [1] 174.4089
##
## $hull
## [,1] [,2] [,3]
## [1,] 9 7 10
## [2,] 5 7 10
## [3,] 8 7 1
## [4,] 8 9 1
## [5,] 8 9 7
## [6,] 11 5 10
## [7,] 11 9 10
## [8,] 11 9 1
## [9,] 2 7 1
## [10,] 2 5 7
## [11,] 2 11 1
## [12,] 2 11 5
##
## $area
## [1] 441.4269
##
## $vol
## [1] 222.4795
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 6 3
## [2,] 1 2 3
## [3,] 1 2 6
## [4,] 4 6 3
## [5,] 4 1 3
## [6,] 4 1 6
##
## $area
## [1] 540.7251
##
## $vol
## [1] 164.438
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 1 4
## [2,] 2 1 4
## [3,] 2 3 4
## [4,] 2 3 1
##
## $area
## [1] 7.671573
##
## $vol
## [1] 0.1675174GolfBall_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") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 2 4 1
## [2,] 2 4 7
## [3,] 5 7 1
## [4,] 5 4 1
## [5,] 8 2 1
## [6,] 8 2 7
## [7,] 9 4 7
## [8,] 9 5 7
## [9,] 9 5 4
## [10,] 6 7 1
## [11,] 6 8 1
## [12,] 6 8 7
##
## $area
## [1] 2.100298
##
## $vol
## [1] 0.1395289
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 10 7
## [2,] 1 10 8
## [3,] 1 11 7
## [4,] 1 11 8
## [5,] 5 11 7
## [6,] 5 10 8
## [7,] 5 11 8
## [8,] 9 10 7
## [9,] 9 5 7
## [10,] 9 5 10
##
## $area
## [1] 0.08475979
##
## $vol
## [1] 0.001249656
##
## $hull
## [,1] [,2] [,3]
## [1,] 10 1 3
## [2,] 2 9 3
## [3,] 2 9 5
## [4,] 11 9 6
## [5,] 11 9 3
## [6,] 11 10 6
## [7,] 11 10 3
## [8,] 7 10 1
## [9,] 7 10 6
## [10,] 7 9 6
## [11,] 7 9 5
## [12,] 4 7 5
## [13,] 4 7 1
## [14,] 4 2 5
## [15,] 4 1 3
## [16,] 4 2 3
##
## $area
## [1] 8.739126
##
## $vol
## [1] 1.024696
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 8 7
## [2,] 6 2 8
## [3,] 10 5 8
## [4,] 10 2 8
## [5,] 1 8 7
## [6,] 1 6 7
## [7,] 1 6 8
## [8,] 3 6 7
## [9,] 3 6 2
## [10,] 3 10 2
## [11,] 3 5 7
## [12,] 3 10 5
##
## $area
## [1] 0.1605849
##
## $vol
## [1] 0.002604847
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 9 10
## [2,] 7 6 9
## [3,] 1 7 10
## [4,] 1 7 6
## [5,] 4 9 10
## [6,] 5 6 9
## [7,] 5 4 9
## [8,] 5 4 6
## [9,] 8 1 10
## [10,] 8 4 10
## [11,] 8 1 6
## [12,] 8 4 6
##
## $area
## [1] 1.410434
##
## $vol
## [1] 0.04458702
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 4 11
## [2,] 2 4 1
## [3,] 9 4 11
## [4,] 9 2 11
## [5,] 9 2 1
## [6,] 8 9 4
## [7,] 3 4 1
## [8,] 3 8 4
## [9,] 3 9 1
## [10,] 3 8 9
##
## $area
## [1] 129.3259
##
## $vol
## [1] 52.68832RoachRun_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") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 4 1 10
## [2,] 9 4 1
## [3,] 3 6 10
## [4,] 3 4 10
## [5,] 8 6 10
## [6,] 8 9 10
## [7,] 8 9 6
## [8,] 11 1 10
## [9,] 11 9 10
## [10,] 11 9 1
## [11,] 7 3 6
## [12,] 7 3 4
## [13,] 7 9 6
## [14,] 7 9 4
##
## $area
## [1] 131.3419
##
## $vol
## [1] 76.84104
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 4 5
## [2,] 1 4 5
## [3,] 1 7 5
## [4,] 3 7 4
## [5,] 2 1 4
## [6,] 2 3 4
## [7,] 2 3 1
## [8,] 6 1 7
## [9,] 6 3 7
## [10,] 6 3 1
##
## $area
## [1] 1142.574
##
## $vol
## [1] 1817.855
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 2 1
## [2,] 3 4 1
## [3,] 6 2 1
## [4,] 6 2 10
## [5,] 6 3 1
## [6,] 6 3 10
## [7,] 8 2 10
## [8,] 8 4 2
## [9,] 9 8 10
## [10,] 9 8 4
## [11,] 9 3 10
## [12,] 9 3 4
##
## $area
## [1] 963.769
##
## $vol
## [1] 797.5841
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 6 1
## [2,] 8 5 1
## [3,] 7 6 1
## [4,] 7 3 1
## [5,] 7 3 6
## [6,] 4 8 5
## [7,] 4 5 6
## [8,] 4 3 6
## [9,] 4 3 1
## [10,] 4 8 1
##
## $area
## [1] 259.3286
##
## $vol
## [1] 168.6815
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 4 2
## [2,] 1 4 5
## [3,] 3 4 2
## [4,] 3 4 5
## [5,] 3 1 2
## [6,] 3 1 5
##
## $area
## [1] 799.6886
##
## $vol
## [1] 522.9569
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 3 4
## [2,] 2 3 4
## [3,] 2 1 4
## [4,] 2 1 3
##
## $area
## [1] 90.83519
##
## $vol
## [1] 8.147371
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 11 1
## [2,] 8 6 9
## [3,] 8 11 9
## [4,] 10 6 9
## [5,] 10 3 6
## [6,] 2 11 9
## [7,] 2 10 9
## [8,] 2 10 3
## [9,] 2 11 1
## [10,] 2 3 1
## [11,] 4 3 6
## [12,] 4 8 6
## [13,] 4 3 1
## [14,] 4 8 1
##
## $area
## [1] 1892.83
##
## $vol
## [1] 3289.526
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 3 4
## [2,] 1 7 3
## [3,] 2 1 4
## [4,] 2 1 7
## [5,] 6 3 4
## [6,] 10 7 3
## [7,] 10 6 3
## [8,] 11 2 7
## [9,] 11 10 7
## [10,] 5 10 6
## [11,] 5 11 10
## [12,] 5 6 4
## [13,] 5 2 4
## [14,] 5 11 2
##
## $area
## [1] 542.4528
##
## $vol
## [1] 664.3903
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 10 3
## [2,] 7 4 3
## [3,] 7 4 6
## [4,] 8 4 6
## [5,] 8 4 10
## [6,] 2 8 6
## [7,] 2 8 10
## [8,] 2 10 3
## [9,] 2 7 3
## [10,] 5 7 6
## [11,] 5 2 6
## [12,] 5 2 7
##
## $area
## [1] 2271.596
##
## $vol
## [1] 3001.615
##
## $hull
## [,1] [,2] [,3]
## [1,] 10 5 4
## [2,] 7 10 1
## [3,] 11 5 4
## [4,] 11 10 1
## [5,] 11 10 5
## [6,] 3 7 1
## [7,] 8 10 4
## [8,] 8 7 10
## [9,] 8 3 4
## [10,] 8 3 7
## [11,] 2 11 1
## [12,] 2 11 4
## [13,] 2 3 1
## [14,] 2 3 4
##
## $area
## [1] 2550.112
##
## $vol
## [1] 4315.367
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 2 5
## [2,] 1 3 5
## [3,] 1 3 2
## [4,] 4 2 5
## [5,] 4 3 5
## [6,] 4 3 2
##
## $area
## [1] 1071.343
##
## $vol
## [1] 954.9406
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 6 2
## [2,] 9 4 10
## [3,] 9 2 10
## [4,] 9 2 4
## [5,] 5 4 10
## [6,] 5 6 10
## [7,] 5 7 4
## [8,] 5 7 6
## [9,] 1 2 10
## [10,] 1 6 10
## [11,] 1 6 2
## [12,] 3 2 4
## [13,] 3 7 4
## [14,] 3 7 2
##
## $area
## [1] 4905.7
##
## $vol
## [1] 9827.118
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 11 5
## [2,] 7 11 5
## [3,] 7 9 5
## [4,] 10 8 5
## [5,] 10 2 9
## [6,] 10 8 11
## [7,] 10 2 11
## [8,] 6 2 9
## [9,] 6 7 9
## [10,] 6 2 11
## [11,] 6 7 11
## [12,] 3 9 5
## [13,] 3 10 5
## [14,] 3 10 9
##
## $area
## [1] 10.97871
##
## $vol
## [1] 1.046881
##
## $hull
## [,1] [,2] [,3]
## [1,] 6 9 7
## [2,] 6 3 9
## [3,] 8 9 7
## [4,] 8 3 9
## [5,] 1 6 3
## [6,] 5 1 6
## [7,] 11 1 3
## [8,] 11 5 1
## [9,] 11 8 3
## [10,] 11 8 7
## [11,] 11 6 7
## [12,] 11 5 6
##
## $area
## [1] 402.5808
##
## $vol
## [1] 484.3723
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 11 4
## [2,] 3 11 5
## [3,] 10 11 5
## [4,] 8 3 4
## [5,] 8 3 5
## [6,] 8 10 4
## [7,] 8 10 5
## [8,] 1 11 4
## [9,] 1 10 11
## [10,] 9 10 4
## [11,] 9 1 4
## [12,] 9 1 10
##
## $area
## [1] 112.3787
##
## $vol
## [1] 66.51645
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 8 2
## [2,] 5 8 2
## [3,] 5 1 8
## [4,] 7 5 2
## [5,] 7 5 1
## [6,] 3 1 2
## [7,] 3 7 2
## [8,] 3 7 1
##
## $area
## [1] 1106.741
##
## $vol
## [1] 1713.092
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 11 1
## [2,] 4 3 11
## [3,] 6 3 11
## [4,] 7 11 1
## [5,] 7 6 11
## [6,] 7 9 1
## [7,] 7 6 3
## [8,] 7 9 3
## [9,] 2 4 1
## [10,] 10 4 3
## [11,] 10 9 3
## [12,] 10 2 4
## [13,] 10 9 1
## [14,] 10 2 1
##
## $area
## [1] 69.08229
##
## $vol
## [1] 35.375
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 1 2
## [2,] 4 3 2
## [3,] 4 3 1
## [4,] 5 1 2
## [5,] 5 4 2
## [6,] 5 4 1
##
## $area
## [1] 221.8438
##
## $vol
## [1] 103.3987
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 1 4
## [2,] 2 3 4
## [3,] 2 3 1
## [4,] 5 1 4
## [5,] 5 3 4
## [6,] 5 3 1
##
## $area
## [1] 420.8609
##
## $vol
## [1] 186.5721
##
## $hull
## [,1] [,2] [,3]
## [1,] 10 8 5
## [2,] 10 6 8
## [3,] 4 8 5
## [4,] 4 6 8
## [5,] 2 10 5
## [6,] 2 10 6
## [7,] 3 4 5
## [8,] 3 2 5
## [9,] 3 2 6
## [10,] 1 4 6
## [11,] 1 3 6
## [12,] 1 3 4
##
## $area
## [1] 18.37455
##
## $vol
## [1] 2.92571
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 5 6
## [2,] 1 5 4
## [3,] 1 5 6
## [4,] 1 7 6
## [5,] 1 10 7
## [6,] 3 5 4
## [7,] 8 7 5
## [8,] 8 3 5
## [9,] 8 3 7
## [10,] 11 10 7
## [11,] 11 3 7
## [12,] 11 3 4
## [13,] 11 1 4
## [14,] 11 1 10
##
## $area
## [1] 3108.163
##
## $vol
## [1] 7063.975
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 3 1
## [2,] 5 8 1
## [3,] 5 8 3
## [4,] 2 5 1
## [5,] 6 5 3
## [6,] 6 2 5
## [7,] 6 3 1
## [8,] 6 2 1
##
## $area
## [1] 1744.045
##
## $vol
## [1] 981.0603
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 8 1
## [2,] 3 6 8
## [3,] 4 8 1
## [4,] 4 7 8
## [5,] 5 6 8
## [6,] 5 7 8
## [7,] 2 4 1
## [8,] 2 3 1
## [9,] 2 3 6
## [10,] 2 5 6
## [11,] 2 4 7
## [12,] 2 5 7
##
## $area
## [1] 840.5023
##
## $vol
## [1] 859.0163
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 8 3
## [2,] 9 8 3
## [3,] 9 10 3
## [4,] 2 10 3
## [5,] 2 1 3
## [6,] 2 1 10
## [7,] 5 1 8
## [8,] 5 1 10
## [9,] 6 9 10
## [10,] 6 5 10
## [11,] 7 9 8
## [12,] 7 6 9
## [13,] 7 5 8
## [14,] 7 6 5
##
## $area
## [1] 998.5678
##
## $vol
## [1] 1937.297
##
## $hull
## [,1] [,2] [,3]
## [1,] 11 7 10
## [2,] 1 10 2
## [3,] 1 11 10
## [4,] 1 7 2
## [5,] 9 10 2
## [6,] 5 11 7
## [7,] 5 1 7
## [8,] 5 1 11
## [9,] 8 7 10
## [10,] 8 9 10
## [11,] 8 7 2
## [12,] 8 9 2
##
## $area
## [1] 473.3126
##
## $vol
## [1] 375.0011
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 10 2
## [2,] 4 10 5
## [3,] 4 10 2
## [4,] 3 8 2
## [5,] 3 4 2
## [6,] 7 3 8
## [7,] 7 3 4
## [8,] 9 8 10
## [9,] 9 7 8
## [10,] 9 10 5
## [11,] 9 7 5
## [12,] 11 4 5
## [13,] 11 7 5
## [14,] 11 7 4
##
## $area
## [1] 13.01947
##
## $vol
## [1] 1.342989
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 4 1
## [2,] 2 4 1
## [3,] 2 3 1
## [4,] 2 3 4
##
## $area
## [1] 562.2438
##
## $vol
## [1] 59.6388
##
## $hull
## [,1] [,2] [,3]
## [1,] 6 9 8
## [2,] 10 3 2
## [3,] 10 9 8
## [4,] 1 3 2
## [5,] 1 6 2
## [6,] 1 6 3
## [7,] 11 3 8
## [8,] 11 6 8
## [9,] 11 6 3
## [10,] 5 6 9
## [11,] 5 10 9
## [12,] 5 6 2
## [13,] 5 10 2
## [14,] 7 3 8
## [15,] 7 10 8
## [16,] 7 10 3
##
## $area
## [1] 437.9139
##
## $vol
## [1] 372.0408
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 4 9
## [2,] 5 3 1
## [3,] 5 3 4
## [4,] 7 3 1
## [5,] 8 7 9
## [6,] 8 7 3
## [7,] 8 4 9
## [8,] 8 3 4
## [9,] 6 7 1
## [10,] 6 5 9
## [11,] 6 7 9
## [12,] 2 5 1
## [13,] 2 6 1
## [14,] 2 6 5
##
## $area
## [1] 1530.431
##
## $vol
## [1] 1851.15
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 3 2
## [2,] 4 3 2
## [3,] 4 1 2
## [4,] 4 1 3
##
## $area
## [1] 202.643
##
## $vol
## [1] 40.04317
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 10 8
## [2,] 2 4 10
## [3,] 2 11 8
## [4,] 2 4 11
## [5,] 9 10 8
## [6,] 9 11 8
## [7,] 9 4 10
## [8,] 9 4 11
##
## $area
## [1] 47.40703
##
## $vol
## [1] 8.250651
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 2 8
## [2,] 6 5 2
## [3,] 6 7 2
## [4,] 6 7 5
## [5,] 4 5 2
## [6,] 4 7 8
## [7,] 4 7 5
## [8,] 3 2 8
## [9,] 3 4 8
## [10,] 3 4 2
##
## $area
## [1] 682.3959
##
## $vol
## [1] 889.4584
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 2 5
## [2,] 9 7 10
## [3,] 9 4 5
## [4,] 9 4 10
## [5,] 1 2 5
## [6,] 1 7 5
## [7,] 1 7 2
## [8,] 11 4 10
## [9,] 11 4 2
## [10,] 11 7 10
## [11,] 11 7 2
## [12,] 3 7 5
## [13,] 3 9 5
## [14,] 3 9 7
##
## $area
## [1] 670.6503
##
## $vol
## [1] 967.0734HalfTire_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") %>%
print()
}## $hull
## [,1] [,2] [,3]
## [1,] 5 1 6
## [2,] 5 1 4
## [3,] 3 5 4
## [4,] 2 1 6
## [5,] 2 1 4
## [6,] 2 3 4
## [7,] 2 5 6
## [8,] 2 3 5
##
## $area
## [1] 368.0524
##
## $vol
## [1] 265.5197
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 5 4
## [2,] 6 3 9
## [3,] 6 3 4
## [4,] 1 5 4
## [5,] 1 3 4
## [6,] 8 7 4
## [7,] 8 6 9
## [8,] 8 6 4
## [9,] 8 5 9
## [10,] 8 7 5
## [11,] 10 3 9
## [12,] 10 1 3
## [13,] 10 5 9
## [14,] 10 1 5
##
## $area
## [1] 2061.382
##
## $vol
## [1] 2699.515
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 4 9
## [2,] 2 4 1
## [3,] 10 11 9
## [4,] 8 11 9
## [5,] 8 2 9
## [6,] 8 11 1
## [7,] 8 2 1
## [8,] 5 10 11
## [9,] 5 4 1
## [10,] 5 11 1
## [11,] 5 4 9
## [12,] 5 10 9
##
## $area
## [1] 164.4332
##
## $vol
## [1] 131.2419
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 5 1
## [2,] 7 5 1
## [3,] 7 5 6
## [4,] 11 3 1
## [5,] 11 7 1
## [6,] 11 7 6
## [7,] 2 3 1
## [8,] 2 8 1
## [9,] 4 11 6
## [10,] 4 11 3
## [11,] 4 5 6
## [12,] 4 8 5
## [13,] 4 2 3
## [14,] 4 2 8
##
## $area
## [1] 2211.743
##
## $vol
## [1] 4187.322
##
## $hull
## [,1] [,2] [,3]
## [1,] 9 3 1
## [2,] 9 3 11
## [3,] 2 3 1
## [4,] 2 6 3
## [5,] 5 3 11
## [6,] 5 6 11
## [7,] 5 6 3
## [8,] 8 6 11
## [9,] 8 9 11
## [10,] 8 6 1
## [11,] 8 9 1
## [12,] 4 6 1
## [13,] 4 2 1
## [14,] 4 2 6
##
## $area
## [1] 396.0309
##
## $vol
## [1] 277.7312
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 9 10
## [2,] 7 1 10
## [3,] 6 1 9
## [4,] 6 7 9
## [5,] 6 7 1
## [6,] 11 9 10
## [7,] 11 8 10
## [8,] 11 8 9
## [9,] 4 1 9
## [10,] 4 8 9
## [11,] 4 1 10
## [12,] 4 8 10
##
## $area
## [1] 13295.71
##
## $vol
## [1] 13320.27
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 5 9
## [2,] 11 8 9
## [3,] 2 8 4
## [4,] 2 8 5
## [5,] 6 11 5
## [6,] 10 5 9
## [7,] 10 11 9
## [8,] 10 11 5
## [9,] 1 8 4
## [10,] 1 6 11
## [11,] 1 6 5
## [12,] 1 2 4
## [13,] 1 2 5
## [14,] 7 11 8
## [15,] 7 1 8
## [16,] 7 1 11
##
## $area
## [1] 3865.125
##
## $vol
## [1] 9360.828
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 6 1
## [2,] 4 3 1
## [3,] 4 3 6
## [4,] 8 9 1
## [5,] 8 3 6
## [6,] 11 9 1
## [7,] 11 8 3
## [8,] 11 8 9
## [9,] 7 6 1
## [10,] 7 8 1
## [11,] 7 8 6
## [12,] 2 3 1
## [13,] 2 11 1
## [14,] 2 11 3
##
## $area
## [1] 262.8885
##
## $vol
## [1] 231.558
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 5 6
## [2,] 3 4 5
## [3,] 7 5 6
## [4,] 7 4 6
## [5,] 1 3 5
## [6,] 1 7 5
## [7,] 8 3 4
## [8,] 8 7 4
## [9,] 2 1 7
## [10,] 2 8 7
## [11,] 2 1 3
## [12,] 2 8 3
##
## $area
## [1] 1212.5
##
## $vol
## [1] 2080.122
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 5 4
## [2,] 7 3 1
## [3,] 7 3 5
## [4,] 2 5 4
## [5,] 2 3 1
## [6,] 2 3 4
## [7,] 2 7 1
## [8,] 2 7 5
##
## $area
## [1] 3.267266e+13
##
## $vol
## [1] 8.507407e+16
##
## $hull
## [,1] [,2] [,3]
## [1,] 6 4 7
## [2,] 2 4 7
## [3,] 2 6 7
## [4,] 5 6 4
## [5,] 5 2 4
## [6,] 5 2 6
##
## $area
## [1] 1011.743
##
## $vol
## [1] 220.5638
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 6 11
## [2,] 2 1 11
## [3,] 4 2 6
## [4,] 8 6 11
## [5,] 8 1 11
## [6,] 3 2 1
## [7,] 3 4 2
## [8,] 7 8 1
## [9,] 7 3 1
## [10,] 7 8 6
## [11,] 7 4 6
## [12,] 7 3 4
##
## $area
## [1] 14463.43
##
## $vol
## [1] 29025.41
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 10 8
## [2,] 9 10 3
## [3,] 9 10 8
## [4,] 5 7 8
## [5,] 4 9 3
## [6,] 2 10 3
## [7,] 2 7 10
## [8,] 2 5 7
## [9,] 1 5 8
## [10,] 1 9 8
## [11,] 1 4 9
## [12,] 1 4 3
## [13,] 1 2 3
## [14,] 1 2 5
##
## $area
## [1] 7988.521
##
## $vol
## [1] 22551.67
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 9 5
## [2,] 10 9 5
## [3,] 10 7 9
## [4,] 4 3 5
## [5,] 4 7 5
## [6,] 4 7 3
## [7,] 8 3 9
## [8,] 8 7 9
## [9,] 8 7 3
## [10,] 6 7 5
## [11,] 6 10 5
## [12,] 6 10 7
##
## $area
## [1] 5748.113
##
## $vol
## [1] 22101.83
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 2 6
## [2,] 3 4 6
## [3,] 3 8 6
## [4,] 3 4 2
## [5,] 3 8 2
## [6,] 1 2 6
## [7,] 1 4 6
## [8,] 1 4 2
##
## $area
## [1] 840.6643
##
## $vol
## [1] 869.9205
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 6 3
## [2,] 5 3 11
## [3,] 8 3 11
## [4,] 8 6 11
## [5,] 8 6 3
## [6,] 9 7 3
## [7,] 9 5 3
## [8,] 2 9 5
## [9,] 2 6 11
## [10,] 2 5 11
## [11,] 2 7 6
## [12,] 2 9 7
##
## $area
## [1] 28.32625
##
## $vol
## [1] 6.732886
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 9 3
## [2,] 7 4 3
## [3,] 10 9 3
## [4,] 8 9 6
## [5,] 8 7 6
## [6,] 5 7 6
## [7,] 5 7 4
## [8,] 11 10 9
## [9,] 11 8 9
## [10,] 11 10 3
## [11,] 11 7 3
## [12,] 11 8 7
## [13,] 2 5 6
## [14,] 2 5 4
## [15,] 2 9 6
## [16,] 2 4 9
##
## $area
## [1] 1024.724
##
## $vol
## [1] 1124.474
##
## $hull
## [,1] [,2] [,3]
## [1,] 11 4 10
## [2,] 11 8 10
## [3,] 6 4 10
## [4,] 2 4 3
## [5,] 2 11 4
## [6,] 2 11 8
## [7,] 9 8 10
## [8,] 9 6 10
## [9,] 5 4 3
## [10,] 5 6 4
## [11,] 7 9 8
## [12,] 7 9 6
## [13,] 7 5 6
## [14,] 7 5 3
## [15,] 7 2 3
## [16,] 7 2 8
##
## $area
## [1] 11169.37
##
## $vol
## [1] 42981.73
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 4 2
## [2,] 3 8 10
## [3,] 11 3 10
## [4,] 11 3 4
## [5,] 7 8 10
## [6,] 7 11 10
## [7,] 7 6 4
## [8,] 7 11 4
## [9,] 9 3 2
## [10,] 9 3 8
## [11,] 9 7 8
## [12,] 9 7 6
## [13,] 1 9 2
## [14,] 1 9 6
## [15,] 1 4 2
## [16,] 1 6 4
##
## $area
## [1] 7377.579
##
## $vol
## [1] 19709.73
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 4 8
## [2,] 7 11 1
## [3,] 7 4 8
## [4,] 6 11 1
## [5,] 6 4 1
## [6,] 6 7 11
## [7,] 6 7 4
## [8,] 5 7 1
## [9,] 5 7 8
## [10,] 5 2 8
## [11,] 5 4 1
## [12,] 5 2 4
##
## $area
## [1] 44.97058
##
## $vol
## [1] 12.71295
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 11 7
## [2,] 4 10 7
## [3,] 4 11 6
## [4,] 4 10 6
## [5,] 2 11 7
## [6,] 2 11 6
## [7,] 9 10 7
## [8,] 9 2 7
## [9,] 1 9 10
## [10,] 3 2 6
## [11,] 3 9 2
## [12,] 3 1 9
## [13,] 3 10 6
## [14,] 3 1 10
##
## $area
## [1] 46.10474
##
## $vol
## [1] 19.64838
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 9 11
## [2,] 3 9 11
## [3,] 10 1 11
## [4,] 8 1 9
## [5,] 8 1 2
## [6,] 4 3 11
## [7,] 4 10 11
## [8,] 4 3 2
## [9,] 4 1 2
## [10,] 4 10 1
## [11,] 5 3 9
## [12,] 5 8 9
## [13,] 5 3 2
## [14,] 6 8 2
## [15,] 6 5 2
## [16,] 6 5 8
##
## $area
## [1] 242.8663
##
## $vol
## [1] 188.1966
##
## $hull
## [,1] [,2] [,3]
## [1,] 6 2 1
## [2,] 6 4 1
## [3,] 11 2 1
## [4,] 11 2 9
## [5,] 11 4 1
## [6,] 11 4 9
## [7,] 5 6 4
## [8,] 5 6 2
## [9,] 5 4 9
## [10,] 5 2 9
##
## $area
## [1] 254.7986
##
## $vol
## [1] 254.7852
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 4 2
## [2,] 11 8 4
## [3,] 5 4 2
## [4,] 5 11 4
## [5,] 3 8 2
## [6,] 3 11 2
## [7,] 3 11 8
## [8,] 10 11 2
## [9,] 10 5 2
## [10,] 10 5 11
##
## $area
## [1] 96.74753
##
## $vol
## [1] 35.0448
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 9 6
## [2,] 5 4 3
## [3,] 8 5 3
## [4,] 1 8 3
## [5,] 1 4 3
## [6,] 1 4 9
## [7,] 10 9 6
## [8,] 10 1 9
## [9,] 10 1 8
## [10,] 7 8 5
## [11,] 7 4 6
## [12,] 7 5 4
## [13,] 7 10 6
## [14,] 7 10 8
##
## $area
## [1] 995.9825
##
## $vol
## [1] 1597.863
##
## $hull
## [,1] [,2] [,3]
## [1,] 2 4 6
## [2,] 2 1 6
## [3,] 2 1 4
## [4,] 5 4 6
## [5,] 5 1 6
## [6,] 5 1 4
##
## $area
## [1] 2044.941
##
## $vol
## [1] 2404.467
##
## $hull
## [,1] [,2] [,3]
## [1,] 8 1 4
## [2,] 5 1 4
## [3,] 3 8 7
## [4,] 3 8 4
## [5,] 3 5 7
## [6,] 3 5 4
## [7,] 6 5 7
## [8,] 6 5 1
## [9,] 6 8 7
## [10,] 6 8 1
##
## $area
## [1] 3068.845
##
## $vol
## [1] 3977.499
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 5 6
## [2,] 2 5 6
## [3,] 3 7 5
## [4,] 3 11 1
## [5,] 3 11 7
## [6,] 4 2 1
## [7,] 4 2 5
## [8,] 4 3 1
## [9,] 4 3 5
## [10,] 8 7 6
## [11,] 8 11 7
## [12,] 10 11 1
## [13,] 10 2 1
## [14,] 10 8 11
## [15,] 10 2 6
## [16,] 10 8 6
##
## $area
## [1] 214.6605
##
## $vol
## [1] 168.8769
##
## $hull
## [,1] [,2] [,3]
## [1,] 11 9 2
## [2,] 11 9 8
## [3,] 3 2 5
## [4,] 3 8 5
## [5,] 3 9 2
## [6,] 3 9 8
## [7,] 1 2 5
## [8,] 1 11 2
## [9,] 6 8 5
## [10,] 6 11 8
## [11,] 6 1 5
## [12,] 6 1 11
##
## $area
## [1] 107.2121
##
## $vol
## [1] 67.94861
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 5 3
## [2,] 7 8 3
## [3,] 7 1 3
## [4,] 11 5 3
## [5,] 11 7 8
## [6,] 11 1 5
## [7,] 11 7 1
## [8,] 2 8 3
## [9,] 2 11 3
## [10,] 2 11 8
##
## $area
## [1] 52.25678
##
## $vol
## [1] 13.29483
##
## $hull
## [,1] [,2] [,3]
## [1,] 9 1 10
## [2,] 6 5 3
## [3,] 6 5 10
## [4,] 7 1 3
## [5,] 7 9 1
## [6,] 7 5 3
## [7,] 7 9 5
## [8,] 4 5 10
## [9,] 4 9 10
## [10,] 4 9 5
## [11,] 11 6 10
## [12,] 11 1 3
## [13,] 11 6 3
## [14,] 2 1 10
## [15,] 2 11 10
## [16,] 2 11 1
##
## $area
## [1] 85.54405
##
## $vol
## [1] 30.50919
##
## $hull
## [,1] [,2] [,3]
## [1,] 3 4 2
## [2,] 1 4 2
## [3,] 1 3 2
## [4,] 1 3 4
##
## $area
## [1] 38.03532
##
## $vol
## [1] 8.209142
##
## $hull
## [,1] [,2] [,3]
## [1,] 7 1 2
## [2,] 11 7 2
## [3,] 3 1 2
## [4,] 3 1 5
## [5,] 3 11 2
## [6,] 8 1 5
## [7,] 4 7 5
## [8,] 4 3 5
## [9,] 4 3 11
## [10,] 6 7 1
## [11,] 6 8 1
## [12,] 6 7 5
## [13,] 6 8 5
## [14,] 10 11 7
## [15,] 10 4 7
## [16,] 10 4 11
##
## $area
## [1] 209.9404
##
## $vol
## [1] 161.0421
##
## $hull
## [,1] [,2] [,3]
## [1,] 5 2 1
## [2,] 7 5 2
## [3,] 3 2 1
## [4,] 4 7 9
## [5,] 4 7 2
## [6,] 4 3 9
## [7,] 4 3 2
## [8,] 8 7 9
## [9,] 8 7 5
## [10,] 11 3 9
## [11,] 11 8 9
## [12,] 11 3 1
## [13,] 11 8 5
## [14,] 11 5 1
##
## $area
## [1] 10644.7
##
## $vol
## [1] 24483.68
##
## $hull
## [,1] [,2] [,3]
## [1,] 11 8 1
## [2,] 9 11 8
## [3,] 4 9 11
## [4,] 10 8 1
## [5,] 10 9 8
## [6,] 2 11 1
## [7,] 2 4 11
## [8,] 6 10 1
## [9,] 6 4 9
## [10,] 6 10 9
## [11,] 3 6 1
## [12,] 3 6 4
## [13,] 3 2 1
## [14,] 3 2 4
##
## $area
## [1] 142.896
##
## $vol
## [1] 81.36266
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 2 11
## [2,] 7 1 2
## [3,] 3 2 11
## [4,] 3 9 11
## [5,] 3 7 2
## [6,] 10 1 11
## [7,] 10 9 11
## [8,] 10 7 1
## [9,] 5 3 9
## [10,] 5 3 7
## [11,] 8 5 9
## [12,] 8 5 7
## [13,] 8 10 9
## [14,] 8 10 7
##
## $area
## [1] 112.6158
##
## $vol
## [1] 69.43801
##
## $hull
## [,1] [,2] [,3]
## [1,] 4 10 5
## [2,] 4 6 5
## [3,] 3 10 9
## [4,] 3 10 5
## [5,] 2 10 9
## [6,] 2 6 9
## [7,] 2 4 10
## [8,] 2 4 6
## [9,] 7 6 5
## [10,] 7 3 5
## [11,] 7 6 9
## [12,] 7 3 9
##
## $area
## [1] 753.3446
##
## $vol
## [1] 503.3326
##
## $hull
## [,1] [,2] [,3]
## [1,] 1 4 2
## [2,] 3 8 2
## [3,] 3 1 2
## [4,] 3 1 8
## [5,] 7 8 2
## [6,] 6 7 8
## [7,] 6 1 8
## [8,] 6 1 4
## [9,] 5 6 4
## [10,] 5 6 7
## [11,] 5 4 2
## [12,] 5 7 2
##
## $area
## [1] 51.04839
##
## $vol
## [1] 5.192115
##
## $hull
## [,1] [,2] [,3]
## [1,] 6 10 1
## [2,] 5 10 1
## [3,] 5 6 1
## [4,] 8 5 10
## [5,] 4 6 7
## [6,] 4 5 6
## [7,] 2 8 10
## [8,] 2 6 7
## [9,] 2 6 10
## [10,] 3 4 7
## [11,] 3 4 5
## [12,] 3 8 5
## [13,] 3 2 7
## [14,] 3 2 8
##
## $area
## [1] 1.034327e+13
##
## $vol
## [1] 2.115084e+17vol_test <- RoachRun_Volume_Post %>%
filter(index == 1) %>%
select(X, Y, Z) %>%
as.matrix() %>%
convhulln("FA")
print(vol_test$vol)## [1] 76.84104Using the 'Hypervolume' package to calculate overlap in occupied volumes by the fish observed in each subsample.
Potential list of tools to use from this package: expectation_convex get_volume get_centroid
for (i in 1:13) {
if (i == 3) {
next
}
if (i == 10) {
next
}
GolfBall_Volume_Pre %>%
filter(index == i) %>%
select(X, Y, Z) %>%
expectation_convex(check.memory = FALSE) %>%
print()
}## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(37.850792, 38.178982, 37.495903, 35.734364, 35.492439,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 3.572776
## Random point density: 15102.540403
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(25.511185, 25.525606, 25.346043, 25.281542, 25.349791,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 0.005369
## Random point density: 10050412.574196
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(25.059107, 25.185759, 25.236544, 25.3286, 25.387087,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 0.010560
## Random point density: 5109527.568638
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(25.34897, 25.42646, 25.363958, 25.197002, 25.156748,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 0.001130
## Random point density: 47763352.560065
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(42.212994, 37.165382, 36.612492, 40.051975, 38.21645,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 6
## Dimensionality: 3
## Volume: 16.181097
## Random point density: 3334.631720
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(-37.342072, -37.40937, -37.436523, -37.699223, -37.347225,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 10
## Dimensionality: 3
## Volume: 1298.271289
## Random point density: 41.561421
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(25.155287, 25.285204, 24.950783, 25.292545, 21.626997,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 17.897031
## Random point density: 3014.913439
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(44.372398, 109.714851, 40.734455, 41.77066, -29.070122,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 4
## Dimensionality: 3
## Volume: 22.316425
## Random point density: 2417.860372
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(-0.106614, -3.117003, -6.070379, -9.2648, -12.205731,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 11
## Dimensionality: 3
## Volume: 34.668470
## Random point density: 1556.399813
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(-37.238571, -37.896358, -37.231556, -37.174625, -37.438675,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 9
## Dimensionality: 3
## Volume: 0.194245
## Random point density: 277783.887519
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)
## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.
## ***** Object of class Hypervolume *****
## Name: Convex expectation for structure(c(-29.73686, -31.581978, -25.707537, -9.604844, 1.236114,
## Method: Adaptive hit and run convex expectation
## Number of data points (after weighting): 5
## Dimensionality: 3
## Volume: 2627.808415
## Random point density: 20.533460
## Number of random points: 53958
## Random point values:
## min: 1.000
## mean: 1.000
## median: 1.000
## max:1.000
## Parameters:
## (none)#look into making a function or creating a list (i.e. list1 = c(HV1, HV2, HV3, etc.) for each forloop scenario)
HV2 <- GolfBall_Volume_Pre %>%
filter(index == "2") %>%
select(X, Y, Z) %>%
as.matrix() %>%
expectation_convex(check.memory = FALSE)## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.HV4 <- GolfBall_Volume_Pre %>%
filter(index == "4") %>%
select(X, Y, Z) %>%
expectation_convex(check.memory = FALSE) ## Calculating linear constraints...done.
## Sampling 53958 random points via hit-and-run, 1000 per chunk...
##
## done.test <- hypervolume_set(HV2, HV4, num.points.max = NULL,
verbose = TRUE, check.memory = FALSE, distance.factor = 1)## Choosing num.points.max=53958 (use a larger value for more accuracy.)
## Using minimum density of 5109527.568638
## Retaining 27431 points in hv1 and 53958 points in hv2.
## Beginning ball queries...
##
## Building tree...
## done.
## Ball query...
##
## done.
##
## Building tree...
## done.
## Ball query...
##
## done.
## Finished ball queries.str(test)## Formal class 'HypervolumeList' [package "hypervolume"] with 1 slot
## ..@ HVList:List of 6
## .. ..$ HV1 :Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Convex expectation for structure(c(25.511185, 25.525606, 25.346043, 25.281542, 25.349791, "
## .. .. .. ..@ Method : chr "Adaptive hit and run convex expectation"
## .. .. .. ..@ Data : num [1:11, 1:3] 25.5 25.5 25.3 25.3 25.3 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.00537
## .. .. .. ..@ PointDensity : num 10050413
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:53958, 1:3] 25.3 25.3 25.4 25.4 25.4 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:53958] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ HV2 :Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Convex expectation for structure(c(25.059107, 25.185759, 25.236544, 25.3286, 25.387087, "
## .. .. .. ..@ Method : chr "Adaptive hit and run convex expectation"
## .. .. .. ..@ Data : num [1:11, 1:3] 25.1 25.2 25.2 25.3 25.4 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.0106
## .. .. .. ..@ PointDensity : num 5109528
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:53958, 1:3] 25.2 25.2 25.4 25.4 25.3 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:53958] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ Intersection:Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Intersection of (Convex expectation for structure(c(25.511185, 25.525606, 25.346043, 25.281542, 25.349791, , Co"| __truncated__
## .. .. .. ..@ Method : chr "Set operations"
## .. .. .. ..@ Data : num [1, 1:3] NaN NaN NaN
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.00282
## .. .. .. ..@ PointDensity : num 10470687
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:29555, 1:3] 25.3 25.2 25.3 25.3 25.2 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:29555] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ Union :Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Union of (Convex expectation for structure(c(25.511185, 25.525606, 25.346043, 25.281542, 25.349791, , Convex ex"| __truncated__
## .. .. .. ..@ Method : chr "Set operations"
## .. .. .. ..@ Data : num [1, 1:3] NaN NaN NaN
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.0131
## .. .. .. ..@ PointDensity : num 5055254
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:66256, 1:3] 25.3 25.4 25.5 25.4 25.4 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:66256] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ Unique_1 :Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Unique component of (Convex expectation for structure(c(25.511185, 25.525606, 25.346043, 25.281542, 25.349791, "| __truncated__
## .. .. .. ..@ Method : chr "Set operations"
## .. .. .. ..@ Data : num [1, 1:3] NaN NaN NaN
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.00255
## .. .. .. ..@ PointDensity : num 5109397
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:13009, 1:3] 25.3 25.4 25.5 25.4 25.4 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:13009] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ Unique_2 :Formal class 'Hypervolume' [package "hypervolume"] with 9 slots
## .. .. .. ..@ Name : chr "Unique component of (Convex expectation for structure(c(25.059107, 25.185759, 25.236544, 25.3286, 25.387087, ) "| __truncated__
## .. .. .. ..@ Method : chr "Set operations"
## .. .. .. ..@ Data : num [1, 1:3] NaN NaN NaN
## .. .. .. ..@ Dimensionality : int 3
## .. .. .. ..@ Volume : num 0.00774
## .. .. .. ..@ PointDensity : num 5017686
## .. .. .. ..@ Parameters : list()
## .. .. .. ..@ RandomPoints : num [1:38825, 1:3] 25.3 25.4 25.3 25.4 25.3 ...
## .. .. .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. .. .. ..$ : NULL
## .. .. .. .. .. ..$ : chr [1:3] "X" "Y" "Z"
## .. .. .. ..@ ValueAtRandomPoints: num [1:38825] 1 1 1 1 1 1 1 1 1 1 ...myvol <- test@HVList$Intersection@Volume
get_volume(HV4)## Convex expectation for structure(c(25.059107, 25.185759, 25.236544, 25.3286, 25.387087,
## 0.01056027