Package 'flfishltm' reference manual

Title:	A package for analyzing FL FWC Freshwater long-term monitoring (LTM) data
Description:	Provides functions for summarizing and analyzing FL FWC Freshwater Fish Long-term Monitoring Data.
Authors:	Jason O'Connor [aut, cre]
Maintainer:	Jason O'Connor <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.4
Built:	2024-11-06 05:13:16 UTC
Source:	https://github.com/jasonO6359/flfishltm

Add zero count data for species missing from sample

Description

Add zero count data for species missing from sample

Usage

add_zero_count(data, sample_ids, species_id, count, format = "long")
add_zero_count(data, sample_ids, species_id, count, format = "long")

Arguments

`data`	dataset as data.frame or tibble
`sample_ids`	sample id variables
`species_id`	species ID field
`count`	field containing species counts
`format`	defaults to "long" - data returned with 1 row per sample_id x species_id, else if not "long" returns data in "wide" format, with one row per sample_id and 1 column per unique species in the species_id.

Value

tibble

Examples

fish_data <- tibble(
  site = c(1:5,3:7),
  species_name = c(rep("BLUE", 5), rep("RESU", 5)),
  ct = c(5, 2, 6, 4, 7,
         8, 6, 7, 2, 9),
  minutes = rep(10, 10))
  
fish_data %>% 
  add_zero_count(sample_ids = c(site,minutes), species_id = species_name, count = ct)
fish_data <- tibble(
  site = c(1:5,3:7),
  species_name = c(rep("BLUE", 5), rep("RESU", 5)),
  ct = c(5, 2, 6, 4, 7,
         8, 6, 7, 2, 9),
  minutes = rep(10, 10))
  
fish_data %>% 
  add_zero_count(sample_ids = c(site,minutes), species_id = species_name, count = ct)

Create age-length key

Description

Generate age-length key from a subsample of aged fish

Usage

age.key(dataset, age_column, length_column, length_unit = NA)
age.key(dataset, age_column, length_column, length_unit = NA)

Arguments

`dataset`	dataset containing at least 2 columns, one containing an age field, and one containing length field
`age_column`	string specifying the name of the column containing ages (in years)
`length_column`	string specifying the name of the column containing lengths
`length_unit`	string specifying the length units options are: "mm" = millimeters; "cm" = centimeters, if none specified defaults to "mm"

Value

returns age-length key

Examples

data(loch_blcr_2020_age)
loch_blcr_alk <- age.key(dataset=loch_blcr_2020_age, age_column="age", length_column="tl", length_unit = 'mm')
data(loch_blcr_2020_age)
loch_blcr_alk <- age.key(dataset=loch_blcr_2020_age, age_column="age", length_column="tl", length_unit = 'mm')

HELPER: check for expected column names

Description

helper function triggers error if expected column names are missing

Usage

check_expected_columns(input_table, expected_names)
check_expected_columns(input_table, expected_names)

Arguments

`input_table`
`expected_names`

Examples

test_data <- data.frame(col1 = 1, col2 = 2, col3 = "A")
check_expected_columns(test_data, c("col1", "col2"))
test_data <- data.frame(col1 = 1, col2 = 2, col3 = "A")
check_expected_columns(test_data, c("col1", "col2"))

Calculate Mean Catch-per-unit Effort (CPUE)

Description

Calculate Mean Catch-per-unit Effort (CPUE)

Usage

cpue(data, count, effort)
cpue(data, count, effort)

Arguments

`data`	`data.frame` or `tibble` containing samples as rows, and species counts as columns along with an effort columnt
`effort`	name of column (unquoted) specifying the sample effort value (typically minutes)
`species`	name of column (unquoted) for species for which CPUE is desired

Value

named vector containing "Mean CPUE" and "SE" estimates

Examples

fish_data <- tibble(
  site = c(1:5,3:7),
  species_name = c(rep("BLUE", 5), rep("RESU", 5)),
  ct = c(5, 2, 6, 4, 7,
         8, 6, 7, 2, 9),
  minutes = rep(10, 10))

fish_data %>% 
  cpue(ct, "minutes") # can specify column name as either a symbol or character
  
fish_data %>% 
  group_by(species_name) %>% # use dplyr::group_by to specify grouping variables.
  cpue(ct, "minutes")
  
# fish_data %>% 
#   add_zero_count(c(site,minutes), species_name, ct) %>% #use [add_zero_count()]to account for missing absence data
#   group_by(species_name) %>% 
#   cpue(ct, "minutes")
fish_data <- tibble(
  site = c(1:5,3:7),
  species_name = c(rep("BLUE", 5), rep("RESU", 5)),
  ct = c(5, 2, 6, 4, 7,
         8, 6, 7, 2, 9),
  minutes = rep(10, 10))

fish_data %>% 
  cpue(ct, "minutes") # can specify column name as either a symbol or character
  
fish_data %>% 
  group_by(species_name) %>% # use dplyr::group_by to specify grouping variables.
  cpue(ct, "minutes")
  
# fish_data %>% 
#   add_zero_count(c(site,minutes), species_name, ct) %>% #use [add_zero_count()]to account for missing absence data
#   group_by(species_name) %>% 
#   cpue(ct, "minutes")

Catch-Per-Unit-Effort Timeseries Plot

Description

Create CPUE timeseries for species and years selected

Usage

cpue_plot(
  datafile,
  speciesList = list(),
  species_size_strata = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1,
  return_object = "data"
)
cpue_plot(
  datafile,
  speciesList = list(),
  species_size_strata = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1,
  return_object = "data"
)

Arguments

`datafile`	data, should be output from ltm.data.summary function
`speciesList`	list of selected species, can specify by common name, scientific name or species code
`species_size_strata`	optional argument, specifies size strata groups for which CPUEs should be calculated, see example below for proper convention.
`years`	list of years to include in figure
`seasons`	—currently functionless, will update in future version, to avoid errors make sure that all seasons in input dataset are the same—
`print`	boolean, if TRUE figure will be saved to file
`figure_filename`	if print=TRUE, figure will be saved to this filename
`fig_scale`	adjust to scale output figure size
`return_object`	string if "data" then function returns the dataset that is used to generate the figure, else if "ggplot" then function returns ggplot object

Value

either summarised data or a ggplot object. Specify the return object type with return_object

Examples

# import and format data
data(newnans)
newn_sum <- ltm.data.summary("Newnans Lake", newnans)
# CPUE plots for bluegill, largemouth bass, and brown bullheads
newnans_cpue <- cpue_plot(newn_sum,
 speciesList=c("BLUE","LMB", "BRBU"),
 years = c(2016:2020))
# CPUE plots by size class
newnans_cpue2 <- cpue_plot(newn_sum,
 speciesList=c("BLUE","LMB","BRBU"),
 species_size_strata = list(
  BLUE = list(
   YOY = c(0,8),
   Quality = c(18,50)),
  LMB = list(
   YOY = c(0,20),
   Quality = c(30,50),
   Trophy = c(51,100))
 ),
 years = c(2016:2020)
)
# import and format data
data(newnans)
newn_sum <- ltm.data.summary("Newnans Lake", newnans)
# CPUE plots for bluegill, largemouth bass, and brown bullheads
newnans_cpue <- cpue_plot(newn_sum,
 speciesList=c("BLUE","LMB", "BRBU"),
 years = c(2016:2020))
# CPUE plots by size class
newnans_cpue2 <- cpue_plot(newn_sum,
 speciesList=c("BLUE","LMB","BRBU"),
 species_size_strata = list(
  BLUE = list(
   YOY = c(0,8),
   Quality = c(18,50)),
  LMB = list(
   YOY = c(0,20),
   Quality = c(30,50),
   Trophy = c(51,100))
 ),
 years = c(2016:2020)
)

Catch-Per-Unit-Effort Timeseries Plot – DEPRECATED May 2024

Description

DEPRECATED as of 5/6/2024 use cpue_plot() instead.

Usage

cpue.plot(
  datafile,
  speciesList = list(),
  species_size_strata = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1
)
cpue.plot(
  datafile,
  speciesList = list(),
  species_size_strata = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1
)

Arguments

`datafile`	data, should be output from ltm.data.summary function
`speciesList`	list of selected species, can specify by common name, scientific name or species code
`species_size_strata`	optional argument, specifies size strata groups for which CPUEs should be calculated, see example below for proper convention.
`years`	list of years to include in figure
`seasons`	—currently functionless, will update in future version, to avoid errors make sure that all seasons in input dataset are the same—
`print`	boolean, if TRUE figure will be saved to file
`figure_filename`	if print=TRUE, figure will be saved to this filename
`fig_scale`	adjust to scale output figure size

Details

Create CPUE timeseries for species and years selected

Value

Returns the summarized data used to construct the plot

Examples

# import and format data
data(newnans)
newn_sum <- ltm.data.summary("Newnans Lake", newnans)
# CPUE plots for bluegill, largemouth bass, and brown bullheads
newnans_cpue <- cpue.plot(newn_sum,
 speciesList=c("BLUE","LMB", "BRBU"),
 years = c(2016:2020))
# CPUE plots by size class
newnans_cpue2 <- cpue.plot(newn_sum,
 speciesList=c("BLUE","LMB","BRBU"),
 species_size_strata = list(
  BLUE = list(
   YOY = c(0,8),
   Quality = c(18,50)),
  LMB = list(
   YOY = c(0,20),
   Quality = c(30,50),
   Trophy = c(51,100))
 ),
 years = c(2016:2020)
)
# import and format data
data(newnans)
newn_sum <- ltm.data.summary("Newnans Lake", newnans)
# CPUE plots for bluegill, largemouth bass, and brown bullheads
newnans_cpue <- cpue.plot(newn_sum,
 speciesList=c("BLUE","LMB", "BRBU"),
 years = c(2016:2020))
# CPUE plots by size class
newnans_cpue2 <- cpue.plot(newn_sum,
 speciesList=c("BLUE","LMB","BRBU"),
 species_size_strata = list(
  BLUE = list(
   YOY = c(0,8),
   Quality = c(18,50)),
  LMB = list(
   YOY = c(0,20),
   Quality = c(30,50),
   Trophy = c(51,100))
 ),
 years = c(2016:2020)
)

HELPER: Create analysis R script for LTM report template

Description

A helper function, intended to be called from within create_ltm_report()

Usage

create_ltm_analysis_template(
  catch_data = "qryFish_standard.csv",
  age_data = NULL,
  habitat_data = "qrySite_comprehensive.csv"
)
create_ltm_analysis_template(
  catch_data = "qryFish_standard.csv",
  age_data = NULL,
  habitat_data = "qrySite_comprehensive.csv"
)

Arguments

`catch_data`	string name of a csv file containing catch data. Data should be in format generated by 'Standard Fish Query' and stored in the /data directory of the ltm report template.
`age_data`	FUTURE FEATURE string name of a csv file containing age data
`habitat_data`	FUTURE FEATURE string name of a csv file containing habitat data. Data should be in format generated by 'Comprehensive Site Data' query and stored in the /data directory of the ltm report template.

Value

string containing r script text

Examples


## Not run: 
create_ltm_analysis_template()

## End(Not run)


## Not run: 
create_ltm_analysis_template()

## End(Not run)

Create a new LTM report template

Description

Create a new LTM report template

Usage

create_ltm_report(dir_name, report_name = dir_name, dir_root = getwd())
create_ltm_report(dir_name, report_name = dir_name, dir_root = getwd())

Arguments

`dir_name`	string specifying the name of the report directory to create
`report_name`	string specifying the report name, used in file names so avoid spaces defaults to name specified in directory
`dir_root`	string specifying the path to the root directory in which to create the report project. Defaults to the current working directory.

Value

boolean - returns TRUE if the function executes without error

Examples


## Not run: 
create_ltm_report(dir_name = "test_report", 
                  report_name ="LakeDoe_LMB_2020")

## End(Not run)

## Not run: 
create_ltm_report(dir_name = "test_report", 
                  report_name ="LakeDoe_LMB_2020")

## End(Not run)

INTERNAL: Create a deprecation warning

Description

INTERNAL: Create a deprecation warning

Usage

deprecate(new_function)
deprecate(new_function)

Value

warning message

Examples

deprecated_function <- function() {
  deprecate("new_function")
}
deprecated_function()
deprecated_function <- function() {
  deprecate("new_function")
}
deprecated_function()

LTM Fish Lookup Table

Description

Species lookup table for freshwater fish.

Usage

fish_table
fish_table

Format

A dataframe with 321 obs and 7 variables

F_code: Species field code
common_name: common name
scientific_name: binomial scientific name
eco_type: categorical variable grouping species as "Exotic", "Fresh", or "Salt"
family_scientific: family name, scientific (latin)
family_common: family name, common
fwc_id: unique identification number for species, can be used as primary key

flfishltm: A package for summarizing and analyzing FL FWC LTM Data

Description

ADD PACKAGE DETAILS See

Details

vignette("flfishltm", package="flfishltm")

intro to flfishltm for more details

flfishltm functions

ltm.data.summary

species.history

len.dist

Extract waypoint data from .gpx files

Description

Extract waypoint data from .gpx files

Usage

get_wpt(gpx)
get_wpt(gpx)

Arguments

gpx

A .gpx file generated using a Garmin GPSmap 78s

Value

data frame containing point ID, lat, long and elevation

Examples

# df <- get_wpt('Waypoints_01-DEC-22.gpx')
# df <- get_wpt('Waypoints_01-DEC-22.gpx')

Guild Composition Plot

Description

Create Guild Composition plot

Usage

guild.comp(
  ltm_dataset,
  waterbody_list = list(),
  years = list(),
  color_scale = "default",
  save = FALSE
)
guild.comp(
  ltm_dataset,
  waterbody_list = list(),
  years = list(),
  color_scale = "default",
  save = FALSE
)

Arguments

`ltm_dataset`	data, should be output from ltm.data.summary function
`waterbody_list`	—currently functionless, will update in future version—
`years`	list of years to include in figure
`color_scale`	color scale for output figure defaults to default r color scale, other options include "grayscale" for black and white, and "viridis" for viridis magma scale
`save`	boolean, if TRUE figure will be saved to file

Value

returns summarize data used to construct figures

Examples

data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_guild <- guild.comp(newn_sum)
data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_guild <- guild.comp(newn_sum)

Helper function to add blank rows to account for missing years in a yearly summary table

Description

currently used within ltm.data.summary()

Usage

helper_addgapyears(sumTable)
helper_addgapyears(sumTable)

Arguments

sumTable

data.frame or tibble

Value

table

Examples


test_data <- structure(list(
Year = c("2006 - 2007", "2007 - 2008", "2008 - 2009","2009 - 2010", 
          "2010 - 2011", "2012 - 2013", "2013 - 2014", "2014 - 2015", 
          "2015 - 2016", "2019 - 2020", "2020 - 2021"),
yr = c(2006, 2007, 2008, 2009, 2010, 2012,2013, 2014, 2015, 2019, 2020),
BOW = c(0.226666666666667, 0.24, 0.188, 0.252, 0.132, 0.02, 0.116, 0.112, 
        0.064, 0.26, 0.128), LMB = c(0.00666666666666667, 0.0733333333333333, 
        0.064, 0.084, 0.04, 0.02, 0.048, 0.148, 0.212, 0.136, 0.12)), 
        row.names = c(NA, -11L), class = c("tbl_df", "tbl", "data.frame"))
        
helper_addgapyears(test_data) 
 

test_data <- structure(list(
Year = c("2006 - 2007", "2007 - 2008", "2008 - 2009","2009 - 2010", 
          "2010 - 2011", "2012 - 2013", "2013 - 2014", "2014 - 2015", 
          "2015 - 2016", "2019 - 2020", "2020 - 2021"),
yr = c(2006, 2007, 2008, 2009, 2010, 2012,2013, 2014, 2015, 2019, 2020),
BOW = c(0.226666666666667, 0.24, 0.188, 0.252, 0.132, 0.02, 0.116, 0.112, 
        0.064, 0.26, 0.128), LMB = c(0.00666666666666667, 0.0733333333333333, 
        0.064, 0.084, 0.04, 0.02, 0.048, 0.148, 0.212, 0.136, 0.12)), 
        row.names = c(NA, -11L), class = c("tbl_df", "tbl", "data.frame"))
        
helper_addgapyears(test_data)

Check if Outlier

Description

Checks whether value is an outlier

Usage

is.outlier(x)
is.outlier(x)

Arguments

`x`	vector of numerical values

Value

boolean

Examples

numbers = c(1:10,1:10,1:10,1000)
numbers_outlier <- is.outlier(numbers)
numbers[numbers_outlier]#'
numbers = c(1:10,1:10,1:10,1000)
numbers_outlier <- is.outlier(numbers)
numbers[numbers_outlier]#'

Standard Fish Query Test

Description

Check if data is from LTM Standard Fish Query

Usage

is.StdFishQry(x)
is.StdFishQry(x)

Arguments

x

dataset

Value

true/false

Examples

data(newnans)
is.StdFishQry(newnans)
####
is.StdFishQry(newnans[, -1])
###
new2 <- newnans
new2$newcolumn <- NA
is.StdFishQry(new2)
data(newnans)
is.StdFishQry(newnans)
####
is.StdFishQry(newnans[, -1])
###
new2 <- newnans
new2$newcolumn <- NA
is.StdFishQry(new2)

Length Distribution Plot

Description

Create Length Distribution histograms for species and years selected

Usage

len_dist(
  datafile,
  speciesList = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1,
  return_object = "data"
)
len_dist(
  datafile,
  speciesList = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1,
  return_object = "data"
)

Arguments

`datafile`	data, should be output from ltm.data.summary function
`speciesList`	list of selected species, can specify by common name, scientific name or species code
`years`	list of years to include in figure
`seasons`	—currently functionless, will update in future version, to avoid errors make sure that all seasons in input dataset are the same—
`print`	boolean, if TRUE figure will be saved to file
`figure_filename`	if print=TRUE, figure will be saved to this filename
`fig_scale`	adjust to scale output figure size
`return_object`	string if "data" then function returns the dataset that is used to generate the figure, else if "ggplot" then function returns ggplot object

Value

either summarised data or a ggplot object. Specify the return object type with return_object

Examples

data(newnans)
newn_sum <- ltm.data.summary("Newnans",newnans)
newnans_LD <- len_dist(newn_sum, speciesList=c("BLUE","LMB", "BRBU"), years = c(2016:2020))
data(newnans)
newn_sum <- ltm.data.summary("Newnans",newnans)
newnans_LD <- len_dist(newn_sum, speciesList=c("BLUE","LMB", "BRBU"), years = c(2016:2020))

Length Distribution Plot – DEPRECATED May 2024

Description

Create Length Distribution histograms for species and years selected

Usage

len.dist(
  datafile,
  speciesList = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1
)
len.dist(
  datafile,
  speciesList = list(),
  years = list(),
  seasons = list(),
  print = FALSE,
  figure_filename = NA,
  fig_scale = 1
)

Arguments

`datafile`	data, should be output from ltm.data.summary function
`speciesList`	list of selected species, can specify by common name, scientific name or species code
`years`	list of years to include in figure
`seasons`	—currently functionless, will update in future version, to avoid errors make sure that all seasons in input dataset are the same—
`print`	boolean, if TRUE figure will be saved to file
`figure_filename`	if print=TRUE, figure will be saved to this filename
`fig_scale`	adjust to scale output figure size

Value

Returns the summarized data used to construct the histogram plot

Examples

data(newnans)
newn_sum <- ltm.data.summary("Newnans",newnans)
newnans_LD <- len.dist(newn_sum, speciesList=c("BLUE","LMB", "BRBU"), years = c(2016:2020))
data(newnans)
newn_sum <- ltm.data.summary("Newnans",newnans)
newnans_LD <- len.dist(newn_sum, speciesList=c("BLUE","LMB", "BRBU"), years = c(2016:2020))

Lochloosa Black Crappie Fall 2020 Aged Subsample

Description

Contains total length and ages for the aged subsample of Black Crappie collected on Lochloosa Lake, FL during fall 2020 LTM trawl survyes

Usage

loch_blcr_2020_age
loch_blcr_2020_age

Format

A dataframe with 103 obs and 5 variables

WaterBody: Waterbody where collection was made
collected: collection date
Species: Species code; BLCR = Black Crappie Pomoxis nigromaculatus
tl: total length in mm
age: age determined from otolith reads assuming Jan 1 birthdate, in yrs

LTM Data Summary

Description

Summarize raw LTM data from "Standard Fish Query"

Usage

ltm.data.summary(
  waterbodyname = "No Waterbody Specified",
  file,
  outtables = 0,
  printfigs = 0,
  print_directory = getwd()
)
ltm.data.summary(
  waterbodyname = "No Waterbody Specified",
  file,
  outtables = 0,
  printfigs = 0,
  print_directory = getwd()
)

Arguments

`waterbodyname`	The name of the selected Waterbody as you would like it to appear in filenames and figure titles
`file`	path to the raw query file, should be a .csv file
`outtables`	option to specify which summary tables to save to working directory, default 0 exports no summary tables
`printfigs`	option to specify which figures to save to print directory, default 0 prints no figures, 1 prints all figures
`print_directory`	specify directory to save exported figures

Value

list of summarized datasets

Examples

#Import demo data for Newnans Lake
data(newnans)
#dplyr::summarise Newnans Lake data
newn_sum <- ltm.data.summary("Newnans Lake", file = newnans)
# Access summary tables within the newn_sum object
newn_sum$SpeciesList
newn_sum$CPUE_number
newn_sum$RawData
newn_sum$Comp_num
#Import demo data for Newnans Lake
data(newnans)
#dplyr::summarise Newnans Lake data
newn_sum <- ltm.data.summary("Newnans Lake", file = newnans)
# Access summary tables within the newn_sum object
newn_sum$SpeciesList
newn_sum$CPUE_number
newn_sum$RawData
newn_sum$Comp_num

Import data from FWC LTM Query

Description

Imports LTM Query data and performs a set of checks that will output warnings if file does not contain expected input, or if file contains data that may not work with 'flfishltm' functions

Usage

ltm.import(datafile)
ltm.import(datafile)

Arguments

datafile

an ltm query dataset, can be either ".csv" or ".Rdata" format

Value

imported dataset

Examples


data(newnans)
newn <- ltm.import(newnans)
data(newnans)
newn <- ltm.import(newnans)

LTM Fish Community Data for Newnans Lake, FL

Description

Dataset containing fish collection for Newnans Lake for the period 2006- 2020. Data is output of "Standard Fish Query"

Usage

newnans
newnans

Format

A data frame with 13,326 rows and 40 variables:

ID: unique record ID #
WaterBody: lake, river or other resource where data were collected, Factor
County: FL County of Collection
Date: collection date
Time: collection time
SamplingType: Sampling protocol: "Standard", "Haphazard", "NS-Random", etc TODO FILL IN ALL PROTOCOLS
Target: Target species
Season: Sampling season
Gear: Sampling gear used
Effort: Effort, expressed as time in seconds
DistanceM: Distance covered during sample, in meters
Site: site identification name or number
BeginLat: Latitude of starting location, decimal degrees
BeginLong: Longitude of starting location, decimal degrees

Predict Weigth at Length

Description

Predict Weigth at Length

Usage

predict_wt(species_code, tl, error = 0.5)
predict_wt(species_code, tl, error = 0.5)

Arguments

`species_code`	FWC LTM species code
`tl`	Total length (mm)
`error`	numeric error scale expressed as a % of estimate, defaults to 0.5

Value

tibble containing tl, prediction, upper and lower bounds

Examples

predict_wt("BLUE", 150)
predict_wt("BLUE", 150)
predict_wt("BLUE", 150)
predict_wt("BLUE", 150)

Santa Fe River Fish Community Electrofishing Data

Description

Dataset containing fish collection for Santa Fe River for the period 2006- 2021. Data is output of "Standard Fish Query"

Usage

sfr_fish
sfr_fish

Format

A dataframe with 321 obs and 7 variables

ID: unique record ID #
WaterBody: lake, river or other resource where data were collected, Factor
County: FL County of Collection
Date: collection date
Time: collection time
SamplingType: Sampling protocol: "Standard", "Haphazard", "NS-Random", etc TODO FILL IN ALL PROTOCOLS
Target: Target species
Season: Sampling season
Gear: Sampling gear used
Effort: Effort, expressed as time in seconds
DistanceM: Distance covered during sample, in meters
Site: site identification name or number
BeginLat: Latitude of starting location, decimal degrees
BeginLong: Longitude of starting location, decimal degrees

Details

** NEED TO UPDATE METADATA **

Species detection history

Description

Create Species detection history plot

Usage

species_history(
  LTMdataset,
  exclude_species = c(),
  save = FALSE,
  filename = NA,
  fig_res = NA,
  fig_width = NA,
  fig_height = NA,
  return_object = "data"
)
species_history(
  LTMdataset,
  exclude_species = c(),
  save = FALSE,
  filename = NA,
  fig_res = NA,
  fig_width = NA,
  fig_height = NA,
  return_object = "data"
)

Arguments

`exclude_species`	vector of string identifying the species names to exclude from the output figure. Should match the spelling of the species names in the figure.
`save`	boolean, if TRUE figure will be saved to file
`filename`	= filename for saved figure
`fig_res`	= specify figure resolution
`fig_width`	= specify figure width
`fig_height`	= specify figure height
`return_object`	string if "data" then function returns the dataset that is used to generate the figure, else if "ggplot" then function returns ggplot object
`ltm_dataset`	data, should be output from ltm.data.summary function

Value

either summarised data or a ggplot object. Specify the return object type with return_object

Examples

data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_dethist <- species.history(newn_sum,save=FALSE)
data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_dethist <- species.history(newn_sum,save=FALSE)

Species detection history – DEPRECATED Aug 2023

Description

DEPRECATED as of 8/22/2023 use species_history() instead.

Usage

species.history(
  LTMdataset,
  exclude_species = c(),
  save = FALSE,
  filename = NA,
  fig_res = NA,
  fig_width = NA,
  fig_height = NA,
  return_object = "data"
)
species.history(
  LTMdataset,
  exclude_species = c(),
  save = FALSE,
  filename = NA,
  fig_res = NA,
  fig_width = NA,
  fig_height = NA,
  return_object = "data"
)

Arguments

`save`	boolean, if TRUE figure will be saved to file
`filename`	= filename for saved figure
`fig_res`	= specify figure resolution
`fig_width`	= specify figure width
`fig_height`	= specify figure height
`return_object`	string if "data" then function returns the dataset that is used to generate the figure, else if "ggplot" then function returns ggplot object
`ltm_dataset`	data, should be output from ltm.data.summary function

Details

Create Species detection history plot

Value

either summarised data or a ggplot object. Specify the return object type with return_object

Examples

data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_dethist <- species.history(newn_sum,save=FALSE)
data(newnans)
newn_sum <- ltm.data.summary(file=newnans)
newnans_dethist <- species.history(newn_sum,save=FALSE)

Custom LTM theme for ggplot

Description

Custom LTM theme for ggplot

Usage

theme_ltm()
theme_ltm()

Value

theme for ggplot figures

Examples

data(newnans)
ggplot2::ggplot(data=newnans, ggplot2::aes(x=TotalLength,y=TotalWeight)) + 
ggplot2::geom_point() +
theme_ltm()
data(newnans)
ggplot2::ggplot(data=newnans, ggplot2::aes(x=TotalLength,y=TotalWeight)) + 
ggplot2::geom_point() +
theme_ltm()

Calculate Relative Weight

Description

Calculates the Relative Weight for a sample of fish with both lengths and weights

Usage

Wr(dataset = NA, length_col = NA, weight_col = NA, a = NA, b = NA)
Wr(dataset = NA, length_col = NA, weight_col = NA, a = NA, b = NA)

Arguments

`dataset`	dataset containing at least 2 columns, one containing a length field, and one containing weight field
`length_col`	string specifying the name of the column containing length
`weight_col`	string specifying the name of the column containing weights
`a`	value of the alpha parameter for relative weight calculation (specific to species of interest)
`b`	value of the beta parameter for relative weight calculation (specific to species of interest)

Value

returns vector of relative weights

Examples

library(dplyr)
data(newnans)
#Calculate relative weight for all LMB collected from Newnans Lake
newn_lmb <- newnans %>% dplyr::filter(SpeciesCode == "LMB" )
newnans_lmb_Wr <- Wr(dataset = newn_lmb, 
                 length_col = "TotalLength",
                 weight_col = "TotalWeight",
                 a = .3245,
                 b = 3.21)
library(dplyr)
data(newnans)
#Calculate relative weight for all LMB collected from Newnans Lake
newn_lmb <- newnans %>% dplyr::filter(SpeciesCode == "LMB" )
newnans_lmb_Wr <- Wr(dataset = newn_lmb, 
                 length_col = "TotalLength",
                 weight_col = "TotalWeight",
                 a = .3245,
                 b = 3.21)

Package 'flfishltm'

Help Index

Add zero count data for species missing from sample

Description

Usage

Arguments

Value

Examples

Create age-length key

Description

Usage

Arguments

Value

Examples

HELPER: check for expected column names

Description

Usage

Arguments

Examples

Calculate Mean Catch-per-unit Effort (CPUE)

Description

Usage

Arguments

Value

Examples

Catch-Per-Unit-Effort Timeseries Plot

Description

Usage

Arguments

Value

Examples

Catch-Per-Unit-Effort Timeseries Plot – DEPRECATED May 2024

Description

Usage

Arguments

Details

Value

Examples

HELPER: Create analysis R script for LTM report template

Description

Usage

Arguments

Value

Examples

Create a new LTM report template

Description

Usage

Arguments

Value

Examples

INTERNAL: Create a deprecation warning

Description

Usage

Value

Examples

LTM Fish Lookup Table

Description

Usage

Format

flfishltm: A package for summarizing and analyzing FL FWC LTM Data

Description

Details

flfishltm functions

Extract waypoint data from .gpx files

Description

Usage

Arguments

Value

Examples

Guild Composition Plot

Description

Usage

Arguments

Value

Examples

Helper function to add blank rows to account for missing years in a yearly summary table

Description

Usage

Arguments

Value