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Read file for the download date.

 fibdata
-#> # A tibble: 74,872 × 18
+#> # A tibble: 76,502 × 18
 #>    area   epchc_station class SampleTime             yr    mo Latitude Longitude
 #>    <chr>          <dbl> <chr> <dttm>              <dbl> <dbl>    <dbl>     <dbl>
-#>  1 Hills…             2 3M    2023-05-10 13:48:00  2023     5     27.9     -82.5
-#>  2 Hills…             6 3M    2023-05-16 09:32:00  2023     5     27.9     -82.5
-#>  3 Hills…             7 3M    2023-05-16 09:41:00  2023     5     27.9     -82.5
-#>  4 Hills…             8 3M    2023-05-16 11:50:00  2023     5     27.9     -82.4
-#>  5 Middl…             9 2     2023-05-16 11:12:00  2023     5     27.8     -82.4
-#>  6 Middl…            11 2     2023-05-16 09:55:00  2023     5     27.8     -82.5
-#>  7 Middl…            13 2     2023-05-16 10:07:00  2023     5     27.8     -82.5
-#>  8 Middl…            14 2     2023-05-16 10:49:00  2023     5     27.8     -82.5
-#>  9 Middl…            16 2     2023-05-23 10:19:00  2023     5     27.7     -82.5
-#> 10 Middl…            19 2     2023-05-23 10:36:00  2023     5     27.7     -82.6
-#> # ℹ 74,862 more rows
+#>  1 Hills…             2 3M    2024-01-08 14:46:00  2024     1     27.9     -82.5
+#>  2 Hills…             6 3M    2024-01-16 09:50:00  2024     1     27.9     -82.5
+#>  3 Hills…             7 3M    2024-01-16 10:07:00  2024     1     27.9     -82.5
+#>  4 Hills…             8 3M    2024-01-16 12:50:00  2024     1     27.9     -82.4
+#>  5 Middl…             9 2     2024-01-16 11:58:00  2024     1     27.8     -82.4
+#>  6 Middl…            11 2     2024-01-16 10:20:00  2024     1     27.8     -82.5
+#>  7 Middl…            13 2     2024-01-16 10:36:00  2024     1     27.8     -82.5
+#>  8 Middl…            14 2     2024-01-16 11:26:00  2024     1     27.8     -82.5
+#>  9 Middl…            16 2     2024-01-30 09:10:00  2024     1     27.7     -82.5
+#> 10 Middl…            19 2     2024-01-30 09:25:00  2024     1     27.7     -82.6
+#> # ℹ 76,492 more rows
 #> # ℹ 10 more variables: Total_Depth_m <dbl>, Sample_Depth_m <dbl>, ecoli <dbl>,
 #> #   ecoli_q <chr>, ecocci <dbl>, ecocci_q <chr>, fcolif <dbl>, fcolif_q <chr>,
 #> #   totcol <dbl>, totcol_q <chr>
@@ -222,20 +222,20 @@

Analyzecol column of the output.

 anlz_fibmap(fibdata)
-#> # A tibble: 74,872 × 12
+#> # A tibble: 76,502 × 12
 #>    area    epchc_station class    yr    mo Latitude Longitude ecoli ecocci ind  
 #>    <chr>           <dbl> <chr> <dbl> <dbl>    <dbl>     <dbl> <dbl>  <dbl> <chr>
-#>  1 Hillsb…             2 3M     2023     5     27.9     -82.5    NA      4 Ente…
-#>  2 Hillsb…             6 3M     2023     5     27.9     -82.5    NA      2 Ente…
-#>  3 Hillsb…             7 3M     2023     5     27.9     -82.5    NA      2 Ente…
-#>  4 Hillsb…             8 3M     2023     5     27.9     -82.4    NA      2 Ente…
-#>  5 Middle…             9 2      2023     5     27.8     -82.4    NA      2 Ente…
-#>  6 Middle…            11 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  7 Middle…            13 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  8 Middle…            14 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  9 Middle…            16 2      2023     5     27.7     -82.5    NA      2 Ente…
-#> 10 Middle…            19 2      2023     5     27.7     -82.6    NA      2 Ente…
-#> # ℹ 74,862 more rows
+#>  1 Hillsb…             2 3M     2024     1     27.9     -82.5    NA     24 Ente…
+#>  2 Hillsb…             6 3M     2024     1     27.9     -82.5    NA      2 Ente…
+#>  3 Hillsb…             7 3M     2024     1     27.9     -82.5    NA      2 Ente…
+#>  4 Hillsb…             8 3M     2024     1     27.9     -82.4    NA     10 Ente…
+#>  5 Middle…             9 2      2024     1     27.8     -82.4    NA      2 Ente…
+#>  6 Middle…            11 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  7 Middle…            13 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  8 Middle…            14 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  9 Middle…            16 2      2024     1     27.7     -82.5    NA      2 Ente…
+#> 10 Middle…            19 2      2024     1     27.7     -82.6    NA      2 Ente…
+#> # ℹ 76,492 more rows
 #> # ℹ 2 more variables: cat <fct>, col <chr>

The thresholds are from EPC and are as follows for E. coli or Enterococcus.

@@ -247,19 +247,19 @@

Analyze E. coli -< 126 +126 - 409 E. coli -126 - 409 +410 - 999 E. coli -> 999 +< 126 E. coli -410 - 999 +> 999 Enterococcus @@ -283,21 +283,21 @@

Analyze 
-anlz_fibmap(fibdata, yrsel = 2022, mosel = 7)
-#> # A tibble: 195 × 12
+anlz_fibmap(fibdata, yrsel = 2023, mosel = 7)
+#> # A tibble: 207 × 12
 #>    area    epchc_station class    yr    mo Latitude Longitude ecoli ecocci ind  
 #>    <chr>           <dbl> <chr> <dbl> <dbl>    <dbl>     <dbl> <dbl>  <dbl> <chr>
-#>  1 Hillsb…             2 3M     2022     7     27.9     -82.5    NA     20 Ente…
-#>  2 Hillsb…             6 3M     2022     7     27.9     -82.5    NA      2 Ente…
-#>  3 Hillsb…             7 3M     2022     7     27.9     -82.5    NA      2 Ente…
-#>  4 Hillsb…             8 3M     2022     7     27.9     -82.4    NA      2 Ente…
-#>  5 Middle…             9 2      2022     7     27.8     -82.4    NA      2 Ente…
-#>  6 Middle…            11 2      2022     7     27.8     -82.5    NA      2 Ente…
-#>  7 Middle…            13 2      2022     7     27.8     -82.5    NA      2 Ente…
-#>  8 Middle…            14 2      2022     7     27.8     -82.5    NA      2 Ente…
-#>  9 Middle…            16 2      2022     7     27.7     -82.5    NA      2 Ente…
-#> 10 Middle…            19 2      2022     7     27.7     -82.6    NA      2 Ente…
-#> # ℹ 185 more rows
+#>  1 Hillsb…             2 3M     2023     7     27.9     -82.5    NA    800 Ente…
+#>  2 Hillsb…             6 3M     2023     7     27.9     -82.5    NA      2 Ente…
+#>  3 Hillsb…             7 3M     2023     7     27.9     -82.5    NA      2 Ente…
+#>  4 Hillsb…             8 3M     2023     7     27.9     -82.4    NA      2 Ente…
+#>  5 Middle…             9 2      2023     7     27.8     -82.4    NA      2 Ente…
+#>  6 Middle…            11 2      2023     7     27.8     -82.5    NA      2 Ente…
+#>  7 Middle…            13 2      2023     7     27.8     -82.5    NA      2 Ente…
+#>  8 Middle…            14 2      2023     7     27.8     -82.5    NA      2 Ente…
+#>  9 Middle…            16 2      2023     7     27.7     -82.5    NA      2 Ente…
+#> 10 Middle…            19 2      2023     7     27.7     -82.6    NA      2 Ente…
+#> # ℹ 197 more rows
 #> # ℹ 2 more variables: cat <fct>, col <chr>

The areasel argument can indicate either "Alafia" or "Hillsborough" to select data for @@ -323,21 +323,21 @@

Analyze"Palm River", "Tampa Bypass Canal", or "Valrico Lake".

-anlz_fibmap(fibdata, yrsel = 2022, mosel = 7, areasel = 'Hillsborough River')
-#> # A tibble: 38 × 12
+anlz_fibmap(fibdata, yrsel = 2023, mosel = 7, areasel = 'Hillsborough River')
+#> # A tibble: 47 × 12
 #>    area    epchc_station class    yr    mo Latitude Longitude ecoli ecocci ind  
 #>    <chr>           <dbl> <chr> <dbl> <dbl>    <dbl>     <dbl> <dbl>  <dbl> <chr>
-#>  1 Hillsb…             2 3M     2022     7     27.9     -82.5    NA     20 Ente…
-#>  2 Hillsb…           105 3M     2022     7     28.0     -82.4    NA      7 Ente…
-#>  3 Hillsb…           106 1      2022     7     28.1     -82.4    73     80 E. c…
-#>  4 Lake T…           107 3F     2022     7     28.0     -82.3   240    700 E. c…
-#>  5 Hillsb…           108 3F     2022     7     28.1     -82.2    60    210 E. c…
-#>  6 Lake T…           118 3F     2022     7     28.1     -82.3     7     10 E. c…
-#>  7 Hillsb…           120 3F     2022     7     28.1     -82.4   220    300 E. c…
-#>  8 Lake T…           135 3F     2022     7     28.1     -82.3     7      7 E. c…
-#>  9 Hillsb…           137 3M     2022     7     28.0     -82.5    NA     40 Ente…
-#> 10 Hillsb…           143 3F     2022     7     28.1     -82.1   327   1240 E. c…
-#> # ℹ 28 more rows
+#>  1 Hillsb…             2 3M     2023     7     27.9     -82.5    NA    800 Ente…
+#>  2 Hillsb…           105 3M     2023     7     28.0     -82.4    NA    128 Ente…
+#>  3 Hillsb…           106 1      2023     7     28.1     -82.4   144    232 E. c…
+#>  4 Lake T…           107 3F     2023     7     28.0     -82.3   570   1550 E. c…
+#>  5 Hillsb…           108 3F     2023     7     28.1     -82.2   187    276 E. c…
+#>  6 Lake T…           118 3F     2023     7     28.1     -82.3     4      7 E. c…
+#>  7 Hillsb…           120 3F     2023     7     28.1     -82.4   100    410 E. c…
+#>  8 Lake T…           135 3F     2023     7     28.1     -82.3     4      4 E. c…
+#>  9 Hillsb…           137 3M     2023     7     28.0     -82.5    NA    680 Ente…
+#> 10 Hillsb…           143 3F     2023     7     28.1     -82.1   800   1333 E. c…
+#> # ℹ 37 more rows
 #> # ℹ 2 more variables: cat <fct>, col <chr>

The anlz_fibmatrix() function creates a summary of FIB categories by station and year as output for the @@ -354,7 +354,7 @@

Analyzecat).

 anlz_fibmatrix(fibdata)
-#> # A tibble: 8,541 × 4
+#> # A tibble: 8,800 × 4
 #>       yr epchc_station gmean cat  
 #>    <dbl> <fct>         <dbl> <chr>
 #>  1  1985 2             52.4  B    
@@ -367,7 +367,7 @@ 
Analyze#>  8  1985 14             6.35 A    
 #>  9  1985 16             4    A    
 #> 10  1985 19             4    A    
-#> # ℹ 8,531 more rows
+#> # ℹ 8,790 more rows

Show @@ -380,17 +380,17 @@

Show month, and area. Unlike anlz_fibmap(), the yrsel and mosel arguments are required.

-show_fibmap(fibdata, yrsel = 2022, mosel = 7, areasel = NULL)
-
-

Sites for the Hillsborough or Alafia river basins can be shown using +show_fibmap(fibdata, yrsel = 2023, mosel = 7, areasel = NULL)

+
+

Sites for the Hillsborough or Alafia river basins can be shown using areasel argument.

-show_fibmap(fibdata, yrsel = 2022, mosel = 7, areasel = 'Hillsborough River')
-
-
-show_fibmap(fibdata, yrsel = 2021, mosel = 6, areasel = 'Alafia River')
-
-

Additional information about a site can be seen by placing the cursor +show_fibmap(fibdata, yrsel = 2023, mosel = 7, areasel = 'Hillsborough River') +

+
+show_fibmap(fibdata, yrsel = 2023, mosel = 7, areasel = 'Alafia River')
+
+

Additional information about a site can be seen by placing the cursor over a location. A map inset can also be seen by clicking the arrow in the button left.

The show_fibmatrix() function creates a stoplight @@ -439,13 +439,13 @@

Show rows.

 show_fibmatrix(fibdata, asreact = TRUE)
-
-

A plotly (interactive, dynamic plot) can be returned by setting the +

+

A plotly (interactive, dynamic plot) can be returned by setting the plotly argument to TRUE.

 show_fibmatrix(fibdata, plotly = TRUE)
-
-

From these plots, we can view locations and general trends in FIB +

+

From these plots, we can view locations and general trends in FIB data for the Hillsborough and Alafia river basins. Additional functions may be added in the future to evaluate FIB data at other locations.

diff --git a/articles/fib_files/figure-html/unnamed-chunk-11-1.png b/articles/fib_files/figure-html/unnamed-chunk-11-1.png index 6741322a..01487ea1 100644 Binary files a/articles/fib_files/figure-html/unnamed-chunk-11-1.png and b/articles/fib_files/figure-html/unnamed-chunk-11-1.png differ diff --git a/articles/fib_files/figure-html/unnamed-chunk-12-1.png b/articles/fib_files/figure-html/unnamed-chunk-12-1.png index d97c0fe3..0840b89d 100644 Binary files a/articles/fib_files/figure-html/unnamed-chunk-12-1.png and b/articles/fib_files/figure-html/unnamed-chunk-12-1.png differ diff --git a/articles/fib_files/figure-html/unnamed-chunk-13-1.png b/articles/fib_files/figure-html/unnamed-chunk-13-1.png index 189ddd35..6a7e6673 100644 Binary files a/articles/fib_files/figure-html/unnamed-chunk-13-1.png and b/articles/fib_files/figure-html/unnamed-chunk-13-1.png differ diff --git a/articles/intro.html b/articles/intro.html index 5c3e11a4..712b6645 100644 --- a/articles/intro.html +++ b/articles/intro.html @@ -242,20 +242,20 @@

Read them from the assigned object:

 phytodata
-#> # A tibble: 23,848 × 8
+#> # A tibble: 21,143 × 8
 #>    epchc_station Date       name           units  count yrqrt         yr mo   
 #>    <chr>         <date>     <chr>          <chr>  <dbl> <date>     <dbl> <ord>
-#>  1 11            1975-07-23 Cyanobacteria  /0.1mL     0 1975-07-01  1975 Jul  
-#>  2 11            1976-01-07 Cyanobacteria  /0.1mL     1 1976-01-01  1976 Jan  
+#>  1 11            1975-07-23 other          /0.1mL     0 1975-07-01  1975 Jul  
+#>  2 11            1976-01-07 other          /0.1mL     1 1976-01-01  1976 Jan  
 #>  3 11            1977-01-05 other          /0.1mL     1 1977-01-01  1977 Jan  
-#>  4 11            1977-04-20 other          /0.1mL     1 1977-04-01  1977 Apr  
-#>  5 11            1977-04-20 Tripos hircus  /0.1mL     1 1977-04-01  1977 Apr  
+#>  4 11            1977-04-20 Tripos hircus  /0.1mL     1 1977-04-01  1977 Apr  
+#>  5 11            1977-04-20 other          /0.1mL     1 1977-04-01  1977 Apr  
 #>  6 11            1977-07-13 other          /0.1mL    12 1977-07-01  1977 Jul  
 #>  7 11            1978-01-11 other          /0.1mL    16 1978-01-01  1978 Jan  
 #>  8 11            1979-02-08 other          /0.1mL     1 1979-01-01  1979 Feb  
 #>  9 11            1979-05-02 Karenia brevis /0.1mL     1 1979-04-01  1979 May  
 #> 10 11            1979-05-30 other          /0.1mL     1 1979-04-01  1979 May  
-#> # ℹ 23,838 more rows
+#> # ℹ 21,133 more rows

These data are highly summarized from the raw data file available online. Cell counts (as number of cells per 0.1mL) for selected taxa are summed for each station by quarters (i.e., Jan/Feb/Mar, Apr/May/Jun, @@ -574,13 +574,13 @@

Show rows.

 show_matrix(epcdata, asreact = TRUE)
-
-

A plotly (interactive, dynamic plot) can be returned by setting the +

+

A plotly (interactive, dynamic plot) can be returned by setting the plotly argument to TRUE.

 show_matrix(epcdata, plotly = TRUE)
-
-

Results can also be obtained for a selected year. Outcomes can be +

+

Results can also be obtained for a selected year. Outcomes can be returned in tabular format with anlz_yrattain(). This table also shows segment averages for chlorophyll and light attenuation, including the associated targets.

@@ -646,8 +646,8 @@

Show by hovering over a location in the plot.

 show_segplotly(epcdata, width = 1000, height = 600)
-
-

From these plots, we can quickly view a summary of the environmental +

+

From these plots, we can quickly view a summary of the environmental history of water quality in Tampa Bay. Degraded conditions were common early in the period of record, particularly for Old Tampa Bay and Hillsborough Bay. Conditions began to improve by the late 1980s and @@ -676,52 +676,52 @@

Reasonable Assurance reporting
-show_annualassess(epcdata, yrsel = 2022)
+show_annualassess(epcdata, yrsel = 2023)
- - + +
- - - + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + +

Segment

Chl-a (ug/L)

Light Penetration (m-1)

Segment

Chl-a (ug/L)

Light Penetration (m-1)

2022

target

2022

target

2023

target

2023

target

OTB

7.1

8.5

0.79

0.83

OTB

6.2

8.5

0.73

0.83

HB

8.9

13.2

0.92

1.58

HB

6.9

13.2

0.79

1.58

MTB

5.0

7.4

0.55

0.83

MTB

3.7

7.4

0.47

0.83

LTB

3.6

4.6

0.66

0.63

LTB

2.6

4.6

0.54

0.63
@@ -729,53 +729,53 @@

Reasonable Assurance reporting
-show_annualassess(epcdata, yrsel = 2022, caption = TRUE)

+show_annualassess(epcdata, yrsel = 2023, caption = TRUE)
- - - + +
Water quality outcomes for 2022.
+ - - - + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + +
Water quality outcomes for 2023.

Segment

Chl-a (ug/L)

Light Penetration (m-1)

Segment

Chl-a (ug/L)

Light Penetration (m-1)

2022

target

2022

target

2023

target

2023

target

OTB

7.1

8.5

0.79

0.83

OTB

6.2

8.5

0.73

0.83

HB

8.9

13.2

0.92

1.58

HB

6.9

13.2

0.79

1.58

MTB

5.0

7.4

0.55

0.83

MTB

3.7

7.4

0.47

0.83

LTB

3.6

4.6

0.66

0.63

LTB

2.6

4.6

0.54

0.63
@@ -788,52 +788,52 @@

Reasonable Assurance reporting
-show_ratab(epcdata, yrsel = 2022, bay_segment = 'OTB')

+show_ratab(epcdata, yrsel = 2023, bay_segment = 'OTB')
- - + +
- - - + + + - - - - - + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - + +

Bay Segment Reasonable Assurance Assessment Steps

DATA USED TO ASSESS ANNUAL REASONABLE ASSURANCE

OUTCOME

Bay Segment Reasonable Assurance Assessment Steps

DATA USED TO ASSESS ANNUAL REASONABLE ASSURANCE

OUTCOME

Year 1 (2022)

Year 2 (2023)

Year 3 (2024)

Year 4 (2025)

Year 5 (2026)

Year 1 (2022)

Year 2 (2023)

Year 3 (2024)

Year 4 (2025)

Year 5 (2026)

**NMC Action 1:** Determine if observed chlorophyll-a exceeds FDEP threshold of 9.3 ug/L

No (7.1)

All years below threshold so far, not necessary for NMC Actions 2-5

**NMC Action 1:** Determine if observed chlorophyll-a exceeds FDEP threshold of 9.3 ug/L

No (7.1)

No (6.2)

All years below threshold so far, not necessary for NMC Actions 2-5

**NMC Action 2:** Determine if any observed chlorophyll-*a* exceedences occurred for 2 consecutive years

No

All years met threshold, not necessary for NMC Actions 3-5

**NMC Action 2:** Determine if any observed chlorophyll-*a* exceedences occurred for 2 consecutive years

No

No

All years met threshold, not necessary for NMC Actions 3-5

**NMC Action 3:** Determine if observed hydrologically-normalized total load exceeds federally-recognized TMDL of 486 tons/year

N/A

Not necessary due to observed water quality and seagrass conditions in the bay segment

**NMC Action 3:** Determine if observed hydrologically-normalized total load exceeds federally-recognized TMDL of 486 tons/year

N/A

N/A

Not necessary due to observed water quality and seagrass conditions in the bay segment

**NMC Actions 4-5:** Determine if any entity/source/facility specific exceedences of 5-yr average allocation occurred during implementation period

Not necessary when chlorophyll-*a* threshold met

**NMC Actions 4-5:** Determine if any entity/source/facility specific exceedences of 5-yr average allocation occurred during implementation period

Not necessary when chlorophyll-*a* threshold met
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Data import and included datasets mapview(trnpts, zcol = 'MonAgency', lwd = 0, legend = F, homebutton = F, col.regions = cols) + mapview(trnlns, zcol = 'MonAgency', homebutton = F, layer.name = 'Monitoring Agency', lwd = 4, color = cols) -
-

The transect data can be downloaded from the Water Atlas using the +

+

The transect data can be downloaded from the Water Atlas using the read_transect() function. The only required argument for this function is training, which indicates if you want to download training data or the complete dataset, i.e., @@ -405,7 +405,7 @@

Plotting resultsspecies argument) and variable (varplo argument).

-show_compplot(traindat, yr = 2022, site = '2', species = 'Halodule', varplo = 'Abundance', base_size = 14)
+show_compplot(traindat, yr = 2023, site = '2', species = 'Halodule', varplo = 'Abundance', base_size = 14)

The rest of the plotting functions work with the complete transect data. Data for an individual transect can be viewed with the @@ -425,8 +425,8 @@

Plotting results 
 show_transect(transect, site = 'S3T10', species = 'Halodule', varplo = 'Abundance', plotly = T)
-
-

The show_transect() function can also be used to plot +

+

The show_transect() function can also be used to plot multiple species. One to many species can be provided to the species argument.

@@ -447,8 +447,8 @@ 
Plotting resultsanlz_transectocc() function as input.

 
 show_transectsum(transectocc, site = 'S3T10')

-

-
A summary matrix of frequency occurrence estimates across all species
+

+
A summary matrix of frequency occurrence estimates across all species
 can be plotted with show_transectmatrix(). This uses
 results from the anlz_transectocc() and
 anlz_transectave() functions to estimate annual averages by
@@ -468,8 +468,8 @@ 
Plotting resultsplotly = TRUE inside the function.

 
 show_transectmatrix(transectocc, plotly = T)

-

-
Time series plots of annual averages of frequency occurrence
+

+
Time series plots of annual averages of frequency occurrence
 estimates by each species can be shown with the
 show_transectavespp() function. By default, all estimates
 are averaged across all bay segments for each species. The plot is a
@@ -488,8 +488,8 @@ 
Plotting resultsplotly = TRUE inside the function.

 
 show_transectavespp(transectocc, bay_segment = 'LTB', species = c('Syringodium', 'Thalassia'), plotly = T)

-

-
As an alternative to plotting the species averages over time with
+

+
As an alternative to plotting the species averages over time with
 show_transectavespp(), a table can be created by setting
 asreact = TRUE. Filtering options that apply to the plot
 also apply to the table, e.g., filtering by the four major bay segments
@@ -497,8 +497,8 @@ 
Plotting results
 
 show_transectavespp(transectocc, asreact = T, bay_segment = c('HB', 'OTB', 'MTB', 'LTB'), yrrng = c(2006, 2012))

-

-
All of the above describes methods in tbeptools for working with
+

+
All of the above describes methods in tbeptools for working with
 transect monitoring data. Seagrass coverage maps are also created
 approximately biennially by the Southwest Florida Water Management
 District, available at https://data-swfwmd.opendata.arcgis.com/.
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index ac4dddc8..4b7a0ddc 100644
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diff --git a/articles/tbbi.html b/articles/tbbi.html
index 31125ed4..74d5fac8 100644
--- a/articles/tbbi.html
+++ b/articles/tbbi.html
@@ -310,8 +310,8 @@ 
Plotting resultsplotly = TRUE inside the function.

 
 show_tbbimatrix(tbbiscr, plotly = T)

-

-
+

+

Additional sediment data @@ -366,17 +366,17 @@

Additional sediment datayrrng argument.

 show_sedimentmap(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022))
-
-

A single year of data can be shown as well.

+
+

A single year of data can be shown as well.

 show_sedimentmap(sedimentdata, param = 'Arsenic', yrrng = 2022)
-
-

A map showing only the concentrations is returned if TEL and PEL +

+

A map showing only the concentrations is returned if TEL and PEL values are not available for a parameter.

 show_sedimentmap(sedimentdata, param = 'Selenium', yrrng = c(1993, 2022))
-
-

Maps for total contaminant values (e.g., Total DDT, Total PAH, Total +

+

Maps for total contaminant values (e.g., Total DDT, Total PAH, Total PCB, Total LMW PAH, Total HMW PAH) can also be returned. Although the totals are not included in the sedimentdata object, they are calculated by tbeptools using the anlz_sedimentaddtot() @@ -385,8 +385,8 @@

Additional sediment data 
 show_sedimentmap(sedimentdata, param = 'Total DDT', yrrng = c(1993, 2022))
-
-

The PEL ratio can also be used to assess relative sediment quality +

+

The PEL ratio can also be used to assess relative sediment quality given the measured contaminants. The show_sedimentpelmap() function creates a map of average PEL ratios graded from A to F for benthic stations monitored in Tampa Bay. The PEL ratio is the @@ -397,8 +397,8 @@

Additional sediment data 
 show_sedimentpelmap(sedimentdata, yrrng = c(1993, 2022))
-
-

The average PEL ratios and grades used to create the map can also be +

+

The average PEL ratios and grades used to create the map can also be returned as a data frame using anlz_sedimentpel().

 anlz_sedimentpel(sedimentdata, yrrng = c(1993, 2022))
@@ -428,8 +428,8 @@ 
Additional sediment dataplotly = T.

 
 show_sedimentave(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022), plotly = T)

-

-
The values used in the plot can be returned with
+

+
The values used in the plot can be returned with
 anlz_sedimentave().

 
 anlz_sedimentave(sedimentdata, param = 'Arsenic', yrrng = c(1993, 2022))
@@ -470,8 +470,8 @@ 
Additional sediment dataplotly = T.

 
 show_sedimentpelave(sedimentdata, yrrng = c(1993, 2022), plotly = T)

-

-
The values used in the plot can be returned with
+

+
The values used in the plot can be returned with
 anlz_sedimentpelave().

 
 anlz_sedimentpelave(sedimentdata, yrrng = c(1993, 2022))
@@ -500,8 +500,8 @@ 
Additional sediment dataplotly = T.

 
 show_sedimentalratio(sedimentdata, param = 'Zinc', bay_segment = c('HB', 'LTB'), plotly = T)

-

-
+

+
 

 

 
References
diff --git a/articles/tbni.html b/articles/tbni.html
index e53b9352..05c3ba54 100644
--- a/articles/tbni.html
+++ b/articles/tbni.html
@@ -226,8 +226,8 @@ 
Data import and included datasets
 fimstations <- read_importfim(csv, download_latest = TRUE, locs = TRUE)
 mapview(fimstations, zcol = 'bay_segment')

-

-
The read_importfim() function processes the observed
+

+
The read_importfim() function processes the observed
 data as needed for the TBNI, including merging the rows with the
 tbnispp and fimstations data. Once imported,
 the metrics and scores can be calculated.

@@ -327,14 +327,14 @@ 
Plotting resultsplotly = TRUE inside each function.

 
 show_tbniscr(tbniscr, plotly = T)

-

-
+

+
 show_tbniscrall(tbniscr, plotly = T)

-

-
+

+
 show_tbnimatrix(tbniscr, plotly = T)

-

-
The breakpoints for the categorical outcomes of the TBNI scores shown
+

+
The breakpoints for the categorical outcomes of the TBNI scores shown
 by the colors in each graph are based on the 33rd and 50th percentiles
 of the distribution of all TBNI scores calculated for Tampa Bay. This
 plotting option is provided for consistency with existing TBEP reporting
diff --git a/articles/tidalcreeks.html b/articles/tidalcreeks.html
index a8b8c04f..cf23ada2 100644
--- a/articles/tidalcreeks.html
+++ b/articles/tidalcreeks.html
@@ -126,8 +126,8 @@ 
Background
 mapview(tidalcreeks, homebutton = F, legend = F)

-

-
The tidal creek assessment framework was established based on data
+

+
The tidal creek assessment framework was established based on data
 from the FDEP Impaired
 Waters Rule database run 56 available here which
 includes data collected through January 10th 2019. However, the this
@@ -269,8 +269,8 @@ 
Report card functions
 
 show_tdlcrk(results)

-

-
A report card style matrix can be plotted using the
+

+
A report card style matrix can be plotted using the
 show_tdlcrkmatrix() function that shows the overall creek
 score and the number of years of data that were used to estimate the
 overall score. The plot shows a matrix with rows for individual creeks
@@ -368,8 +368,8 @@ 
Indicator functions
 cntdat <- anlz_tdlcrkindic(tidalcreeks, iwrraw, yr = 2023)
 show_tdlcrkindic(id = id, cntdat = cntdat, thrsel = TRUE)

-

-
The show_tdlcrkindiccdf() function is similar except
+

+
The show_tdlcrkindiccdf() function is similar except
 that empirical cumulative distribution functions (CDF) are plotted to
 evaluate outcomes for a specific creek relative to the entire
 distribution of creeks in southwest Florida. Each indicator and each
@@ -381,8 +381,8 @@ 
Indicator functions
 show_tdlcrkindiccdf(id = id, cntdat = cntdat, thrsel = TRUE)

-

-
+

+

diff --git a/favicon-16x16.png b/favicon-16x16.png index 1b14a11e..25e2f453 100644 Binary files a/favicon-16x16.png and b/favicon-16x16.png differ diff --git a/favicon-32x32.png b/favicon-32x32.png index b930efa6..cbbb1102 100644 Binary files a/favicon-32x32.png and b/favicon-32x32.png differ diff --git a/pkgdown.yml b/pkgdown.yml index 59efb550..2440370b 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -9,7 +9,7 @@ articles: tbbi: tbbi.html tbni: tbni.html tidalcreeks: tidalcreeks.html -last_built: 2024-03-20T12:24Z +last_built: 2024-03-20T13:09Z urls: reference: https://tbep-tech.github.io/tbeptools/reference article: https://tbep-tech.github.io/tbeptools/articles diff --git a/reference/anlz_fibmap.html b/reference/anlz_fibmap.html index f182aaa6..f02f8073 100644 --- a/reference/anlz_fibmap.html +++ b/reference/anlz_fibmap.html @@ -97,20 +97,20 @@

DetailsExamples

# assign categories to all
 anlz_fibmap(fibdata)
-#> # A tibble: 74,872 × 12
+#> # A tibble: 76,502 × 12
 #>    area    epchc_station class    yr    mo Latitude Longitude ecoli ecocci ind  
 #>    <chr>           <dbl> <chr> <dbl> <dbl>    <dbl>     <dbl> <dbl>  <dbl> <chr>
-#>  1 Hillsb…             2 3M     2023     5     27.9     -82.5    NA      4 Ente…
-#>  2 Hillsb…             6 3M     2023     5     27.9     -82.5    NA      2 Ente…
-#>  3 Hillsb…             7 3M     2023     5     27.9     -82.5    NA      2 Ente…
-#>  4 Hillsb…             8 3M     2023     5     27.9     -82.4    NA      2 Ente…
-#>  5 Middle…             9 2      2023     5     27.8     -82.4    NA      2 Ente…
-#>  6 Middle…            11 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  7 Middle…            13 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  8 Middle…            14 2      2023     5     27.8     -82.5    NA      2 Ente…
-#>  9 Middle…            16 2      2023     5     27.7     -82.5    NA      2 Ente…
-#> 10 Middle…            19 2      2023     5     27.7     -82.6    NA      2 Ente…
-#> # ℹ 74,862 more rows
+#>  1 Hillsb…             2 3M     2024     1     27.9     -82.5    NA     24 Ente…
+#>  2 Hillsb…             6 3M     2024     1     27.9     -82.5    NA      2 Ente…
+#>  3 Hillsb…             7 3M     2024     1     27.9     -82.5    NA      2 Ente…
+#>  4 Hillsb…             8 3M     2024     1     27.9     -82.4    NA     10 Ente…
+#>  5 Middle…             9 2      2024     1     27.8     -82.4    NA      2 Ente…
+#>  6 Middle…            11 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  7 Middle…            13 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  8 Middle…            14 2      2024     1     27.8     -82.5    NA      2 Ente…
+#>  9 Middle…            16 2      2024     1     27.7     -82.5    NA      2 Ente…
+#> 10 Middle…            19 2      2024     1     27.7     -82.6    NA      2 Ente…
+#> # ℹ 76,492 more rows
 #> # ℹ 2 more variables: cat <fct>, col <chr>
 
 # filter by year, month, and area
diff --git a/reference/anlz_fibmatrix.html b/reference/anlz_fibmatrix.html
index cd330a1f..68bcc5cb 100644
--- a/reference/anlz_fibmatrix.html
+++ b/reference/anlz_fibmatrix.html
@@ -99,7 +99,7 @@ 
See also
     
Examples

     
anlz_fibmatrix(fibdata)
-#> # A tibble: 8,541 × 4
+#> # A tibble: 8,800 × 4
 #>       yr epchc_station gmean cat  
 #>    <dbl> <fct>         <dbl> <chr>
 #>  1  1985 2             52.4  B    
@@ -112,7 +112,7 @@ 
Examples#>  8  1985 14             6.35 A    
 #>  9  1985 16             4    A    
 #> 10  1985 19             4    A    
-#> # ℹ 8,531 more rows
+#> # ℹ 8,790 more rows
 

     


 # defaults to current year
-anlz_yrattain(epcdata, yrsel = 2022)
+anlz_yrattain(epcdata, yrsel = 2023)
 #> # A tibble: 4 × 6
 #>   bay_segment chla_val chla_target la_val la_target outcome
 #>   <fct>          <dbl>       <dbl>  <dbl>     <dbl> <chr>  
-#> 1 OTB             7.12         8.5  0.787      0.83 green  
-#> 2 HB              8.86        13.2  0.922      1.58 green  
-#> 3 MTB             5.05         7.4  0.553      0.83 green  
-#> 4 LTB             3.59         4.6  0.658      0.63 green  
+#> 1 OTB             6.21         8.5  0.734      0.83 green  
+#> 2 HB              6.93        13.2  0.791      1.58 green  
+#> 3 MTB             3.68         7.4  0.472      0.83 green  
+#> 4 LTB             2.60         4.6  0.536      0.63 green