Updated rmds built with new plot functions

Plot functions adjusted for missing data required a catalog rebuild. Added catalog_files folder to gitignore

.gitignore

@@ -6,4 +6,5 @@
 # local files/folders
 _book
 _bookdown_files
-docs
+docs
+catalog_files

chapters/New Indicator.rmd

@@ -69,11 +69,9 @@ The relative dominance of large and small copepods in Northeast US ecosystems ha
 
 **Variable definitions**
 
-Naming key * *Calanus finmarchicus* (calfin_100m3) = “calfin” , 
-* Large copepods (calfin_100m3, mlucens_100m3, calminor_100m3, euc_100m3, calspp_100m3) = “lgcopeALL”, 
+Naming key * *Calanus finmarchicus* (calfin_100m3) = “calfin” , * Large copepods (calfin_100m3, mlucens_100m3, calminor_100m3, euc_100m3, calspp_100m3) = “lgcopeALL”, 
 * Small copepods (all) (ctyp_100m3, pseudo_100m3, tlong_100m3, cham_100m3, para_100m3, acarspp_100m3, clauso, acarlong_100m3, fur_100m3, ost_100m3, temspp_100m3, tort_100m3, paraspp_100m3) = “smallcopeALL” and 
-* Small copepods (SOE) (ctyp_100m3, pseudo_100m3, tlong_100m3, cham_100m3) = “smallcopeSOE”.   
-Variables **TO BE ADDED ONCE DATA IN ECODATA, NOT ALL DATASETS COULD BE READ INTO FORM**
+* Small copepods (SOE) (ctyp_100m3, pseudo_100m3, tlong_100m3, cham_100m3) = “smallcopeSOE”.   Variables **TO BE ADDED ONCE DATA IN ECODATA, NOT ALL DATASETS COULD BE READ INTO FORM**
 
 
 No Data

chapters/SAV.rmd

@@ -21,6 +21,14 @@ Underwater grass beds are critical to the Chesapeake Bay ecosystem. They provide
 ## Key Results and Visualizations
 SAV increased in the Mesohaline and Polyhaline zones, where SAV continued to recover from recent declines in some areas. The Mesohaline zone showed a 28% increase from 2021 (2,768 hectares, 6,840 acres). The Polyhaline zone showed a 17% increase from 2021 (1,145 hectares, 2,828 acres). The Tidal Fresh zone stayed essentially the same with a small decrease (29 hectares, 73 acres) while the Oligohaline zone showed a 15% decrease (501 hectares, 1,239 acres). The increases in the Mesohaline and Polyhaline zone largely reflect recovery following the SAV crash in 2019. Those losses in 2019 were largely due to declines in widgeongrass which has expanded over the past decade due to increases in water quality but is sensitive to wet springs like the one experienced in 2019.  The expansion in polyhaline zone is also attributable to a La Nina climate cycle which has resulted in cooler summers, benefiting eelgrass.  The primary losses in the Oligohaline were concentrated in a small area, the Gunpowder River, the Middle River, and the adjacent mainstem.  These declines may have been influenced by phytoplankton blooms observed in those segments in the spring and summer of 2022.
 
+### MAB
+
+```{r plot_SAVMAB}
+# Plot indicator
+ggplotObject <- ecodata::plot_SAV(report='MidAtlantic',n=10)
+ggplotObject
+```
+
 
 ## Indicator statistics 
 Spatial scale: The data covers the tidal Chesapeake Bay region.

chapters/abc_acl.rmd

@@ -27,12 +27,14 @@ Catch divided by ABC/ACL for MAFMC managed fisheries. Red line indicates the med
 ```{r plot_abc_aclMidAtlanticStacked}
 # Plot indicator
 ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Stacked')
+ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Stacked')
 ggplotObject
 ```
 
 ```{r plot_abc_aclMidAtlanticCatch}
 # Plot indicator
 ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Catch')
+ggplotObject <- ecodata::plot_abc_acl(report= 'MidAtlantic', plottype= 'Catch')
 ggplotObject
 ```
 
@@ -41,12 +43,14 @@ ggplotObject
 ```{r plot_abc_aclNewEnglandStacked}
 # Plot indicator
 ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Stacked')
+ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Stacked')
 ggplotObject
 ```
 
 ```{r plot_abc_aclNewEnglandCatch}
 # Plot indicator
 ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Catch')
+ggplotObject <- ecodata::plot_abc_acl(report= 'NewEngland', plottype= 'Catch')
 ggplotObject
 ```
 

chapters/aggregate_biomass.rmd

@@ -21,6 +21,30 @@ The NEFSC has been conducting bi-annual bottom trawl surveys along the Northeast
 ## Key Results and Visualizations
 Aggregate biomass levels have been relatively stable over time.
 
+### MAB
+
+```{r plot_aggregate_biomassMAB}
+# Plot indicator
+ggplotObject <- ecodata::plot_aggregate_biomass(report='MidAtlantic',n=10)
+ggplotObject
+```
+
+### GB
+
+```{r plot_aggregate_biomassNEGB}
+# Plot indicator
+ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GB',n=10)
+ggplotObject
+```
+
+### GOM
+
+```{r plot_aggregate_biomassNEGOM}
+# Plot indicator
+ggplotObject <- ecodata::plot_aggregate_biomass(report='NewEngland',EPU='GOM',n=10)
+ggplotObject
+```
+
 
 ## Indicator statistics 
 Spatial scale: By EPU

chapters/aquaculture.rmd

@@ -55,8 +55,7 @@ Aquaculture production contributes to overall seafood production in the Northeas
 
 **Variable definitions**
 
-Pieces: number of oysters produced (all regions)                 
-Shellfish lease Acres: area used for shellfish production (New England states only), acres  
+Pieces: number of oysters produced (all regions)                 Shellfish lease Acres: area used for shellfish production (New England states only), acres  
 Production/Acre: Pieces divided by Shellfish lease acres (New England states only)
 
 ```{r vars_aquaculture}

chapters/bottom_temp_insitu.rmd

@@ -21,6 +21,22 @@ The bottom temperature index incorporates near-bottom temperature measurements c
 ## Key Results and Visualizations
 _No response_
 
+### MAB
+
+```{r plot_bottom_temp_insituMAB}
+# Plot indicator
+ggplotObject <- ecodata::plot_bottom_temp_insitu(report='MidAtlantic',n=10)
+ggplotObject
+```
+
+### NE
+
+```{r plot_bottom_temp_insituNE}
+# Plot indicator
+ggplotObject <- ecodata::plot_bottom_temp_insitu(report='NewEngland',n=10)
+ggplotObject
+```
+
 
 ## Indicator statistics 
 Spatial scale: by EPU

chapters/bottom_temp_model_anom.rmd

@@ -29,39 +29,39 @@ Time series plots for seasonal bottom temperature anomaly for each EPU shows a l
 
 ```{r plot_bottom_temp_model_anomMidAtlanticseasonalMAB}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'MidAtlantic', varName= 'seasonal' ,EPU= 'MAB')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'MidAtlantic', varName= 'seasonal' ,EPU= 'MAB',n=10)
 ggplotObject
 ```
 
 ```{r plot_bottom_temp_model_anomMidAtlanticannualMAB}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'MidAtlantic', varName= 'annual' ,EPU= 'MAB')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'MidAtlantic', varName= 'annual' ,EPU= 'MAB',n=10)
 ggplotObject
 ```
 
 ### NewEngland
 
 ```{r plot_bottom_temp_model_anomNewEnglandseasonalGB}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GB')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GB',n=10)
 ggplotObject
 ```
 
 ```{r plot_bottom_temp_model_anomNewEnglandseasonalGOM}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GOM')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'seasonal' ,EPU= 'GOM',n=10)
 ggplotObject
 ```
 
 ```{r plot_bottom_temp_model_anomNewEnglandannualGB}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'annual' ,EPU= 'GB')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'annual' ,EPU= 'GB',n=10)
 ggplotObject
 ```
 
 ```{r plot_bottom_temp_model_anomNewEnglandannualGOM}
 # Plot indicator
-ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'annual' ,EPU= 'GOM')
+ggplotObject <- ecodata::plot_bottom_temp_model_anom(report= 'NewEngland', varName= 'annual' ,EPU= 'GOM',n=10)
 ggplotObject
 ```
 
@@ -91,11 +91,9 @@ Bottom temperature is an important driver for benthic and demersal species growt
 
 **Variable definitions**
 
-Season: 1 = winter (January – March), 2 = spring (April – June), 3 = summer (July – September), 4 = fall (October – December) 
-Subarea: EPU name 
+Season: 1 = winter (January – March), 2 = spring (April – June), 3 = summer (July – September), 4 = fall (October – December) Subarea: EPU name 
 Source:  ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) 
-bt_temp : mean bottom temperature for each year/season across entire EPU 
-ref_bt: bottom temperature climatology for season/EPU based on 1990-2020
+bt_temp : mean bottom temperature for each year/season across entire EPU ref_bt: bottom temperature climatology for season/EPU based on 1990-2020
 
 ```{r vars_bottom_temp_model_anom}
 # Pull all var names

chapters/bottom_temp_model_gridded.rmd

@@ -55,8 +55,7 @@ Bottom temperature is a key environmental parameter in defining the habitat and
 
 **Variable definitions**
 
-- Time: year - Lat: latitude - Lon: longitude - Variable: season 
-- Value: bottom temperature (degrees Celcius)
+- Time: year - Lat: latitude - Lon: longitude - Variable: season - Value: bottom temperature (degrees Celcius)
 
 ```{r vars_bottom_temp_model_gridded}
 # Pull all var names

chapters/cetacean_dist.rmd

@@ -57,8 +57,7 @@ Shifting species distributions alter both species interactions and fishery inter
 
 **Variable definitions**
 
-1) Time=time period of centroid location.  2) species=cetacean species.  3) season.  
-4) wlat=latitude of centroid.  5) wlon=longitude of centroid.
+1) Time=time period of centroid location.  2) species=cetacean species.  3) season.  4) wlat=latitude of centroid.  5) wlon=longitude of centroid.
 
 ```{r vars_cetacean_dist}
 # Pull all var names

chapters/ches_bay_sst.rmd

@@ -61,9 +61,7 @@ In the fall season, there were warmer-than-average temperatures in the Western S
 
 **Variable definitions**
 
-1) sst: sea surface temperature 2023, Celsius 
-2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius 
-3) sst_anomaly: sea surface temperature anomaly 2023 minus 2007-2022, Celsius
+1) sst: sea surface temperature 2023, Celsius 2) sst_climatol: sea surface temperature climatology 2007-2022, Celsius 3) sst_anomaly: sea surface temperature anomaly 2023 minus 2007-2022, Celsius
 
 ```{r vars_ches_bay_sst}
 # Pull all var names

chapters/ches_bay_wq.rmd

@@ -27,6 +27,14 @@ The multimetric indicator quantifies the fraction of segment-DU-criterion combin
 ## Key Results and Visualizations
 The indicator provides an integrated measure of Chesapeake Bay’s water quality condition (Figure 1). In 2019-2021, 28.1% of all tidal water segment-DU-criterion combinations are estimated to have met or exceeded applicable water quality criteria thresholds. Overall, the indicator has a positive and statistically significant trend between 1985-1987 and 2019-2021, which shows that Chesapeake Bay is on a positive trajectory toward recovery. This pattern has been statistically linked to total nitrogen reduction, indicating responsiveness of attainment status to management actions implemented to reduce nutrients ([@zhang_chesapeake_2018]).
 
+### MAB
+
+```{r plot_ches_bay_wqMAB}
+# Plot indicator
+ggplotObject <- ecodata::plot_ches_bay_wq(report='MidAtlantic',n=10)
+ggplotObject
+```
+
 
 ## Indicator statistics 
 Spatial scale: Chesapeake Bay
@@ -53,13 +61,10 @@ Patterns of attainment of individual designated uses are variable (Figure 1). Th
 
 **Variable definitions**
 
-Period: Assessment period Year 1: Starting year of the assessment period 
-Year 2: Ending year of the assessment period Total: The overall attainment indicator 
+Period: Assessment period Year 1: Starting year of the assessment period Year 2: Ending year of the assessment period Total: The overall attainment indicator 
 MSN-DO: Estimated attainment of the dissolved oxygen criterion for the migratory spawning and nursery designated use 
-OW-DO: Estimated attainment of the dissolved oxygen criterion for the open water designated use 
-DW-DO: Estimated attainment of the dissolved oxygen criterion for the deep water designated use 
-DC-DO: Estimated attainment of the dissolved oxygen criterion for the deep channel designated use 
-OW-CHLA: Estimated attainment of the chlorophyll-a criterion 
+OW-DO: Estimated attainment of the dissolved oxygen criterion for the open water designated use DW-DO: Estimated attainment of the dissolved oxygen criterion for the deep water designated use 
+DC-DO: Estimated attainment of the dissolved oxygen criterion for the deep channel designated use OW-CHLA: Estimated attainment of the chlorophyll-a criterion 
 SW-Clarity/SAV: Estimated attainment of the bay grasses / water clarity criterion for the shallow water designated use
 
 ```{r vars_ches_bay_wq}

chapters/cold_pool.rmd

@@ -27,39 +27,39 @@ Time series plots of the three cold pool indices. Cold pool index shows the mean
 
 ```{r plot_cold_poolMidAtlanticcold_pool}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'cold_pool')
+ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'cold_pool',n=10)
 ggplotObject
 ```
 
 ```{r plot_cold_poolMidAtlanticpersistence}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'persistence')
+ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'persistence',n=10)
 ggplotObject
 ```
 
 ```{r plot_cold_poolMidAtlanticextent}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'extent')
+ggplotObject <- ecodata::plot_cold_pool(report= 'MidAtlantic', varName= 'extent',n=10)
 ggplotObject
 ```
 
 ### NewEngland
 
 ```{r plot_cold_poolNewEnglandcold_pool}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'cold_pool')
+ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'cold_pool',n=10)
 ggplotObject
 ```
 
 ```{r plot_cold_poolNewEnglandpersistence}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'persistence')
+ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'persistence',n=10)
 ggplotObject
 ```
 
 ```{r plot_cold_poolNewEnglandextent}
 # Plot indicator
-ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'extent')
+ggplotObject <- ecodata::plot_cold_pool(report= 'NewEngland', varName= 'extent',n=10)
 ggplotObject
 ```
 
@@ -92,11 +92,8 @@ Changes in the cold pool habitat can affect species distribution, recruitment, a
 **Variable definitions**
 
 1) Source: ROMS (bias-corrected ROMS-NWA bottom temperature [@dupontavice_ocean_2022]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature) 
-2) year 3) cold_pool_index: measure of mean temperature within cold pool 
-4) se_cold_pool_index: standard error of cold_pool_index 
-5) persistence_index: measure of duration of cold pool 
-6) se_persistence_index: standard error of persistence_index 
-7) extent_index: measure of spatial extent of cold pool 8) se_extent_index: standard error of extent_index
+2) year 3) cold_pool_index: measure of mean temperature within cold pool 4) se_cold_pool_index: standard error of cold_pool_index 5) persistence_index: measure of duration of cold pool 
+6) se_persistence_index: standard error of persistence_index 7) extent_index: measure of spatial extent of cold pool 8) se_extent_index: standard error of extent_index
 
 ```{r vars_cold_pool}
 # Pull all var names

chapters/comdat.rmd

@@ -28,51 +28,51 @@ Commercial revenue by managed species has generally been down.  The exception is
 
 ```{r plot_comdatMidAtlanticlandingstotal}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'total')
+ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'total',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatMidAtlanticlandingsguild}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'guild')
+ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'landings', plottype = 'guild',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatMidAtlanticrevenuetotal}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'total')
+ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'total',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatMidAtlanticrevenueguild}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'guild')
+ggplotObject <- ecodata::plot_comdat(report= 'MidAtlantic', varName= 'revenue', plottype = 'guild',n=10)
 ggplotObject
 ```
 
 ### NewEngland
 
 ```{r plot_comdatNewEnglandlandingstotal}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'total')
+ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'total',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatNewEnglandlandingsguild}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'guild')
+ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'landings', plottype = 'guild',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatNewEnglandrevenuetotal}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'total')
+ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'total',n=10)
 ggplotObject
 ```
 
 ```{r plot_comdatNewEnglandrevenueguild}
 # Plot indicator
-ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'guild')
+ggplotObject <- ecodata::plot_comdat(report= 'NewEngland', varName= 'revenue', plottype = 'guild',n=10)
 ggplotObject
 ```
 

chapters/commercial_div.rmd

@@ -95,8 +95,7 @@ In the Mid-Atlantic, stability in commercial fleet diversity metrics suggests st
 **Variable definitions**
 
 1) Name: Permit revenue species diversity; Definition: Diversity of revenue across species averaged across permits; Units: effective Shannon.  
-2) Name: Fleet diversity in revenue; Definition: Diversity of revenue across fleet segments; Units: effective Shannon. 
-3) Name: Fleet count; Definition: Number of active fleets; Units: number of fleets.
+2) Name: Fleet diversity in revenue; Definition: Diversity of revenue across fleet segments; Units: effective Shannon. 3) Name: Fleet count; Definition: Number of active fleets; Units: number of fleets.
 
 ```{r vars_commercial_div}
 # Pull all var names

chapters/condition.rmd

@@ -86,8 +86,7 @@ These changes in condition have direct implications for stock assessments, catch
 
 **Variable definitions**
 
-Species: common name for fish species  EPU: Ecological Production Unit YEAR: year of condition data 
-MeanCond: annual mean by EPU and species of relative condition (unitless)
+Species: common name for fish species  EPU: Ecological Production Unit YEAR: year of condition data MeanCond: annual mean by EPU and species of relative condition (unitless)
 
 ```{r vars_condition}
 # Pull all var names