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jcaracappa1jcaracappa1
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Merge pull request #364 from NOAA-EDAB/plot-abc-acl-aesthetics
Rework ABC/ACL plot function (`plot_abc_acl`)
2 parents f7d168b + f84031b commit 52a0fe6

3 files changed

Lines changed: 109 additions & 132 deletions

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‎DESCRIPTION‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -37,7 +37,8 @@ Imports:
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forcats,
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patchwork,
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ggpubr,
40-
arfit
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arfit,
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Polychrome
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Suggests:
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knitr,
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rmarkdown,

‎R/plot_abc_acl.R‎

Lines changed: 103 additions & 127 deletions
Original file line numberDiff line numberDiff line change
@@ -13,13 +13,13 @@
1313
#' @export
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#'
1515

16-
plot_abc_acl <- function(shadedRegion = NULL,
17-
report="MidAtlantic",
18-
plottype = "Stacked") {
19-
16+
plot_abc_acl <- function(
17+
shadedRegion = NULL,
18+
report = "MidAtlantic",
19+
plottype = "Stacked"
20+
) {
2021
# generate plot setup list (same for all plot functions)
21-
setup <- ecodata::plot_setup(shadedRegion = shadedRegion,
22-
report=report)
22+
setup <- ecodata::plot_setup(shadedRegion = shadedRegion, report = report)
2323

2424
# which report? this may be bypassed for some figures
2525
if (report == "MidAtlantic") {
@@ -38,69 +38,90 @@ plot_abc_acl <- function(shadedRegion = NULL,
3838
# splitting code by report for now
3939

4040
if (report == "MidAtlantic") {
41-
4241
ABCs <- ecodata::abc_acl |>
4342
dplyr::filter(EPU == filterEPUs) |>
4443
tidyr::separate(col = Var, into = c("Fishery", "Var"), sep = "_") |>
4544
dplyr::filter(Var == "Quota") |>
46-
dplyr::mutate(Fishery = gsub("Commercial", "C", Fishery),
47-
Fishery = gsub("Recreational", "R", Fishery)) |>
45+
dplyr::mutate(
46+
Fishery = gsub("Commercial", "C", Fishery),
47+
Fishery = gsub("Recreational", "R", Fishery)
48+
) |>
4849
dplyr::group_by(Fishery, Time) |>
49-
dplyr::summarise(Value = sum(Value),
50-
.groups="drop")
50+
dplyr::summarise(Value = sum(Value), .groups = "drop")
51+
52+
# Determine species order for stacked bar plot based on first year
53+
stackedOrder <- ABCs |>
54+
dplyr::filter(Time == min(ABCs$Time)) |>
55+
dplyr::arrange(Value) |>
56+
dplyr::mutate(
57+
Order = 1:nrow(dplyr::filter(ABCs, Time == min(ABCs$Time)))
58+
) |>
59+
dplyr::select(Fishery, Order)
60+
61+
# Join stacked bar plot subset with ordered species list based on first year
62+
# Convert 'Fishery' column into a factor with levels based on this order
63+
ABCs <- ABCs |>
64+
dplyr::left_join(stackedOrder) |>
65+
dplyr::mutate(Order = ifelse(is.na(Order), 0, Order)) |>
66+
dplyr::arrange(Order) |>
67+
dplyr::mutate(Fishery = factor(Fishery, levels = unique(Fishery)))
5168

5269
CatchABC <- ecodata::abc_acl |>
53-
unique()|>
70+
unique() |>
5471
dplyr::filter(EPU == filterEPUs) |>
5572
tidyr::separate(col = Var, into = c("Fishery", "Var"), sep = "_") |>
56-
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
73+
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
5774
#tidyr::separate(Catch, into = c("Catch", "X"), sep = ",") %>%
58-
dplyr::mutate(Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
59-
Quota = as.numeric(stringr::str_extract(Quota, pattern = "\\d+")),
60-
Value = Catch/Quota#,
61-
#Time = as.character(Time)
75+
dplyr::mutate(
76+
Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
77+
Quota = as.numeric(stringr::str_extract(Quota, pattern = "\\d+")),
78+
Value = Catch / Quota #,
79+
#Time = as.character(Time)
6280
) |>
6381
dplyr::filter(!is.na(Value))
6482

6583
meanCatchABC <- CatchABC |>
6684
dplyr::group_by(Time) |>
67-
dplyr::summarise(val = mean(Value),
68-
.groups="drop") |>
85+
dplyr::summarise(val = mean(Value), .groups = "drop") |>
6986
dplyr::ungroup() |>
7087
dplyr::mutate(Time = as.numeric(Time))
71-
7288
}
7389

7490
if (report == "NewEngland") {
75-
7691
ABCs <- ecodata::abc_acl |>
7792
dplyr::filter(EPU == filterEPUs) |>
78-
tidyr::separate(col = Var, into = c("FMP", "Fishery", "Var"), sep = "_") |>
93+
tidyr::separate(
94+
col = Var,
95+
into = c("FMP", "Fishery", "Var"),
96+
sep = "_"
97+
) |>
7998
dplyr::filter(Var == "ABC") |>
8099
dplyr::group_by(Fishery, Time) |>
81-
dplyr::summarise(Value = sum(Value),
82-
.groups="drop")
100+
dplyr::summarise(Value = sum(Value), .groups = "drop")
83101

84102
CatchABC <- ecodata::abc_acl |>
85-
unique()|>
103+
unique() |>
86104
dplyr::filter(EPU == filterEPUs) |>
87-
tidyr::separate(col = Var, into = c("FMP", "Fishery", "Var"), sep = "_") |>
88-
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
105+
tidyr::separate(
106+
col = Var,
107+
into = c("FMP", "Fishery", "Var"),
108+
sep = "_"
109+
) |>
110+
tidyr::pivot_wider(names_from = Var, values_from = Value) |>
89111
#tidyr::separate(Catch, into = c("Catch", "X"), sep = ",") %>%
90-
dplyr::mutate(Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
91-
Quota = as.numeric(stringr::str_extract(ABC, pattern = "\\d+")),
92-
Value = Catch/ABC#,
93-
#Time = as.character(Time)
94-
) |>
112+
dplyr::mutate(
113+
Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
114+
Quota = as.numeric(stringr::str_extract(ABC, pattern = "\\d+")),
115+
Value = Catch / ABC #,
116+
#Time = as.character(Time)
117+
) |>
95118
dplyr::filter(!is.na(Value))
96119

97120
meanCatchABC <- CatchABC |>
98121
dplyr::group_by(Time) |>
99-
dplyr::summarise(val = mean(Value),
100-
.groups="drop") |>
122+
dplyr::summarise(val = mean(Value), .groups = "drop") |>
101123
dplyr::ungroup() |>
102124
dplyr::mutate(Time = as.numeric(Time))
103-
104125
}
105126

106127
# code for generating plot object p
@@ -109,108 +130,63 @@ plot_abc_acl <- function(shadedRegion = NULL,
109130
# xmin = setup$x.shade.min , xmax = setup$x.shade.max
110131
#
111132
if (plottype == "Stacked") {
112-
113-
p <- ABCs |>
114-
ggplot2::ggplot()+
115-
ggplot2::geom_bar(ggplot2::aes( y = Value, x = Time, fill = Fishery), stat="identity", position = "stack" )+
116-
ggplot2::scale_x_continuous(breaks= scales::pretty_breaks()) +
117-
ggplot2::ggtitle("ABC or ACL for Managed Species")+
118-
ggplot2::theme(legend.text = ggplot2::element_text(size = 8),
119-
legend.key.height = ggplot2::unit(2, "mm"))+
120-
ggplot2::ylab("ABC or ACL, metric tons")+
121-
ggplot2::xlab(ggplot2::element_blank())+
122-
ecodata::theme_ts()+
123-
ggplot2::guides(fill=ggplot2::guide_legend(ncol=1))+
124-
ecodata::theme_title()
133+
# Define a new palette that accommodates 18+ data classes
134+
if (report == "MidAtlantic") {
135+
customPalette <- rev(Polychrome::light.colors(19))
136+
} else {
137+
customPalette <- Polychrome::palette36.colors()
138+
}
139+
140+
p <- ABCs |>
141+
ggplot2::ggplot() +
142+
ggplot2::geom_bar(
143+
ggplot2::aes(y = Value, x = Time, fill = Fishery),
144+
stat = "identity",
145+
position = "stack"
146+
) +
147+
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks()) +
148+
ggplot2::ggtitle("ABC or ACL for Managed Species") +
149+
ggplot2::theme(
150+
legend.text = ggplot2::element_text(size = 8),
151+
legend.key.height = ggplot2::unit(2, "mm")
152+
) +
153+
ggplot2::ylab("ABC or ACL, metric tons") +
154+
ggplot2::xlab(ggplot2::element_blank()) +
155+
ecodata::theme_ts() +
156+
ggplot2::guides(fill = ggplot2::guide_legend(ncol = 1)) +
157+
ecodata::theme_title() +
158+
ggplot2::scale_fill_manual(values = unname(customPalette[-16]))
125159

126160
return(p)
127-
128161
}
129162

130163
if (plottype == "Catch") {
131-
132164
p <- CatchABC |>
133-
ggplot2::ggplot()+
165+
ggplot2::ggplot() +
134166
#geom_boxplot()+
135-
ggplot2::geom_point(ggplot2::aes(x = Time, y = Value))+
136-
ggplot2::geom_point(data = meanCatchABC, ggplot2::aes(x = Time, y = val), color = "red")+
137-
ggplot2::geom_line(data = meanCatchABC, ggplot2::aes(x = Time, y = val), color = "red")+
138-
ggplot2::geom_hline(yintercept = 1, linetype='dashed', col = 'gray')+
139-
ggplot2::scale_x_continuous(breaks= scales::pretty_breaks()) +
140-
ggplot2::ggtitle("Catch per ABC or ACL")+
141-
ggplot2::ylab(expression("Catch / ABC or ACL"))+
142-
ggplot2::theme(legend.title = ggplot2::element_blank())+
143-
ggplot2::xlab(ggplot2::element_blank())+
144-
ecodata::theme_ts()+
167+
ggplot2::geom_point(ggplot2::aes(x = Time, y = Value)) +
168+
ggplot2::geom_point(
169+
data = meanCatchABC,
170+
ggplot2::aes(x = Time, y = val),
171+
color = "red"
172+
) +
173+
ggplot2::geom_line(
174+
data = meanCatchABC,
175+
ggplot2::aes(x = Time, y = val),
176+
color = "red"
177+
) +
178+
ggplot2::geom_hline(yintercept = 1, linetype = 'dashed', col = 'gray') +
179+
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks()) +
180+
ggplot2::ggtitle("Catch per ABC or ACL") +
181+
ggplot2::ylab(expression("Catch / ABC or ACL")) +
182+
ggplot2::theme(legend.title = ggplot2::element_blank()) +
183+
ggplot2::xlab(ggplot2::element_blank()) +
184+
ecodata::theme_ts() +
145185
ecodata::theme_title()
146186

147187
return(p)
148188
}
149-
150189
}
151190

152-
attr(plot_abc_acl,"report") <- c("MidAtlantic","NewEngland")
153-
attr(plot_abc_acl,"plottype") <- c("Stacked","Catch")
154-
155-
156-
# Paste commented original plot code chunk for reference
157-
#
158-
# Stacked
159-
# mean<- ecodata::abc_acl %>%
160-
# ecodata::abc_acl |>
161-
# dplyr::filter(EPU == "MAB") |>
162-
# tidyr::separate(col = Var, into = c("Fishery", "Var"), sep = "_") |>
163-
# dplyr::filter(Var == "Quota") |>
164-
# dplyr::mutate(Fishery = gsub("Commercial", "C", Fishery),
165-
# Fishery = gsub("Recreational", "R", Fishery)) |>
166-
# dplyr::group_by(Fishery, Time) |>
167-
# dplyr::summarise(Value = sum(Value)) |>
168-
# ggplot2::ggplot()+
169-
# ggplot2::geom_bar(ggplot2::aes( y = Value, x = Time, fill = Fishery), stat="identity", position = "stack" )+
170-
# ggplot2::ggtitle("ABC or ACL for MAFMC Managed Species")+
171-
# ggplot2::theme(legend.text = ggplot2::element_text(size = 8),
172-
# legend.key.height = ggplot2::unit(2, "mm"))+
173-
# ggplot2::ylab("ABC or ACL")+
174-
# ggplot2::xlab(ggplot2::element_blank())+
175-
# ecodata::theme_ts()+
176-
# ggplot2::guides(fill=ggplot2::guide_legend(ncol=2))+
177-
# ecodata::theme_title()
178-
#
179-
# Catch
180-
# mean<- ecodata::abc_acl %>%
181-
# dplyr::filter(EPU == "MAB") %>%
182-
# tidyr::separate(col = Var, into = c("FMP", "Var"), sep = "_") %>%
183-
# tidyr::pivot_wider(names_from = Var, values_from = Value) %>%
184-
# #tidyr::separate(Catch, into = c("Catch", "X"), sep = ",") %>%
185-
# dplyr::mutate(Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
186-
# Quota = as.numeric(stringr::str_extract(Quota, pattern = "\\d+")),
187-
# Value = Catch/Quota,
188-
# Time = as.character(Time)) %>%
189-
# filter(!Value == "NA") %>%
190-
# dplyr::group_by(Time) %>%
191-
# dplyr::summarise(val = mean(Value)) %>%
192-
# dplyr::ungroup() %>%
193-
# dplyr::mutate(Time = as.numeric(Time))
194-
#
195-
# ecodata::abc_acl %>%
196-
# dplyr::filter(EPU == "MAB") %>%
197-
# tidyr::separate(col = Var, into = c("FMP", "Var"), sep = "_") %>%
198-
# tidyr::pivot_wider(names_from = Var, values_from = Value) %>%
199-
# #tidyr::separate(Catch, into = c("Catch", "X"), sep = ",") %>%
200-
# dplyr::mutate(Catch = as.numeric(stringr::str_extract(Catch, pattern = "\\d+")),
201-
# Quota = as.numeric(stringr::str_extract(Quota, pattern = "\\d+")),
202-
# Value = Catch/Quota,
203-
# Time = as.numeric(Time))%>%
204-
# filter(!Value == "NA") %>%
205-
# ggplot2::ggplot()+
206-
# #geom_boxplot()+
207-
# geom_point(aes(x = Time, y = Value))+
208-
# geom_point(data = mean, aes(x = Time, y = val), color = "red")+
209-
# geom_line(data = mean, aes(x = Time, y = val), color = "red")+
210-
# geom_hline(yintercept = 1, linetype='dashed', col = 'gray')+
211-
# ggplot2::ggtitle("MAFMC Catch per ABC or ACL")+
212-
# ggplot2::ylab(expression("Catch / ABC or ACL"))+
213-
# ggplot2::theme(legend.title = element_blank())+
214-
# ggplot2::xlab(element_blank())+
215-
# ecodata::theme_ts()+
216-
# ecodata::theme_title()
191+
attr(plot_abc_acl, "report") <- c("MidAtlantic", "NewEngland")
192+
attr(plot_abc_acl, "plottype") <- c("Stacked", "Catch")

‎data-raw/comparisons/abc_acl.html‎

Lines changed: 4 additions & 4 deletions
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