Goals are to:
- Provide an easy, mostly automated way to produce the “standard” crss figures accross many scenarios.
- Provide a set of plots with a common API for creating new CRSS figures for any slot/variable. These functions can be wrapped in code from goal 1 in this package or used in other analyses/frameworks.
library(remotes)
remotes::install_github('rabutler-usbr/crssplot')
- Setup a yaml file that determines which scenarios are used and which
plots are created. yaml
Specification
includes the details about the yaml file configuration.
- It does not matter where you save this file. It can be saved in a data analysis folder or in the CRSS folder.
- Then call:
create_results_package("path/to/yml/file.yml")
See doc/README.md for more details on how this process works in the overall CRSS publication process.
This section demonstrates how to use the generic plotting functions to recreate the standard CRSS results, though it does so using a truncated/sample set of them. The available plotting functions are described below. Except for the last function, all plots are time series plots.
scens_plot_*()- these functions compare scenarios using different colors and show different variables as facets.scens_plot_range()- show 10/50/90th percentiles as line plots.scens_plot_cloud()- show 10/50/90th percentiles as a shaded “cloud”. Can additionally show historical data.scens_plot_probs()- show probabilities of binary flags occurring.scens_plot_boxplot()- show boxplots of data each year.
vars_plot_*()- these functions compare variables using different colors and show different scenarios as facets.vars_plot_probs()- show probabilities of binary flags occurring as a line plot.vars_plot_heatmap()- show probabilities of binary flags occurring using a heat map.
var_plot_trace_scatter()- show a scatter plot of a variable’s value vs. trace for a single variable and single year of data. Different scenarios are shown as facets.
library(crssplot)
library(ggplot2)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(patchwork)
library(RWDataPlyr)
library(CRSSIO)
# ex_pe is sample data frame included with this package
# TODO: add in sample code that shows how to generate ex_pe from RWDataPlyr and
# add in the ScenarioGroup and add in the initial conditionsex_pe is a data frame included with this package. It includes multiple
variables that are typically used in the generation of CRSS results. It
was created using RWDataPlyr, similar to the following example which
repeats the process, but uses the “system conditions”, i.e., binary
flags of system and operating conditions.
# use example data from RWDataPlyr
scen_path <- system.file("extdata/Scenario", package = "RWDataPlyr")
scens <- c(
"I.C. 1" = "ISM1988_2014,2007Dems,IG,2002",
"I.C. 2" = "ISM1988_2014,2007Dems,IG,Most"
)
sys <- rw_scen_aggregate(scens, sys_cond_rwa(), scen_dir = scen_path)
#> Processing 2 total scenarios.
#> ------------------
#> ** Starting scenario 1 of 2: ISM1988_2014,2007Dems,IG,2002
#> Processing 17 slots in SystemConditions.rdf
#> ** Starting scenario 2 of 2: ISM1988_2014,2007Dems,IG,Most
#> Processing 17 slots in SystemConditions.rdf
# crssplot needs ScenarioGroup variable. Since we are not combining multiple
# scenarios together, it is the same as Scenario
sys$ScenarioGroup <- sys$Scenario# Cloud figures
# get historical mead and powell data (examples stored with this pacakge)
h_mead <- read.csv("inst/extdata/HistMeadPE.csv")
h_powell <- read.csv("inst/extdata/HistPowellPE.csv")
hh <- h_mead
colnames(hh)[2] <- "mead_dec_pe"
hh$powell_dec_pe <- h_powell[,2]
pal <- c(
"April ST 2007 UCRC" = "#138d75",
"April ST CT" = "#f1c40f"
)
scens_plot_cloud(
ex_pe,
c("powell_dec_pe", "mead_dec_pe"),
historical = hh,
legend_wrap = 15,
plot_colors = pal,
y_lab = "feet",
years = 2020:2026,
facet_scales = "free_y",
fill_label = "10th to 90th percentile of full range"
) +
theme_cloud()
p1 <- scens_plot_probs(
ex_pe, years = 2021:2026,
vars = "powell_wy_min_lt_3525",
plot_colors = pal,
subtitle = "Percent of traces less than elevation 3,525' in any water year"
) +
expand_limits(y = c(0, 1))
p2 <- scens_plot_probs(
ex_pe, years = 2021:2026,
vars = "powell_wy_min_lt_3490",
plot_colors = pal,
subtitle = "Percent of traces less than elevation 3,490' in any water year"
) +
expand_limits(y = c(0, 1))
p1 + p2 +
plot_layout(guides = "collect") +
plot_annotation(title = "Lake Powell:")
# uses different data, so use default colors
p1 <- scens_plot_probs(
sys, years = 2018:2026,
vars = "lbShortage",
subtitle = "Percent of traces in Shortage Conditions"
) +
expand_limits(y = c(0, 1))
p2 <- scens_plot_probs(
sys, years = 2018:2026,
vars = "lbSurplus",
subtitle = "Percent of traces in Surplus Conditions"
) +
expand_limits(y = c(0, 1))
p1 + p2 +
plot_layout(guides = "collect") +
plot_annotation(title = "Lower Basin:")
p1 <- scens_plot_probs(
ex_pe, years = 2021:2026,
vars = "mead_dec_lt_1025",
plot_colors = pal,
subtitle = "Percent of traces less than elevation 1,025' in December"
) +
expand_limits(y = c(0, 1))
p2 <- scens_plot_probs(
ex_pe, years = 2021:2026,
vars = "mead_min_lt_1000",
plot_colors = pal,
subtitle = "Percent of traces less than elevation 1,000' in any month"
) +
expand_limits(y = c(0, 1))
p1 + p2 +
plot_layout(guides = "collect") +
plot_annotation(title = "Lake Mead:")
tmp_labs <- c("April ST 2007 UCRC" = "Scenario 1", "April ST CT" = "Scenario 2")
scens_plot_boxplot(
ex_pe,
vars = c("powell_dec_pe", "mead_dec_pe"),
years = 2021:2036,
title = "Mead and Powell", subtitle = "End-of-December Elevation",
y_lab = "(feet)", caption = "Results from April 20xx",
facet_scales = "free_y",
plot_colors = pal,
scen_labels = tmp_labs,
legend_wrap = 10
)
Need LB Shortage added to data frame. Not a limitation of the plotting, rather a limitation of the example data I’m using.
vv <- c(
"mead_min_lt_1020" = "Mead < 1,020' in Any Month",
"powell_wy_min_lt_3490" = "Powell < 3,490' in Any Month in the WY",
"powell_dec_lt_3525" = "Powell < 3,525' in December",
"mead_min_lt_1000" = "Mead < 1,000' in Any Month",
"mead_min_lt_1025" = "Mead < 1,025' in Any Month",
"powell_wy_min_lt_3525" = "Powell < 3,525' in Any Month in the WY"
)
vars_plot_probs(
ex_pe, "April ST CT",
years = 2021:2026,
vars = names(vv),
var_labels = vv,
legend_wrap = 15
) +
theme(legend.position = "bottom") +
guides(color = guide_legend(nrow = 2)) +
labs(color = NULL)
vv <- c(
"lbShortageStep1" = "Step 1 Shortage",
"lbShortageStep2" = "Step 2 Shortage",
"lbShortageStep3" = "Step 3 Shortage"
)
vars_plot_probs(
sys, "I.C. 1",
years = 2018:2026,
vars = names(vv),
var_labels = vv,
legend_wrap = 15,
plot_type = "stacked bar",
title = "Lower Basin Shortages by Tier"
) +
theme(legend.position = "bottom") +
guides(color = guide_legend(nrow = 1)) +
labs(color = NULL)
vv <- c(
"eq" = "Equalization Tier (Powell >= Equalization [EQ] Elevation)",
"ueb" = "Upper Elevation Balancing Tier (Powell < EQ Elevation and >= 3,575')",
"mer" = "Mid-Elevation Release Tier (Powell < 3,575' and >= 3,525')",
"leb" = "Lower Elevation Balacing Tier (Powell < 3,525')"
)
# combine existing variables together
tmp_df <- bind_rows(
filter(sys, Variable == "eq"),
filter(sys, Variable %in% c("uebGt823", "ueb823", "uebLt823")) %>%
group_by(Year, TraceNumber, ScenarioGroup) %>%
summarise(Value = sum(Value)) %>%
mutate(Variable = "ueb"),
filter(sys, Variable %in% c("mer748", "mer823")) %>%
group_by(Year, TraceNumber, ScenarioGroup) %>%
summarise(Value = sum(Value)) %>%
mutate(Variable = "mer"),
filter(sys, Variable %in% c("lebGt823", "leb823", "lebLt823")) %>%
group_by(Year, TraceNumber, ScenarioGroup) %>%
summarise(Value = sum(Value)) %>%
mutate(Variable = "leb")
)
#> `summarise()` has grouped output by 'Year', 'TraceNumber'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'Year', 'TraceNumber'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'Year', 'TraceNumber'. You can override using the `.groups` argument.
vars_plot_heatmap(
tmp_df, unique(tmp_df$ScenarioGroup),
years = 2018:2026,
vars = names(vv),
var_labels = vv,
legend_wrap = 15,
title = "Lake Powell Conditions from CRSS",
subtitle = "Percent of Traces in each Elevation Range"
)
var_plot_trace_scatter(
ex_pe,
vars = "mead_dec_pe",
years = 2021,
scenarios = c("April ST CT", "April ST 2007 UCRC")
)
- 2021-08-30: v0.0.3
- 2021-04-25: v0.0.2
- 2021-04-01: v0.0.1