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test.R
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test.R
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##### steps #####
[X] add variable thresholds (7d), 2%
[ ] explore adding longest drought events first? - # of drought days doesn't = # of tiles
[ ] plot by region - # static 2%, variable 7d 2%
[ ] try using different % thresholds
# idea - take expanded table. already right# of rows.
##### update casc #####
p2_casc_list <- list(NW = c('WA','OR','ID'),
SW = c('CA','UT','NV','AZ'),
SC = c('OK','TX','LA','NM'),
NC = c('MT','ND','SD','WY','CO','NE','KS'),
MW = c('WI','MN','IA','IN','IL','OH','MI', 'MO'),
NE = c('ME','VT','NH','NY','NJ','PA','MA','RI','CT','WV','VA','MD','DE','KY'),
SE = c('AR','MS','TN','NC','SC','AL','FL','GA','PR'),
PI = c('HI','AS','GU'),
AK = c('AK'))
# intentionally not including PI and AK?
##### test swarm ####
library(targets)
library(tidyverse)
library(data.table)
library(ggplot2)
library(lubridate)
library(scales)
library(ggforce)
library(showtext)
library(paletteer)
library(BAMMtools)
library(scico)
library(scales)
library(ggthemes)
library(usmap)
library(cowplot)
library(ggbeeswarm)
# tar_load(p2_site_prop_2)
tar_load(p2_1951_2020_drought_prop_jd_7d)
tar_load(p2_1951_2020_metadata)
p2_casc_list <- list(NW = c('WA','OR','ID'),
SW = c('CA','UT','NV','AZ'),
SC = c('OK','TX','LA','NM'),
NC = c('MT','ND','SD','WY','CO','NE','KS'),
MW = c('WI','MN','IA','IN','IL','OH','MI', 'MO'),
NE = c('ME','VT','NH','NY','NJ','PA','MA','RI','CT','WV','VA','MD','DE','KY'),
SE = c('AR','MS','TN','NC','SC','AL','FL','GA','PR'),
PI = c('HI','AS','GU'),
AK = c('AK'))
metadata <- p2_1951_2020_metadata %>%
mutate(CASC = case_when(
STATE %in% p2_casc_list$NW ~ 'NW',
STATE %in% p2_casc_list$SW ~ 'SW',
STATE %in% p2_casc_list$SC ~ 'SC',
STATE %in% p2_casc_list$NC ~ 'NC',
STATE %in% p2_casc_list$MW ~ 'MW',
STATE %in% p2_casc_list$NE ~ 'NE',
STATE %in% p2_casc_list$SE ~ 'SE'
)) %>%
filter(!is.na(CASC))
selected_national_prop <- p2_1951_2020_drought_prop_jd_7d %>%
left_join(metadata %>%
select(StaID:STATE, HCDN_2009, CASC)) %>%
filter(threshold==2)
prop_expanded <- expand_drought_prop(selected_national_prop)
prop_chunks <- identify_drought_chunks(prop_expanded, 365/3)
n_chunks <- nrow(prop_chunks)
prop_swarm <- purrr::pmap_dfr(prop_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = selected_national_prop,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
prop_swarm_plot <- event_swarm_plot(prop_swarm)
ggsave('3_visualize/out/swarm_jd7d_2.png',
prop_swarm_plot,
width = 100, height = 10, dpi = 300, limitsize = FALSE)
##### test regional ######
CASC_regions <- selected_national_prop %>% filter(!is.na(CASC)) %>% pull(CASC) %>% unique()
# source('2_process/src/prep_stripswarm.R')
# source('3_visualize/src/plot_stripswarm.R')
# selected_region <- CASC_regions[1]
# national_prop <- selected_national_prop
regional_duration_chart <- function(national_prop, selected_region, outfile_template) {
# browser()
message(sprintf('filtering to %s', selected_region))
regional_prop <- national_prop %>%
filter(CASC==selected_region)
message('expanding region')
regional_expanded <- expand_drought_prop(regional_prop)
message(sprintf('nrow(regional_expanded): %s', nrow(regional_expanded)))
message('identifying drought chunks')
regional_drought_chunks <- identify_drought_chunks(regional_expanded, min_chunk_days=365/3)
message(sprintf('sum(regional_drought_chunks$total_drought_days): %s', sum(regional_drought_chunks$total_drought_days)))
message('generating swarm')
n_chunks <- nrow(regional_drought_chunks)
regional_swarm <- purrr::pmap_dfr(regional_drought_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = regional_prop,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
message(sprintf('nrow(regional_swarm): %s', nrow(regional_swarm)))
message('plotting swarm')
regional_plot <- event_swarm_plot(swarm_data = regional_swarm)
message('saving swarm plot')
outfile <- sprintf(outfile_template, selected_region)
ggsave(outfile,
regional_plot,
width = 100, height = 10, dpi = 300, limitsize = FALSE)
return(outfile)
}
CASC_regions <- 'SW'
plot_files <- purrr::map(CASC_regions, function(region) {
regional_plot <- regional_duration_chart(selected_national_prop, region, outfile_template='3_visualize/out/swarm_jd7d_2_%s.png')
return(regional_plot)
})
##### 2010-2019 2% #####
### national
tar_load(p2_1951_2020_drought_prop_jd_7d)
selected_national_prop <- p2_1951_2020_drought_prop_jd_7d
prop_2010_2019 <- selected_national_prop %>%
left_join(metadata %>%
select(StaID:STATE, HCDN_2009, CASC)) %>%
filter(threshold==2) %>%
filter(start >= '2010-01-01' & end <= '2019-12-31')
prop_2010_2019_expanded <- expand_drought_prop(prop_2010_2019)
prop_2010_2019_chunks <- identify_drought_chunks(prop_2010_2019_expanded, min_chunk_days = 365/3)
n_chunks <- nrow(prop_2010_2019_chunks)
swarm_2019_2019 <- purrr::pmap_dfr(prop_2010_2019_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = prop_2010_2019,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
swarm_plot_2010_2019 <- event_swarm_plot(swarm_2019_2019)
ggsave('3_visualize/out/swarm_jd7d_2_2010_2019.png', swarm_plot_2010_2019, width = 100, height = 10, dpi = 300, limitsize = FALSE)
### regional
regions_2010_2019 <- prop_2010_2019 %>% filter(!is.na(CASC)) %>% pull(CASC) %>% unique()
regional_plots_2010_2019 <- purrr::map(regions_2010_2019, function(region) {
regional_plot <- regional_duration_chart(prop_2010_2019, region, outfile_template='3_visualize/out/swarm_jd7d_2_2010_2019_%s.png')
return(regional_plot)
})
### western states
western_states <- c('AZ','CA','CO','ID','MT','NV','NM','OR','UT','WA','WY')
western_prop_2010_2019 <- prop_2010_2019 %>%
filter(STATE %in% western_states)
western_prop_expanded <- expand_drought_prop(western_prop_2010_2019)
western_prop_chunks <- identify_drought_chunks(western_prop_expanded, 365/3)
n_chunks <- nrow(western_prop_chunks)
western_swarm <- purrr::pmap_dfr(western_prop_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = western_site_2010_2019,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
western_swarm_plot_2010_2019 <- event_swarm_plot(western_swarm)
ggsave('3_visualize/out/swarm_jd7d_2_2010_2019_western_states.png', western_swarm_plot_2010_2019, width = 100, height = 10, dpi = 300, limitsize = FALSE)
state_duration_chart <- function(national_prop, selected_state, outfile_template) {
message(sprintf('filtering to %s', selected_state))
state_prop <- national_prop %>%
filter(STATE==selected_state)
message('expanding state prop')
state_expanded <- expand_drought_prop(state_prop)
message(sprintf('nrow(state_expanded): %s', nrow(state_expanded)))
message('identifying drought chunks')
state_drought_chunks <- identify_drought_chunks(state_expanded, min_chunk_days=365/3)
message(sprintf('sum(state_drought_chunks$total_drought_days): %s', sum(state_drought_chunks$total_drought_days)))
message('generating swarm')
n_chunks <- nrow(state_drought_chunks)
message(sprintf('data will be processed in %s chunks', n_chunks))
state_swarm <- purrr::pmap_dfr(state_drought_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = state_prop,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
message(sprintf('nrow(state_swarm): %s', nrow(state_swarm)))
message('plotting swarm')
state_plot <- event_swarm_plot(swarm_data = state_swarm)
message('saving swarm plot')
outfile <- sprintf(outfile_template, selected_state)
ggsave(outfile,
state_plot,
width = 100, height = 10, dpi = 300, limitsize = FALSE)
return(outfile)
}
western_state_plots_2010_2019 <- purrr::map(western_states, function(state) {
state_plot <- state_duration_chart(site_2010_2019, state, outfile_template='3_visualize/out/swarm_jd7d_2_2010_2019_%s.png')
return(state_plot)
})
##### test percentiles #####
library(targets)
library(tidyverse)
library(data.table)
library(ggplot2)
library(lubridate)
library(scales)
library(ggforce)
library(showtext)
library(paletteer)
library(BAMMtools)
library(scico)
library(scales)
library(ggthemes)
library(usmap)
library(cowplot)
library(ggbeeswarm)
tar_load(p2_1951_2020_metadata)
tar_load(p2_1951_2020_drought_prop_site)
tar_load(p2_1951_2020_drought_prop_jd_7d)
selected_national_prop <- p2_1951_2020_drought_prop_jd_7d
percentile_levels <- selected_national_prop %>% pull(threshold) %>% unique()
percentile_levels <- percentile_levels[!(percentile_levels==2)] # drop 2 b/c already plotted
min_date <- '1951-01-01' #min(selected_national_prop$start)
max_date <- '1952-12-31' # min_date %m+% years(2)
source('2_process/src/prep_stripswarm.R')
source('3_visualize/src/plot_stripswarm.R')
# selected_percentile <- percentile_levels[1]
national_prop_filter <- selected_national_prop %>%
filter(start >= min_date & end <= max_date)
# metadata <- p2_1951_2020_metadata
# outfile_template<-'3_visualize/out/swarm_site_%s_1951_1952.png'
percentile_duration_chart <- function(national_prop, selected_percentile, metadata, outfile_template) {
message(sprintf('filtering to %s threshold', selected_percentile))
percentile_prop <- national_prop %>%
filter(threshold == selected_percentile) %>%
left_join(metadata %>%
select(StaID:STATE, HCDN_2009, CASC))
message('expanding data')
percentile_expanded <- expand_drought_prop(percentile_prop)
message(sprintf('nrow(percentile_expanded): %s', nrow(percentile_expanded)))
message('identifying drought chunks')
percentile_drought_chunks <- identify_drought_chunks(percentile_expanded, min_chunk_size=365/3)
message(sprintf('sum(percentile_drought_chunks$total_drought_days): %s', sum(percentile_drought_chunks$total_drought_days)))
message('generating swarm')
n_chunks <- nrow(percentile_drought_chunks)
percentile_swarm <- purrr::pmap_dfr(percentile_drought_chunks, function(...) {
current_chunk <- tibble(...)
swarm_data <- create_event_swarm_dt(event_data = percentile_prop,
start_period = current_chunk$start_date,
end_period = current_chunk$break_date,
max_droughts = current_chunk$max_single_day_droughts)
message(sprintf('chunk %s of %s complete', current_chunk$chunk_num, n_chunks))
return(swarm_data)
}, n_chunks)
message(sprintf('nrow(percentile_swarm): %s', nrow(percentile_swarm)))
message('plotting swarm')
percentile_plot <- event_swarm_plot(swarm_data = percentile_swarm)
message('saving swarm plot')
outfile <- sprintf(outfile_template, selected_percentile)
ggsave(outfile,
percentile_plot,
width = 100, height = 10, dpi = 300, limitsize = FALSE)
return(outfile)
}
percentile_levels <- 2
percentile_plot_files <- purrr::map(percentile_levels, function(percentile) {
percentile_plot <- percentile_duration_chart(national_prop_filter, percentile, p2_1951_2020_metadata, outfile_template='3_visualize/out/swarm_jd7d_%s_1951_1952.png')
return(percentile_plot)
})
##### timing #####
result_of_timing <- tibble(
fxns = c('_dt','_dt','_dplyr','_dplyr', '_dplyr','_dt'),
years = c(1,5,1,5, 10, 10),
seconds = c(11.5,123.9,20.1,104.6,277.3, 337.6) #579 with targets
)
ggplot2::ggplot(result_of_timing) +
geom_line(aes(x=years,y=seconds,color=fxns))
##### date chunking #####
# fully expand drought dataset - every day with drought is represented
# identify dates that have no droughts - pull for chunking
# process chunks of data then merge
# also identify max # of droughts on given date
p2_site_prop_2_expanded <- p2_site_prop_2 %>%
uncount(duration, .remove=FALSE, .id="id") %>%
mutate(date = start+id-1, .after=end)
# pull unique drought dates
# drought_dates <- unique(p2_site_prop_2_expanded$date)
drought_dates <- p2_site_prop_2_expanded %>%
count(date, name='n_droughts')
# get full sequence of dates
full_date_sequence <- seq.Date(from=min(drought_dates$date),to=max(drought_dates$date),by=1)
# find dates that didn't have droughts
dates_w_o_drought <- full_date_sequence[!(full_date_sequence %in% drought_dates$date)]
# for each time chunk, find max # of droughts on single day in that period
# figure out chunks if used all dates w/o drought as breaks
drought_chunks <-
tibble(break_date = c(dates_w_o_drought, max(full_date_sequence))) %>%
mutate(chunk_num = row_number(),
start_date = case_when(
chunk_num==1 ~ min(full_date_sequence),
TRUE ~ lag(break_date)+1
),
chunk_length_days = as.numeric(break_date-start_date)) %>%
select(chunk_num, start_date,break_date, chunk_length_days) #%>%
# group_by(chunk_num) %>%
# group_modify( ~ {
# drought_dates_subset <- drought_dates %>%
# filter(date >= .x$start_date & date<=.x$break_date)
# .x <- mutate(.x, max_single_day_droughts = max(drought_dates_subset$n_droughts),
# total_drought_days = sum(drought_dates_subset$n_droughts))
# })
# drought_chunks <- pmap_dfr(drought_chunks, function(...) {
# current_chunk <- tibble(...)
# drought_dates_subset <- drought_dates %>%
# filter(date >= current_chunk$start_date & date<=current_chunk$break_date)
# current_chunk %>%
# mutate(max_single_day_droughts = max(drought_dates_subset$n_droughts),
# total_drought_days = sum(drought_dates_subset$n_droughts))
# })
get_drought_info <- function(drought_df, chunk_df) {
pmap_dfr(chunk_df, function(...) {
current_chunk <- tibble(...)
drought_subset <- drought_df %>%
filter(date >= current_chunk$start_date & date<=current_chunk$break_date)
current_chunk %>%
mutate(max_single_day_droughts = max(drought_subset$n_droughts),
total_drought_days = sum(drought_subset$n_droughts),
n_days_w_droughts = nrow(drought_subset))
})
}
drought_chunks <- get_drought_info(drought_dates, drought_chunks)
# %>%
# group_by(break_num) %>%
# group_modify( ~ {
# drought_dates_subset <- drought_dates %>%
# filter(date >= .x$start_date & date<=.x$break_date)
# .x <- mutate(.x, max_single_day_droughts = max(drought_dates_subset$n_droughts),
# total_drought_days = sum(drought_dates_subset$n_droughts))
# })
# subset to larger chunks only
combined_drought_chunks <- drought_chunks %>%
filter(chunk_length_days>=365/3) %>%
select(break_date) %>%
# recalcuate break_num, start date, and break length
mutate(chunk_num = row_number(),
start_date = case_when(
chunk_num==1 ~ min(full_date_sequence),
TRUE ~ lag(break_date)+1
),
chunk_length_days = as.numeric(break_date-start_date),
chunk_length_year = chunk_length_days/365) #%>%
combined_drought_chunks <- get_drought_info(drought_dates, combined_drought_chunks)
check <- p2_site_prop_2 %>%
filter(start >= selected_breaks$start_date[1]) %>%
filter(start <= selected_breaks$break_date[1]) %>%
mutate(onset_day = as.integer(start - selected_breaks$start_date[1])+1) %>%
mutate(end_day = as.integer(end - selected_breaks$start_date[1])+1) %>%
mutate(end_day_2 = onset_day+duration) %>%
arrange(onset_day) %>%
mutate(idx = row_number())
##### targets #####
##### functions #####
create_event_swarm <- function(event_data, start_period, end_period){
event_subset <- event_data %>%
filter(start > start_period) %>%
filter(start <= end_period) %>%
mutate(onset_day = as.integer(start - start_period)) %>%
mutate(end_day = as.integer(end - start_period)) %>%
arrange(onset_day)
# set up an empty "swarm grid" to place drought events into
n <- 600 # set arbitrarily large number of possible simultaneous drought events positions. Trimmed prior to plotting
E <- as_tibble(matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1))
E <- E %>% mutate(priority = 1:n)
E <- E %>% arrange(desc(priority)) %>%
bind_rows(E) %>%
mutate(rnum = 1:(2*n))
# loop through each event and place into best available spot in grid
progress_bar <- txtProgressBar(min = 1, max = nrow(event_subset), style = 3)
for (idx in 1:nrow(event_subset)){
temp_dur <- event_subset[[idx,'duration']]
temp_startd <-event_subset[[idx,'onset_day']]
# find available spots looking within 1 day +/- the start date (to encourage a little compactness)
avail_rows <- E %>% select(all_of(temp_startd:(temp_startd + temp_dur - 1)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 0)
avail_rows_plus1d <- E %>% select(all_of((temp_startd+1):(temp_startd + temp_dur)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 1)
avail_rows_minus1d <- E %>% select(all_of((temp_startd-1):(temp_startd + temp_dur - 2)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = -1)
# find spot closest to central axis
all_avail_rows <- bind_rows(avail_rows, avail_rows_minus1d, avail_rows_plus1d) %>%
arrange(priority) %>%
group_by(priority) %>%
slice_sample(prop = 1) %>% # adds a little randomness by assigning to spot above or below central axis randomly
ungroup()
temp_rnum <- all_avail_rows[[1,'rnum']]
temp_pos_key <- all_avail_rows[[1, 'pos']]
if (temp_startd == 1){
temp_pos_key <- 0
}
# assign event to identified spot by using duration value
E[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- event_subset[[idx,'duration']]
E[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
setTxtProgressBar(progress_bar, idx)
}
# trim unused rows
ind <- E %>% select(-priority,-rnum) %>%
apply( 1, function(x) all(is.na(x)))
E <-E[ !ind, ]
E[E == 0] = NaN # remove spaces added to avoid events appearing connected
ncols = ncol(E)-2
E <- mutate(E, decade = as.factor(floor(year(start_period)/10)*10))
plot_dat <- E %>% select(-priority) %>%
pivot_longer(cols=1:ncols, names_to = "names", values_to = "duration")
plot_dat$names<- str_remove(plot_dat$names,"V")
plot_dat <- plot_dat %>% mutate(dt = as.integer(names)) %>%
mutate(date = as.Date(start_period + dt - 1)) %>%
mutate(rnum = rnum - n) %>%
drop_na()
return(plot_dat)
}
# event_subset_2000_2009 <- p2_site_prop_2 %>%
# filter(start > as.Date('2000-01-01')) %>%
# filter(start <= as.Date('2009-12-31')) %>%
# mutate(onset_day = as.integer(start - as.Date('2000-01-01'))) %>%
# mutate(end_day = as.integer(end - as.Date('2000-01-01'))) %>%
# arrange(onset_day) %>%
# mutate(idx = row_number())
# sum(event_subset_2000_2009$duration) # 116667
# event_subset_2000_2004 <- p2_site_prop_2 %>%
# filter(start > as.Date('2000-01-01')) %>%
# filter(start <= as.Date('2004-12-31')) %>%
# mutate(onset_day = as.integer(start - as.Date('2000-01-01'))) %>%
# mutate(end_day = as.integer(end - as.Date('2000-01-01'))) %>%
# arrange(onset_day) %>%
# mutate(idx = row_number())
# sum(event_subset_2000_2004$duration) #66677
# event_subset_2000 <- p2_site_prop_2 %>%
# filter(start > as.Date('2000-01-01')) %>%
# filter(start <= as.Date('2000-12-31')) %>%
# mutate(onset_day = as.integer(start - as.Date('2000-01-01'))) %>%
# mutate(end_day = as.integer(end - as.Date('2000-01-01'))) %>%
# arrange(onset_day) %>%
# mutate(idx = row_number())
# sum(event_subset_2000$duration) #12340
# create_event_swarm() for 1 year (2000-01-01 -> 2000-12-31) = (a) 73.44 seconds, (b) 64 secs, (c) 69 secs [(a) 10961 obs, (b) 10725, (c) 10683] || w/o progress bar 60 seconds [10962]
# create_event_swarm_mod() w/ group_modify for 1 year (2000-01-01 -> 2000-12-31) = 81 seconds
# create_event_swarm_mod() w/ purrr w/i for loop for 1 year (2000-01-01 -> 2000-12-31) = 66 seconds
# create_event_swarm_mod() w/ filter before slice_sample (a) = 38 seconds, (b) 34 secs, (c) 31 secs, (d) 33 secs [(b) 10918, (c) 10648, (d) 10669] || w/o progress bar 26 seconds [10968]
# create_event_swarm_dt() w/ data.table = (a) 18 seconds, (b) 17 secs, (c) 18 secs [(a) 12340, (b) 12340, (c) 12340 ] || w/o progress bar 14 seconds [12340] || dt for 200-2004 [66677]
# create_event_swarm_dplyr 2000-2004 [57726]
# tar_make() w/ create_event_swarm() for 20 year (2000-01-01 -> 2020-12-31) = 1940s = 30 min
# tar_make() w/ create_event_swarm_dt() for 20 year (2000-01-01 -> 2020-12-31) = 2502s = 40 min
create_event_swarm_mod <- function(event_data, start_period, end_period, max_droughts){
#browser()
event_subset <- event_data %>%
filter(start > start_period) %>%
filter(start <= end_period) %>%
mutate(onset_day = as.integer(start - start_period)) %>%
mutate(end_day = as.integer(end - start_period)) %>%
arrange(onset_day) %>%
mutate(idx = row_number())
# set up an empty "swarm grid" to place drought events into
n <- round(max_droughts/2)+10 #600 # set arbitrarily large number of possible simultaneous drought events positions. Trimmed prior to plotting
#### alt options :
###### keep as matrix
###### use data.table
E <- as_tibble(matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1))
E <- E %>% mutate(priority = 1:n)
E <- E %>% arrange(desc(priority)) %>%
bind_rows(E) %>%
mutate(rnum = 1:(2*n))
# loop through each event and place into best available spot in grid
progress_bar <- txtProgressBar(min = 1, max = nrow(event_subset), style = 3)
#### alt options:
###### use purrr
###### use data.table
for (idx in 1:nrow(event_subset)){
temp_dur <- event_subset[[idx,'duration']]
temp_startd <-event_subset[[idx,'onset_day']]
search_windows <- tibble(
window = c('d','plus1d', 'minus1d'),
start = c(temp_startd, temp_startd+1, temp_startd-1), # 12, 13, 11
end = c(temp_startd + temp_dur - 1, temp_startd + temp_dur, temp_startd + temp_dur - 2), # 18, 19, 17
pos = c(0, 1, -1)
)
# find available spots looking within 1 day +/- the start date (to encourage a little compactness)
all_avail_rows <- purrr::pmap_dfr(search_windows, function(...) {
current_window <- tibble(...)
E %>% select(all_of(current_window$start:current_window$end),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = current_window$pos)
}) %>%
filter(priority==min(priority)) %>%
slice_sample(prop = 1) # adds a little randomness by assigning to spot above or below central axis randomly
temp_rnum <- all_avail_rows[[1,'rnum']]
temp_pos_key <- all_avail_rows[[1, 'pos']]
if (temp_startd == 1){
temp_pos_key <- 0
}
# assign event to identified spot by using duration value
E[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- temp_dur #event_subset[[idx,'duration']]
E[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
setTxtProgressBar(progress_bar, idx)
}
# purrr
# window_list <- list()
# event_subset %>%
# group_by(idx) %>%
# group_map( ~ {
# # browser()
# current_matrix <- E
# temp_dur <- .x$duration
# temp_startd <- .x$onset_day
#
# search_windows <- tibble(
# window = c('d','plus1d', 'minus1d'),
# start = c(temp_startd, temp_startd+1, temp_startd-1),
# end = c(temp_startd + temp_dur - 1, temp_startd + temp_dur, temp_startd + temp_dur - 2),
# pos = c(0, 1, -1)
# )
#
# all_avail_rows <- purrr::pmap_dfr(search_windows, function(...) {
# current_window <- tibble(...)
# current_matrix %>% select(all_of(current_window$start:current_window$end),priority,rnum) %>%
# filter(is.na(if_all(starts_with("V")))) %>%
# mutate(pos = current_window$pos)
# }) %>%
# arrange(priority) %>%
# group_by(priority) %>%
# slice_sample(prop = 1) %>% # adds a little randomness by assigning to spot above or below central axis randomly
# ungroup()
# temp_rnum <- all_avail_rows[[1,'rnum']]
# temp_pos_key <- all_avail_rows[[1, 'pos']]
# if (temp_startd == 1){
# temp_pos_key <- 0
# }
#
# # taken_window <- tibble(
# # dt = ((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key)),
# # rnum = temp_rnum,
# # duration = temp_dur
# # )
# #
# # dt_run_tupes <- sets::tuple(taken_window$rnum, taken_window$dt)
#
# # assign event to identified spot by using duration value
# current_matrix[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- temp_dur #event_subset[[idx,'duration']]
# current_matrix[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
#
# E <<- current_matrix
#
# setTxtProgressBar(progress_bar, .y$idx)
# })
# trim unused rows
ind <- E %>% select(-priority,-rnum) %>%
apply( 1, function(x) all(is.na(x)))
E <-E[ !ind, ]
E[E == 0] = NaN # remove spaces added to avoid events appearing connected
ncols = ncol(E)-2
E <- mutate(E, decade = as.factor(floor(year(start_period)/10)*10))
plot_dat <- E %>% select(-priority) %>%
pivot_longer(cols=1:ncols, names_to = "names", values_to = "duration")
plot_dat$names<- str_remove(plot_dat$names,"V")
plot_dat <- plot_dat %>% mutate(dt = as.integer(names)) %>%
mutate(date = as.Date(start_period + dt - 1)) %>%
mutate(rnum = rnum - n) %>%
drop_na()
return(plot_dat)
}
create_event_swarm_dplyr <- function(event_data, start_period, end_period, max_droughts){
#browser()
event_subset <- event_data %>%
filter(start >= start_period) %>%
filter(start <= end_period) %>%
mutate(onset_day = as.integer(start - start_period)) %>%
mutate(end_day = as.integer(end - start_period)) %>%
arrange(onset_day) %>%
mutate(idx = row_number())
# set up an empty "swarm grid" to place drought events into
n <- max_droughts+10 #600 # set arbitrarily large number of possible simultaneous drought events positions. Trimmed prior to plotting
#### alt options :
###### keep as matrix
###### use data.table
E <- as_tibble(matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1))
E <- E %>% mutate(priority = 1:n)
E <- E %>% arrange(desc(priority)) %>%
bind_rows(E) %>%
mutate(rnum = 1:(2*n))
# loop through each event and place into best available spot in grid
progress_bar <- txtProgressBar(min = 1, max = nrow(event_subset), style = 3)
#### alt options:
###### use purrr
###### use data.table
# for (idx in 1:nrow(event_subset)){
# temp_dur <- event_subset[[idx,'duration']]
# temp_startd <-event_subset[[idx,'onset_day']]
# search_windows <- tibble(
# window = c('d','plus1d', 'minus1d'),
# start = c(temp_startd, temp_startd+1, temp_startd-1), # 12, 13, 11
# end = c(temp_startd + temp_dur - 1, temp_startd + temp_dur, temp_startd + temp_dur - 2), # 18, 19, 17
# pos = c(0, 1, -1)
# )
#
# # find available spots looking within 1 day +/- the start date (to encourage a little compactness)
# all_avail_rows <- purrr::pmap_dfr(search_windows, function(...) {
# current_window <- tibble(...)
# E %>% select(all_of(current_window$start:current_window$end),priority,rnum) %>%
# filter(is.na(if_all(starts_with("V")))) %>%
# mutate(pos = current_window$pos)
# }) %>%
# filter(priority==min(priority)) %>%
# slice_sample(prop = 1) # adds a little randomness by assigning to spot above or below central axis randomly
#
# temp_rnum <- all_avail_rows[[1,'rnum']]
# temp_pos_key <- all_avail_rows[[1, 'pos']]
# if (temp_startd == 1){
# temp_pos_key <- 0
# }
#
# # assign event to identified spot by using duration value
# E[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- temp_dur #event_subset[[idx,'duration']]
# E[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
#
# setTxtProgressBar(progress_bar, idx)
# }
#
# # purrr
# window_list <- list()
event_subset %>%
group_by(idx) %>%
group_map( ~ {
# browser()
current_matrix <- E
temp_dur <- .x$duration
temp_startd <- .x$onset_day
search_windows <- tibble(
window = c('d','plus1d', 'minus1d'),
start = c(temp_startd, temp_startd+1, temp_startd-1),
end = c(temp_startd + temp_dur - 1, temp_startd + temp_dur, temp_startd + temp_dur - 2),
pos = c(0, 1, -1)
)
all_avail_rows <- purrr::pmap_dfr(search_windows, function(...) {
current_window <- tibble(...)
current_matrix %>% select(all_of(current_window$start:current_window$end),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = current_window$pos)
}) %>%
filter(priority==min(priority)) %>%
slice_sample(prop = 1)
# arrange(priority) %>%
# group_by(priority) %>%
# slice_sample(prop = 1) %>% # adds a little randomness by assigning to spot above or below central axis randomly
# ungroup()
temp_rnum <- all_avail_rows[[1,'rnum']]
temp_pos_key <- all_avail_rows[[1, 'pos']]
if (temp_startd == 1){
temp_pos_key <- 0
}
# taken_window <- tibble(
# dt = ((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key)),
# rnum = temp_rnum,
# duration = temp_dur
# )
#
# dt_run_tupes <- sets::tuple(taken_window$rnum, taken_window$dt)
# assign event to identified spot by using duration value
current_matrix[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- temp_dur #event_subset[[idx,'duration']]
current_matrix[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
E <<- current_matrix
setTxtProgressBar(progress_bar, .y$idx)
})
# trim unused rows
ind <- E %>% select(-priority,-rnum) %>%
apply( 1, function(x) all(is.na(x)))
E <-E[ !ind, ]
E[E == 0] = NaN # remove spaces added to avoid events appearing connected
ncols = ncol(E)-2
E <- mutate(E, decade = as.factor(floor(year(start_period)/10)*10))
plot_dat <- E %>% select(-priority) %>%
pivot_longer(cols=1:ncols, names_to = "names", values_to = "duration")
plot_dat$names<- str_remove(plot_dat$names,"V")
plot_dat <- plot_dat %>% mutate(dt = as.integer(names)) %>%
mutate(date = as.Date(start_period + dt - 1)) %>%
mutate(rnum = rnum - n) %>%
drop_na()
return(plot_dat)
}
create_event_swarm_mod_2 <- function(event_data, start_period, end_period, max_droughts){
#browser()
event_subset <- event_data %>%
filter(start >= start_period) %>%
filter(start <= end_period) %>%
mutate(onset_day = as.integer(start - start_period)) %>%
mutate(end_day = as.integer(end - start_period)) %>%
arrange(onset_day) %>%
mutate(idx = row_number())
# set up an empty "swarm grid" to place drought events into
n <- max_droughts+10 #600 # set arbitrarily large number of possible simultaneous drought events positions. Trimmed prior to plotting
#### alt options :
###### keep as matrix
###### use data.table
E <- as_tibble(matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1))
E <- E %>% mutate(priority = 1:n)
E <- E %>% arrange(desc(priority)) %>%
bind_rows(E) %>%
mutate(rnum = 1:(2*n))
mat <- matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1)
E_1 <- setDT(as.data.frame(mat))[]
E_1[,priority:=1:n]
E_2 <- E_1[order(-priority)]
E_3 <- rbindlist(list(E_2, E_1))
E_3[,rnum:=1:(2*n)]
# loop through each event and place into best available spot in grid
progress_bar <- txtProgressBar(min = 1, max = nrow(event_subset), style = 3)
# store priority, rnum for all rows
rnum_priority <- E_3[,.(priority,rnum)]
#### alt options:
###### use purrr
###### use data.table
for (idx in 1:nrow(event_subset)){
temp_dur <- event_subset[[idx,'duration']]
temp_startd <-event_subset[[idx,'onset_day']]
# if(idx==50) {
# browser()
# }
#
# find available spots looking within 1 day +/- the start date (to encourage a little compactness)
avail_rows <- E %>% select(all_of(temp_startd:(temp_startd + temp_dur - 1)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 0)
avail_rows_plus1d <- E %>% select(all_of((temp_startd+1):(temp_startd + temp_dur)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 1)
avail_rows_minus1d <- E %>% select(all_of((temp_startd-1):(temp_startd + temp_dur - 2)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = -1)
# find spot closest to central axis
all_avail_rows <- bind_rows(avail_rows, avail_rows_minus1d, avail_rows_plus1d) %>%
filter(priority==min(priority)) %>%
slice_sample(prop = 1)
dt_d <- E_3[,temp_startd:(temp_startd + temp_dur - 1)]
dt_d <- cbind(dt_d, rnum_priority)
dt_d <- dt_d[rowSums(is.na(dt_d))==(ncol(dt_d)-2),] # pull rows where all but priority and rnum are NA
dt_d[,pos:=0]
dt_plus1d <- E_3[,(temp_startd+1):(temp_startd + temp_dur)]
dt_plus1d <- cbind(dt_plus1d, rnum_priority)
dt_plus1d <- dt_plus1d[rowSums(is.na(dt_plus1d))==(ncol(dt_plus1d)-2),]
dt_plus1d[,pos:=1]
dt_minus1d <- E_3[,(temp_startd-1):(temp_startd + temp_dur - 2)]
dt_minus1d <- cbind(dt_minus1d, rnum_priority)
dt_minus1d <- dt_minus1d[rowSums(is.na(dt_minus1d))==(ncol(dt_minus1d)-2),]
dt_minus1d[,pos:=-1]
all_dt <- rbindlist(list(dt_d, dt_minus1d, dt_plus1d), fill=TRUE) # rbindlist(list(dt_d, dt_plus1d, dt_minus1d)) # DROPS columns that aren't shared
all_dt <- all_dt[priority==min(priority)]
all_dt <- all_dt[sample(.N,1)]
temp_rnum <- all_avail_rows[[1,'rnum']]
temp_pos_key <- all_avail_rows[[1, 'pos']]
temp_rnum_dt <- all_dt[,rnum]
temp_pos_key_dt <- all_dt[,pos]
if (temp_startd == 1){
temp_pos_key <- 0
temp_pos_key_dt <- 0
}
# assign event to identified spot by using duration value
E[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- temp_dur #event_subset[[idx,'duration']]
E[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
E_3[temp_rnum_dt,((temp_startd + temp_pos_key_dt):(temp_startd + temp_dur - 1 + temp_pos_key_dt))] <- temp_dur #event_subset[[idx,'duration']]
E_3[temp_rnum_dt,(temp_startd + temp_dur + temp_pos_key_dt)] <- 0 # enforces a space between subsequent events
setTxtProgressBar(progress_bar, idx)
}
browser()
# trim unused rows
ind <- E %>% select(-priority,-rnum) %>%
apply( 1, function(x) all(is.na(x)))
E <-E[ !ind, ]
E_3_ind <- rowSums(is.na(E_3))==(ncol(E_3)-2)
E_3 <- E_3[ !E_3_ind, ]
E[E == 0] = NaN # remove spaces added to avoid events appearing connected
E_3[E_3 == 0] <- NaN # remove spaces added to avoid events appearing connected
ncols = ncol(E)-2
E <- mutate(E, decade = as.factor(floor(year(start_period)/10)*10))
E_3[,decade:=as.factor(floor(year(start_period)/10)*10)]
plot_dat <- E %>% select(-priority) %>%
pivot_longer(cols=1:ncols, names_to = "names", values_to = "duration")
plot_dat$names<- str_remove(plot_dat$names,"V")
plot_dat <- plot_dat %>% mutate(dt = as.integer(names)) %>%
mutate(date = as.Date(start_period + dt - 1)) %>%
mutate(rnum = rnum - n) %>%
drop_na()
E_3 <- E_3[,priority:=NULL] # drop priority column
plot_dat_dt <- melt(E_3, measure.vars=colnames(E_3)[1:(ncol(E_3)-2)], # all but last 2 columns (rnum, decade)
variable.name="names",value.name = "duration")
plot_dat_dt[,dt:=as.integer(str_remove(names,"V"))]
plot_dat_dt[,date:=as.Date(start_period + dt - 1)]
plot_dat_dt[,rnum:=rnum - n]
plot_dat_dt <- na.omit(plot_dat_dt)
plot_dat_df <- as.data.frame(plot_dat_dt)
# return(plot_dat)
return(plot_dat_df)
}
create_event_swarm_max_droughts <- function(event_data, start_period, end_period, max_droughts){
event_subset <- event_data %>%
filter(start >= start_period) %>%
filter(start <= end_period) %>%
mutate(onset_day = as.integer(start - start_period)+1) %>%
mutate(end_day = onset_day+duration-1) %>% #as.integer(end - start_period)
arrange(onset_day)
# event_subset_2 <- event_data %>%
# filter(start >= start_period) %>%
# mutate(onset_day = as.integer(start - start_period)+1) %>%
# mutate(end_day = onset_day+duration-1) %>% #as.integer(end - start_period)
# filter(end <= end_period) %>%
# arrange(onset_day)
# set up an empty "swarm grid" to place drought events into
n <- round(max_droughts/2)+10 #600 # set arbitrarily large number of possible simultaneous drought events positions. Trimmed prior to plotting
E <- as_tibble(matrix(NaN,nrow=n,ncol=max(event_subset$end_day)+1))
E <- E %>% mutate(priority = 1:n)
E <- E %>% arrange(desc(priority)) %>%
bind_rows(E) %>%
mutate(rnum = 1:(2*n))
# loop through each event and place into best available spot in grid
progress_bar <- txtProgressBar(min = 1, max = nrow(event_subset), style = 3)
for (idx in 1:nrow(event_subset)){
temp_dur <- event_subset[[idx,'duration']]
temp_startd <-event_subset[[idx,'onset_day']]
# find available spots looking within 1 day +/- the start date (to encourage a little compactness)
avail_rows <- E %>% select(all_of(temp_startd:(temp_startd + temp_dur - 1)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 0)
avail_rows_plus1d <- E %>% select(all_of((temp_startd+1):(temp_startd + temp_dur)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = 1)
avail_rows_minus1d <- E %>% select(all_of((temp_startd-1):(temp_startd + temp_dur - 2)),priority,rnum) %>%
filter(is.na(if_all(starts_with("V")))) %>%
mutate(pos = -1)
# find spot closest to central axis
all_avail_rows <- bind_rows(avail_rows, avail_rows_minus1d, avail_rows_plus1d) %>%
arrange(priority) %>%
group_by(priority) %>%
slice_sample(prop = 1) %>% # adds a little randomness by assigning to spot above or below central axis randomly
ungroup()
temp_rnum <- all_avail_rows[[1,'rnum']]
temp_pos_key <- all_avail_rows[[1, 'pos']]
if (temp_startd == 1){
temp_pos_key <- 0
}
# assign event to identified spot by using duration value
E[temp_rnum,((temp_startd + temp_pos_key):(temp_startd + temp_dur - 1 + temp_pos_key))] <- event_subset[[idx,'duration']]
E[temp_rnum,(temp_startd + temp_dur + temp_pos_key)] <- 0 # enforces a space between subsequent events
setTxtProgressBar(progress_bar, idx)
}
# trim unused rows
ind <- E %>% select(-priority,-rnum) %>%
apply( 1, function(x) all(is.na(x)))
E <-E[ !ind, ]
E[E == 0] = NaN # remove spaces added to avoid events appearing connected
ncols = ncol(E)-2
E <- mutate(E, decade = as.factor(floor(year(start_period)/10)*10))
plot_dat <- E %>% select(-priority) %>%
pivot_longer(cols=1:ncols, names_to = "names", values_to = "duration")
plot_dat$names<- str_remove(plot_dat$names,"V")
plot_dat <- plot_dat %>% mutate(dt = as.integer(names)) %>%
mutate(date = as.Date(start_period + dt - 1)) %>%
mutate(rnum = rnum - n) %>%
drop_na()
return(plot_dat)
}
create_event_swarm_dt <- function(event_data, start_period, end_period, max_droughts){
event_subset <- event_data %>%
filter(start >= start_period) %>%
filter(start <= end_period) %>%