#load packages
library(tidyverse)
library(dplyr)
#library(plyr)
library(ggplot2)
library(stringr)
library(lubridate)
library(kableExtra)
# load data
obs <- read.csv("CAIC_obs.csv", na.strings = c("", "NA"))
compass <- read.csv("compass.csv")

obs <- obs %>% 
  na.omit() %>% 
  # convert aspect from cardinal to degrees using join
  left_join(compass, by=c("Aspect"="Cardinal")) %>% 
  # remove letter from size measurement and convert to number
  mutate(R.Size = as.numeric(str_remove_all(R.Size, "R"))) %>% 
  mutate(D.Size = as.numeric(str_remove_all(D.Size, "D"))) %>% 
  # convert date format so time series stays in chronological order
  mutate(Date = ymd(mdy(Date)))

Figure 1.

series <- data.frame(
    count = count(obs %>% group_by(Date)))

series %>% 
  ggplot(aes(x=count.Date,y=count.n)) +
  geom_line(size=0.5, color="#3375B2") +
  theme_light() +
  scale_x_date(date_labels = "%m-%Y", date_breaks = "1 month", date_minor_breaks = "1 week") +
  theme(panel.grid.major = element_line(color="lightgray",size=0.4)) +
  xlab("Date") +
  ylab("Number of Avalanches") +
  labs(title="Number of Recorded Avalanches per Day",
       subtitle = "Colorado 2021-2022 Season",
       caption = "Source: Colorado Avalanche Information Center (CAIC)")

Figure 2.

obs_pivot <- obs %>% 
  mutate(Date = str_remove_all(as.character(Date),"-")) %>% 
  filter(Date %in% c("20211209", "20211210", "20211231","20220101", "20220223", "20220224")) %>% 
  group_by(Date, Zone) %>% 
  tally() %>% 
  pivot_wider(
            names_from = Date,
            names_prefix = "day",
            values_from = n) %>% 
  mutate("Dec910" = replace_na(day20211209 + day20211210, 0),
         "Dec311" = replace_na(day20211231 + day20220101, 0),
         "Feb2324" = replace_na(day20220223 + day20220224, 0)) %>% 
  arrange(by=desc(Feb2324)) 

# obs_perc <- obs_pivot %>% 
#   select(Dec910, Dec311, Feb2324)
# colSums(obs_perc)

obs_pivot <- obs_pivot %>% 
  mutate("percDec910" = round(Dec910/356*100, digits=1),
         "percDec311" = round(Dec311/301*100, digits=1),
         "percFeb2324" = round(Feb2324/474*100, digits=1))

obs_table <- obs_pivot %>% 
  select(Zone, percDec910, percDec311, percFeb2324)

kbl(obs_table,
    col.names = c("Zone", "Dec 9-10, 2021", "Dec 31-Jan 1, 2022", "Feb 23-24, 2022"),
    caption = "Percent of Avalanches Per Zone during Spikes in the Colorado 2021-2022 Season") %>% 
  kable_styling(bootstrap_options="condensed",full_width = F) %>% 
  footnote(general = "Source: Colorado Avalanche Information Center (CAIC)", general_title="")
Percent of Avalanches Per Zone during Spikes in the Colorado 2021-2022 Season
Zone Dec 9-10, 2021 Dec 31-Jan 1, 2022 Feb 23-24, 2022
Gunnison 68.0 52.5 33.1
North San Juan 14.3 9.0 27.2
Aspen 10.4 15.3 14.3
South San Juan 6.2 9.3 14.1
Sawatch 1.1 2.0 8.0
Vail & Summit County 0.0 3.0 3.2
Front Range 0.0 9.0 0.0
Grand Mesa 0.0 0.0 0.0
Sangre de Cristo 0.0 0.0 0.0
Steamboat & Flat Tops 0.0 0.0 0.0
Source: Colorado Avalanche Information Center (CAIC)

Figure 3.

#compile data for chart
polar <- obs %>% 
  subset(Aspect!="All" & Aspect!="U") %>% # only avys with cardinal aspects
  group_by(Aspect, Elevation)

#specify order of stacked bars
polar$Elevation <- factor(polar$Elevation, levels=c("All",">TL","TL","<TL")) 

#all aspects but not centered
ggplot(polar, aes(x = Degrees, fill = Elevation)) +
  geom_histogram(binwidth = 45, boundary=-22.5, color = "black", size = .25) + #center=0
  coord_polar() + #makes circular
  scale_x_continuous(limits = c(-22.5,360-22.5), #limits not include 0 data but not centered
                     breaks = compass$Degrees,
                     labels = compass$Cardinal) +
  scale_fill_brewer(labels = c("All","Above Treeline","At Treeline","Below Treeline")) +
  theme_minimal() +
  labs(title = "Recorded Avalanches by Aspect and Elevation",
       subtitle = "Colorado 2021-2022 Season",
       caption = "Source: Colorado Avalanche Information Center (CAIC)") + 
  xlab("Aspect") +
  ylab("Number of Avalanches")

Figure 4.

size_count <- obs %>% 
  group_by(D.Size, R.Size) %>% 
  tally()

ggplot(size_count, aes(x=D.Size, y=R.Size)) +
  geom_point(aes(size=n, color=n)) +
  scale_x_continuous(limits = c(0, 5)) +
  scale_y_continuous(limits = c(0, 5)) +
  scale_size(range=c(1,10), breaks=c(10, 100, 500, 1000)) +
  scale_fill_brewer() +
  guides(color=FALSE, size=FALSE) + # to keep guide legends use =guide_legend()
  geom_text(aes(label = n), size = 3, vjust = 3) +
  coord_fixed(ratio=1) +
  theme_light() +
  labs(title="Size of Avalanches", 
       subtitle="Colorado 2021-2022 Season, 3863 Total Avalanches",
       x="Destructive Size (destructive potential)",
       y="Relative Size (volume relative to path size)",
       caption = "Source: Colorado Avalanche Information Center (CAIC)")

References

Chang, Winston. (2022). R Graphics Cookbook 2nd Edition, “8.16 Making a Circular Plot”. https://r-graphics.org/recipe-axes-polar.

Colorado Avalanche Information Center. (2022). Observation Explorer [Data set]. https://forecasts.avalanche.state.co.us/explorer/.

Notes