Mount Shasta Summit Pass Data

–Visualizing Search and Rescue Data
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Author

RJ Cody Markelz

Published

January 5, 2024

I am collaborating with the Mount Shasta Avalanche Center to visualize data sets and build models to help them forecast avalanches in the area better. A small dataset that they have been collecting since 1992 is the number of Summit Passes sold along with the Search and Rescue (SAR) activities that the Rangers perform. This data is available in the Avalanche Center Annual Reports and SAR statistics.

See the other posts in the Avalanche Data series: Post 1, Post 2, Post 3 and Post 4.

Here are a few plots I made for the upcoming reports.

Load the libraries and in-file the data.

library(ggplot2)
library(tidyverse)
library(gridExtra)

# Read in the CSV file
pass_data <- read.csv("~/DATA/data/mount-shasta-summit-pass-data.csv")

I always take a quick look at the data to make sure it infiled correctly.

head(pass_data)
  YEAR SUMMIT.PASSES.SOLD FATALITIES SEARCH RESCUE SELF.RESCUE Total
1 1992                  0          0      0      3           0     0
2 1992                  0          0      0      0           0     0
3 1993                  0          1      0      3           0     0
4 1994                  0          1      0      4           0     0
5 1995                  0          1      0      3           0     0
6 1996                  0          0      0      4           0     0
summary(pass_data)
      YEAR      SUMMIT.PASSES.SOLD   FATALITIES         SEARCH     
 Min.   :1992   Min.   :   0       Min.   :0.0000   Min.   : 0.00  
 1st Qu.:1999   1st Qu.:4628       1st Qu.:0.0000   1st Qu.: 1.00  
 Median :2007   Median :5679       Median :1.0000   Median : 3.00  
 Mean   :2007   Mean   :5104       Mean   :0.8125   Mean   : 3.03  
 3rd Qu.:2015   3rd Qu.:6817       3rd Qu.:1.0000   3rd Qu.: 4.00  
 Max.   :2023   Max.   :9349       Max.   :3.0000   Max.   :10.00  
                                   NA's   :1                       
     RESCUE       SELF.RESCUE         Total      
 Min.   : 0.00   Min.   : 0.000   Min.   : 0.00  
 1st Qu.: 4.00   1st Qu.: 0.000   1st Qu.: 9.00  
 Median : 6.00   Median : 2.000   Median :12.00  
 Mean   : 8.03   Mean   : 4.576   Mean   :15.82  
 3rd Qu.:10.00   3rd Qu.: 6.000   3rd Qu.:20.00  
 Max.   :32.00   Max.   :20.000   Max.   :54.00

Transform the data into long format for easier plotting.

data_long <- pass_data %>% 
  pivot_longer(cols = c("FATALITIES", "SEARCH","RESCUE","SELF.RESCUE"), 
               names_to = "variable", 
               values_to = "value")

Create the base plots for summit passes sold and the other Search and Rescue statistics collected.

summit_pass1 <- ggplot() +
  geom_line(data = pass_data, aes(x = YEAR, y = SUMMIT.PASSES.SOLD), color = "blue", size = 1) +
  scale_y_continuous(name = "Summit Passes Sold") +
  theme_minimal() +
  labs(x = "Year")
summit_pass1

SAR_1 <- ggplot() +
  geom_bar(data = data_long, aes(x = YEAR, y = value, fill = variable), stat = "identity", position = position_dodge()) +
  scale_y_continuous(name = "Number of Incidents") +
  theme_minimal() +
  labs( x = "Year", fill = "")
SAR_1

I like to default to a minimal theme to see how it looks and then add gridlines and color back to the plots. Minimal plots are great for people who are used to looking at plots, however the end readers of this report are mostly from the general public so I decided to add in each year along the x-axis. I want to stack the plots so I will also move the legend for the SAR data to the bottom.

summit_pass2 <- ggplot() +
  geom_line(data = pass_data, aes(x = YEAR, y = SUMMIT.PASSES.SOLD), color = "blue", size = 1) +
  scale_x_continuous(breaks = seq(1992, 2023, 1))  +
  scale_y_continuous(name = "Summit Passes Sold") +
  theme(axis.text.x = element_text(angle = -75, vjust = 0.5)) +
  labs(x = "Year")
summit_pass2

SAR_2 <- ggplot() +
  geom_bar(data = data_long, aes(x = YEAR, y = value, fill = variable), stat = "identity", position = position_dodge()) +
  scale_x_continuous(breaks = seq(1992, 2023, 1))  +
  scale_y_continuous(name = "Number of Incidents") +
  theme(legend.position="bottom", 
        axis.text.x = element_text(angle = -75, vjust = 0.5)) +
  labs( x = "Year", fill = "")
SAR_2

Using the gridExtra package we can combine the plots we already generated.

combined_plot <- grid.arrange(summit_pass2, SAR_2)

combined_plot
TableGrob (2 x 1) "arrange": 2 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]

These plots are going to be published and printed so we want to make sure they are large enough and have at least a 300 dpi resolution.

ggsave(plot = summit_pass2, "~/DATA/images/summit-passes.png", width = 10, dpi = 300)

ggsave(plot = SAR_2, "~/DATA/images/sar-metrics.png", width = 10, dpi = 300)

ggsave(plot = combined_plot, "~/DATA/images/pass-sar-metrics.png", width = 10, height = 8, dpi = 300)

There we have it. Some basic tidy-verse plots for inclusion in the Annual and SAR reports.

See the other posts in the Avalanche Data series: Post 1, Post 2, Post 3 and Post 4.