brouwern/countpva
What the Package Does (One Line, Title Case)

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In brouwern/countpva: What the Package Does (One Line, Title Case) 

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

#library(gamkapva)

Table 6, page 15, Yellowstone Grizzley Bear Investigations 2018

Data TODO: R object for focal data

year <- 1980:2034
N.obs.bwc <- c(NA,NA,NA,13,17,
                9,25,13,19,16,
               25,24,25,20,20,
               17,33,31,35,33,
               37,42,52,38,49,
               31,47,50,44,42,
               51,39,49,58,50,
               46,50,58,58,NA,
               NA,NA,NA,NA,NA,
               NA,NA,NA,NA,NA,
               NA,NA,NA,NA,NA)

N.chao2.bwc<-c(NA,NA,NA,19,22,
               18,28,17,21,18,
               25,38,41,21,23,
               43,38,39,37,36,
               51,48,58,46,58,
               31,45,53,56,44, #31 - outlier?
               56,47,59,60,64,
               51,56,64,57,NA,
               NA,NA,NA,NA,NA,
               NA,NA,NA,NA,NA,
               NA,NA,NA,NA,NA)
length(year)
length(N.obs.bwc)
length(N.chao2.bwc)


# TODO: adult total or just adults?
N.adult1 <- N.obs.bwc*(1/0.274)
N.adult2 <- N.chao2.bwc*(1/0.274)

bears <- data.frame(year, N.obs.bwc, N.chao2.bwc,
                    N.adult1, N.adult2)

bears$N.adult2.log <- log(bears$N.adult2)

# Table 5
litter.size <- c(1.69, 1.82, 1.78, 1.92, 2.23,
                 2.16, 1.81, 2.32, 1.87,2.40,
                 2.05, 2.35, 2.18, 2.18, 2.00,
                 2.00, 1.91, 1.95, 1.86, 1.96,
                 1.97, 1.96, 1.84, 2.04, 2.16,
                 1.91, 2.12, 1.98, 1.90, 1.92,
                 2.17, 1.92, 1.98, 1.96, 1.98,
                 2.05)

Wolves: https://en.wikipedia.org/wiki/History_of_wolves_in_Yellowstone Bison: Emigration and Density Dependence in Yellowstone Bison

Take a look at the data

bears

R is great for plots!

An awesome plot about yellowstone bears

library(ggplot2)
library(ggpubr)
i.outlier <- which(bears$N.chao2.bwc == 31)
bear.K <- 250
ggbear <- ggpubr::ggline(data = bears,
       y = "N.adult2",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 1.5,
       point.size = 4,
       shape = 16,
       xlab = "Year",
       ylab = "Adult bears (N)",
       title = "Estimated adult Grizzly Bear population of\n Yellowstone National Park, 1983-2016") +
  geom_hline(yintercept = bear.K, linetype = 2) +
  geom_segment(aes(x = 1988, xend = 1988, y = 40, yend = 0),
               col = "red",
               size = 3,
               arrow = arrow()) +
  geom_segment(aes(x = 1995, xend = 1995, y = 40, yend = 0),
               col = "darkgrey",
               size = 3,
               arrow = arrow()) +
  annotate("text",label = "Fire",
            x = 1988, y = 50) +
  annotate("text",label = "Wolves",
            x = 1996, y = 50) +
  annotate("text",label = "Bear Carrying Capacity (K)?",
            x = 1990, y = 258)

ggbear +  geom_smooth(method = "lm",se = F, 
              formula = y ~ x + I(x^2),
              fullrange = T)

#linear trend
sz <- 0.15
ggbear +  geom_smooth(method = "lm",se = F, 
              formula = y ~ x + I(x^2),
              fullrange = T) +
#bracket - "annotations"
geom_segment(x = 2000,  xend = 2002, 
               y = 0,  yend = 0, size = sz) +
geom_segment(x = 2000,  xend = 2002, 
               y = 50,  yend = 50, size = sz) +
geom_segment(x = 2002,  xend = 2002, 
               y = 0,  yend = 50, size = sz) +
annotate("text",label = "Annotations",
            x = 2008, y = 27) +
# arrow - "trend line"
geom_segment(aes(xend = 2021, x = 2028, yend = bear.K-30, y = bear.K-80),
               col = "darkgrey",
               size = sz+0.5,
               arrow = arrow(type = "closed",angle = 20))  +
annotate("text",label = "\nTrend line\n(regression line\nor smoother)",
            x = 2030, y = bear.K-95, parse = F) +
# arrow - "reference line"
geom_segment(aes(x = 1990, xend = 1999, y = bear.K-30, yend = bear.K-6),
               col = "darkgrey",
               size = sz+0.5,
               arrow = arrow(type = "closed",angle = 20))  +
annotate("text",label = "Reference line",
            x = 1990, y = bear.K-40, parse = F) 

#large drop - many die
ggbear +  geom_smooth(method = "lm",se = F, 
              formula = y ~ x + I(x^2),
              fullrange = T) +
  geom_rect(xmin = 2006, xmax = 2035,
            ymin = 100, ymax = 250, fill = "white") +
   geom_rect(xmin = 2005.1, xmax = 2035,
            ymin = 120, ymax = 250, fill = "white") +
  geom_curve(x = 2006, y = 110, xend = 2006, yend = 220)



#gam
ggbear +  geom_smooth(method = "gam",se = F, 
              fullrange = T) 

#loess
ggbear +  geom_smooth(method = "loess",se = F, 
              fullrange = T)

Biology Carrying Capacity (K) Logistic equation (show data prior to 1980s to evoke this?) Exponential population growth (plot exponential population growth?) Fire: succession Wolves: competition whitebark pine: climate change, disease population dynamics N.t+1 = N.t + B - D

R/Stats Vocab: quiz folks on this? assignment object Annotation: a label or note included within a plot (unsed many other ways in science) Smoother: a statistical tool used to visualize trends in data that moves around a lot. Instead of a straight "line of best fit" for the entire dataset, a smoother creates a flexible line that attempts to show variablity in the trend. non-linear/non-linearities: not a straight line lm: linear model; regression line, line of best fit ~: a tilda. Kinda like =, or f(x) :: library, package: external software loaded into R se: standard error stochastic/stochasticity * asymptote

shnow log(bears)

Create data that fall perfectly on a line to compare random/stoch data to perfect data? Show data from prior to 1980s to evoke logistic equation

Questions What does "/n" do in the title

Log 

ggpubr::ggline(data = bears,
       y = "N.adult2.log",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 1.5,
       point.size = 4,
       shape = 16,
       xlab = "Year",
       ylab = "log(Adult bears)",
       title = "Population trend Grizzly Bear population of\n Yellowstone National Park, 1983-2016") +
  geom_smooth(method = "gam",se = F, 
             #formula = y ~ x + I(x^2)  + I(x^3) ,
              fullrange = T)

Practice

elk <- c(9609,10469,NA,NA,6380,
         6534,6534,4272, 4355, 5593,7326,
         8290,10135,10739,12754,
         12354,
         13047, 12941,
         11149,
         NA,NA,
         16363,
         NA,
         NA,
         NA,
         16742,17901,19272,
         17023,
         15644,
         12335,
         15587,
         18066,
         19299,
         17290,
         15397,
         14246,
         12025,
         12075,
         14682,
         13673,
         11969,
         9215,
         8335)

year <- 1960:2003
length(elk)
length(year)

plot(elk ~ year, type = "b")

Rotate arrows to look like paper Romme et al 200x "Twenty Years After the 1988 Yellowstone Fires: Lessons About Disturbance and Ecosystems"

ggline(data = YNP_north_elk,
       y = "N.elk",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 1.5,
       point.size = 4,
       shape = 16,
       xlab = "days",
       ylab = "Adult bears (N)",
       title = "Estimated adult Grizzly Bear population of\n Yellowstone National Park, 1983-2016") +
  geom_smooth(method = "gam",se = F,
              fullrange = T) +
  geom_hline(yintercept = 17500, linetype = 2) +
  geom_segment(aes(x = 1988, xend = 1988, y = 4000, yend = 0),
               col = "red",
               size = 3,
               arrow = arrow()) +
    geom_segment(aes(x = 1995, xend = 1995, y = 4000, yend = 0),
               col = "darkgrey",
               size = 3,
               arrow = arrow()) +
  annotate("text",label = "Fire",
            x = 1988, y = 5000) +
  annotate("text",label = "Wolves",
            x = 1996, y = 5000) +
  annotate("text",label = "Bear Carrying Capacity (K)?",
            x = 1985, y = 17500)

Assignment

Key theme of class - don't freak out when you see code - pick it apart

Comment things out to determine what things do

wolves <- read.csv(here::here("wolf_data.csv"))
wolves <- read.csv("wolf_data.csv")

Fix the errors change change method = "lm" to "gam"

ggline(data = wolves,
       y = "wolves",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 0.5,
       point.size = 0,
       shape = 16,
       xlab = "day",
       ylab = "Adult bears (t)",
       title = "Estimated adult Grizzly Bear population of\n Allegheny State Park, 1983-2016") +
  geom_smooth(method = "lm",se = F) +
  geom_hline(yintercept = 250, linetype = 2) +
  annotate("text",label = "Bear Carrying Capacity (K)?",
            x = 1995, y = 256) +
  geom_point(aes(size = packs, color = packs)) +
  scale_colour_gradientn(colours = terrain.colors(10))

ggline(data = wolves,
       y = "wolves",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 0.5,
       point.size = 5,
       shape = 16,
       xlab = "Year",
       ylab = "Wolves (N)",
       xlim = c(1995, 2006),
       title = "Estimated wolf population of\n Yellowstone National Park") #+
  #geom_smooth(method = "lm",se = F) +
  #geom_hline(yintercept = 250, linetype = 2) #+
  #annotate("text",label = "Wolf Carrying Capacity (K)?",
  #          x = 1995, y = 256) +
  #geom_point(aes(size = packs, color = packs)) +
  #scale_colour_gradientn(colours = terrain.colors(10))

ggline(data = wolves,
       y = "wolves",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 0.5,
       point.size = 5,
       shape = 16,
       xlab = "Year",
       ylab = "Wolves (N)",
       #xlim = c(1995, 2006),
       title = "Estimated wolf population of\n Yellowstone National Park") +
  #geom_smooth(method = "lm",se = F) +
  geom_hline(yintercept = 100, linetype = 2) #+
  #annotate("text",label = "Wolf Carrying Capacity (K)?",
  #          x = 1995, y = 256) +
  #geom_point(aes(size = packs, color = packs)) +
  #scale_colour_gradientn(colours = terrain.colors(10))

A brief field guide to plots in R

Another approach to graphics is xxx, but since the rise of ggplot2 this is rarely used.

Plot in base R

Brief walk through

plot(N.adult2 ~ year, data = bears)

Type = "l" adds line

plot(N.adult2 ~ year, data = bears, type = "l")

Type = "b" adds line and keeps points

plot(N.adult2 ~ year, data = bears,type = "b")

What has changed about this plot? What caused the change?

plot(N.adult2 ~ year, data = bears,type = "b",
     ylim = c(0,250))

TODO: add axis labels

Yellowstone fire occured in 1988. Add a vertical reference line using abline()

plot(N.adult2 ~ year,data = bears, type = "b",
     ylim = c(0,250))
abline(v = 1988)

Make line red using col = "red

plot(N.adult2 ~ year,data = bears, type = "b",
     ylim = c(0,250))
abline(v = 1988, col = "red")

Make line thick using lwd = 3. What do you think lwd means?

plot(N.adult2 ~ year,data = bears, type = "b",
     ylim = c(0,250))
abline(v = 1988, col = "red", lwd = 3)

Plots in ggplot2

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point() +
  geom_line()

Set them to white

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point() +
  geom_line() +
  theme_bw()

Increase size of points

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw()

Lael axis. What command is doing this

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year")

Label y axis

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)")

Set y axis limit to 0. What command does this?

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225)

Add trend line

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "lm", se = F)

Add smoother

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F)

Compare

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F) +
  geom_smooth(method  = "lm", se = F) 

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F) +
  geom_hline(yintercept = 210)

Make line dashed

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F) +
  geom_hline(yintercept = 210, linetype = 2)

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F) +
  geom_hline(yintercept = 210, linetype = 2, col = "red")

Add vertical line for fire

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F) +
  geom_hline(yintercept = 210, linetype = 2) +
  geom_vline(xintercept = 1988)

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,225) +
  geom_smooth(method  = "gam", se = F,
              fullrange=TRUE) +
  geom_hline(yintercept = 210, linetype = 2) +
  #geom_vline(xintercept = 1988) +
  geom_segment(aes(x = 1988, xend = 1988, y = 40, yend = 0),
               col = "red",
               size = 3,
               arrow = arrow())

ggplot(data = bears,
       aes(y = N.adult2,
           x = year)) +
  geom_point(size = 3) +
  geom_line() +
  theme_bw() +
  xlab("Year") +
  ylab("Adult bears (N)") +
  ylim(0,250) +
  geom_smooth(method = "gam")+
  # geom_smooth(method  = "lm", se = F,
  #             formula = y ~ x + I(x^2),
  #             fullrange=TRUE) +
  geom_hline(yintercept = 215, linetype = 2) +
  #geom_vline(xintercept = 1988) +
  geom_segment(aes(x = 1988, xend = 1988, y = 40, yend = 0),
               col = "red",
               size = 3,
               arrow = arrow())

ggpubr

ggline(data = bears,
       y = "N.adult2",
       x = "year")

ggscatter(data = bears,
       y = "N.adult2",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 2,
       point.size = 4,
       add = "reg.line",
       shape = 10)

ggline(data = bears,
       y = "N.adult2",
       x = "year",
       color = "black",
       point.color = "red",
       linetype = "solid",
       plot_type = "b",
       size = 2,
       point.size = 4,
       shape = 10)

ggline(data = bears,
       y = "N.adult2",
       x = "year") +
       ylim(0,250) +
       xlab("Year") +
       ylab("Adult bears (N)") +
       geom_smooth(type = "gam") +
       geom_hline(yintercept = 250)

lm.mod <- lm(N.adult2 ~ year +I(year^2), data = bears)
x.hat <- predict.lm(lm.mod, newdata = bears)

year.wolves <- 1995
year.fire <- 1988
plot(N.chao2.bwc*(1/0.274) ~ year, type = "b",
     ylim = c(0,700))
abline(v = year.wolves)
abline(v = year.fire)
points(N.obs.bwc ~ year, pch = 2, col =2)


brouwern/countpva documentation built on Dec. 19, 2021, 11:48 a.m.

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