# ESCI 197C  Intro to Field Research
# Maple seed dispersal project 10/16/2026

##########################################################################
# This section contains R commands only; see below for R output
# import *.csv file of maple seed dispersal data

# Set Working Directory: R_Book p.101
setwd("C:\\Downloads")
file.exists("seed.data.low.csv")
maple.low <- read.csv("seed.data.low.csv")
length(maple.low)
maple.low[1,]
attach(maple.low)

# import *.csv file of maple seed dispersal data from high release site

# Set Working Directory: R_Book p.101
setwd("C:\\Users\\jmcl\\Documents")
file.exists("seed.data.high.csv")
# in ATUS labs: file.exists("C:\\Users\\jmcl\\Downloads\\seed.data.high.csv")
maple.hi <- read.csv("seed.data.high.csv")    # Seeds released from high site
length(maple.hi)
length(maple.hi[,1])
maple.hi[1,]
attach(maple.hi)
mean(maple.hi$distance)
sd(maple.hi$distance)
mean(maple.hi$time)
sd(maple.hi$time)
hist(maple.hi$distance)

hist(maple.hi$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
   main="High release site")

# Plot Normal distribution with mean, sd from sample values:
x.cm <- seq(0, 1000, by=10)
lines(x.cm, 100*100*dnorm(x.cm, mean(maple.hi$distance), sd(maple.hi$distance)))
# multiply by 100 because n=100
# multiply by 100 because interval width=100cm
# Plot Uniform distribution over interval [0,1000]
lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)

# Save figure in JPEG format:
jpeg(file="maple.dist.2026.jpg")
hist(maple.hi$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
   main="High release site")
# Plot Normal distribution with mean, sd from sample values:
x.cm <- seq(0, 1000, by=10)
lines(x.cm, 100*100*dnorm(x.cm, mean(maple.hi$distance), sd(maple.hi$distance)))
# multiply by 100 because n=100
# multiply by 100 because interval width=50cm
# Plot Uniform distribution over interval [0,1000]
lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)
dev.off()
#############################
# Low release site:
file.exists("C:\\Users\\jmcl\\Downloads\\seed.data.high.csv")
maple.low <- read.csv("C:\\Users\\jmcl\\Downloads\\seed.data.high.csv")
length(maple.low)
maple.low[1,]
attach(maple.low)
mean(maple.low$distance)
sd(maple.low$distance)
mean(maple.low$time)
sd(maple.low$time)
hist(maple.low$distance)

hist(maple.low$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
   main="Low release site")

# Plot Normal distribution with mean, sd from sample values:
x.cm <- seq(0, 1000, by=10)
lines(x.cm, 50*460*dnorm(x.cm, mean(maple.low$distance), sd(maple.low$distance)))
# multiply by 46 because n=46
# multiply by 50 because interval width=50cm
# Plot Uniform distribution over interval [0,1000]
lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)

# Save figure in JPEG format:
jpeg(file="maple.dist.2026.jpg")
hist(maple.hi$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
   main="High release site")
# Plot Normal distribution with mean, sd from sample values:
x.cm <- seq(0, 1000, by=10)
lines(x.cm, 50*46*dnorm(x.cm, mean(maple.hi$distance), sd(maple.hi$distance)))
# multiply by 46 because n=46
# multiply by 50 because interval width=50cm
# Plot Uniform distribution over interval [0,1000]
lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)
dev.off()

##########################################################################
> # ESCI 197C  Maple seed dispersal project 10/16/2026
> # This section contains R commands and R output

> # Set Working Directory: R_Book p.101
> setwd("C:\\Users\\jmcl\\Documents")
> file.exists("seed.data.low.csv")
[1] TRUE
> maple.low <- read.csv("seed.data.low.csv")
> length(maple.low)
[1] 3
> maple.low[1,]
  seed.no distance time
1       1      220  1.8
> attach(maple.low)
> mean(distance)
[1] 108.7174
> mean(time)
[1] 2.4
> hist(distance)
> hist(time)

> # Set Working Directory: R_Book p.101
> setwd("C:\\Users\\jmcl\\Documents")
> file.exists("seed.data.high.csv")
[1] TRUE
> maple.hi <- read.csv("seed.data.high.csv")    # Seeds released from high site
> length(maple.hi)
[1] 3
> maple.hi[1,]
  seed.no distance time
1       1       65  4.4
> attach(maple.hi)

> length(maple.hi[,1])
[1] 100
> mean(maple.hi$distance)
[1] 211.25
> sd(maple.hi$distance)
[1] 130.4401
> mean(maple.hi$time)
[1] 3.398
> sd(maple.hi$time)
[1] 1.459416
> hist(maple.hi$distance)
> hist(maple.hi$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
+    main="High release site")
> 
> # Plot Normal distribution with mean, sd from sample values:
> x.cm <- seq(0, 1000, by=10)
> lines(x.cm, 100*100*dnorm(x.cm, mean(maple.hi$distance), sd(maple.hi$distance)))
> # multiply by 100 because n=100
> # multiply by 100 because interval width=100cm
> # Plot Uniform distribution over interval [0,1000]
> lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)
> 
> # Save figure in JPEG format:
> jpeg(file="maple.dist.2026.jpg")
> hist(maple.hi$distance, xlim=c(0, 1000), xlab="Dispersal distance (cm)", 
+    main="High release site")
> # Plot Normal distribution with mean, sd from sample values:
> x.cm <- seq(0, 1000, by=10)
> lines(x.cm, 100*100*dnorm(x.cm, mean(maple.hi$distance), sd(maple.hi$distance)))
> # multiply by 100 because n=100
> # multiply by 100 because interval width=50cm
> # Plot Uniform distribution over interval [0,1000]
> lines(x.cm, 100*50*dunif(x.cm, 0, 1000), lty=2)
> dev.off()
windows 
      2 
>