#Print your name at the top of the script. Include the prefix: “Plotting Basics:” such that it

#appears “Plotting Basics: Firstname Lastname ”

print(“Plotting Basics : Tianyu Zhang”)

#Import libraries including: FSA, FSAdata, magrittr, dplyr, plotrix, ggplot2, and moments

#NOTE: You must use R version 3.6.3 to gain access to the FSA data set. If you installed a

#later version of R, you must uninstall Rstudio and R. Then reinstall R version 3.6.3; then

#reinstall Rstudio

install.packages(“FSA”)

install.packages(“FSAdata”)

install.packages(“magrittr”)

install.packages(“dplyr”)

install.packages(“plotrix”)

install.packages(“ggplot2”)

install.packages(“moments”)

library(FSA)

library(FSAdata)

library(magrittr)

library(dplyr)

library(plotrix)

library(ggplot2)

library(moments)

#Load the BullTroutRML2 dataset (BullTroutRML2.csv)

data(BullTroutRML2)

# Print the first 5 records

print(head(BullTroutRML2, 5))

# Print the last 5 records

print(tail(BullTroutRML2, 5))

#Remove all records except those from Harrison Lake (hint: use dplyr::filter() function)

#NOTE: From this point forward any reference to BullTroutRML2 always refers to the

#filtered dataset (Harrison Lake only data is used). You may choose to rename the

#dataset at this point.

BullTroutRML2_filtered <- dplyr::filter(BullTroutRML2, lake == “Harrison”)

#Display the first 4 and last 4 records from the filtered BullTroutRML2 dataset

# Display the first 4 records

print(head(BullTroutRML2_filtered, 4))

# Display the last 4 records

print(tail(BullTroutRML2_filtered, 4))

#Display the structure of the filtered BullTroutRML2 dataset

str(BullTroutRML2_filtered)

#Display the summary of the filtered BullTroutRML2 dataset

summary(BullTroutRML2_filtered)

#Create a scatterplot for “Age (yrs)” (y variable) and “Fork Length (mm)” (x variable)

#with the following specifications:

#• Limit of x axis is (0,500)

#• Limit of y axis is (0,15)

#• Title of graph is “Plot 1: Harrison Lake Trout”

#• Y axis label is “Age (yrs)”

#• X axis label is “Fork Length (mm)”

#• Use small solid circles for the plotted data points

plot(BullTroutRML2_filtered$fl, BullTroutRML2_filtered$age,

xlim = c(0,500), ylim = c(0,15),

main = “Plot 1 : Harrison Lake Trout”,

xlab = “Fork Length (mm))”, ylab = “Age(yrs)”,

pch = 21, cex = 0.5)

#Plot an “Age” histogram with the following specifications

#• Y axis label is “Frequency”

#• X axis label is “Age (yrs)”

#• Title of the histogram is “Plot 2: Harrison Fish Age Distribution”

#• The color of the frequency plots is “lightblue”

#• The color of the Title is “red”

hist(BullTroutRML2_filtered$age, xlab = “Age (yrs)”, ylab = “Frequency”,

main = “Plot 2: Harrison Fish Age Distribution”,

col = “lightblue”)

title(main = “Plot 2: Harrison Fish Age Distribution”, col.main = “red”)

#Create a plot using the same specifications as the previous scatterplot. But,

#• Title the plot “Plot 3: Harrison Density Shaded by Era”

#• Y axis label is “Age (yrs)”

#• Y axis limits are 0 to 15

#• X axis label is “Fork Length (mm)”

#• X axis limits are 0 to 500

#• include two levels of shading of blue for the data points based on era values.

#• Plot solid diamonds as data points

ggplot(BullTroutRML2_filtered, aes(x = fl, y = age, color = era)) +

geom_point(shape = 4, size = 3) +

ggtitle(“Plot 3: Harrison Density Shaded by Era”)+

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

scale_color_manual(values = c(“darkblue”,”lightblue”)) +

xlim(0,500) + ylim(0,15)

#Create a new object called “tmp” that includes the first 3 and last 3 records of the

#BullTroutRML2 dataset.

head(BullTroutRML2, 3)

tail(BullTroutRML2, 3)

tmp <- rbind(head(BullTroutRML2, 3), tail(BullTroutRML2, 3))

#Display the “era” column (variable) in the new “tmp” object

tmp$era

#Create a pchs vector with the argument values for + and x.

#Create a cols vector with the two elements “black” and “red”

pchs <- c(“+”, “x”)

cols <- c(“black”, “red”)

# Convert the tmp era values to numeric values.

# Initialize the pchs and cols vector conditional on the tmp era values

tmp$era <- as.numeric(tmp$era)

tmp$pch <- pchs[tmp$era]

tmp$col <- cols[tmp$era]

#Create a plot of “Age (yrs)” (y variable) versus “Fork Length (mm)” (x variable) with the

#following specifications:

# • Title of graph is “Plot 4: Symbol & Color by Era”

#• Limit of x axis is (0,500)

#• Limit of y axis is (0,15)

#• Y axis label is “Age (yrs)”

#• X axis label is “Fork Length (mm)”

#• Set pch equal to pchs era values

#• Set col equal to cols era values

ggplot(tmp, aes(x = fl, y = age, pch = pch, col = col)) +

geom_point() +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 4: Symbol & Color by Era”)

#Plot a regression line (blue color) overlay on Plot 4 and title the new graph “Plot 5:

#Regression Overlay”.

ggplot(tmp, aes(x = fl, y = age)) +

geom_point() +

geom_smooth(method = “lm”, se = FALSE, color = “blue”) +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 5: Regression Overlay”)

#Place a legend of on Plot 5 and call the new graph “Plot 6: Legend Overlay”

ggplot(tmp, aes(x = fl, y = age, pch = pch, col = col)) +

geom_point() +

geom_smooth(method = “lm”, se = FALSE, color = “blue”) +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 6: Legend Overlay”)