Claremont, CA has had a local winter Christmas Bird Count (CBC for short) station since 1972. We are going to interact with the CBC data for four focal species:
COHA - Cooper’s HawkACWO - Acorn woodpeckerSPTO - Spotted towheeYRWA - Yellow-rumped warblerIn the following exercises, please discuss as a group:
Number and NumNormal?Note that in the assignment ClaremontCBC, you’ll see a ClaremontCBC.R script that you can use to follow along with this discussion activity.
First, let’s load the packages that we’ll need.
###==================================================
### Loading packages & keys
###==================================================
library("dplyr") # load package for wrangling data
library("readr") # package for reading tabular data
library("ggplot2") # package for plotting###==================================================
### Importing the data
###==================================================
claremontCBC <- read_tsv("https://raw.githubusercontent.com/chchang47/BIOL104PO/master/data/ClaremontCBCdata.txt") # pulling in the spreadsheet from this internet location and storing it in claremontCBC
### Inspecting the data
claremontCBC %>%
head() %>%
View()
# The above is equivalent to:
# View( head( claremontCBC )) # delete the leading # to runThe Claremont CBC data has these four columns:
Species: the shortened code for each speciesYear: the year of the CBC observationNumber: the number of each species observed in that yearNumNormal: Number/effort, where effort is the number of people participating that yearYour task is to:
Number and NumNormal columnsFirst, let’s consider how to filter the data to just one of the species.
### Create your subset data
clareSub <- claremontCBC %>%
filter(Species=="...") # select one of the 4 species and replace the ... with its short code name
### View your subset data
clareSubOf the different plots (see Week2b FMI), because we have observations of distinct numeric x- and y-variables (x = time and y = species counts), a scatterplot would be appropriate.
### Creating a scatterplot
# Note that we will store the scatterplot in an object named "p" for plot
p <- ggplot(clareSub, aes(x=Year, y=Number)) # specifying an aesthetic where x is Year and y is the count of that species
p <- p + geom_point() # adding on points to create a scatterplot
p <- p + labs(x="...", y="...") # changing the default x and y axis labels to more informative labels (replace the ... with your text)
p # calling p which will be displayed in the plot viewer
### Slightly more advanced move to save plots using commands
# ?ggsave # uncomment and run to load the help page
#ggsave(plot= p, filename="BirdPlot.jpeg") # replace filename with something more informative; remove leading # to runHow would you repeat the plot above but have NumNormal on the y-axis? Why might this be more appropriate in some cases?
We’ll start by modifying the data filter to include two species.
First, let’s consider how to filter the data to just one of the species.
### Create your subset data
clareSub <- claremontCBC %>%
filter(Species=="..." | Species=="...") # select two of the 4 species and replace the ... with their short code name
# NB: the | symbol means OR - match species 1 OR species 2
### View your subset data
clareSubOf the different plots (see Week2b FMI), because we have observations of distinct numeric x- and y-variables (x = time and y = species counts), a scatterplot would be appropriate.
### Creating a scatterplot
# Note that we will store the scatterplot in an object named "p" for plot
p <- ggplot(clareSub, aes(x=Year, y=Number, color=Species)) # specifying an aesthetic where x is Year and y is the count of that species
# We're adding on another dimension to the visualization, which is using color to differentiate species
p <- p + geom_point() # adding on points to create a scatterplot
p <- p + labs(x="...", y="...") # changing the default x and y axis labels to more informative labels (replace the ... with your text)
p <- p + theme_classic() # choosing a simpler black and white background
p # calling p which will be displayed in the plot viewer
### Slightly more advanced move to save plots using commands
# ?ggsave # uncomment and run to load the help page
#ggsave(plot= p, filename="BirdPlot2.jpeg") # replace filename with something more informative; remove leading # to runHow would you repeat the plot above but have NumNormal on the y-axis? Why might this be more appropriate in some cases?
### Creating a scatterplot
# Note that we will store the scatterplot in an object named "p" for plot
p <- ggplot(clareSub, aes(x=Year, y=NumNormal)) # specifying an aesthetic where x is Year and y is the count of that species
p <- p + geom_point() # adding on points to create a scatterplot
p <- p + labs(x="Year", y="Count (normalized)") # changing the default x and y axis labels to more informative labels (replace the ... with your text)
p # calling p which will be displayed in the plot viewer
### Slightly more advanced move to save plots using commands
# ?ggsave # uncomment and run to load the help page
#ggsave(plot= p, filename="BirdPlot.jpeg") # replace filename with something more informative; remove leading # to run### Creating a scatterplot
# Note that we will store the scatterplot in an object named "p" for plot
p <- ggplot(clareSub, aes(x=Year, y=NumNormal, color=Species)) # specifying an aesthetic where x is Year and y is the count of that species
# We're adding on another dimension to the visualization, which is using color to differentiate species
p <- p + geom_point() # adding on points to create a scatterplot
p <- p + labs(x="Year", y="Count (normalized)") # changing the default x and y axis labels to more informative labels (replace the ... with your text)
p <- p + theme_classic() # choosing a simpler black and white background
p # calling p which will be displayed in the plot viewer
### Slightly more advanced move to save plots using commands
# ?ggsave # uncomment and run to load the help page
#ggsave(plot= p, filename="BirdPlot2.jpeg") # replace filename with something more informative; remove leading # to run