Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set( echo = TRUE, message = FALSE, warning = FALSE, collapse = TRUE, comment = "#>", fig.align = "center",
fig.retina = 2)
## ---- include = TRUE, messgae = FALSE-----------------------------------------
# First make sure you have the package downloaded!
# devtools::install_github("mcconvil/pdxTrees")
# Loading the required libraries
library(pdxTrees)
library(ggplot2)
library(dplyr)
library(forcats)
## -----------------------------------------------------------------------------
# Leaving the argument field blank pulls data for all of the parks!
pdxTrees_parks <- get_pdxTrees_parks()
## ---- fig.width= 6, fig.height=4----------------------------------------------
# A histogram of the inventory date
pdxTrees_parks %>%
count(Inventory_Date) %>%
# Setting the aesthetics
ggplot(aes(x = Inventory_Date)) +
# Specifying a histogram and picking color!
geom_histogram(bins = 50,
fill = "darkgreen",
color = "black") +
labs( x = "Inventory Date",
y = "Count",
title= "When was pdxTrees_parks Inventoried?") +
# Adding a theme
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
## ----leaflet packages---------------------------------------------------------
# Loading the leaflet packages
library(leaflet)
library(leaflet.extras)
## ----leaflet graph, fig.width= 8, fig.height=6--------------------------------
# Making the leaf popup icon
greenLeaflittle <- makeIcon(
iconUrl = "http://leafletjs.com/examples/custom-icons/leaf-green.png",
iconWidth = 10, iconHeight = 20,
iconAnchorX = 10, iconAnchorY = 10,
shadowUrl = "http://leafletjs.com/examples/custom-icons/leaf-shadow.png",
shadowWidth = 10, shadowHeight = 15,
shadowAnchorX = 5, shadowAnchorY = 5
)
# Pulling the data for Berkely Park
berkeley_prk <- get_pdxTrees_parks(park = "Berkeley Park")
# Creating the popup label
labels <- paste("</b>", "Common Name:",
berkeley_prk$Common_Name,
"</b></br>", "Factoid: ",
berkeley_prk$Species_Factoid)
# Creating the map
leaflet() %>%
# Setting the lng and lat to be in the general area of Berekely Park
setView(lng = -122.6239, lat = 45.4726, zoom = 17) %>%
# Setting the background tiles
addProviderTiles(providers$Esri.WorldTopoMap) %>%
# Adding the leaf markers with the popup data on top of the circles markers
addMarkers( ~Longitude, ~Latitude,
data = berkeley_prk,
icon = greenLeaflittle,
popup = ~labels) %>%
# Adding the mini map at the bottom right corner
addMiniMap()
## -----------------------------------------------------------------------------
library(gganimate)
## ----animated graph-----------------------------------------------------------
# Refactoring the categorical mature_size variable
berkeley_prk <- berkeley_prk %>%
mutate(mature_size = fct_relevel(Mature_Size, "S", "M", "L"))
# First creating the graph using ggplot and saving it!
berkeley_graph <- berkeley_prk %>%
# Piping in the data
ggplot(aes(x = Tree_Height,
y = Pollution_Removal_value,
color = Mature_Size)) +
# Creating the scatterplot
geom_point(size = 2, alpha = 0.5) +
theme_minimal() +
# Adding the labels
labs(title = "Pollution Removal Value of
Berkeley Park Trees",
x = "Tree Height",
y = "Pollution Removal Value ($'s annually)",
color = "Mature Size") +
# Adding a color palette
scale_color_brewer(type = "seq", palette = "Set1") +
# Customizing the title font
theme(plot.title = element_text(hjust = 0.5,
size = 8,
face = "bold"),
axis.title.x = element_text(size = 6),
axis.text = element_text(size = 4),
axis.title.y = element_text(size = 6),
legend.title = element_text(size = 6),
legend.text = element_text(size= 4))
## ---- out.width = "90%"-------------------------------------------------------
# Then adding the animation with gganimate functions
berkeley_graph +
# Choosing which variable we want to annimate
transition_states(states = Mature_Size,
# How long each point stays before fading away
transition_length = 10,
# Time the transition takes
state_length = 8) +
# Animation for the points entering
enter_grow() +
# Animation for the points exiting
exit_shrink()
## ----linear regression graph, warning = FALSE, fig.width = 6, fig.height = 4----
# Visualizing the relationship between the two variables.
ggplot(pdxTrees_parks, aes(x = Tree_Height,
y = Pollution_Removal_value)) +
# Creating a scatter plot
geom_point(alpha = 0.05) +
# Adding the line of best fit
stat_smooth(method = lm, se = FALSE) +
theme_minimal() +
labs(x = "Tree Height",
y = "Pollution Removal Value ($)")
## ----linear regression, warning = FALSE---------------------------------------
# moderndive is where the get_regression_table() function lives
library(moderndive)
# Running a linear regression of Pollution_Removal_value on Tree_Height
mod <- lm(Pollution_Removal_value ~ Tree_Height, data = pdxTrees_parks)
# Printing the coefficients table
get_regression_table(mod)
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