pdxTrees is a data package composed of information on inventoried trees in Portland, OR. There are two datasets that can be accessed with this package:
get_pdxTrees_parks() pulls in data on up to 25,534 trees from 174 Portland parks.
get_pdxTrees_streets() pulls in data on up to 218,602 trees located on Portland’s streets. A street tree is loosely defined as a tree generally in the public right-of-way, usually between the sidewalk and the street.
The street trees are categorized by one of the 96 Portland neighborhoods and the park trees are categorized by the public parks in which they grow.
Here are some examples of the different ways
pdxTrees can be used in an educational setting!
# 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)
First we have to grab the data. To do this we use the
get_pdxTrees_streets() functions. In this vignette, we only explore the parks dataset.
# Leaving the argument field blank pulls data for all of the parks! pdxTrees_parks <- get_pdxTrees_parks()
# 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))
ggplot2 we can create a histogram of the
pdxTrees_parks inventory dates. The trees were inventoried from 2017 to 2019 with the majority of the trees inventoried in the summer months, when the weather is nice in Portland.
This graph is just one of example of how
pdxTrees can be used to create data visualizations. With a healthy mix of categorical and quantitative variables in both datasets, you can make scatterplots, bar graphs, density plots, etc. For more advanced visualizations, you can add animation with
gganimate or create an interactive map with
The following code creates an interactive map with
leaflet and the
pdxTrees_parks data. It showcases:
# 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$Esri.WorldTopoMap) %>% # Adding the leaf markers with the popup data on top of the circles markers addMarkers( ~Longitude, ~Latitude, data = berkeley_prk, icon = greenLeaflittle, popup = ~labels) %>% # Adding the mini map at the bottom right corner addMiniMap()
Before you animate a graph with
gganimate you have to create and save a graph with
# Refactoring the categorical mature_size variable berkeley_prk <- berkeley_prk %>% mutate(mature_size = fct_relevel(Mature_Size, "S", "M", "L")) # First creating the graph using ggplot and saving it! berkeley_graph <- berkeley_prk %>% # Piping in the data ggplot(aes(x = Tree_Height, y = Pollution_Removal_value, color = Mature_Size)) + # Creating the scatterplot geom_point(size = 2, alpha = 0.5) + theme_minimal() + # Adding the labels labs(title = "Pollution Removal Value of Berkeley Park Trees", x = "Tree Height", y = "Pollution Removal Value ($'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))
Now we can add animation!
# 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()