The Many Uses of pdxTrees

Graphing with ggplot2

# 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))

Using 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 leaflet.

An interactive map with leaflet

The following code creates an interactive map with leaflet and the pdxTrees_parks data. It showcases:

• Adding popups for each tree where you can customize the displayed information.
• Changing the background map.
• Including a mini-map.

# Loading the leaflet packages 
library(leaflet)
library(leaflet.extras)

# 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()

Creating an animated graph using gganimate

library(gganimate)

Before you animate a graph with gganimate you have to create and save a graph with ggplot2.

# 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()

Unsurprisingly it seems that large trees have the highest degree of pollution removal. However, there is a lot of overlap between the categories that could likely be explained by other key variables, like Species. This is a great opportunity to practice multivariate thinking!

A Linear Regression

Aside from visualizations, the data can also be used to build linear regression models, create confidence intervals, and perform many other forms of statistical inference.

Let’s look at the relationship between Tree_Height and Pollution_Removal_value.

# 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 ($)")

# 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)
#> # A tibble: 2 x 7
#>   term        estimate std_error statistic p_value lower_ci upper_ci
#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
#> 1 intercept      1.31      0.071      18.5       0    1.17     1.44 
#> 2 Tree_Height    0.104     0.001     114.        0    0.102    0.106

Looking at the graph and the slope coefficient from the regression table, it does seem like tree height positively correlates with pollution removal. In particular, with a \(\hat\beta_1\) of \(0.104\), we’d estimate that the Pollution_Removal_value increases by \(0.104\) with each additional foot of Tree_Height.