nyc_tree_census_2015_dash_2.Rmd

---
title: "NYC 2015 Tree Census"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}
# libraries
library(flexdashboard)
library(tidyverse)
library(leaflet)
library(DT)
library(plotly)

# load data
trees_2015 <- read_csv("new_york_tree_census_2015.csv")
```

Built using the the [NYC 2015 Tree Census dataset](https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh).

The entire dataset is available on the last tab and is searchable.

Column {data-width=650}
-----------------------------------------------------------------------

### 1200 Random Trees

```{r tree_map}
trees_2015 %>% 
  # filter(boroname == "Queens") %>% 
  sample_n(1200) %>%
  leaflet() %>%
  addTiles() %>%
  addMarkers()
```

Column {.tabset}
-----------------------------------------------------------------------

### Status

```{r status}
trees_2015 %>% 
  count(status, sort = TRUE) %>% 
  mutate(status = as.factor(status)) %>% # will make this searchable by being selectable instead of having to type the status
  DT::datatable(colnames = c("Status", "Number of Trees"),
                filter = "top")
```

### Tree diameter distribution

```{r diameter_dist}
diameter_graph <- trees_2015 %>% 
  filter(tree_dbh < 120) %>% # to make the graph more interesting to look at - don't do this unless you have a good business reason
  ggplot(aes(tree_dbh)) +
  geom_histogram(bins = 100) + # more bins gives me more detail
  labs(title = "Diameter of NYC trees, 2015",
       x = "Diameter (in)",
       y = "# of trees")

plotly::ggplotly(diameter_graph)
```

### Whole dataset (searchable)
```{r whole_dataset}
trees_2015 %>% 
  sample_n(1200) %>% # it's actually a sample of the dataset. the entire dataset is too large for datatable to handle
  select(tree_id, block_id, created_at, 
         tree_dbh, stump_diam, 
         curb_loc, 
         status, health, 
         spc_latin, spc_common, 
         address, zipcode, boroname) %>% 
  mutate_if(is.character, as.factor) %>% # making character variables into factors will allow the
  DT::datatable(colnames = c("Tree ID", "Block ID", "Created At", 
                             "Tree Diameter (in)", "Stump Diameter (in)",
                             "Curb location", 
                             "Status", "Health",
                             "Species name (Latin)", "Species Name (Common)",
                             "Nearest Address", "Zip Code", "Borough"),
                filter = "top")
```