Southern Magnolia tree in urban San Francisco

San Francisco Trees

The data this week comes from San Francisco’s open data portal.

There are dozens of tree species, and many other intresting features to explore in this dataset! I did drop a few columns that were either > 75% missing or redundant, feel free to check out the source for the fully original dataset.

Also - make sure to follow @tidypod - they’ll have some interesting #TidyTuesday updates to come this week!

Some interesting articles: - Trees of Life in SF - Landmark trees - Non-native trees - Friends of the urban forest - SF Tree Guide

Get the data here

# Get the Data

sf_trees <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-01-28/sf_trees.csv')

# Or read in with tidytuesdayR package (https://github.com/dslc-io/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO UPDATE tidytuesdayR from GitHub

# Either ISO-8601 date or year/week works!

# Install via pak::pak("dslc-io/tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2020-01-28') 
tuesdata <- tidytuesdayR::tt_load(2020, week = 5)


sf_trees <- tuesdata$sf_trees

Data Dictionary

sf_trees.csv

A full data dictionary is available at: the source but it’s fairly sparse.

variable	class	description
tree_id	double	Unique ID
legal_status	character	LegalLegal staus: Permitted or DPW maintained
species	character	Tree species includes common name after the :: separator
address	character	Street Address
site_order	double	Order of tree at address where multiple trees are at same address. Trees are ordered in ascending
address order
site_info	character	Site Info - Where the tree resides
caretaker	character	Agency or person that is primary caregiver to tree – Owner of Tree
date	double	Date Planted (NA if before 1955)
dbh	double	Diameter at breast height
plot_size	character	Dimension of plot - typically in feet
latitude	double	Latitude
longitude	double	Longitude

Cleaning Script

library(tidyverse)
library(here)
library(tidytuesdaymeta)
library(pryr)
library(visdat)
library(skimr)
library(lubridate)
library(leaflet)

create_tidytuesday_folder()

raw_df <- read_csv(here::here("2020", "2020-01-28", "Street_Tree_Map.csv"),
                   col_types = 
                   cols(
                     TreeID = col_double(),
                     qLegalStatus = col_character(),
                     qSpecies = col_character(),
                     qAddress = col_character(),
                     SiteOrder = col_double(),
                     qSiteInfo = col_character(),
                     PlantType = col_character(),
                     qCaretaker = col_character(),
                     qCareAssistant = col_character(),
                     PlantDate = col_character(),
                     DBH = col_double(),
                     PlotSize = col_character(),
                     PermitNotes = col_character(),
                     XCoord = col_double(),
                     YCoord = col_double(),
                     Latitude = col_double(),
                     Longitude = col_double(),
                     Location = col_character()
                   )) %>% 
  janitor::clean_names()

small_df <- raw_df %>% 
  select(-x_coord,-y_coord,-q_care_assistant, -permit_notes) %>% 
  filter(plant_type != "Landscaping") %>% 
  select(-plant_type) %>% 
  separate(plant_date, into = c("date", "time"), sep = " ") %>% 
  mutate(date = parse_date(date, "%m/%d/%Y")) %>% 
  select(-time, -location) %>% 
  arrange(date) %>% 
  rename(legal_status = q_legal_status,
         species = q_species,
         address = q_address,
         site_info = q_site_info,
         caretaker = q_caretaker)

small_df %>% skimr::skim()

small_df %>% 
  write_csv(here::here("2020", "2020-01-28", "sf_trees.csv"))