Welcome
The purpose of this analysis is to look at the clustering of litter for the cleanup done during the Startup Grind 2020 event, by the Rubbish, co. team. We can use clustering to evaluate the placement of collection objects (i.e, trash cans, etc.) and possibly improve on the cleanup policies. We can also use this to gain insights on the habits of people who throw trash on the ground!
This Analysis uses R vizualization and Python scripting together, utilizing the reticulate package. The data was already cleaned using R, and the script to do so can be found as clean_script.R. A Python function was also created for the clustering algorithm, which is located in euclidean_script.py. Both files can be found in my GitHub Repository for this analysis.
This write-up will not be discussing the impacts of litter type and litter production during an event. If you want to see a very well done analysis of the mentioned attributes, please visit this blog post by Rubbish’s Founder, Emin Israfil.
A little about who Rubbish, co. is
Rubbish, co. is a fresh startup based in California. They recently created the Rubbish iOS Application where you can clean litter and track it! The app allows users to take pictures of the collected item, while simultaniously tracking where the item was collected. The user then self annotates what classification the item belonds to (such as, plastic, paper, tobacco, etc.). They have even created a Rubbish Beam, which is a trash picker-upper dedicated to automatically taking pictures and tracking the litter, making this process easier and faster for the user!
About Event and Cleanup
Rubbish partnered with Startup Grind and the Fox Theatre Redwood City Team (Team Fox) with a goal to make Startup Grind Global 2020 the first conference in the nation to audit their litter and quantify their litter footprint.
They started the litter audit with a baseline cleanup on Sunday where they found a measly 392 pieces of litter. Monday was the Opening Night Event, and the Rubbish team had gone out to cleanup after the event to find 613 pieces of litter. On Day 1 of the event, Tuesday, Rubbish did another cleanup run after the event where they found 868 pieces of litter. On the second day of the event, Wednesday, they again went out to collect 564 pieces of litter after the days activities.
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
####### ALL LIBRARIES USED ###########
###### DATA MANIPULATION ######
options(stringsAsFactors = FALSE)
library(dplyr) # for main data manipulations
library(reticulate) # for python script
library(reshape2) # for dcast()
library(lubridate) # for hours()
source_python("euclidean_script.py") # python script
###### GRAPHING ######
library(RColorBrewer) # for colors in graphs
library(ggplot2) # for lollipop graph
library(gganimate) # for animated time ggplot
library(hrbrthemes) # for ggplot theme
library(sunburstR) # for sunburst graph
library(streamgraph) # for stream graph
library(heatmaply) # for heatmap graph
library(DT) # for data table
######################################
# placeholders for plot facet wrap titles
day_names <- c(
'Sunday' = "Sunday (Baseline)"
,'Monday' = "Monday (Opening Night)"
,'Tuesday' = "Tuesday (Day 1)"
,'Wednesday' = "Wednesday (Day 2)"
)
# setup to get all types
collection = c('trashCan', 'recyclingCan', 'tobaccoAshCan')
litter = c('paper', 'tobacco', 'unknown', 'plastic', 'food', 'glass')
# load in cleaned raw data
raw <- read.csv('clean/clean_rubbish.csv') %>%
mutate( # change days from shorthand to full
day = factor(ifelse(day =="Sun", "Sunday",
ifelse(day =="Mon", "Monday",
ifelse(day == "Tue", "Tuesday", "Wednesday"))),
levels = c("Sunday", "Monday", "Tuesday", "Wednesday"))
)How much litter was cleaned?
Lets start with getting a baseline for the volume of litter collected on each of our four days.
# prepping data for plot
plot_data<- raw %>%
subset(city == 'Redwood City') %>%
subset(is_litter == 1) %>%
group_by(rubbishType, day) %>%
summarise(
num_litter = n()
) %>%
group_by(day) %>%
arrange(-num_litter) %>%
ungroup() %>%
arrange(day, num_litter) %>%
mutate(order = row_number())
# plotting lollipop graphs
plot_data %>%
ggplot() +
geom_segment( aes(x=order, xend=order, y=0, yend=num_litter), color="pink", size = 1.5) +
geom_point( aes(x=order, y=num_litter, color="pink"), size=5 ) +
coord_flip() +
theme_ipsum() +
theme(
panel.grid.minor.y = element_blank()
,panel.grid.major.y = element_blank()
,legend.position = "none"
,panel.border = element_blank()
,panel.spacing = unit(0.1, "lines")
,strip.text.x = element_text(size = 15)
) +
xlab("") +
ylab("Pieces of Litter Collected") +
labs(title = "Total Litter Collected by Rubbish Team"
,subtitle = "during the Startup Grind 2020 Conference"
) +
facet_wrap(~day, ncol=1, scale="free_y", labeller = as_labeller(day_names)) +
scale_x_continuous(
breaks = plot_data$order
,labels = plot_data$rubbishType)