Mariah Research Notebook

[mariahgammon]

Idea:
My idea as of now is to work with data driven marketing in animal shelters.

Some questions that I want to answer:
Are certain characteristics of animals (breed, age, size, color) associated with higher or lower adoption rates?
Does the way animals are marketed (through online platforms, social media campaigns, personalized profiles) influence their likelihood of being adopted?
Are these demographic factors (age, gender, location) that affect the preferences of potential adopters and their likelihood of adopting certain types of animals?
Can predictive models be developed to forecast adoption rates and inform targeted marketing strategies?

Some things to look into:
Look at characteristics of animals most likely to be adopted (breed, age, gender, size, health status)
Marketing strategies most effective at attracting potential adopters (online listings, social media campaigns, adoption events)
Characteristics of adopters (age, gender, income, living situation) that are most strongly associated with adopting certain types of animals

Things to consider:
Maybe certain kinds of animals are more likely to be adopted if they are featured in engaging videos or photos, of if they are showcased in adoption events that cater to specific audiences

Select specific animal shelters to work with for research - how will I select which shelters to include in my study?
What data sources do I plan to use for my analysis? Publicly available or collecting my own data through surveys or other methods?
How do I plan to measure the success of my marketing interventions? Will I be tracking adoption rates over time, or will I be using other metrics?
What ethical considerations will I need to take into account in my research? For example, how will I ensure that the animals and potential adopters are treated with respect and care throughout the process?

Data Collection and Analysis: collect data from animal shelters and potentially from adopters as well ; analyze this data to identify patterns and trends related to adoption rates and the factors that influence them
Marketing Strategies: based on analysis, develop marketing strategies that are tailored to the needs of different types of animals and potential adopters ; could include online listings, social media campaigns, and adoption events
Implementation and Testing: once the marketing strategies are developed, I will need to implement them at animal shelters and track their success over time ; could involve partnering with shelter staff to train them on the new strategies and working with them to track adoption rates and other metrics
Evaluation and Reporting: evaluate the effectiveness of my marketing strategies and report my findings to stakeholders ; this may involve presenting or creating reports for animal shelter organizations

Create dashboard can help present findings in a clear, concise, and visually engaging way, making it easier for stakeholders to understand and act on your recommendations
Help identify patterns and trends in the data more easily → make more informed decisions about the marketing strategies that I develop ; ex: identify which types of animals are most likely to be adopted in different regions of the country, or which marketing channels are most effective at attracting potential adopters
Use tools such as charts, graphs, and maps to present data in a visually compelling way ; maybe include interactive features that allow users to explore the data more deeply and gain additional insights
Communicate research findings and help animal shelters to make data-driven decisions about marketing strategies

Software to Use:

Debating between Streamlit, Tableau, & Qlik
Streamlit & Tableau seem easy to use but I have experience creating a Qlik Dashboard and access to training videos and visuals

Data Needed
Need access to data on animal shelters, including info on the animals that are available for adoption, their characteristics, and their adoption history
Animal characteristics - breed, age, size, color, gender, and temperament of the animals available for adoption
Adoption history - how long an animal has been available for adoption, whether it has been adopted previously, and if so, how many times
Demographic info - characteristics of adopters, such as age, gender, income, and household size
Shelter info - location, size, staffing, and resources of the animal shelter
Sources to obtain this data - publicly available datasets, data collected by animal shelters themselves, and data collected by third-party organizations that work with animal shelters
Search for publicly available datasets on animal shelters or adoption rates ; reach out to local animal shelters and rescue organizations to request access to their data
Maybe conduct surveys of adopters or potential adopters to collect info on their preferences and behaviors
Collect data on adoption rates of specific animals by tracking their progress through the adoption process

Work done so far:

• began reaching out and working with local animal shelters
• in contact with Erie Anna Shelter & requested the data that I need, but also reached out to various other ones this week in case that one doesn't work out
• Austin Animal Shelter has publicly available data that I have begun to look into --> currently making graphs on excel but want to begin a dashboard

[mariahgammon]

I've been in contact with multiple animal shelters, requesting data and scheduling meetings.
In the meantime, I have been continuing to work on the publicly available data that I have, from the Austin Animal Shelter & other databases.
I have been working with both excel and R Studio/Quarto, getting accustomed to using R & Quarto & also connecting it to my data.
I also have been researching animal shelters, their marketing strategies, statistics, etc. to help me prove my project's worth and feasibility.

[mariahgammon]

Finding graphs that represent trends such as days of week, months, breeds, and ages most popular can help to drive marketing decisions.
For example, most popular months/days of week can help influence promotion strategies - what days of week animal shelters should be posting on, what months they should do promotional activities, etc. Finding which breeds are most and least popular to be adopted can help focus an emphasis on which breeds to highlight focus of advertisements on.

[mariahgammon]

animal shelter social media statistics:

• social media increases public support and awareness
• Facebook most prevalent source

Roughly 6.5 million animals enter shelters each year but only 3.2 million are adopted. (American Society for the Prevention of Cruelty to Animals)

Animal Euthanasia
"Pet overpopulation is a serious problem in the United States, leaving animal shelters over capacity and overwhelmed."

[mariahgammon]

different parts of code I was trying to combine breeds:

library(dplyr)

Define a function to combine rows based on conditions

combine_rows <- function(data) {
if (any(grepl("Alaskan Husky/", data$breed))) {
husky_count <- sum(data$Count[grepl("Alaskan Husky/", data$breed)])
} else {
husky_count <- 0
}

if (any(grepl("Airedale Mix/", data$breed))) {
retriever_count <- sum(data$Count[grepl("Airedale Mix/", data$breed)])
} else {
airedale_count <- 0
}

Create a new data frame with combined counts

combined_data <- data %>%
filter(!(grepl("Alaskan Husky/|Airedale Mix/", breed))) %>%
bind_rows(data.frame(breed = "Husky Mix", count = husky_count),
data.frame(breed = "Airedale Mix", count = airedale_count))

return(combined_data)
}

Group the data by Breed and apply the combine_rows function

combined_counts <- breed_counts_df %>%
group_by(breed) %>%
do(combine_rows(.))
library(dplyr)

Define a function to combine rows based on conditions

combine_rows <- function(data) {
if (any(grepl("Alaskan Husky/", data$breed))) {
husky_count <- sum(data$Count[grepl("Alaskan Husky/", data$breed)])
} else {
husky_count <- 0
}

if (any(grepl("Airedale Mix/", data$breed))) {
retriever_count <- sum(data$Count[grepl("Airedale Mix/", data$breed)])
} else {
airedale_count <- 0
}

Create a new data frame with combined counts

combined_data <- data %>%
filter(!(grepl("Alaskan Husky/|Airedale Mix/", breed))) %>%
bind_rows(data.frame(breed = "Husky Mix", count = husky_count),
data.frame(breed = "Airedale Mix", count = airedale_count))

return(combined_data)
}
{r}

Apply the combine_breeds function to create the CombinedBreed column

breed_counts_df <- breed_counts_df %>%
mutate(CombinedBreed = sapply(breed, combine_breeds))

{r}
#breed_counts_df <- breed_counts_df %>%
#mutate(CombinedBreed = ifelse(grepl("Alaskan Husky", breed), "Alaskan Husky Mix", breed))
#mutate(CombinedBreed = ifelse(grepl("Airedale Mix", breed), "Airedale Mix", breed))

library(dplyr)
library(ggplot2)

Define a function to combine breeds based on specific conditions

combine_breeds <- function(breed) {
if (grepl("Alaskan Husky/", breed)) {
return("Alaskan Husky Mix")
} else if (grepl("Airedale Mix/", breed)) {
return("Airedale Mix")
} else if (grepl("Akita/", breed)) {
return("Akita Mix")
} else if (grepl("Alaskan Malamute", breed)) {
return("Alaskan Malamute Mix")
} else if(grepl("American Bulldog/", breed)) {
return("American Bulldog Mix")
} else if (grpl("American Staffordshire Terrier/", breed)) {
return("American Staffordshire Terrier Mix")
} else {
return(breed) # Keep the original breed if no condition matches
}
}

Apply the combine_breeds function to create the CombinedBreed column

breed_counts_df <- breed_counts_df %>%
mutate(CombinedBreed = sapply(breed, combine_breeds))

{r}
breed_mapping <- data.frame(
OriginalBreed = c("Boxer Mix", "Labrador Mix", "Poodle Mix"),
CombinedBreed = c("Mixed Breed", "Mixed Breed", "Mixed Breed"),
stringsAsFactors = FALSE
)
{r}
breed_counts_df <- breed_counts_df %>%
mutate(CombinedBreed = ifelse(grepl("Airedale Terrier", breed), "Airedale Mix", breed))

[mariahgammon]

rm(list = ls()) # clear out the variables from memory to make a clean execution of the code.# If you want to remove all previous plots and clear the console, run the following two lines.graphics.off() # clear out all plots from previous work.cat("\014") # clear the console

[mariahgammon]

weekly tasks after meeting with gmk this past week:
Here’s a couple of articles I was looking at that talk about cat concerns & overpopulation that I mentioned to you:
https://www.cbsnews.com/news/animal-shelters-overburdened-with-cats-in-summer/
https://www.dailybreeze.com/2009/08/17/summer-is-cat-season-at-shelters-now-overflowing/
https://www.drelseys.com/2023/04/10/everything-you-need-to-know-about-kitten-season/
https://www.americanhumane.org/position-statement/animal-population-control/
I’m also reaching out & planning to reach out to more animal shelters to get their insights & thoughts (to get quotes like you said) & also to get pricing & food brand thoughts
I’m also continuing to do background research & compiling an annotated list of helpful & approved/scholarly sources to help with the writing portion of my project & give support to the need for this project
And continuing to work on touching up the interactive document on R studio
Please let me know if I am forgetting anything!

[mariahgammon]

The past few weeks I have been working on doing my research and background work, compiling these notes into outlines for my thesis, continuing to work with my project in R and helping to push forward with my research and thesis. I have also been in contact with multiple animal shelters and talked to them about their data, how they use it, how they feed/spend money on animals, how they market, etc. Most, if not all, of the animal shelters that I talk to don't utilize data when it comes to their marketing strategies. Most, if not all, are also at capacity for their animals, and with limited staff to help, they are usually utilizing 110% efforts.
I have done research into a) why data is important in animal shelters and how it can be used
b) how marketing can help
c) overpopulation & at capacity & what that means for a shelter plus what it means for the animals within that shelter
d) what it costs to run shelters & how diff factors like size, age, and length of stay affect finances in a shelter
e) why adopting from shelters is important
f) how being at capacity can stretch the workers thin.

I have continued to improve and update my work in R, utilizing interactive shiny apps to help shelters notice trends and determine who and when to market.

[gkapfham]

Hello @mariahgammon, you have made an acceptable start on your research notebook. With that said, it is important to note that there are ways in which you can improve your research notebook during the upcoming semester. Here is a list of tasks to consider that we can discuss at the start of the upcoming semester:

• Decrease the gaps between entries so that there are more entries overall
• Make sure that all source code is correctly formatted
• Provide direct references to research papers and provide an analysis of those papers
• Provide direct references to your data sets and explain how you will analyze them
• Make more clear the overall motivation and specific technical analysis your work offers
• Give specifics about when you conduct an outreach to a shelter and what was the outcome
• Use TODO lists and then make it clear when you have completed the specific tasks

[mariahgammon]

Notes from 1/23 meeting:

• Technology → R packages [...] → R Books ; posit company ; Hadley Wickham
  Technique
  Methodology
  Data visualization
  Data analysis (e.g., stats)
  Machine learning
  “Domain” → animal shelters → broaden?? → above?? → non-profits
  Data analysis associated with non-profit organizations
  Title? / Overall Contributions (3-5)? → related work/background → evaluation → People?? → IRB
  “BEST” ??
  metrics

[mariahgammon]

Notes from 1/31 Meeting:
What a researcher would need to do in order to install the tool, run the tool, perform certain analyses, etc.
If marketing is in your title, have data and method of approach to help shelter market
Market vs. Analysis
Taking a step beyond - do the marketing and study how it was effective or not
Experimental study - evaluate
Ask and answer research questions - formulate as a testable hypothesis ; dataset from shelter 1 and dataset from shelter 2, etc.
Does my tool given multiple datasets help me answer questions about ….
On average, animal stay in shelter is …
How likely an animal is to stay in an animal shelter for x amt of time
The average stay for cat or x type of cat is …
Data analysis vs. data driven marketing decisions
Data analysis about intake/outtake data … prove and then next step is future work which is data driven marketing ; study shows that would be feasible to do data driven marketing decision making

[mariahgammon]

These couple weeks, I've been working on reframing my overall project goals/questions:

• developing a shiny application dashboard as opposed to an interactive document ; allows for it to be more user friendly for shelters that are not used to computer science
• reorganizing what I had as the interactive document into a comprehendable dashboard --> dashboard reads csv file and outputs data based on that
• developing a form for users to utilize that will create a csv file for them if they do not have the data and resources themselves

[mariahgammon]

library(shiny)

UI definition

ui <- fluidPage(

titlePanel("Animal Shelter Data Entry"),
sidebarLayout(
sidebarPanel(
textInput("file_path", "File Path", placeholder = "Enter file path"),
textInput("animal_id", label = "Animal ID", placeholder = "Enter Animal ID"),
selectInput("animal_type", label = "Animal Type", choices = c("Select Animal Type", "Dog", "Cat", "Rabbit", "Other")),
textInput("breed", label = "Breed", placeholder = "Enter Breed"),
numericInput("age", label = "Age", value = NULL),
selectInput("sex", label = "Sex", choices = c("Select Sex", "Intact Male", "Intact Female", "Neutered Male", "Spayed Female", "Unknown")),
textInput("color", label = "Color", placeholder = "Enter Color"),
textInput("name", label = "Name", placeholder = "Enter Name"),
selectInput("size", label = "Size", choices = c("Select Size", "Small", "Medium", "Large")),
selectInput("outcome_type", label = "Outcome Type", choices = c("Select Outcome Type", "Adoption", "Euthanasia", "Transfer", "Return to Owner", "Other")),
selectInput("intake_type", label = "Intake Type", choices = c("Select Intake Type", "Stray", "Relinquished by Owner", "Transferred In", "Owner Intended Euthanasia", "Other")),
selectInput("intake_condition", label = "Intake Condition", choices = c("Select Intake Condition", "Aged", "Feral", "Injured", "Normal", "Nursing", "Other", "Pregnant", "Sick")),
selectInput("intake_weekday", label = "Intake Weekday", choices = c("Select Intake Weekday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")),
selectInput("intake_month", label = "Intake Month", choices = c("Select Intake Month", month.name)),
selectInput("outcome_weekday", label = "Outcome Weekday", choices = c("Select Outcome Weekday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")),
selectInput("outcome_month", label = "Outcome Month", choices = c("Select Outcome Month", month.name)),
dateInput("intake_date", label = "Intake Date", value = NULL),
dateInput("outcome_date", label = "Outcome Date", value = NULL)
),
mainPanel(
actionButton("submit", "Submit"),
br(),
tableOutput("recent_entry_table")
)
)
)

Server logic

server <- function(input, output, session) {

Store the Data

data <- reactiveValues(entries = data.frame())

Show the most recent entry

output$recent_entry_table <- renderTable({
data$entries
})

Submit button

observeEvent(input$submit, {

Show loading

showModal(modalDialog(
title = "Processing",
"Please wait while the data is being processed...",
footer = NULL,
size = "m",
backdrop = "static",
keyboard = FALSE
))

validate fields

if (input$animal_id == "" || input$animal_type == "Select Animal Type" || input$breed == "" || is.null(input$age) || input$sex == "Select Sex" || input$color == "" || input$name == "" || input$size == "Select Size" || input$outcome_type == "Select Outcome Type" || input$intake_type == "Select Intake Type" || input$intake_condition == "Select Intake Condition" || input$intake_weekday == "Select Intake Weekday" || input$intake_month == "Select Intake Month" || input$outcome_weekday == "Select Outcome Weekday" || input$outcome_month == "Select Outcome Month" || is.null(input$intake_date) || is.null(input$outcome_date)) {
# close loading
removeModal()

# validation error
showModal(modalDialog(
  title = "Validation Error",
  "Please fill out all required fields.",
  easyClose = TRUE
))
return()

}

tryCatch({
# data frame creation
new_entry <- data.frame(
animal_id = input$animal_id,
animal_type = input$animal_type,
breed = input$breed,
age = input$age,
sex = input$sex,
color = input$color,
name = input$name,
size = input$size,
outcome_type = input$outcome_type,
intake_type = input$intake_type,
intake_condition = input$intake_condition,
intake_weekday = input$intake_weekday,
intake_month = input$intake_month,
outcome_weekday = input$outcome_weekday,
outcome_month = input$outcome_month,
intake_date = input$intake_date,
outcome_date = input$outcome_date
)

# length of stay
new_entry$time_in_shelter_days <- as.numeric(difftime(input$outcome_date, input$intake_date, units = "days"))

# new entry
data$entries <- rbind(data$entries, new_entry)

# file path
file_path <- input$file_path

# CSV file
if (!file.exists(file_path)) {
  write.csv(new_entry, file_path, row.names = FALSE)
} else {
  write.table(new_entry, file_path, append = TRUE, sep = ",", row.names = FALSE, col.names = FALSE)
}

# clear form after submit
updateTextInput(session, "animal_id", value = "")
updateSelectInput(session, "animal_type", selected = "Select Animal Type")
updateTextInput(session, "breed", value = "")
updateNumericInput(session, "age", value = NULL)
updateSelectInput(session, "sex", selected = "Select Sex")
updateTextInput(session, "color", value = "")
updateTextInput(session, "name", value = "")
updateSelectInput(session, "size", selected = "Select Size")
updateSelectInput(session, "outcome_type", selected = "Select Outcome Type")
updateSelectInput(session, "intake_type", selected = "Select Intake Type")
updateSelectInput(session, "intake_condition", selected = "Select Intake Condition")
updateSelectInput(session, "intake_weekday", selected = "Select Intake Weekday")
updateSelectInput(session, "intake_month", selected = "Select Intake Month")
updateSelectInput(session, "outcome_weekday", selected = "Select Outcome Weekday")
updateSelectInput(session, "outcome_month", selected = "Select Outcome Month")
updateDateInput(session, "intake_date", value = NULL)
updateDateInput(session, "outcome_date", value = NULL)

# close
removeModal()

# success
showModal(modalDialog(
  title = "Success",
  "Successful Data Submission:)",
  easyClose = TRUE
))

}, error = function(e) {
# close
removeModal()

# error
showModal(modalDialog(
  title = "Error",
  paste("An error occurred:(", e$message),
  easyClose = TRUE
))

})
})

}

Run shiny App

shinyApp(ui = ui, server = server)

[mariahgammon]

^ above is the code for the csv file creator

[mariahgammon]

The dashboard ui : ui <- dashboardPage(
dashboardHeader(title = "Animal Shelter Analysis"),
dashboardSidebar(
sidebarMenu(
menuItem("Upload CSV", tabName = "upload_csv", icon = icon("upload")), # New menu item for uploading CSV
menuItem("Breed Counts", tabName = "breed_counts"),
menuItem("Average Length of Stay", tabName = "length_of_stay"),
menuItem("Breed by Animal Type", tabName = "breed_by_animal_type"),
menuItem("Interactive Breed Chart", tabName = "interactive_breed_chart"),
menuItem("Interactive Age Chart",tabName = "interactive_age_chart"),
menuItem("Intake Months by Breed", tabName = "intake_months_by_breed"),
menuItem("Outcome Months by Breed", tabName = "outcome_months_by_breed"),
menuItem("Intake Months by Color", tabName = "intake_months_by_color"),
menuItem("Outcome Months by Color", tabName = "outcome_months_by_color")
# Add additional menu items for other analyses or tasks
)
),
dashboardBody(
tabItems(
tabItem(tabName = "upload_csv",
fileInput("file", "Upload CSV file")), # File input control in the upload tab

  tabItem(tabName = "breed_counts",
          plotlyOutput("breed_counts_plot")),
  
  tabItem(tabName = "length_of_stay",
          plotOutput("length_of_stay_plot")),
  
  tabItem(tabName = "breed_by_animal_type",
          plotlyOutput("breed_by_animal_type_plot")),
  
  tabItem(tabName = "interactive_breed_chart",
          uiOutput("breed_selection"), # Render breed selection UI
          plotOutput("avg_los_plot")), # Render plot of average length of stay by breed
  
  tabItem(tabName = "interactive_age_chart",
          uiOutput("age_selection"), # Render breed selection UI
          plotOutput("avg_los_plot_age")),
  
  tabItem(tabName = "intake_months_by_breed",
          uiOutput("breed_selection_intake"),
          plotOutput("intake_months_plot_by_breed")),
  
  tabItem(tabName = "outcome_months_by_breed",
          uiOutput("breed_selection_outcome"),
          plotOutput("outcome_months_plot_by_breed"),
          plotOutput("outcome_type_plot")),
  
  
  tabItem(tabName = "intake_months_by_color",
          uiOutput("color_selection_intake"),
          plotOutput("intake_months_plot_by_color")),
  
  tabItem(tabName = "outcome_months_by_color",
          uiOutput("color_selection_outcome"),
          plotOutput("outcome_months_plot_by_color"))
)

)
)

[gkapfham]

Hello @mariahgammon you have made an acceptable contribution to your research notebook. With that said, I wanted to point out that you did not have any entries in April 2024 and your total number of entries was below the minimum number needed to earn the baseline assessment score for the SE/CS/DS/INF 610 course, as outlined in the syllabus for those classes.

[mariahgammon]

Hi Professor Kapfhammer, I am so sorry for that - I forgot that when I would meet with you I would typically take notes in my hardcopy notebook and google slides that I was editing on and forgot to transfer it over to the research notebook. Would it be okay if I share those notes with you?

…

On Mon, May 6, 2024 at 12:04 PM Gregory M. Kapfhammer < ***@***.***> wrote: Hello @mariahgammon <https://github.com/mariahgammon> you have made an acceptable contribution to your research notebook. With that said, I wanted to point out that you did not have any entries in April 2024 and your total number of entries was below the minimum number needed to earn the baseline assessment score for the SE/CS/DS/INF 610 course, as outlined in the syllabus for those classes. — Reply to this email directly, view it on GitHub <#12 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AQZ2WGQJUHAB67MPDQ2OT2LZA6S2DAVCNFSM6AAAAAA42B2PAOVHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4TGMZRGIZTC> . You are receiving this because you were mentioned.Message ID: <ReadyResearchers-2023-24/syllabus/repo-discussions/12/comments/9331231@ github.com>