finally completed assignment 3 sorry for delay

02_activities/assignments/assigment_3_done/Python/Python markdown.ipynb

@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0dea6ae",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc23d96c",
+   "metadata": {},
+   "source": [
+    " > What software did you use to create your data visualization? \n",
+    " I created my data visualizations using Python, specifically the libraries pandas, seaborn, and matplotlib.\n",
+    "\n",
+    "    > Who is your intended audience? \n",
+    "    My intended audience includes researchers studying social issues, ssocial workers, government and policy organizations that are interested in learning more about the intimate partner and family violence.\n",
+    "\n",
+    "    > What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories. The goal is to show how incidents are distributed and how trends change year-to-year, rather than to make claims about true prevalence.\n",
+    "\n",
+    "    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    I was interested in clarity & simplicity so I avoided clutter and limited the number of variables shown at once. For example, aggregating by year reduced noise and made trends clearer. Furthermore, I considered color choice I used color-blind friendly palettes (e.g., viridis) to ensure distinctions between categories are visible to most viewers. Also, readability which prompted me to have clear axis labels, descriptive titles and logical ordering of categories (e.g., years in ascending order)\n",
+    "\n",
+    "    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    The visualization is reproducible because because I pulled the code data directly from the public OpenData link for developers. Furthermore, all transformations are scripted and no manual edits were made to the dataset to ensure that anyone can rerun the code to recreate results.\n",
+    "    \n",
+    "    > How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "\n",
+    "    > Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "\n",
+    "    > How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I was hoping to simply identify if rates of IPFV were changing over the year and also if certain days of the week had higher reports of IPFV. So, this helped me to hone in on the features of that data that made the most sense to visualize that. Then I thought it might be interesting to see the relationship of the reports to see if there are any trends there as well.\n",
+    "\n",
+    "    > What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    The “underwater labour” included learning how to query the provided data from OpenData Toronto, cleaning and converting data types, aggregating counts correctly, testing different visualizations, debugging code errors, deciding how to present sensitive data responsibly and iterating on design choices.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visualization-env (3.11.13)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

02_activities/assignments/assigment_3_done/Python/Pythonmarkdown.ipynb

@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0dea6ae",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - Python: https://open.toronto.ca/dataset/intimate-partner-and-family-violence/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc23d96c",
+   "metadata": {},
+   "source": [
+    " > What software did you use to create your data visualization? \n",
+    " I created my data visualizations using Python, specifically the libraries pandas, seaborn, and matplotlib.\n",
+    "\n",
+    "> Who is your intended audience? \n",
+    "    My intended audience includes researchers studying social issues, ssocial workers, government and policy organizations that are interested in learning more about the intimate partner and family violence.\n",
+    "\n",
+    "> What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories. The goal is to show how incidents are distributed and how trends change year-to-year, rather than to make claims about true prevalence.\n",
+    "\n",
+    "> What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    I was interested in clarity & simplicity so I avoided clutter and limited the number of variables shown at once. For example, aggregating by year reduced noise and made trends clearer. Furthermore, I considered color choice I used color-blind friendly palettes (e.g., viridis) to ensure distinctions between categories are visible to most viewers. Also, readability which prompted me to have clear axis labels, descriptive titles and logical ordering of categories (e.g., years in ascending order)\n",
+    "\n",
+    "> How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    The visualization is reproducible because because I pulled the code data directly from the public OpenData link for developers. Furthermore, all transformations are scripted and no manual edits were made to the dataset to ensure that anyone can rerun the code to recreate results.\n",
+    "    \n",
+    "> How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "\n",
+    "> Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "\n",
+    "> How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I was hoping to simply identify if rates of IPFV were changing over the year and also if certain days of the week had higher reports of IPFV. So, this helped me to hone in on the features of that data that made the most sense to visualize that. Then I thought it might be interesting to see the relationship of the reports to see if there are any trends there as well. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "\n",
+    "> What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    The “underwater labour” included learning how to query the provided data from OpenData Toronto, cleaning and converting data types, aggregating counts correctly, testing different visualizations, debugging code errors, deciding how to present sensitive data responsibly and iterating on design choices.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visualization-env (3.11.13)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

02_activities/assignments/assigment_3_done/R/R markdown.ipynb

@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb5d7848",
+   "metadata": {},
+   "source": [
+    "    > What software did you use to create your data visualization?\n",
+    "    I created my data visualizations using RStudio with the R programming language. I primarily used the packages ggplot2 for visualization and dplyr for data cleaning and aggregation. The dataset was accessed from the Toronto Open Data portal and processed within R.\n",
+    "\n",
+    "    > Who is your intended audience? \n",
+    "    The intended audience includes researchers studying social and public health issues, policy makers and service organizations, community groups focused on violence prevention and a general audience interested in understanding trends in reported violence.\n",
+    "    \n",
+    "    > What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories or temporal grouping (day of the week). The goal is to highlight trends and distributions rather than make claims about true prevalence.\n",
+    "    \n",
+    "    > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    Similar to my other visualization, I prioritized clarity and simplicity by using aggregated data and avoided overcrowded plots. For example, grouping by day of week or relation made patterns easier to interpret. Color and visual factors as I used accessible color palettes (e.g., viridis) to support viewers with color vision differences. Lastly, I aimed to support readability with the use of clear titles and axis labels, logical ordering of categorical variables, and minimal visual clutter through clean themes (e.g., theme_minimal())\n",
+    "    \n",
+    "    > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    Reproducibility was ensured by writing all steps in R scripts, using publicly accessible datasets, documenting data transformations and aggregation steps and avoiding manual edits to the data\n",
+    "\n",
+    "    > How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "    \n",
+    "    > Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "    \n",
+    "    > How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I selected variables that directly supported the goal of showing trends in the counts of reports by relation and one certain days. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "    \n",
+    "    > What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    Similar to my other visualization the “underwater labour” included learning to work with the dataset, cleaning and transforming variables, converting date and categorical fields, testing multiple visualization types, debugging R code, reflecting on ethical considerations, and iterating on design decisions."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

02_activities/assignments/assigment_3_done/R/Rmarkdown.ipynb

@@ -0,0 +1,52 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1283f3e0",
+   "metadata": {},
+   "source": [
+    "Assignment 3 - R: https://open.toronto.ca/dataset/intimate-partner-and-family-violence/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5d7848",
+   "metadata": {},
+   "source": [
+    "> What software did you use to create your data visualization?\n",
+    "    I created my data visualizations using RStudio with the R programming language. I primarily used the packages ggplot2 for visualization and dplyr for data cleaning and aggregation. The dataset was accessed from the Toronto Open Data portal and processed within R.\n",
+    "\n",
+    "> Who is your intended audience? \n",
+    "    The intended audience includes researchers studying social and public health issues, policy makers and service organizations, community groups focused on violence prevention and a general audience interested in understanding trends in reported violence.\n",
+    "    \n",
+    "> What information or message are you trying to convey with your visualization? \n",
+    "    The visualization aims to communicate patterns in reported intimate partner and family violence over time and across relationship categories or temporal grouping (day of the week). The goal is to highlight trends and distributions rather than make claims about true prevalence.\n",
+    "    \n",
+    "> What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? \n",
+    "    Similar to my other visualization, I prioritized clarity and simplicity by using aggregated data and avoided overcrowded plots. For example, grouping by day of week or relation made patterns easier to interpret. Color and visual factors as I used accessible color palettes (e.g., viridis) to support viewers with color vision differences. Lastly, I aimed to support readability with the use of clear titles and axis labels, logical ordering of categorical variables, and minimal visual clutter through clean themes (e.g., theme_minimal())\n",
+    "    \n",
+    "> How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? \n",
+    "    Reproducibility was ensured by writing all steps in R scripts, using publicly accessible datasets, documenting data transformations and aggregation steps and avoiding manual edits to the data\n",
+    "\n",
+    "> How did you ensure that your data visualization is accessible?  \n",
+    "    One of the main consideration I made for accessibility purposes for my data visualization was around using color-blind friendly palletes. \n",
+    "    \n",
+    "> Who are the individuals and communities who might be impacted by your visualization?  \n",
+    "    Potentially impacted groups include survivors of violence, affected families and communities, advocacy organizations,policymakers, and law enforcement and service providers. As this data is directly tied to some community members lived experiences.\n",
+    "    \n",
+    "> How did you choose which features of your chosen dataset to include or exclude from your visualization? \n",
+    "    I selected variables that directly supported the goal of showing trends in the counts of reports by relation and one certain days. I avoided overly granular geographic data that could stigmatize specific neighborhoods without context.\n",
+    "    \n",
+    "> What ‘underwater labour’ contributed to your final data visualization product?\n",
+    "    Similar to my other visualization the “underwater labour” included learning to work with the dataset, cleaning and transforming variables, converting date and categorical fields, testing multiple visualization types, debugging R code, reflecting on ethical considerations, and iterating on design decisions."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}