Data Visualization (Statistics Project Sample)

INF4000 Data VisualizationStudent NameInstitutionDepartmentCourseModuleLecturerSubmission date Knowledge Building Data visualization is a term that refers to the methods that are used to transmit content visually or information by storing it as graphic elements (Tschandl et al., 2018). The topic selected is; “Prevalence of Mental Disorders and Substance use Disorders”. With data visualization, the selected dataset shall be analyzed and provide more information on how psychological disorders prevail in various parts of the world. The visual charts creates will also work to provide further information on the nature of mental conditions for the last three decades. The dataset is made up of 9 columns and 6840 rows. The nine columns include entity which holds the country name or region where the data is collected from, year column, and the rest of the columns hold prevalence percentage for specific mental illnesses. These columns are schizophrenia, alcohol, drug use, anxiety, depression, bipolar, eating disorders. From a glimpse of the dataset it is evident that depression as well as anxiety is the most prevalent among all the psychological illnesses. Datasets are often created for specific research or practical purposes, and can be obtained from a variety of sources such as government agencies, research institutions, and online databases (Vieira et al., 2018). In some cases, a researcher or organization may create their own dataset by collecting data through surveys, experiments, or other methods. There are many possible sources for datasets on mental disorders. Some examples include: * Government agencies such as the Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) may collect data on the prevalence and treatment of mental disorders. * Research institutions and universities may conduct studies on mental disorders and make their data available to the public. * Online databases such as Kaggle or the Open Science Framework may host datasets on mental disorders that have been shared by researchers. My choice of using this particular dataset on mental disorders was largely informed by the kind of visualization topic chosen and other variety of factors, including the research question being addressed, the availability of the data, and the suitability of the data for the intended analysis (Mirman, 2017). It is important to carefully consider the limitations and biases of any dataset, and to properly cite the source of the data in any published work. There are a number of observations discovered from visualization of the dataset used. Firstly, the dataset contains records from all over the world; country wise, per continent as well as per region. For fast visualization, there was need to filter the data into various continents, regions and some countries. For instance a filtration of England shows that bipolar disorder has been on the increase for the last three decades, as seen in figure 1 below. Additionally, the condition is determined to be influenced mainly by depression and anxiety though anxiety seems to have emerged towards the end of the last decade. From the visualization also it is clear that depression factor of Bipolar disorder in England is reducing over the years. Figure 2 below shows bipolar disorder prevalence in England with other factors (anxiety and depression) involved. Figure SEQ Figure \* ARABIC 1: England’s Bipolar disorder with anxiety Figure SEQ Figure \* ARABIC 2: England chart on Bipolar against years Figure SEQ Figure \* ARABIC 3: England Bipolar chart with trend lines As shown in figure 3, the trend lines shows a steady growth in bipolar cases in England. This visualization is informed by the fact that majority of people who are diagnosed with bipolar disorder also suffer from an anxiety issue. These include post-traumatic stress disorder (PTSD), generalized anxiety disorder (GAD), panic disorder, as well as social phobia (Lee and Yoon, 2017). Anxiety and depression, either on their own or in conjunction with another mental health condition, have been linked to a heightened likelihood of suicidal behavior as well as relational problems. Figure SEQ Figure \* ARABIC 4: Depression in England is declining From the above visualizations, it is clear that depression levels were high at the beginning of the study. Also notable is that there is a slight increase in depression immediately after 2010. This could be the cause of inflation and economic meltdown of the year 2008. Theoretical Framework ASSERT Framework The ASSERT model is comprised of the following six components: Ask a question to be answered in the visualization work, investigate possible answers to the question by looking for evidence. Organize this information so that it can provide a response to the query, Imagine other ways to respond to the question using the information that is currently accessible. Finally, after you have represented the data in a relevant visualization for the purpose of answering the question, Use these words to tell a story with some meaning (Rees and Laramee, 2019). The diagram below shows various levels of the assert framework utilized. Ask The question asked for the above visualization is: “What is the relationship of bipolar disorder to anxiety and depression disorders?” The question provides a clear information of what specific data are to be searched in the internet or the dataset being analyzed. Search The dataset was obtained from Our Word in Data website, https://ourworldindata.org/. This dataset fully corresponds to the type of visualization envisioned for this task. Structure Filtered the data per country to analyze data from England only. Envision I researched on the internet the previous visualizations related to mental disorders especially in England and how they were visualized the kind of questions answered. Represent Use of R Studio to design visual scatterplot charts to determine the relationship between bipolar disorder with anxiety and depression. Tell Explained the results visualized conclusively. Grammar Graphics Data: The data for this visualization consists of a table with nine columns: "Entity", “Year”, “schizophrenia”, “alcohol”, “drug use”, “anxiety”, “depression”, “bipolar”, “eating disorders” The rows represent the population in percentage for individuals living with the psychological disorders stated, from different countries in different years. Aesthetics: The variable "Anxiety" is mapped to the color aesthetic, so each anxiety level is represented with a different color. The variable "Year" is mapped to the x-axis position, so the populations are plotted at different points along the x-axis depending on the year. The variable "Bipolar" is mapped to the y-axis position, so the height of each point on the plot represents the population of the corresponding country in the corresponding year. Geometry: The geometry used in this visualization is points, with each point representing the population of bipolar disorder patients in a particular year. Scales: The y-axis uses a linear scale, with the minimum value set to 0 and the maximum value determined by the maximum bipolar prevalence in the data. The x-axis uses an ordinal scale, with the values corresponding to the years in the data. Coordinate systems: The visualization uses a Cartesian coordinate system, with the x-axis representing the years and the y-axis representing the populations. Annotations: The title and axis labels provide additional context for the visualization. Accessibility Accessibility in visualization refers to the design and use of visualizations in a way that is inclusive and usable for people with a wide range of abilities and disabilities. This includes considerations such as visual acuity, color perception, and cognitive abilities, as well as factors such as cultural and linguistic diversity (Linderman et al., 2019). By considering these factors, visualizations can be made more accessible and usable for a wider audience. These visualizations are designed to represent data in a way that is easy to understand and interact with, even for users who may have visual impairments, hearing impairments, cognitive impairments, or motor impairments. The figure below shows a chart designed with high contrasting colors to ensure visibility. Figure SEQ Figure \* ARABIC 5: England chart with trendlines There are a number of different approaches that can be taken when creating accessibility dataset visualizations. For example, designers can use high-contrast colors, large font sizes, and clear labels to make the visualization easier to read for people with visual impairments (Kraak and Ormeling, 2020). They can also provide audio descriptions of the visualization, allowing users with hearing impairments to access the information. Additionally, they can simplify the visualization by using simple shapes, patterns, and colors to represent the data, making it easier to understand for users with cognitive impairments. Finally, they can make the visualization touch-based, allowing users with motor impairments to interact with the data using touch. There are many benefits to using accessibility dataset visualizations. By making data more accessible and understandable, these visualizations can help to promote more informed decision-making and better understanding of complex concepts. They can also help to break down barriers to information and ensure that all users, regardless of their abilities, have equal access to data and the insights it can provide. Accessibility in visualizations is an important tool for making data more accessible and understandable for all users. By designing these visualizations with accessibility in mind, we can help to ensure that everyone has the opportunity to fully engage with and understand the data that is so central to our world today. * Visual clarity: Visualizatio...