Open Jimmi-Kr opened 4 months ago
21f1000120 - Pranav Wankhedkar
After deliberating on the most effective visualization type, I initially contemplated portraying the "estimates of survival rates" from week 4 lecture 1, as a potential option. However, upon further consideration, it became apparent that this approach might not adequately convey the intended message through mere visual interpretation. Consequently, I opted for a more intuitive solution: the bar chart.
In order to enhance clarity and facilitate immediate comprehension, I strategically employed a color scheme wherein blue signifies positivity, while red conveys negativity. This deliberate choice ensures that viewers can swiftly discern the underlying sentiment associated with each data point.
Moreover, to accentuate the distinction between positive and negative elements, I opted for a nuanced approach to width. By employing narrower bars for negative data and broader bars for positive data, I aimed to provide a visual cue that reinforces the directional interpretation of the information presented. This deliberate design decision serves to further clarify and enhance the overall impact of the visualization.
Design Decisions:
Scale, Color and Visual Encoding:
Tools
Name & Roll No - Saikat Samanta (21f1003501)
Name: ROYCE TOMY (21F1001916)
Aspect Emphasized
Design Decisions
Tool(s)
Name: Soumya V Namboodiripad Roll Number: 21F1004752
Overview: The purpose of this visualization is to clearly convey the effectiveness of different antibiotics named Neomycin, Penicillin and Streptomycin against various bacterial infections.
Visualization Type: The selected visualization type for this representation is a grouped bar chart. This choice allows for a straightforward comparison of MIC values among various antibiotics across bacteria exhibiting negative and positive Gram staining. The grouped bars make it simple to see the differences in antibiotic effectiveness between these two groups. Putting each antibiotic together for different bacteria makes it easier for us to compare the MIC required for each of them.
Color Encoding: Assigned a unique color to each antibiotic to enhance clarity. The chart's background color was adjusted to a slightly darker shade, improving the visibility of the bars.
Grouping Decision: The decision to group bacteria based on Gram staining facilitates a direct comparison of antibiotic effectiveness within each staining category. This grouping helps draw attention to the variations in MIC values between the two groups, emphasizing the impact of Gram staining on bacterial response to antibiotics.
Sorting and Scale: Bacteria within each group were sorted based on their respective MIC values, allowing for a smooth progression of bars from lowest to highest MIC values. This sorting helps in identifying patterns and trends within each Gram staining category. The values are scaled using the logarithmic scale, maintaining consistency across both negative and positive Gram staining groups and ensuring a fair visual representation of MIC values. The scale is consistent across both negative and positive Gram staining groups, ensuring a fair visual representation of MIC values.
Size and Clarity: The size of the chart was chosen to be large enough for easy readability while avoiding unnecessary clutter. Adequate spacing between bars and clear labels contribute to the overall clarity of the visualization. Axes are appropriately labeled to provide context, and data labels are added for precise identification of MIC values.
Key findings:
Tools Used: Tableau Public
Name: Kaushik V Roll No: 21f1001083
Emphasis: To present the efficacy of each of the antibiotics (Neomycin, Penicillin and Streptomycin) against different types of bacteria. This is measured in terms of the Minimum Inhibitory Concentration (MIC) which indicates the minimum level of concentration required to inhibit the growth of a specific bacteria. Consequently, lower the MIC, the more potent the antibiotic is in inhibiting that particular bacteria.
Design Choice:
Where this visualization may not be effective
Tool used: Tableau
Name : Viraj Sharma Roll No: 21f1003723
The chosen visualization is a heatmap, which effectively communicates the varying effectiveness of three antibiotics across sixteen bacteria.
Sorting and Scale: The sorting of bacteria is alphabetical, which might obscure trends related to Gram staining; sorting by Gram reaction or MIC values could reveal additional patterns. Scaling choices, such as the logarithmic transformation often used for MIC values, are implicit in the heat-map's design.
Color Encoding: The colors blue and green are traditionally associated with medical and scientific visuals, conveying a sense of clinical precision and efficacy.
The heat-map was chosen for its ability to display quantitative information across two categories (bacteria and antibiotics) in a dense yet readable format, making it ideal for comparing multiple variables simultaneously. Overall, the design decisions—from the choice of visualization type to the color scale and text annotations—were made to facilitate an effective communication of the data's complexity in a simple, visually engaging format.
Tools Used: Plotly
Name: Asswin Karuppaiah PL Roll No: 21F1001419
Visualization Type: To effectively communicate the antibiotic efficacy data, a grouped bar chart is chosen as the visualization type. This decision is driven by the need to compare the effectiveness of each antibiotic across different bacterial strains while considering the Gram staining reaction. Grouped bars allow for easy comparison between antibiotics within each bacterial strain, facilitating insights into the most effective treatment options.
Chart layout and Color Coding: In the chart, each group of bars represents one bacterial strain, grouped by their Gram staining reaction (Gram-positive or Gram-negative). The x-axis denotes the bacterial strains, while the y-axis represents the minimum inhibitory concentration (MIC) of the antibiotics. Antibiotics are color-coded for easy differentiation, with Penicillin in blue, Streptomycin in orange, and Neomycin in green. Shorter bars indicate lower MIC values, signifying greater effectiveness.
Sorting and scale: The data is transformed to calculate the MIC values for each antibiotic. Since MIC values can vary significantly, a logarithmic scale is employed to better visualize the data range without losing details at lower concentrations. This transformation helps mitigate the skewness in the data distribution and ensures that differences across antibiotics and bacterial strains are clearly depicted.
Furthermore, the bacterial strains are sorted alphabetically to aid readability and ease of comparison. This arrangement ensures that similar strains are grouped together, allowing viewers to quickly identify patterns and trends. Including a legend clarifies the color encoding for each antibiotic, while axis labels and a title provide context and guidance for interpretation.
How These Decisions Facilitate Effective Communication:
Clarity: Each design decision, from the choice of visualization type to the color encoding and sorting, is made to enhance clarity and minimize ambiguity in the communication of antibiotic efficacy data. Comparability: The use of grouped bars and color encoding enables easy comparison between antibiotics within bacterial strains, facilitating insights into the most effective treatment options. Readability: Alphabetical sorting and clear labeling ensure that viewers can easily navigate the chart and understand the relationships between bacterial strains, Gram staining reactions, and antibiotic efficacy.
Purpose and Conclusion Overall, this grouped bar chart effectively communicates the relative effectiveness of antibiotics across bacterial strains, enabling viewers to identify the most potent treatments for specific infections. However, the chart may obscure specific MIC values, which could be addressed by providing additional annotations or tooltips for finer detail. Additionally, considerations for color accessibility should be made to ensure clarity for all viewers.
Name: SHRI KRISHNA PANDEY Roll No: 21f1006966
The data given to us had 3 column of antibiotics and its MIC (minimum inhibitory concentration) on 16 type of bacteria, labeled with gram staining positive or negative. We were required to come up with visualization which effectively provides a rigorous rationale for my design choice that is side-by-side bar chart.
Link to view visualization
The visualization effectively presents the MIC (Minimum Inhibitory Concentration) values of antibiotics across different bacteria types, categorized by their gram staining characteristics. Few notable observations I have seen are:
Excel for viewing the data and changing the value and Tableau public for visualization.
After World War II, antibiotics were considered "wonder drugs", since they were easy cures for what had been intractable ailments. To learn which drug worked most effectively for which bacterial infection, the performance of the three most popular antibiotics on 16 bacteria was gathered. The values in the table represent the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro. The reaction of the bacteria to Gram staining is described by the covariate “gram staining”. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.
Your task is to design a chart that you believe effectively communicates the data and provide a short write-up (no more than 4 paragraphs) describing your design.
- Your chart image should be interpretable without recourse to your short write-up.
- Do not forget to include a title, axis labels, or legends as needed!
As different visualizations can emphasize different aspects of a data set, you should document what aspects of the data you are attempting to most effectively communicate. Just as important, also note which aspects of the data might be obscured or downplayed due to your visualization design.
In your write-up, you should provide a rigorous rationale for your design decisions. Document the visual encodings you used and why they are appropriate for the data. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication?
Here is the link for the dataset: Butin_antibiotic_data.xlsx
The heatmap illustrates how effective three antibiotics (Neomycin, Penicillin, and Streptomycin) are against different types of bacteria. Each color-coded cell represents the Minimum Inhibitory Concentration (MIC) value, indicating the concentration needed to inhibit bacterial growth. Darker shades suggest higher MIC values, implying lower antibiotic efficacy. Neomycin generally requires higher concentrations, indicating lower effectiveness compared to Penicillin and Streptomycin, which show lower MIC values, indicating higher potency.
Notably, bacteria with positive Gram staining tend to be more susceptible to Penicillin and Streptomycin, showing lower MIC values, while those with negative Gram staining exhibit higher MIC values, indicating lower susceptibility to these antibiotics. This information is crucial for clinicians, helping them choose the most effective antibiotic for specific bacterial infections, while also highlighting the need for new antibiotics and treatment strategies to combat antibiotic resistance.
Name : Phani Kumar Jallipalli Roll no : 21f3002478
The stacked bar chart visualizes the effectiveness of three antibiotics (Penicilin, Streptomycin, and Neomycin) against various bacteria, categorized by Gram staining (positive and negative). Each bar in the chart represents 100% of the bacteria for each antibiotic, with different colors indicating the proportion of effectiveness attributed to each antibiotic.
The x-axis lists the names of the bacteria species tested, while the y-axis represents the percentage of effectiveness of the antibiotics. The chart is divided into two sections: Gram-positive bacteria (denoted as "positive") and Gram-negative bacteria (denoted as "negative").
For each bacterium, the chart illustrates the relative effectiveness of the three antibiotics. The height of each segment within a bar indicates the percentage of bacteria inhibited by the corresponding antibiotic, with the total height representing 100% effectiveness. The segments are stacked on top of each other, allowing for easy comparison of antibiotic effectiveness within each Gram staining category.
The segment of each antibiotic within the bar represents the percentage of effectiveness attributed to that antibiotic.
The highest MIC levels are represented by the segments that are positioned at the top of the bars, indicating the proportion of bacteria for which the corresponding antibiotic is less effective or ineffective in inhibiting growth at lower concentrations.
Through this visualization, patterns in antibiotic effectiveness across different bacteria and Gram staining classes become apparent. Users can quickly identify which antibiotics are more effective against specific bacteria and how Gram staining classification influences antibiotic efficacy.
The chart provides a comprehensive overview of antibiotic effectiveness against a range of bacteria, facilitating comparisons and insights into bacterial susceptibility to different antibiotics based on Gram staining characteristics.
Name: MANISH KUMAR Roll No: 21f1004259
The data given to us is about 16 types of bacteria antibiotics and its MIC (minimum inhibitory concentration), each bacteria is categorized into gram staining positive or negative. Our requirement is to come up with visualization.
Line chart with markers with Logarithmic Scale
I chose it because the markers in the line help to clearly identify the MIC value for the particular bacteria, it allows a clear comparison of antibiotic effectiveness. The y-axis represents the Minimum Inhibitory Concentration (MIC), which is logarithmically scaled with base 10. This scale emphasizes differences in effectiveness while accommodating the wide range of MIC values. The reason I chose this visualization design it clearly helps identify one interesting aspect of the data, For Penicillin all the bacteria have MIC values less than one for the Gram-positive while it is greater than all for all the bacteria with Gram-negative.
While this design effectively communicates antibiotic effectiveness and the trend of MIC values for the bacteria over the two groups in grams, it doesn’t reveal the actual value of MIC since the y-axis is logarithmically transformed data.
Name: Amol HATWAR Roll No: 21f1000451
The Minimum Inhibitory Concentration (MIC) of three antibiotic compounds were given for 16 bacteria along with gram-staining properties. Since the data is important from a health-care perspective and may directly influence patient outcomes, simplicity, ease-of-comprehension, and minimalism were chosen as the guiding principles for the visualisation. High Resolution PDF here.
Tools Used: Microsoft Excel
Name : Om Sharma Roll No. 21f1004424
The visualization aims to compare and emphasize the effectiveness of three antibiotic drugs against various bacteria strains, aiding in the selection of the most suitable antibiotic for combating specific bacteria.
The design choices were deliberate. A bar chart was chosen for its ability to highlight differences in drug performance across different bacteria. A logarithmic scale was applied to the x-axis to accommodate the wide range of Minimum Inhibitory Concentration (MIC) values, ranging from 0.001 to 870. This scale allows for the representation of vastly different MIC values on a single axis.
Most effective drugs against each bacteria is highlighted in golden color.
The data was sorted based on gram staining, facilitating the grouping of negative and positive gram staining bacteria together and in alphabetical order.
Gram staining labels were placed alongside bacteria names on the y-axis, streamlining the identification of positive and negative gram staining groups without the need to reference other sections of the visualization.
Name: Gokulakrishnan B Roll: 21f1006866
Spreadsheet link: DVD Week 5
The goal of visualization is to pinpoint the most effective antibiotic against the target bacteria and determine its gram property, aiding in treatment decision-making.
With 16 cells representing different bacteria, which are discrete and categorical, plotting them on an axis would overly complicate visualization. Unlike the scenario of choosing a medicine for a disease, here we aim to identify the medicine for a given disease. Hence, plotting antibiotics for all bacteria together lacks coherence. Individually plotting each makes more sense.
Rather than comparing the ratio of penicillin to streptomycin, our focus lies on identifying the best-performing antibiotic. Hence, bars with fixed heights are employed to simplify interpretation, avoiding complexity introduced by outliers.
To facilitate antibiotic preference identification, bars are colored red for the least preferred, yellow for intermediate, and green for the most preferred. This aids in discerning the most appropriate antibiotics. Additionally, MIS values and gram properties are labeled and color-coded for clarity.
Bacteria names are arranged in cells in a sorted fashion, enhancing the ease of locating the desired bacteria.
By not capturing the ratio, we may overlook the extent to which one antibiotic outperforms another. For instance, plots depicting drugs with MIS values of 4 for one antibiotic and 5 for another might appear identical to plots where the MIS values are 4 for one antibiotic and 1000 for the other. This limitation could obscure nuanced differences in effectiveness between antibiotics.
Derived columns were added using Colab in Python, while Google Sheets facilitated visualization creation.
Introduction
Bacterial Strains: 16 distinct bacterial strains are included in the dataset. Every strain, such as Aerobacter aerogenes, Escherichia coli, Staphylococcus aureus, etc., is identified by name. These bacteria are the subjects, and the way they react to antibiotics is noted.
Antibiotics: Three antibiotics are tested for effectiveness: Neomycin, Streptomycin, and Penicillin. The Minimum Inhibitory Concentration (MIC) values of these antibiotics are determined against individual bacteria, and they are frequently used to treat bacterial infections.
Gram Staining: Every bacterium's Gram staining classification is included in this dataset. Based on how they react to Gram staining, bacteria are categorized as either Gram-positive or Gram-negative. The dataset contains this classification as a covariate.
Important details regarding the interactions between antibiotics and bacteria are provided by each of these features. Effective data visualization allows us to see patterns, trends, and relationships that shed light on the antibiotics that work best against particular bacterial strains and whether or not their efficacy varies with Gram staining classification.
Aim of visualization
The visualization's goal is to show how effective three antibiotics—Penicilin, Streptomycin, and Neomycin—are in comparison to one another against various bacterial species while taking into account their minimum inhibitory concentrations (MIC) values.
The visualization attempts to draw attention to any possible patterns or trends in the effectiveness of antibiotics depending on the bacteria's Gram staining characteristics (Gram-positive vs. Gram-negative).
Design Selection and scaling
Data Partitioning: Based on the bacteria's positive and negative Gram staining characteristics, it was decided to divide the data into two distinct sheets. This division makes it possible to compare and analyze Gram-positive and Gram-negative bacteria with greater precision, providing more insight into how well antibiotics work against various bacterial species.
Visualization Composition: Each sheet has distinct panes for displaying the total amount of penicilin, streptomycin, and neomycin for each species of bacteria. Circle marks are used to represent individual bacteria within each pane; the size of the circles indicates the total of the MIC values for the corresponding antibiotic. The design guarantees coherence and makes it simple to compare the antibiotic efficacy of various bacteria within each Gram staining category by using uniform visual encodings across all panes.
Min-Max scaling is employed as a technique to standardize the data, aiming to mitigate the influence of extreme values and ensure a more uniform representation across the scatter plot.
Limitations
A significant disparity in the sizes of the individual data points has been noted by me in my analysis of the scatter plot. Certain points seem comparatively small, but there are others that are disproportionately big. Due to the difficulty in interpreting the data, this variation in size distribution among the data points causes inefficiencies in the visualization. In particular, it is difficult to identify significant patterns or trends in the data due to the wide range of point sizes.
Tool used : Tableau
Name : Purva Sharma Roll No : 21f1006847
Name : Sarthak Khandelwal Roll Number : 21f1004405
https://public.tableau.com/app/profile/sarthak.khandelwal7221/viz/DVD_GA3/Sheet12?publish=yes
Objective: The objective of the visualization is to compare the Minimum Inhibitory Concentration (MIC) values of three antibiotics (Penicillin, Streptomycin, and Neomycin) across 16 different bacteria strains. The visualization aims to provide insight into the effectiveness of each antibiotic against various bacterial strains and the influence of Gram staining (positive or negative) on antibiotic response.
Aspects:
Design Decisions:
Other Considerations:
Limitations:
Tool used : Tableau & MS Excel
Roll No. : 21F1006597 Tool used : Google sheet
There are 16 Bacteria and to cure there are 3 antibiotic.Each antibiotic works differently on different bacteria. Effectiveness is measured in MIC value, less MIC value means best effective.
The chart given shows the MIC (Minimum Inhibitory Concentration) values for three antibiotics, Penicillin, Streptomycin and Neomycin, and their effectiveness towards 16 different kinds of bacteria, which data was collected after World War II. The bacteria are divided into two groups: Gram-positive and Gram-negative.
Following are the descriptions of the visualization:
Choice of visualization and color A table is chosen because it is the most appropriate way to display small datasets where both rows and columns have important information to be conveyed. The antibiotics are represented by medicine bottles to make the chart interesting as well as relevant to the medical field. It allows for easy comparison of MIC values between different antibiotics and bacteria.
Color A color gradient is used across the bottom of the table to represent the range of MIC values, from the lowest (0.001) in green to the highest (870) in red. This helps viewers easily identify which bacteria are most susceptible (low MIC values) and resistant (high MIC values) to each antibiotic. Also, the bacteria are colored accordingly to represent Gram-positive Bacteria as blue and the Gram-negative Bacteria are red. This is because the Gram-positive bacteria in the gram-staining process turn violet or blue, and the Gram-negative bacteria do not.
Sorting The bacteria are sorted alphabetically within their respective Gram-positive and Gram-negative groups. This makes it easy for viewers to find specific bacteria of interest.
Additional Elements The chart includes a title, labels for the rows and columns, and a legend for the color gradient. These elements all help to improve the clarity and readability of the chart.
Improvement: We can overlay the color gradient with additional visual markers, like symbols or text labels, to indicate established resistance categories based on established breakpoints for MIC values. This would provide a more direct interpretation of susceptibility and resistance levels for clinicians. While a table is effective for small datasets, a heatmap could offer a quicker visual comparison of MIC values, especially across different antibiotics. Colors could represent the same gradient as the table, with darker shades indicating higher MIC values and lighter shades indicating lower values. This could be particularly helpful if the number of antibiotics or bacteria types increases significantly.
Tools Used: Microsoft Excel, Canva
Name: Manaswita Mandal Roll No: 21f1004567
Name: A Aniruddha Roll: 21f1005327
Identify the most effective drug for a given bacterial infection. The dataset consists of MIC (minimum inhibitory concentration) values of three antibiotics (Penicilin, Streptomycin and Neomycin) on 16 bacteria. The MIC values indicate the effectiveness of the drug with lower values inidcating better performance. Information regarding the Gram Stain test is also provided which gives rise to two categorical values, which are, Gram Stain Positive and Gram Stain Negative.
Name: Aditya Dhar Dwivedi Roll Number: 21F1001069
A bar chart was chosen for this visualization to present the Minimum Inhibitory Concentration (MIC) values of various antibiotics against different strains of bacteria. Bar charts excel in comparing MIC values across multiple bacterial strains and antibiotics simultaneously.
On the x-axis, the bacteria strains are sorted alphabetically for clarity. The y-axis is divided into three axes, each corresponding to one of the three antibiotics. This organization minimizes the gap between extreme MIC values.
Higher MIC values, represented by taller bars, indicate a need for higher concentrations (greater than 1 unit or 1 microgram per milliliter) of antibiotics to inhibit bacterial growth, suggesting reduced effectiveness against that strain. Conversely, lower MIC values, reflected in shorter bars, suggest that lower concentrations (less than 1) are needed to inhibit the bacteria's growth. A value of 0 indicates the precise requirement of 1 unit or 1 microgram per milliliter to inhibit bacterial growth.
Bacterial strains are categorized as positive or negative, aligning with conventional microbiology practices, providing further context for interpretation.
Tableau was used as the tool for creating this visualization.
NAME: UTKARSH GAURAV ROLL NO. : 21F1001336
The primary objective of this study is to evaluate and compare the effectiveness of three widely used antibiotics—penicillin, neomycin, and streptomycin—on 16 different bacterial strains. The study focuses on determining the minimum inhibitory concentration (MIC), a key metric indicating the concentration required to prevent bacterial growth in vitro.Lesser the value better is the effectiveness of the drug.
As an integral part of the study, the Gram staining reaction of bacteria serves as a covariate. Bacteria are categorized as either Gram-positive or Gram-negative based on their staining characteristics. Gram-positive bacteria exhibit a dark blue or violet staining, while Gram-negative bacteria lack these colors. The inclusion of this covariate aims to correlate antibiotic effectiveness with the Gram staining properties of the bacteria.
Name: DEENA GAUTAM Roll Number: 21F1001012
Highlighting the effectiveness of Penicillin for Gram-positive bacteria
UNDERSTANDING THE GRAPH
NEGATIVES
POTENTIAL IMPROVEMENTS
TOOLS USED:
Name : Gaikwad Sanket Sanjay Roll no : 21f1007096
Visualizing Effectiveness of Antibiotics on Different Bacteria
Design Decisions: In this data, I have four variables; three are categorical and one is numerical. There is no need to do any data aggregation or grouping. I can show one numerical variable on one axis and use another variable as the second axis. The remaining two variables can be represented using color and shape as legends in the plot. The best way to have two legends without much clutter is to use a scatter plot. The obvious choice for the x-axis would be the variable with the highest number of distinct values; hence, bacteria is the best choice. For gram staining, I can use color as it captures the real-world semantics of the data. The data from the numerical variable ranges from 0.001 to 870. Hence, it is better to use a log scale for the y-axis. The remaining variable is antibiotics, and I can use shape to represent it.
Emphasis: The emphasis is on ensuring that the graph captures every single aspect of data points. The plot is pretty much self-explanatory. The impact of colors as legends is clearly visible and shows the difference between the two categories of gram staining.
Limitations: Reading the shapes representing antibiotics is a little bit hard.
Penicilin
Streptomycin
Neomycin
This chart is employed to represent antibiotic effectiveness across bacterial strains. Circles of varying sizes are used to convey the minimum inhibitory concentration (MIC) values for each antibiotic and bacterium.
Each circle represents a bacterial strain. The size of the circle corresponds to the MIC value, with larger circles indicating higher MIC values.
Effective Communication: This visualization provides a quick and visually appealing overview of antibiotic effectiveness. Larger circles immediately draw attention to bacteria with higher MIC values, offering a clear comparison between antibiotics and bacterial strains. Stakeholders can easily identify patterns, such as which antibiotics are generally more effective or less effective against specific bacteria.
Limitations: While this visualization effectively communicates the relative magnitude of MIC values, it may not be suitable for comparing detailed differences between closely spaced values. Users seeking a more precise analysis may need to refer to the original data or utilize complementary visualizations for specific insights.
Yalini S 21f1004138
Puravasu Jaideep Sesha 21f1000162
MIC antibiotic effectiveness visualization
Tool used: Excel, Tableau
Introduction The dataset includes information on the minimum inhibitory concentration (MIC) of the three most popular antibiotics for 16 bacterial infections. It also includes the Gram staining reaction of the bacteria, distinguishing between Gram-positive and Gram-negative.
Goal The goal of this visualization was to provide a simple view of the MIC data to understand the effectiveness of antibiotics like Penicilin, Streptomycin and Neomycin.
Chart Design Thought Process
Name : Arunkumar N Roll No : 21f3000442
The Data:
The Visualization:
Name: Sushmita Nandy Roll: 21f1005425
Objective:
The study mainly focusses on effectively communicate the antibiotic sensitivity of different bacterial strains while considering their Gram staining reaction. The data provided allows for easy comparison between the three most popular antibiotics (Penicillin, Streptomycin, and Neomycin) across the bacterial strains.
Data Interpretation and Chart Designing
Observations
Neeraj Rajeev Shetkar 21F1006328
Antibiotic effectiveness visualisation
The visualization created in Microsoft Excel presents a straightforward table showcasing the effectiveness of three prominent antibiotics across 16 different bacteria strains. Each cell in the table is adorned with a vibrant background color, indicating the efficacy of the respective drug against the particular bacterium. The colours used red, yellow, and green serve as visual cues, enabling easy interpretation of the data by pharmacists.
Moreover, the table incorporates an additional layer of information by subtly integrating the Gram staining reaction of the bacteria. Gram-positive bacteria typically exhibit shades of purple to blue, while Gram-negative bacteria lean towards pink to red. By mirroring these color schemes in the table, the visualization seamlessly communicates the Gram staining characteristics alongside antibiotic effectiveness without the need for a separate legend.
Name: Chandana Nisankara Roll Number : 21f1005727 Tool used : Power Bi Antibiotic Effectiveness Across Bacteria: A Gram-Staining Perspective Objective : To learn which drug worked most effectively for which bacterial infection, the performance of the three most popular antibiotics on 16 bacteria was gathered. The values in the table represent the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro. The reaction of the bacteria to Gram staining is described by the covariate “gram staining”. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.
Visualization : The grouped bar chart is selected for comparative analysis of Penicillin, Streptomycin and Neomycin for 16 types of bacterial strains. Horizontal grouped bar chart is chosen for better readability and easier comparisons.
Y-axis represents all Bacterial strains and X-axis represents log scaled MIC as the range of the values falls between 0 - 1000 , Concentration of values is high between 0-2 and very few large numbers. The values are not uniformly distributed so large values are compressed using log to make them visually comparable with Large values.
There are two classes of Bacteria negative and positive , they are represented using two different graphs for better readability . they are separated vertically. For every bacteria , all three anti biotics and their performance is plotted. Three different colors Pink , Yellow and Blue are considered to represent Penicillin, Neomycin and Streptomycin.
Name: Rohan Khandelwal Roll number: 21f1005976 Tool: Flourish(Drawing) , Google Sheets (scaling)
Introduction: The Objective aims to communicate antibiotic sensitivity in diverse bacterial strains, considering Gram staining reactions. It evaluates the effectiveness of Penicillin, Streptomycin, and Neomycin across 16 bacteria by using minimum inhibitory concentration (MIC) values. The Gram staining reaction serves as a covariate, distinguishing between Gram-positive and Gram-negative bacteria. The research aims to identify the most effective drug for specific bacterial infections based on these analyses.
Visualisation:
To address the considerable variability among the data values, I employed the Min-Max scaler. This transformation was essential to standardize the data and bring it within a uniform range of 0 to 1, facilitating more effective visualization and analysis.
The visual representation was crafted using the Flourish platform, featuring two distinct images based on filtering criteria—Gram staining of positive and negative. This segregation allowed for a focused examination of the dataset under different conditions.
The chart itself was constructed as a Grouped Bar Chart, chosen for its ability to clearly display the relationships between variables. This format enhances the visual communication of trends and patterns within the data.
A meticulous approach was taken in selecting distinct colors for each antibiotic. This color-coded scheme was deliberately chosen to ensure clarity and ease of interpretation, allowing stakeholders to quickly grasp the nuances of antibiotic performance and relationships within the dataset.
Name: V Ajith Roll No: 21f1005919
Title: Antibiotic Effectiveness Grouped Bar Chart
Introduction: In the quest to effectively communicate data on antibiotic efficacy against various bacteria, a meticulous selection of visualization type is pivotal. The chosen approach is a grouped bar chart, renowned for its ability to present complex datasets with clarity and precision. This choice ensures a straightforward comparison of Minimum Inhibitory Concentration (MIC) values of each antibiotic across diverse bacterial strains.
Visualisation:
Conclusion: In summation, this design choice is tailored to foster effective communication of antibiotic efficacy data. The grouped bar chart serves as a powerful tool for discerning trends and patterns, allowing viewers to ascertain the most effective antibiotics for specific bacterial infections. Furthermore, it underscores disparities in efficacy across bacterial strains and antibiotics, thereby facilitating informed decision-making in clinical settings.
Name: Priyanka Nathani Roll no: 21f1005807 Tools used: Excel Power Bi
Objective: The effectiveness of various known antibiotics was measured through MCI (Minimum Inhibitory Concentration) against common disease causing bacteria. This index indicates the quantity of antibiotic required to prevent in-vitro growth. additionally information is provided whether the bacteria are Gram-positive or Gram-negative.
Approach
Visualization
Limitations:
Name: Harsh Bardhan Roll No 21f1004807
The visualization was meticulously crafted to facilitate a comprehensive comparison of the efficacy of three antibiotic drugs across a spectrum of bacteria strains, aiding in the informed selection of the most appropriate antibiotic for targeting specific bacterial infections.
A deliberate choice was made to utilize a bar chart format, renowned for its ability to visually emphasize disparities in drug effectiveness across different bacterial strains. To accommodate the wide range of Minimum Inhibitory Concentration (MIC) values spanning from 0.001 to 870, a logarithmic scale was implemented on the x-axis. This logarithmic scale enables the representation of vastly divergent MIC values on a single axis, ensuring clarity and coherence in the visualization.
To enhance readability and interpretation, the most effective drug against each bacterial strain is highlighted in a distinctive golden color, drawing immediate attention to the optimal treatment option.
The data organization strategy involved sorting based on gram staining, strategically grouping together bacteria with negative and positive gram staining. Moreover, the arrangement was executed in alphabetical order, simplifying the identification and comparison of bacterial strains without the need for cumbersome cross-referencing.
In addition to the visually prominent representation of drug efficacy, gram staining labels were strategically positioned alongside bacteria names on the y-axis. This strategic placement facilitates seamless identification and differentiation of positive and negative gram staining groups, streamlining the interpretation process and enabling swift decision-making regarding antibiotic selection based on bacterial characteristics.
Name: Debapriyo Saha Roll No: 21f1004645
Objective: To study the effectiveness of the drug towards 16 different kinds of bacterial infection. The above chart shows the MIC (Minimum Inhibitory Concentration) values for three antibiotics, Penicillin, Streptomycin and Neomycin. The data was collected post World War II. The bacteria are divided into two groups: Gram-positive and Gram-negative based on their reactions.
Choice of visualization and colour: A heatmap is plotted using python for each antibiotic which brings in more meaning with respect to the scale of the value. A colour gradient is being used across each drug category to represent the range of MIC values. Highest value is in red and lowest value is in blue This brings more intuitive way to understand and compare the response of the bacteria to each antibiotic.
Grouping & Sorting: The bacteria are grouped based on Gram-Staining values that is positive and negative.
Data Transformation: Applied log transformation to scale the numerical values.
Visual Elements: The chart comes with a title and labels for both X and Y axis. It also contains a gradient colour scale to understand the max and min value. It also shows the grouping of the bacteria based on the Gram-Staining.
Improvement: More icons related to medical field can be used to make the visualization better.
Tools Used: Python PowerPoint
This visualization, titled "Krimul," is designed to intuitively convey the effectiveness of three antibiotics against 16 different bacteria, and consequently convey the hidden information of various types of bacterias themselves.
The chart utilizes size and color to encode MIC data meaningfully. The size of each bubble corresponds to the MIC value; larger bubbles indicate a higher concentration requirement and, thus a lower antibiotic effectiveness. The bubbles are given primary colors due to the absence of any apparent semantic color. log2
scaling has been done to acheive meaningful visualization. Clamping was considered but was quicly side-lined.
The area graph cleverly conveys the strength of the bacteria, calculated by the MIC value of the efficient antibiotic, allowing for additional insights that may not be visible in a tabular chart. The bigger the area, the more strong the bacteria. This is calculated by radius of inner circle - MIC value of effective antibiotic
The donut chart encodes how the best antibiotic compare against the second-best antibiotic to aid the drug-administerers in case of the absence of the first-best antibiotic. This is achieved by (MIC)/(Sum of MIC of Top-2 Antibiotic)
Three labels with 3 distinc colors for Penicillin, Streptomycin, and Neomycin is placed in the center. This allows for an easy visual trace from each bacterium to its corresponding MIC value for each antibiotic.
A single thick layer was chosen for Gram-Positive, and multiple thin layers were chosen for Gram-Negative, which coincides with their biological appearance.
The "Krimul" chart's circular hierarchy, multiple graphs, color usage, and scale choices are all deliberate to facilitate the communication of complex data in a comprehensive and aesthetically pleasing manner. This novel visualization allows for at-a-glance insights into antibiotic effectiveness, while also providing a deeper level of detail upon closer examination, effectively balancing the need for simplicity and informational depth.
Name: Aditi Krishana Roll No: 21f1004270
Introduction:
The dataset provided to us included three columns detailing antibiotics and their Minimum Inhibitory Concentration (MIC) values across 16 types of bacteria, each labeled according to their Gram staining characteristics as either positive or negative. Our task was to develop a visualization that not only effectively presents this information but also offers a solid justification for our choice of design. We decided on a column chart as the most suitable format to achieve this goal, as it allows for a clear comparison of the MIC values for each antibiotic against the various types of bacteria, while also highlighting the distinction between Gram-positive and Gram-negative bacteria.
Design Rationale and Observations:
Title: "Antibiotic Efficacy Across Bacterial Strains" Visualization
Our decision to employ a column chart for the Title: "Antibiotic Efficacy Across Bacterial Strains" Visualization was driven by the objective to cohesively represent three critical elements: the MIC values of various antibiotics, the diversity of bacteria types, and their Gram staining attributes. This layout, featuring bacteria names along the x-axis and corresponding antibiotics on the y-axis, offers an integrated snapshot of the data, ensuring a comprehensive analysis within a singular graphical representation. The decision to use normalized values, particularly through the application of the natural logarithm, was aimed at bolstering the clarity and comparability of antibiotic effectiveness across differing bacterial strains.
The addition of color coding to distinguish between Gram-positive and Gram-negative bacteria further augments the chart's readability, allowing for an intuitive grasp of the data. A notable inclusion at the bottom of the x-axis is a listing of the most effective antibiotics against each bacteria type, providing a quick reference for optimal treatment options.
Normalization Process:
The normalization of MIC values, especially through natural logarithm transformation, plays a pivotal role in standardizing the dataset. This process facilitates a more straightforward comparison of the antibiotics' potency against various bacteria, irrespective of their Gram stain classification. By focusing on relative differences rather than absolute values, this approach significantly enhances the visualization's interpretability, empowering stakeholders to make more informed choices regarding antibiotic usage.
Key Observations:
The visualization successfully delineates the MIC values of antibiotics against a range of bacteria, segmented by their Gram staining properties. Several critical insights emerge from this analysis:
Antibiotics with lower MIC values are more effective, as indicated by their superior capability to inhibit bacterial growth. Penicillin is notably more potent against Gram-positive bacteria, which is consistent with its targeted mechanism of action against cell wall synthesis. Neomycin demonstrates enhanced effectiveness against Gram-negative bacteria, underscoring the variability in susceptibility based on Gram staining characteristics. Streptomycin's performance is comparatively weaker across the board, highlighting the necessity of exploring alternative antibiotics for certain bacterial infections.
Analytical Tool Utilized:
The data was meticulously analyzed and visualized using Google-sheets, enabling the detailed examination and presentation of the findings.
Name: Himadri Dixit Roll no.: 21f1006310
Burtin's antibiotic dataset shows us the effectiveness of an antibiotic on a given bacteria based on their dosage in Minimum Inhibitory Concentration (MIC). Additional classification information about the bacteria has been provided in the dataset.
This simple visualization aims to show the most effective antibiotic for each bacteria among the 3 given antibiotics. The most effective antibiotic is shown in darker color. The choice of 3 bar charts stacked over was made to show the distinction between values clearly. X and Y axis are labeled clearly showing bacteria names/ gram staining and Penicilin, Streptomycin, Neomycin respectively. Since the dataset had outliers which would overshadow the other values the data was logarithmically scaled with base 2 along with linear scaling to offset the negative values the log scale gives.
Some notable observation:
Name: Anirudh Singh Siddhu Roll No: 21f1001587
Tools : MS Excel, Tableau
For this task, I have used a grouped bar chart to effectively communicate the data on antibiotic effectiveness against different bacteria, considering the covariate of Gram staining. Grouped bar charts are suitable for comparing the performance of multiple categories across different groups.
The bars are color-coded according to the Gram staining of the bacteria, allowing for easy visual differentiation between the two categories. I have represented the Gram staining with positive and negative signs for better understanding.
The x-axis contains the bacteria strains split into two parts: first name and last name, to group them for easy identification. The y-axis is also split into three categories for the different antibiotics. The y-axis labels for all three antibiotics are different according to the scale of each individual antibiotic. Data labeling is being used to showcase the value corresponding to each bar.
Name : Rajkishore Nandi Roll No : 21f1006016 Tools Used : MS Excel, Tableau Public
Objective :
The aim of this study is to compare the effectiveness of three widely used antibiotics - Penicillin, Neomycin, and Streptomycin - on 16 different bacterial strains. The antibiotics are compared according to their minimum inhibitory concentration (MIC) levels. The antibiotic with least MIC value is the most effective one for a particular bacteria.
Designing Approach
Conclusion :
The design choices are made to foster effective communication of antibiotic efficacy. The bar chart serves as a powerful tool for comparison to find the most effective antibiotics for specific bacterial infections.
Name : Lehani Raj Mohanta Roll No: 21F1003574
Tableau public link: https://public.tableau.com/app/profile/lehani.raj.mohanta5078/viz/21F1003574_GA3/Dashboard1?publish=yes
The chosen type of visualization is a group bar chart, grouped by different antibiotics namely: Penicillin, Neomycin, Streptomycin; against their effectiveness against various bacteria. This visualization aims to show the effectiveness of the antibiotics against various bacteria.
In general the given data for each antibiotic ranges from 0.001 to 2-3 digit number. When plotting these data due to some values being very large, the scaling of the plot puts more emphasis on the datapoints with larger values. To solve this problem, a log (base 10) transformation was done in order to fix the scaling issue.
The following data are plotted as grouped bar charts. for each chart the result of gram staining test (negative or positive) is represented by different colors. Green for positive and Red for negative, to provide clear distinction.
The size of the chart is optimized for readability, with ample spacing between bars and clear labels contributing to its clarity. Axes are appropriately labeled to provide context.
Key findings from the visualization include the effectiveness of Penicillin against Gram-positive bacteria and Neomycin's superiority against Gram-negative bacteria.
Program used: Tableau Public
Name: Mukesh K
Roll no: 21F1000478
Tools used: Excel, Matplotlib
Each antibiotic is represented by a horizontal bar chart, with bacteria labeled on the Y-axis and the minimum inhibitory concentration (MIC) values on the X-axis.
The charts are designed to fit an A4 size paper, ensuring clear visibility and readability when printed.
Bars are color-coded to correspond with the Gram staining characteristics of the bacteria: blue for Gram-positive and red for Gram-negative bacteria. This intuitive color scheme facilitates quick visual differentiation.
A logarithmic scale (log10) is utilized for the X-axis to accommodate the wide range of MIC values. This approach ensures that the charts are informative and easy to interpret, even with values spanning several orders of magnitude.
The MIC value for each bacterium is annotated directly on its respective bar, providing immediate and precise reference without the need to cross-check with an axis.
Bacteria are sorted in descending alphabetical order, maintaining a consistent and predictable organization across all three antibiotics. This standardized sorting streamlines the search process for specific bacteria across the charts.
The charts are structured to serve as a practical reference tool for medical practitioners. They deliver essential information on MIC values, relate these values to the Gram staining properties of each bacterium, and present the data in a way that is both accessible and systematically arranged. The direct annotation of MIC values on the bars allows for quick and easy reference, while the consistent alphabetical sorting ensures that users can efficiently locate specific bacteria across the various antibiotics.
While the logarithmic (log10) scale is effective for displaying a wide range of MIC values, it may obscure the finer distinctions between closely clustered MIC values. This scaling can also potentially understate the significance of differences among lower MIC values, which might be crucial for clinical decision-making.
Name: Kruthiventi M R S Sai Charan Roll No: 21f1004450
A stacked column chart with percentages could be a useful visualization to show the proportion of each antibiotic within each bacterium, especially when you want to emphasize the relative distribution of antibiotic concentrations. However, it's important to consider both the strengths and limitations of this type of visualization:
Relative Comparison: Stacked column charts with percentages allow for a clear comparison of the contribution of each antibiotic to the total concentration within each bacterium. This is useful for understanding the distribution of antibiotics.
Visualizing Trends: Patterns and trends in antibiotic concentrations can be easily identified, helping to discern which antibiotics dominate or are less prevalent for different bacteria.
Facilitates Cross-Bacteria Comparison: Comparing the distribution of antibiotics across different bacteria becomes more straightforward with this visualization, as percentages normalize the data.
Potential for Misinterpretation: While percentages provide a relative measure, users should be cautious about potential misinterpretation. A small percentage for one antibiotic does not necessarily imply ineffectiveness; it might just mean a lower concentration relative to others.
Limited Absolute Values: Stacked percentages might obscure the actual concentration values, making it challenging to grasp the absolute antibiotic amounts. Consider providing both stacked percentages and the original concentration values for a comprehensive view.
Sensitivity to Total Values: The perception of the stacked percentages depends on the total concentration of antibiotics for each bacterium. Extreme values might overly influence the visual representation.
Complexity for Many Categories: If you have a large number of bacteria, the chart might become cluttered and less readable. Consider filtering or grouping bacteria to maintain clarity.
Color Selection: Ensure that the color palette chosen for the chart is accessible and interpretable by a diverse audience. Be mindful of colorblindness considerations.
In conclusion, a stacked column chart with percentages can be a valuable visualization tool for exploring the relative distribution of antibiotics across different bacteria.
Name: Abhishek Gupta Roll no: 21F1004820 Tools used: Excel, Flourish
Flourish public link : https://public.flourish.studio/visualisation/17007417/
Overview : I have opted for a group bar chart that categorizes antibiotics, including Penicillin, Neomycin, and Streptomycin. This visualization is designed to illustrate the effectiveness of these antibiotics against different bacteria strains.
Data Transformation: Typically, the provided data for each antibiotic varies from 0.001 to several digits. When plotting this data, the presence of significantly larger values may cause the plot's scaling to prioritize those data points, potentially overshadowing others. To address this issue, a min-max transformation was implemented to normalize the data and ensure a more balanced scaling across all data points.
Visualization:
To make the data easier to understand, I used a technique called Min-Max scaling. This helped to make all the numbers in the data fall between 0 and 1, so they could be compared more easily.
I created two separate pictures using a tool called Flourish. These pictures showed the data in two different ways, depending on whether the bacteria tested positive or negative in Gram staining. This split helped to focus on specific parts of the data.
For the charts themselves, I chose a type called Grouped Bar Charts. These charts are good at showing how different things relate to each other, making it easier to spot patterns in the data.
I carefully picked different colors for each antibiotic in the charts. This color-coding made it simpler to see how well each antibiotic worked, helping people understand the data more quickly.
Name: Devansh Gandhi Roll Number: 21f1002115
This visualization aims to empower the public with knowledge about the effectiveness of three common drugs against various bacteria. It focuses on Minimum Inhibitory Concentration (MIC) values, which indicate the drug concentration needed to inhibit bacterial growth. The data is categorized by bacterial type (Gram-positive or Gram-negative) and displayed in easy-to-compare charts. Each bacterium has a unique color for clear identification, and a logarithmic scale is used to effectively represent the diverse range of MIC values. Overall, the design prioritizes simplicity and clarity to ensure accessibility for the general public.
Goal: Inform the public about the effectiveness of three common drugs against different bacteria.
Data:
Visualization:
Tools used:
Name- Prakhar Bansal Roll No- 21f1003810 Tools used- Excel, Flourish
Flourish Public link - https://public.flourish.studio/visualisation/17007459/
About the Data:
Data Transformation Since the antibiotic data ranges greatly, from 0.001 to 870, directly plotting it can make smaller values visually insignificant. To prevent this, a min-max transformation was applied, effectively rescaling the data to ensure all points are displayed fairly.
Visualization
Name: Nidhish Kumar Roll No.: 21F1003758
Tableau Link - https://prod-apnortheast-a.online.tableau.com/#/site/21f10037584b90bc2348/views/EffectivenessofAntibodies/Sheet1
The selected visualization format is a horizontal bar chart, organized by distinct antibiotics specifically, Penicillin, Neomycin and Streptomycin. The chart is trying to illustrate the effectiveness of these antibiotics against different bacteria.
The data has been transformed to a logarithmic scale to simplify visual comparisons.
Name: P V Shabarish Roll No: 21f1001346
Data Transformation: I have transformed the data with positive and negative values for all MIC values based on the "Gram Staining" Column
Visualization: The chart illustrates the Minimum Inhibitory Concentration (MIC) of three antibiotics (Penicilin, Streptomycin, Neomycin) against 16 different bacteria. Each antibiotic is represented by a grouped bar chart, where the x-axis displays the MIC values scale and the y-axis represents the bacteria names. The bars are color-coded to differentiate between bacteria. Negative MIC values indicate bacterial resistance to the antibiotic, while positive values signify susceptibility.
Utilizing a grouped bar chart allows for clear comparison of MIC values of each antibiotic across different bacteria. This layout facilitates easy identification of trends and variations in susceptibility/resistance among bacteria.
Tools Used: Flourish
**Name : Trivikram Umanath
Roll No: 21f1005359**
Aim Of The Visualization To learn which drug worked most effectively for which bacterial infection through a thorough analysis on the relationship between minimum inhibitory concentration (MIC) which is a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro and the 16 Bacterias.Additional Parameters of Gram Staining is also provided i.e Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.For the case study given the three most popular antibiotics are considered i.e Penicilin,Streptomycin and Neomycin.
The thumb rule for the analysis based on the Problem Statement and some additional research is a lower MIC value is desired as lower the value for a drug used on a bacteria i.e lower the concentration of it is required to kill or stop the growth of it and to end any ailments altogether. Relationship of Staining on each of the bacteria for that is also worth exploring. A positive stained means the drug has had an adverse impact on the bacteria of an order enough to change the colour itself but in some case this impact also is coupled with killing the bacteria and in some not so much.
Analysis For the Drug Pencilin all the bacterias are stained negative for some of them and the concentration is absurdly high in MIC magnitude for those of which are stained as negative and for those with a positive stained the MIC is virtually 0 essentially stating the fact that Penicilin works perfectly effectively well for bacterias such as Brucella anthracis,Streptococcus hemolyticus,etc.The MIC value for the above bacterias are lowest in the case of Penicilin. But for the other ones such as Aerobacter aerogenes,Mycobacterium tuberculosis with a negative stain the MIC value is the highest i.e the poorest performance of Peniciln on these drugs.
For these drugs which are stained Negatively by Penicilin they are easily destroyed by the drug Neomycin stained as negative as the MIC values for all of them is almost of the order of 0.5 on an average..which is an excellent performance considering all three drugs for these bacterias.But Neomycin doesn't work as well on those on which the bacteria's stains change colors and some of them require a very high MIC..essentially discounting the fact that it's an absolute drug.
For the drug Streptomycin the MIC value on average irrespective of staining is on the lower side when compared to the total average for the other two drugs but for each specific bacteria the other drugs outperform Streptopmycin but a pattern of negative stains having a better performance for Streptomycin can be seen.
Design Decision The chart was designed on the basis of the aim of the exercise to understand the relationship between staining on bacterias for each drug on the efficacy or the performance of the drug(MIC). The chart is divided into three sections for each drug. The xaxis has Bacterias as well as Gram Staining on it i.e first foundation of basis on Bacterias and further subdivided on whether the bacteria stains on the impact of a drug or not. The y axis entails the MIC value for each drug on each of bacteria. Legends are mentioned as well for the color coding. Labeling for each vertical sections is clearly mentioned as Bacteria stained Postive/Negative and each of the bars are labelled with the 2 decimal rounded value of the MIC.
Scale, Visual Encoding, and color Scale for the yaxis entailing the MIC in each sub chart is customly selected such that the difference between the MIC values for each bacteria is clearly visible i.e expanded by choosing a scale with the minimum as 0 and maximum as the max MIC only for that drug and not a total of all drugs for each bacteria i.e when the scale is selectively constricted the information is magnified. Visual Encoding has been set as a comparison between the positive and the negative strained bacteria. The color for a negative stained is taken as dark blue and that of postive is taken as dark orange i.e this is done in principles of a chart being visually apealling and giving a clean and clear distinct picture discriminating based on staining intensly. The background color is chosen as white as the contrast with dark blue and dark orange works best with a white background. Labelling is carried on top of each bar with a black font again for contrast reason of a black font being visually appealing due to the best contrast with a white background. Legends are mentioned on top right stating the color coding for bacterial staining.
Tools used Tableau
Thanks and Best Regards, Trivikram Umanath
Name: Pratham Bhalla Roll No: 21f1003052
Title: Antibiotic Effectiveness Across Bacterial Strains
Gram Positive:
Gram Negative:
Visualisation link: https://public.flourish.studio/visualisation/17008875/ Overview: Our visualization aims to provide insights into the effectiveness of three popular antibiotics across 16 different bacterial strains, categorized based on Gram staining into Gram-negative and Gram-positive bacteria. In the post-World War II era, antibiotics revolutionized medical treatment by offering cures for previously incurable ailments. To understand which antibiotics work best against various bacterial infections, we collected data on minimum inhibitory concentrations (MIC), representing the concentration of antibiotic required to prevent growth in vitro.
Visualization Type:
Data Encoding:
Design Rationale:
Communication of Data:
Overall, the design decisions, including the choice of visualization type, logarithmic scaling, and categorization based on Gram staining, facilitate effective communication of the data. The visualization allows viewers to identify patterns in antibiotic effectiveness across bacterial strains while considering the covariate of Gram staining. Despite potential limitations in obscuring absolute MIC values, the visualization effectively conveys the relative effectiveness of antibiotics, aiding in informed decision-making for medical treatment strategies.
Name: Vijay Suryakant Kapse Roll Number: 21f1000202
Objective: The visualization aims to effectively communicate the comparative effectiveness of three antibiotics (Penicilin, Streptomycin, and Neomycin) against various bacteria strains. It provides insights into which antibiotics are more effective against specific bacteria and highlights any notable patterns or trends in antibiotic susceptibility.
A heatmap was chosen as the visualization type due to its ability to display a matrix of data values using color intensity. Heatmaps are effective for comparing multiple variables across different categories, making them suitable for this dataset.
Min-Max scaling was applied to normalize the antibiotic susceptibility data. Normalization ensures that all values fall within the same range (0 to 1), allowing for a fair comparison between antibiotics and bacteria strains. This decision prevents bias that may arise from differences in the original scales of the data.
The heatmap allows viewers to compare the effectiveness of Penicilin, Streptomycin, and Neomycin against different bacteria strains at a glance. The color intensity indicates the relative susceptibility of each bacterium to the antibiotics, facilitating quick identification of the most and least effective antibiotics.
The heatmap does not explicitly highlight the influence of Gram staining on antibiotic susceptibility. While the 'Gram Staining' variable is included in the dataset, it is not directly visualized in the heatmap.
Tools: Python
After World War II, antibiotics were considered "wonder drugs", since they were easy cures for what had been intractable ailments. To learn which drug worked most effectively for which bacterial infection, the performance of the three most popular antibiotics on 16 bacteria was gathered. The values in the table represent the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic, which represents the concentration of antibiotic required to prevent growth in vitro. The reaction of the bacteria to Gram staining is described by the covariate “gram staining”. Bacteria that are stained dark blue or violet are Gram-positive. Otherwise, they are Gram-negative.
Your task is to design a chart that you believe effectively communicates the data and provide a short write-up (no more than 4 paragraphs) describing your design.
As different visualizations can emphasize different aspects of a data set, you should document what aspects of the data you are attempting to most effectively communicate. Just as important, also note which aspects of the data might be obscured or downplayed due to your visualization design.
In your write-up, you should provide a rigorous rationale for your design decisions. Document the visual encodings you used and why they are appropriate for the data. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication?
Here is the link for the dataset: Butin_antibiotic_data.xlsx