Graded Assignment -3 (Jan Term 2024):- Effective Visualization of Data

[Jimmi-Kr]

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

[Astroficboy]

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.

[Saikat88]

Design Decisions:

• A clustered column chart is utilized to showcase the minimum inhibitory concentration (MIC) of Penicillin, Streptomycin, and Neomycin.
• Initially, the data is segregated into Positive and Negative Gram Staining categories.
• This categorization facilitates the visualization of Gram Staining on the chart.
• Subsequently, the data is organized by the average MIC of all three antibiotics, arranged from the smallest to the largest.
• This sorting simplifies the interpretation, with bacteria exhibiting low MIC scores appearing first.

Scale, Color and Visual Encoding:

• A Logarithmic Scale is employed instead of a linear one due to the wide range of values in the data.
• The colors assigned to the antibiotics are distinct from each other, while maintaining a harmonious and visually appealing contrast.
• For Gram staining, positive and negative categories are represented by violet and pink colors respectively. This choice is based on the convention where bacteria stained violet are considered positive, while those stained pink are deemed negative.
• Visual encodings are intentionally kept simple to ensure the chart remains uncluttered, neat, and easy to interpret.

Tools

• MS Excell
• Power BI

Name & Roll No - Saikat Samanta (21f1003501)

[rt1916-IITM]

Name: ROYCE TOMY (21F1001916)

Aspect Emphasized

• Effectiveness of various Antibiotics against Different Bacterial Strains

Design Decisions

• A Bar Chart has been used for this visualization, to showcase the Minimum Inhibitory Concentration (MIC) values of different antibiotics against various bacteria strains. It's because Bar chart is effective in comparing MIC values across multiple bacterial strains and antibiotics simultaneously.
• The x-axis represents the bacteria strains, sorted alphabetically while the y-axis is further split into 3 axes, corresponding to the 3 different antibiotics.
• Values have been scaled down to $log_{e} \space (MIC)$ values. This is done to lessen the gap between extreme values present.
• $+$ ve values suggest higher concentrations (greater than 1 unit or 1 $\frac{\mu g}{ml}$ microgram per millilitre) of antibiotics required to inhibit the bacteria, indicating reduced effectiveness against that bacteria strain.
• Conversely, $-$ ve values suggest requirement of low concentrations (less than 1 $\frac{\mu g}{ml}$) to inhibit that bacteria's growth.
• 0 indicates the requirement of exact 1 unit or 1 $\frac{\mu g}{ml}$ to inhibit the bacterial growth.
• Bacterial strains have been stained BLUE for gram $+$ ve and RED for gram $-ve$ , aligning with conventional microbiology.

Tool(s)

• Tableau

[soumyanamboo]

Visualizing Effectiveness of Antibiotics on Different Bacteria

Name: Soumya V Namboodiripad
Roll Number: 21F1004752

https://public.tableau.com/views/AntibioticsVsBacteria/Sheet1?:language=en-GB&publish=yes&:sid=&:display_count=n&:origin=viz_share_link

Overview:
The purpose of this visualization is to clearly convey the effectiveness of different antibiotics named Neomycin, Penicillin and Streptomycin against various bacterial infections.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Key findings:

• Penicillin works good against Gram Positive bacteria.
• Neomycin is the best against Gram Negative bacteria.

7. Tools Used:
   Tableau Public

[kaushikpatriot]

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:

1. The key objective was to represent, the effectiveness of each of the antibiotics against each type of bacteria.
2. Given there are 3 antibiotics, the objective was to present the best, average and the least potent antibiotics for each bacterial type
3. The depiction should also represent the relative potency for each of the antibiotic. To avoid a wide variance, for visual purposes, a calculated field $ln(1/MIC)$ was used to smoothen the variance between antibiotics.
4. A simple dot plot was used that encoded the relative potency using size of the circle and the best, average and least effective being indicated by colors as shown in the legend above.
5. Additionally, Bacteria with positive and negative Gram staining are grouped together for ease of visualization.
6. Within each group, the bacteria are sorted alphabetically
7. In summary, one can infer the most effective antibiotic for each type of bacteria and its relative levels of concentration required.

Where this visualization may not be effective

1. If one were to look for a specific bacteria by name without knowing what sort of Gram staining it has, one may have to go through both the lists individually instead of going through one list. This can be difficult if the number of bacteria types are many.
2. Given there are only 3 antibiotics, the color encoding for the best one is straight forward. In the real world, there are many antibiotics available. In such cases, this type of visualization may be cluttered.
3. Displaying the MIC on the side looks a bit clunky, but has been retained for the sake of completeness of information.

Tool used:
Tableau

[viraj19r]

Name : Viraj Sharma
Roll No: 21f1003723

Design Description:

The chosen visualization is a heatmap, which effectively communicates the varying effectiveness of three antibiotics across sixteen bacteria.

Data Organization and Representation:

• 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.

Legibility, Contextual Information and Pattern Analysis:

• Size and Clarity: The MIC values are presented in a legible font size, directly superimposed on their respective cells, and the color contrast is adjusted to maintain readability against the varying color intensities of the background. The addition of Gram staining information next to each bacterium's name serves as a categorical annotation that provides additional context, enabling users to discern patterns based on bacterial classification.

• A colour bar legend on the right translates the colour gradient back to MIC values, ensuring that the viewer can quantify the effectiveness precisely.

Interpretation of Data and Visualization Choice and Design Rationale:

• The granularity of the data might be obscured, particularly when the MIC values are closely clustered. Additionally, the focus on antibiotic-bacterium pairs might make it less straightforward to extract broader patterns.

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

[asswin-123]

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.

[shrikrishna97]

Name: SHRI KRISHNA PANDEY
Roll No: 21f1006966

Introduction:

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

Design Choice

Title: "MIC antibiotic vs Bacteria"

1. First, The decision to create a side-by-side bar chart titled "MIC antibiotic vs Bacteria" stems from the need to effectively present three main components: MIC values of antibiotics, bacteria types, and gram staining characteristics.
2. By placing bacteria names on the x-axis and antibiotics on the y-axis, the design ensures a comprehensive view of all components in a single visualization.
3. The utilization of normalized values enhances clarity and understanding, facilitating easier comparison of antibiotic efficacy across different bacteria strains.
4. Additionally, color-coding the bars based on gram staining positive and negative values enhances the interpretability of the visualization.
5. Finally, I gave the best working antibiotics for the bacteria in the bottom x-axis.

Normalization

• Normalizing MIC values, particularly using natural logarithm, aids in standardizing the data and improving comparability across different antibiotics and bacteria gram strains.
• Standardization of MIC values allows for easier identification of patterns in antibiotic resistance or susceptibility within and across bacterial groups.
• By focusing on relative differences rather than absolute MIC values, the normalization approach enhances the interpretability of the visualization, enabling stakeholders to make informed decisions regarding antibiotic selection.

Observation:

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:

• Lower MIC values indicate better antibiotic performance, suggesting higher efficacy in inhibiting bacterial growth.
• Penicillin demonstrates efficacy against gram-positive bacteria, aligning with its mechanism of action targeting cell wall synthesis.
• Neomycin exhibits better performance against gram-negative bacteria, indicating differential susceptibility based on gram staining characteristics.
• Streptomycin appears less efficient compared to penicillin and neomycin across the bacteria strains analyzed, emphasizing the importance of considering alternative treatment options.

Tool used:

Excel for viewing the data and changing the value and Tableau public for visualization.

[nikhil2003272]

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.

[phanijallipalli]

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.

[maniesh1]

Name: MANISH KUMAR
Roll No: 21f1004259

Introduction:

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.

Visualization Design: Antibiotic Effectiveness line chart with markers

Objective and Aspects:

• The primary objective is to compare the effectiveness of three antibiotics (Penicillin, Streptomycin, and Neomycin) across different bacteria.
• Want to highlight which antibiotic is most effective for each bacterial strain.
• The Gram staining response (Gram-positive or Gram-negative) is an important covariate, so I'll incorporate it into the visualization.

Design Decisions:

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.

Considerations:

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.

[MajorHamol]

Name: Amol HATWAR
Roll No: 21f1000451

Introduction

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.

Data Transformation

1. While gram-staining is an important property for detection, it has little to no bearing on the line of treatment with antibiotics once the bacteria has been identified. As a result, the gram-staining property was not used and elided on purpose from the chart.
2. Minimum Inhibitory Concentration is the measure of minimum concentration required to inhibit further growth of bacterial culture. Lesser concentrations mean that the antibiotic compound is strong and effective. Higher concentrations mean that the antibiotic is weak and not as effective. So, to fit the mental pattern of more means better, the MIC values were inverted; that is 1/MIC was used instead of MIC.
3. The resulting MIC values had a huge range from fractional values to ones in the thousands. To fit and compress such a vast range, a logarithmic (log10) scale was used. This resulted in negative values for log of inverted MICs that had factional values (below 1). However, this doesn't mean that negative values means that the antibiotic compound is ineffective, or worse encourages bacterial growth.
4. To stop the above interpretation of negative values, all the data points had to be kept on positive side of the axis. To do this, the lowest log inverted MIC value was added to all the data points. This shifted the Y axis well left of the least data point.

Design Choices

1. The clean and easily understandable form of grouped bar charts was chosen as this allows choosing the antibiotic compound simple given the name of the bacteria. The fact that the data had only three antibiotic agents (not more) aided this decision.
2. The legend was put directly under the title so that the reader knows the number and the names of the antibiotic compounds the chart is about while adding to the symmetry in the heading.
3. The Y-axis was chosen for the categorical data of bacteria names, as the names are longish and putting them aligned vertically on the X-axis would have hampered readability.
4. Bacteria names were sorted alphabetically so that the reader can find particular species easily without hunting or searching.
5. Text on how to interpret the effectiveness scale, and the formula used to derive it from MIC have been mentioned on the lower right.

Colour Scheme

1. White background was chosen for easy readability and reproducibility (photocopying for instance).
2. The colour black was chosen for bacteria name, scales and ticks on the axis for contrast and easy readability.
3. Where lesser visual weight was required, for example on the note in the lower right, the colour grey with a reduced font-size was employed
4. Various colours rather than shades of a single colour the bars as all the compounds are unique and different. This also encourages visual separation.

Other Considerations

1. Adding a (+), or a (-) after the bacteria name could have helped encode the gram positivity or negativity of the bacteria, but it would have resulted in more elements. Also it has no bearing on the effectiveness and would have just added to clutter, hence it was elided.

Tools Used: Microsoft Excel

[Sharmaom24]

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.

[gokulakrishnanbalaji]

Name: Gokulakrishnan B
Roll: 21f1006866

Spreadsheet link: DVD Week 5

Visualization Overview:

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.

Data Representation:

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.

Size Comparison:

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.

Color Coding:

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.

Sorted Order:

Bacteria names are arranged in cells in a sorted fashion, enhancing the ease of locating the desired bacteria.

Limitations:

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.

Visualization Tools:

Derived columns were added using Colab in Python, while Google Sheets facilitated visualization creation.

[Purva0808]

Antibiotic effectiveness

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

1. 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.

2. 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

1. 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.

2. 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.

3. 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

[sarthak-iitm]

Visualization Design: Antibiotic Effectiveness

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:

1. Design Decisions:

   • Graph Type: A grouped bar chart is chosen to represent the MIC values of the three antibiotics for each bacteria strain. This allows for easy comparison across antibiotics and bacteria.
   • Data Transformation: The data is transformed to arrange bacteria along the y-axis in alphabetical order and divide the x-axis into three parts, each representing one antibiotic. Gram staining (positive or negative) is labeled beside each bacteria name to provide additional context.
   • Color Scheme: Each bacteria strain is encoded with a unique color to facilitate clear visualization and easy identification. The detailed name of each bacteria is provided in the legend along with its corresponding color.
   • Scale: The scale for each antibiotic is chosen to accommodate the available data and ensure optimal visualization. Streptomycin is scaled from 0 to 16, Penicillin from 0 to 1000, and Neomycin from 0 to 40.

2. Other Considerations:

   • The visualization aims to provide a comprehensive overview of antibiotic effectiveness against different bacteria strains while considering the influence of Gram staining.
   • Clear labeling and a detailed legend are provided to aid interpretation and understanding of the visualization.

Limitations:

• Sorted Order: While bacteria are sorted alphabetically, other sorting methods could be considered based on specific criteria such as MIC values or Gram staining response. However, alphabetical sorting provides a straightforward organization for quick reference.

Tool used : Tableau & MS Excel

[CoreManish]

Name : Manish kumar

Roll No. : 21F1006597
Tool used : Google sheet

Intro

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.

Visualisation approach

• I want to visualise effectiveness using stack bar chart.
• Effectiveness is inversely proportional to MIC concentration. So to show effectiveness on bar chart, finding 1/MIC would be great.
• Another problem - scaling differences, some values are too high. So first normalise data.
  $ value = (given value - min value )/ (max value - min value) $
• Another problem- for min value, above calculation gives 0, so add 1.
  $ value = (given value - min value +1 )/ (max value - min value) $
• Now find inverse of all values.
  

More about above visual

• "Gram staining" of each bacteria is written as label on the corresponding stacked bar.
• For a stacked bar,length represents the comparative effectiveness of antibiotics for a bacteria.
• For a stacked bar, MIC of different antibiotics is also written as label.

[Manaswita06]

MIC value comparison for three Antibiotics

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

[AlapeAniruddha]

Name: A Aniruddha
Roll: 21f1005327

Objective

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.

Design choices and Rationale

• The primary objective was to compare the effectiveness of the antibiotic given a bacteria using MIC values. Since the comparison is between three values, the MIC values are shown as is.
• Emphasis on the optimal drug is shown with a popout effect that immediately draws the attention of the viewer. The lowest MIC value that indicates the highest performance are highlighted in each row.
• The Gram Stain test results in two possible values Gram stain positive and Gram stain negative. This is indicated through color coding as well as the use of icons next to the name of each bacteria. A legend is also provided for the same.
• The scale of the values is not altered as the values themselves convey the difference in effectiveness of say a drug like Penicilin on certain bacteria
• The bacteria are sorted in alphabetical order for readability after the initial separation based on Gram stain test results

Limitation

• The size of bubbles for each value is the same. Varying the size would impact the visibility of the numerical values themselves. Due to this, better readability of the numbers was prioritized over size of the bubble.

[TheMandalorian1]

Visualizing Effectiveness of Antibiotics on Different Bacteria

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.

[Tris-utkarsh]

NAME: UTKARSH GAURAV
ROLL NO. : 21F1001336

Aim of the Study:

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.

Covariate: Gram Staining Reaction

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.

Visualisation approach :

• Bacteria classified into Gram-positive and Gram-negative groups for systematic comparison.
• Rigorous assessment of each antibiotic (penicillin, neomycin, and streptomycin) against others for effectiveness.
• Y-axis oriented with higher values descending to visually represent increased efficacy.
• X-axis depicts bacteria names for clear antibiotic performance across strains.
• Dots strategically placed on the graph for accurate visualization and insightful interpretation.

Observations:

• Neomycin and Streptomycin are more effective as compared to penicillin against Gram negative bacterias .
• Streptomycin perform better against gram positive Bacteria in comparison to neomycin.
• Penicillin are more effective against gram Positive Bacterias .

Improvements :

• Scaling can be done . Also the area of circles can be used to show the effectiveness .
• The overlapping of color tends put strain on understanding the difference .

Tools Used :

• Excel
• Tableau

[DEENA0503]

Name: DEENA GAUTAM
Roll Number: 21F1001012

Highlighting the effectiveness of Penicillin for Gram-positive bacteria

UNDERSTANDING THE GRAPH

• The range of MIC values were scaled down to log10 for ease of visualization within one frame
• Clustered column bar chart segmented into two rows of positive negative gram test was chosen to communicate values of all three antibiotics required individually, to prevent bacteria growth
• Lower level of MIC suggests higher effectiveness of antibiotic against the bacteria
• The visualization is segmented into the two sections of gram-negative (pink) and gram-positive (dark violet)
• The background colours chosen have a semantic emphasis towards gram staining as gram negative test tend to turn pink (mostly) and gram positive test turn to dark shades of violet/blue
• A pattern of penicillin in both tests can be observed as it tends to have positive log value for negative gram staining test and negative log value for positive gram staining
• Penicillin is a narrow-spectrum antibiotic and is more effective against Gram-positive (which turns dark blue/violet) bacteria which is there reason to choose dark blue bar colour for penicillin
• Gram-negative bacteria are protected against penicillin's effects because they have a protective outer membrane that prevents penicillin from accessing their peptidoglycan layer
• Other two antibiotics don't have a prominent pattern for this test, so they have faint coloured bars
• The data labels have the background colour same as their respective bars for ease the of value reading
• positive gram staining segment is placed below the negative segment to create and understanding of lower level of penicillin concentration is required to be effective against gram positive bacteria
• The bacteria names are sorted alphabetically in the x-axis for ease of reading
• The legends are placed just below the title to instantly communicate the list of antibiotics visualized in the graph
• A narrative about MIC is placed at the center of the graph for easy comprehension of the purpose of visualization

NEGATIVES

• As the log scale is used 0 value can be misinterpreted as absence of quantity
• Log scale also normalizes the scale which can create false visual impression of quantity difference

POTENTIAL IMPROVEMENTS

• The segmentation of cluster graphs for different bacteria can be made more clear
• Better placement of bacteria names

TOOLS USED:

1. Excel - converting concentration values to logarithmic values
2. PowerBI - for visualization

[sanket21f1007096]

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.

[Yalinisaravanan]

Antibiotic Effectiveness Visualization

Penicilin

Streptomycin

Neomycin

1. 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.

2. 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]

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

1. A bar chart with Bacteria on the vertical axis and the antibiotics on the horizontal axis split into three parts seemed the cleanest and most efficient way to show magnitude.
2. The bars were colored based on the results of Gram staining, as gram positive means the bacteria are stained either blue or purple, so I made it blue and similarly, gram negative stains bacteria red or pink, hence the red bar.
3. Since the higher the MIC, the less effective the antibiotic on the bacteria, I labeled the highest and lowest MIC value for each antibiotic to highlight its best and worst performance is against which bacteria.
4. I was debating between a normal scale or a logarithmic scale, but settled with the logarithmic scale since the largest penicilin values skewed the visualization. It mostly displayed only the top values, which doesn't give any insight into its best effectiveness.

• Observations

1. The logarithmic scale makes it hard to distinguish between the values, such that, 800, 850 and 870 values for penicilin have bars the same size. The advantages outweigh the disadvantages and hence I kept the logarithmic scale itself.
2. The labels could have been better aligned with better coloring, but I couldn't figure out how to do that neatly in Tableau.

[arunkumariitm]

Name : Arunkumar N
Roll No : 21f3000442

The Data:

• This information shows how effective three antibiotics (Penicillin, Streptomycin, and Neomycin) are against 16 different types of bacteria.
• The bacteria are classified into two groups based on a staining test: Gram-positive and Gram-negative.
• Each number in the table shows the minimum amount of antibiotic needed to stop the bacteria from growing (MIC value).

The Visualization:

• The information is presented in a table format, which is suitable for small datasets like this one.
• Medicine bottles are used to represent the antibiotics, making the chart visually interesting and relevant to medicine.
• Colors are used to help you understand the information quickly:
• Green indicates low MIC values (bacteria are more susceptible to the antibiotic).
• Red indicates high MIC values (bacteria are more resistant to the antibiotic).
• The bacteria names are listed alphabetically within their Gram-positive and Gram-negative groups, making it easier to find specific ones.

[SushmitaNandy]

Antibiotic Sensitivity of Bacteria

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

• I have used colab to understand trends in the data provided. Click here
• I have used Standard Scaler to scale the MIC values in order to put all the antibiotics within similar scale.
• I have used Flourish to plot the final charts from the scaled data.
• I have represented the data into Grouped bar chart and used Gram staining(Negative/Positive) as filtering parameter.
• The choice of color for the bars is crucial for facilitating effective communication. I have selected distinct colors for each antibiotic to ensure clarity and ease of interpretation.
• The inclusion of a legend helps viewers understand the color coding and associate each color with its respective antibiotic.

Observations

• While this visualization effectively communicates the antibiotic sensitivity of different bacterial strains and their Gram staining reaction, it may not capture more nuanced relationships or trends present in the dataset.
• If more attributes were provided then there can be a possibility of exploring specific aspects of the data in greater detail.

[ineerajrajeev]

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.

[HungryPanda0212]

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.

[rrohnyy02]

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:

1. 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.

2. 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.

3. 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.

4. 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.

[ajith1119]

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:

• The chart's architecture is structured to optimize comprehension. Bacterial strains adorn the x-axis, offering a comprehensive overview, while the y-axis meticulously denotes MIC values.
• Each antibiotic - Neomycin, Penicillin, and Streptomycin - is distinctly color-coded, facilitating easy identification and comparison.
• Within each group, bars are grouped by bacterial strain, with the height of each bar representing the MIC value. Lower bars signify higher efficacy, thereby offering a straightforward interpretation.
• Alphabetical sorting of bacterial strains enhances readability and streamlines comparison, ensuring a seamless analytical experience. Additionally, the inclusion of a legend elucidates the color coding for each antibiotic, offering viewers a quick reference point for interpretation.
• Axis labels and a meticulously crafted title provide essential context and guidance. The title, "Antibiotic Efficacy Across Bacterial Strains," succinctly encapsulates the chart's purpose, while axis labels meticulously delineate bacterial strains and MIC values, empowering viewers with a comprehensive understanding of the dataset at a glance.

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.

[PriyaNathani]

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

1. The data could be sorted in many ways:
   • Whether the bacteria is Gram-positive or Gram-negative.
   • on the basis of reducing effectiveness
   • whether to have separate graphs for effectiveness of different antibiotics to various bacteria.
2. It could be seen that the MICs of the antibiotics to different bacteria has very large variation. There seems to be no correlation between effectiveness of antibiotics on different bacteria.
3. I reached a conclusion that it is better to segregate the data on the basis of whether the bacteria are Gram-positive or Gram-negative. Create two different graphs depicting relative MICs of the antibiotics.

Visualization

1. Since there is very large difference in MIC values, I chose logarithmic scale for my graphs
2. Since, I wanted to show relative difference in effectiveness of the antibiotics, I feel that bar-chart is the best choice
3. I chose multi colours for the bars to clearly identify the antibiotics
4. I chose pink as background for Gram-negative bacteria and violet for Gram-positive bacteria based on the stain colour of the Gram stain.
5. The chart title, axis and legends are clearly indicated for ease of comprehending the graph.

Limitations:

1. Since the graph is logarithmic, it may sometimes lead to visual misunderstanding that a given antibiotic has similar effectiveness to many bacteria while that is not correct.
2. The data does not list any side-effects of the antibiotics or their doses which can indicate whether large dosage, e.g., of penicillin is permissible to counter bacteria like Klebsiella pneumoniae.
3. The data also does not include information on whether antibiotics can be combined, e.g. Neomycin requires small dosage for most of the bacteria except for Diplococcus pneumoniae, Streptococcus hemolyticus and Streptococcus viridans against which small doses of Penicillin is effective.

[harshOpensource]

Name: Harsh Bardhan
Roll No 21f1004807

Visualisation

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.

Effectiveness

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.

Observation

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.

Conclusion

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.

[DebapriyoSaha]

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

[hanani8]

Insights that can be readily gleaned:

1. Gram-negative bacteria are more intrinsically resistant to antibiotics - they don't absorb the toxin into their insides, due to their multi-layers. Read More
2. Penicillin performs best in Gram-Positive Bacteria.
3. Neptomycin is the best antibiotic amongst all types of Bacteria.
4. For Gram-Negative Bacteria, the top 2 effective antibiotics are comparable in their effectiveness.
5. Whereas, in Gram-Positive Bacteria, the effectiveness of top 2 antibiotics differ hugely.
   etc,..There are many more.

Name: Hanani Bathina

Roll: 21f1006169

---

Objective

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.

Design Rationale

1. It adopts a circular hierarchical structure, which represents the data in a way that allows for immediate visual comparison across multiple dimensions:

• the antibiotic effectiveness (through minimum inhibitory concentration or MIC values) through the variably-sized boundary circles
• the type of bacteria (Gram-positive or Gram-negative) through boundary-wall colors
• the relative strength of bacteria against antibiotics through area graph
• the relative effectiveness of best bacteria with the second best bacteria, through donut graph
• numbers for labels to deter visual clutter

2. The circular format was chosen for its ability to display relational data compactly while maintaining clarity. It encourages the viewer's eye to move around the chart, promoting engagement and comparison.

Visual Encodings and Color Choices

1. 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.

2. 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

3. 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)

4. 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.

5. A single thick layer was chosen for Gram-Positive, and multiple thin layers were chosen for Gram-Negative, which coincides with their biological appearance.

Data Emphasis and Limitations

1. Due to logarithmic scaling, the difference between 2 antibiotics may appear larger or smaller than they actually are, which may mislead the viewer. To alleviate this, a website was created which will show the actual MIC data on-hovering on the bubble.
   Click
   

• If the circles are jumbled, zoom in and refresh.

2. Apart from that, every single data point in the given table was considered, and more was derived to offer more insights.

Conclusion

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.

Tool

• d3.js

[krishnaditi]

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.

[himadridixit]

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:

1. gram-positive bacteria show less resistance towards antibiotics i.e. even less amount of antibiotics are effective enough.
2. On the other hand, gram-negative bacteria requires larger amount of the same antibiotic to be effective.
3. Above 3 points are supporting the properties of gram positive and negative bacteria. Gram positive bacteria has thick layer which absorbs the antibiotic and dies quickly whereas gram-negative ones have multiple layers which are harder to penetrate.
4. Most effective antibiotic overall for the given bacteria is Neomycin.
5. Penicilin performs poorly for gram-negative but gives good result for gram-positive.

[anirudhssidhu]

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.

[Rajkishore2904]

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

• Used Bar Chart to show effectiveness of different Antibiotics against specific Bacterias and as length is easily conceivable which helps in comparing, Bar chart seemed to be the right choice.
• Analysed the Positive Gram staining Bacterias and Negative staining Bacterias separately .
• The Antibiotic with lower MIC value shows most effectiveness against that specific Bacteria so the reciprocal of the MIC values in the data were taken so that lower MIC value antibiotics can have longer bars and becomes easier to compare the effectiveness of that antibiotic against the other antibiotics for a specific Bacteria.
• The colour coding for Negative strain bacterias is used as Red colour just to potray that in general for negative strain Bacterias the MIC values of antibiotics are larger than positive strain Bacterias which are coded by Green colour which depicts for positive strain Bacterias the concentration of antibiotic needed to prevent the growth in vitro is quite less.

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.

[lehani1]

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

Overview:

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.

Data transformation

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.

Data representation

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.

Conclusion

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

[mukeshmlb92]

Name: Mukesh K
Roll no: 21F1000478
Tools used: Excel, Matplotlib

Visualization Type:

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.

Size:

The charts are designed to fit an A4 size paper, ensuring clear visibility and readability when printed.

Color Scheme:

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.

Scale:

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.

Annotation:

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.

Sorting:

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.

Effective Communication:

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.

Potential Limitations:

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.

[Charan1152]

Visualization Name: Stacked Column Chart for Butin Antibiotic Data

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:

• Strengths:

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.

• Critique and Considerations:

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.

[Abhishekgu]

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.

[DevanshGandhi-16]

Visualizing Effectiveness of Antibiotics on Different Bacteria

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.

1. Goal:
   Inform the public about the effectiveness of three common drugs against different bacteria.

2. Data:

• Bacteria: Classified as Gram-positive or Gram-negative.
• Values: Minimum Inhibitory Concentration (MIC) for each drug-bacteria combination.
• Sorting: Bacteria names sorted alphabetically within each Gram classification. (This ensures consistent label order across charts for easier comparison.)

3. Visualization:

• Chart Type: Likely bar or column charts.
• Color Coding: Each bacterial type has a unique color for easy identification.
• Scale: Logarithmic scale used to accommodate the wide range of MIC values.
• Design: Simple and uncluttered for improved readability.

Tools used:

• Microsoft Excel
• Flourish

[prakhar-20]

Name- Prakhar Bansal
Roll No- 21f1003810
Tools used- Excel, Flourish

Comparative Study of Different Antibiotics

Flourish Public link - https://public.flourish.studio/visualisation/17007459/

About the Data:

• The data shows the minimum inhibitory concentration (MIC), a measure of the effectiveness of the antibiotic for the 3 antibiotics (Penicillin , Streptomycin, Neomycin) for 16 different types of bacteria.
• The bacteria are classified into 2 classes (Gram-positive, Gram Negative) based on gram-staining.

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

• I have used Microsoft excel to do Min-Max scaling to bring the values from 0-1. This helps in comparison between different bacteria as all the values come on one scale.
• After this I have used Flourish to visualize the transformed data. The filter is applied for the positive-gram and negative-gram to create 2 different bar graphs for each category of bacteria.
• I opted for Grouped Bar Charts as the preferred chart type due to their effectiveness in illustrating relationships between various elements, aiding in the identification of data patterns.
• I thoughtfully assigned distinct colours to each antibiotic in the charts as it will improve the clarity.
• After that proper labels, legends and title is added that will help in getting to know what the graph is all about.

[nk-droid]

Name: Nidhish Kumar
Roll No.: 21F1003758

Tableau Link - https://prod-apnortheast-a.online.tableau.com/#/site/21f10037584b90bc2348/views/EffectivenessofAntibodies/Sheet1

Overview

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.

Visualization

• The chart depicts Minimum Inhibitory Concentration (MIC) data for three antibiotics (Penicillin, Streptomycin, Neomycin) across 16 bacteria.
• Each antibiotic is showcased through a horizontal grouped bar chart, with the x-axis indicating MIC values and the y-axis representing bacteria names.
• Bars are color-coded to distinguish between bacteria, facilitating visual clarity.
• Negative MIC values signify bacterial resistance, while positive values indicate susceptibility, providing a clear understanding of antibiotic effectiveness.

Data Transformation

The data has been transformed to a logarithmic scale to simplify visual comparisons.

Tools Used

• MS Excel
• Tableau

[Shabarish1403]

Visualization on different Drugs MIC for different Bacteria

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

[21f1005359]

**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

[pkbhalla]

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:

• Two separate bar charts within the same graph, one for Gram-negative and one for Gram-positive bacteria
• Bar chart chosen for its ability to display categorical data and facilitate easy comparison of antibiotic effectiveness across bacterial strains
• Flourish used as the visualization tool for its user-friendly interface and flexibility in creating interactive charts.

Data Encoding:

• Logarithmically scaled MIC values used to ensure visibility of small differences in effectiveness across a wide range of values
• Each bar represents the scaled MIC values for three antibiotics: Penicilin, Streptomycin, and Neomycin
• Color encoding used to distinguish between antibiotics in the legend
• Bacteria names encoded along the y-axis for easy identification
• Axis labels indicate bacteria names and logarithmically scaled MIC values

Design Rationale:

• Goal was communicate the effectiveness of antibiotics across bacterial strains while considering Gram staining covariate.
• Visualization type chosen based on ability to effectively display categorical data and facilitate comparison.
• Logarithmic scaling applied to ensure visibility of small differences in effectiveness across wide range of MIC values.
• Categorization based on Gram staining enables comparison within each bacterial category.
• Color encoding and legends used to enhance interpretability of the chart

Communication of Data:

• Visualization effectively communicates relative effectiveness of antibiotics across bacterial strains.
• Viewer can identify patterns in effectiveness and make informed decisions for medical treatment strategies.
• However, absolute MIC values may be obscured due to logarithmic scaling, emphasizing relative differences instead.

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.

[vijay-kapse]

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