2c:null 2d:["$","title",null,{"children":"Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11) | NYU | Observable"}] 32:T9a8,# Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11) This is a short exercised designed to further familiarize yourselves with **Vega-Lite** operations and conducting some basic critiques of visual charts. This exercise requires that we work with an ["1000 Cameras Dataset"](https://www.kaggle.com/datasets/crawford/1000-cameras-dataset) provided by Chris Crawford on Kaggle. We will be looking at some trends across the dataset's 13 features. This assignment is broken into **4** major sections: 1. Part 1: Importing Vega-Lite and the Camera dataset (10%) 2. Part 2: Camera Features Across Time (30%) 3. Part 3: Comparing Features (25%) 4. Part 4: Extending Our Understanding: Bar Charts, Boxplots, and Facets (35%) ## Assignment Instructions **Fork this notebook to make sure that you are working on your own copy!** Work your way down the notebook, making sure to run code blocks whenever they appear. Some parts may require you to fill out markdown blocks. Others will require code work. And some are not interactive and are just for instructions! All exercises will be marked with a **[TO-DO]**, so pay attention to them throughout the notebook. You may refer to either [Week 1 Lab](https://observablehq.com/@rk2546/2025-infovis-cse_week-1-lab) for reference or additional online resources such as documentation and public forums for help with these questions. Please be mindful of our academic integrity policies when completing this assignment. ## Submission Instructions This assignment is due **September 11, 2025 @ 11:59pm**. Please follow the submission instructions: 1. _Make the notebook visible to the public._ This is accomplished by clicking the "Share" button at the top of the page and changing the "Access" settings for the Public to "Can view". 2. _Copy the Notebook URL in the same "Share" panel._ Feel free to use either an automatically-generated or custom URL. 3. In the markdown block below, _please fill out the following information._ The "Notebook URL" is the URL you copied in Step 2. **Not filling out the details below will result in a 0 for this assignment!** 4. _Download the notebook as a `.tgz` zip file._ This can be done by: 1. Scroll up to the top of the notebook, and click the "..." button. 2. `Export` -> `Download Code`. This produces a `.tgz` zip file. 3. On Brightspace, _navigate to "Assignments" and locate the corresponding assignment submission page._ 4. _Upload the `.tgz` zip file._33:T40b,--- ### Part 1c. Re-Mapping Feature Labels (6%) Redirect to the original ["1000 Cameras Dataset"](https://www.kaggle.com/datasets/crawford/1000-cameras-dataset) on another tab, and look at the data features provided by this dataset. Notice that many of the feature names contain parentheses and/or spaces, which are not very optimal for data handling. We need to change that by using JavaScript's `.map()` method to change data labels as so: |Original Feature Name|Modified Feature Name| |:--|:--| |"Model"|"model"| |"Release date"|"release_date"| |"Max resolution"|"max_resolution"| |"Low resolution"|"min_resolution"| |"Effective pixels"|"pixels"| |"Zoom wide (W)"|"zoom_wide"| |"Zoom tele (T)"|"zoom_tele"| |"Normal focus range"|"normal_focus_range"| |"Macro focus range"|"macro_focus_range"| |"Storage included"|"storage"| |"Weight (inc. batteries)"|"weight"| |"Dimensions"|"dimensions"| |"Price"|"price"| #### [TO-DO] Complete the `.map()` code below to re-label all feature labels. The first three are provided to you to help.34:T4c8,> ========================= > >There is a strong positive correlation between maximum resolution and release date, with resolution increasing over time. Outliers include some cameras with resolutions around 3000 before 2000 and others above 5000–6000 after 2000. The range of resolutions also widens post-2000, reflecting improvements in camera technology. > Normal focus range shows no strong trend, with scattered values across years and a few outliers such as a focus range of 120. Similarly, camera dimensions remain relatively stable over time, typically between 60–160, with some outliers above 200 or below 40. Price also shows no clear trend. Most cameras are priced below 1000, though a few more expensive models (4000–8000) appear after 2000, suggesting an expansion of higher-end options alongside affordable ones. > Some features, like resolution, display clear trends because they are technology-driven and highly valued by consumers. Other features, such as dimensions or focus range are likely less desirable compared to other features so they remain more stable. Prices, meanwhile, are influenced by market segmentation, with both budget and premium models coexisting. > > =========================35:T419,> ========================= > >There is no clear trend between maximum resolution and price. Most models cluster below 1000, with newer cameras generally offering higher resolutions. It seems that high-priced cameras are also generally the ones with high resolutions. > Weight and dimensions show a strong positive correlation, as larger dimensions typically indicate heavier weight. Outliers include models with unusually large dimensions but low weight, as well as very heavy models (≥1600). > Zoom wide and zoom tele also tend to increase together (positive relationship), although the trend is not very strong. Newer cameras show higher tele zooms, with a few outliers reaching extremely high levels. > Overall, weight and dimensions are strongly related because of physical design constraints, while wide and tele zooms likely correlate due to shared technological advances and consumer demand for both features. Price, however, is more market-driven and does not depend as directly on physical camera features. > =========================36:T7aa,## Part 4: Extending Our Understanding: Bar Charts, Boxplots, and Facets (35%) Scatter plots can only give us so much context to the data. We need additional charts to start observing trends more easily. One thing we hadn't considered thus far in our data is the number of cameras across time. What if our data is actually biased by having more recent cameras than pre-2000 cameras, for example? Are there other ways to visualize the same trends we've explored above? Let's try to understand the distributions of our data in different ways.. For this part, we need to extend into different chart types other than scatter plots. Make sure to get acquainted with the possible [mark types](https://vega.github.io/vega-lite/docs/mark.html#types) provided by **Vega-Lite**. We'll be focused on two distinct types: Bar Charts, and Boxplots. ### Bar Charts v.s. Histograms: Which to Use? Bar charts and histograms are very useful tools for demonstrating the distribution of data across either a spectrum of data or across aggregated sections of data. There is a distinction between them, however. - _Bar charts_ describe distinct, discrete categories in the data. This can be features such as labels, company names, or seasons of a year. The bins of bar charts can be either nominal or ordinal. - _Histograms_ describe the distribution of data across a spectrum of continuous data. The feature that is binned is usually quantitative and requires some intuition on how to divide the continuous data into equal segments. For the purposes of this assignment, we'll explore bar charts. However, just know that **Vega-Lite** offers the ability to generate both kinds of graphs using the `markBar()` method; it's all about what kind of data you feed into it that determines whether it's a bar chart or a histogram. For the determined, we recommend at least viewing the [possible types of encodings useful for bar charts](https://vega.github.io/vega-lite/docs/bar.html).37:T7dd,--- ### Part 4c. Multi-Boxplot Weight vs. Dimension, Across Years (10%) We're generating a unique boxplot where we're adding **release_date** as a 3rd dimension to our boxplots. However, unlike the scatter plot in Part 3 where we used colors to represent years, we're binning the data by **release_date** and separating the data across several subplots that each represent a 5-year period. **Vega-Lite** uses a concept called ["facets"](https://vega.github.io/vega-lite/docs/encoding.html#facet) as a way to create these subplots across one or more data features. This is very similar to the concept of adding subplots to Python's "Matplotlib" plots, except that we need to define very specifically which data feature we want to generate the subplots from. In the code sample we're generating, we're producing several subplots. Each subplot will generate the data across time bins of 5 years. This means one subplot will show from 1990-1994, another from 1995-1999, and so on until 2009. In order to do this, we need to introduce two new concepts: `transform()` and `column()`. #### [TO-DO] (5%) The `transform()` method lets you modify the data provided in `data()` just for this chart. In this case, we need to transform the data by doing the following: 1. Using `calculate()` and the following string comprehension to generate 5-year bins across our **release_date** feature: ```javascript calculate("floor(datum.release_date / 5) * 5 + '-' + (floor(datum.release_date / 5) * 5 + 4)") ``` 2. rename this new bin column as **year_bin** so that we can reference it in the `encode()` method later. Modify the `transform()` method in the code sample as instructed. #### [TO-DO] (5%) The `column()` is an encoding method similar to `x()` and `y()` in that it must be called within the `encode()` function. We are effectively telling **Vega-Lite** to generate subplots by column. We want to generate this facet based on **year_bin**. Add one more `column()` encoding, using **year_bin** as its ORDINAL field.38:T44e,> ========================= > > The relationship between weight and dimensions remains relatively the same in terms of trend i.e. larger the dimensions, heavier the weight across every period. The main differences across the year bins lie in the amount of variation and the presence of outliers, rather than in the overall trend itself. This could be because of camera design constraints which means that higher dimensions by design lead to heavier cameras. > When comparing 2000–2004 with 2005–2009, the later period shows fewer outliers and less variation in camera weight and dimensions. For example, in the 2000-2004 period we can notice experimentation with cameras of dimensions such as 200 or around 10 which doesn't exist post 2005. The plots between 1995-1995 have slightly heavier cameras for the same dimensions compared to later. Overall there is still a greater range of designs that can be noticed in the pre 2005 period. It is likely that most recent cameras have become more standardised with less cameras having unusually high dimensions or weights. > > =========================39:[{"id":"b616e9d6e3f60aae6125175c81b52a3b0996d232b5747d6bab5055b2f98809cf7c4c04102175d2b85b2660d77f02f116bc0f85c2b97189b45ec2d4abf465aadc","href":"https://static.observableusercontent.com/files/b616e9d6e3f60aae6125175c81b52a3b0996d232b5747d6bab5055b2f98809cf7c4c04102175d2b85b2660d77f02f116bc0f85c2b97189b45ec2d4abf465aadc","name":"camera_dataset.csv","type":"text/csv","lastModified":1757465827258.187,"size":86961}] 30:{"id":"813f1d4d81f9fbb9","slug":null,"title":"Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11)","thumbnail":"035d8ac1822fa767e1137b36960f1ef1a70a752ff3c3aca77f5e6207924e7afa","access_level":"public","create_time":"$D2025-09-10T00:56:05.235Z","update_time":"$D2025-09-12T03:34:47.622Z","view_time":null,"first_public_time":"$D2025-09-10T00:56:05.235Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"9f0e7a056e171f1e","login":"nyu34","name":"NYU","avatar_url":"https://avatars-next.observableusercontent.com/2d4bc1e34109ca4a768a9988c04daf63bb3b75d51019c1264cf163e913a8562b","tier":"starter_2024"},"version":597,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"03df06c91a2bb0e9","login":"anushri-iyer","name":"Anushri Iyer","avatar_url":"https://avatars-next.observableusercontent.com/17802ee9d3daba50a72b15f0997290e6a630e2c752bc2c0f872a839f9ab5a4e3","tier":null}],"members":[{"type":"individual","id":"03df06c91a2bb0e9","login":"anushri-iyer","name":"Anushri Iyer","avatar_url":"https://avatars-next.observableusercontent.com/17802ee9d3daba50a72b15f0997290e6a630e2c752bc2c0f872a839f9ab5a4e3","tier":null,"role":"editor"},{"type":"team","id":"9f0e7a056e171f1e","login":"nyu34","name":"NYU","avatar_url":"https://avatars-next.observableusercontent.com/2d4bc1e34109ca4a768a9988c04daf63bb3b75d51019c1264cf163e913a8562b","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11)","cells":[{"id":0,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"---\n\n#### [TO-DO]\n\nFill out the following submission details:\n\n* **Name:** ANUSHRI IYER;\n* **NetID:** ai2185;\n* **SHARE URL:** https://observablehq.com/d/813f1d4d81f9fbb9;\n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"## Part 1: Importing Vega-Lite and the Camera dataset (10%)\n\n---\n### Part 1a. Importing Vega-Lite (2%)\n#### [TO-DO]\nImport the **Vega-Lite** API into this notebook.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"// [TO-DO]\nimport {vl} from \"@vega/vega-lite-api\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"---\n\n### Part 1b. Importing the Camera dataset (2%)\n#### [TO-DO]\nImport the \"camera_dataset.csv\" provided alongside this notebook by completing the code below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":380,"value":"camera_data = FileAttachment(\"camera_dataset.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":388,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":350,"value":"cameras = camera_data.map(d => ({\n model: d[\"Model\"],\n release_date: d[\"Release date\"],\n max_resolution: d[\"Max resolution\"],\n min_resolution: d[\"Low resolution\"],\n pixels: d[\"Effective pixels\"],\n zoom_wide: d[\"Zoom wide (W)\"],\n zoom_tele: d[\"Zoom tele (T)\"],\n normal_focus_range: d[\"Normal focus range\"],\n macro_focus_range: d[\"Macro focus range\"],\n storage: d[\"Storage included\"],\n weight: d[\"Weight (inc. batteries)\"],\n dimensions: d[\"Dimensions\"],\n price: d[\"Price\"]\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":339,"value":"---\n\nInspecting the `cameras` dataset reveals that there are 13 columns and 1038 rows. This means that 13 features of cameras are recorded, and have 1038 unique cameras that were analyzed. In addition, there is only one kind of nominal data type: the **model** name. All other data features appear to be either quantitative or temporal, in the case of **release_date**. However, for the purposes of this assignment, **we will treat the release_date as an ORDINAL data feature**. This is because the **release_date** feature isn't a full timestamp and there's an implied order given the way years come one after another.\n\nThe table below shows the first 5 rows of this cameras dataset:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":70,"value":"html`\n \n \n ${Object.keys(cameras[0]).map(key => html``)}\n \n \n \n ${cameras.slice(0, 5).map(row => html`\n ${Object.values(row).map(value => html``)}\n `)}\n \n

${key}
${value}

`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"This looks like a pretty comprehensive dataset. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"## Part 2: Camera Features Across Time (30%)\n\nOne of the more salient things that may be interesting to observe is how cameras have changed over time. Let's try to do some charting to visually understand what's going on with cameras. In fact, let's observe changes over time based on the features we've listed above.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":397,"value":"---\n### Part 2a. Max resolution v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **release_date** as an ORDINAL data type. (2%)\n2. Y-axis: **max_resolution** as a QUANTITATIVE data type. (2%)\n3. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":105,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldO(\"release_date\"), \n vl.y().fieldQ(\"max_resolution\"), \n vl.tooltip().fieldN(\"model\") \n )\n .render() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":405,"value":"---\n### Part 2b. Normal focus range v.s. Release Date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **release_date** as an ORDINAL data type. (2%)\n2. Y-axis: **normal_focus_range** as a QUANTITATIVE data type. (2%)\n3. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":116,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldO(\"release_date\"), \n vl.y().fieldQ(\"normal_focus_range\"), \n vl.tooltip().fieldN(\"model\") \n )\n .render() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":410,"value":"---\n### Part 2c. Dimensions v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **release_date** as an ORDINAL data type. (2%)\n2. Y-axis: **dimensions** as a QUANTITATIVE data type. (2%)\n3. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":114,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldO(\"release_date\"), \n vl.y().fieldQ(\"dimensions\"), \n vl.tooltip().fieldN(\"model\") \n )\n .render() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":124,"value":"---\n### Part 2d. Price v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **release_date** as an ORDINAL data type. (2%)\n2. Y-axis: **price** as a QUANTITATIVE data type. (2%)\n3. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":110,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldO(\"release_date\"), \n vl.y().fieldQ(\"price\"), \n vl.tooltip().fieldN(\"model\") \n )\n .render() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"---\n### Part 2e. Observing and Describing Trends (10%)\n#### [TO-DO]\n\nAnswer the following questions about the four features we plotted in Parts 2a-2d:\n\n1. Where there any trends that you observed in the data features as time passed?\n2. If so, describe their nature: are they necessarily strong trends, and do they have any outliers?\n3. Why do some features display trends while others may not necessarily display trends?\n\n_No need to go full statistical analysis or technical answers about cameras - rather, what does the data visually tell you?_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":420,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":154,"value":"---\n\n## Part 3: Comparing Features (25%)\n\nTrends across time may indicate the possibility of certain features being related to one another. Let's try to visually analyze for any relationships between features. Let's also add some color variation to visualize **release_date** as 3rd dimension to our data analysis.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":427,"value":"---\n### Part 3a. Max resolution v.s. Price v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **max_resolution** as an QUANTITATIVE data type. (1%)\n2. Y-axis: **price** as a QUANTITATIVE data type. (1%)\n3. Color: **release_date** as an ORDINAL data type. (2%)\n4. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":33,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldQ(\"max_resolution\"), \n vl.y().fieldQ(\"price\"), \n vl.color().fieldO(\"release_date\"),\n vl.tooltip().fieldN(\"model\") \n )\n .render() \n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":430,"value":"---\n### Part 3b. Dimensions v.s. Weight v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **dimensions** as an QUANTITATIVE data type. (1%)\n2. Y-axis: **weight** as a QUANTITATIVE data type. (1%)\n3. Color: **release_date** as an ORDINAL data type. (2%)\n4. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":183,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldQ(\"dimensions\"), \n vl.y().fieldQ(\"weight\"), \n vl.color().fieldO(\"release_date\"),\n vl.tooltip().fieldN(\"model\") \n )\n .render() ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":432,"value":"---\n### Part 3c. Zoom wide v.s. Zoom tele v.s. Release date (5%)\n#### [TO-DO]\nGenerate a scatter chart (can be circles, points, or squares) with the following encodings:\n\n1. X-axis: **zoom_wide** as an QUANTITATIVE data type. (1%)\n2. Y-axis: **zoom_tele** as a QUANTITATIVE data type. (1%)\n3. Color: **release_date** as an ORDINAL data type. (2%)\n4. Tooltip: **model** name as a NOMINAL data type. (1%)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":207,"value":"vl.markCircle() \n .data(cameras) \n .encode(\n vl.x().fieldQ(\"zoom_wide\"), \n vl.y().fieldQ(\"zoom_tele\"), \n vl.color().fieldO(\"release_date\"),\n vl.tooltip().fieldN(\"model\") \n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":214,"value":"---\n### Part 3d. Observing and Describing Trends (5%)\n#### [TO-DO]\nFor the three graphs we generated above, provide a similar analysis as in **Part 2e**.\n\n1. Where there any trends that you observed in the data features as time passed?\n2. If so, describe their nature: are they necessarily strong trends, and do they have any outliers?\n3. Why do some features display trends while others may not necessarily display trends?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":436,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":217,"value":"---\n### Part 3e. Colors and Data Interpretation (5%)\n#### [TO-DO]\nAs mentioned during Week 1's Lab session, color can be an important tool for visualization. Consider the new colors we've applied to our three comparison charts and answer the following question in a few sentences (no more than a paragraph):\n\n- In which graphs did coloration appear to work well to add context to the data? And which graphs did coloration not help at all?\n- Were there any charts where you believed that the coloration actually may misconstrue your understanding of possible trends?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":440,"value":"> =========================\n> \n> Coloration helps in the max resolution vs price graph as it indicates that newer cameras tend to have higher resolutions in a given price range. In the Zoom Wide vs Zoom Tele colors are able to show that newer cameras are generally the ones with high tele zoom but there is not too much clear information that can be gauged. This gives some more context to development in zoom over time. The graph for Dimensions vs Weight shows a strong correlation between the two features. However, the points are densely clustered together making it difficult to identify any relationship with time.\n>\n> This dense cluster can also allow for misconstruing of trends as the colour shades appear darker which might indicate that newer cameras are heavier or larger which might not be true.\n>\n> =========================","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":220,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":222,"value":"---\n### Part 4a. How many camera entries per year? (10%)\n#### [TO-DO] (5%)\nGenerate a bar chart that visualizes the number of camera models across each year (x-axis). To do this, you need to take advantage of the `markBar()` mark type and play around with the `aggregate()` encoding for the y-axis. There is no need to render a tooltip or colors for this bar chat. We emphasize \"bar chart\" as we are treating the **release_date** as an ordinal data type.\n\n#### [TO-DO] (5%)\nIn the markdown block after generating the bar chart, in a few sentences, describe how the number of camera entries per year may or may not have skewed our initial analyses of the data in Parts 2 and 3.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":224,"value":"// [TO-DO]\nvl.markBar()\n .data(cameras) \n .encode(\n vl.x().fieldO(\"release_date\"), \n vl.y().fieldQ(\"model\").aggregate(\"valid\"), \n )\n .render()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":237,"value":"> =========================\n> \n> The number of camera entries per year increased significantly after 1998, with the the highest number of models being between 2002 and 2007. This may have skewed our previous findings, since trends observed in features such as resolution or zoom are more strongly influenced by the later years where more data points exist. Less data points for cameras in the earlier years make it harder to detect patterns from that period. Therefore, some of the trends noticed earlier may partly reflect the much greater sample size in the 2000s compared to pre-2000s.\n> \n> =========================","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":243,"value":"---\n### Part 4b. Weight v.s. Dimension, Revisited via Boxplots (10%)\n#### [TO-DO] (5%)\nGenerate a boxplot of the relationship between **dimensions** (x-axis) and **weight** (y-axis). While we recommend that you bin the cameras' dimensions by magnitudes of 10, it's up to you. To use boxplots, make sure to use the `markBoxplot()` method provided by **Vega-Lite**.\n\n#### [TO-DO] (5%)\nIn the markdown block below the code for the chart, describe the relationship between the dimensions and weight of cameras. How does the boxplot affect your ability to interpret the data trend, if any?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":266,"value":"// [TO-DO]\nvl.markBoxplot()\n .data(cameras) \n .encode(\n vl.x().bin({step:10}).fieldQ(\"dimensions\"), \n vl.y().fieldQ(\"weight\"), \n )\n .render()\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":458,"value":"> =========================\n> \n> The boxplot shows a clear positive relationship between camera dimensions and weight: as dimensions increase, cameras tend to become heavier. The boxplot organises the data and is able to give more insights such as the spread (IQR) and median. For example, it is easier to notice the higher variablity in weight for cameras with high dimensions.\nOutliers are visible at both lower and higher weights, but they do not disrupt the overall trend. \n\n>The use of boxplots makes the trend easier to interpret than scatterplots alone, since the distribution within each dimension bin is summarized, highlighting both central tendencies and the presence of outliers.\n> \n> =========================","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":274,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":284,"value":"vl.markBoxplot()\n .data(cameras)\n .transform(\n vl.calculate(\"floor(datum.release_date / 5) * 5 + '-' + (floor(datum.release_date / 5) * 5 + 4)\").as(\"year_bin\")\n )\n .encode(\n vl.x().fieldQ(\"dimensions\").bin({ step: 10 }),\n vl.y().fieldQ(\"weight\"),\n vl.column().fieldO(\"year_bin\"),\n vl.tooltip().fieldN(\"model\"),\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"---\n### Part 4d. Boxplots Across Time (5%)\n#### [TO-DO]\n\nObserve the relationship between **dimensions** and **weight** across each of the 5-year periods. Answer the following questions:\n\n1. Has the relationship between weight and dimension really changed at all across the yearly bins? Why or why not do you think so?\n2. Looking at Part 4A, we should see a multitude of entries in the 2005-2009 plot. However, we see very few outliers and variation in cameras in this yearly period than in 2000-2004, for example. What can you intuit about the nature of more recent cameras compared to their older siblings?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 31:"$30:body"