[4 points] There should be 100 points using a circle shape that are randomly generated and placed on the plot. Each point’s x coordinate should be a random number between 10 and 250 inclusively (i.e., [10, 250]), and so should each point’s y coordinate. A point’s x and y coordinates should be independently computed.[2 points] The plot must have visible X and Y axes that scale according to the generated points. The ticks on these axes should adjust automatically based on the randomly generated scatter-plot points.[2 points] Use a single linear scale and apply it to both the X and Y coordinates of each circle to map the domain of X and Y values to the range of [1,5]. Set each circle’s radius attribute to be the euclidean distance between the points (X, 0) and (0, Y) where X and Y denote the circle’s scaled X and Y values.[3 points] All points with a scaled X value greater than the average of the scaled X value of all scatter-plot points should be outlined in blue. All other points should be outlined green. The points should use a transparent fill to enable visualization of overlapping points.可视化分析代写Hint: Modify the ‘stroke’ parameter of the ‘circle’.[3 points] It is often desirable to emphasize some important data points in a dataset. Accomplish this by displaying a text annotation for the point that contains the smallest y value. The annotation should read “Min Y: value ” where value is the minimum y. If there are multiple points that contain the same minimum y value, you can pick whichever point you like to annotate. Optional: experiment with different approaches to style the annotation text, e.g., try using different positioning settings, font-sizes, font-weights, and/or color to make it stand out. Use unscaled Y values here rather than scaled Y values.[1 point] Your GT username, in lower-case, (e.g., jdoe3) should appear above the scatterplot. Also set the HTML title tag (e.g., title jdoe3 /title ) to your GT username (also in lower-case).The scatterplot should appear similar to but not exactly equivalent to the sample plot provided below. Remember that the plot will contain random data. Note: No external libraries should be used. The index.html file can only refer to d3.v3.min.js within the d3 folder using the js file’s relative path. Absolute/local paths are specific locations that exist only on your computer, which means your code won’t run on our machines we grade (and you will lose points).可视化分析代写Q3 [15分] D3预热和教程●在此处浏览在线D3 v3教程。●完成步骤01-16（完成“ 16.轴”的操作）。●确保您使用的是D3库的v3。注意：这是一个简单而重要的教程，奠定了家庭作业2的基础。最新的D3版本是v5，但只有v3教程可在线获得。你在此v3教程中赚取的收入可以转移到v5。在“作业2”中，您将大量使用D3。我们正在将作业2升级到v5。所有佐治亚理工学院的学生都可以免费使用Safari图书，其中包括本手册及其更新的v4教程。 https://www.safaribooksonline.com。只需使用您的GT帐户登录即可。注意：我们建议使用Mozilla Firefox或Google Chrome，因为它们具有相对强大的内置开发人员工具。可交付成果：将下面列出的所有文件/文件夹放在Q3文件夹中●名为d3的文件夹，其中包含文件d3.v3.min.js（下载）●index.html：在浏览器中运行时，它应显示具有以下规格的散点图：一种。 [4分]使用圆形的100个点应随机生成并放置在图上。每个点的x坐标应为10到250之间（包括10和250）的随机数（即[10，250]），每个点的y坐标也应为10。点的x和y坐标应独立计算。b。 [2个点]该图必须具有可见的X轴和Y轴，这些轴会根据生成的点进行缩放。这些轴上的刻度应根据随机生成的散点图点自动调整。C。 [2分]使用单个线性比例尺并将其应用于每个圆的X和Y坐标，以将X和Y值的域映射到[1,5]的范围。将每个圆的“半径”属性设置为点（X，0）和（0，Y）之间的欧式距离，其中X和Y表示圆的缩放X和Y值。可视化分析代写d。 [3分]所有标度X值大于所有散点图点的标度X值的平均值的点都应以蓝色框出。所有其他点都应以绿色勾勒出轮廓。这些点应使用透明填充以使重叠点可视化。提示：修改“圆”的“冲程”参数。e。 [3分]通常需要强调数据集中的一些重要数据点。通过显示包含最小y值的点的文本注释来实现此目的。注释应显示为“最小值Y： 值 ”，其中 值 是最小值y。如果有多个点包含相同的y最小值，则可以选择要注释的点。可选：尝试使用不同的方法来设置注释文本的样式，例如，尝试使用不同的定位设置，字体大小，字体粗细和/或颜色使其突出。在此使用未缩放的Y值，而不是缩放的Y值。F。 [1分]您的GT用户名（小写）（例如jdoe3）应显示在散点图上方。还要将HTML标题标签（例如 title jdoe3 / title ）设置为您的GT用户名（也要小写）。散点图应看起来与下面提供的样本图相似但不完全相同。请记住，该图将包含随机数据。可视化分析代写注意：不应使用任何外部库。 index.html文件只能使用js文件的相对路径引用d3文件夹中的d3.v3.min.js。绝对/本地路径是仅在您的计算机上存在的特定位置，这意味着您的代码将无法在我们评分的我们的计算机上运行（并且您将失去分）。Q4 [10 pt] OpenRefine可视化分析代写 Watch the videos on the OpenRefine’s homepage for an overview of its features. Download and install OpenRefine (latest release : 3.1) 2.Import Dataset:Launch OpenRefine. It opens in a browser (127.0.0.1:3333).We use a products dataset from Mercari, derived from a competition on Kaggle (Mercari Price Suggestion Challenge). If you are interested in the details, please refer to the data description page. We have sampled a subset of the dataset as the given properties.csv .Choose Create Project - This Computer - properties.csv . Click Next . You will now see a preview of the dataset. Click Create Project in the upper right corner.3.Clean/Refine the data:Note: OpenRefine maintains a log of all changes. You can undo changes. See the Undo/Redo button on the upper left corner.i.a [1 pt] Select the “category_name” column and choose ‘Facet by Blank’ (Facet - Customized Facets - Facet by blank) to filter out the records that have blank values in this column. Provide the number of rows that return True. Remove these rows.i.b [1 pt] Split the column “category_name” into multiple columns without removing the original column. For example, a row with “Kids/Toys/Dolls Accessories” in the category_name column, would be split across the newly created columns as “Kids”, “Toys” and “Dolls Accessories”. Use the existing functionality in OpenRefine that creates multiple columns from an existing column based on a separator (i.e., in this case ‘/’). Provide the number of columns that are created in this operation. Remove any newly created columns that do not have values in all rows.可视化分析代写ii. [2 pt] Select the column “name” and apply the Text Facet (Facet - Text Facet). Click the Cluster button which opens a window where you can choose different “methods” and “keying functions” to use while clustering. Choose the keying function that produces the highest number of clusters under the “Key Collision” method. Provide the number of clusters found using this keying function. Click on ‘Select All’ and ‘Merge Selected Close’.iii. [2 pt] Replace the null values in the “brand_name column” with the text “Unbranded” (Edit Cells - Transform). Provide the General Refine Evaluation Language (GREL) expression used.iv. [2 pt] Create a new column “high_priced” with the values 0 or 1 based on the “price” column with the following conditions: If the price is greater than 100, “high_priced” should be set as 1, else 0. Provide the GREL expression used to perform this. v. [2 pt] Create a new column “has_offer” with the values 0 or 1 based on the “item_description” column with the following conditions: If it contains the text “discount” or “offer” or “sale”, then set the value in “has_offer” as 1, else 0. Provide the GREL expression used to perform this.可视化分析代写 Note: There has been a slight confusion with c) v. and thus, we will be giving full credit to GREL statements that fulfill either of the following:a) Look for the following words “sale”, “offer”, discount” without converting the original value in the “item description” column to lowercaseb) Look for the following words “sale”, “offer”, discount” after converting the original value in the “item description” column to lowercaseDeliverables: Place all the files listed below in the Q4 folderproperties_clean.csv : Export the final table as a comma-separated values (.csv) file.changes.json : Submit a list of changes made to file in json format. Use the “Extract Operation History” option under the Undo/Redo tab to create this file. Q4Observations.txt : A text file with answers to parts c.i.a, c.i.b, c.ii, c.iii, c.iv and c.v. Provide each answer in a new line.4 [10分] OpenRefine一种。观看OpenRefine主页上的视频，以了解其功能概述。下载并安装OpenRefine（最新版本：3.1）b。导入数据集：●启动OpenRefine。它在浏览器中打开（127.0.0.1:3333）。●我们使用来自Mercari的产品数据集，该数据集来自Kaggle（Mercari价格建议挑战）竞赛。如果您对这些细节感兴趣，请参考数据描述页面。我们已经采样了数据集的一个子集作为给定的“ properties.csv”。●选择“创建项目”- “本计算机”- “ properties.csv”。点击下一步”。●现在，您将看到数据集的预览。点击右上角的“创建项目”。C。清理/优化数据：注意：OpenRefine维护所有更改的日志。您可以撤消更改。请参阅左上角的“撤消/重做”按钮。可视化分析代写i.a [1 pt]选择“ category_name”列，然后选择“ Facet by Blank”（Facet- Customized Facets- Facet by blank）以过滤出该列中具有空白值的记录。提供返回True的行数。删除这些行。i.b [1 pt]将“ category_name”列拆分为多个列，而不删除原始列。例如，category_name列中包含“孩子/玩具/娃娃和配件”的行将被拆分为新创建的列，分别为“孩子”，“玩具”和“娃娃和配件”。使用OpenRefine中的现有功能，该功能会基于分隔符（即本例中的“ /”）从现有列中创建多个列。提供在此操作中创建的列数。删除所有在所有行中都没有值的新创建的列。ii。 [2 pt]选择“名称”列并应用Text Facet（Facet- Text Facet）。单击“群集”按钮，这将打开一个窗口，您可以在其中选择不同的“方法”和“键”群集中使用的“功能”。在“键碰撞”方法下，选择产生最多簇数的键控功能。提供使用此键控功能找到的群集数量。点击“全选”和“合并选择并关闭”。可视化分析代写iii。 [2分]将“ brand_name列”中的空值替换为文本“ Unbranded”（编辑单元格- 转换）。提供使用的通用优化评估语言（GREL）表达式。iv。 [2分]基于“价格”列，并在以下情况下，创建一个新的列“高价”，其值为0或1：如果价格大于100，则“高价”应设置为1，否则设置为0。用于执行此操作的GREL表达式。v。[2 pt]基于具有以下条件的“ item_description”列，创建一个值为0或1的新列“ has_offer”：如果它包含文本“ discount”或“ offer”或“ sale”，则进行设置“ has_offer”中的值为1，否则为0。提供用于执行此操作的GREL表达式。注意：与c）v。略有混淆，因此，我们将充分考虑满足以下任一条件的GREL语句：a）查找以下单词“ sale”（销售），“ offer”（优惠），Discount（折扣），而无需将“ item description”（商品说明）列中的原始值转换为小写b）将“商品说明”列中的原始值转换为小写字母后，查找以下单词“ sale”，“ offer”，“ Discount”可交付成果：将下面列出的所有文件放在Q4文件夹中●properties_clean.csv：将最终表导出为逗号分隔值（.csv）文件。●changes.json：以json格式提交对文件所做的更改的列表。使用“撤消/重做”选项卡下的“提取操作历史记录”选项来创建此文件。●Q4Observations.txt：一个文本文件，其中包含部分c.i.a，c.i.b，c.ii，c.iii，c.iv和c.v的答案。在新行中提供每个答案。Extremely Important: folder structure and content of submission zip file可视化分析代写Extremely Important: We understand that some of you may work on this assignment until just prior to the deadline, rushing to submit your work before the submission window closes. Take the time to validate that all files are present in your submission and that you do not forget to include any deliverables! If a deliverable is not submitted, you will receive zero credit for the affected portion of the assignment this is a very sad way to lose points, since you’ve already done the work!You are submitting a single zip file named HW1-{GT username}.zip. The files included in each question’s folder have been clearly specified at the end of the question’s problem description. The zip file’s folder structure must exactly be (when unzipped):HW1-{GT username}/Q1/movie_ID_name.csvmovie_ID_sim_movie_ID.csvgraph.png / graph.svggraph_explanation.txtmetrics.txt 可视化分析代写script.pyQ2/movie-cast.txtmovie-name-score.txtmovie-overview.txtQ2.SQL.txtQ3/index.htmld3/d3.v3.min.jsQ4/properties_clean.csvchanges.jsonQ4Observations.txtVersion 6

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