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GitHub Repo https://github.com/riggiobill/Front-End-HTML-Web-Application

riggiobill/Front-End-HTML-Web-Application

# Web Design Homework - Web Visualization Dashboard (Latitude) ## Background Data is more powerful when we share it with others! Let's take what we've learned about HTML and CSS to create a dashboard showing off the analysis we've done. ![Images/landingResize.png](Images/landingResize.png) ### Before You Begin 1. Create a new repository for this project called `Web-Design-Challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the web challenge. Use a folder name to correspond to the challenge: **WebVisualizations**. 4. Add your **html** files to this folder as well as your **assets**, **Resources** and **visualizations** folders. 5. Push the above changes to GitHub or GitLab. 6. Deploy to GitHub pages. ## Latitude - Latitude Analysis Dashboard with Attitude For this homework we'll be creating a visualization dashboard website using visualizations we've created in a past assignment. Specifically, we'll be plotting [weather data](Resources/cities.csv). In building this dashboard, we'll create individual pages for each plot and a means by which we can navigate between them. These pages will contain the visualizations and their corresponding explanations. We'll also have a landing page, a page where we can see a comparison of all of the plots, and another page where we can view the data used to build them. ### Website Requirements For reference, see the ["Screenshots" section](#screenshots) below. The website must consist of 7 pages total, including: * A [landing page](#landing-page) containing: * An explanation of the project. * Links to each visualizations page. There should be a sidebar containing preview images of each plot, and clicking an image should take the user to that visualization. * Four [visualization pages](#visualization-pages), each with: * A descriptive title and heading tag. * The plot/visualization itself for the selected comparison. * A paragraph describing the plot and its significance. * A ["Comparisons" page](#comparisons-page) that: * Contains all of the visualizations on the same page so we can easily visually compare them. * Uses a Bootstrap grid for the visualizations. * The grid must be two visualizations across on screens medium and larger, and 1 across on extra-small and small screens. * A ["Data" page](#data-page) that: * Displays a responsive table containing the data used in the visualizations. * The table must be a bootstrap table component. [Hint](https://getbootstrap.com/docs/4.3/content/tables/#responsive-tables) * The data must come from exporting the `.csv` file as HTML, or converting it to HTML. Try using a tool you already know, pandas. Pandas has a nifty method approprately called `to_html` that allows you to generate a HTML table from a pandas dataframe. See the documentation [here](https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.to_html.html) The website must, at the top of every page, have a navigation menu that: * Has the name of the site on the left of the nav which allows users to return to the landing page from any page. * Contains a dropdown menu on the right of the navbar named "Plots" that provides a link to each individual visualization page. * Provides two more text links on the right: "Comparisons," which links to the comparisons page, and "Data," which links to the data page. * Is responsive (using media queries). The nav must have similar behavior as the screenshots ["Navigation Menu" section](#navigation-menu) (notice the background color change). Finally, the website must be deployed to GitHub pages. When finished, submit to BootcampSpot the links to 1) the deployed app and 2) the GitHub repository. Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file ### Considerations * You may use the [weather data](Resources/cities.csv) or choose another dataset. Alternatively, you may use the included [cities dataset](Resources/cities.csv) and pull the images from the [assets folder](Resources/assets). * You must use Bootstrap. This includes using the Bootstrap `navbar` component for the header on every page, the bootstrap table component for the data page, and the Bootstrap grid for responsiveness on the comparison page. * You must deploy your website to GitHub pages, with the website working on a live, publicly accessible URL as a result. * Be sure to use a CSS media query for the navigation menu. * Be sure your website works at all window widths/sizes. * Feel free to take some liberty in the visual aspects, but keep the core functionality the same.
GitHub Repo https://github.com/benjiaminhanks2/stylee.css

benjiaminhanks2/stylee.css

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GitHub Repo https://github.com/lananhbui2899/-EDA-Coursera-Course-Project-1

lananhbui2899/-EDA-Coursera-Course-Project-1

This assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site: Dataset: Electric power consumption [20Mb] Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. The following descriptions of the 9 variables in the dataset are taken from the UCI web site: Date: Date in format dd/mm/yyyy Time: time in format hh:mm:ss Global_active_power: household global minute-averaged active power (in kilowatt) Global_reactive_power: household global minute-averaged reactive power (in kilowatt) Voltage: minute-averaged voltage (in volt) Global_intensity: household global minute-averaged current intensity (in ampere) Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. Review criteria less Criteria Was a valid GitHub URL containing a git repository submitted? Does the GitHub repository contain at least one commit beyond the original fork? Please examine the plot files in the GitHub repository. Do the plot files appear to be of the correct graphics file format? Does each plot appear correct? Does each set of R code appear to create the reference plot? Reviewing the Assignments Keep in mind this course is about exploratory graphs, understanding the data, and developing strategies. Here's a good quote from a swirl lesson about exploratory graphs: "They help us find patterns in data and understand its properties. They suggest modeling strategies and help to debug analyses. We DON'T use exploratory graphs to communicate results." The rubrics should always be interpreted in that context. As you do your evaluation, please keep an open mind and focus on the positive. The goal is not to deduct points over small deviations from the requirements or for legitimate differences in implementation styles, etc. Look for ways to give points when it's clear that the submitter has given a good faith effort to do the project, and when it's likely that they've succeeded. Most importantly, it's okay if a person did something differently from the way that you did it. The point is not to see if someone managed to match your way of doing things, but to see if someone objectively accomplished the task at hand. To that end, keep the following things in mind: DO Review the source code. Keep an open mind and focus on the positive.≤/li> When in doubt, err on the side of giving too many points, rather than giving too few. Ask yourself if a plot might answer a question for the person who created it. Remember that not everyone has the same statistical background and knowledge. DON'T: Deduct just because you disagree with someone's statistical methods. Deduct just because you disagree with someone's plotting methods. Deduct based on aesthetics. Loading the data less When loading the dataset into R, please consider the following: The dataset has 2,075,259 rows and 9 columns. First calculate a rough estimate of how much memory the dataset will require in memory before reading into R. Make sure your computer has enough memory (most modern computers should be fine). We will only be using data from the dates 2007-02-01 and 2007-02-02. One alternative is to read the data from just those dates rather than reading in the entire dataset and subsetting to those dates. You may find it useful to convert the Date and Time variables to Date/Time classes in R using the \color{red}{\verb|strptime()|}strptime() and \color{red}{\verb|as.Date()|}as.Date() functions. Note that in this dataset missing values are coded as \color{red}{\verb|?|}?. Making Plots less Our overall goal here is simply to examine how household energy usage varies over a 2-day period in February, 2007. Your task is to reconstruct the following plots below, all of which were constructed using the base plotting system. First you will need to fork and clone the following GitHub repository: https://github.com/rdpeng/ExData_Plotting1 For each plot you should Construct the plot and save it to a PNG file with a width of 480 pixels and a height of 480 pixels. Name each of the plot files as \color{red}{\verb|plot1.png|}plot1.png, \color{red}{\verb|plot2.png|}plot2.png, etc. Create a separate R code file (\color{red}{\verb|plot1.R|}plot1.R, \color{red}{\verb|plot2.R|}plot2.R, etc.) that constructs the corresponding plot, i.e. code in \color{red}{\verb|plot1.R|}plot1.R constructs the \color{red}{\verb|plot1.png|}plot1.png plot. Your code file should include code for reading the data so that the plot can be fully reproduced. You must also include the code that creates the PNG file. Add the PNG file and R code file to the top-level folder of your git repository (no need for separate sub-folders) When you are finished with the assignment, push your git repository to GitHub so that the GitHub version of your repository is up to date. There should be four PNG files and four R code files, a total of eight files in the top-level folder of the repo. The four plots that you will need to construct are shown below.
GitHub Repo https://github.com/Ayshehgithub/Exploratory-Data-Analysis

Ayshehgithub/Exploratory-Data-Analysis

his assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site: Dataset: Electric power consumption [20Mb] Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. The following descriptions of the 9 variables in the dataset are taken from the UCI web site: Date: Date in format dd/mm/yyyy Time: time in format hh:mm:ss Global_active_power: household global minute-averaged active power (in kilowatt) Global_reactive_power: household global minute-averaged reactive power (in kilowatt) Voltage: minute-averaged voltage (in volt) Global_intensity: household global minute-averaged current intensity (in ampere) Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. Review criteria less Criteria Was a valid GitHub URL containing a git repository submitted? Does the GitHub repository contain at least one commit beyond the original fork? Please examine the plot files in the GitHub repository. Do the plot files appear to be of the correct graphics file format? Does each plot appear correct? Does each set of R code appear to create the reference plot? Reviewing the Assignments Keep in mind this course is about exploratory graphs, understanding the data, and developing strategies. Here's a good quote from a swirl lesson about exploratory graphs: "They help us find patterns in data and understand its properties. They suggest modeling strategies and help to debug analyses. We DON'T use exploratory graphs to communicate results." The rubrics should always be interpreted in that context. As you do your evaluation, please keep an open mind and focus on the positive. The goal is not to deduct points over small deviations from the requirements or for legitimate differences in implementation styles, etc. Look for ways to give points when it's clear that the submitter has given a good faith effort to do the project, and when it's likely that they've succeeded. Most importantly, it's okay if a person did something differently from the way that you did it. The point is not to see if someone managed to match your way of doing things, but to see if someone objectively accomplished the task at hand. To that end, keep the following things in mind: DO Review the source code. Keep an open mind and focus on the positive.≤/li> When in doubt, err on the side of giving too many points, rather than giving too few. Ask yourself if a plot might answer a question for the person who created it. Remember that not everyone has the same statistical background and knowledge. DON'T: Deduct just because you disagree with someone's statistical methods. Deduct just because you disagree with someone's plotting methods. Deduct based on aesthetics. Loading the data less When loading the dataset into R, please consider the following: The dataset has 2,075,259 rows and 9 columns. First calculate a rough estimate of how much memory the dataset will require in memory before reading into R. Make sure your computer has enough memory (most modern computers should be fine). We will only be using data from the dates 2007-02-01 and 2007-02-02. One alternative is to read the data from just those dates rather than reading in the entire dataset and subsetting to those dates. You may find it useful to convert the Date and Time variables to Date/Time classes in R using the \color{red}{\verb|strptime()|}strptime() and \color{red}{\verb|as.Date()|}as.Date() functions. Note that in this dataset missing values are coded as \color{red}{\verb|?|}?. Making Plots less Our overall goal here is simply to examine how household energy usage varies over a 2-day period in February, 2007. Your task is to reconstruct the following plots below, all of which were constructed using the base plotting system. First you will need to fork and clone the following GitHub repository: https://github.com/rdpeng/ExData_Plotting1 For each plot you should Construct the plot and save it to a PNG file with a width of 480 pixels and a height of 480 pixels. Name each of the plot files as \color{red}{\verb|plot1.png|}plot1.png, \color{red}{\verb|plot2.png|}plot2.png, etc. Create a separate R code file (\color{red}{\verb|plot1.R|}plot1.R, \color{red}{\verb|plot2.R|}plot2.R, etc.) that constructs the corresponding plot, i.e. code in \color{red}{\verb|plot1.R|}plot1.R constructs the \color{red}{\verb|plot1.png|}plot1.png plot. Your code file should include code for reading the data so that the plot can be fully reproduced. You must also include the code that creates the PNG file. Add the PNG file and R code file to the top-level folder of your git repository (no need for separate sub-folders) When you are finished with the assignment, push your git repository to GitHub so that the GitHub version of your repository is up to date. There should be four PNG files and four R code files, a total of eight files in the top-level folder of the repo.
GitHub Repo https://github.com/TeniaKovacs/ExploratoryDataProject1

TeniaKovacs/ExploratoryDataProject1

Instructions This assignment uses data from the UC Irvine Machine Learning Repository, a popular repository for machine learning datasets. In particular, we will be using the “Individual household electric power consumption Data Set” which I have made available on the course web site: Dataset: Electric power consumption [20Mb] Description: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available. The following descriptions of the 9 variables in the dataset are taken from the UCI web site: Date: Date in format dd/mm/yyyy Time: time in format hh:mm:ss Global_active_power: household global minute-averaged active power (in kilowatt) Global_reactive_power: household global minute-averaged reactive power (in kilowatt) Voltage: minute-averaged voltage (in volt) Global_intensity: household global minute-averaged current intensity (in ampere) Sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). Sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. Sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. Review criterialess Criteria Was a valid GitHub URL containing a git repository submitted? Does the GitHub repository contain at least one commit beyond the original fork? Please examine the plot files in the GitHub repository. Do the plot files appear to be of the correct graphics file format? Does each plot appear correct? Does each set of R code appear to create the reference plot? Reviewing the Assignments Keep in mind this course is about exploratory graphs, understanding the data, and developing strategies. Here's a good quote from a swirl lesson about exploratory graphs: "They help us find patterns in data and understand its properties. They suggest modeling strategies and help to debug analyses. We DON'T use exploratory graphs to communicate results." The rubrics should always be interpreted in that context. As you do your evaluation, please keep an open mind and focus on the positive. The goal is not to deduct points over small deviations from the requirements or for legitimate differences in implementation styles, etc. Look for ways to give points when it's clear that the submitter has given a good faith effort to do the project, and when it's likely that they've succeeded. Most importantly, it's okay if a person did something differently from the way that you did it. The point is not to see if someone managed to match your way of doing things, but to see if someone objectively accomplished the task at hand. To that end, keep the following things in mind: DO Review the source code. Keep an open mind and focus on the positive.≤/li> When in doubt, err on the side of giving too many points, rather than giving too few. Ask yourself if a plot might answer a question for the person who created it. Remember that not everyone has the same statistical background and knowledge. DON'T: Deduct just because you disagree with someone's statistical methods. Deduct just because you disagree with someone's plotting methods. Deduct based on aesthetics. Loading the dataless When loading the dataset into R, please consider the following: The dataset has 2,075,259 rows and 9 columns. First calculate a rough estimate of how much memory the dataset will require in memory before reading into R. Make sure your computer has enough memory (most modern computers should be fine). We will only be using data from the dates 2007-02-01 and 2007-02-02. One alternative is to read the data from just those dates rather than reading in the entire dataset and subsetting to those dates. You may find it useful to convert the Date and Time variables to Date/Time classes in R using the strptime() and as.Date() functions. Note that in this dataset missing values are coded as ?. Making Plotsless Our overall goal here is simply to examine how household energy usage varies over a 2-day period in February, 2007. Your task is to reconstruct the following plots below, all of which were constructed using the base plotting system. First you will need to fork and clone the following GitHub repository: https://github.com/rdpeng/ExData_Plotting1 For each plot you should Construct the plot and save it to a PNG file with a width of 480 pixels and a height of 480 pixels. Name each of the plot files as plot1.png, plot2.png, etc. Create a separate R code file (plot1.R, plot2.R, etc.) that constructs the corresponding plot, i.e. code in plot1.R constructs the plot1.png plot. Your code file should include code for reading the data so that the plot can be fully reproduced. You must also include the code that creates the PNG file. Add the PNG file and R code file to the top-level folder of your git repository (no need for separate sub-folders) When you are finished with the assignment, push your git repository to GitHub so that the GitHub version of your repository is up to date. There should be four PNG files and four R code files, a total of eight files in the top-level folder of the repo.