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Did you know you can use Google Data Studio Community Visualizations to customize your report for clearer data?

Depicting figures is the norm in workplaces to understand the dynamics of progress in the company. But sometimes we need to represent a set of data in our own defined set.

Here we’ll look at some of the featured charts of the Community Visualizations Tool of Google Data Studio (now called Looker Studio):

Let’s get started!

What are Community Visualizations?

We learned about all the different standard chart types in Google Data Studio, but what if the type of visualizations that we are after is not possible to achieve with any of those default chart types? 

What if we want something completely different? The good news is within Data Studio, we have community visualizations and components.

The feature is right now in the developer preview mode as we speak.

Here’s the page Data Studio Community Visualizations and you can learn about everything that you need to put together to create a currency visualization. 

As you can see, it says that you will have the flexibility to use any visualization library, any kind of JavaScript library, and CSS to create your visualizations.

Then you can define how to style your element, and how to tell the story with your data with that JavaScript library, CSS, and some coding knowledge. 

You don’t have to be a developer or a technical expert to create your own visualizations. It’s possible for anyone that creates a custom visualization to share it with others. 

People and companies are creating community visualizations, and they’re sharing them with us if Google reviews them. Then, they are shared in this little menu in Google Data Studio. 

Instead of adding a chart menu and using one of the standard charts, you can open a little menu and access community visualizations that are approved by Google.

They work quite similarly to the rest of the standard Data Studio charts. 

Gauge Chart

Let’s look at the Gauge chart by Data Studio and see how it works.

For example, we have a Metric and we can Sort it. We can apply the Default date range. 

On the STYLE tab, we can apply a minimal gauge value at zero. 

We can set the maximum. Since we have 43,474 new users, let’s put the maximum at 50,000 because of the major tick label and the number of minor tick sections, etc.

We need to define three different sections – green section, yellow section, and red section. 

If the business owner is happy with 40 to 50,000 users in the time slot, it is green. They may start to worry if it’s somewhere between 25,000, and 40,000 users. 

They have fewer users than they expected. Anything between zero and 25,000 users is bad and that someone’s gonna get red. 

Basically, that’s it. So it has the same data Properties tab and it has the same install tab, just like any other standard chart in Data Studio.

Sunburst Chart

The Sunburst chart shows one metric over multiple dimensions. It uses this kind of ring to show the distribution of that metric across different categories within that dimension.

Here we have revenue by Product Category as a product. 

The main ring in the middle, which is a darker shade, represents the product category. We can see the nest, apparel, and office drinkware.

It’s similar to the Treemap visualization that we saw in the previous lesson. So the underlying data set is basically the same revenue, product, category, and product. 

However, here we have a different kind of visualization. If you hover over any of these, we can see that the product category, for example, had a 71% share of revenue across all categories.

For example, we can move the mouse cursor to the outer ring to see the actual products within that category to actual NEST products within the NEST category, and how much was the share of revenue across the single category. 

If required, we can have another ring and another dimension, as well. 

Let’s take a look at another example. Here we have one metric session and three dimensions – the landing page, second page, and exit page. 

We are visualizing a kind of a story of how people enter the website, which pages they land on, and what are the second pages that they see. 

Finally, we see what is the last page that they see, and what is the page that they exit from.

The ring in the middle represents the landing page. We can see that the homepage was responsible for 57% of the sessions. 

Of those people, 75,000 of them reloaded the homepage, which corresponds to 26% of people who landed on the homepage and 15% of all the stuff sections put on the website. 

If we move another level out, we can see that the homepage could have been the exit session for our website as well. 

There could have been other pages, so from homepage to homepage and then exit on the basket. 

Remember that there can be different pages between the second page and the exit page, it doesn’t mean that they directly went from the second page to the exit page. So that was Sunburst. 

Animated Bar Chart

Next, we have an Animated Bar chart. 

We have the top 10 cities by revenue from 2023 to date, and this chart type uses animation to show how the top 10 cities by revenue change from month to month during this time period. 

Here, we’ve chosen to highlight New York, San Francisco, and Mountain View. 

But depending on the story you want to tell, you can decide which value from your categories you want to highlight. 

Maybe you want to see the change in product revenue, or product categories revenue over time, you can highlight that and see how it changes over time.

Heat Map

A heat map is quite straightforward and can apply to dimension and one metric. In this example, we have users by day of the week and hour of the day. 

This type of visualization uses different shades of color to show the highest and lowest values for that metric, in this case, users for the combinations for the different combinations of those two categories. 

For example, Thursday at 10 am, we had this number of users, but on Wednesday, and Tuesdays we’re not so busy.

If the owner of this business wants to allocate resources to their customers, they know that Mondays, Thursdays, Fridays, and Saturdays are quite busy. Perhaps some of the employees could take a day off on Tuesdays and Wednesdays.

Funnel Visualization

Moving on to the next area, here is the funnel visualization, which I’m sure would be exciting for most people.

For a long time, when people wanted to create a funnel visualization in Google Data Studio, they had to use a static image as a background, and then just overlay some scorecards over a static image. 

But now with the introduction of this funnel diagram, it’s easier than ever to create a funnel visualization Data Studio. 

All you need to do is just pick your metrics, and then place them in order. The chart will take care of the rest. Here we see how users go through the purchase funnel on this website. 

How many sessions do we have? How many people? What percentage of them view the collection page?

How many product detail views do we have? How many products were added to the basket? How many checkouts? Finally, how many transactions?

Immediately, we can see the bottleneck from the collection page to the product detail. Maybe they have ugly product images. 

If this is the issue, then they would have to look at their website and see what can be done to increase the percentage of product detail views. 

Range Filter

Let’s move on to the next one – the range filter. This one is not a visualization, but it’s a community component. 

It’s a filter menu that allows us to select a range between a minimum and a maximum number, or during the date range to filter the rest of the charts and components on the page. 

Here we have 3rd of January to 20th of May, 

We can use this range filter to limit the amount of data that is represented on the rest of the charts on this page. 

Did you notice that as we adjusted the range, the trendline changed and the scorecard above was also refreshed to reflect the new numbers?

Sankey Chart

Let’s take a look at the final example – the Sankey chart. The Sankey chart is a good means to show the relationship between categories of two or more dimensions. Here, we have gender, device category, and age range.

We can hover the mouse pointer, and each of these sections highlights the relationship between each pair of categories, male users on mobile, and female users on desktop.

🚨 Note: Make sure to build mobile-friendly dashboards to not miss out on traffic from mobile users.

For example, desktop users that are 18 to 24 years old could be a category. 

Now you know that within Data Studio, it is possible to create custom visualizations and components and share them with others. 

You know where to find them and how to use them, and we also reviewed some of the most interesting ones together.

FAQ How can I access Community Visualizations in Google Data Studio?

To access Community Visualizations, you need to sign up for a new account on the Data Studio Community Visualizations page. Once you have an account, you can use approved community visualizations by opening the menu in Google Data Studio and selecting the desired visualization.

What is a Gauge Chart?

A Gauge Chart is a type of community visualization in Google Data Studio that allows you to display a metric and define different sections to represent different ranges or thresholds. It is useful for visualizing data in relation to predefined thresholds or targets.

How does the Range Filter component work?

The Range Filter is a community component in Google Data Studio that allows you to select a range between a minimum and maximum value or set a date range to filter the data displayed in other charts and components on the page. It helps in analyzing data within specific ranges or time periods.

Summary

This is how we can use the Community Visualizations on the Google Data Studio.

These charts provide the freedom to get creative with the report, customized to serve you. The pros of Data Studio Community Visualizations are many.

The key merit of Community Visualization is the freedom to use any tool to represent data in a model that deems fit.

With easy legends and interactive elements, data analysis is a piece of cake!  You can make it more fun by adding interactive elements and filters!

When it comes to interactive elements, Google Data Studio interactive dashboards can bring your reports to the next level.

You're reading Google Data Studio Community Visualizations Best Reports

How To Create Jaw Dropping Data Visualizations On The Web With D3.Js?

Introduction

Data Visualization is the way a data scientist expresses himself / herself. Creating a meaningful visualization requires you to think about the story, the aesthetics of the visualization and various other aspects.

In this article, we would learn some of D3’s powers and use them to create magic of our own! By end of the article, you will be able to create awesome stories for yourself!

This article is a continuation of my previous article, Beginner’s guide to build data visualizations on the web with chúng tôi . Which I would recommend you to read before going further for a better understanding 🙂

Table of Contents

Refreshing previous concepts of D3.js

Building basic charts and code reusability

Line Chart

Area Chart

Chart as a modular structure

Visualizing Game of Thrones Social Network  – Force Directed Graph in action!

Case Studies – Some interesting charts built using D3.js

Introduction to chúng tôi – D3 made easy!

1. Refreshing previous concepts of D3.js

Let’s quickly refresh some of the important concepts we have learned in the previous article!

Selections

d3.select(“body”);

Adding Elements

var svg = d3.select(“body”).append(“svg”);

Setting attributes

svg.attr({“width”:500, “height”:500});

Appending data to DOM

In D3, if you want to add some data to DOM elements, you would create what is called a “Enter, Update, Exit” cycle. For example, You want to create a set of rectangles each having a height as given in an array you’d do :

//data values

var data_values = [10,20,30,40,50,5,2,12,70,26];

//create rectangles

var bars = svg.selectAll("rect")

 .data(data_values)

 .enter()

 .append("rect")

 .attr("width","25px")

 .attr("height", function(d){ return d; });

Now, having refreshed some of the important concepts, let’s learn some new topics.

These are some of the most useful features that come out of the box with D3. We will cover them one by one:

2.1 Scales

As data enthusiasts, you had probably come across cases where you have data in different scales. For any meaningful inference, it had to be “scaled” to a common range. For example, when you normalize for values between 0 and 1. Here if you want to rescale a data set, you’d need to create a linear scale with fixed domain and range values. Then call the scale with the required value:

// Create a linear scale

var normalize = d3.scale.linear().domain([0,50]).range([0,1]);

Here domain(..) is used to set the max and min values of the input data and range(..) is used to set the max and min values of the output needed. A domain of [0,50] and a range of [0,1] means we have created a linear scale that’d map any value in [0,50] to a value in [0,1]. Let’s check how well our scale works :

It looks the scale is working quite well! D3 supports plenty of scales to cover almost all of your data needs. Read more here.

2.2 Axes

//create an svg element

var svgElement = d3.select("body")

.append("svg")

.attr({"width" : 500, "height" : 500});

//create a linear scale to map data to pixels, domain is [0,50] and range is [10,400]

var xScale = d3.scale.linear().domain([0,50]).range([10,400]);

//create a axis based on the scale

var xAxis = d3.svg.axis().scale(xScale)

.ticks(5)

//limit number of ticks to 5

.orient("bottom");

//horizontal axis with labels to  the bottom

//create a new group to hold the axis

var x = svgElement.append("g")

.call(xAxis);

Let’s beautify the axis a little by adding the following code to the CSS :

path{

stroke: steelblue;

fill: none;

stroke-width: 2;

}

That looks quite better! I personally feel CSS is like “parameter tuning” of visualization with D3.

What happened here?

var xAxis = d3.svg.axis().scale(xScale)

.ticks(5)

//limit number of ticks to 5

.orient("bottom");

//horizontal axis with labels to the bottom

We created an axis using d3.svg.axis() method. The scale() method then sets the scale to our newly created scale. ticks()  sets the number of ticks our axis will have, the default is 10.  The above axis without ticks(5) would look like :

var x = svgElement.append("g")

.call(xAxis);

2.3 Loading data from external sources

As data scientists , we deal with a variety of data formats like JSON, CSV, TSV, XML,HTML etc. D3 supports all of these formats and much more by default. For example, loading data from a tsv is as simple as calling a function :

if(error)

// If error, print the error message in the console

console.log(error);

else

// Print the data as a table in the console

console.table(data);

});

Things to note:

console.table(data)

– Nicely prints the data as a table.

Now that we have learned how to create the building blocks of a visualization, let’s create some charts using D3.

3. Building basic charts and code reusability

All of the concepts that we have learned till now are used as modules for creating a visualization in D3. We will create two charts of our own, a line chart and an area chart.

3.1 Line Chart

Line charts are one of the most widely used chart types when it comes to showing time series based data to depict trends over time. Let’s create the above line chart!

About Dataset

Data preprocessing – Handling date and type conversions

Notice that both the date and price is in string format. Before we can use them to make plots, we need to convert them into usable formats. Make the following changes to your earlier code of loading external data:

//Create a date parser

var ParseDate = d3.time.format("%d-%b-%y").parse;

d3.tsv("data.tsv", function(error, data){

//Parse Data into useable format

data.forEach(function(d){

d.date = ParseDate(d.date);

d.close = +d.close;

//the + sign converts numeric string to number

});

//Print the data as a table in the console

console.table(data);

});

Let’s view our data once again :

Looks like we have successfully formatted our data. Let’s take a closer look at the code:

We first create a date parser object using D3’s

time.format.parse()

function. Notice that we have given the format of the date as “%d-%b-%y”. You can read more about various

formats here

. This part tells our D3 parser how to correctly extract date info from the given string and D3 does all the work of converting the dates for you 🙂

We then iterate through every data value using D3’s

forEach()

function. This function executes any code inside it for every element of the given array.

To format date we use our previously defined

ParseDate()

function.

Notice the ‘+’ sign in the next line, the ‘+’ sign converts numerical strings to the numeric type.

Now that our data is properly preprocessed, we are ready to convert the data into visuals.

Plotting line

When we plot a line in an SVG, we give coordinates of the path to follow but that’s a tedious process. Enter D3, here we just need to provide data values and the scale they should follow and D3 does all the heavy lifting of calculating coordinates for us! The following code will create a reusable line generator for our data :

//Create a line generator

var valueline = d3.svg.line()

.x(function(d){

return xScale(d.date);

})

.y(function(d){

return yScale(d.close);

});

Bringing it all together

Now that we have all the pieces together, let’s build the final chart! The following code should make the above chart:

body{ font: Arial 18px; text-align: center; }

path{

stroke: steelblue;

fill: none;

stroke-width: 2;

}

.axis path, .axis line{

fill: none;

stroke: grey;

stroke-width: 1;

shape-rendering: crispEdges;

}

//Set margins and sizes

var margin = {

top: 20,

bottom: 50,

right: 30,

left: 50

};

var width = 700 – chúng tôi – margin.right;

var height = 500 – chúng tôi – margin.bottom;

//Create date parser

var ParseDate = d3.time.format(“%d-%b-%y”).parse;

//Create x and y scale to scale inputs

var xScale = d3.time.scale().range([0, width]);

var yScale = d3.scale.linear().range([height, 0]);

var xAxis = d3.svg.axis().scale(xScale)

.orient(“bottom”)

.ticks(5);

var yAxis = d3.svg.axis().scale(yScale)

.orient(“left”)

.ticks(5);

//Create a line generator

var valueline = d3.svg.line()

.x(function(d){

return xScale(d.date);

})

.y(function(d){

return yScale(d.close);

});

//Create an SVG element and append it to the DOM

var svgElement = d3.select("body").append("svg")

.attr({"width": width+margin.left+margin.right, "height": height+margin.top+margin.bottom})

.append("g")

.attr("transform","translate("+margin.left+","+margin.top+")");

//Read TSV file

//Parse Data into useable format

data.forEach(function(d){

d.date = ParseDate(d.date);

d.close = +d.close;

//the + sign converts string automagically to number

});

//Set the domains of our scales

xScale.domain(d3.extent(data, function(d){ return d.date; }));

yScale.domain([0, d3.max(data, function(d){ return d.close; })]);

//append the svg path

var path = svgElement.append("path")

.attr("d", valueline(data));

var x = svgElement.append("g")

.attr("transform", "translate(0,"+height+")")

.call(xAxis);

//Add Y Axis

var y = svgElement.append("g")

.call(yAxis);

y.append("text")

.attr("fill", "#000")

.attr("transform", "rotate(-90)")

.attr("y", 6)

.attr("dy", "0.71em")

.attr("text-anchor", "end")

.text("Price ($)");

});

3.2 Area Chart

One of the magical things about D3 is if you design your code smartly, a lot of it can be reused in other visualizations. An area chart can be thought as a modified version of the line chart, here we shade the region of the chart that comes under the line.

The interesting thing here is, you can create the above chart by changing 4-5 lines in the previous chart’s code! The final code for area chart would be :

body{ font: Arial 12px; text-align: center;}

.axis path, .axis line{

fill: none;

stroke: black;

stroke-width: 1;

shape-rendering: crispEdges;

}

//Set margins and sizes

var margin = {

top: 20,

bottom: 50,

right: 30,

left: 50

};

var width = 960 - chúng tôi - margin.right;

var height = 500 - chúng tôi - margin.bottom;

//Create date parser

var ParseDate = d3.time.format("%d-%b-%y").parse;

//Create x and y scale to scale inputs

var xScale = d3.time.scale().range([0, width]);

var yScale = d3.scale.linear().range([height, 0]);

var xAxis = d3.svg.axis().scale(xScale)

.orient("bottom")

.ticks(5);

var yAxis = d3.svg.axis().scale(yScale)

.orient("left");

var area = d3.svg.area() .x(function(d){ return xScale(d.date); }) .y1(function(d){ return yScale(d.close); });

//Create an SVG element and append it to the DOM

var svgElement = d3.select("body")

.append("svg").attr({"width": width+margin.left+margin.right, "height": height+margin.top+margin.bottom})

.append("g")

.attr("transform","translate("+margin.left+","+margin.top+")");

//Read TSV file

//Parse Data into useable format

data.forEach(function(d){

d.date = ParseDate(d.date);

d.close = +d.close;

//the + sign converts string automagically to number

});

//Set the domains of our scales

xScale.domain(d3.extent(data, function(d){ return d.date; }));

yScale.domain([0, d3.max(data, function(d){ return d.close; })]);

area.y0(yScale(0));

//append the svg path

var path = svgElement.append("path")

.attr("d", area(data))

.attr("fill", "steelblue");

//Add X Axis

var x = svgElement.append("g")

.attr("transform", "translate(0,"+height+")")

.attr("class", "x axis")

.call(xAxis);

//Add Y Axis

var y = svgElement.append("g")

.call(yAxis)

.attr("class", "y axis");

y.append("text")

.attr("fill", "#000")

.attr("transform", "rotate(-90)")

.attr("y", 6)

.attr("dy", "0.71em")

.attr("text-anchor", "end")

.text("Price ($)");

});

Notice that we have only made the following changes :

Replaced line generation code with area generation code, because we need D3 to calculate area and coordinates for it based on our data set.

Now that we have successfully built some visualizations let’s look at some case studies with D3.

3.3 Chart as a modular structure

You have already built a bunch of charts using D3, did you notice any pattern or structure in them? After working with D3 for a while, I have noticed a general structure that I follow in my code to create charts. The following structure is how I logically layout my code :

Basic HTML and CSS

 

Setting the stage

Setting the margins, sizes, and variables that would be needed later on

Building scales, axes, and color scales

Creating data preprocessing structures like data parser, percentage formatter

Visualization specific setup

– Sometimes we need to write chart specific code, Eg: line generator in line chart and area generator in area chart

Create the SVG

– The most important step, the base SVG element, and all its properties should be prepared in this step

Loading external data

Code for loading data based on format, source

Applying pre-processing, cleaning if required on the data using the structures set up in step 1.c

Verify loaded data and its format once before going to the next step

Bringing it all together

– Now that everything is set up and ready to be attached to the chart. One by one all the axes, bars, paths etc. should be drawn on the SVG in this step

Adding Extras

– After all who doesn’t like extras? Animations, Labels, etc. are to be put in at this step

Note that sometimes you won’t be able to follow the order because of other factors and that is alright. This is not a rigid rule but just a way to divide and conquer the process of building a chart in D3 so that it can be easily understood and reused whenever possible. Confused much? Let’s understand it with a practice problem!

3.4 Case Study

This section is very important because here, it’s your turn to build! You will be building the following beautiful bar chart. After the basic chart is built a huge task awaits you, give your best. This chart is built on a data set of character frequency. The dataset can be found here. Let’s follow the general structure that we just learned and try to build this chart!

Step 1. Basic HTML and CSS

   .axis path,

   .axis line {

     fill: none;

     stroke: #000;

     shape-rendering: crispEdges;

   }

   .x.axis path{

     display: none;

    }    body{

font: 10px arial;

     text-align: center;

   }

Step 2. Setting the stage

   //Set up margin and percentage formatter

   var margin = {top:20, right: 30, bottom: 30, left:40};

   var width = 800-margin.left-margin.right;

   var height = 400-margin.top-margin.bottom;

  

   //Creating a percentage formatter

   var formatPercent = d3.format("%.0");

   

   

 //Create x and y scale

   var yScale = d3.scale.linear().range([height,0]);

   var xScale = d3.scale.ordinal().rangeRoundBands([0,width],0.1,0.2);

   //Create category 10 scale for color

   var c10 = d3.scale.category10();

In the above code, we started out by setting up basic margins and width, height values. Then we created a percentage formatter using D3’s format(..) so that we can convert our y axis labels to %. Then we defined scales for x and y axis and defined the range for the same. Lastly, since we would be needing color for our bars, we will use one of D3’s color bands, category10. The category10 contains the following 10 colors :

D3 has more color scales. Read more here.

Step 3. Visualization specific code

For the above simple bar chart, we don’t need any visualization specific code.

Step 4. Create the SVG

Now that we have set the stage, it is time to create the SVG along with the above settings:

   //Create SVG Element

   var svg = d3.select("body")

         .append("svg")  .attr({"width":width+margin.left+margin.right,"height":height+margin.top+margin.bottom})

         .append("g")

         .attr("transform","translate("+margin.left+","+margin.right+")");

Step 5. Loading external data

Since we have a URL to load data from and it is in tsv format, we can just make a single function call to load data:

Remember some preprocessing of data? Now that we get our data, we need to set the domain of our x and y scales which we couldn’t set earlier because we didn’t have the data:

     yScale.domain([0,d3.max(data, function(d){ return d.frequency; })]);

     xScale.domain(data.map(function(d){ return d.letter; }));

Step 6. Bringing it all together

We have our data nicely formatted and ready, we also have our axes, colors ready. Let’s bring it all together and attach them to our chart. Notice the new code added in external data loading function :

     //Load data from external tsv file

     //Set domain of x and y scales based on loaded data

     yScale.domain([0,d3.max(data, function(d){ return d.frequency; })]);

     xScale.domain(data.map(function(d){ return d.letter; }));

     //Create X and Y Axis based on scales and data

     var xAxis = d3.svg.axis()

               .scale(xScale)

               .orient("bottom");

     var yAxis = d3.svg.axis()

               .scale(yScale)

               .orient("left")

               .tickFormat(formatPercent);

     //Add bars

     var bars = svg.selectAll("rect")

                   .data(data)

                   .enter()

                   .append("rect")

                   .attr("class","bar")

                   .attr("width", xScale.rangeBand())

                   .attr("fill","steelblue")

                   .attr("fill", function(d,i){

                     return c10(Math.random()*10*i);

                   })

                   .attr("y", function(d){

                     return yScale(d.frequency);

                   })

                   .attr("x", function(d){

                     return xScale(d.letter);

                   })              

                  .attr("height", function(d){

                     return height-yScale(d.frequency);

                   });

     //Add X Axis

     svg.append("g")

     .attr("transform","translate(0,"+height+")")

     .call(xAxis)

    .attr("class","x axis");

     //Add Y Axis

     svg.append("g")

       .attr("class", "y axis")

       .call(yAxis);

   });

Now that you have the basic chart drawn, it is time for you to jump into action and do something creative!

Task – Add Step 7. Extras to the bar chart

This is the step where you come in. After all, who doesn’t like something extra? Visualization is no different! Since we already have a basic bar chart, I challenge you to go ahead and add animation/interactivity to this chart. The following is a list of common effects that you can easily add to your chart using D3:

Tooltips Interactivity Animation

The above is just a minor set of options. You’ll find enough resources in the endnotes to refer. You can post about your approach/discuss your doubts with the community here :

Go ahead, surprise me with your creativity!

4. Visualizing the Game of Thrones Social Network: Force-Directed Graph in action!

Now that you have pretty much learnt all the basics of D3 and made plenty of charts on your own, it is time to move to the next level and do something that is niche to D3, you’ll be building one of the case study – Force-Directed Graph to visualize a very popular TV Series, Game of Thrones.

A little context about Game of Thrones

The TV sensation Game of Thrones is based on George R. R. Martin’s epic fantasy novel series, “A Song of Ice and Fire.” The series is famous for its sweeping plotlines, its cast of hundreds of characters, and its complex web of character dynamics. In addition, “Game of Thrones” is an ensemble piece, featuring many prominent characters with intertwined relationships.

Here, interaction among characters and the strength of their relationship is important. Also, there are some characters who are much more influential than others and steer the course of the story.

About the dataset

The dataset for this visualization is based on Andrew Beveridge’s data set of “A Storm of Swords”, the third book in the series. In Andrew’s words,

“We represented each character in the book as a vertex. We then added a link between two characters whenever their names appeared within 15 words of one another. So a link between characters means that they interacted, spoke of one another, or that another character spoke of them together. Characters that interacted frequently are connected multiple times.”

Using the above data set, I calculated the influence of character based on the “the number of times her/his interaction has appeared in the book”. For example, if Sansa has 6 records where she is the source and 4 records where she is the target of an interaction, her influence will be 6+4=10.

I then formatted the data so that D3 can easily use it. The result was a simple JSON file here.

Data format for force-directed graph

The force directed graph can be divided into two major components – nodes and links. The nodes represent entities whose relationship is to be plotted and similarly links are those relationships. These are analogous to vertex and edge in a graph.

D3 expects two arrays for force layout. The first one should be an array of nodes and the second one an array of links. The links refer to the index of the node and should have two attributes “source” and “target”. This is exactly how I have laid out our JSON.

Now that we are versed in the basics, let’s build our Social Network Graph by following the earlier steps :

Step 1. Basic HTML and CSS

body{ font: Arial 12px; text-align: center;}

.link {

 stroke: #ccc;

}

.node text {

pointer-events: none;

 font: sans-serif;

}

Step 2. Setting the stage

//Set margins and sizes

var margin = {

top: 20,

bottom: 50,

right: 30,

left: 50

};

var width = 960 - chúng tôi - margin.right;

var height = 700 - chúng tôi - margin.bottom;

//Load Color Scale

var c10 = d3.scale.category10();

Step 3. No visualization specific code

Step 4. Create the SVG

//Create an SVG element and append it to the DOM

var svgElement = d3.select("body")

.append("svg").attr({"width": width+margin.left+margin.right, "height": height+margin.top+margin.bottom})

.append("g")

.attr("transform","translate("+margin.left+","+margin.top+")");

Step 5. Load external data

//Load External Data

//Extract data from dataset

var nodes = dataset.nodes,

links = dataset.links;

Step 6. Bringing it all together – force layout, nodes, links

//Create Force Layout

var force = d3.layout.force()

.size([width, height])

.nodes(nodes)

.links(links)

.gravity(0.05)

.charge(-200)

.linkDistance(200);

//Add links to SVG

var link = svgElement.selectAll(".link")

.data(links)

.enter()

.append("line")

.attr("stroke-width", function(d){ return d.weight/10; })

.attr("class", "link");

//Add nodes to SVG

var node = svgElement.selectAll(".node")

.data(nodes)

.enter()

.append("g")

.attr("class", "node")

.call(force.drag);

Step 7. Adding extras – Labels, Starting the simulation

//Add labels to each node

var label = node.append("text")

.attr("dx", 12)

.attr("dy", "0.35em")

.text(function(d){ return d.character; });

//Add circles to each node

var circle = node.append("circle")

.attr("fill", function(d){ return c10(d.zone*10); });

//This function will be executed once force layout is done with its calculations

force.on("tick", function(){

//Set X and Y of node

node.attr("r", function(d){ return d.influence; })

.attr("cx", function(d){ return d.x; })

.attr("cy", function(d){ return d.y; });

//Set X, Y of link

link.attr("x1", function(d){ return d.source.x; })

link.attr("y1", function(d){ return d.source.y; })

link.attr("x2", function(d){ return d.target.x; })

link.attr("y2", function(d){ return d.target.y; });

//Shift node a little

node.attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });

});

//Start the force layout calculation

force.start();

});

What’s happening here?

First, we perform steps 1 to 4 of building a D3 chart as discussed earlier. Then we create a force layout here  :

//Create Force Layout

var force = d3.layout.force()

.size([width, height])

.nodes(nodes)

.links(links)

.gravity(0.05)

.charge(-200)

.linkDistance(200);

d3.layout.force() –

initializes a new force layout

.size() –

set the size of the layout

.nodes(nodes) –

pass in the nodes array

.links(links) –

pass the links array

.gravity() –

sets a pseudo-gravity force that attracts our nodes to the center of the area

.charge() –

sets a charge force between nodes. If positive will be an attractive force, repulsive if negative values.

.linkDistance() –

set the minimum distance in pixels that each node should have. In other words, the minimum length of a link.

When the above code is executed, it asks D3 to make the necessary calculations for the position of each node, the distance between them, calculating coordinates of links joining them etc. All of this happens in the background and D3 takes care of it all. While the above calculations are being done, we need to keep updating the position of the nodes and links. We attach a listener to our force layout :

//This function will be executed for every tick of force layout

force.on("tick", function(){

//Set X and Y of node

node.attr("r", function(d){ return d.influence; })

.attr("cx", function(d){ return d.x; })

.attr("cy", function(d){ return d.y; });

//Set X, Y of link

link.attr("x1", function(d){ return d.source.x; })

link.attr("y1", function(d){ return d.source.y; })

link.attr("x2", function(d){ return d.target.x; })

link.attr("y2", function(d){ return d.target.y; });

//Shift node a little

node.attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });

});

The above code basically tells D3 for every “tick” (single step of the simulation), redraw(updated) all the nodes and their links. Now when everything is done, we start the simulation :

//Start the force layout simulation

force.start();

If you want to dig deeper into force layouts, you can read its documentation here. With this, you have your very own GoT Social Graph!

Live Demo

Few things to note about the visualization:

The nodes represent the characters and the links their interactions.

The size of node and name represents the influence of character. Tyrion, way to go!

The thickness of link denotes the weight of the relationship between the characters. See the thick link between Jaime & Brienne, Jon & Sam, Bran & Hodor, Daenerys & Sir Jorah? 😀 and how can you forget Jon and Ygritte! 😛

The node is color-coded based on an influential character’s current geolocation and supporting characters around it. There you go Jon, Daenerys, Tyrion, Robb, Bran, Arya, Stannis 🙂

5. Case Studies – Different Charts using D3.js

D3 has been used on a versatile set of visualization problems. Let’s look into some of the interesting ones.

Bubble Chart – Visualizing 3 Dimensional data

Source Link

What would you do if you want to show 3-dimensional data in 2 dimensions? You’d use a bubble chart! The area of the circle and the x and y coordinates are used to encode all the 3 dimensions.

Concept Map – Relationship between concepts

Source Link

A concept map typically represents ideas and information as boxes or circles, which it connects with labeled arrows in a downward-branching hierarchical structure. The relationship between concepts can be articulated in linking phrases such as causes, requires, or contributes to. In this example, D3 is used to show an improvised concept map to better convey the story. Notice how the branches get highlighted when you select a topic. This is a good example of interactivity D3 enables in your visualizations.

Map Chart – Visualizing Demographical Data

Source Link

D3 provides amazing inbuilt support to create interactive, map-based visualizations that can be used to show demographically distributed data.

Sankey Diagrams

Source Link 

Sankey diagrams visualize the magnitude of flow between nodes in a network. This intricate diagram shows a possible scenario for UK energy production and consumption in 2050: energy supplies are on the left, and demands are on the right. Intermediate nodes group related forms of production and show how energy is converted and transmitted before it is consumed (or lost!). The thickness of each link encodes the amount of flow from source to target.

Parallel Coordinates

Source Link 

Parallel coordinates is a visualization technique used to plot individual data elements across many dimensions. Each of the dimensions corresponds to a vertical axis and each data element is displayed as a series of connected points along the dimensions/axes. In the above example, the very famous iris dataset is plotted.

6. A Brief Introduction to chúng tôi – D3 made easy!

D3 code can be quite verbose. That begs the question, is there a simpler way of creating basic charts using D3 but with fewer lines of code? That’s exactly what chúng tôi lets you do.

It is a library that is directly built on top of D3 and provides an easy interface to build visualizations. It can be loosely compared to the relationship between keras and tensorflow in python. Let’s build a basic visualization using dimple :

   //Create the SVG

   var svg = dimple.newSvg("#chartContainer", 590, 400);

   //Load external data

     //Create a chart object using svg and data

     var myChart = new dimple.chart(svg, data);

     //Set the size of the plot within SVG

     myChart.setBounds(60, 30, 510, 330

     //Add an axis of categorical values

     myChart.addCategoryAxis("x", ["Price Tier", "Channel"]);

     //Add an axis of continuous values

     myChart.addMeasureAxis("y", "Unit Sales");

     //Make a barchart

     myChart.addSeries("Channel", dimple.plot.bar);

     //Add legends

     myChart.addLegend(65, 10, 510, 20, "right");

     //Draw the chart

     myChart.draw();

   });

What happened here?

dimple.newSvg()

– create a SVG element

new dimple.chart(svg, data)

– create a new dimple chart

.setBounds()

– set boundaries of the chart within the svg

.addCategoryAxis()

– add an axis that will have categorical values

.addMeasureAxis()

– add an axis that will have continuous values

.addSeries(, dimple.plot.bar)

– make a barplot

.addLegends()

– add legends to the chart

Note: that with only a few lines of code, you were able to create a beautiful visualization. Also, dimple took care of basic interaction and animation. If you want to explore dimple more, check out these other examples created using dimple.js.

End Notes

In this article, we refreshed some of D3’s basics and further learned a lot of techniques and new functionalities. We also learned how to preprocess data and successfully build a line chart and modified it into an area chart.

We used a practice problem to create a colorful bar chart. We also saw some of the most useful and amazing visualizations created using chúng tôi We ended up analyzing Game of Thrones data using one of the case studies – Force-Directed Graph and with this,

I hope, this article has given you the necessary impetus on your D3 journey!

All of the code used in this article is available at GitHub.

Here are some useful links for chúng tôi :

Related

20 Best Visual Studio Code Themes You Should Use

Microsoft’s Visual Studio Code is arguably one of the best code editors out there. With its vast collection of community-supported extensions, you can use VS Code to fuel almost all of your programming needs. The app is available across Windows, macOS, and Linux, making it the perfect program to write code on, regardless of the operating system. One key aspect of VS Code is its customizability and robust theming capabilities. While the default themes are good enough, there are many other visually pleasing themes that you should not miss out on. Here are the 20 best Visual Studio Code themes you should try in 2023.

Best Visual Studio Code Themes to Try in 2023

1. Ariake Dark

2. Rosé Pine

Download Rosé Pine Theme

3. Celestial

Download Celestial Theme

4. In Bed By 7pm

In Bed by 7pm is a theme from the creator of widely used VS Code theme Night Owl (added in the seventh position on this list). As the name suggests, this theme is best suited for those who don’t often stay up late for coding sessions. This theme has excellent contrast levels to help you effortlessly differentiate various elements in the code.

Download In Bed By 7pm Theme

5. ReUI

If you’re searching for a VS Code theme, chances are, you’ve visited React JS’ official website at least once. If you fancy the color palette used in React website’s code snippets, the ReUI theme brings it to VS Code. The result is an aesthetic theme with colors that go easy on the eyes.

Download ReUI Theme

6. Horizon

Download Horizon Theme

7. Night Owl

Night Owl is a theme created with accessibility in mind. It offers a neat contrast for better readability, and the color scheme here is such that it would not feel uncomfortable to people with colorblindness. This theme meets the needs of those who often find themselves working late into the night.

Download Night Owl Theme

8. Andromeda

Andromeda is a theme with vibrant colors and a dark background. The developer has also created a variant of this theme with italic keywords that go well with fonts, including Operator Mono and Catograph Mono. There’s also a bordered variant if you like having a separate distinction between multiple tabs.

Download Andromeda Theme

9. Nord

Nord VS Code theme is based on Arctic Ice Studio’s Nord color palette inspired by the beauty of the arctic. One noteworthy part of Nord is that its color scheme even extends to popular third-party syntax extensions to offer you a unified experience. If you are someone who likes minimal setups, you will probably admire this theme. However, it may look a bit too pale in bright lighting conditions.

10. Tokyo Night

As the name hints, Tokyo Night is a theme that celebrates the beautiful night lights of downtown Tokyo. It intentionally sets many UI elements to low-contrast so as not to distract coders. The theme comes bundled with two additional variants, namely Tokyo Night Light and Tokyo Night Storm. In the theme’s listing, the developer recommends disabling semantic syntax highlighting from settings for best results. There are also configurations for disabling italics, brightening Codelens text, and customizing active and inactive borders.

Download Tokyo Night Theme

11. Mayukai Mirage

Download Mayukai Mirage Theme

12. One Dark Pro

Before VS Code went mainstream, the Atom code editor was the preferred choice of many developers, and there’s no denying that Atom’s One Dark theme looks great. If you miss Atom’s one dark theme, then One Dark Pro is what you need to install on VS Code. The creators of this theme have also included snippets in the listing to help you customize the colors and add italics to the UI.

Download One Dark Pro Theme

13. LaserWave

LaserWave is an 80’s synthwave-inspired theme with a warm retro look and feel. It currently supports Python, React JS, Angular, Ruby, Markdown, Java, C#, and Dart. If you’re looking for a theme that isn’t dark and works well with your lo-fi playlist, you won’t go wrong with LaserWave. It has a unique feel, and you are going to love it.

Download LaserWave Theme

14. Darcula

Darcula (not to be confused with the popular Dracula Official) brings the familiar Darcula theme seen on Jetbrains IDEs and Android Studio to VS Code. However, the creator points out that there are a few modifications in tow to deliver a better experience to programmers. You can use this theme for a unified experience if you use Jetbrains’ IDE offerings.

15. City Lights

Another great VS Code theme is City Lights that brings predominant matte dark blue tones. It supports over eight popular programming languages. While you try this theme, make sure you don’t miss out on the City Lights icon package with over 60 file-type icons. It is also available for other popular code editors such as Sublime Text and Atom.

Download City Lights Theme

16. Quiet Light

Download Quiet Light Theme

17. GitHub Theme

GitHub is an official VS Code theme that brings the GitHub experience to VS Code. If you like the look and feel of the code hosting platform, you will feel right at home on switching to this theme.

Download GitHub Theme

18. Brackets Light Pro

Brackets Light Pro ports Adobe’s Brackets code editor’s light theme to VS Code. If you’re someone migrating to VS Code from Brackets since Adobe has ended support for Brackets from September 1, 2023, you’ll enjoy using the Brackets Light Pro theme. The recommended fonts and icons for this theme are Fantasque Sans Mono and VSCode Great Icons.

Download Brackets Light Pro Theme

19. Aofuji Light

Aofuji Light is a minimal theme for VS Code. This theme is relatively new, and the syntax highlighting has been tested with HTML, JavaScript, CSS, and Markdown files. If you prefer light themes, you can give Aofuji Light a shot to see if it fits perfectly in your workflow.

20. Bluloco Light

Lastly, we have Bluloco Light theme. It is a fork of the One Light theme with a Bluloco color palette that’s visually pleasing. This theme looks good even when you have Apple’s NightShift mode enabled, as per the creator. It means the theme plays along well with a blue light filter.

Download Bluloco Light Theme

VS Code Themes to Rejuvenate Your Coding Experience

Creating And Visualizing Reports Automatically

Introduction to Kibana Reporting

You can generate a report proper format including the Kibana dashboard, data visualization, and other tools which make your report more meaningful about data. We can also save our project based on the image and pdf format which depends upon the requirements of yours like either in PNG, PDF. Kibana reporting we can generate not only automatically but also manually. In this blog, we will do both generations manually and automatically. Apart from this, we can also generate a report with the help of a script and Watcher. Kibana reporting makes our analytics more powerful by seeing all data in a single place.

Kibana Reporting Tool

To find where is Kibana reporting tool available, we have to follow the below steps:

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1.  Open the Kibana and it will take to your first page of the Kibana.

4. Now, we have to look Kibana reporting option in that as shown below in the screenshot.

Features of Kibana Reporting

Below there is a customize feature display option, which provides all details of different kinds of tools charts, visualization, etc. that we can use in reporting.

1. Reporting Permission

To generate a report in Kibana, you must have a granted privilege to create a report which we called resporting_user, and also you have the privilege to access the index data which you want to create report else data will not display.

2. Creating a Manual Report

We can also create a report manually. To create a report manual we have to follow the following steps:

Index your data and access that index in the Kibana main dashboard.

We can choose the option according to our requirements:

If we want to make a report of visualization and charts, so we can choose either Pdf or image option.

we can also convert the whole canvas into pdf.

We can also opt for the option of CSV.

Now we can save that file anywhere in our local system.

3. Size of the Report

The size of the report in which we generate either pdf or image form that depends upon through which we generating. If we are using canvas, then it has to determine the size before creating the report depends upon the requirements. But, if you are using any other app then might be it depends upon the size of that app like if we want to reduce the report size we can just decrease the size of the browser.

4. Automatic Report Creation

We can also create a report using POST through HTTP (with the help of the script). We can do for both PDF or CSV.

Steps to create POST link for PDF:

Go to the visualization of the Kibana and choose the time filter option. For an absolute time we have to use the time filter option.

Now the Kibana toolbar will show the share option as shown (Fig. no. 5) above. We have to choose a PDF option and create a PDF link.

Now copy the link showing in the below screenshot.

Fig. no. 6: PDF URL link

5. Steps to Create POST Link for CSV

We have to load the data from the search option and for the absolute time, we can use the time filter option. The Discover option is available in the left toolbar at the top.

Now the Kibana toolbar will show the share option as shown (Fig. no. 5) above. We have to choose the CSV option and create a CSV link.

Now copy the link showing in the below screenshot.

6. Using a Script

We can also generate an automatic link for PDF and CSV through the script. This script requests a POST link and will get the result in the form of JSON and also contain a link to download the report. To get the report through a link we have to give GET request in the script itself.

curl

Code:

Here kbn-version is telling what version we are using currently. And -u elastic will check grant permission of the user to create a report. XPOST is basically for the POST.

Final report result:

After we have done all activities of reporting then our report will look like this. I just uploaded the saved projects. You can see a lot of saved projects which option is shown in fig. no. 3 left side.

Conclusion

The Kibana reporting option is very awesome to visualize all things in a single place to decision making. It has a lot of features that you explore while going through the above mention methods. Kibana has also another option to create reports automatically using Watcher. For that, we have to link the Watcher certificate to the Kibana, so that it can access Watcher.

Recommended Article

This is a guide to Kibana Reporting. Here we discuss the Introduction to Kibana Reporting and its tools along with different features with example. You can also go through our other suggested articles to learn more –

When Controversy Hits Your Community

On a sunny summer weekend, the reality of a cruel world found us in the foothills of Virginia’s Blue Ridge Mountains. Normally, we’d simply be preparing for a new school year, but this year in Albemarle County, the end of summer became something else entirely when a “Unite the Right” gathering of white nationalists protesting the removal of a statue of Robert E. Lee in Charlottesville, the county seat, clashed with counter-demonstrators .

Three weeks later, our children have filled our learning spaces. They are walking, playing, talking, and studying together in our 25 schools across 726 square miles. Some of those schools sit within blocks of the scenes that made our area a symbol of American dysfunction and discord, while others are so distant and isolated that children may not even feel the connection.

Creating a Resilient Community

The answer to the question, “How do we respond to a tragedy in our community?” lies in the work we have done before tragedy strikes. This will be true for every school. If children are prized, if their needs lie at the center of every decision, if they have true voice, true agency, and true power over their own environment, you will have a learning community that is resilient and able to emotionally support itself.

Our work depends on an understanding and acknowledgement of our community’s past. Charlottesville and Albemarle County have occupied a complex place in national history. From its early days as home to Thomas Jefferson and the University of Virginia to its role in the American Civil War and Reconstruction and later the “massive resistance” movement of the 1950s and 1960s, the area has experienced some of the best and worst of America’s public and hidden histories.

Today, our area is home to both liberal and relatively conservative communities and both highly educated families and ones with minimal formal education. When children arrive in our schools, they bring all the perspectives, values, and understandings of diverse geographic communities—rural, suburban, and urban. We have children who live in poverty and ones who have all the amenities of wealth. Our students speak 91 languages; many of them come from international refugee camps. Ours is a community where opportunity gaps abound, gentrification threatens families, and a legacy of racism is still unresolved.

Albemarle schools had been working to create more resilient students long before tragedy struck on August 12 of this year. We had already developed the Seven Competencies Framework to help address the changing demographics of 21st-century students, for example, and our “All Means All” philosophy was firmly in place, creating the expectation that every child would be treated with full respect by both adults and peers. To support that mission, we had established a strong commitment to trauma-sensitive education that built on our focus on culturally responsive classrooms, and on social and emotional learning in general. Those efforts led directly to this year’s commitment to providing focused team support in our urban ring schools for social, emotional, and academic development. We also already had a plan in place for our high schools to engage in a grant from the National Writing Project to implement project-based learning to deepen students’ understanding of the meaning of various local historical memorials and monuments.

You can never be entirely prepared for events like the deadly protests that occurred in our community—reality will always have the capacity to surprise and shock—but in Albemarle we had done the hard work that laid a foundation of resilience in our students. We are incredibly fortunate that our educators were able to draw upon the trauma-sensitive work we began last year, which has been critical to allowing our opening days this year to be as healthy and happy as can be expected for everyone.

To support our staff, we aggregated resources from some of our previous and newer work on how to talk to students about tragic or violent events. These resources include some that are specific to Charlottesville as well as materials for all grade levels on teaching controversial issues and creating a kinder classroom.

In high schools, the point when students received their one-to-one laptops doubled as a safe time for students to talk with their teachers and to each other. Students at one middle school began by spending four and a half hours in each of the first four days working on issues around humanity, community, citizenship, and responsibility. In our elementary schools, educators capitalized on our existing responsive-classroom model, which has built-in social and emotional conversation time and established routines for safe and open conversations with children.

As we have listened to our young people—from 5 to 18 years of age—gather and talk together in the opening days of school about what they want their school communities to be like, their words represent the best of who we are as humans. One young man in a middle school classroom said it best: “If we really believe in and do the things that show respect for each other, no one gets left out.”

This has been a very difficult time for us, but it has also showed us that our belief in moving education forward is making a real difference.

How To Bypass Reddit Private Community?

People are making Reddit private to join the Reddit blackout, one of the largest user-driven protests against the company’s new policy.

They show their dissatisfaction and solidarity with the protestors by making their subreddits private.

However, what if you are curious about bypassing Reddit private community and discovering what happens in those private subreddits?

Bypassing Reddit private is not simple and ethical. However, Reddit may warn you about your violation, temporary or permanent account suspension or take legal action against you.

In this article, we will explore some genuine and proven tricks to bypass Reddit private community with their legal and ethical implications.

What Is Reddit Private Community?

Many subreddit have gone private recently due to the protest against Reddit’s plan to raise its API pricing. 

Thousands of Reddit forums have shut themselves down in a protest called Reddit blackout.

It involves charging millions of dollars for third-party apps to continue accessing Reddit’s data via its API.

Users and developers are unhappy with this change. As a result, they are making their subreddit private.

A Reddit private community is a subreddit that is not open to the public.

It is usually created for specific purposes such as testing, moderation, invitation-only discussion, etc.

Only the moderators and approved community members can view and post content there.

You can find if a subreddit is private by the lock icon next to its name.

You will also get the message that you must be invited to visit this community when you try to access the private subreddit.

Some private subreddit is only accessible to users who participate in Reddit’s annual gift exchange, such as r/secretSanta.

Note: The Reddit blackout was initially planned to last 48 hours, starting Monday, June 13, 2023. However, it is unclear when the blackout will end or how it will affect users or Reddit’s revenue.

How To Bypass Reddit Private Community?

A private subreddit is accessible only by those communities’ moderators and approved members.

Bypassing a private Reddit community is not an easy and ethical way. 

If you want to join a private subreddit, you will need to message the moderators of that community and request an invitation.

However,  if you are a new or inactive user, they may not respond or accept your request.

Some subreddits may have specific criteria or rules for accepting new members.

Therefore, you must check the About Community and Rules tabs on the subreddit before sending the request.

Alternatively, you can try finding Reddit alternatives that cover similar topics and are open to the public.

Disclaimer: Bypassing the Reddit private community is considered unethical and illegal as it violates the terms of the services of Reddit. You might face legal action from Reddit or the moderators of the private subreddit.

Is It Safe To Bypass Reddit Private Community?

It is unsafe to bypass Reddit private community as it violates the terms of service and content policy.

Bypassing the Reddit private community is considered malicious or fraudulent.

You could risk getting your account suspended, banned, or legal action from Reddit.

Moreover, bypassing Reddit private community would also infringe on the privacy of the other users who have chosen to participate in a restricted community.

The Bottom Line

Hopefully, this article helps you understand everything about Reddit private and its bypassing tips to access the content.

You should always respect the privacy of other users and not try to access or use Reddit private community without their approval.

Reddit is what you make of it, so make it awesome.

Read and explore why your Reddit app might not work with its fixes.

Update the detailed information about Google Data Studio Community Visualizations Best Reports on the Daihoichemgio.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!