A lolipop chart is a type of modified bar graph. These charts can either be visually attractive or just confusing depending on your data. It is very important to recoginze what data you have when it comes to creating charts and this is also true with lolipop charts. If your data has high number values then a bar graph will look too agressive with columns being too high. This is when a lolipop chart should be used. Instead of a traditional bars in a bar graph, a thin bar will replace the columns and the thin line will have a bulbous end. Below is a lolipop chart representing a high amount of calls that a company recieved within 10 days.
A lollipop graph is a visualization that is great for comparing up to three different types of data such as year to year or before, during and after. Typically, a lollipop graph can be used where a bar graph would be used and is often more visually appealing. Lollipop graphs are not ideal when you are using a dataset with stacked-bars or with data that has very similar end results. An example of this would be an experimental dataset with thousands of results ranging from 0.00 to 0.05. It would be extremely difficult for an audience to differentiate what exact values the bars are representing.
How to create a lollipop graph…
When looking for a dataset to create a lollipop graph with it is important that you choose one that is appropriately measured by a scatter plot, as this is used to create your final visualization. Keeping this in mind, I selected to create my visual using the average gas prices over the past twenty years, this dataset is pictured below.
After selecting a dataset I moved into Excel to begin creating my lollipop graph! First, you should translate your selected dataset into a table in Excel, as pictured below using my example dataset.
Once you have your dataset translated into an Excel table, you will insert a basic scatter plot graph to represent your data and make a few other formatting changes. These will include adding horizontal error bars, changing the error bars direction & altering the error amount. Once you have completed this your lollipop graph should look similar to the one I produced using my dataset below.
As you can see, a lollipop graph is a much more visually appealing when compared to a simple bar graph. While it may be a little more work to create, it can make a huge difference in what your audience takes away from your visualization.
Infographics are an amazing way of being able to visualize and communicate information to people in a couple of fun images and visuals. They are great marketing tools and they will take the attention of your audience much quicker than a simple power point. With an infographic comes pictures, graphs, data-based visualizations and a very minimal amount of text that sums up the point you are trying to get across. It is also very important to know and to be as clear as possible so that your audience can understand what you are trying to show them.
KNOW YOUR AUDIENCE
Always know who your audience is! It is important to make sure that you are able to communicate clearly and find ways to connect with them through the information given in your infographic.
SHOW MORE, TELL LESS
I think that it is best to show your audience your information instead of telling them. In other words, put in a data-based visual like a graph or a pie chart and show them how the numbers corelate with your given topic. Amy Balliet suggested in Smashing magazine, “Every once in a while, turn off the text layer and see whether the infographic still makes sense. If there isn’t any data viz, or if a bunch of pictures are missing context, then you are doing too much telling and not enough showing.” If you are able to remove a piece of text from your infographic, and your visuals make sense on their own you are on the path of having an effective end product.
THE PRESENTATION OF YOUR INFOGRAPHIC
From viewing many different kinds of infographics most of them are created vertically rather than horizontally. With that downward-like layout, you should have clear transitions. Having good transitions both on your infographic as well as when you are presenting gives your audience a brief moment to let them know that you are going on to something else. Good transitions in your presentations also help your audience see the connection and relationship between one idea to the next.
I choose this website to introduce with you that relate to technology field.
However, I see the difference of benchmark and benchmarking
“Benchmarks are reference points or measurements used for comparison, usually with the connotation that the benchmark is a ‘good’ standard against which comparison can be made. Benchmarking is a process of finding good practice and of learning from others”
In business, benchmarking is used to measure a company’s success. If you can see a clear and present gap, you can figure out what to fix before things get out of hand. Companies can gain a competitive edge by studying their competitors and can have a fighting chance.
There are lots of different ways to represent a benchmark on a graph. Sometimes it’s as simple as making a line, other times you have to compare multiple things so one line won’t do.
In this example, we have a combo chart. this explains the many benchmarks that were set each month.
A benchmark is essential a goal for a company to set. You reach that goal by just passing the line. In my first example, you can see in June, the company set the benchmark very low and were able to pass it.
In this graph, we’re now counting two variables (Fatalities and Total Accidents)
In this graph, the company us representing the frequency for all of their fatalities. This is a benchmark that you would want to see decreasing.
Companies can use this data to show how well their products are doing. While the overall satisfaction is remaining constant, the company has seen a dramatic decrease in the usage of their product. This can tell a company that they need to make a change in their production.
The graph above (via nytimes.com) displays several major natural disasters and alongside them, the amount of money required to get things back to a normal state.
The graph below (also via nytimes.com) displays the importance and reasoning behind flattening the curve of COVID cases. In this graph, the benchmark is the healthcare system capacity.
From what I understand from the textbook, benchmarks look at a actual data, and compare them to a target data. The target data does not need to be actualised necessarily. It canbe theoretical. In this case, the best example of this to me are the cases of Covid-19, theoretical impact versus the actual impact.
the ‘benchmarks ‘ are coloured.
Notice how the cases in India are compared to possible cases that may occur depending on the responses.
Also the big concept thrown around at the start of the virus’ spread was the “flatten the curve” rhetoric. This lead to many cases of comparing ideal scenarios to the actual cases and data that were being collected.
Example of ideal curve behaviourExample of cases being put on a similar trend-line in comparison to the target curves.
Although benchmarks can show us a variety of things, I think they are best at showing us comparisons. You can compare almost anything and everything. When you compare something you are basically showing how well something or someone performs compared to one another. For example you can compare the brands Tesla and Honda; you could compare the pricing, the style, how much you would save on gas and so on.
An article by Pew Research Center does some analysis on our presidential candidates. The article is meant to reflect on traits of both candidates and how voters are viewing each one. This particular benchmark to the left is the percentage of voters that think Trump or Biden would be a Great, Good, Average, Poor, or Terrible president. It is interesting because Trump has the highest percentage for both great and terrible.
Similar to the benchmark previously mentioned, the one to the right shows the percentage of voters that think the. candidates are great, good, average, poor, or terrible. It goes a little more in detail by adding the percentages for both sides of the spectrum. It gives Biden’s supporters on Trump and Trump’s supporters on Biden.
In general, both graphs are just comparisons of performances from both of the candidates. This doesn’t explain to us which one is the better candidate because that is up to your own personal beliefs on each stance but it gives you a look into what other people are thinking about your candidates and you can kind of get an insight on how the election could go but it is not a definite answer.
2020 has been a mess of year. With outbreak of Covid-19 in early march, which has us now in a pandemic, the term essential workers has been bought up a million times. At first, many people were not to sure who exactly essential workers were. To clear this up, the New York Times posted a handy benchmark graph as seen below.
More intrestingly, This graph shows the share of essential workers who are women. 52% of ALL essential workers are women. Evenmore, they make up for more than half of the workers in social work and in the healthcare field. The simplicity of this benchmark graph makes it easy to anayzle the data of who are essential workers in the Covid-19 pandemic.
The presidential election is a topic that brings about many different types of visualizations. One search on google and you will be bombarded with statistics for each candidate. Below we will look at simple visualizations that have been generated by CNN for each presidential candidate.
Above you see a visualization that depicts how each candidate is polling nationally. You can find the article this visual is captured from here. It is quite apparent to see that according to this visual, Biden has the overall support for the presidency. One thing that this visual does that is very helpful for the audience is highlighting the trending candidate’s (in this case Biden) numbers with their associated party color (blue for Biden, red for Trump). This visualization also included other news sources & polling agencies results to ensure that there were no extreme cases.
In the visualization above you immediately notice that there are not as many polling agencies listed for results. This is because after viewing the national polling for each candidate, the article allows us to narrow in on exactly how each candidate is polling within the states, here I selected New Jersey. Based off this visual for NJ, you notice that Biden has over half of its support with trump staying consistent around 35%.
In the above visual Texas was selected and as you can see right away, this visualization looks a bit different then the two prior. Immediately your eyes are drawn to the red highlighted percentages for Trump. However, as you view the entire visualization you notice that only two of the entire five polling agencies were able to determine a difference between the two candidates. The last three polling agencies were unable to determine if there was more or less support for Trump/ Biden because the final percentages they came up with, were within the margin of error.
