#MakeoverMonday Week 2019-26 Diary

This week’s #MakeoverMonday data set examines the twenty -five countries in the world with the highest consumption of pure alcohol per capita. Below is a picture of the original viz, let’s see what we can do to improve it.

orig

What Does Not Work and Why?

In looking over the original visualization, it became clear quickly that a few small tweaks could drastically improve our audience’s ability to consume the data. So, what doesn’t work and how can it be improved?

  • The Title – it’s misleading and could have us believe we’re looking at actual rates (percentages) of consumption when in fact the data displayed are liters of alcohol consumed. To improve this, we grabbed the title from the y-axis and made it our main title. While exploring the data, I noticed a majority of the countries were European countries, so decided this would be the focus of our viz. To call out the fact that only three of the countries in the Top 25 were non-European countries, we leveraged a light gray/dark red color combination, to bring attention to those three non-European countries. The subtitle coloring ties into the coloring within the viz (which we’ll see shortly), grabbing the reader’s attention.
title1
Original title
title2
Updated title

There are several issues with the chart itself, so instead of showing a before/after snapshot for each individual issue, we’ll first cover what doesn’t work and then provide one before/after that captures all of the updates made.

  • The Truncated Y-Axis – this is a HUGE no-no when working with bar charts. Truncating the axis of a bar chart will ALWAYS result in an inaccurate representation of the data!! My favorite quote on this topic is from Curtis Harris and his Pluralsight course, “Data Visualization: Best Practices.”

curtis.JPG

Check out the two charts below, the top one has the same truncated axis as the original, while the bottom has a zero baseline. Just look at how the truncated axis distorts the data!! It looks as though the value of Belarus (the top country) is nearly 5x the value of Slovenia (the bottom country) when in reality, the value of Belarus (17.5 liters) is only 1.5x that of Slovenia (11.6 liters). Again, repeat after Curtis…I cannot stress this enough.

bars1bars2

  • The Country Labels – it takes our brains longer to read text that is presented vertically or at an angle, so avoid this whenever possible. A simple flip of the chart allows us to display the country names horizontally and is much easier to read.
  • The Grid Lines – I’m a big fan of labeling my bar charts directly when the situation allows for it and felt this was an instance where we could remove the grid lines and simply label the ends of the bars instead.
  • The Color – Nothing in the original viz grabs the reader’s attention. This is where we can leverage the color mentioned earlier to guide the reader’s focus to whatever our particular insights may be; in this example, we wanted the reader to quickly see that out of a list of 25 countries, just three were non-European.

Now that we’ve covered a few items from the original viz that don’t quite work out, let’s take a look back at it, as well as the updates we’ve made, below. Here’s what changed;

  • By flipping the viz we are now able to display the country labels horizontally, thus eliminating the strain on our audience.
  • By removing the truncated axis and setting a zero baseline, we’re able to accurately display the data.
  • We’ve removed the grid lines and labeled the bars directly. What this does is remove any distraction that may be caused by the grid lines and turns our focus to the labeled ends of the bars, instead. Also worth noting, since the bars are labeled directly, we can remove the y-axis (x-axis in my viz), as it no longer provides value.
  • Lastly, we color the three non-European countries to match the red coloring in the title. Notice how quickly your eyes are drawn to those three countries; Grenada, South Korea and Australia.

bars1

chart2

So there you have it, just a few small changes to the original visualization and we’ve transformed a difficult to read chart with inaccurately displayed data into a clean, crisp looking chart, that leverages color to guide our audience. Thanks, I hope you enjoyed reading this and were able to take away something useful. Have a great day!!

final

#MakeoverMonday Week 2019-24 Diary

In celebration of pride month, this week’s #MakeoverMonday looks at the question, “Is it wrong for same-sex adults to have sexual relations?” The original visualization by GSS Data Explorer (below), tracks the progress over time of the percentage of the population to answer that it is “Not wrong at all,” broken down by four different age groups. It’s going to be a shorter post this week, so let’s get right to it.

originalStep 1. What Works and What Does Not Work?

Since we’re trending the percentages over time, the original line chart is a logical decision. However, there are a few things that don’t quite work for me. After downloading the data, I noticed there are several years missing and that doesn’t appear to be called out anywhere on the visualization. With data missing between the starting and ending points, a slope chart would be another way to effectively show the change over time. A slope chart would also prevent the lines from overlapping and crossing one another so often. Slope chart or line chart, the colors in the original viz could also be improved upon and I know somewhere where you can find a ton of awesome color palettes…thanks Neil!! Lastly, I would have labeled the ends of the lines…either with the value or with the age group. Labeling the ends of the lines with the age group would allow us to get rid of the color legend that is forcing us to look back and forth between the legend and the graph, to see which color represents which age group.

Step 2. Know and Understand the Data

The data set this week was super clean, with the exception of some missing years like I mentioned earlier. Once opened in Tableau, a quick pivot brought the years and their values into rows as opposed to columns. So after pivoting, we end up with a tall data set instead of the original wide data set. Now we’re ready to head into Tableau to begin building our visualization.

datadata2

Step 3. Choosing the Right Chart Type

Earlier I mentioned that a slope chart would be a good way to visualize this data set, given the fact that several years were missing in the data. I wanted to show the difference from the first year (1973) to the last year (2018), without showing any of the data in between those two years. But, I also wanted to show that, despite considerable growth over this current 45-year period, each age group was still very far away from 100%. So, with this in mind, I began by building a dot plot that looked like the below chart. This was a good start, but now I needed to show the gaps in each age group. For instance, for the 18-34 year old age group, I wanted to highlight the 71% to 100% section.

dotplot

So, I changed the colors of the dots in the dot plot, as I would later tie their gray color into my title. Next, I thickened the line connecting the two dots, which, if you recall, represent 1973 and 2018 and ended up with this. I liked how simple the visualization was to read, each age group has increased its percentage of the population answering our question “Not wrong at all” by quite a lot, over the years. However, those are still huge gaps to reach 100% and it is quite disappointing to think that such a large portion of our society is this close minded. So, I wanted to make sure to capture the gaps that still remain.

dots2

To do this, I would leverage Tableau’s transparent sheets as well as a video from Andy Kriebel. I would use Andy’s tip to create a rounded bar chart, but instead of starting the bars at 0, I wanted mine to start at the 1973 value for each age group, to ensure they didn’t extend to the left of the gray dot plot, shown above. Here’s how my worksheet was set up to achieve this, you can view Andy’s video above to master the steps required to get there. Ok, so we had two worksheets, now we just needed to build the dashboard and layer one worksheet on top of the other.

dots3

Step 4. Finishing Touches

We didn’t need a big dashboard for this, so I just set mine to a fixed size of 1200px by 500px and probably could have gone 800px wide to be honest. I tiled my title text box, the sheet with the blue rounded bar charts and my footer text boxes and then laid the gray, thickened dot plot on top of the blue rounded bars. If you’ve never used transparent sheets before, the key is to float the top sheet on top of the bottom sheet and set the size and position to exactly match the bottom sheet. Also, in order for the sheet to be transparent, the background must be set to None. Here’s how my floating, transparent sheet was set up.

float

The last thing to do was add the title, where I tied in the colors to match those in the visualization. Then the information in the footer and some tooltips and we’re done! I was short on time this week, but still feel this quick visualization provides a good look into not only how far each age group has come, but also how far there still is to go, on this subject. Thanks for reading, I hope you enjoyed and were able to take away something useful. Have a great day!!

final2

#MakeoverMonday Week 2019-22 Diary

The data set for this week’s #MakeoverMonday is CO2 emissions per capita, per country, with the original visualization (below) showing the trends of nine selected countries, from 1960 through 2014. So, what works and what doesn’t work with this chart? I don’t mind a line chart displaying the trends of CO2 emissions by country. However, here are a few things I don’t like about the original. The colors are difficult to deal with and I would prefer a solid line vs. the dashed line in the original viz. The country labels block the last 5-10 years of the viz, depending on what line you’re following, so that’s not ideal either. I see in the original, the user has the option of toggling the labels on or off. But, if you turn the labels on and they end up covering part of the viz, I would have gone for an alternative approach to labeling the lines. Alright, let’s get down to business.

 

Step 1. Understanding the Data

The data set is a nice and easy one to work with, giving us Country Name, Country Code and the CO2 emissions for each year, from 1960 to 2014. However, in looking through the data set, the first thing that caught my attention was there are several additional rows of aggregated data, such as ‘Arab World’ and ‘Caribbean small states’ below. Depending on your analysis, you may want to use these, so just be aware that they are there. If not interested in using them, consider throwing a data source filter on Country Name, before jumping into Tableau, and filtering these out, so you don’t have to deal with them.

data

The only other thing with the data set is when pulling it into Tableau, you’ll likely need to take a few small steps to reshape the data;

  • You’ll notice the field names are in row 1 and the headers read F1, F2, F3, etc. To fix this, from the Data Source pane, click on the drop down of the sheet you pulled onto the canvas and select ‘Field Names are in first row.’
  • Next, the years 1960 to 2018 are in columns and we want those in rows instead, so we’ll pivot our data, giving us a tall data set as opposed to the current wide data set.
    • To do this click on the header of the year 1960, hold shift and scroll to 2014, click on that as well. This will select all years from 1960 to 2014. Next, right-click and select pivot.
    • Since there’s no data in the years 2015 to 2018, feel free to hide them.
    • Now, rename your new columns;
      • Change ‘Pivot Field Names’ to ‘Year’
      • Change ‘Pivot Field Values’ to ‘Value’
  • That should leave us with four columns; Country Name, Country Code, Year and Value. Alright, now we’re ready to jump into Sheet 1.

Step 2. Recreating the Original

After taking some time to explore the data, I decided to try something I’m not sure I’ve ever actually done as part of a #MakeoverMonday and that is to make a recreation of the original visualization with the exact same chart types. So, I’ll make a replica of the line chart and look to incorporate the bar charts into my viz as well. With this approach, I’ve defined three goals;

  • Make the viz cleaner
  • Better solution for the labels
  • Improve the interactivity

Usually my goal for #MakeoverMonday is to come up with a better way to visualize the data through the use of a different chart type. However, with this visualization, I feel the line chart and bar chart are good choices, the line chart just needs to be cleaned up and the bar chart is a little blah. What better time to try out Tableau’s BRAND NEW Parameter Actions, featured in the recent 2019.2 release?!!

 

Step 3. Effective Use of Color

I took to Tableau and built exact replicas of the original line chart and bar chart. While I changed a few things formatting-wise, the only thing different with the charts themselves is the use of color. Instead of several different colors on the line chart, I used parameter actions to highlight, in red, the country being hovered on. Likewise, I followed this coloring through to the bar chart, which will end up in a viz in tooltip. Here they are below, as stand alone charts. Using color to highlight a certain country helps the audience to see how that country differs from the others.

 

Tableau’s parameter actions are so easy to use. If you’ve used Set Actions before, the set up is very similar. Here’s all that is required for the three parameter actions in my viz.

  1. Create a parameter using the Country Name field. I called it Country Parameter. This one parameter will be used in all the parameter actions.

countryparam

 

 

 

 

 

 

 

 

2.  Create a Boolean calculation called Country T/F and drag it to both the size card and the color card. Then simply adjust the size to your liking for both the True and False values and do the same for coloring. I adjusted my sizing and color, so when a Country was selected, the line thickened and turned red in color, while the other countries are thin gray lines, pushing them to the background but keeping them plenty visible for comparisons. Quick note: I also dropped this calculation on the color card of my viz in tooltip bar chart, allowing it to highlight the country being hovered on, just like the line chart.

3. Create a calculation that checks to see if the Country Name = the Country Parameter. If True, then it displays the Country Name, if False then it is blank. I dragged this to the label card to label the country being highlighted via the parameter actions. All other countries will receive no label.

Here are the calculations as well as the sheet. To get the label to fit at the end of the line chart, I both fixed the Year axis to add a few additional years and also added 25 pixels of right outer padding to this sheet, once it was dropped onto the dashboard. I could have just done more padding without fixing the axis and got the same result.

 

linesheet

4. Once the sheet was pulled onto the dashboard it was time to set up the Parameter Actions. This is literally all there is to it; from the Menu go to Dashboard –> Actions –> Add Action –> Change Parameter. An Edit Parameter Action dialogue box will pop up. Simply name your action if you’d like, select your Source Sheets, Target Parameter and Field and then set the action to run on either Hover, Select or Menu. I chose to run the action on Hover, as it made the most sense for the interactivity in this viz.

paramaction

Step 4. Formatting

Alright, with the Parameter Actions set up, it was time to finish this thing off with a little formatting. Here are some formatting steps I took to clean up the viz from its original version.

  • Changed the Y-axis tick marks to an interval of 5 instead of 2
  • Changed the X-axis tick marks to an interval of 10 instead of 5
  • Removed the grid lines
  • Changed the default axis font to Tableau Book 8pt, bold and gave it a darker color to help push it to the background
  • Replaced the default tooltips with the viz in tooltip, featuring the bar chart with selected country highlight
  • Added a small message letting the user know to hover for interactivity
  • Changed the background to a darker color…just personal preference

There we go, that’s it. Nothing crazy, but I feel like we gave the original viz a nice makeover, cleaning it up and making it more user friendly. The final product is below and you can play around with the interactive version right here. Thanks for reading and have a wonderful day!!

co2snip

#MakeoverMonday Week 2019-19 Diary

For this week’s #MakeoverMonday, we’re looking into cost effectiveness in Major League Baseball. More specifically, how does a player/team salary translate into productivity on the field, across a variety of statistical categories. For instance, if Player X made $20 million in 2015 and hit 20 home runs that year, you could say the team is paying $1 million per home run hit by Player X. Alright, let’s get started.

original

Step 1. Understanding the Data

Since I’m a lifelong baseball fan, this is a data scenario that is familiar to me. However, Andy and Eva did a great job of including links on the data sets page for those who may be less familiar with the sport. If this were, say, Rugby data, I would absolutely be diving into those resources, so if you ever feel uncomfortable with the data set, be sure to do a little bit of research. One thing I will mention is that it looks like the data set is focused on hitting stats only and does not include pitching stats. However, pitchers are included in the data set, because they do compile hitting stats in certain situations. If you’re unfamiliar with the rules and why this could potentially matter for this data set, here are a few notes;

  • Pitchers ONLY hit in games that are played in National League ballparks
  • Starting pitchers ONLY start every 4th or 5th game
  • MOST (not all) pitchers are not very good at hitting the ball
  • MOST (not all) good pitchers have high salaries

So, if the average National League pitcher starts every 5th game (32 starts) and gets three plate appearances per game, that comes to 96 at bats, for the season. So why does any of this matter? Like we mentioned, pitchers typically aren’t great at hitting the ball, so their hitting stats could look very poor when compared to the average position player (all other players on the field, other than the pitcher are referred to as position players). So, if we’re analyzing the cost a team pays players per home run, for instance, let’s look at an example of what it could look like when comparing a pitcher vs. a good position player.

  • Pitcher X
    • makes $25 million and hits 1 home run in 96 plate appearances
      • This would suggest we pay Pitcher X $25 million per home run hit
  • Position Player Y
    • makes $25 million and hits 25 home runs in 432 plate appearances
      • We pay Position Player Y $1 million per home run hit

This scenario would lead you to believe that Position Player Y is a much more cost effective player, when the reality is simply that he is paid to hit the ball, while Pitcher X is paid primarily to pitch the ball. And since the data set does not include a field for each player’s “position,” we’re unable to simply filter pitchers out of the data set. Therefore, it may make sense to set a filter on plate appearances and set it to a minimum of 200 per season. This would filter the pitchers out, as in my opinion, it does not make sense to include them in this analysis. I apologize for the long-winded explanation, but in my first glance at the data, I saw this as potentially slipping up some participants who may not be familiar with the game. Ok, what about the original viz?

Step 2. The Original Viz

The scatter plots on the original viz are easy enough to understand, but to be honest the way in which it was labeled, made it difficult for me to follow, especially the bottom, team section. Also, I didn’t find the team section all that interesting, because basically it was just showing us what teams have the lowest payrolls (Houston Astros) and which teams have the highest payrolls (New York Yankees and Los Angeles Dodgers). I guess the most interesting part of the team section was it tells us just how unbelievably bad the Miami Marlins were, offensively, in 2013. Wow!! Last in the league in all five categories.

Step 3. Try New Things

Awhile back, I saw this really good video by Andy, on how to build a no-whisker box plot and have been waiting for the right opportunity to try creating something similar. I was hopeful this data set would provide that opportunity, but after working through a few different scenarios, I was unhappy with the results. So, we’ll continue to file that chart type away for a different day and move onto something else. Another recent viz I really liked was this beautiful viz by Lindsey Poulter, which used a stepped line and dot combo chart to capture the magical 2018 season of Kansas City Chiefs QB, Patrick Mahomes. In his #WorkoutWednesday challenge for Week 4 of 2019, Curtis Harris built a similar chart that tracked headcount. I really loved not only the look of these vizzes, but also the ease of understanding them. So, I decided to go with this chart type, but the question was what data would it work well with? It’s probably worth mentioning that in a business setting, choosing your chart type first is probably not going to be the best approach. However, one of the great things about Tableau Public and community projects like #MakeoverMonday are that they offer us great opportunities to try new things and approach data visualization in different ways, in a safe environment.

Step 4. Finding the Story

The next step was to begin playing around with the data to find a story that fit the vision I had in my head. Early on, I had ruled out looking at team data, as I wanted to focus on players instead. Looking at hits, runs, RBI and home runs, I worked through some different ideas before landing on a viz that would feature the most recent members of the 500 home run club. Leveraging the stepped line dot combo chart, I felt it would be fun to visualize each player’s home runs by season, along with their team’s cost per home run (or the player’s salary per home run, whichever way you prefer looking at it). What I expected to see was as a player’s salary increased throughout their career, their salary per home run would increase fairly closely along with it. While this was true in a majority of cases, it certainly was not the case for all players on the list and in other cases, the increase in cost per home run was not as steep as I had guessed. Now that I had found a story, the next step was communicating it with a clean, engaging design.

Step 5. Simplicity in Design

I used just two colors in the viz, with a third for non-data related text. My colors were shades of red and blue, the colors of the MLB logo. For the chart, I set home runs as the stepped line chart and salary per home run as the sized dots. Here’s what it looked like.

step

The chart looked nice, but something very important was missing; salary by season. I immediately thought back to a fantastic blog post by Ryan Sleeper, in which he shares creative ways to use transparent sheets to add context to your vizzes. This was exactly what I needed, context. The moment I saw it, I fell in love with Ryan’s bar chart trend pushed to the background. So, I implemented this strategy, with player salary set as the trend in the background, with an opacity of 20%. This way it would be there for context, but not draw attention away from the other chart. With home runs set to running total for the player’s career, this worked out well, because as home runs increased, a player’s salary typically increased as well. So, for the most part, they increased along with one another. Here’s what it looked like after floating the stepped line on top of the bar chart.

step2

To add more context, I included text for each player’s career salary, home runs and salary per home run, as well as the years they played. Lastly, I wanted the reader to be able to see the differences in all three measures, so I fixed the y-axis for the home runs stepped chart from 0 to 800, fixed the salary bar chart from 0 to $40 million and fixed the salary per home run dot size from $0 to $5 million. Below is a view after adding the text. Since I didn’t show any axes, I included an explanation through the use of an info button.

step3

Step 6. Sense Checking the Data

It was not until after building the entire viz that I really took the time to look closely at the numbers to make sure everything made sense. Guess what? It didn’t!! For a while, I couldn’t figure out why a few players had extreme spikes in their salaries. Take a look at the below comparison of before and after I found the issue. Look at those spikes!! Why on earth would Gary Sheffield randomly make nearly $30 million dollars in one season and then go back to making $9-10 for the next several years? Answer? He wouldn’t, so there was clearly something wrong with either the data or one of my calculations.

After digging around, here’s what I found. My ‘FIXED Player Salary’ calculation had originally been set up as SUM([Salary]), as I had not taken into account the fact that if a player had played for more than one team during the same season, they would have more than one row of data for that season. Here’s what the incorrect calculation and the result looked like.

calcs

sheffsal

I was certain $29.9 million was incorrect, but I also wanted to be sure that the $14.9 million figure was correct, so I checked the trusty old baseball-reference.com and saw that the numbers matched and Sheffield did indeed make that amount in 1998. So, I needed to change my calculation to pull in the MIN([Salary]) as opposed to SUM.

bbref

calc

Overall, I enjoyed working with the data set this week and wound up spending a lot more time on this viz than on a typical #MakeoverMonday, mostly due to just playing around with exploring the data. Below is a look at my final viz, the interactive version can be found here. Thanks for reading, I hope you enjoyed!!

Home Runs

#MakeoverMonday Week 2019-18 Diary

This week for #MakeoverMonday, the data set is ISS Spacewalks, which takes a look at the 216 Spacewalks at the International Space Station, from December 7, 1998 through April 8, 2019. The original visualization (pictured below) shows the difference in spacewalks in U.S. Spacesuits vs. Russian Spacesuits. Let’s get started. issspacewalks

Step 1. Understand the Data

There were two data sets this week. For my analysis, I’ll be sticking to the ISS Spacewalks data set, which is super simple to understand. However, if you choose to build a visualization using the Spacewalk Detail data set, you may want to do a little research to get a better idea of the history of the various missions throughout the years, as that data set contains more information pertaining to each individual mission. Some light reading could help you tell a more compelling story, should you choose to go that route. Ok, back to the ISS Spacewalks data set, here’s what it looks like; we have Year, Nation and Number of Spacewalks. Yep, that’s it. So, what did we like about the original viz?

data

Step 2. The Original Viz

I liked the original viz, as it was easy to understand that since 1998, many more spacewalks have taken place by American astronauts than by Russian astronauts. However, stacked bar charts can be difficult to read, so my goal was to move away from that chart type in favor of an easier to read chart type that would highlight the fact that the United States has had more spacewalks than the Russians in all but three years.

Step 3. Keeping it Simple

My main goal here was to display the data in a way that is easy to understand. I feel that when highlighting the variance in American spacewalks vs. Russian spacewalks, from year to year, the bar in bar chart does a better job than the stacked bar. Of course, there are other chart types that would work as well, but we’ll go with the bar in bar this time. Now, I’m also a big fan of work that is visually appealing and bar charts aren’t the most elegant, especially when you put one inside of another. So, after starting out with a regular old bar in bar, I quickly decided I needed something a little more pleasing to the eyes…insert the rounded bar chart. This video by Andy Kriebel is a great resource if you’re not sure how to make them in Tableau. Below are the final charts, my eyes are much more drawn to the rounded version, how about you?

regbar
Standard Bar in Bar
roundbar
Rounded Bar in Bar

Step 4. Finishing Touches

So, with the chart built, you can quickly see 2004, 2013 and 2014 are the only three years in which Russia had more spacewalks than the United States. You’ll also noticed I stuck with the colors from the original viz (however, I softened them a bit). I used them because they are familiar to anyone who has seen the original and I wanted them to be the only colors used, other than a shade of gray for text not related to the U.S. or Russia. A few other changes I made was to add a y-axis for the number of spacewalks, while removing the grid lines and rotating the labels for year, so they are easier to read. I went with a shade of black for my background, added a tooltip, title and some info in the footer area and was ready to publish. Here’s what the final viz looks like. An interactive version can be found on my Tableau Public page, here. Thanks for reading!!

final

 

#MakeoverMonday Week 50 Diary

After a few weeks away, we’re back at it for #MakeoverMonday, Week 50 of 2018. This week, we’re looking at the land use (in square meters) required to produced one gram of protein and this is broken down by several different food types. Below is the original visualization, which I like. A basic bar chart that allows you to quickly see how much more land is required to produce a gram of protein for Beef/Mutton. After a quick glance at the data, I knew that fact would be my focus. My goal was simply to bring a little more attention to it and clean up the viz a bit. Let’s get started. 2018-50orig

Step 1.  Understand the Data

This was a pretty easy step this week, as the data set essentially contains just two columns (food type and land use in sqm) and ten rows (one for each food type). It also contains a column for year, but with the only year being 2017, it’s there more for reference than anything, as we can’t compare year over year trends, etc.

Step 2. Simplicity in Design

With a very straight-forward data set, we’re ready to jump right into the design. If the goal was to try something big and challenging, I would definitely go to my sketch pad here and begin sketching out a possible design. However, in this scenario, as I mentioned above, the original viz was good and the goal was to simply iterate on it, bringing more focus to just how much land use the Beef/Mutton food type requires in comparison to the other food types. Back in September, I saw this blog post by Charlie Hutcheson, where he took apart a #MakeoverMonday viz by Ruchika Agrawal. I thought the use of a bar inside of 100% box was pretty slick and a nice visual for comparisons. Having wanted to try this for quite some time now, I felt this data set would provide a decent use case. So, in this case the 1.024 square meters needed to produce one gram of protein, for Beef/Mutton, would serve as the 100% and the square meters required for the other food types would fill up that “100%” bar. For instance, in the example below, the Pork food type requires 13% of the land that the Beef/Mutton food type requires, to produce one gram of protein.pork

However, in my tooltip, instead of choosing to show how much less land is required by the other food types, since the focus is on the Beef/Mutton food type, I chose to show how much more land is required by Beef/Mutton than by the other food types.

tooltip2

So, at this point I had a pretty basic bar in bar chart that I felt was easy to understand and didn’t stray too far from the original viz.

2018-50bars

The last step was to really drive the point home and what better way to do this than with

BIG ASS NUMBERS!!!

Step 3. Effective Use of Text

Using a descriptive title allowed me to include some text on the right side of the viz, that helped explain the situation. Again, the first thing I noticed in the original viz was how much more land use was required for Beef/Mutton. My natural instinct was to compare that one food type to all the others, combined. The results are pretty astonishing. It turns out that Beef/Mutton requires more than twice the amount of land needed by all nine other food types, combined. Pretty eye opening if you ask me. So, the finishing touches on the viz was to expand on the fact stated in the title. The final viz is below. Thank you so much for reading, have a great day!!

2018-50final

 

 

#MakeoverMonday Week 45 Diary

My second #MakeoverMonday Diary looks at an Aging America, as the U.S. Census Bureau projects that for the first time in U.S. history, adults aged 65+ will outnumber children aged under 18, by the year 2034. I felt like the original viz was straightforward and did a nice job of showing the anticipated shift. However, I wanted more detail than just total adults and total children, so my goal was to include something that resembled the top part of the original viz, while also adding more detail below. Let’s get started…Screen Shot 2018-11-08 at 9.30.34 PM

9:11am – My first step was to pivot the data from its original format, this would leave me with one column for age and a second for the population of each age. However, this would also leave me with duplicate data, so it was important to then go ahead and filter the data appropriately for my analysis. After digging through the data a bit, it was decided that I would not be using the Race field, so I threw a Data Source Filter on that to keep only “All Races.” This way I wouldn’t have to deal with all of the other Race options once I began my analysis. I did not do this with the Sex and Origin fields, as my viz would include sheets both at the highest level, as well as more detailed, so I chose to filter those from the worksheets. It was important to keep an eye on my filters to ensure I wasn’t reporting data that had been duplicated. Once inside Tableau, I started with two quick calculations to first parse out the word “age” from the age field and then group the ages so I had my children under 18 and adults 65+ age groups. Then it was time to replicate the original viz, the only differences here was that I went with a stepped line chart and included actual populations for each group as opposed to percent of total population, like in the original. After adding some text and a highlight circle focused on the point when adults 65+ outnumber children under 18, here’s what the top part of my viz looked like.

topline

9:42am – So, now that we had this high level overview, it was time to show some detail and find out what groups were projected to cause such a giant shift. Again, after playing around with the data, I felt the Origin field would succeed in telling the story here and since it only had a few values (Hispanic, Not Hispanic and Total), would require fewer visuals than telling the story through the Race field, which had seven values. The differences in population projections among people with Hispanic origins vs. those without Hispanic origins was quite jaw dropping at first glance, which is why I went this route. While adult males and females 65+ with Hispanic origins are projected to close the gap on children under 18 with Hispanic origins, the trend was much more gradual than that of people without Hispanic origins.

9:57am – Unfortunately, I’ve been getting pulled away with a few phone calls, so the timing of the diary this week may not make a ton of sense. Either way, we’ll forge on!! So, as we mentioned above, the projections for people with and without Hispanic origins were vastly different. Below are the final visuals displaying each Origin group for both Females and Males.

Females and Males with Hispanic Originswithhispanicorigin

Females and Males without Hispanic Originswohisporg

10:22am – As I was just about to publish the viz, a last minute idea came to me, to change the title to a sort of diverging color palette, so that it more aligned with the rest of the viz. My hope was that this also helps show the projected shift in population. Here are a before and after of the title.

Beforeuwa1

Afteruwa2

Bringing it all together now, below is my final viz for #MakeoverMonday Week 45. The interactive version can be found here. Before we wrap this up, I want to thank Neil Richards again for the awesome color palette. If you haven’t seen it yet, his Color Palette viz can be found here. It’s such a nice resource if you’re anything like me and struggle with putting colors together that compliment one another. Thanks again Neil!!

Thank you for reading and have a wonderful day!!

2018-45final