Protein (Part 1)

protein, food, chicken, bean, rice, avocado, beef, egg, bean, nut, milk

What’s up ? This is THE stephane ANDRE. With my training, I’m interested in nutrition to stay in shape. I read some advices from Arnold Schwarzenegger and I learned some good stuff.

Your body uses protein to build, repair and maintain muscle tissue. Bodybuilders have a great knowledge of nutrition, especially the protein for building muscle. Since they want to consume much more muscle than the average person, they consume more protein than average.

Your body uses protein to build muscle only if all the necessary amino acids are available. The small problem is that your body doesn’t produce by itself all the necessary amino acids. Amino acids that your body can’t produce are called essential amino acids and fortunately they’re available in foods.

Proteins are made of carbon, hydrogen and oxygen like other macronutrients, but proteins have one more element, nitrogen. Bodybuilders often explain that they have a positive or negative nitrogen balance, which means that they’re in an anabolic state (muscle gain) or in a catabolic state (loss of muscle).

Food and protein

There are foods called complete proteins, which means that they provide all the amino acids needed to create usable proteins. Milk, eggs, meat, fish and many vegetable products have complete proteins. Be careful because these foods don’t have the same amount of usable protein by weight. For example a food that contains 10 grams of protein, your body can use only 7 grams.

Here is a table that shows the amount of protein by weight and the protein’s amount used in your body as a percentage for the most common foods :

Food% Protein by weight% Net Protein Utilization
Eggs1294
Milk482
Fish18-2580
Cheese22-3670
Brown rice870
Meat and flowl19-3168
Soybean flour4261

(Whey is a byproduct of milk that contain much more protein than eggs)

This table tells us that an egg contains only 12% protein by weight. Despite this small percentage, an egg contains an amino acids balance that allows your body to use 94% of it. However, the table tells us that soy flour contains 42% protein by weight. But because of the amino acids composition, your body can use only 61%. This shows us that there is a difference between the protein’s amount in a food and the protein’s amount your body can use.

This is the end of Part 1.In Part 2, I show you that in cases where you don’t have complete protein foods, it’s possible to make combination of food to have the maximum protein used by your body.

Subscribe to my newsletter and share this article if you think it can help someone you know. Thank you.

-Steph

P.S. If you’re in Miami and you like Caribbean food, go to my cousin’s bistro to eat Haitian food, click here .

Combine 2 charts

tableau chart compare paralell data mining science

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

We’ll move to the next level. We’ll work with 2 bar charts in parallel to have a more efficient data mining. In a previous article, we created 2 different bar charts. The 1st was an A/B test (actually, it’s a classification test) that told us in which age range the clients were most likely to leave the bank. The 2nd was a bar chart showing the age distribution of clients in our sample of 10 000 clients.

Let’s go. We’re going to have an A/B test with age range and we’ll add a bar chart of the client distribution below. To add a bar chart, we must start by choosing what we want to keep and what we want to add. In our case, we want to keep the columns because they’re the same in the 2 bar charts.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

And we just want to add a new line so we will add a new variable in « Rows ». As we want to add a bar chart of distribution, we will use the variable which corresponds to the number of observation « Number of Records ».

In « Measures » moves the variable « Number of Records » in « Rows » to the right of « SUM(Number of Records).

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

We have a 2nd bar chart below the 1st bar chart. As you can see, these 2 bar charts are in one column. « Columns » is « Age(bins) ». These 2 bar charts are in 2 different lines which are the lines that correspond to the 2 « SUM(Number of Records) » in « Rows ».

The space on the left has also changed. There is « All » which represents the 2 bar charts at the same time. It means, when your select « All », you make change in the 2 bar charts.

tableau chart compare paralell data mining science

Below this tab « All » we have 2 tabs. The 1st tab represents the 1st bar chart so the 1st « SUM(Number of Records) » in « Rows » and the 2nd tab represents the 2nd bar chart so the 2nd « SUM(Number of Records) » in « Rows ».

tableau chart compare paralell data mining science

Which means that if you want to make changes on the 2 bar charts at the same time, you make the changes in the tab « All ». If you want to make changes only in the first bar chart, you select the first tab below « All ». If you want to make changes only in the 2nd bar chart, you select the second tab below « All ».

So if you change the color in tab « All », our 2 bar charts will be colored by the same color.

Select the « All » tab and click on « Colors ».

tableau chart compare paralell data mining science

Click on « Edit Colors… » and select « Stayed ». Select the green color and click on the « OK » button.

tableau chart compare paralell data mining science

As you can see, the color changed in the 2 bar charts.

tableau chart compare paralell data mining science

Click on the tab of the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Removes the « Exited » variable from « Colors » to remove colors only in the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Removes the « SUM(Number of Records) » variable from « Label » to remove the labels only in the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

We will add color on this 2nd bar chart. Click on « Colors », click on « More colors… » and select the blue color. Click on the « OK » button.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Now, we would like to see the colors vary in intensity depending on the number of observations. Take « SUM(Number of Records) » from the 2nd line in « Rows » and holding « Ctrl » or « Command », move it to « Colors ».

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Cool ! We will take care of the 1st bar chart. Select the tab of the 1st bar chart.

tableau chart compare paralell data mining science

Click on « Colors ». Click on « Edit Colors… ». Select « Stayed ». Select the brown color and click on the « OK » button.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

For more clarity, we will add labels in 2nd bar chart. Click on the tab of the 2nd bar chart. Take « SUM(Number of Records) » from « Colors » and holding « Ctrl » or « Command » and move it to « Labels ».

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Perfect. Now we will change the location of the bar chart. We will put the 2nd bar chart instead of the 1st bar chart. According to the logic of « Rows » and « Columns », simply put the 2nd line « SUM(Number of Records) » to the left to pass in 1st line.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

BOOM, the bar chart of the age distribution is going over because it’s in the 1st line in « Rows ». With these changes, tabs to change the bar charts have changed order.

Observation

What we can observe with these bar chart is that we see on the 1st bar chart that the majority of bank’s clients are in the age group of 30 to 34 years old and 35 to 39 years old. In these 2 age groups, we see on the 2nd bar chart that client of 30 to 34 years old are less likely to leave the bank than clients between 35 and 39 years old. Look at ages 30 to 34, the rate of clients leaving the bank is 8% while in the 35 to 39 age group, the number of clients leaving the bank is 13%.

In the age group of 40 to 54 years old, we see on the 2nd bar chart that the rate of clients leaving the bank is increasing and is above of the average rate of clients leaving the bank (20%). But we see in the 1st bar chart that the number of clients in the age group of 40 to 54 years old decrease with the age groups.

Do you remember the potential for anomalies in age groups 75, 85 and 90 ? We’ll check it. In the 1st bar chart we can see that there are 11 clients in the age group of 80 to 84 years old, 2 clients in the age group of 85 to 89 years old and 2 clients in the age group of 90 to 94 years old. We can conclude that these observations in age group of 80, 85 and 90 aren’t very significant from a statistical point of view because 2 clients is something negligible in this sample of 10 000 clients.

In the first age group of 15 to 19 years old, we can see that there are 49 clients, which is not very significant.

Compare these 2 bar chart in parallel allows us to have additional insights.

Subscribe to my newsletter and share this article if you think it can help someone you know. Thank you.

-Steph

Add a Reference Line

reference line tableau data science

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

In the previous article we learned how to work with aliases. We will learn how to add a reference line in our bar chart.

Before I start, I’ll show you a trick in Tableau.

In our bar chart we can see the labels in this order : percentage and below : « Stayed » or « Exited ».

We will reverse this order. You go in this rectangle.

reference line tableau data science

And you place the label « Exited » above the label « SUM(Number of Records ».

reference line tableau data science

Look, the label « Stayed » is above percentage.

reference line tableau data science

With that, we can understand the bar chart more easily.

Let’s add a reference line, let’s go . But before, I think you’d like to know why I’m talking to you about a reference line.

A reference line helps us to compare bar chart results with a benchmark. This benchmark is represented by this reference line.

In our case, the benchmark is the percentage of clients who left the bank in our sample of 10 000 people.

The first thing to do is find this percentage in our bar chart. To be able to do that, remove « Gender » from « Columns ».

reference line tableau data science

Boom, we have a new bar chart.

reference line tableau data science

Look, we only have the percentage of clients who left the bank and the percentage of clients who stayed in the bank.

We see that on our sample of 10 000 people, there are 20% of the clients who left the bank and 80% of the clients stayed in the bank. This means that the churn rate (client departure rate) is 20%.

What we’re going to do is we will add this churn rate in our A/B test. To return to our A/B test, press 2 times on Ctrl+Z or Command+Z or you can click 2 times on the « Back » button in the menu bar.

reference line tableau data science

Now we know that the average clients who left the bank is 20%.

We will add a horizontal line in the Y axis (Y = 20%) to compare the 20% of the churn rate and the 2 categories male and female.

Let’s go. Right-click on the vertical axis (Y axis) and select « Add Reference Line ».

reference line tableau data science

A window appears with several options.

reference line tableau data science

You have the choice to add a line, a band, a distribution or a box plot.

We will use the line for the entire table.

Click on the « Line » button and activate the « Entire Table » checkbox. In « Value » selects « Constant ».

reference line tableau data science

The constant is 20%, so it’s necessary that you put 0.20 in « Value ».

reference line tableau data science

It’s possible to put a label on this reference line. For example, if the line reference corresponds to a formula, the label displays the formula. But for our case, our constant is 20% and it’s already displayed on the vertical axis so we will select « None ».

reference line tableau data science

For the format of the line, select the continuous line and click on the « OK » button.

reference line tableau data science

We have our reference line is added to our chart.

reference line tableau data science

Here is what we can see. Female clients are more likely to leave the bank than average clients. Male clients are less likely to leave the bank than average clients. 

In our case, it’s obvious to see that because there is only 2 categories, men and women.

Now you know how to add a reference line in a bar chart.

Share this article if you think it can help someone you know. Thank you.

-Steph

Average Not Acceptable

mediocrity

Being average isn’t acceptable, it’s simple. I encourage people to pay attention to their ecosystem because you chose the lifestyle you want. It’s true, it’s sound cliché or corny but it’s the truth. If you’re in a group of people who doesn’t motivate you, who doesn’t motivate each other to improve, you will fall in a mediocrity circle. If you stay long in avergage, after a while, you begin to decline.

This is what is called a plateau in training. If you lift 20kg (44lbs) in your training each time and you keep that weight a long time , your body will get used and it will begin to decline.

Being in average will take you to mediocrity ! It’s something you don’t want, you want to stay motivated, you want to be around people who motivate you and you want to motivate others. When you’re afraid to do something, you want your friends motivate you and you want to motivate your friends when it happens. That’s how you grow and you become better.

mediocrity

Sometimes a person gives you good advice to help you to advance or help you to avoid to make a mistake. If you don’t apply the advice, it’s your story, you keep it in your head. That’s why you have to keep an open mind to accept advices from others. You must train yourself to have this state of mind to be open and go to places where you can get good advice. All things can be a tool that help you to move forward either a positive or negative situation.

When you are facing a difficulty, don’t be overwhelmed by your emotions too long because there is always something that can help you to overcome this difficulty. Don’t be afraid, be brave and if in your entourage nobody has this mindset, it’s better to be lonely until you find one.

In your life, you will meet many types of people but you don’t want to be surrounded by people who put you down, who are sadistic, who slow you down. You want people who motivate you, it’s a steady progress.

mediocrity

For me, life is something in constantly progress. I don’t mean that’s the definition of life, I don’t know what is the definition of life but it’s a point of view that I have and I apply it every day . I’m personally in progress in all areas of my life and it makes me happy.

I know some people is living difficult times and that’s why I like to motivate people because they can overcome these difficulties. Me too, people motivated me when I had hard times and it’s my turn to motivate others.

If I can motivate you a little, that’s cool but around you with people who motivates you. Avoid people who put you down, you don’t need them. Let me say to this kind of people « Fuck you ».

-Steph