Manage Your Carbohydrate

carbohydrate source food

What’s up ? This is THE stephane ANDRE !!! I watched a Jamcore DZ’s video  and I learned some good stuff.

If you want to learn how to manage your protein, click here and for amino acids, click here.

Carbohydrates are the main source of energy for the body. It’s true that we can have energy with proteins and lipids but they provide less quantity of energy.

To put it simply, when you eat carbohyrates, they turn into glucose and then into glycogen. To better understand, I’ll take the example of a gasoline tank. Imagine that the tank is a muscle and that the gasoline is glycogen. Gasoline in the tank helps move the car and glycogen in the muscle helps move your body.

This glycogen is shared in many places in your body : 80% in your muscles, 14% in your liver and 6% in your blood.

Carbohydrate type

 

Most people know 2 types of carbohydrates, slow carbohydrates and simple carbohydrates. There is a 3rd type, it’s fibrous carbohydrate and unfortunately many people neglect them.

Slow carbohydrates :

  • Whole wheat bread / white bread (refined carbohydrate)

  • Whole wheat rice / white rice (refined carbohydrate)

  • Oatmeal

  • Cereals (muelsi is excellent because there are often lipids and carbohydrates that are added like nuts, raisins, etc. ).

  • Sweet potato / potato

  • Whole wheat pasta / white pasta (refined carbohydrate)

  • Kinoa

  • Couscous

Simple carbohydrates :

  • Fruit (fructose)

  • Honey

  • Dextrose

  • Maltodextrin

  • Lactose (it’s sugar in the milk)

  • Cluster dextrin (it’s perfect during your training session because it has a low glycemic index).

Fibrous carbohydrates (vitamin, fiber et minerals) :

  • Vegetables (aspargus, carrot, cauliflower, salad, etc)

Carbohydrate, insulin and insulin spike

insulin spike blood sugar

There is a special relationship between carbohydrates and insulin. When you eat carbohydrates, they turn into glucose, which is a type of sugar, and then glucose is turned into glycogen. Glucose is managed by insulin. Insulin is a hormone created by the pancreas. The insulin’s role is to manage the sugar’s level in the blood. This avoids having too much sugar in the blood (hyperglycemia) or not enough sugar in the blood (hypoglycemia).

The problem is that the majority of people eat any type of carbohydrate anyway and they may quickly get a lot of fat. It’s important to understand that each carbohydrate has a different gylcemic index and this glycemic index will cause different insulin spikes.

Glycemic index

A glycemic index indicates how quickly carbohydrates become glucose in the blood. It’s for this reason that it’s recommended to eat carbohydrate with a low glycemic index rather than those with a high glycemic index.

Carbohydrates with low glycemic index  :

  • Sweet potato

  • Oatmeal

  • Kinoa

  • Sugar free fruit juice

  • Whole wheat rice

  • Whole wheat bread

  • Whole wheat pasta

  • Fruits (apple, orange, cherry, pear, apricot)

  • Cluster dextrin (for your training session)

 

Carbohydrate with high glycemic index :

  • Potato

  • Couscous

  • Table sugar

  • White rice

  • White bread

  • White pasta

  • Cereal (Kellog, Nestle, etc.)

  • Dextrose

  • Sweet drink (soda)

  • Fruit (melon, watermelon)

Here is an exemple of low glycemic index carbohydrates to eat. For high glycemic carbohydrates, it’s recommended to eat them in moderation because if you eat them in large quantities, after several years, you have the risk to becoming diabetic or having other health problems.

Consumption

Always take into consideration your bodyweight and your training’s intensity. There 2 bad situations :

  1. Have a not very intense training and eat too much carbohydrates, which results in having too much fat in the body.

  2. Have intense training and eat a little carbohydrate, which results in being weak.

Take for example :

  • A man who weighs 85kg and is a beginner. In this case, it’s recommended to start with 3.5gr per kilo of bodyweight so 3.5 x 85 = 297.5gr of carbohydrates to eat a day.

  • A man who weighs 85kg and who is advanced or pro. In this case, it’s 5gr per kilo of bodyweight so 5 x 85 = 425gr of carbohydrates to eat a day. As an advanced person, the training sessions are really intense, that’s why you need this amount of energy.

  • A man who weighs 85kg and who wants to be shredded. Let things be clear, it’s being shredded and not losing weight. In this case is between 1.2 ans 2.5gr per kilo of bodyweight so it’s between 102 and 212.5gr of carbohydrates to eat per day.

The carbohydrates amount to eat is to be divided into 4-5 meals a day for ease of digestion. Each meal is balanced in carbohydrates, proteins and lipids.

To be shredded

shredded lean lose weight vs versus

Let things be clear, be shredded isn’t a weight loss. In this situation, leptin must be taken into consideration. Leptin is a hormone that regulates your appetite. When you start to lower your glucides, leptin will aslo decrease and you’ll be hungry. These feelings of hunger are created because of ghrelin. Ghrelin is a hormone called « hormone hunger » because it stimulates the appetite. Ghrelin also affects your insulin and testosterone.

This means that people who too quicly decrease their carbohydrates will fail because the lack of energy will be too brutal. The feeing of hunger will be too intense because the body has not had time to get used to the new diet. This is where nutrition is out of control.

Never reduce carbohydrate to 0. The people who do that, make a diet ketogenic and you have to master this type of diet. The professional bodybuilder is doing this diet for 4 weeks, but they can do that because of chemical aid (steroids) and they have a dietician. The problem is that those who aren’t professional athletes and who are on a ketogenic diet do that because someone told them it was good. That’s why in the gyms there are people who lose consciousness because they don’t have enough energy. You need 50 to 130 grams of carbohydrates, just for your brain, it’s not a simple diet to lose weight.

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.

Create Bins and View Distributions

tableau, bins, bar, chart, distribution, age, data, science

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

It’s cool, you finished the 1st part. Now we’re going to do more deep Data Mining analysis with this bank’s dataset.

tableau, bins, bar, chart, distribution, age, data, science

To make these analyzes more deep, we’ll create a more statistical approach.

To do that we will create a new tab.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

For this new tab, we want to understand how client distributed according to their age. Is there a majority of young or old people ?

tableau, bins, bar, chart, distribution, age, data, science

Move the variable « Age » in « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

As we want to see the distribution of client ages, we need to use the variable « Number of Records » to see the number of observations. Move the variable « Number of Record » to « Rows ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Boom, we have a chart but there is only one point on the top right. What happened is that Tableau took the sum of the ages of all the bank’s clients and the sum of all the « Number of Records », it means the total number of clients, 10 000 clients.

We’ll find a solution but before we’ll change the format to better see the chart. Right-click in the middle of the chart and select « Format ».

tableau, bins, bar, chart, distribution, age, data, science

For the font’s size, select « 12 ».

tableau, bins, bar, chart, distribution, age, data, science

Here you can see that the total age is 39 218 but that’s not what we’re looking for. What we want to see is the number of clients for each age.

I’ll explain what’s going on. We took the aggregated sums of our variables. Aggregate means that we took the total sum of the variable for each category. We added the ages but in fact we want to see the total number of observations for each age separately.

To have that, just click on the arrow in « SUM(Age) » in « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

Then select « Dimensions »

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

You see, Tableau doesn’t take the aggregated sum of ages but it takes ages separately. We have a curve that shows us the continuous distribution of our clients ages. That is to say, for each age, the curve gives is the number of clients of this age.

We’ll look at the dataset. Right-click on « Churn Modelling » and select « View Data… ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

There is window that appears that shows us the data in detail. If you scroll to the right, you will find the column « Age ».

tableau, bins, bar, chart, distribution, age, data, science

We see that the ages rounded. As all ages rounded, Tableau is able to group clients by age. By positioning the mouse on the curve, we can see that there are 200 clients who are 26 years old.

tableau, bins, bar, chart, distribution, age, data, science

If in the dataset, ages weren’t rounded, you would have seen clients with 26.5 or 26.3 years. It would create a lot of irregularity, there would be plenty of spikes with lots of variations.

Oooooh look, there is a variation that isn’t normal.

tableau, bins, bar, chart, distribution, age, data, science

Let’s analyze it in detail. Around this peak, we see that there are 348 clients who are 29 years old.

tableau, bins, bar, chart, distribution, age, data, science

Here, 404 clients who are 31 years old.

tableau, bins, bar, chart, distribution, age, data, science

And this peak down that shows us that there are 327 clients who are 30 years old.

tableau, bins, bar, chart, distribution, age, data, science

How to explain this irregularity ? It’s possible that many people of 29 years old are about to turn 30 years old and many people of 31 years old who just had 31 years old. It’s chance that make us have inaccuracies. You may have other inaccuracies if you data isn’t precise and rounded. In our case, the ages are rounded but we want to get rid of our small irregularity that we see on our curve.

There is way to see our distribution without our irregularities, it’s « bins ». « Bins » consists of grouping the information into different categories. That is we’re going to regroup our clients in different age groups.

Right-click on « Age » in « Measures ». Select « Create » and select « Bins… ».

tableau, bins, bar, chart, distribution, age, data, science

A window appears. We’ll group our clients in 5-years increments. In « Size of bins », write « 5 » and click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

As you can see, the variable « Age » has remained in « Measures » but there is a new variable in « Dimensions ».This is the variable we created « Age(bins) ».

tableau, bins, bar, chart, distribution, age, data, science

Our « Age(bins) » variable was correctly placed in « Dimensions » because it is a category variable because each category corresponds to a 5-year age group.

For example, one category is 20 to 24 age group. Now we’ll create a new distribution based on « bins ».

To do that, we’ll remove the variable « Age » from « Columns » with a click and drag outside.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

You move the variable « Age(bins) » from « Dimensions » to « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Note

In this case, it’s not possible to directly replace « Age » by « Age(bins) » over « Age » on « Columns ». This is because « Age » is a measure and « Age(bins) is a dimension.

That’s nice distribution, it’s usually the type of distribution (chart) we see in economics or mathematics. The difference with the old chart is that this chart is discrete. This chart is discrete because the clients grouped by age group while the previous chart was continuous.

On this distribution (chart), each bar corresponds to an age range. For example, this bar corresponds to the 25-29 age group.

tableau, bins, bar, chart, distribution, age, data, science

Now, we’ll change the colors.

In « Row », move « SUM(Number of Record) » while holding down the « Ctrl » or « Command » key on your keyboard to « Colors ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

We get our distribution in blue but we’ll change the color to red. Click on « Colors » and click on « Edit Colors »

tableau, bins, bar, chart, distribution, age, data, science

In the window that appears, click on the blue square on the right to display the color pallet.

tableau, bins, bar, chart, distribution, age, data, science

Select the red color and click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

Click on the « OK » button of the « Edit Colors » window.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

To facilitate the reading of the bar chart, we’ll add the number of clients in each age group. In « Row », move « SUM (Number of Record) » while holding the « Ctrl » or « Command » key on your keyboard to « Label ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

That’s it, we can see how many clients there are in each age group.

We see that the dominant bar is the 35-39 age bracket and the second dominant bar is the 30-34 age bracket. Overall, we can see that most clients are between 25 and 40 years old, which seems consistent.

On our bar chart, we have absolute values. We’ll replace that with percentages. Click in the little arrow in « SUM(Number of Records) » in « Label » and you select « Add Table Calculation… » but I’ll show you another way to do it.

tableau, bins, bar, chart, distribution, age, data, science

Instead of clicking « Add Table Calculation… », click on « Quick Table Calculation » and select « Percent of total ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

It’s cool, we have the exact percentage of people in each age bracket. Now, we can see that in the 25 to 40 age group, we have 20 + 23 +17= 60% of clients.

I’ll show you one last thing.You can change the size of the slices easily, just click on « Age(bins) » and select « Edit ».

tableau, bins, bar, chart, distribution, age, data, science

In the windows, you can change the size of the slices (bins). Put « 10 » instead of « 5 » to get 10-years slices. Click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Now, we have a distibution with fewer slices and the dominant slice is 30 to 39 years old.

Well, it was just to show you how to change the size of bins. To go back to the old distribution with the 5-years slices, click on « Back » button.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

As you can see, the values on bars are in percentages but the values on the axis are in absolutes values. Here is an exercise that I ask you to do : « Put the values of the axis in percentage ». I’ll give you the answer the next article.

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

-Steph