Analyzing Nutritional Information of Fast Food Restaurants using SAP Lumira
It was two months ago that I first saw the Data Geek Challenge at SCN and I even got a badge for accepting it.
Then time flew really fast before I got to work. Last month I got into serious business and started searching for some datasets. The real challenge was finalizing a dataset. The datasets available ranged from serious issues like heart disease and global warming to funny ones like the color of M&Ms in a packet 😆 . So finally what did I settle for? Something in between.
The following analysis is on the dataset I created from the nutrition information available from famous fast-food chains(as published by the restaurant chains). The top five brands to be particular. Only the menu listings for food are considered here. I have left out the beverage list (too confusing with different measurements and serving sizes). Believe me when I say that the hardest part was transferring all the nutrition information from the tables in pdf to excel. The rest was just child’s play in SAP Lumira.
About the data:
5 fast food restaurants – Mc Donalds, Subway, Wendy’s, Burger King, Taco Bell
715 menu items
16 attributes – 16 attributes – ServingSize(g), Calories, Calories from Fat, Total Fat (g), Saturated Fat (g), Trans Fat (g), Cholesterol (mg), Sodium (mg), Carbohydrates (g), Dietary Fiber (g), Sugars (g), Protein (g), Vitamin A, Vitamin C, Calcium, Iron
About the Analyst:
He is a Data Geek aspirant and not your nutritionist 😛 .
To start with, I wanted to find the restaurant that offers a wide range of choices. How do the restaurants rank in terms of menu size?
Pretty easy, I just used a simple column chart with the restaurant names in X-axis and Menu(count distinct) in the Y-axis.
Subway tops the list with 171.
Next I wanted to see who serves me the highest amount of calories.
For this I used a donut chart with all menu items and calories represented by the pie sectors and I used the total calories for the pie depth.
Colorful, but definitely not very useful. So, I narrowed it to the restaurant names instead of the menu items. But this time I used the ‘pie chart with variable slice depth’.
I expected it to be Subway, since it had the biggest list of menu items, but surprisingly I see Burger King steals the spot.
What about fat you say. Let’s see about that. I use a 3D column chart to see the total fat in all menu listings and the calories that came from all that fat. Wendy’s did not have the information about ‘Calories from Fat’ so I filtered it out.
As a nutritionist…oops data analyst, I am worried about the heart too. Let’s check out the cholesterol levels of the food that we eat at these places.
Let’s see the top ten bad-boys in this category,
Of course there are a lot of good things in there called proteins and vitamins. Let’s get our facts straight on these using an area chart. We can do this only for three brands since Wendy’s and Burger King do not have any information on vitamins served.
Mc Donald’s seems to fare well than Subway even though it has a lesser count of menu items.
And lastly, my favorite. What goes into to the menu names that make it appealing to us?
I split the menu names across all restaurants into individual words and fed it to Lumira as a separate file and what better chart to analyze this information than the ‘tag cloud. The tag cloud is one of my favorites.
All the analyses has left me hungry and wanting a Grilled Sandwich with Cheese and Chicken and Egg.
And if you don’t like that filter it out and you have other options too,