seaVegNames = {"dulse", "kombu", "nori", "kelp seaweed",
"irishmoss seaweed", "wakame", "watercress", "agar-agar",
"spirulina", "watercress", "water spinach", "lemongrass"};
seaVegData =
AssociationThread[seaVegNames,
Map[Interpreter["Food"][#] &, seaVegNames]];
seaVegLabels = AssociationThread[seaVegNames, Map[# &, seaVegNames]];
magnesiumContentSeaVeg =
EntityValue[Values[seaVegData], "RelativePotassiumContent",
"EntityAssociation"];
nutNames = {"almonds", "Brazil nuts", "cashews", "peanuts", "walnuts",
"pecans", "hazelnuts"};
nutData =
AssociationThread[nutNames, Map[Interpreter["Food"][#] &, nutNames]];
nutLabels = AssociationThread[nutNames, Map[# &, nutNames]];
magnesiumContentNuts =
EntityValue[Values[nutData], "RelativePotassiumContent",
"EntityAssociation"];
combinedMagnesiumContent =
Join[magnesiumContentSeaVeg, magnesiumContentNuts];
combinedMagnesiumContentLabels = Keys[Join[seaVegLabels, nutLabels]];
BarChart[Values[combinedMagnesiumContent],
ChartLabels ->
Placed[Map[Rotate[#, Pi/4] &, combinedMagnesiumContentLabels],
Below], ChartStyle -> "Rainbow",
PlotLabel -> "Potassium Content Comparison"]
And that is the detailed exploration of aquatic foods that we focus on, the nutritional aspects and safety that they have in our culinary uses; it's not like we're a couple of scholars going out for lunch it's more like we discuss the analysis of risks that we have with cooking recipes..and leave that to the sea vegetables and bivalves because the oysters, the mussels, and the clams, they know that they integration of the Wolfram Language for visualization and data analysis strengthens their informational value, and showcases the comparisons of magnesium content. But it's not just magnesium, we can also provide actionable insights into potassium.
redAlgae =
Interpreter["Species"][{"Porphyra", "Pyropia", "Palmaria palmata",
"Gracilaria", "Gelidium", "Delesseria sanguinea",
"Chondrus crispus", "Eucheuma", "Gigartina",
"Rhodymenia palmata"}];
speciesDetails =
EntityValue[redAlgae, {"Phylum", "Genus"}, "PropertyAssociation"];
ResourceFunction["NiceGrid"][speciesDetails]
And I wouldn't want to give the wrong impression but I'm pretty sure that potassium content between nuts and sea vegetables is a singular thing and if we only knew the iron content in various bivalves compared to beef us culinary professionals and enthusiasts will be softly weeping, when we observe the safety aspects of food safety rules. I mean what if you wanted to include some bivalves and you had them, if it makes your evening out that much more palatable. You could apply computational tools in food science until you're purple and blue in the face but the thing is to eat it, and that's what I would do honestly I would probably cook a bunch of bivalves and sea vegetables in order to more properly adhere to FDA guidelines.
bivalvesAndBeefNames = {"oysters", "beef", "scallops", "crab",
"shrimp"};
bivalvesAndBeefInterpret =
AssociationThread[
bivalvesAndBeefNames, (Interpreter["Food"][#1] &) /@
bivalvesAndBeefNames];
ironContent =
AssociationThread[Keys[bivalvesAndBeefInterpret],
EntityValue[Values[bivalvesAndBeefInterpret],
"RelativePotassiumContent"]];
ironContentSorted =
ReverseSortBy[ironContent, #["RelativePotassiumContent"] &];
bivalveLabels = Keys[ironContentSorted];
customColors =
Map[If[# === "beef", ColorData["HTML", "DarkRed"],
ColorData["Aquamarine"][3]] &, bivalveLabels];
BarChart[ironContentSorted,
ChartLabels -> Placed[Map[Rotate[#, Pi/4] &, bivalveLabels], Below],
PlotLabel -> "Potassium Content in Bivalves vs Beef",
AxesLabel -> "mg/g", ChartStyle -> customColors]
