\n", + "
![\"Logo\"](\"img/logo.png\"){kind=logo}
\n",
+ "From the original dataset, we will be excluding the `Item` and `Serving Size` columns, as they are not relevant to the machine learning model we are about to build. Integrating these columns with the predictors used in this worksheet is beyond the scope of this camp."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "e0c2b2aa-1d8c-49f4-8c1d-16e77ea092fd",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.1 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n",
+ "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
+ "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m| Category | Item | Serving Size | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Breakfast | Egg McMuffin | 4.8 oz (136 g) | 300 | 120 | 13 | 20 | 5 | 25 | 0 | ⋯ | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | Egg White Delight | 4.8 oz (135 g) | 250 | 70 | 8 | 12 | 3 | 15 | 0 | ⋯ | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | Sausage McMuffin | 3.9 oz (111 g) | 370 | 200 | 23 | 35 | 8 | 42 | 0 | ⋯ | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | Sausage McMuffin with Egg | 5.7 oz (161 g) | 450 | 250 | 28 | 43 | 10 | 52 | 0 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | Sausage McMuffin with Egg Whites | 5.7 oz (161 g) | 400 | 210 | 23 | 35 | 8 | 42 | 0 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |
- 260
- 24
| Category | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | Cholesterol | Cholesterol (% Daily Value) | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Breakfast | 300 | 120 | 13 | 20 | 5 | 25 | 0 | 260 | 87 | ⋯ | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | 250 | 70 | 8 | 12 | 3 | 15 | 0 | 25 | 8 | ⋯ | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | 370 | 200 | 23 | 35 | 8 | 42 | 0 | 45 | 15 | ⋯ | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | 450 | 250 | 28 | 43 | 10 | 52 | 0 | 285 | 95 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | 400 | 210 | 23 | 35 | 8 | 42 | 0 | 50 | 16 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |
| Category | Count |
|---|---|
| <fct> | <int> |
| Breakfast | 42 |
| Beef & Pork | 15 |
| Chicken & Fish | 27 |
| Salads | 6 |
| Snacks & Sides | 13 |
| Desserts | 7 |
| Beverages | 27 |
| Coffee & Tea | 95 |
| Smoothies & Shakes | 28 |
| Category | Count | Percentage |
|---|---|---|
| <fct> | <int> | <dbl> |
| Breakfast | 42 | 16.15 |
| Beef & Pork | 15 | 5.77 |
| Chicken & Fish | 27 | 10.38 |
| Salads | 6 | 2.31 |
| Snacks & Sides | 13 | 5.00 |
| Desserts | 7 | 2.69 |
| Beverages | 27 | 10.38 |
| Coffee & Tea | 95 | 36.54 |
| Smoothies & Shakes | 28 | 10.77 |
\n", + "
![\"Chef\"](\"img/chef.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce472c67",
+ "metadata": {},
+ "source": [
+ "#### 2.1 Train, Test and Split\n",
+ "Let's use the \"Train, Test, and Split\" method to divide our `mcdonalds` dataset into training and testing data, setting the split ratio (initial_split) to $80$%. How many rows and columns of data do we have in each set?\n",
+ "\n",
+ "Why do we need to split our data into train and test sets? Explain in one or two sentences. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e65ff3f5",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "### YOUR CODE HERE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "cfccdf32-b043-4f91-8a49-5503e663823b",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dimensions of the training set are: 208 x 22 \n",
+ "Dimensions of the testing set are: 52 x 22"
+ ]
+ }
+ ],
+ "source": [
+ "mcdonalds_split <- initial_split(mcdonalds, prop=0.8)\n",
+ "mcdonalds_train <- training(mcdonalds_split)\n",
+ "mcdonalds_test <- testing(mcdonalds_split)\n",
+ "\n",
+ "cat(paste0(\"Dimensions of the training set are: \", paste(dim(mcdonalds_train), collapse = \" x \")), \"\\n\")\n",
+ "cat(paste0(\"Dimensions of the testing set are: \", paste(dim(mcdonalds_test), collapse = \" x \")))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25d5854e",
+ "metadata": {},
+ "source": [
+ "> **YOUR ANSWER HERE**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53d37ba3",
+ "metadata": {},
+ "source": [
+ "#### 2.2 Building Model and Recipe\n",
+ "Let's create a classification model using the k-Nearest Neighbor (k-NN) approach with $k=3$, following these guidelines:\n",
+ "- **Recipe**: Utilize all predictors in the dataset, excluding the `Category` column, which serves as our target variable.\n",
+ "- **Modeling**: Initialize the modeling strategy with `weight_func=\"rectangular\"`, use \"kknn\" as the engine, and set \"classification\" as the mode."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d1c89010",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "### YOUR CODE HERE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "497b1f72-4cac-4743-8d9c-521388ab03fd",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mcdonalds_recipe <- recipe(Category ~ ., data=mcdonalds_train) |>\n",
+ " step_scale(all_predictors()) |>\n",
+ " step_center(all_predictors())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "0f5e5d21-4c81-495d-acb0-c94cad725b33",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "knn_spec <- nearest_neighbor(weight_func = \"rectangular\", neighbors = 3) |>\n",
+ " set_engine(\"kknn\") |>\n",
+ " set_mode(\"classification\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c6ce735",
+ "metadata": {},
+ "source": [
+ "#### 2.3 Fit the Data into the Workflow\n",
+ "Let's combine the recipe and model we previously defined to fit our `mcdonalds_train` data into a workflow. Follow these steps:\n",
+ "1. Initialize the workflow.\n",
+ "2. Specify the recipe.\n",
+ "3. Incloude the model.\n",
+ "4. Fit the training data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "b94b7cec-f198-4171-9c7f-5dccefda2ccb",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "knn_fit <- workflow() |>\n",
+ " add_recipe(mcdonalds_recipe) |>\n",
+ " add_model(knn_spec) |>\n",
+ " fit(data=mcdonalds_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d3d99c1",
+ "metadata": {},
+ "source": [
+ "#### 2.4 Calculate Model Accuracy\n",
+ "Now, we will determine how accurate our model is. First, we will add a new column in our `mcdonalds_test` dataset that contains predictions made by our model. Then, we will calculate the model accuracy by evaluating how well these predictions match the actual Category values in the dataset. This accuracy assessment helps us understand how effectively our model can classify new items based on the training data it learned from.\n",
+ "\n",
+ "This section of the worksheet needs you to fill in the blanks with the right answers or code. Uncomment the code and replace all `...` with what you think is the right answer. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "ba70c247",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "### REPLACE ALL ...\n",
+ "\n",
+ "# validation <- predict(..., mcdonalds_test) |>\n",
+ "# bind_cols(...) |>\n",
+ "# rename(Category_pred = ...)\n",
+ "# head(validation, 5)\n",
+ "\n",
+ "# acc <- validation |>\n",
+ "# metrics(truth = Category, estimate = ...) |>\n",
+ "# filter(.metric == ...) |>\n",
+ "# select(...) |>\n",
+ "# pull()\n",
+ "\n",
+ "# acc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "9d08b9c2-1677-42cb-85e2-bc2bca0eddc3",
+ "metadata": {
+ "vscode": {
+ "languageId": "r"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| Category_pred | Category | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | Cholesterol | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Breakfast | Breakfast | 370 | 200 | 23 | 35 | 8 | 42 | 0 | 45 | ⋯ | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | Breakfast | 430 | 210 | 23 | 36 | 9 | 46 | 1 | 300 | ⋯ | 31 | 10 | 4 | 18 | 3 | 26 | 15 | 2 | 30 | 20 |
| Breakfast | Breakfast | 410 | 180 | 20 | 32 | 11 | 56 | 0 | 35 | ⋯ | 36 | 12 | 2 | 7 | 3 | 20 | 2 | 8 | 15 | 10 |
| Breakfast | Breakfast | 410 | 180 | 20 | 31 | 8 | 41 | 0 | 30 | ⋯ | 41 | 14 | 2 | 6 | 3 | 17 | 0 | 2 | 6 | 15 |
| Chicken & Fish | Breakfast | 400 | 140 | 15 | 24 | 7 | 34 | 0 | 35 | ⋯ | 47 | 16 | 2 | 9 | 16 | 20 | 2 | 10 | 15 | 10 |
| Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | Cholesterol | Cholesterol (% Daily Value) | Sodium | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| 6.155322 | 5.15345 | 11.04645 | 1.127663 | 9.370986 | 9.675415 | 16.24805 | 7.406411 | 10.93117 | 3.405241 | ⋯ | 17.64331 | 5.607077 | 7.969758 | 15.25102 | 13.38043 | 4.092243 | 7.150497 | 7.189502 | 13.80581 | 10.71622 |
| .pred_class |
|---|
| <fct> |
| Beef & Pork |
| artist_name | track_name | album_release_date | genres | danceability | energy | loudness | key | tempo | duration_ms | time_signature | popularity | pop |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <date> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <chr> |
| Miley Cyrus | Flowers | 2023-08-18 | ['pop'] | 0.706 | 0.691 | -4.775 | 0 | 118.048 | 200600 | 4 | 94 | yes |
| SZA | Kill Bill | 2022-12-08 | ['pop', 'r&b', 'rap'] | 0.644 | 0.735 | -5.747 | 8 | 88.980 | 153947 | 4 | 86 | yes |
| Harry Styles | As It Was | 2022-05-20 | ['pop'] | 0.520 | 0.731 | -5.338 | 6 | 173.930 | 167303 | 4 | 95 | yes |
| Jung Kook | Seven (feat. Latto) (Explicit Ver.) | 2023-11-03 | ['k-pop'] | 0.790 | 0.831 | -4.185 | 11 | 124.987 | 183551 | 4 | 90 | yes |
| Eslabon Armado | Ella Baila Sola | 2023-04-28 | ['corrido', 'corridos tumbados', 'sad sierreno', 'sierreno'] | 0.668 | 0.758 | -5.176 | 5 | 147.989 | 165671 | 3 | 86 | no |
| Taylor Swift | Cruel Summer | 2019-08-23 | ['pop'] | 0.552 | 0.702 | -5.707 | 9 | 169.994 | 178427 | 4 | 99 | yes |
\n", "
\n",
"From the original dataset, we will be excluding the `Item` and `Serving Size` columns, as they are not relevant to the machine learning model we are about to build. Integrating these columns with the predictors used in this worksheet is beyond the scope of this camp."
@@ -16,14 +16,49 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 1,
"id": "e0c2b2aa-1d8c-49f4-8c1d-16e77ea092fd",
"metadata": {
"vscode": {
"languageId": "r"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.1 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n",
+ "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n",
+ "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
+ "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
+ "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m| Category | Item | Serving Size | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | ... | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Category | Item | Serving Size | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
| <chr> | <chr> | <chr> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Breakfast | Egg McMuffin | 4.8 oz (136 g) | 300 | 120 | 13 | 20 | 5 | 25 | 0 | ... | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | Egg White Delight | 4.8 oz (135 g) | 250 | 70 | 8 | 12 | 3 | 15 | 0 | ... | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | Sausage McMuffin | 3.9 oz (111 g) | 370 | 200 | 23 | 35 | 8 | 42 | 0 | ... | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | Sausage McMuffin with Egg | 5.7 oz (161 g) | 450 | 250 | 28 | 43 | 10 | 52 | 0 | ... | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | Sausage McMuffin with Egg Whites | 5.7 oz (161 g) | 400 | 210 | 23 | 35 | 8 | 42 | 0 | ... | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |
| Breakfast | Egg McMuffin | 4.8 oz (136 g) | 300 | 120 | 13 | 20 | 5 | 25 | 0 | ⋯ | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | Egg White Delight | 4.8 oz (135 g) | 250 | 70 | 8 | 12 | 3 | 15 | 0 | ⋯ | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | Sausage McMuffin | 3.9 oz (111 g) | 370 | 200 | 23 | 35 | 8 | 42 | 0 | ⋯ | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | Sausage McMuffin with Egg | 5.7 oz (161 g) | 450 | 250 | 28 | 43 | 10 | 52 | 0 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | Sausage McMuffin with Egg Whites | 5.7 oz (161 g) | 400 | 210 | 23 | 35 | 8 | 42 | 0 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |
| Category | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | Cholesterol | Cholesterol (% Daily Value) | ... | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ... | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Category | Calories | Calories from Fat | Total Fat | Total Fat (% Daily Value) | Saturated Fat | Saturated Fat (% Daily Value) | Trans Fat | Cholesterol | Cholesterol (% Daily Value) | ⋯ | Carbohydrates | Carbohydrates (% Daily Value) | Dietary Fiber | Dietary Fiber (% Daily Value) | Sugars | Protein | Vitamin A (% Daily Value) | Vitamin C (% Daily Value) | Calcium (% Daily Value) | Iron (% Daily Value) |
| <fct> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | ⋯ | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | <dbl> |
| Breakfast | 300 | 120 | 13 | 20 | 5 | 25 | 0 | 260 | 87 | ... | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | 250 | 70 | 8 | 12 | 3 | 15 | 0 | 25 | 8 | ... | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | 370 | 200 | 23 | 35 | 8 | 42 | 0 | 45 | 15 | ... | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | 450 | 250 | 28 | 43 | 10 | 52 | 0 | 285 | 95 | ... | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | 400 | 210 | 23 | 35 | 8 | 42 | 0 | 50 | 16 | ... | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |
| Breakfast | 300 | 120 | 13 | 20 | 5 | 25 | 0 | 260 | 87 | ⋯ | 31 | 10 | 4 | 17 | 3 | 17 | 10 | 0 | 25 | 15 |
| Breakfast | 250 | 70 | 8 | 12 | 3 | 15 | 0 | 25 | 8 | ⋯ | 30 | 10 | 4 | 17 | 3 | 18 | 6 | 0 | 25 | 8 |
| Breakfast | 370 | 200 | 23 | 35 | 8 | 42 | 0 | 45 | 15 | ⋯ | 29 | 10 | 4 | 17 | 2 | 14 | 8 | 0 | 25 | 10 |
| Breakfast | 450 | 250 | 28 | 43 | 10 | 52 | 0 | 285 | 95 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 15 | 0 | 30 | 15 |
| Breakfast | 400 | 210 | 23 | 35 | 8 | 42 | 0 | 50 | 16 | ⋯ | 30 | 10 | 4 | 17 | 2 | 21 | 6 | 0 | 25 | 10 |