library(readr)

## Warning: package 'readr' was built under R version 4.3.3

nutrition <- read_csv("C:/Users/cisco/Downloads/nutrition_subset")

## Rows: 961 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): food item
## dbl (3): weight_in_grams, saturated_fat, cholesterol
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

1.The elements in the data set are food items of various sizes, ranging from a teaspoon of cinnamon to an entire carrot cake.

1.1: Add an index field.

n <- dim(nutrition)[1]
nutrition$Index <- c(1:n)
head(nutrition)

## # A tibble: 6 × 5
##   `food item`                    weight_in_grams saturated_fat cholesterol Index
##   <chr>                                    <dbl>         <dbl>       <dbl> <int>
## 1 GELATIN; DRY                 …             7             0             0     1
## 2 SEAWEED; SPIRULINA; DRIED    …            28.4           0.8           0     2
## 3 YEAST; BAKERS; DRY; ACTIVE   …             7             0             0     3
## 4 PARMESAN CHEESE; GRATED      …            28.4           5.4          22     4
## 5 PARMESAN CHEESE; GRATED      …           100            19.1          79     5
## 6 PARMESAN CHEESE; GRATED      …             5             1             4     6

1.2: Sort the data set by the saturated fat (saturated_fat) and produce a listing of the five food items highest in saturated fat.

nutrition_sort <- nutrition[ order(- nutrition$saturated_fat), ]
nutrition_sort[ 1:5, ]

## # A tibble: 5 × 5
##   `food item`                    weight_in_grams saturated_fat cholesterol Index
##   <chr>                                    <dbl>         <dbl>       <dbl> <int>
## 1 CHEESECAKE                   …            1110         120.         2053   379
## 2 ICE CREAM; VANLLA; RICH 16% F…            1188         118.          703   536
## 3 YELLOWCAKE W/ CHOCFRSTNG;COMM…            1108          92           609   459
## 4 CREME PIE                    …             910          90.1          46   582
## 5 LARD                         …             205          80.4         195   891

print(nutrition_sort)

## # A tibble: 961 × 5
##    `food item`                   weight_in_grams saturated_fat cholesterol Index
##    <chr>                                   <dbl>         <dbl>       <dbl> <int>
##  1 CHEESECAKE                  …            1110         120.         2053   379
##  2 ICE CREAM; VANLLA; RICH 16% …            1188         118.          703   536
##  3 YELLOWCAKE W/ CHOCFRSTNG;COM…            1108          92           609   459
##  4 CREME PIE                   …             910          90.1          46   582
##  5 LARD                        …             205          80.4         195   891
##  6 ICE CREAM; VANLLA; REGULR 11…            1064          71.3         476   467
##  7 CARROT CAKE;CREMCHESE FRST;R…            1536          66          1183   423
##  8 BUTTER; SALTED              …             113          57.1         247   710
##  9 BUTTER; UNSALTED            …             113          57.1         247   711
## 10 DEVIL'S FOOD CAKE;CHOCFRST;F…            1107          55.6         598   411
## # ℹ 951 more rows

1.3: Comment on the validity of comparing food items of different sizes.

Without standardization, comparing the raw values would be misleading since you’d be comparing nutrients across different total amounts of food. For example, 1 CUP of parmesan cheese (100g) showing 19.1g of saturated fat versus 1 TBSP (5g) showing 1.0g of saturated fat appears to be a huge difference,but it’s simply due to the 20x difference in portion size.
However valid comparisons can be made if nutrient values are standardized using the weight_in_grams column. By calculating nutrient content per gram (dividing saturated_fat or cholesterol by weight_in_grams), you can make direct, meaningful comparisons between foods, regardless of their original serving sizes.

2.Derive a new variable, saturated_fat_per_gram, by dividing the amount of saturated fat by the weight in grams.

nutrition$saturated_fat_per_gram <- nutrition$saturated_fat / nutrition$weight_in_grams

2.1: Sort the data set by saturated_fat_per_gram and produce a listing of the five food items highest in saturated fat per gram.

nutrition_sort2 <- nutrition[ order(- nutrition$saturated_fat_per_gram), ]
nutrition_sort2[ 1:5, ]

## # A tibble: 5 × 6
##   `food item`                    weight_in_grams saturated_fat cholesterol Index
##   <chr>                                    <dbl>         <dbl>       <dbl> <int>
## 1 BUTTER; SALTED               …              14           7.1          31   909
## 2 BUTTER; UNSALTED             …              14           7.1          31   910
## 3 BUTTER; SALTED               …             113          57.1         247   710
## 4 BUTTER; UNSALTED             …             113          57.1         247   711
## 5 BUTTER; SALTED               …               5           2.5          11   913
## # ℹ 1 more variable: saturated_fat_per_gram <dbl>

print(nutrition_sort2)

## # A tibble: 961 × 6
##    `food item`                   weight_in_grams saturated_fat cholesterol Index
##    <chr>                                   <dbl>         <dbl>       <dbl> <int>
##  1 BUTTER; SALTED              …            14             7.1          31   909
##  2 BUTTER; UNSALTED            …            14             7.1          31   910
##  3 BUTTER; SALTED              …           113            57.1         247   710
##  4 BUTTER; UNSALTED            …           113            57.1         247   711
##  5 BUTTER; SALTED              …             5             2.5          11   913
##  6 BUTTER; UNSALTED            …             5             2.5          11   914
##  7 LARD                        …            13             5.1          12   900
##  8 LARD                        …           205            80.4         195   891
##  9 IMITATION CREAMERS; POWDERED…             2             0.7           0   921
## 10 CHOCOLATE; BITTER OT BAKING …            28.4           9             0   211
## # ℹ 951 more rows
## # ℹ 1 more variable: saturated_fat_per_gram <dbl>

2.2: Which food has the most saturated fat per gram? 1 TBSP of Butter (both salted and unsalted) has the most saturated fat per gram (sfpg), at 0.507 sfpg.

3.Derive a new variable, cholesterol_per_gram, by dividing the amount of cholesterol by the weight in grams.

nutrition$cholesterol_per_gram <- nutrition$cholesterol / nutrition$weight_in_grams

3.1: Sort the data set by cholesterol_per_gram and produce a listing of the five food items highest in cholesterol fat per gram.

nutrition_sort3 <- nutrition[ order(- nutrition$cholesterol_per_gram), ]
nutrition_sort3[ 1:5, ]

## # A tibble: 5 × 7
##   `food item`                    weight_in_grams saturated_fat cholesterol Index
##   <chr>                                    <dbl>         <dbl>       <dbl> <int>
## 1 EGGS; RAW; YOLK              …              17           1.6         213   120
## 2 CHICKEN LIVER; COOKED        …              20           0.4         126    59
## 3 BEEF LIVER; FRIED            …              85           2.5         410    46
## 4 EGGS; COOKED; FRIED          …              46           1.9         211   168
## 5 EGGS; RAW; WHOLE             …              50           1.6         213   185
## # ℹ 2 more variables: saturated_fat_per_gram <dbl>, cholesterol_per_gram <dbl>

print(nutrition_sort3)

## # A tibble: 961 × 7
##    `food item`                   weight_in_grams saturated_fat cholesterol Index
##    <chr>                                   <dbl>         <dbl>       <dbl> <int>
##  1 EGGS; RAW; YOLK             …              17           1.6         213   120
##  2 CHICKEN LIVER; COOKED       …              20           0.4         126    59
##  3 BEEF LIVER; FRIED           …              85           2.5         410    46
##  4 EGGS; COOKED; FRIED         …              46           1.9         211   168
##  5 EGGS; RAW; WHOLE            …              50           1.6         213   185
##  6 EGGS; COOKED; HARD-COOKED   …              50           1.6         213   187
##  7 EGGS; COOKED; POACHED       …              50           1.5         212   186
##  8 EGGS; COOKED; SCRAMBLED/OMEL…              61           2.2         215   190
##  9 BUTTER; SALTED              …              14           7.1          31   909
## 10 BUTTER; UNSALTED            …              14           7.1          31   910
## # ℹ 951 more rows
## # ℹ 2 more variables: saturated_fat_per_gram <dbl>, cholesterol_per_gram <dbl>

3.2: Which food has the most cholesterol fat per gram? Raw egg yolk has the most cholesterol per gram (cpg), at 12.5 cpg.

4.Standardize the field saturated_fat_per_gram.

nutrition$saturated_fat_per_gram_z <- scale(x = nutrition$saturated_fat_per_gram)

4.1: Produce a listing of all the food items that are outliers at the high end of the scale.

nutrition_outliersA <- nutrition[ which(nutrition$saturated_fat_per_gram_z > 3) , ]
print(nutrition_outliersA)

## # A tibble: 15 × 8
##    `food item`                   weight_in_grams saturated_fat cholesterol Index
##    <chr>                                   <dbl>         <dbl>       <dbl> <int>
##  1 CHOCOLATE; BITTER OT BAKING …            28.4           9             0   211
##  2 COCONUT; RAW; SHREDDED      …            80            23.8           0   449
##  3 COCONUT; DRIED; SWEETND;SHRE…            93            29.3           0   493
##  4 COCONUT; RAW; PIECE         …            45            13.4           0   577
##  5 BUTTER; SALTED              …           113            57.1         247   710
##  6 BUTTER; UNSALTED            …           113            57.1         247   711
##  7 LARD                        …           205            80.4         195   891
##  8 FATS; COOKING/VEGETBL SHORTE…            13             3.3           0   899
##  9 LARD                        …            13             5.1          12   900
## 10 FATS; COOKING/VEGETBL SHORTE…           205            51.3           0   908
## 11 BUTTER; SALTED              …            14             7.1          31   909
## 12 BUTTER; UNSALTED            …            14             7.1          31   910
## 13 BUTTER; SALTED              …             5             2.5          11   913
## 14 BUTTER; UNSALTED            …             5             2.5          11   914
## 15 IMITATION CREAMERS; POWDERED…             2             0.7           0   921
## # ℹ 3 more variables: saturated_fat_per_gram <dbl>, cholesterol_per_gram <dbl>,
## #   saturated_fat_per_gram_z <dbl[,1]>

4.2 How many food items are outliers at the low end of the scale?

nutrition_outliersB <- nutrition[ which(nutrition$saturated_fat_per_gram_z < -3) , ]
print(nutrition_outliersB)

## # A tibble: 0 × 8
## # ℹ 8 variables: food item <chr>, weight_in_grams <dbl>, saturated_fat <dbl>,
## #   cholesterol <dbl>, Index <int>, saturated_fat_per_gram <dbl>,
## #   cholesterol_per_gram <dbl>, saturated_fat_per_gram_z <dbl[,1]>

There are no (0) outliers at the low end of the scale.

5.Standardize the field cholesterol_per_gram.

nutrition$cholesterol_per_gram_z <- scale(x = nutrition$cholesterol_per_gram)

5.1: Produce a listing of all the food items that are outliers at the high end of the scale.

nutrition_outliersC <- nutrition[ which(nutrition$cholesterol_per_gram_z > 3) , ]
print(nutrition_outliersC)

## # A tibble: 8 × 9
##   `food item`                    weight_in_grams saturated_fat cholesterol Index
##   <chr>                                    <dbl>         <dbl>       <dbl> <int>
## 1 BEEF LIVER; FRIED            …              85           2.5         410    46
## 2 CHICKEN LIVER; COOKED        …              20           0.4         126    59
## 3 EGGS; RAW; YOLK              …              17           1.6         213   120
## 4 EGGS; COOKED; FRIED          …              46           1.9         211   168
## 5 EGGS; RAW; WHOLE             …              50           1.6         213   185
## 6 EGGS; COOKED; POACHED        …              50           1.5         212   186
## 7 EGGS; COOKED; HARD-COOKED    …              50           1.6         213   187
## 8 EGGS; COOKED; SCRAMBLED/OMELE…              61           2.2         215   190
## # ℹ 4 more variables: saturated_fat_per_gram <dbl>, cholesterol_per_gram <dbl>,
## #   saturated_fat_per_gram_z <dbl[,1]>, cholesterol_per_gram_z <dbl[,1]>

Exercise1Dataprep_Part1

Francisco Javier Estrada

2025-01-20

1.The elements in the data set are food items of various sizes, ranging from a teaspoon of cinnamon to an entire carrot cake.

2.Derive a new variable, saturated_fat_per_gram, by dividing the amount of saturated fat by the weight in grams.

3.Derive a new variable, cholesterol_per_gram, by dividing the amount of cholesterol by the weight in grams.

4.Standardize the field saturated_fat_per_gram.

5.Standardize the field cholesterol_per_gram.