80 Cereal Brands. How close are they to the daily nutritional recommendation by the World Health Organization (WHO)?
Kenneth Childs Jr
4/1/2022
Install packages
library(tidyverse)
library(readr)
library(dplyr)
library(janitor)
library(plotly)
library(forcats)
library(plyr)
library(scales)Ask
The purpose of my analysis is to find out how healthy are the cereals listed.
- Find out which cereal is the healthiest and why?
- How does the healthiest cereal match up to the recommended daily intakes proved by the World Health Organization(WHO)?
I’m the primary stack-holder for this project.
Prepare
This dataset was provided by Chris Crawford on Kaggle. Latest update was 10-24-2017. Data was downloaded as a .CSV and stored safely on an external hard drive.
References
- Sodium intake for adults and children World Health Organization(WHO)
- Potassium intake for adults and children World Health Organization(WHO)
cereal <- read.csv("cereal.csv")
food_group <- read.csv("high_potassium_food.csv")Process
Used glimpse to find out we have 77 rows, 16 columns,the datatypes involved, and make sure we have no duplicates
glimpse(cereal)## Rows: 77
## Columns: 16
## $ name <chr> "100% Bran", "100% Natural Bran", "All-Bran", "All-Bran with ~
## $ mfr <chr> "N", "Q", "K", "K", "R", "G", "K", "G", "R", "P", "Q", "G", "~
## $ type <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "~
## $ calories <int> 70, 120, 70, 50, 110, 110, 110, 130, 90, 90, 120, 110, 120, 1~
## $ protein <int> 4, 3, 4, 4, 2, 2, 2, 3, 2, 3, 1, 6, 1, 3, 1, 2, 2, 1, 1, 3, 3~
## $ fat <int> 1, 5, 1, 0, 2, 2, 0, 2, 1, 0, 2, 2, 3, 2, 1, 0, 0, 0, 1, 3, 0~
## $ sodium <int> 130, 15, 260, 140, 200, 180, 125, 210, 200, 210, 220, 290, 21~
## $ fiber <dbl> 10.0, 2.0, 9.0, 14.0, 1.0, 1.5, 1.0, 2.0, 4.0, 5.0, 0.0, 2.0,~
## $ carbo <dbl> 5.0, 8.0, 7.0, 8.0, 14.0, 10.5, 11.0, 18.0, 15.0, 13.0, 12.0,~
## $ sugars <int> 6, 8, 5, 0, 8, 10, 14, 8, 6, 5, 12, 1, 9, 7, 13, 3, 2, 12, 13~
## $ potass <int> 280, 135, 320, 330, -1, 70, 30, 100, 125, 190, 35, 105, 45, 1~
## $ vitamins <int> 25, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25~
## $ shelf <int> 3, 3, 3, 3, 3, 1, 2, 3, 1, 3, 2, 1, 2, 3, 2, 1, 1, 2, 2, 3, 2~
## $ weight <dbl> 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.33, 1.00, 1.00, 1~
## $ cups <dbl> 0.33, 1.00, 0.33, 0.50, 0.75, 0.75, 1.00, 0.75, 0.67, 0.67, 0~
## $ rating <dbl> 68.40297, 33.98368, 59.42551, 93.70491, 34.38484, 29.50954, 3~n_distinct(cereal)## [1] 77summary(cereal)## name mfr type calories
## Length:77 Length:77 Length:77 Min. : 50.0
## Class :character Class :character Class :character 1st Qu.:100.0
## Mode :character Mode :character Mode :character Median :110.0
## Mean :106.9
## 3rd Qu.:110.0
## Max. :160.0
## protein fat sodium fiber
## Min. :1.000 Min. :0.000 Min. : 0.0 Min. : 0.000
## 1st Qu.:2.000 1st Qu.:0.000 1st Qu.:130.0 1st Qu.: 1.000
## Median :3.000 Median :1.000 Median :180.0 Median : 2.000
## Mean :2.545 Mean :1.013 Mean :159.7 Mean : 2.152
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:210.0 3rd Qu.: 3.000
## Max. :6.000 Max. :5.000 Max. :320.0 Max. :14.000
## carbo sugars potass vitamins
## Min. :-1.0 Min. :-1.000 Min. : -1.00 Min. : 0.00
## 1st Qu.:12.0 1st Qu.: 3.000 1st Qu.: 40.00 1st Qu.: 25.00
## Median :14.0 Median : 7.000 Median : 90.00 Median : 25.00
## Mean :14.6 Mean : 6.922 Mean : 96.08 Mean : 28.25
## 3rd Qu.:17.0 3rd Qu.:11.000 3rd Qu.:120.00 3rd Qu.: 25.00
## Max. :23.0 Max. :15.000 Max. :330.00 Max. :100.00
## shelf weight cups rating
## Min. :1.000 Min. :0.50 Min. :0.250 Min. :18.04
## 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:0.670 1st Qu.:33.17
## Median :2.000 Median :1.00 Median :0.750 Median :40.40
## Mean :2.208 Mean :1.03 Mean :0.821 Mean :42.67
## 3rd Qu.:3.000 3rd Qu.:1.00 3rd Qu.:1.000 3rd Qu.:50.83
## Max. :3.000 Max. :1.50 Max. :1.500 Max. :93.70Removed the rows that have negative values. Which brought me to 74 rows and 16 columns after removing the rows with negative values.
cereal_without_neg <- subset(cereal,carbo != -1 & potass != -1)
glimpse(cereal_without_neg)## Rows: 74
## Columns: 16
## $ name <chr> "100% Bran", "100% Natural Bran", "All-Bran", "All-Bran with ~
## $ mfr <chr> "N", "Q", "K", "K", "G", "K", "G", "R", "P", "Q", "G", "G", "~
## $ type <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "~
## $ calories <int> 70, 120, 70, 50, 110, 110, 130, 90, 90, 120, 110, 120, 110, 1~
## $ protein <int> 4, 3, 4, 4, 2, 2, 3, 2, 3, 1, 6, 1, 3, 1, 2, 2, 1, 1, 3, 2, 2~
## $ fat <int> 1, 5, 1, 0, 2, 0, 2, 1, 0, 2, 2, 3, 2, 1, 0, 0, 0, 1, 3, 0, 1~
## $ sodium <int> 130, 15, 260, 140, 180, 125, 210, 200, 210, 220, 290, 210, 14~
## $ fiber <dbl> 10.0, 2.0, 9.0, 14.0, 1.5, 1.0, 2.0, 4.0, 5.0, 0.0, 2.0, 0.0,~
## $ carbo <dbl> 5.0, 8.0, 7.0, 8.0, 10.5, 11.0, 18.0, 15.0, 13.0, 12.0, 17.0,~
## $ sugars <int> 6, 8, 5, 0, 10, 14, 8, 6, 5, 12, 1, 9, 7, 13, 3, 2, 12, 13, 7~
## $ potass <int> 280, 135, 320, 330, 70, 30, 100, 125, 190, 35, 105, 45, 105, ~
## $ vitamins <int> 25, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25~
## $ shelf <int> 3, 3, 3, 3, 1, 2, 3, 1, 3, 2, 1, 2, 3, 2, 1, 1, 2, 2, 3, 3, 3~
## $ weight <dbl> 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.33, 1.00, 1.00, 1.00, 1~
## $ cups <dbl> 0.33, 1.00, 0.33, 0.50, 0.75, 1.00, 0.75, 0.67, 0.67, 0.75, 1~
## $ rating <dbl> 68.40297, 33.98368, 59.42551, 93.70491, 29.50954, 33.17409, 3~summary(cereal_without_neg)## name mfr type calories
## Length:74 Length:74 Length:74 Min. : 50
## Class :character Class :character Class :character 1st Qu.:100
## Mode :character Mode :character Mode :character Median :110
## Mean :107
## 3rd Qu.:110
## Max. :160
## protein fat sodium fiber carbo
## Min. :1.000 Min. :0 Min. : 0.0 Min. : 0.000 Min. : 5.00
## 1st Qu.:2.000 1st Qu.:0 1st Qu.:135.0 1st Qu.: 0.250 1st Qu.:12.00
## Median :2.500 Median :1 Median :180.0 Median : 2.000 Median :14.50
## Mean :2.514 Mean :1 Mean :162.4 Mean : 2.176 Mean :14.73
## 3rd Qu.:3.000 3rd Qu.:1 3rd Qu.:217.5 3rd Qu.: 3.000 3rd Qu.:17.00
## Max. :6.000 Max. :5 Max. :320.0 Max. :14.000 Max. :23.00
## sugars potass vitamins shelf
## Min. : 0.000 Min. : 15.00 Min. : 0.00 Min. :1.000
## 1st Qu.: 3.000 1st Qu.: 41.25 1st Qu.: 25.00 1st Qu.:1.250
## Median : 7.000 Median : 90.00 Median : 25.00 Median :2.000
## Mean : 7.108 Mean : 98.51 Mean : 29.05 Mean :2.216
## 3rd Qu.:11.000 3rd Qu.:120.00 3rd Qu.: 25.00 3rd Qu.:3.000
## Max. :15.000 Max. :330.00 Max. :100.00 Max. :3.000
## weight cups rating
## Min. :0.500 Min. :0.2500 Min. :18.04
## 1st Qu.:1.000 1st Qu.:0.6700 1st Qu.:32.45
## Median :1.000 Median :0.7500 Median :40.25
## Mean :1.031 Mean :0.8216 Mean :42.37
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:50.52
## Max. :1.500 Max. :1.5000 Max. :93.70Arranged the cereal by rating in descending order
cereal_by_rating <- cereal_without_neg %>% arrange(desc(rating))
food_group <-clean_names(food_group)Analyze and Visualize
As seen below, the higher rating cereals have a lower calorie count.
rating_calories <- ggplot(data = cereal_by_rating) + geom_jitter(aes(x= calories, y = rating, fill = name)) +
labs(title = "80 Cereals", subtitle = "Calorie vs Rating trend", x = "Calories", y= "Rating", caption = "Data Source: Chris Crawford")
rating_calorie <- ggplotly(rating_calories)
rating_calorieThe World Health Organization(WHO) recommends a reduction in sodium intake to reduce blood pressure and risk of cardiovascular disease,stroke and coronary heart disease in adults. WHO recommends a reduction to <2g/day sodium(5g/day salt) in adults. Adults include individuals > or = 16 years of age. I decided to compare this recommendation to the highest rated cereal on our list to see if this would be a good product to eat.I also converted the sodium of the cereal to grams.
cereal_max_rating <- cereal_by_rating [1,]
cereal_max_rating["sodium_in_grams"] <- cereal_max_rating$sodium / 1000
cereal_max <- select(cereal_max_rating, name, sodium_in_grams)
recommended_sodium <- c("WHO_recommendation", 7)
sodium_intake <- rbind(cereal_max, recommended_sodium)
sodium_intake$sodium_in_grams <- as.double(sodium_intake$sodium_in_grams)ggplot(data = sodium_intake) + geom_col(aes(x = name, y = sodium_in_grams, fill = name)) +
labs(title = "Sodium", x= "", y = "Sodium (g)", subtitle = "All-Bran with Extra Fiber vs WHO recommendation for daily sodium intake 3/27/2022", caption = "Data Source: Chris Crawford") +
geom_text(aes(x= name, y = sodium_in_grams, label = sodium_in_grams, vjust=-.1))
The meta-analysis of 21 studies with 21 comparisons found that increased potassium resulted in a decrease in resting systolic blood pressure. The results suggest that the greatest impact on blood pressure was achieved when the increased potassium intake was approximately 3,519–4,692mg/day. Let’s see how our close the top rated cereal compares to the WHO recommendation.
cereal_by_potassium <- cereal_by_rating %>% arrange(desc(potass))
potass_top_5 <- head(cereal_by_potassium,5)
ggplot(data = potass_top_5) + geom_col(mapping = aes(x = name, y = potass, fill = name)) +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=.4)) +
labs(x = " ",y = "Potassium (mg)", title = "Potassium", subtitle = "Top Five brands with the highest amount of Potassium", caption = "Data Source: Chris Crawford") +
geom_text(aes(x= name, y = potass, label = potass, vjust=-.1))
cereal_max_potass <- select(cereal_max_rating, name, potass)
recommended_potassium <- c("WHO_recommendation", 3519)
potass_intake <- rbind(cereal_max_potass, recommended_potassium)
potass_intake$potass <- as.double(potass_intake$potass)ggplot(data = potass_intake) + geom_col(aes(x = name, y = potass, fill = name)) +
labs(title = "Potassium", x= "", y = "Potassium (mg)", subtitle = "All-Bran with Extra Fiber vs WHO recommendation for daily potassium intake 3/27/2022", caption = "Data Source: Chris Crawford") +
geom_text(aes(x= name, y = potass, label = potass, vjust=-.1))
Pros
- Low calories
- Contains some potassium
- Low in sodium
Cons
- Isn’t close to filling the daily recommendation of potassium
Conclusion
The highest rated cereal may be good for weight loss due to it’s low calorie and sodium, but if you plan to eat this cereal I suggest eating foods rich in potassium along with it. Examples:
## food_group potassium_content_mg
## 1 Beans and peas 1300
## 2 Nuts 600
## 3 Green vegetables 550
## 4 Root vegetables 200
## 5 Other vegetables 300
## 6 Fruits 300