Exploratory Data Analysis - 80 Cereals Data
Carlios Eryan
2022-08-02
Introduction
Context
Variables in the dataset:
Name: Name of cereal
mfr: Manufacturer of cereal
A = American Home Food Products;
G = General Mills
K = Kelloggs
N = Nabisco
P = Post
Q = Quaker Oats
R = Ralston Purina
type:
cold
hot
calories: calories per serving
protein: grams of protein
fat: grams of fat
sodium: milligrams of sodium
fiber: grams of dietary fiber
carbo: grams of complex carbohydrates
sugars: grams of sugars
potass: milligrams of potassium
vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended
shelf: display shelf (1, 2, or 3, counting from the floor)
weight: weight in ounces of one serving
cups: number of cups in one serving
rating: a rating of the cereals (Possibly from Consumer Reports?)
1. Explanation
The dataset is from Kaggle https://www.kaggle.com/datasets/crawford/80-cereals. This dataset contains information about 80 different cereal products from 7 manufacturers. Manufacturers include American Home Food Products, General Mills, Kellogs, Nabisco, Post, Quaker Oats, and Ralston Purina. This analysis will provide understanding of what goes in the cereal that we eat everyday for breakfast.
2. Read data and initial inspection
Loading libraries
library(readr)
library(dplyr)
library(reshape)
library(ggplot2)2.1. Reading data
Read data using read.csv (data is in the same folder)
cereal <- read.csv("cereal.csv")2.2. Initial data inspection
Data dimension is 77 rows by 16 columns
dim(cereal)## [1] 77 16The columns of the dataset
names(cereal)## [1] "name" "mfr" "type" "calories" "protein" "fat"
## [7] "sodium" "fiber" "carbo" "sugars" "potass" "vitamins"
## [13] "shelf" "weight" "cups" "rating"head(cereal, 10)## name mfr type calories protein fat sodium fiber carbo
## 1 100% Bran N C 70 4 1 130 10.0 5.0
## 2 100% Natural Bran Q C 120 3 5 15 2.0 8.0
## 3 All-Bran K C 70 4 1 260 9.0 7.0
## 4 All-Bran with Extra Fiber K C 50 4 0 140 14.0 8.0
## 5 Almond Delight R C 110 2 2 200 1.0 14.0
## 6 Apple Cinnamon Cheerios G C 110 2 2 180 1.5 10.5
## 7 Apple Jacks K C 110 2 0 125 1.0 11.0
## 8 Basic 4 G C 130 3 2 210 2.0 18.0
## 9 Bran Chex R C 90 2 1 200 4.0 15.0
## 10 Bran Flakes P C 90 3 0 210 5.0 13.0
## sugars potass vitamins shelf weight cups rating
## 1 6 280 25 3 1.00 0.33 68.40297
## 2 8 135 0 3 1.00 1.00 33.98368
## 3 5 320 25 3 1.00 0.33 59.42551
## 4 0 330 25 3 1.00 0.50 93.70491
## 5 8 -1 25 3 1.00 0.75 34.38484
## 6 10 70 25 1 1.00 0.75 29.50954
## 7 14 30 25 2 1.00 1.00 33.17409
## 8 8 100 25 3 1.33 0.75 37.03856
## 9 6 125 25 1 1.00 0.67 49.12025
## 10 5 190 25 3 1.00 0.67 53.31381Data structure
str(cereal)## 'data.frame': 77 obs. of 16 variables:
## $ name : chr "100% Bran" "100% Natural Bran" "All-Bran" "All-Bran with Extra Fiber" ...
## $ mfr : chr "N" "Q" "K" "K" ...
## $ type : chr "C" "C" "C" "C" ...
## $ calories: int 70 120 70 50 110 110 110 130 90 90 ...
## $ protein : int 4 3 4 4 2 2 2 3 2 3 ...
## $ fat : int 1 5 1 0 2 2 0 2 1 0 ...
## $ sodium : int 130 15 260 140 200 180 125 210 200 210 ...
## $ fiber : num 10 2 9 14 1 1.5 1 2 4 5 ...
## $ carbo : num 5 8 7 8 14 10.5 11 18 15 13 ...
## $ sugars : int 6 8 5 0 8 10 14 8 6 5 ...
## $ potass : int 280 135 320 330 -1 70 30 100 125 190 ...
## $ vitamins: int 25 0 25 25 25 25 25 25 25 25 ...
## $ shelf : int 3 3 3 3 3 1 2 3 1 3 ...
## $ weight : num 1 1 1 1 1 1 1 1.33 1 1 ...
## $ cups : num 0.33 1 0.33 0.5 0.75 0.75 1 0.75 0.67 0.67 ...
## $ rating : num 68.4 34 59.4 93.7 34.4 ...Data summary
summary(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. :93.70insight
There are negative value in carbo, sugars, and potass
insight
There are a lot of “outliers” in categories and weight which might be caused by different company size classification (small, medium, large)
3. Data cleansing and transformation
3.1. Check missing data
Check if data has missing value
anyNA(cereal)## [1] FALSEinsight
No missing value from data. However, we need to replace negative value with zero because none of the columns can have value below zero
cereal[cereal < 0] <- 03.2. Transform data type
cereal<-cereal %>%
mutate(mfr = as.factor(mfr),
type = as.factor(type),
mfr=recode(mfr,
A="American Home Food Products",
G="General Mills",
K="Kelloggs",
N="Nabisco",
P="Post",
Q="Quarker Oats",
R="Ralston Purnia"),
type=recode(type,
C="Cold",
H="Hot"))levels(cereal$mfr)## [1] "American Home Food Products" "General Mills"
## [3] "Kelloggs" "Nabisco"
## [5] "Post" "Quarker Oats"
## [7] "Ralston Purnia"levels(cereal$type)## [1] "Cold" "Hot"Manufacturer(mfr) of cereal: - A = American Home Food Products;
G = General Mills
K = Kelloggs
N = Nabisco
P = Post
Q = Quaker Oats
R = Ralston Purina
Type of cereal:
C = Cold
H = Hot
4. Further exploratory analysis
1. Correlation heatmap of numeric columns value
cereal_is.numeric <- cereal %>%
select_if(is.numeric)
cereal_heatmap <- round(cor(cereal_is.numeric), 1)
cereal_heatmap <- melt(cereal_heatmap)
ggplot(cereal_heatmap, aes(x=X1, y=X2, fill=value)) +
geom_tile()+
scale_fill_gradient(low = "pink", high = "dark red")+
labs(title="Correlation Heatmap",x="", y="")+
geom_text(aes(x=X1, y=X2, label=value), color="white")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), plot.title = element_text(hjust=0.5))
insight
There seems to be a very strong correlation between fiber and potassium content in cereal. Weight also correlate positively with calories, this makes sense because in physics, mass equates to energy, however not all of the energy from food (in this case, cereal) can be digested therefore it is not a perfect correlation.
2. Maybe it is more crucial to look at the correlation specific to the rating column
cereal_ratcorr <- data.frame(cor(cereal_is.numeric)) %>% select(rating) %>% filter(rating != 1)
cereal_ratcorr$id <- row.names(cereal_ratcorr)
ggplot(cereal_ratcorr, mapping=aes(x=id, y=rating, fill=rating)) +
geom_col()+
ylim(-1,1)+
scale_fill_gradient2(low = "#3D2F38", mid="#715266", high = "#A3918C", limits=c(-1,1))+
labs(title="Correlation with Rating",x="", y="Correlation Value")+
coord_flip()+
theme(plot.title = element_text(hjust=0.5))+
theme_minimal()
insight
There aren’t a strong positive correlation between rating and the cereal’s ingredients. The largest positive correlation value is only 0.58. Meanwhile, there are two strong negative correlation that contribute to the cereal’s rating, sugars and calories which the correlation value of -0.76 and -0.69 respectively.
3. Calories and Sugar have a pretty negative correlation with ratings
cereal_calcor<- melt(cereal %>%
select(rating, calories, sugars), id.vars="rating")
ggplot(cereal_calcor, mapping=aes(x=rating, y=value)) +
facet_wrap(vars(variable), scale="free_y")+
geom_point(aes(col=variable), show.legend=FALSE)+
geom_smooth(method="lm", aes(col=variable), show.legend=FALSE)+
labs(title="Scatter Plot Correlation of Both Calories and Sugars with Ratings",x="Rating", y="")+
theme(plot.title = element_text(hjust=0.5))+
theme_minimal()
insight
As we can see from this scatter plot, the rating of a cereal seems to go down the more the calories and sugars it contains.
4. Manfacturer ranked by average sugars in their products
mfr_sugar <- cereal %>%
group_by(mfr) %>%
summarise(mean_sugars = mean(sugars)) %>%
ungroup() %>%
arrange(desc(mean_sugars))
ggplot(mfr_sugar, mapping=aes(x=reorder(mfr, mean_sugars), y=mean_sugars, fill=mean_sugars)) +
geom_col(show.legend=FALSE)+
scale_fill_gradient(low = "#5ADBFF", high = "#004BA8")+
labs(
title="Average Sugar Content in Each Manufacturer's Products",
x="",
y="Average Sugar Content (grams)"
)+
coord_flip()+
theme(plot.title = element_text(hjust=0.5))+
theme_minimal()
insight
The difference between manufacturers with the lowest and highest average sugar content in their products is astonishing. We should really becareful when buying cereals from Post, General Mills, and Kelloggs as their products contain the most sugar.
5. Top 10 cereal with highest sugar level
sugar_highest <- head(cereal %>%
arrange(desc(sugars)) %>%
select(c(name, sugars)), 10)
ggplot(sugar_highest, mapping=aes(x=reorder(name, sugars), y=sugars))+
geom_col(aes(fill=sugars), show.legend = FALSE)+
geom_hline(yintercept = mean(cereal$sugars), col="white", size=1)+
labs(
title="Top 10 Cereal with Most Sugar Content",
x="",
y="Sugar Content (grams)"
)+
geom_text(aes(0, mean(cereal$sugars), label=round(mean(cereal$sugars), 1), vjust=0))+
scale_fill_gradient(low="#C1A5A9", high="#694873")+
theme(plot.title = element_text(hjust=0.5))+
theme_minimal()+
coord_flip()
insight
These cereal products have sugar content exceeds average cereal (6.9 grams) by 3-10 grams or 60-200%. This is a very surprising fact and I think we should avoid eating these cereals. However, there are cereal brands without sugar content which are:
w.o_sugar <- cereal %>%
select(c(name, sugars)) %>%
filter(sugars == 0)
print(w.o_sugar$name)## [1] "All-Bran with Extra Fiber" "Cream of Wheat (Quick)"
## [3] "Puffed Rice" "Puffed Wheat"
## [5] "Quaker Oatmeal" "Shredded Wheat"
## [7] "Shredded Wheat 'n'Bran" "Shredded Wheat spoon size"5. Conclusion
Generally, cereal that contain more sugar and calories will have lower ratings. While containing more fiber and protein can help with the ratings. Here we notice that cereal that are healthier are rated higher, so I assume that the rating is rated according to the health value. There are also cereals that we absolutely have to avoid eating regularly because the sugar content are just too high.