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Lab 9: Candy Mini-Project
Cecilia Wang (A18625854)
Background
In this mini-project, we will explore FiveThirtyEight’s Halloween Candy dataset.
Data Import
Our dataset is a CSV file so we used read.csv() function.
candy_file <- read.csv("candy-data.csv")
candy = data.frame(candy_file,row.names = 1)
head(candy)
chocolate fruity caramel peanutyalmondy nougat crispedricewafer
100 Grand 1 0 1 0 0 1
3 Musketeers 1 0 0 0 1 0
One dime 0 0 0 0 0 0
One quarter 0 0 0 0 0 0
Air Heads 0 1 0 0 0 0
Almond Joy 1 0 0 1 0 0
hard bar pluribus sugarpercent pricepercent winpercent
100 Grand 0 1 0 0.732 0.860 66.97173
3 Musketeers 0 1 0 0.604 0.511 67.60294
One dime 0 0 0 0.011 0.116 32.26109
One quarter 0 0 0 0.011 0.511 46.11650
Air Heads 0 0 0 0.906 0.511 52.34146
Almond Joy 0 1 0 0.465 0.767 50.34755
Q1. How many different candy types are in this dataset?
sum(nrow(candy))
[1] 85
Q2. How many fruity candy types are in the dataset?
sum(candy$fruity==1)
[1] 38
Q3. What is your favorite candy (other than Twix) in the dataset and what is it’s winpercent value?
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
candy |>
filter(row.names(candy)=="Nerds") |>
select(winpercent)
winpercent
Nerds 55.35405
Q4. What is the winpercent value for “Kit Kat”?
candy |>
filter(row.names(candy)=="Kit Kat") |>
select(winpercent)
winpercent
Kit Kat 76.7686
Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy |>
filter(row.names(candy)=="Tootsie Roll Snack Bars") |>
select(winpercent)
winpercent
Tootsie Roll Snack Bars 49.6535
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
library("skimr")
skim(candy)
| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Winpercent
Q7. What do you think a zero and one represent for the candy$chocolate column?
skim(candy$chocolate)
| Name | candy$chocolate |
| Number of rows | 85 |
| Number of columns | 1 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| data | 0 | 1 | 0.44 | 0.5 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▆ |
nothing below this percentile.
Exploratory analysis
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
library(ggplot2)
ggplot(data.frame(winpercent = candy$winpercent),
aes(x = winpercent)) +
geom_histogram(bins = 20)
Q9. Is the distribution of winpercent values symmetrical?
No
Q10. Is the center of the distribution above or below 50%?
mean(candy$winpercent)
[1] 50.31676
summary((candy$winpercent))
Min. 1st Qu. Median Mean 3rd Qu. Max.
22.45 39.14 47.83 50.32 59.86 84.18
The center is below 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
mean(candy$winpercent[candy$chocolate == 1]) >
mean(candy$winpercent[candy$fruity == 1])
[1] TRUE
Chocolate candy ranked higher than fruit candy
Q12. Is this difference statistically significant?
t.test(candy$winpercent[candy$chocolate == 1],
candy$winpercent[candy$fruity == 1])
Welch Two Sample t-test
data: candy$winpercent[candy$chocolate == 1] and candy$winpercent[candy$fruity == 1]
t = 6.2582, df = 68.882, p-value = 2.871e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
11.44563 22.15795
sample estimates:
mean of x mean of y
60.92153 44.11974
p-value is less than 0.05, the difference statistically significant.
Overall Candy Rankings
Q13. What are the five least liked candy types in this set?
candy |> arrange(winpercent) |> head(5)
chocolate fruity caramel peanutyalmondy nougat
Nik L Nip 0 1 0 0 0
Boston Baked Beans 0 0 0 1 0
Chiclets 0 1 0 0 0
Super Bubble 0 1 0 0 0
Jawbusters 0 1 0 0 0
crispedricewafer hard bar pluribus sugarpercent pricepercent
Nik L Nip 0 0 0 1 0.197 0.976
Boston Baked Beans 0 0 0 1 0.313 0.511
Chiclets 0 0 0 1 0.046 0.325
Super Bubble 0 0 0 0 0.162 0.116
Jawbusters 0 1 0 1 0.093 0.511
winpercent
Nik L Nip 22.44534
Boston Baked Beans 23.41782
Chiclets 24.52499
Super Bubble 27.30386
Jawbusters 28.12744
Q14. What are the top 5 all time favorite candy types out of this set?
candy |> arrange(winpercent) |> tail(5)
chocolate fruity caramel peanutyalmondy nougat
Snickers 1 0 1 1 1
Kit Kat 1 0 0 0 0
Twix 1 0 1 0 0
Reese's Miniatures 1 0 0 1 0
Reese's Peanut Butter cup 1 0 0 1 0
crispedricewafer hard bar pluribus sugarpercent
Snickers 0 0 1 0 0.546
Kit Kat 1 0 1 0 0.313
Twix 1 0 1 0 0.546
Reese's Miniatures 0 0 0 0 0.034
Reese's Peanut Butter cup 0 0 0 0 0.720
pricepercent winpercent
Snickers 0.651 76.67378
Kit Kat 0.511 76.76860
Twix 0.906 81.64291
Reese's Miniatures 0.279 81.86626
Reese's Peanut Butter cup 0.651 84.18029
Q15. Make a first barplot of candy ranking based on winpercent values.
ggplot(candy) +
aes(x = winpercent, y = rownames(candy)) +
geom_bar(stat = "identity") +
theme_minimal() + theme(axis.text.y = element_text(size = 4))
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"
ggplot(candy) +
aes(x = winpercent, y =reorder(rownames(candy),winpercent)) +
geom_bar(stat = "identity") +
theme_minimal() + theme(axis.text.y = element_text(size = 4))+ geom_col(fill=my_cols)
Q17. What is the worst ranked chocolate candy?
Sixlets
Q18. What is the best ranked fruity candy?
Starburst
Taking a look at pricepercent
library(ggrepel)
# How about a plot of win vs price
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=2, max.overlaps = 15)
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
Reese’s Miniatures
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )
pricepercent winpercent
Nik L Nip 0.976 22.44534
Nestle Smarties 0.976 37.88719
Ring pop 0.965 35.29076
Hershey's Krackel 0.918 62.28448
Hershey's Milk Chocolate 0.918 56.49050
Nik L Nip
Exploring the correlation structure
library(corrplot)
corrplot 0.95 loaded
cij <- cor(candy)
corrplot(cij)
Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)?
Chocolate with fruity are anti-correlated. This is reasonable because fruity candy should not be overlapping with chocolate, it make the candy taste very bad.
Q23. Similarly, what two variables are most positively correlated?
Chocolate with winpercent.
Principal Component Analysis
pca <- prcomp(candy,scale=T )
summary(pca)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
PC8 PC9 PC10 PC11 PC12
Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[,1:2], col=my_cols, pch=16)
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols,alpha=0.8)
p
library(ggrepel)
p + geom_text_repel(size=1.5, col=my_cols, max.overlaps = 8) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")
ggplot(pca$rotation) +
aes(y = reorder(rownames(pca$rotation), PC1), x = PC1) +
geom_bar(stat = "identity") +
theme_grey()
Q24. Complete the code to generate the loadings plot above. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? Where did you see this relationship highlighted previously?
Fruity, pluribus, and hard are in the positive direction. Chocolate,bar,winpercent,and pricepercent are in the negative direction. We see the negative correlation between chocolate and fruity and positive correlation between chocolate and bar, chocolate and winpercent.
Summary
Q25. Based on your exploratory analysis, correlation findings, and PCA results, what combination of characteristics appears to make a “winning” candy? How do these different analyses (visualization, correlation, PCA) support or complement each other in reaching this conclusion?
Visualization, correlation, and PCA support that the combination of chocolate, peanuty/alomndy, and bar make a winning candy.
Q26. Are popular candies more expensive? In other words: is price significantly different between “winners” and “losers”? List both average values and a P-value along with your answer.
losers = candy[which(candy$winpercent < 50),]
winners = candy[which(candy$winpercent >= 50),]
mean(winners$pricepercent)
[1] 0.580359
mean(losers$pricepercent)
[1] 0.3743696
t.test(winners,candy$pricepercent)
Welch Two Sample t-test
data: winners and candy$pricepercent
t = 6.2917, df = 468.34, p-value = 7.216e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
3.542237 6.759764
sample estimates:
mean of x mean of y
5.6198831 0.4688824
Yes, popular candies are more expansive. And the difference is statistically significant since the p-value is less than 0.05.
Q27. Are candies with more sugar more likely to be popular? What is your interpretation of the means and P-value in this case?
mean(winners$sugarpercent)
[1] 0.5343077
mean(losers$sugarpercent)
[1] 0.4314565
t.test(winners,candy$sugarpercent)
Welch Two Sample t-test
data: winners and candy$sugarpercent
t = 6.2799, df = 468.31, p-value = 7.741e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
3.532496 6.749976
sample estimates:
mean of x mean of y
5.6198831 0.4786471
Yes, candies with more sugar more likely to be popular. And the difference is statistically significant since the p-value is less than 0.05.