Bioinformatics Portfolio
My coursework from BIMM 143. Showcasing genomic analysis techniques, introductory machine learning, and protein folding and structure prediction.
Class 9: Candy Mini-Project
Aadhya Tripathi (PID: A17878439)
- Background
- Data Import
- Exploratory analysis
- Overall Candy Rankings
- Taking a look at pricepercent
- Exploring the correlation structure
- Principal Component Analysis
- Summary
Background
In today’s mini-project we will analyze candy data with exploratory graphics, ggplot, basic statistics, correlation analysis, and principal component analysis methods we have been learning.
Data Import
The data comes as a CSV file from FiveThirtyEight.
candy <- read.csv("candy-data.csv", 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?
There are 85 different candy types.
Q2. How many fruity candy types are in the dataset?
There are 38 fruity candy types.
Q3. What is your favorite candy (other than Twix) in the dataset and what is it’s winpercent value?
My favorite candy is “Milky Way” with a winpercent of 73.099556.
Q4. What is the winpercent value for “Kit Kat”?
The winpercent for “Kit Kat” is 76.7686.
Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
The winpercent for “Tootsie Roll Snack Bars” is 49.653503.
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 | ▃▇▆▅▂ |
The winpercent variable is on a different scale in comparison to the other columns.
Q7. What do you think a zero and one represent for the candy$chocolate column?
It is a true/false for whether the candy type is chocolate or not.
Exploratory analysis
Q8. Plot a histogram of winpercent values using both base R an ggplot2.
Using base R:
hist(candy$winpercent)
Using ggplot:
library(ggplot2)
ggplot(candy) +
aes(winpercent) +
geom_histogram(bins = 10, fill = "lightblue", col = "darkblue")
Q9. Is the distribution of winpercent values symmetrical?
No, there is a skew to the right.
Q10. Is the center of the distribution above or below 50%?
mean(candy$winpercent)
[1] 50.31676
median(candy$winpercent)
[1] 47.82975
summary(candy$winpercent)
Min. 1st Qu. Median Mean 3rd Qu. Max.
22.45 39.14 47.83 50.32 59.86 84.18
The mean is slightly above 50%. However, the median is below 50%. Based on the median, the center is determined to be below 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
Steps to solve this problem: 1. Find all chocolate candy in the dataset
- Find winpercent of these 3. Calculate mean winpercent 4. Repeat 1-3 for fruity candy 5. Compare chocolate mean and fruity mean
choc_win <- candy$winpercent[as.logical(candy$chocolate)]
fruity_win <- candy$winpercent[as.logical(candy$fruity)]
print(mean(choc_win))
[1] 60.92153
print(mean(fruity_win))
[1] 44.11974
mean(choc_win) > mean(fruity_win)
[1] TRUE
Based on their mean values, chocolate candy is ranked higher than fruit candy.
Q12. Is this difference statistically significant?
t.test(choc_win, fruity_win)
Welch Two Sample t-test
data: choc_win and fruity_win
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
Based on the extremely low p-value, we can reject the null hypothesis and the difference is statistically significant.
Overall Candy Rankings
Q13. What are the five least liked candy types in this set?
Use dplyr to rearrange the data by winpercent.
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 |>
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
The 5 least liked candy types in this dataset are “Nik L Nip”, “Boston Baked Beans”, “Chiclets”, “Super Bubble”, and “Jawbusters”.
Q14. What are the top 5 all time favorite candy types out of this set?
candy |>
arrange(desc(winpercent)) |>
head(5)
chocolate fruity caramel peanutyalmondy nougat
Reese's Peanut Butter cup 1 0 0 1 0
Reese's Miniatures 1 0 0 1 0
Twix 1 0 1 0 0
Kit Kat 1 0 0 0 0
Snickers 1 0 1 1 1
crispedricewafer hard bar pluribus sugarpercent
Reese's Peanut Butter cup 0 0 0 0 0.720
Reese's Miniatures 0 0 0 0 0.034
Twix 1 0 1 0 0.546
Kit Kat 1 0 1 0 0.313
Snickers 0 0 1 0 0.546
pricepercent winpercent
Reese's Peanut Butter cup 0.651 84.18029
Reese's Miniatures 0.279 81.86626
Twix 0.906 81.64291
Kit Kat 0.511 76.76860
Snickers 0.651 76.67378
The 5 all time favorite candy types in this dataset are “Reese’s Peanut Butter cup”, “Reese’s Miniatures”, “Twix”, “Kit Kat”, and “Snickers”.
Q15. Make a first barplot of candy ranking based on winpercent values.
ggplot(candy) +
aes(winpercent, rownames(candy)) +
geom_col()
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent.
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col() +
ylab("")
Color the bars based on candy type. Brown is for chocolate, blue for bar candies, and green for fruity candies.
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "#BD5C00"
my_cols[as.logical(candy$bar)] = "#458CED"
my_cols[as.logical(candy$fruity)] = "#0BAD00"
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) +
ylab("")
Q17. What is the worst ranked chocolate candy?
Sixlets.
Q18. What is the best ranked fruity candy?
Starburst.
Taking a look at pricepercent
Use ggrepel to make overalpping data point labels easier to read.
library(ggrepel)
Make a plot of winpercent vs pricepercent:
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 7)
Warning: ggrepel: 40 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
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 have a high winpercent while having a relatively low pricepercent.
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
candy |>
arrange(desc(pricepercent)) |>
head(5)
chocolate fruity caramel peanutyalmondy nougat
Nik L Nip 0 1 0 0 0
Nestle Smarties 1 0 0 0 0
Ring pop 0 1 0 0 0
Hershey's Krackel 1 0 0 0 0
Hershey's Milk Chocolate 1 0 0 0 0
crispedricewafer hard bar pluribus sugarpercent
Nik L Nip 0 0 0 1 0.197
Nestle Smarties 0 0 0 1 0.267
Ring pop 0 1 0 0 0.732
Hershey's Krackel 1 0 1 0 0.430
Hershey's Milk Chocolate 0 0 1 0 0.430
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
The top 5 most expensive candy types are “Nik L Nip”, “Nestle Smarties”, “Ring pop”, “Hershey’s Krackel”, and “Hershey’s Milk Chocolate”. The least popular of these is “Nik L Nip”.
Exploring the correlation structure
Pearson correlation values range from -1 to +1.
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)?
Fruity and chocolate
Q23. Similarly, what two variables are most positively correlated?
Chocolate and winpercent
Principal Component Analysis
pca <- prcomp(candy, scale=TRUE)
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
The main results figure is the PCA score plot:
plot(pca$x[,1:2])
plot(pca$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$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols) +
labs(title="PCA Candy Space Map")
p
p <- p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (brown), bar (blue), fruity (green), other (black)",
caption="Data from 538")
p
Warning: ggrepel: 43 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Make an interactive plot with plotly (excluded for PDF render):
# library(plotly)
# ggplotly(p)
Q24. Complete the code to generate the loadings plot above. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? Where did you see this relationship highlighted previously?
ggplot(pca$rotation) +
aes(PC1, reorder(rownames(pca$rotation), PC1)) +
geom_col() +
ylab("")
Fruity, pluribus, and hard are in the positive direction. It makes sense for these characteristics to be correlated as many fruity candies come in multiples and may be hard more often than chocolate. This relationship was previously highlighted in the correlation matrix.
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?
Chocolate, bar candy types appear to be the “winning” candy. The correlation matrix shows that winpercent has strongest positive correlation with chocolate, and it has a medium positive correlation with bar as well. The PCA plot supports that chocolate and bar candy are more similar to each other compared to fruity candy, as they are closer together on the PC1 axis than chocolate and fruity. The winpercent ordered barplot visualization shows that that brown and blue, representing chocolate and bar candies, tend to be higher compared to the green fruity candies.