Class09

Author

Samuel Fisher (A18131929)

Data Import

The data comes as a CSV file from 538

candy_file <- "candy-data.csv"

candy = read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/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?

nrow(candy)

[1] 85

There are 85 different candy types in this dataset

Q2. How many fruity candy types are in the dataset?

sum(candy$fruity == 1)

[1] 38

There are 38 fruity candy types in this dataset

Q3. What is your favorite candy (other than Twix) in the dataset and what is it’s winpercent value?

candy["100 Grand", "winpercent"]

[1] 66.97173

My favorite candy in the dataset is 100 Grand with a winpercent of 66.97173.

Q4. What is the winpercent value for Kit Kat?

candy["Kit Kat", "winpercent"]

[1] 76.7686

The winpercent for Kit Kat is 76.7686.

Q5. What is the win percent for Tootsie Roll Snack Bars?

candy["Tootsie Roll Snack Bars", "winpercent"]

[1] 49.6535

library("skimr")
skim(candy)

Data summary
Name	candy
Number of rows	85
Number of columns	12
_______________________
Column type frequency:
numeric	12
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
chocolate	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
fruity	1	0.45	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
caramel	1	0.16	0.37	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▂
peanutyalmondy	1	0.16	0.37	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▂
nougat	1	0.08	0.28	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
crispedricewafer	1	0.08	0.28	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
hard	1	0.18	0.38	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▂
bar	1	0.25	0.43	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▂
pluribus	1	0.52	0.50	0.00	0.00	1.00	1.00	1.00	▇▁▁▁▇
sugarpercent	1	0.48	0.28	0.01	0.22	0.47	0.73	0.99	▇▇▇▇▆
pricepercent	1	0.47	0.29	0.01	0.26	0.47	0.65	0.98	▇▇▇▇▆
winpercent	1	50.32	14.71	22.45	39.14	47.83	59.86	84.18	▃▇▆▅▂

Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?

Yes, winpercent is on a different scale. Most of the columns are binary, but winpercent is a continuous percentage (20 - 85)

Q7. What do you think a zero and one represent for the candy$chocolate column?

0 = does not contain chocolate, while 1 = does contain chocolate.

Q8. Plot a histogram of winpercent values using both base R an ggplot2.

hist(candy$winpercent,
     main = "Histogram of Win Percent",
     xlab = "Win Percent",
     col = "lightblue",
     breaks = 15)

library(ggplot2)

ggplot(candy, aes(x = winpercent)) +
  geom_histogram(bins = 15, fill = "lightgreen", color = "black") +
  labs(title = "Histogram of Win Percent",
       x = "Win Percent",
       y = "Count")

Q9. Is the distribution of winpercent values symmetrical?

No — the distribution of winpercent values is not symmetrical; it is slightly skewed.

Q10.Is the center of the distribution above or below 50%?

The center of the distribution is over 50%.

Q11. On average is chocolate candy higher or lower ranked than fruit candy?

steps to solve this: 1. find all chocolate candy in the dataset 2. extract or find their winpercent values 3. calculate the mean of these values

find all fruit candy
find their winpercent values
calculate their mean value

choc.candy <- candy[candy$chocolate==1, ] 
choc.win <- choc.candy$winpercent
mean(choc.win)

[1] 60.92153

The mean winpercent value for chocolate candy is 60.92153.

fruit.candy <- candy[candy$fruity==1, ] 
fruit.win <- fruit.candy$winpercent
mean(fruit.win)

[1] 44.11974

The mean winpercent value for fruity candy is 44.11974.

Q12. Is this difference statistically significant?

t.test(choc.win, fruit.win)


    Welch Two Sample t-test

data:  choc.win and fruit.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

The p-value is less than 0.05, therefore we can reject the null hypothesis that there is no difference in means. Therefore, the difference in means between fruity candies and chocolate candies are statistically significant from each other.

Q13. What are the five least liked candy types in this set?

least <- candy[order(candy$winpercent), ]
head(least, 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 candies 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?

top <- candy[order(candy$winpercent, decreasing=TRUE), ]
head(top, 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 top 5 candies 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.

library(ggplot2)

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()

Adding Useful Color

my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "brown"
my_cols[as.logical(candy$bar)] = "orange"
my_cols[as.logical(candy$fruity)] = "hotpink"
ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +
  geom_col(fill=my_cols)

Q17. What is the worst ranked chocolate candy?

The worst ranked chocolate candy is Sixlets.

Q18. What is the best ranked fruity candy?

The best ranked fruity candy is Starburst.

Looking at Pricepoints

library(ggrepel)
ggplot(candy) +
  aes(winpercent, pricepercent, label = rownames(candy)) +
  geom_point(col = my_cols) +
  geom_text_repel(col = my_cols, size = 3.3, max.overlaps = Inf)

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?

The candy that has the most bang for your buck is Reese’s Miniatures. This is because Reese’s Miniatures have a very high winpercent, while still having a relatively low pricepoint.

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

The top 5 most expensive candies 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 with a winpercent of just 22.44.