Question

3 dplyr
Let’s work with the data set diamonds :

data(diamonds)
head(diamonds)

A) Calculate the average price of a diamond:

[your code here]

B) Use group_by() to group diamonds by color, then use summarise() to calculate the average price and the standard deviation in price by color:

[your code here)

C) Use group_by() to group diamonds by cut, then use summarise() to count the number of observations by cut:

[your code here]

D) Use filter() to remove observations with a depth greater than 62, then use group_by() to group diamonds by clarity, then use summarise() to find the maximum price of a diamond by clarity:

[your code here]

E) Use mutate() and log() to add a new variable to the data called “log_price”:

[your code here]

Answer 1

Solution-A:

Rcode:

library(ggplot2)
library(dplyr)

diamonds %>%
summarise(Average = mean(price),

)

Output:

Average
<dbl>
1 3933.

Solution-B:

Rcode;

diamonds %>%
group_by(color) %>%
summarise(Avg_price = mean(price),
std_deviation = sd(price))

Output:

color Avg_price std_deviation
<ord> <dbl> <dbl>
1 D 3170. 3357.
2 E 3077. 3344.
3 F 3725. 3785.
4 G 3999. 4051.
5 H 4487. 4216.
6 I 5092. 4722.
7 J 5324. 4438.

Solution-c:

Rcode:

diamonds %>%
group_by(cut) %>%
summarise(counts = n())

Output:

cut counts
<ord> <int>
1 Fair 1610
2 Good 4906
3 Very Good 12082
4 Premium 13791
5 Ideal 21551

Solution-D

depgt_62 <- filter(diamonds, depth > 62)
depgt_62 %>%
group_by(clarity) %>%
summarise(max_price = max(price))

Output:

clarity max_price
<ord> <int>
1 I1 18531
2 SI2 18804
3 SI1 18818
4 VS2 18791
5 VS1 18500
6 VVS2 18768
7 VVS1 18777
8 IF 18552

Rscreenshot: