diamond_sizes

We have data about 53940 diamonds. Only 126 are larger than 2.5 carats. The distribution of the remainder is shown in Figure 1:

From the frequency polygon chart we can see sharp spikes in count at specific carat values, suggesting that most diamonds are cut to these sizes due to market/industry standards. Despite uneven distribution with peaks, we can still observe the decline in the number of diamonds as the carats increase.

percent_larger <- mean(diamonds$carat > 2.5)
cat(round(percent_larger * 100, 2), "%\n")

0.23 %

Exploring diamond sizes by color, cut, and clarity

diamonds |>
  group_by(cut) |>
  summarize(median_carat = median(carat), .groups = "drop") |>
  knitr::kable()

cut	median_carat
Fair	1.00
Good	0.82
Very Good	0.71
Premium	0.86
Ideal	0.54

diamonds |>
  group_by(color) |>
  summarize(median_carat = median(carat), .groups = "drop") |>
  knitr::kable()

color	median_carat
D	0.53
E	0.53
F	0.70
G	0.70
H	0.90
I	1.00
J	1.11

diamonds |>
  group_by(clarity) |>
  summarize(median_carat = median(carat), .groups = "drop") |>
  knitr::kable()

clarity	median_carat
I1	1.12
SI2	1.01
SI1	0.76
VS2	0.63
VS1	0.57
VVS2	0.44
VVS1	0.39
IF	0.35

The Largest 20 Diamonds

top20 <- diamonds |> arrange(desc(carat)) |> slice_head(n = 20)
knitr::kable(top20[, c("carat", "cut", "color", "clarity")])

carat	cut	color	clarity
5.01	Fair	J	I1
4.50	Fair	J	I1
4.13	Fair	H	I1
4.01	Premium	I	I1
4.01	Premium	J	I1
4.00	Very Good	I	I1
3.67	Premium	I	I1
3.65	Fair	H	I1
3.51	Premium	J	VS2
3.50	Ideal	H	I1
3.40	Fair	D	I1
3.24	Premium	H	I1
3.22	Ideal	I	I1
3.11	Fair	J	I1
3.05	Premium	E	I1
3.04	Very Good	I	SI2
3.04	Premium	I	SI2
3.02	Fair	I	I1
3.01	Premium	I	I1
3.01	Premium	F	I1

knitr::kable(head(diamonds, 5))

carat	cut	color	clarity	depth	table	price	x	y	z
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75