Diamonds Report with Data Visualization

Sprint 05 - Data Visualization Homework

: Explore dataframe diamonds and create 5 visualizations

Diamond_ring

The dataset diamonds is a built-in dataset in ggplot2 library which containing the prices and other attributes of almost 54,000 round cut diamonds.

The variable in diamonds(53,940 rows x 10 columns) are as follows:

carat : weight of the diamond (0.2–5.01)
cut : quality of the cut (Fair, Good, Very Good, Premium, Ideal)
color : diamond color, from D (best) to J (worst)
clarity : a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
price : price in US dollars ($326–$18,823)
x : length in mm (0–10.74)
y : width in mm (0–58.9)
z : depth in mm (0–31.8)
depth : total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43–79)
table : width of top of diamond relative to widest point (43–95)

1. Introduction

Objective Let’s say I’m looking for an engagement ring (for myself). I have done some research in these diamond attributes and come across some questions:

What is the distribution of price in round cut diamonds ?
I heard that in diamonds, table should be fairly large. Therefore, I want to compare the tablevalues between cut grade
As diamond is grading by its clarity, let’s explore this variable by comparing each clarity group
Color and carat also play a major role in diamond price. What is the correlation between these two variables with respect to clarity?
For buying engagement ring on a suitable budget, What is the average price of diamonds by color and clarity?

2. Loading Libraries and Data

# Import Library
library(tidyverse)
library(dplyr)
library(patchwork)
library(scales)

# loading dataset
data(diamonds)

3. Understanding the dataset

Review dataset

head(diamonds)

## # A tibble: 6 × 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

Review data structure

glimpse(diamonds)

## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…

The Diamonds Grading System

To gain a better understanding in this dataset, here’s some study that I did on diamond grading system and its value (in this case – price). The grading system is based on four attributes: carat, color, clarity, and cut, known as The Four Cs of Diamonds.

Carat

Diamond weight is measured in carats. One carat (or 1 ct, when abbreviated) equals 0.2 gram. The carat weight is sometimes directly related to price and the prices go up as diamond carat weight increase.

Color

Diamonds are graded on a color scale from D-Z. According to GIA color grading scale, a diamond graded D-F is considered to be “colorless” and diamond graded G-J are considered to be “near colorless”.

GIA color chart

Clarity

Diamonds clarity are graded on a scale from “Internal Flawless (IF)” to “Included (I3)”. Most diamonds have inclusions (defined as anything that will interfere with the free passage of light) but the less inclusions it has, the more valuable it is.

Clarity grading are as follows:

IF: Internally Flawless
VVS1, VVS2: Very, Very Slightly Included
VS1, VS2: Very Slightly Included
SI1,SI2: Slightly Included
I1: Included

Diamond Clarity Chart

Cut

Diamond cut quality is what gives a diamond its sparkle. Therefore, Cut is considered to be the most important determining the diamond’s overall beauty. The overall quality of the cut is determined by the anatomy of diamond such as table, total depth percentage, etc. as well as the quality of the polish.

For round brilliant diamonds, cut grading are as follows:

Ideal or Excellence
Premium
Very Good
Good
Fair
Poor (does not include in this dataset)

4. Data Visualization

Chart 01 - Diamonds prices distribution

To simply explore the distribution of diamonds price, the histogram along with boxplot is plotted as shown below:

# Using sampling method
set.seed(42) 
p1 <- ggplot(diamonds %>% sample_n(5400),
       aes(price)) +
  geom_histogram(bins=10, fill = "#b0c4b1", alpha = 0.5, color = "#4a5759" ) +
  theme_minimal() +
  ggtitle("Histogram")

p2 <- ggplot(diamonds %>% sample_n(5400),
       aes(price)) +
  geom_boxplot(fill = "#b0c4b1", alpha = 0.5, color = "#4a5759",
               outlier.colour = "#bc4b51", outlier.shape = 1) +
  theme_minimal() +
  ggtitle("Boxplot")

patchwork <- p1 / p2
patchwork + 
  plot_annotation(
  title = "The Distribution of Diamonds Prices",
  subtitle = 'The "prices" distribution is positive skew with outliers (red marks)',
  caption = 'Source: Diamonds from ggplot2'
)

The diamonds “prices” distribution is positive skew, and there are outliers (red marks) as shown in the boxplot.

Chart 02 - Boxplot of Table by Cut Grade

As table is one of the attribute that affect the cut quality. I want to compare the distribution of table between cut grade side-by-side. The boxplot of table is created as shown below:

diamonds %>%
  ggplot(aes( cut ,table,fill=cut, col=cut)) +
  #geom_point(position = "jitter") +
  geom_jitter(alpha=0.1, show.legend = FALSE) +
  geom_boxplot(alpha=0.9, fill= NA, color = "#463f3a", show.legend = FALSE)+
  theme_minimal() +
  scale_color_manual(values = c(
    "#ffcdb2", "#ffb4a2", "#e5989b", "#b5838d", "#9e2a2b")) +
  labs(title = "Boxplot of Table by Cut Grade",
       subtitle = "Table: width of top of diamond relative to widest point",
       x = "Cut Grade", 
       y = "Table",
       caption = "Source: Diamonds from ggplot2")

From this chart, it can be seen that only diamonds with Good-cut has no outlier, and the range of table is much more narrower in Ideal-cut compared to the Fair-cut (worst).
The median of Ideal-cut table is also lower than other cut grade. Therefore, there is a certain suitable range for diamond table.

Chart 03 - Percentage of diamonds by Clarity Segments

As in this dataset, clarity is scaling from IF to I1, and the flaw is usually noticeable in SI1 to I1. Therefore, instead of showing all scaling percentage in pie chart, the clarity is grouped into 4 segments as follows:

Internal Flawless (IF)
Very, Very Slightly Included (VVS1-2)
Very Slightly Included (VS1-2)
All other (SI1 to I1)

In this case, I can focus more on comparing the clean and unnoticeable grade.

## create new segment level
# data transformation
dia_cl <- diamonds %>%
  mutate(segment="")

dia_cl$segment[dia_cl$clarity == "IF"] <- "Internal Flawless (IF)"
dia_cl$segment[dia_cl$clarity %in% c("VVS1","VVS2")] <- "Very, Very Slightly Included (VVS1-2)"
dia_cl$segment[dia_cl$clarity %in% c("VS1","VS2")] <- "Very Slightly Included (VS1-2)"
dia_cl$segment[dia_cl$clarity %in% c("SI1","SI2", "I1")] <- "All Other (SI1 to I1)"

dia_cl$segment <- factor(dia_cl$segment, levels = c("Internal Flawless (IF)", "Very, Very Slightly Included (VVS1-2)","Very Slightly Included (VS1-2)","All Other (SI1 to I1)"))

dia_cl <- dia_cl %>%
  group_by(segment)  %>% 
  count() %>%
  ungroup() %>%
  mutate(pc =`n`/sum(`n`)) %>%
  arrange(pc ) %>%
  mutate(labels = scales::percent(pc, accuracy = 0.1))
dia_cl

## # A tibble: 4 × 4
##   segment                                   n     pc labels
##   <fct>                                 <int>  <dbl> <chr> 
## 1 Internal Flawless (IF)                 1790 0.0332 3.3%  
## 2 Very, Very Slightly Included (VVS1-2)  8721 0.162  16.2% 
## 3 Very Slightly Included (VS1-2)        20429 0.379  37.9% 
## 4 All Other (SI1 to I1)                 23000 0.426  42.6%

Create Pie Chart

# build pie chart
ggplot(dia_cl, aes(x="", y = pc,  fill= segment)) +
  geom_col(alpha=0.8) +
  geom_text(aes(x = 1.65, label = labels),
            position = position_stack(vjust = 0.5),
            size = 3)+
  coord_polar(theta = "y") +
  theme_void() + #empty theme 
  scale_fill_manual(values=c("#8338ec","#e29578","#ccc5b9","#252422")) +
  guides(fill = guide_legend(title = "Clarity Segments")) +
  labs(title = "Percentage of diamonds by Clarity Segments",
       subtitle = "Total n = 53,940",
       caption = "Source: Diamonds from ggplot2")

It can be seen that the smallest fraction in this case is IF or Internal Flawless diamond with 3.3%.

Chart 04 - Distribution of Price by Color

As diamond prices depend on various variables. I want to explore the correlation between carat and price in colorless diamonds (D, E, and F), focusing solely on clean & unnoticeable flaw clarity grades (IF to VS2).

Scatter plots with trendlines are used to illustrate these correlations.

## Data Transformation
colorless_dia <- diamonds %>% 
  filter(color %in% c("D","E","F") & cut == "Ideal"
         & clarity %in% c("IF","VVS1","VVS2","VS1","VS2"))

# summary(colorless_dia)


## Scatterplot
ggplot(colorless_dia, aes(x=carat, y=price, col = color) ) +
  geom_point(size=1, position = "jitter", alpha=0.4) +
  geom_smooth(method="lm", aes(color=color),
            formula = (y ~ exp(x)),
             se = FALSE) +
  scale_x_continuous(limits =c(0.20,2.00) , n.breaks = 10) +
  scale_y_continuous(limits = c(500,20000), n.breaks = 10) +
  theme_minimal() +
  scale_color_manual( values = c("#4575b4", "#91cf60", "#fc8d59"))  +
  facet_wrap(~ clarity, ncol = 2) +
  labs(title = "Correlation between Carat and Price in Colorless Diamonds",x = "Carat", y = "Price (USD)",
       subtitle = "Cut: Ideal grade, Color: D-E-F, Clarity: IF to VS2 ",
       caption = "Source: Diamonds from ggplot2")

According to the chart above, there’s a positive correlation between carat and price in all colorless diamonds
In terms of clarity grade, the price jump between D&E-color was found in IF-grade as the price of D-color diamonds increase exponentially

Chart 05 - Average Price of Colorless Diamonds by Clarity

Assume the engagement ring that I’m looking for is 1.00-1.25 carat diamond with Ideal-cut. Before making decision, I want to know the average price of ideal cut diamond based on color and clarity.

  colorless_dia %>% 
  filter(carat >= 1 & carat <= 1.25) %>%
  group_by(color,clarity) %>%
  summarise(avg_price = round(mean(price))) %>%
  ggplot( aes(x=color, y=avg_price, fill=clarity)) +
  geom_bar(alpha = 0.8,stat="identity", position=position_dodge())+
  geom_text(aes(label=avg_price), vjust=1.6, color="black",
            position = position_dodge(0.9), size=2)+
  scale_fill_brewer(palette="BuPu", direction = 1) +
  theme_minimal() +
  scale_y_continuous(limits = c(0,18000), n.breaks = 8) +
  labs(title = "Average Price of Colorless Diamonds by Clarity",
       subtitle = "Cut: Ideal grade and Carat: 1.00 to 1.25",
       x = "Color", 
       y = "Average Price (USD)",
       caption = "Source: Diamonds from ggplot2")

Refer to the chart above, it can be seen that the average price of diamond increase rapidly with the clarity and color grade
This could be helpful information if I wish to trade off some aspects to lower the price.