Assignment 4

Import data

Code

library(tidyverse)
# result <- read.csv(file = 'total_elements_mindat.csv')
mineral <- read.csv(file = 'mineral.csv')
# result <- read.csv(file = 'total_elements_mindat.csv')
# df_72 <- read.csv(file = 'hardness.csv')
df_30 <- read.csv(file = 'hardness_30.csv')

Expressiveness and Effectiveness

Corrected Version

Code

# head(df_30)
# # ggplot(df_30, aes(x=elements, y=X)) +
# ggplot(df_30, aes(x=factor(elements, level=elements), y=hmax)) +
#   geom_point(size=2) + 
#   labs(title = "Fig 1. Expressiveness and Effectiveness",
#               subtitle = "Plot of element hardness by length",
#               caption = "The hardness for elements")

# head(mineral)
ggplot(mineral, aes(x=hmax)) + geom_histogram()+ 
  labs(title = "Fig 1. The hardness distribution among minerals",
              subtitle = "Channel: Length, Mark: Lines",
              caption = "The hardness for elements")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Code

# # Change the width of bins
# ggplot(mineral, aes(x=hmax)) +
#   geom_histogram(binwidth=1)
# # Change colors
# p<-ggplot(mineral) +
#   geom_histogram(color="black", fill="white")
# p

Wrong Version

Our main theme is about the hardness of minerals. To best demonstrate the characteristic of our elements list, I choose to visualize the index of the elements.

Code

# head(df_30)
# ggplot(df_30, aes(x=elements, y=X)) +
# ggplot(df_30, aes(x=factor(elements, level=elements), y=X)) +
#   geom_point(size=2, shape=as.integer(df_30$X)) + 
#   labs(title = "Fig 2. The hardness distribution among minerals",
#               subtitle = "Plot of elements by shapes",
#               caption = "The hardness for elements")

ggplot(df_30, aes(x=factor(elements, level=elements), y=X)) +
  geom_point(size=2, shape=as.integer(df_30$hmax)) + 
  labs(title = "Fig 2. The hardness distribution among elements",
              subtitle = "Channel: Shape, Mark: Points",
              caption = "The hardness for elements")

Discriminability

Corrected Version

Code

#head(df_30)
# ggplot(df_30, aes(x=elements, y=X)) +
ggplot(df_30, aes(x=factor(elements, level=elements), y=hmax)) +
  geom_point(size=2) +
  labs(title = "Fig 3. The hardness distribution among elements",
              subtitle = "Channel: Position, Mark: Points",
              caption = "The hardness for elements")

Wrong Version

Code

ggplot(mineral, aes(x=sigelements, y=hmax)) +
  geom_point(size=2) +
  labs(title = "Fig 4. The hardness distribution among sigelements",
              subtitle = "Channel: Position, Mark: Points",
              caption = "The hardness for elements")

Separability

Corrected Version

Code

ggplot(df_30, aes(x=factor(elements, level=elements), y=hmax)) +
  geom_point(size=2, shape=23) + 
  labs(title = "Fig 5. Distribution of element-wised hardness",
              subtitle = "Channel: Position, Mark: Points",
              caption = "The hardness for elements")

Code

  #scale_y_continuous(trans='log2')

Wrong Version

We noticed that the values of ‘hmin’ are too small to tell, so we amplified it by applying exponential transform.

Code

# ggplot(df_30, aes(x=elements, y=hmax)) +
#   geom_point(size=2, shape=23) + 
#   labs(title = "Separability",
#               subtitle = "Plot of element hardness with exponential transform on hmin.",
#               caption = "The hardness for elements")
#   #scale_y_continuous(trans='log2')
# Basic piechart
ggplot(df_30, aes(x=factor(elements, level=elements), y=hmax, fill=factor(elements, level=elements))) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) + 
  labs(title = "Fig 6. Pie chart of element-wised hardness",
              subtitle = "Channel: Color, Mark: Length",
              caption = "The hardness for elements")

Popout

Corrected Version

Code

ggplot(df_30, aes(x=elements, y=hmax)) +
  geom_point(size=2, shape=23, colour = factor(as.integer(df_30$hmax))) + 
  labs(title = "Fig 7. Pie chart of element-wised hardness",
              subtitle = "Channel: Position, Color, Mark: Points",
              caption = "The hardness for elements")

Wrong Version

Nothing can be more striking than the areas of the scatters. And guess what? We will also add some colors to make it as eye-catching as rainbow!

Code

ggplot(df_30, aes(x=elements, y=hmax)) +
  geom_point(size=df_30$hmax, shape=23, colour = factor(as.integer(df_30$hmax))) + 
  labs(title = "Fig 6. Pie chart of element-wised hardness",
              subtitle = "Channel: Position, Color, Mark: Points, Area",
              caption = "The hardness for elements")