In-class Exercise 2

The first part of the lesson was to mingle with Tableau. We have published our first date with Tableau here. Moving on to R studio, we will be referring to Visualising Distributions read more here for this exercise.

#Load Package

pacman::p_load(ggrepel, patchwork, ggthemes, 
               tidyverse, ggridges, ggdist,
               colorspace) 

exam_data <- read_csv("Exam_data.csv")
summary(exam_data)

      ID               CLASS              GENDER              RACE          
 Length:322         Length:322         Length:322         Length:322        
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
    ENGLISH          MATHS          SCIENCE     
 Min.   :21.00   Min.   : 9.00   Min.   :15.00  
 1st Qu.:59.00   1st Qu.:58.00   1st Qu.:49.25  
 Median :70.00   Median :74.00   Median :65.00  
 Mean   :67.18   Mean   :69.33   Mean   :61.16  
 3rd Qu.:78.00   3rd Qu.:85.00   3rd Qu.:74.75  
 Max.   :96.00   Max.   :99.00   Max.   :96.00  

Histogram

Using the steps you have learned, build a histogram.

ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
geom_histogram(bins=30,            
                 color="black",      
                 fill="light blue") 

Probability Density Plot

ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_density(
     color = "#1696d2",
     adjust = .54,
     alpha = .6
  )

The alternative design. (Missing median_eng because the class abruptly ended.)

median_eng <- median(exam_data$ENGLISH)
mean_eng <- mean(exam_data$ENGLISH)
std_eng <- (exam_data$ENGLISH)

ggplot(exam_data,
       aes(x= ENGLISH)) +
  geom_density(
    color = "#1696d2",
    adjust = .65,
    alpha = .6) +
  stat_function(
    fun = dnorm, 
    args = list(mean = mean_eng,
                sd = std_eng),
    col = "grey10",
    size = .8) +
  
  geom_vline(
    aes(xintercept = mean_eng), 
    color="#4d5887",
    linewidth = .6,
    linetype = "dashed") +
  annotate(geom="text", 
           x = mean_eng - 8,
           y = 0.04, 
           label = paste0("Mean ENGLISH: ",round((mean_eng),2)),
           color = "#4d5887") 

Visualising Distribution with Ridgeline Plot

Read more here.

ggplot(exam_data, aes(x = ENGLISH, y = fct_relevel(CLASS, rev(unique(CLASS))))) +
  geom_density_ridges() +
  scale_y_discrete(labels = rev) + # This is to ensure the order of classes is from top to bottom
  labs(title = "Distribution of English Scores by Class",
       x = "English Score",
       y = "Class") +
  theme_ridges(grid = FALSE) 

ridgeline1 <- ggplot(exam_data, 
       aes(x = ENGLISH, 
           y = CLASS)) +
  geom_density_ridges(
    scale = 3,
    rel_min_height = 0.01,
    bandwidth = 3.4,
    fill = lighten("#7097BB", .3),
    color = "white"
  ) +
  scale_x_continuous(
    name = "English grades",
    expand = c(0, 0)
    ) +
  scale_y_discrete(name = NULL, expand = expansion(add = c(0.2, 2.6))) +
  theme_ridges()

ridgeline2 <- ggplot(exam_data, 
       aes(x = ENGLISH, 
           y = CLASS,
           fill = stat(x))) +
  geom_density_ridges_gradient(
    scale = 3,
    rel_min_height = 0.01) +
  scale_fill_viridis_c(name = "Temp. [F]",
                       option = "C") +
  scale_x_continuous(
    name = "English grades",
    expand = c(0, 0)
  ) +
  scale_y_discrete(name = NULL, expand = expansion(add = c(0.2, 2.6))) +
  theme_ridges()

ridgeline3 <- ggplot(exam_data,
       aes(x = ENGLISH, 
           y = CLASS, 
           fill = 0.5 - abs(0.5-stat(ecdf)))) +
  stat_density_ridges(geom = "density_ridges_gradient", 
                      calc_ecdf = TRUE) +
  scale_fill_viridis_c(name = "Tail probability",
                       direction = -1) +
  theme_ridges()

ridgeline4 <- ggplot(exam_data,
       aes(x = ENGLISH, 
           y = CLASS, 
           fill = factor(stat(quantile))
           )) +
  stat_density_ridges(
    geom = "density_ridges_gradient",
    calc_ecdf = TRUE, 
    quantiles = c(0.025, 0.975)
    ) +
  scale_fill_manual(
    name = "Probability",
    values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
    labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
  ) +
  theme_ridges()

print(ridgeline1)

print(ridgeline2) 

print(ridgeline3) 

print(ridgeline4)

☁ Rainploud Plot

ggplot(exam_data, 
       aes(x = RACE, 
           y = ENGLISH)) +
  stat_halfeye(adjust = 0.5,
               justification = -0.2,
               .width = 0,
               point_colour = NA)

ggplot(exam_data, 
       aes(x = RACE, 
           y = ENGLISH)) +
  stat_halfeye(adjust = 0.5,
               justification = -0.2,
               .width = 0,
               point_colour = NA) +
  geom_boxplot(width = .20,
               outlier.shape = NA)

ggplot(exam_data, 
       aes(x = RACE, 
           y = ENGLISH)) +
  stat_halfeye(adjust = 0.5,
               justification = -0.2,
               .width = 0,
               point_colour = NA) +
  geom_boxplot(width = .20,
               outlier.shape = NA) +
  stat_dots(side = "left", 
            justification = 1.2, 
            binwidth = .5,
            dotsize = 2)

ggplot(exam_data, 
       aes(x = RACE, 
           y = ENGLISH)) +
  stat_halfeye(adjust = 0.5,
               justification = -0.2,
               .width = 0,
               point_colour = NA) +
  geom_boxplot(width = .20,
               outlier.shape = NA) +
  stat_dots(side = "left", 
            justification = 1.2, 
            binwidth = .5,
            dotsize = 1.5) +
  coord_flip() +
  theme_economist()