Hands-on_Ex02

Getting Started

Install and loading required libraries

pacman::p_load(ggrepel, patchwork, 
               ggthemes, hrbrthemes,
               tidyverse) 

Importing data

exam_data <- read_csv("data/Exam_data_2.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Working with ggrepel

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5) +  
  geom_label(aes(label = ID), 
             hjust = .5, 
             vjust = -.5) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

`geom_smooth()` using formula = 'y ~ x'

Alternate code to get rid of annotation

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5) +  
  geom_label_repel(aes(label = ID), 
                   fontface = "bold") +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")

`geom_smooth()` using formula = 'y ~ x'

Warning: ggrepel: 317 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Beyond ggplot2 theme

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  theme_minimal() +
  ggtitle("Distribution of Maths scores") 

There are 8 built-in themes of ggplot2, which are: theme_gray(), theme_bw(), theme_classic(), theme_dark(), theme_light(), theme_linedraw(), theme_minimal(), and theme_void()

Working with ggtheme package

ggtheme offers an even wider variety of plotting styles, such as The Economist (theme_economist) or Stata (theme_stata)

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_economist()

Working with hrbthems package

Working with hrbthemes package. Why use this package? It has 2 goals. First, it provides a base theme that focuses on typographic elements. Second, it centers around productivity for a production workflow

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_ipsum()

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_ipsum(axis_title_size = 10,
              base_size = 10,
              grid = "Y")

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
font family not found in Windows font database

Beyond single graphs

Graphing of Math scores

p1 <- ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") + 
  coord_cartesian(xlim=c(0,100)) +
  ggtitle("Distribution of Maths scores")
print(p1)

Graphing of English scores

p2 <- ggplot(data=exam_data, 
             aes(x = ENGLISH)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  coord_cartesian(xlim=c(0,100)) +
  ggtitle("Distribution of English scores")
print(p2)

Graphing scatterplot for English score versus Maths score

p3 <- ggplot(data=exam_data, 
             aes(x= MATHS, 
                 y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5) +  
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")
print(p3)

`geom_smooth()` using formula = 'y ~ x'

Using patchwork extension to create composite figures by combining graphs

install.packages(“devtools”)

library(patchwork)

Combining 2 ggplot2 graphs

p1+p2

Additional attempt 1 : use different code to combine the 2 graphs

wrap_plots(p1,p2)

Combining 3 ggplot2 graphs

(p1 / p2) | p3

`geom_smooth()` using formula = 'y ~ x'

Additional attempt 2: Trying different style of stacking

(p1 | p2) / p3

`geom_smooth()` using formula = 'y ~ x'

Creating a composite figure with tag

((p1 / p2) | p3) + 
  plot_annotation(tag_levels = 'I')

`geom_smooth()` using formula = 'y ~ x'

Additional attempt 3: Trying to make it more beautiful

(p1 | p2) / p3 +
  plot_annotation(tag_levels = 'I') & 
  theme(plot.tag = element_text(size = 10))

`geom_smooth()` using formula = 'y ~ x'

Creating figure with insert

p3 + inset_element(p2, 
                   left = 0.01, 
                   bottom = 0.7, 
                   right = 0.5, 
                   top = 1)

`geom_smooth()` using formula = 'y ~ x'

Additional attempt 4:

p2_density <- ggplot(exam_data, aes(x = ENGLISH)) +
  geom_density(fill = "#EEDFCC", alpha = 0.5) +
  ggtitle("English score density") + 
  theme_minimal(base_size = 8) +
  theme(
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    #plot.title = element_blank(),
    panel.border = element_rect(color = "black", fill = NA, linewidth = 0.3)
  )
print (p2_density)

p3 + inset_element(
  p2_density,
  left = 0.01, bottom = 0.7,
  right = 0.5, top = 1
)

`geom_smooth()` using formula = 'y ~ x'

Creating a composite figure by using patchwork and ggtheme combined

patchwork <- (p1 / p2) | p3
patchwork & theme_economist()

`geom_smooth()` using formula = 'y ~ x'

Additional attempt 5a:

patchwork <- (p1 | p2) / p3
patchwork & theme_bw()

`geom_smooth()` using formula = 'y ~ x'

Additional attempt 5b:

patchwork <- (p1 / p2) | p3
patchwork & theme_bw() + theme(plot.title = element_text(size=8))

`geom_smooth()` using formula = 'y ~ x'

Suggested solution 1: increase figure size

patchwork <- (p1 / p2) | p3 
patchwork & theme_bw()

`geom_smooth()` using formula = 'y ~ x'

Suggested solution 2: Wrap text - divide text into 2 lines

Use stringr::str_wrap