In-class Exercise 1

Getting started

1. Using p_load() of pacman package to load tidyverse on

pacman:: p_load(tidyverse) 

2. Importing data

exam_data <- read_csv("data/Exam_data.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.

Plotting the graph

1) Horizontal Bar Graph

Changing the colors of plot panel background of theme_minimal() to light blue and the color of grid lines to white.

Output:

ggplot(data= exam_data,
       aes(x = RACE)) +
       geom_bar() +
       coord_flip() +
       theme_minimal() +
       theme(panel.background = element_rect(fill = 'lightblue') ,
       panel.grid.minor=element_line(colour="white"),
       panel.grid.major=element_line(colour="white")) +
       ggtitle("Horizontal Bar Chart: \nLight Blue theme and White grid lines ") +  
       theme(plot.title = element_text(hjust = 0.5))

2) Vertical Bar Graph

With reference to the critics on the earlier slide, create a makeover looks similar to the figure on the right.

Output 1:

ggplot(data= exam_data,
      aes(x = fct_infreq(RACE))) +
      geom_bar() +
      xlab("Race") +
      ylab("No.of\nPupils") +
      ylim(0,220) +
      geom_text(aes(label = paste(..count..,",", scales::percent(..count../sum(..count..),accuracy = 0.1))), 
      stat= "count", vjust = -0.5) +
      ggtitle("Vertical Bar Chart: \nSorted Frequency + Labelling ") +
      theme(plot.title = element_text(hjust = 0.5))

Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

Output 2: Forcats Package.:

exam_data %>%
  mutate(RACE = fct_infreq(RACE)) %>%
  ggplot(aes(x = RACE)) + 
  geom_bar()+
  ylim(0,220) +
  geom_text(stat="count", 
      aes(label=paste0(..count.., ", ", 
      round(..count../sum(..count..)*100,
            1), "%")),
      vjust=-1) +
  xlab("Race") +
  ylab("No. of\nPupils") +
  theme(axis.title.y=element_text(angle = 0))

3) Histogram

• Adding mean and median lines on the histogram plot.

• Change fill color and line color

Output:

ggplot(data= exam_data,
       aes(x = MATHS)) +
       geom_histogram(color="black",fill="light blue",bins = 30) +
       geom_vline(aes(xintercept=mean(MATHS)),
            color="red", linetype="dashed", size=1) +
       geom_vline(aes(xintercept=median(MATHS)),
            color="black", linetype="dashed", size=1) +
       ggtitle("Histogram: \nAdd lines and color ") +  
       theme(plot.title = element_text(hjust = 0.5))

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

3.1) By Gender

• The background histograms show the distribution of English scores for all pupils.

Output:

d <- exam_data   
d_bg <- d[, -3] 

ggplot(d, aes(x = ENGLISH)) +
       geom_histogram (data= d_bg, bins=30, alpha = 0.2) +
       geom_histogram (bins=30, color = 'black') +
       facet_wrap(~ GENDER) + 
       theme_bw()       

d <- exam_data   
d_bg <- d[, -3]  

ggplot(d, aes(x = ENGLISH, fill = GENDER)) +
  geom_histogram(data = d_bg, fill = "grey", alpha = .5) +
  geom_histogram(colour = "black") +
  facet_wrap(~ GENDER) +
  theme_bw() 

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

d <- exam_data   
d_bg <- d[, -3] 

ggplot(data = exam_data, aes(x = ENGLISH, fill= GENDER, )) +
    geom_histogram(bins = 30) +
    facet_wrap(~ GENDER) +
    guides(fill = FALSE) 

Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
of ggplot2 3.3.4.

4) Scatterplot

• The scatterplot show the relationship between English and Maths for all pupils.

Output:

ggplot(data = exam_data, 
        aes (x= MATHS, y= ENGLISH)) +
        geom_point() +
        geom_hline(yintercept=50, linetype="dashed", color = "darkgrey") +
        geom_vline(xintercept=50, linetype="dashed", color = "darkgrey") +
        coord_cartesian(xlim=c(0,100), ylim=c(0,100))