ggplot2-visualizations/12 Raincloud chart AE Attendances.R at main · Pablo-source/ggplot2-visualizations · GitHub

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# 12 Raincloud chart AE Attendances

# Reference: ggdist: Make a Raincloud Plot to Visualize Distribution in ggplot2 | R-bloggers
# https://www.r-bloggers.com/2021/07/ggdist-make-a-raincloud-plot-to-visualize-distribution-in-ggplot2/

# Load required packages at once (readxl,here,dplyr,janitor)
pacman::p_load(readxl,here,tidyverse,janitor)


# Load England AE Attendances
# Import (Type 1, Type 2 and Type 3 AE Attendances)
AE_ATT <- read_excel(here("data","AE_England_data.xls"),
                                sheet = 1,skip =17, range = "C18:G123",na = "") %>%
clean_names()

AE_ATT

names(AE_ATT)

# [1] "period"
# [2] "type_1_departments_major_a_e"
# [3] "type_2_departments_single_specialty"
# [4] "type_3_departments_other_a_e_minor_injury_unit"
# [5] "total_attendances"

AE_ATT <- AE_ATT %>%
                select(
                   Period = period,
                   Major_att = type_1_departments_major_a_e ,
                   Single_spec_att = type_2_departments_single_specialty,
                   Other_att = type_3_departments_other_a_e_minor_injury_unit

                )
AE_ATT

# Create variable for year

AE_ATT <- AE_ATT %>%
          mutate(
            year = as.numeric(format(Period, "%Y"))
          )
AE_ATT

AE_ATT_arrange <- AE_ATT %>%
select(Period,year,Major_att,Single_spec_att,Other_att)
AE_ATT_arrange

# Prep.1 Pivot long the initial data set
AE_ATT_long<- AE_ATT_arrange %>%
              pivot_longer(names_to = "Metrics",
                           cols = 3:ncol(AE_ATT_arrange))
AE_ATT_long


# Prep.2 Group value by year and Metric
AE_ATT_long_year <-  AE_ATT_long %>%
                      select(year,Metrics,value) %>%
                      group_by(year,Metrics) %>%
                      summarise(Value = sum(value))
AE_ATT_long_year


## Start building raincloud plot

## 1. MAJOR ATTENDANCES RAINCLOUD
# Filter data to account for 2010-2014 years

# 1.1 Create boilerplate chart for value by period split by Metrics

# Include all years in the visualization
AE_ATT_sel_major <- AE_ATT_long %>%  select(year,Metrics,value)

AE_ATT_sel_major
names(AE_ATT_sel_major)

# Include all years in the visualization
# Subset data for 2010-2014 period
AE_ATT_period <- AE_ATT_long %>%  select(year,Metrics,value) %>%
                  filter(Metrics == "Major_att" &
                           (year == 2010 | year == 2011 |year == 2012 | year == 2013 )
                         ) %>%
                  select(year,value)
AE_ATT_period

table(AE_ATT_period$year)

# 1.2 Create Raincloud visualization for 2010=2014 period

MAJOR_ATT <- ggplot(AE_ATT_period,aes(x = factor(year), y = value,
                              fill = factor(year))) +
  # Include half-violin from {ggdist} package
  # Function: stat_halfeye() from ggdist package
  ggdist::stat_halfeye(
    adjust = 0.5,
    justification = -.2,
    .width = 0,
    point_colour = NA
  ) +
# Function: geom_boxplot() from ggplot package
geom_boxplot(
  width = .12,
  outlier.color = NA,
  alpha = 0.5

) +

# DOTS chart. Function: stat_dots() from ggdis package
ggdist::stat_dots(
  side = "left", # left orientation
  justification = 1.1,
  binwidth = .25

)
MAJOR_ATT

# 2. Improve plot layout feel and look
# functions: scale_fill_tq(), theme_tq()
# Improve theme and plot layout
library(tidyquant)

MAJOR_ATT_layout <- ggplot(AE_ATT_period,aes(x = factor(year), y = value,
                                      fill = factor(year))) +
  # Include half-violin from {ggdist} package
  # Function: stat_halfeye() from ggdist package
  ggdist::stat_halfeye(
    adjust = 0.5,
    justification = -.2,
    .width = 0,
    point_colour = NA
  ) +
  # Function: geom_boxplot() from ggplot package to be used
  geom_boxplot(
    width = .12,
    outlier.color = NA,
    alpha = 0.5

  ) +

  # DOTS chart. Function: stat_dots() from ggdis package
  ggdist::stat_dots(
    side = "left", # left orientation
    justification = 1.1,
    binwidth = .25

  )  +
  # Improve theme and plot layout
  # library(tidyquant)
  scale_fill_tq() +
  theme_tq() +   # Apply theme
  labs (
    title = "Major A&E Attendances. England 2010-2013",
    subtitle = "Raincloud plot example",
    x = "Years",
    y = "Major Attendances",
    fill = "Years"
  ) +
  coord_flip() ## Flip coordinates

MAJOR_ATT_layout

ggsave("plots/24_AE_Attendances_Raincloud_chart.png", width = 6, height = 4)