basic_ggplot_code.R

https://github.com/danieloc89/SWG-introduction-to-R/tree/main/sessions/session-4-plotting/data/wine-imports


install.packages("afcharts")


library(afcharts)
library(dplyr)
library(ggplot2)
library(janitor)
library(jsonlite)
library(readr)
library(readxl)
library(tidyr)


"C:\Users\tb000087\Downloads\May_2025_Over_10m_vessel_list.xlsx"



cereals_df <- read_csv("C:/Users/tb000087/Downloads/cereals (1).csv", name_repair = make_clean_names) |> 
  pivot_longer(
    cols = -year,
    names_to = "cereal",
    values_to = "yield",
    values_drop_na = TRUE
  )

vessels_df <- read_xlsx("C:/Users/tb000087/Downloads/May_2025_Over_10m_vessel_list.xlsx", skip = 4, .name_repair = make_clean_names)

"C:\Users\tb000087\Downloads\May_2025_Over_10m_vessel_list.xlsx"


# UK GDP deflators series
gdp_deflators_df <- read_json("C:/Users/tb000087/Downloads/gdp-deflators (1).json", simplifyVector = TRUE)

# MMO vessel list
vessels_df <- read_xlsx("data/May_2025_Over_10m_vessel_list.xlsx", skip = 4, .name_repair = make_clean_names)


# scatter plot
plot(x = vessels_df$overall_length, y = vessels_df$vessel_capacity_units)


# line plot
plot(x = gdp_deflators_df$year, y = gdp_deflators_df$index, type = "l")



# Create the plot canvas
gdp_deflators_df |> 
  # apply aesthetics
  ggplot(mapping = aes(x = year, y = pct_change))

# Create the plot canvas
gdp_deflators_df |> 
  ggplot(aes(x = year)) +
  # create a line plot
  geom_bar()


gdp_deflators_df |> 
  # create a variable to show if it is a forecast
  mutate(forecast = if_else(year > 2024, "yes", "no")) |> 
  # apply this a mapping to distinguish between forecast and observed
  ggplot(aes(x = year, y = pct_change, fill = forecast)) +
  geom_col()


gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, fill = if_else(year > 2024, "yes", "no"))) +
  geom_col()


gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, fill = if_else(pct_change > 3, "yes", "no"))) +
  geom_col()

gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, color = if_else(year > 2020 ,"yes", "no"))) +
  geom_line()


gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, color = if_else(year > 2020 ,"yes", "no"))) +
  geom_line()



gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, linetype = if_else(year > 2020 ,"yes", "no"))) +
  geom_line()


gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change)) +
  geom_line()





gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, color = if_else(year > 2024, "yes", "no"))) +
  geom_line() +
  # applying lables to the plot
  labs(
    title = "Changes in the UK GDP Deflator Index",
    subtitle = "From 1956 to 2029",
    x = "Year",
    y = "Percentage Change",
    colour = "Forecast",
    caption = "Source: HMT"
  )


gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, fill = if_else(year > 2024, "yes", "no"))) +
  geom_col() +
  labs(
    title = "Changes in the UK GDP Deflator Index",
    subtitle = "From 1956 to 2029",
    x = NULL,
    y = "Percentage Change",
    fill = "Forecast",
    caption = "Source: HMT"
  ) +
  scale_fill_brewer(palette = "Set2")



gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, fill = if_else(year > 2024, "yes", "no"))) +
  geom_col() +
  labs(
    title = "Changes in the UK GDP Deflator Index",
    subtitle = "From 1956 to 2029",
    x = NULL,
    y = "Percentage Change",
    fill = "Forecast",
    caption = "Source: HMT"
  ) +
  scale_fill_manual(values = c(no = "cyan", yes = "red"))





afcharts::use_afcharts()
gdp_deflators_df |> 
  ggplot(aes(x = year, y = pct_change, fill = if_else(year > 2024, "yes", "no"))) +
  geom_col() +
  labs(
    title = "Changes in the UK GDP Deflator Index",
    subtitle = "From 1956 to 2029",
    x = NULL,
    y = "Percentage Change",
    fill = "Forecast",
    caption = "Source: HMT"
  ) +
 scale_fill_discrete_af(scale_name = "Forecast") +
  theme_af()



ggsave(
  filename = "images/gdp-deflators.png",
  width = 20,
  height = 12,
  units = "cm",
  dpi = 400
)


cereals_df |>
  ggplot(aes(x = year, y =yield, colour = cereal)) +
  geom_line()


cereals_df |>
  ggplot(aes(yield, fill = cereal)) +
  geom_histogram(bins = 8)+
  facet_wrap(vars(cereal))

  






library("ggplot2")
library("afcharts")


d <- subset(mpg, manufacturer == "ford")

d |>
  ggplot(aes(x = model, y = hwy)) +
  geom_line()


f <- data.frame(air
                )


my_df <- data.frame(USAccDeaths)
my_df |>
  ggplot(aes(x,y = AirPassengers)) +
  geom_point()
USAccDeaths



my_df <- data.frame(
  year = as.numeric(time(lynx)),
  lynx = as.numeric(lynx)
)

my_df |> ggplot(aes(x = year, y = lynx)) +
  geom_line() +
  labs(title = "Annual Canadian Lynx Trappings", x = "Year", y = "Lynx Count")


library("ggplot2")
library("afcharts")

d <- subset(mpg, manufacturer == "ford")

ggplot(d, aes(x = class, fill = class)) +
  geom_bar() +
  scale_fill_discrete_af()