load libraries and data

library(tidyverse)
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 # loads relevant syntax as well as ggplot2 packages
library(wesanderson)
library(ggridges)
library(scales)
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library(plotly)
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dat <- iris
glimpse(dat)
## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…

Color choice

show_col(wes_palettes$GrandBudapest2)

my_cols <- c(wes_palettes$GrandBudapest2)

Use ggplot to visualize Iris data set with a density plot

# basic example
p1 <- ggplot(dat) +
  aes(x = Sepal.Length, y = Species, fill = Species) +
  geom_density_ridges() +
  theme_ridges() + 
  theme(legend.position = "none")

p1 + scale_fill_manual(values = my_cols)
## Picking joint bandwidth of 0.181

## Make an interactive plot where points can be animated for more information Using interactive graphing with the plotly function

p2 <- ggplot(dat) +
  aes(x = Sepal.Length, y = Petal.Length, col = Species) +
  geom_point() + geom_smooth() + xlab("Sepal Length") +
  ylab("Sepal Width")

p2 + scale_fill_manual(values = my_cols)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

ggplotly(p2)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'