On my honor as a student, I have neither given nor accepted unauthorized aid on this assignment or exam. Nhi Nguyen

library(tidyverse)

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## ✔ purrr     1.0.1     
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library(plotly)

## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
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##     last_plot
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##     filter
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##     layout

data = as.data.frame(iris)
head(data)

create a scatterplot of petal length vs petal width colored by species using ggplot2. Set you colors manually.

p1 = ggplot(data, aes(x=Petal.Length, y=Petal.Width, color=Species))+
  geom_point()+
  scale_color_manual(values=c("#6C91BF","#5FB0B7","#5BC8AF"))+
  theme_minimal()+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  labs(x="Petal Length", y="Petal Width")

p1

Convert your plot above to an interactive graph using `ggplotly`

ggplotly(p1)

Now code the the above plot directly in `plot_ly`

Manipulate data as needed

setosa = data %>% filter(Species == "setosa")
versicolor = data %>% filter(Species == "versicolor")
virginica = data %>% filter(Species == "virginica")

initialize your plotly plot

p2 = plot_ly(type = 'scatter', mode = 'markers')

add your Setosa trace

# values=c("#6C91BF","#5FB0B7","#5BC8AF")

p2 = p2 %>%
  add_trace(
    x = setosa$Petal.Length,
    y = setosa$Petal.Width,
    marker = list(
      color = "#6C91BF",
      size = 10),
    name = 'Setosa'
      )

p2

Add your versicolor trace

p2 = p2 %>%
  add_trace(
    x = versicolor$Petal.Length,
    y = versicolor$Petal.Width,
    marker = list(
      color = "#5FB0B7",
      size = 10),
    name = 'Versicolor'
      )

p2

Add your virginica trace

p2 = p2 %>%
  add_trace(
    x = virginica$Petal.Length,
    y = virginica$Petal.Width,
    marker = list(
      color = "#5BC8AF",
      size = 10),
    name = 'Virginica'
      )

p2

Update the tooltip to read petal width: x.x petal length: x.x

# had to initialize again bc my graph key was showing double for each of the flower specie
p2 = plot_ly(type = 'scatter', mode = 'markers') 

p2 = p2 %>%
  add_trace(
    x = setosa$Petal.Length,
    y = setosa$Petal.Width,
    marker = list(
      color = "#6C91BF",
      size = 10),
    name = 'Setosa',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2 = p2 %>%
  add_trace(
    x = versicolor$Petal.Length,
    y = versicolor$Petal.Width,
    marker = list(
      color = "#5FB0B7",
      size = 10),
    name = 'Versicolor',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2 = p2 %>%
  add_trace(
    x = virginica$Petal.Length,
    y = virginica$Petal.Width,
    marker = list(
      color = "#5BC8AF",
      size = 10),
    name = 'Virginica',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2

Add graph title and remove the gridlines

p2 = p2 %>%
  layout(title = 'Scatterplot of Petal Length & Petal Width',
         xaxis = list(title = 'Petal Length',
                      showgrid = FALSE),
         yaxis = list(title = 'Petal Width',
                      showgrid = FALSE, zeroline = FALSE))
p2

Lighten the marker, create a dark outline of the same color

p2 = plot_ly(type = 'scatter', mode = 'markers') 

p2 = p2 %>%
  add_trace(
    x = setosa$Petal.Length,
    y = setosa$Petal.Width,
    opacity = 0.5, 
    marker = list(
      color = "#6C91BF",
      size = 10,
      line = list(
        color = "#6C91BF",
        width = 2)),  # outline hard to see bc they're the same color, but I think 2 is pretty dark and thick
    name = 'Setosa',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2 = p2 %>%
  add_trace(
    x = versicolor$Petal.Length,
    y = versicolor$Petal.Width,
    opacity = 0.5, 
    marker = list(
      color = "#5FB0B7",
      size = 10,
      line = list(
        color = "#5FB0B7",
        width = 2)),
    name = 'Versicolor',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2 = p2 %>%
  add_trace(
    x = virginica$Petal.Length,
    y = virginica$Petal.Width,
    opacity = 0.5, 
    marker = list(
      color = "#5BC8AF",
      size = 10,
      line = list(
        color = "#5BC8AF",
        width = 2)),
    name = 'Virginica',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x}'
      )

p2 = p2 %>%
  layout(title = 'Scatterplot of Petal Length & Petal Width',
         xaxis = list(title = 'Petal Length',
                      showgrid = FALSE),
         yaxis = list(title = 'Petal Width',
                      showgrid = FALSE, zeroline = FALSE))

p2

Homework:

Take the above graph and make it 3D by adding Sepal Length on the z-axis.

# referenced class notes 'plotly-1.Rmd'
p3 = plot_ly(type = 'scatter3d', mode = 'markers') 

p3 = p3 %>%
  add_trace(
    x = setosa$Petal.Length,
    y = setosa$Petal.Width,
    z = setosa$Sepal.Length,
    opacity = 0.5, 
    marker = list(
      color = "#6C91BF",
      size = 10,
      line = list(
        color = "#6C91BF",
        width = 2)),  # outline hard to see bc they're the same color, but I think 2 is pretty dark and thick
    name = 'Setosa',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x} <br>Sepal Length: %{z}'
      )

p3 = p3 %>%
  add_trace(
    x = versicolor$Petal.Length,
    y = versicolor$Petal.Width,
    z = versicolor$Sepal.Length,
    opacity = 0.5, 
    marker = list(
      color = "#5FB0B7",
      size = 10,
      line = list(
        color = "#5FB0B7",
        width = 2)),
    name = 'Versicolor',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x} <br>Sepal Length: %{z}'
      )

p3 = p3 %>%
  add_trace(
    x = virginica$Petal.Length,
    y = virginica$Petal.Width,
    z = virginica$Sepal.Length,
    opacity = 0.5, 
    marker = list(
      color = "#5BC8AF",
      size = 10,
      line = list(
        color = "#5BC8AF",
        width = 2)),
    name = 'Virginica',
    hovertemplate = 'Petal Width: %{y} <br>Petal Length: %{x} <br>Sepal Length: %{z}'
      )

x.axis = list(
  title = "Petal Length"
)

y.axis = list(
  title = "Petal Width"
)

z.axis = list(
  title = "Sepal Length"
)

p3 = p3 %>%
  layout(title = 'Scatterplot of Petal Length, Petal Width & Sepal Length',
         scene = list(xaxis = x.axis,
                      yaxis = y.axis,
                      zaxis = z.axis))

p3

Create grouped boxplots where each iris dataset measure in on the x-axis with 3 boxplots for each measure representing the species. Use your own cohesive color palette using coolors.co. Don’t forget to include all the parts of a chart.

Reshaping the data to make column names into column values

# Renaming to take away the dot in the column names
names = c("Sepal Length","Sepal Width","Petal Length","Petal Width","Species")

setosa1 = setosa
colnames(setosa1) = names

versicolor1 = versicolor
colnames(versicolor1) = names

virginica1 = virginica
colnames(virginica1) = names

# Source: https://campus.datacamp.com/courses/abc-intro-2-r/data-wrangling?ex=2
library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

# id.vars is kind of the identifying column; everything else but the id.vars gets stretched out
setosa.new = melt(setosa1, id.vars = c("Species")) 
versicolor.new = melt(versicolor1, id.vars = c("Species"))
virginica.new = melt(virginica1, id.vars = c("Species"))

p4 = plot_ly(type = 'box')

p4 = p4 %>%
  add_trace(
    x = setosa.new$variable,
    y = setosa.new$value,
    marker = list(
      color = "#E3D7FF"
    ),
    name = "Setosa"
  ) %>%
  add_trace(
    x = versicolor.new$variable,
    y = versicolor.new$value,
    marker = list(
      color = "#AFA2FF"
    ),
    name = "Versicolor"
  ) %>%
  add_trace(
    x = virginica.new$variable,
    y = virginica.new$value,
    marker = list(
      color = "#7A89C2"
    ),
    name = "Virginica"
  ) %>%
  layout(title = 'Boxplot of All Measures Grouped by Species',
         xaxis = list(title = 'Measures'),
         yaxis = list(title = 'Value',
                      showgrid = FALSE, zeroline = FALSE)) 



p4

## Warning: Can't display both discrete & non-discrete data on same axis

Plotly Lab

create a scatterplot of petal length vs petal width colored by species using ggplot2. Set you colors manually.

Convert your plot above to an interactive graph using ggplotly

Now code the the above plot directly in plot_ly

Convert your plot above to an interactive graph using `ggplotly`

Now code the the above plot directly in `plot_ly`