Creating interactive plots with ggplotly

Metabolites and lipids descriptive statistical analysis in R

The plotly library contains the ggplotly() function, which will allow you to turn some of your ggplot2 graphics into an interactive plotly object. Such interactive figures can be used for exploratory data analysis or as a part of a presentation, e.g. for your lab meetings. Here is how the ggplotly() function works:

# Changing ggplot2 box plots with dots into interactive plot:
# Calling libraries:
library(plotly)
library(tidyverse)

# Consulting the documentation:
?ggplotly()

# Create 'data.long' tibble:
data.long <- data %>%
  select(`Label`,
         `SM 39:1;O2`,
         `SM 40:1;O2`,
         `SM 41:1;O2`,
         `SM 42:1;O2`) %>%
  pivot_longer(cols = `SM 39:1;O2`:`SM 42:1;O2`,
               names_to = 'Lipids',
               values_to = 'Concentrations')

# Creating a plot:
box.plots <- 
  ggplot(data.long, aes(x = `Label`, 
                      y = `Concentrations`,
                      fill = `Label`)) +
  geom_boxplot(outlier.shape = NA) +
  geom_point(position = position_jitter(width = 0.05), shape = 21) +
  scale_fill_manual(values = c('royalblue', 'orange', 'red2')) +
  facet_grid(. ~ Lipids) + 
  theme_classic() +
  theme(strip.placement = "outside",
        strip.background = element_blank(),
        panel.border = element_blank(),
        panel.spacing.x = unit(0, 'cm')) 

# Changing the plot into an interactive version:
ggplotly(box.plots)

The output:

Interactive box plots are generated using ggplotly() function from the plotly library.

We can even create entire interactive correlation heat maps:

# Interactive correlation heat maps.
# Calling library:
library(reshape2)
library(ggsci)

# Computing correlation matrix:
corr.matrix <- 
  data %>%
  filter(Label == "N") %>%
  select(starts_with("SM"),
         -`Sample Name`,
         -Label) %>%
  cor()
  
# Melting a wide correlation matrix into a long correlation matrix:
long.corr.matrix <- melt(corr.matrix)

# Selecting colors for the continuous color scaling:
colors <- c("#002060", "#0d78ca", "#00e8f0", "white", "#FF4D4D", "red", "#600000")

# Creating a ggplot2 correlation heat map:
corr.heat.map <-
  ggplot(long.corr.matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradientn(colours = colors, limits = c(-1,1)) +
  theme(axis.text.x = element_text(angle = 90, 
                                   hjust = 1, 
                                   vjust = 0.5))
                                   
# Creating the interactive plotly heat map:
ggplotly(corr.heat.map)

The output:

The ggplot2 correlation heat map turned into a plotly interactive plot using ggplotly().

We encourage you to explore more of the possibilities offered by the ggplotly() function.