Plotly is a powerful interactive visualization library that allows users to create web-based, dynamic charts right from R. It offers a wide variety of graph types, such as scatter plots, bar charts, line graphs, 3D plots, maps, and more. One of Plotly’s biggest advantages is the interactivity it brings to R visualizations, including zooming, panning, and tooltips that display information when hovering over data points.
Interactivity: Unlike static plots, Plotly’s graphs are highly interactive. Web-Integration: Outputs can be embedded in web apps, dashboards, or HTML reports. Customization: Full control over elements like axes, colors, legends, and layouts. 3D Capabilities: Support for 3D scatter plots, surface plots, and more. Integration: Works seamlessly with R Markdown, Shiny apps, and even ggplot2 through ggplotly().
A scatter plot is used to visualize relationship between two variables.
library(plotly)
fig = plot_ly(
x = ~c(1, 2, 3, 4, 5),
y = ~c(10, 11, 12, 13, 14),
type = 'scatter',
mode = 'markers'
)
fig| # Bar Plot |
r library(plotly) fig = plot_ly( x = ~c("A", "B", "C"), y = ~c(10, 20, 15), type = 'bar' ) %>% layout(title = "Bar Plot Example") |
The Air Quality dataset in R is a built-in dataset that contains daily air quality measurements in New York from May to September 1973. It includes variables such as ozone levels, solar radiation, wind speed, temperature, and more. The dataset provides an excellent opportunity to explore and visualize air quality data.
The Air Quality dataset provides a wealth of information that can be visualized to explore patterns and relationships in air quality data. The examples below demonstrate how to create different types of interactive plots using Plotly in R, enhancing the analysis of this dataset.
This plot shows the relationship between ozone levels and temperature.
library(plotly)
plot_ly(data = airquality,
x = ~Temp,
y = ~Ozone,
type = 'scatter',
mode = 'markers',
marker = list(size = 10,color = 'purple', opacity = 0.5))%>%
layout(title = "Ozone Levels vs Temperature",
xaxis = list(title = "Temperature(°F)"),
yaxis = list(title = "Ozone (ppb)"))| # Box Plot of Ozone by Month |
| This box plot helps visualize the distribution of ozone levels across different months. |
r plot_ly(data = airquality, x = ~factor(Month), y = ~Ozone, type = 'box') %>% layout(title = "Ozone Levels by Month", xaxis = list(title = "Month"), yaxis = list(title = "Ozone (ppb)")) |
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This line plot shows ozone levels change over time.
airquality$Date = as.Date(paste(1973, airquality$Month, airquality$Day, sep = "-"))
plot_ly(data = airquality,
x = ~Date,
y = ~Ozone,
type = 'scatter',
mode = 'lines+markers',
line = list(color = 'red')) %>%
layout(title = "Ozone Levels Over Time",
xaxis = list(title = "Date"),
yaxis = list(title = "Ozone (ppb)"))