Data Visualizations in Python and R

Python:

1. Matplotlib:

• Matplotlib is a widely-used plotting library in Python. It provides a flexible and comprehensive set of plotting functions.
• Simple Example:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

y = [2, 4, 6, 8, 10]

plt.plot(x, y)

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Simple Line Plot')

plt.show()

2. Seaborn:

• Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics.
• Simple Example:

import seaborn as sns

tips = sns.load_dataset('tips')

sns.barplot(x='day', y='total_bill', data=tips)

3. Plotly:

• Plotly is an interactive visualization library that produces high-quality and interactive plots. It offers a wide range of chart types and customization options.
• Simple Example:

import plotly.express as px

df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")

fig.show()

4. Bokeh:

• Bokeh is another interactive visualization library that targets modern web browsers for presentation. It supports both simple and complex visualizations.
• Simple Example:

from bokeh.plotting import figure, output_file, show

x = [1, 2, 3, 4, 5]

y = [2, 4, 6, 8, 10]

output_file("line_plot.html")

p = figure(title="Simple Line Plot", x_axis_label='X-axis', y_axis_label='Y-axis')

p.line(x, y)

show(p)

***************************************************************************

R:

1. ggplot2:

• ggplot2 is a widely-used data visualization library in R that implements the grammar of graphics. It provides a layered approach to creating plots.
• Simple Example:

library(ggplot2)

data <- data.frame(x = c(1, 2, 3, 4, 5), y = c(2, 4, 6, 8, 10))

ggplot(data, aes(x = x, y = y)) + geom_line() +

?xlab("X-axis") + ylab("Y-axis") +

?ggtitle("Simple Line Plot")

2. lattice:

• lattice is a powerful and flexible library for creating conditioned plots in R. It allows for easy creation of trellis plots, which are multi-panel displays.
• Simple Example:

library(lattice)

data <- data.frame(x = c(1, 2, 3, 4, 5), y = c(2, 4, 6, 8, 10))

xyplot(y ~ x, data = data, type = "b",

????xlab = "X-axis", ylab = "Y-axis",

????main = "Simple Line Plot")

3. plotly:

• Plotly is also available for R and provides interactive and dynamic visualizations with a wide range of options. It supports various types of charts.
• Simple Example:

library(plotly)

data <- iris

fig <- plot_ly(data, x = ~Petal.Width, y = ~Petal.Length, color = ~Species, mode = "markers")

fig

4. ggvis:

• ggvis is an interactive data visualization package for R based on the principles of ggplot2. It supports interactive features like tooltips and dynamic filtering.
• Simple Example:

library(ggvis)

data <- data.frame(x = c(1, 2, 3, 4, 5), y = c(2, 4, 6, 8, 10))

data %>% ggvis(~x, ~y) %>% layer_lines() %>%

?add_axis("x", title = "X-axis") %>%

?add_axis("y", title = "Y-axis") %>%

?set_options(width = 500, height = 300)