`ggplot2` vs. `matplotlib`

Below is a markdown table comparing the features of ggplot2 (R) and matplotlib (Python) for creating visualizations:

Plot basics

ggplot2 (R)	Feature	matplotlib (Python)
aes()	Used inside `ggplot()` to map variables to aesthetics	Not used; column names are specified directly in `plot()`
+() %>%	Used to add layers and modify the plot	Not applicable
ggsave()	Save the plot to a file	Not applicable; plots are saved using `plt.savefig()` in Matplotlib
quickplot()	Simple, intuitive function for quick plots	Not available

Layers

ggplot2 (R)	Feature	matplotlib (Python)
geom_abline()	Add an arbitrary line to the plot	`axhline()` and `axvline()` in Matplotlib
geom_hline()	Add horizontal lines to the plot	`axhline()` in Matplotlib
geom_vline()	Add vertical lines to the plot	`axvline()` in Matplotlib
geom_bar()	Create bar charts	`kind='bar'` in `plot()`
geom_col()	Create column charts	`kind='bar'` with `position='identity'` in `plot()`
stat_count()	Create bar charts with automatic counting	`kind='bar'` with `position='identity'` in `plot()`
geom_boxplot()	Create boxplots	`kind='box'` in `plot()`
stat_boxplot()	Create boxplots with statistical summaries	`kind='box'` in `plot()`
geom_map()	Plot spatial data on maps	Not available
geom_point()	Create scatter plots	`kind='scatter'` in `plot()`
geom_label()	Add text labels to points	Not available
geom_text()	Add text annotations to the plot	`text()` in Matplotlib
geom_violin()	Create violin plots	Not available
stat_ydensity()	Compute density for violin plots	Not available

Position adjustment

ggplot2 (R) Feature matplotlib (Python)

position_dodge() Dodge overlapping elements Not available
Annotations

ggplot2 (R) Feature matplotlib (Python)

annotate() Add annotations to the plot Not available

ggplot2 (R)	Feature	matplotlib (Python)
position_dodge()	Dodge overlapping elements	Not available

ggplot2 (R)	Feature	matplotlib (Python)
annotate()	Add annotations to the plot	Not available

Scales

ggplot2 (R)	Feature	matplotlib (Python)
labs()	Modify plot labels and titles	Not available
xlab()	Modify the x-axis label	`set_xlabel()` in Matplotlib
ylab()	Modify the y-axis label	`set_ylabel()` in Matplotlib
ggtitle()	Add a plot title	`set_title()` in Matplotlib
lims()	Set plot limits	`set_xlim()` and `set_ylim()` in Matplotlib
xlim()	Set x-axis limits	`set_xlim()` in Matplotlib
ylim()	Set y-axis limits	`set_ylim()` in Matplotlib
scale_x_continuous()	Modify x-axis scales	`set_xscale()` in Matplotlib
scale_y_continuous()	Modify y-axis scales	`set_yscale()` in Matplotlib
scale_x_date()	Modify x-axis scales for date data	Not available
scale_y_date()	Modify y-axis scales for date data	Not available
scale_x_discrete()	Modify x-axis scales for discrete data	Not available
scale_y_discrete()	Modify y-axis scales for discrete data	Not available

Facetting

ggplot2 (R)	Feature	matplotlib (Python)
facet_wrap()	Create small multiples in a wrap layout	`subplots=True` with multiple plots in Pandas
facet_grid()	Create small multiples in a grid layout	`subplots=True` with multiple plots in Pandas
coord_flip()	Flip the x and y-axis	Not available

Themes

ggplot2 (R)	Feature	matplotlib (Python)
element_blank()	Remove an element from the plot	Not available
element_rect()	Modify rectangle elements in the plot	Not available
element_line()	Modify line elements in the plot	Not available
element_text()	Modify text elements in the plot	Not available

autoplot

ggplot2 (R) Feature matplotlib (Python)

autoplot() Create basic plots automatically Not available

ggplot2 (R)	Feature	matplotlib (Python)
autoplot()	Create basic plots automatically	Not available

Please note that ggplot2 and matplotlib are both powerful visualization libraries, but they have different philosophies and strengths. The syntax for creating visualizations can differ significantly between the two libraries.