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Migration from R

This guide helps R users of the MSUthemes R package transition to the Python version.

Overview

The Python implementation of MSUthemes closely mirrors the R package's functionality, making migration straightforward. The main differences are syntax-related due to Python vs R conventions.

Package Comparison

Feature R Package Python Package
Installation install.packages("MSUthemes") pip install msuthemes
Import library(MSUthemes) import msuthemes or from msuthemes import ...
Colors msu_green, msu_white colors.MSU_GREEN, colors.MSU_WHITE
Palettes msu_palettes$seq palettes.msu_seq
Theme theme_msu() theme_msu()
Big Ten bigten_colors("MSU") get_bigten_colors("MSU")
Dataset bigten (data.frame) load_bigten_data() (DataFrame)

Installation

# From CRAN
install.packages("MSUthemes")

# From GitHub
devtools::install_github("emilioxavier/MSUthemes")

# From PyPI
pip install msuthemes

# From GitHub
git clone https://github.com/emilioxavier/msuthemes-py.git
cd msuthemes-py
pip install -e .

Loading the Package

library(MSUthemes)
library(ggplot2)

from msuthemes import theme_msu, colors, palettes
import matplotlib.pyplot as plt

Using Colors

# Access colors
msu_green
msu_white
msu_orange

# In ggplot2
ggplot(data, aes(x, y)) +
  geom_line(color = msu_green)

# Access colors
colors.MSU_GREEN
colors.MSU_WHITE
colors.MSU_ORANGE

# In matplotlib
plt.plot(x, y, color=colors.MSU_GREEN)

Color Palettes

# Get palette colors
msu_palettes$seq
msu_palettes$div
msu_palettes$qual1

# Use in ggplot2
ggplot(data, aes(x, y, fill = category)) +
  geom_bar() +
  scale_fill_manual(values = msu_palettes$qual1)

# Get palette colors
palettes.msu_seq.as_hex(n_colors=5)
palettes.msu_div.as_hex(n_colors=7)
palettes.msu_qual1.as_hex()

# Use in matplotlib
colors_list = palettes.msu_qual1.as_hex()
plt.bar(categories, values, color=colors_list)

Applying Themes

# Apply MSU theme to ggplot2
ggplot(data, aes(x, y)) +
  geom_point() +
  theme_msu()

# With options
ggplot(data, aes(x, y)) +
  geom_point() +
  theme_msu(base_size = 14, use_grid = TRUE)

# Apply MSU theme to matplotlib
theme_msu()
plt.plot(x, y)
plt.show()

# With options
theme_msu(base_size=14, use_grid=True)
plt.plot(x, y)
plt.show()

Big Ten Colors

# Single institution
bigten_colors("MSU")

# Multiple institutions
bigten_colors(c("MSU", "Michigan", "Ohio State"))

# Create palette
bigten_palette(c("MSU", "Michigan"))

# Single institution
get_bigten_colors("MSU")

# Multiple institutions
get_bigten_colors(["MSU", "Michigan", "Ohio State"])

# Create palette
bigten_palette(["MSU", "Michigan"])

Working with Data

# Load dataset
data(bigten)

# Filter data
msu_data <- bigten %>%
  filter(name == "Michigan State")

# Plot with ggplot2
ggplot(msu_data, aes(x = entry_term, y = UGDS)) +
  geom_line(color = msu_green) +
  theme_msu()

# Load dataset
df = load_bigten_data()

# Filter data
msu_data = df[df['name'] == 'Michigan State']

# Or filter at load time
msu_data = load_bigten_data(institutions=['MSU'])

# Plot with matplotlib
theme_msu()
plt.plot(msu_data['entry_term'], msu_data['UGDS'],
         color=colors.MSU_GREEN)
plt.show()

Complete Example

R Version

library(MSUthemes)
library(ggplot2)
library(dplyr)

# Load and filter data
data(bigten)
rivalry <- bigten %>%
  filter(name %in% c("Michigan State", "Michigan", "Ohio State"))

# Get school colors
school_colors <- bigten_palette(c("MSU", "Michigan", "Ohio State"))

# Create plot
ggplot(rivalry, aes(x = entry_term, y = ADM_RATE * 100,
                    color = name)) +
  geom_line(linewidth = 1.5) +
  scale_color_manual(values = school_colors) +
  labs(
    title = "Big Ten Admission Rates Over Time",
    x = "Year",
    y = "Admission Rate (%)",
    color = "Institution"
  ) +
  theme_msu(use_grid = TRUE)

Python Version

from msuthemes import (
    theme_msu,
    get_bigten_colors,
    load_bigten_data
)
import matplotlib.pyplot as plt

# Apply theme
theme_msu(use_grid=True)

# Load and filter data
rivalry = load_bigten_data(
    institutions=['MSU', 'Michigan', 'Ohio State'],
    columns=['name', 'entry_term', 'ADM_RATE']
)

# Get school colors
school_colors = get_bigten_colors(['MSU', 'Michigan', 'Ohio State'])

# Create plot
fig, ax = plt.subplots(figsize=(10, 6))

for school in ['MSU', 'Michigan', 'Ohio State']:
    school_data = rivalry[rivalry['name'] == school]
    ax.plot(
        school_data['entry_term'],
        school_data['ADM_RATE'] * 100,
        label=school,
        color=school_colors[school],
        linewidth=1.5
    )

ax.set_xlabel('Year')
ax.set_ylabel('Admission Rate (%)')
ax.set_title('Big Ten Admission Rates Over Time')
ax.legend(title='Institution')
plt.show()

Key Differences

Naming Conventions

R Convention Python Convention
snake_case for functions snake_case for functions
snake_case for variables UPPER_CASE for constants
. for list access . for attribute access
$ for list/dataframe columns . for attributes, [] for dict keys

Data Structures

R Python
data.frame pandas.DataFrame
list dict or list
vector list or numpy.array
c() []

Plotting Libraries

R Python
ggplot2 matplotlib (primary)
Base R graphics matplotlib
- seaborn (statistical)
- plotly (interactive)

Common Gotchas

Theme Application

  • R: Theme is applied per-plot with + theme_msu()
  • Python: Theme is applied globally with theme_msu() before creating plots

Color Names

  • R: msu_green (lowercase, no quotes)
  • Python: colors.MSU_GREEN (uppercase constant)

Palette Access

  • R: msu_palettes$seq (dollar sign)
  • Python: palettes.msu_seq (dot notation)

Data Loading

  • R: data(bigten) loads into environment
  • Python: df = load_bigten_data() returns DataFrame

Tips for R Users Learning Python

  1. Indexing: Python uses 0-based indexing (R uses 1-based)
  2. Indentation: Python requires proper indentation (no {})
  3. Lists: Use [] for lists, not c()
  4. Assignment: Use = for assignment (though <- enthusiasts exist!)
  5. Help: Use help(function_name) or ?function_name in IPython

Additional Resources

Getting Help

If you encounter issues during migration: