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Automatically Choose orientation="y" When Aes Y Is Discrete

In [1]:
import pandas as pd

from lets_plot import *
from lets_plot.mapping import as_discrete
In [2]:
LetsPlot.setup_html()
In [3]:
df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/gapminder.csv')
albania_df = df.loc[df['country'] == 'Albania']
df.head(3)
Out[3]:
country continent year lifeExp pop gdpPercap
0 Afghanistan Asia 1952 28.801 8425333 779.445314
1 Afghanistan Asia 1957 30.332 9240934 820.853030
2 Afghanistan Asia 1962 31.997 10267083 853.100710

1. How Does Automatic Setting orientation="y" Work

In [4]:
gggrid([
    ggplot(df, aes('continent', 'pop', fill='continent')) + geom_bar() + ggtitle('Default'),
    ggplot(df, aes('pop', 'continent', fill='continent')) + geom_bar() + ggtitle('Automatic setting orientation="y"')
], ncol=2)
Out[4]:

2. Marking by as_discrete()

If you're using continuous data for the Y-axis, the plot may be incorrect. Try marking the Y-axis data with the as_discrete() function, this will rotate your geom.

In [5]:
gggrid([
    ggplot(albania_df) + geom_bar(aes('lifeExp', 'year'), stat='sum', size=0),
    ggplot(albania_df) + geom_bar(aes('lifeExp', as_discrete('year')), stat='sum', size=0)
], ncol=2)
Out[5]:

3. Other Kinds of Geoms Support Automatic Choosing of Orientation

Such manipulations with automatic rotation can be done for geoms geom_boxplot(), geom_violin(), geom_bar() and geom_lollipop().

In [6]:
gggrid([
    ggplot(df, aes('continent', 'lifeExp', fill='continent')) + geom_boxplot(),
    ggplot(df, aes('lifeExp', 'continent', fill='continent')) + geom_boxplot(),
    ggplot(df, aes('continent', 'lifeExp', fill='continent')) + geom_violin(),
    ggplot(df, aes('lifeExp', 'continent', fill='continent')) + geom_violin(),
    ggplot(albania_df, aes(as_discrete('year'), 'lifeExp')) + geom_lollipop(stat='sum', size=2),
    ggplot(albania_df, aes('lifeExp', as_discrete('year'))) + geom_lollipop(stat='sum', size=2),
], ncol=2)
Out[6]:

4. stat='boxplot', stat='boxplot_outlier', stat_summary()

In [7]:
gggrid([
    ggplot(df, aes('continent', 'lifeExp')) + geom_pointrange(stat='boxplot'),
    ggplot(df, aes('lifeExp', 'continent')) + geom_pointrange(stat='boxplot'),
    ggplot(df, aes('continent', 'lifeExp')) + geom_pointrange(stat='boxplot_outlier'),
    ggplot(df, aes('lifeExp', 'continent')) + geom_pointrange(stat='boxplot_outlier'),
    ggplot(df, aes('continent', 'lifeExp')) + stat_summary(),
    ggplot(df, aes('lifeExp', 'continent')) + stat_summary()
], ncol=2)
Out[7]: