In [33]:
import plotnine as p9
import pandas as pd
surveys_complete = pd.read_csv('data/surveys.csv')
surveys_complete = surveys_complete.dropna()
Exercise¶
In [5]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='weight', y='hindfoot_length'))
+ p9.geom_point())
Out[5]:
Challenge - bar chart¶
In [6]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='plot_id'))
+ p9.geom_bar()
)
Out[6]:
Challenge - Bar plot adaptations¶
Adapt the bar plot of the previous exercise by mapping the sex variable to the color fill of the bar chart. Change the scale of the color fill by providing the colors blue and orange manually (see API reference to find the appropriate function).
In [13]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='plot_id',fill='sex'))
+ p9.geom_bar()
+ p9.scale_fill_manual(["blue", "orange"])
)
Out[13]:
Challenge - distributions¶
- Replace the box plot with a violin plot, see geom_violin()
In [21]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='species_id',y='weight'))
+ p9.geom_violin()
)
Out[21]:
- Represent weight on the log10 scale, see scale_y_log10()
In [22]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='species_id',y='weight'))
+ p9.geom_violin()
+ p9.scale_y_log10()
)
Out[22]:
- Add color to the datapoints on your boxplot according to the plot from which the sample was taken (plot_id)
In [30]:
(p9.ggplot(data=surveys_complete,
mapping=p9.aes(x='species_id',y='weight',color='factor(plot_id)'))
+ p9.geom_jitter(alpha=0.2)
+ p9.geom_boxplot()
+ p9.scale_y_log10()
)
Out[30]:
Challenge - facetting¶
- Create a separate plot for each of the species that depicts how the average weight of the species changes through the years.
In [43]:
df = surveys_complete.groupby(['year', 'species_id'])['weight'].mean()
df
Out[43]:
In [44]:
df_res = df.reset_index(name='weights')
df_res
Out[44]:
In [49]:
(p9.ggplot(data=df_res,mapping=p9.aes(x='year',y='weights'))
+ p9.geom_line()
+ p9.facet_wrap("species_id")
)
Out[49]:
- Based on the previous exercise, visually compare how the weights of male and females has changed through time by creating a separate plot for each sex and an individual color assigned to each species_id.
In [51]:
df2 = surveys_complete.groupby(['sex', 'year', 'species_id'])['weight'].mean().reset_index()
df2
Out[51]:
In [55]:
(p9.ggplot(data=df2,mapping=p9.aes(x='year',y='weight',color='species_id'))
+ p9.geom_line()
+ p9.facet_wrap('sex')
)
Out[55]:
Challenge - customizations¶
- See if you can change thickness of lines for the line plot .
In [61]:
(p9.ggplot(data=df2,mapping=p9.aes(x='year',y='weight',color='species_id'))
+ p9.geom_line(size=1.2)
+ p9.facet_wrap('sex')
)
Out[61]:
- Can you find a way to change the name of the legend? What about its labels?
In [62]:
(p9.ggplot(data=df2,mapping=p9.aes(x='year',y='weight',color='species_id'))
+ p9.geom_line(size=1.5)
+ p9.facet_wrap('sex')
+ p9.labs(color='Species')
)
Out[62]:
- Use a different color palette (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2))
In [91]:
(p9.ggplot(data=df2,mapping=p9.aes(x='year',y='weight',color='species_id'))
+ p9.geom_line(size=1.5)
+ p9.facet_wrap('sex')
+ p9.labs(color='Species')
+ p9.scales.scale_color_brewer(type='qual', palette=1)
)
Out[91]: