Supporting Statistical Analysis for Research

3.4 Relationship between more than two variables

These exercises use the Mroz.csv data set that was imported in the prior section.

1. Import the Mroz.csv data set.

   from pathlib import Path
   import pandas as pd
   import plotnine as p9

   mroz_path = Path('..') / 'datasets' / 'Mroz.csv'
   mroz = pd.read_csv(mroz_path)
   
   print(mroz.dtypes)

   Unnamed: 0      int64
   lfp            object
   k5              int64
   k618            int64
   age             int64
   wc             object
   hc             object
   lwg           float64
   inc           float64
   dtype: object

2. Create a scatter plot for age against lwg. Use color to display women college attendance status.

   print(
       p9.ggplot(mroz, p9.aes(x='age', y='lwg', color='wc')) + 
       p9.geom_point() +
       p9.theme_bw())

   <ggplot: (143590174868)>

   

3. Facet the prior plot on hc.

   print(
       p9.ggplot(mroz, p9.aes(x='age', y='lwg', color='wc')) + 
       p9.geom_point() +
       p9.facet_wrap('~hc') +
       p9.theme_bw())

   <ggplot: (143590151239)>

   

4. Add a loess smoothing line hc.

   print(
       p9.ggplot(mroz, p9.aes(x='age', y='lwg', color='wc')) + 
       p9.geom_point() +
       p9.geom_smooth(color='blue') +
       p9.facet_wrap('~hc') +
       p9.theme_bw())

   <ggplot: (143590949661)>
   
   C:\PROGRA~3\ANACON~1\lib\site-packages\plotnine\stats\smoothers.py:146: UserWarning: Confidence intervals are not yet implementedfor lowess smoothings.
     warnings.warn("Confidence intervals are not yet implemented"
   C:\PROGRA~3\ANACON~1\lib\site-packages\plotnine\stats\smoothers.py:146: UserWarning: Confidence intervals are not yet implementedfor lowess smoothings.
     warnings.warn("Confidence intervals are not yet implemented"

   

5. If the prior plot produces a message or warning, change the code to avoid the warning.

   The plotnine package does not support confidence intervals. These need to be turned off. The se parameter is difficult to find. If you google "plotnine stat_smooth", you will find a page for this stat_smooth() method. The parameter is documented there.

   print(
       p9.ggplot(mroz, p9.aes(x='age', y='lwg', color='wc')) + 
       p9.geom_point() +
       p9.geom_smooth(
           se=False,
           color='blue') +
       p9.facet_wrap('~hc') +
       p9.theme_bw())

   <ggplot: (143590968696)>

   

6. Add a title and provide better axis labels.

   print(
       p9.ggplot(mroz, p9.aes(x='age', y='lwg', color='wc')) + 
       p9.geom_point() +
       p9.geom_smooth(
           se=False,
           color='blue') +
       p9.facet_wrap('~hc') +
       p9.theme_bw() +
       p9.ggtitle('Womens Wages and Ages') +
       p9.theme(plot_title=p9.element_text(hjust=0.5)) +
       p9.xlab('Log Womens Wages') +
       p9.ylab('Age'))

   <ggplot: (-9223371893263836942)>

   

7. Create a plot that explores the relationship between at least three variables. Use at least one different value than was used in the prior exercise.