Module stikpetP.visualisations.vis_dot_plot

Functions

def vi_dot_plot(data, dotSize=1)

Dot Plot

The Oxford Dictionary of Statistics defines a dot plot as "an alternative to a bar chart or line graph when there are very few data values. Each value is recorded as a dot, so that the frequencies for each value can easily be counted" (Upton & Cook, 2014, p. 129).

This function uses matplotlib.pyplot scatter() to create a simple dot plot.

A YouTube video on dot plots.

This function is shown in this YouTube video and the visualisation is also described at PeterStatistics.com

Parameters

data : list or pandas series

 

dotSize : float, optional

indicator for how big the dots need to be. Default is 1

Notes

In the definition a bar chart is mentioned. A bar chart can be defined as “a graph in which bars of varying height with spaces between them are used to display data for variables defined by qualities or categories” (Zedeck, 2014, p. 20). Together this indicates that a dot plot is used for categorical data.

However, Zedeck sees the dot plot as an alternative name for a scatterplot, which is for continuous data. A third version comes from the Cambridge Dictionary of Statistics: "A more effective display than a number of other methods, for example, pie charts and bar charts, for displaying quantitative data which are labelled" (Everitt, 2004, p. 123). They also show an example where we see a categorical variable on one axis, and a continuous variable on another.

This function was only for the original definition for categorical data.

See Also

Before the visualisation you might first want to get an impression using a frequency table: * [tab_frequency](../other/table_frequency.html#tab_frequency)

After visualisation you might want some descriptive measures: * [me_mode](../measures/meas_mode.html#me_mode) for the mode * [me_qv](../measures/meas_qv.html#me_qv) for Measures of Qualitative Variation

or perform a test: * [ts_pearson_gof](../tests/test_pearson_gof.html#ts_pearson_gof) for Pearson Chi-Square Goodness-of-Fit Test * [ts_freeman_tukey_gof](../tests/test_freeman_tukey_gof.html#ts_freeman_tukey_gof) for Freeman-Tukey Test of Goodness-of-Fit * [ts_freeman_tukey_read](../tests/test_freeman_tukey_read.html#ts_freeman_tukey_read) for Freeman-Tukey-Read Test of Goodness-of-Fit * [ts_g_gof](../tests/test_g_gof.html#ts_g_gof) for G (Likelihood Ratio) Goodness-of-Fit Test * [ts_mod_log_likelihood_gof](../tests/test_mod_log_likelihood_gof.html#ts_mod_log_likelihood_gof) for Mod-Log Likelihood Test of Goodness-of-Fit * [ts_multinomial_gof](../tests/test_multinomial_gof.html#ts_multinomial_gof) for Multinomial Goodness-of-Fit Test * [ts_neyman_gof](../tests/test_neyman_gof.html#ts_neyman_gof) for Neyman Test of Goodness-of-Fit * [ts_powerdivergence_gof](../tests/test_powerdivergence_gof.html#ts_powerdivergence_gof) for Power Divergence GoF Test

Alternatives for this visualisation could be: * [vi_bar_simple](../visualisations/vis_bar_simple.html#vi_bar_simple) for Simple Bar Chart * [vi_cleveland_dot_plot](../visualisations/vis_cleveland_dot_plot.html#vi_cleveland_dot_plot) for Cleveland Dot Plot * [vi_pareto_chart](../visualisations/vis_pareto_chart.html#vi_pareto_chart) for Pareto Chart * [vi_pie](../visualisations/vis_pie.html#vi_pie) for Pie Chart

References

Everitt, B. (2004). The Cambridge dictionary of statistics (2nd ed). Cambridge University Press.

Upton, G. J. G., & Cook, I. (2014). Dictionary of statistics (3rd ed.). Oxford University Press.

Zedeck, S. (Ed.). (2014). APA dictionary of statistics and research methods. American Psychological Association.

Author

Made by P. Stikker

Companion website: https://PeterStatistics.com
YouTube channel: https://www.youtube.com/stikpet
Donations: https://www.patreon.com/bePatron?u=19398076

Examples

Example 1: pandas series

>>> df1 = pd.read_csv('https://peterstatistics.com/Packages/ExampleData/GSS2012a.csv', sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
>>> ex1 = df1['mar1']
>>> vi_dot_plot(ex1);
>>> vi_dot_plot(ex1, dotSize=50);

Example 2: a list

>>> ex2 = ["MARRIED", "DIVORCED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "NEVER MARRIED", "MARRIED", "MARRIED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "MARRIED"]
>>> vi_dot_plot(ex2);

Expand source code

def vi_dot_plot(data, dotSize = 1):
    '''
    Dot Plot
    --------
    
    The Oxford Dictionary of Statistics defines a dot plot as "an alternative to a bar chart or line graph when there are very few data values. Each value is recorded as a dot, so that the frequencies for each value can easily be counted" (Upton & Cook, 2014, p. 129). 
    
    This function uses matplotlib.pyplot scatter() to create a simple dot plot.
    
    A [YouTube](https://youtu.be/qs1nh0CMiIY) video on dot plots.

    This function is shown in this [YouTube video](https://youtu.be/gSLI-2oqwiQ) and the visualisation is also described at [PeterStatistics.com](https://peterstatistics.com/Terms/Visualisations/DotPlot.html)
    
    Parameters
    ----------
    data : list or pandas series
    dotSize : float, optional
        indicator for how big the dots need to be. Default is 1
    
    Notes
    -----
    In the definition a *bar chart* is mentioned. A bar chart can be defined as “a graph in which bars of varying height with spaces between them are used to display data for variables defined by qualities or categories” (Zedeck, 2014, p. 20). Together this indicates that a dot plot is used for categorical data.
    
    However, Zedeck sees the dot plot as an alternative name for a scatterplot, which is for continuous data. A third version comes from the Cambridge Dictionary of Statistics: "A more effective display than a number of other methods, for example, pie charts and bar charts, for displaying quantitative data which are labelled" (Everitt, 2004, p. 123). They also show an example where we see a categorical variable on one axis, and a continuous variable on another.
    
    This function was only for the original definition for categorical data.

    See Also
    --------
    Before the visualisation you might first want to get an impression using a frequency table:
    * [tab_frequency](../other/table_frequency.html#tab_frequency)

    After visualisation you might want some descriptive measures:
    * [me_mode](../measures/meas_mode.html#me_mode) for the mode
    * [me_qv](../measures/meas_qv.html#me_qv) for Measures of Qualitative Variation

    or perform a test:
    * [ts_pearson_gof](../tests/test_pearson_gof.html#ts_pearson_gof) for Pearson Chi-Square Goodness-of-Fit Test
    * [ts_freeman_tukey_gof](../tests/test_freeman_tukey_gof.html#ts_freeman_tukey_gof) for Freeman-Tukey Test of Goodness-of-Fit
    * [ts_freeman_tukey_read](../tests/test_freeman_tukey_read.html#ts_freeman_tukey_read) for Freeman-Tukey-Read Test of Goodness-of-Fit
    * [ts_g_gof](../tests/test_g_gof.html#ts_g_gof) for G (Likelihood Ratio) Goodness-of-Fit Test
    * [ts_mod_log_likelihood_gof](../tests/test_mod_log_likelihood_gof.html#ts_mod_log_likelihood_gof) for Mod-Log Likelihood Test of Goodness-of-Fit
    * [ts_multinomial_gof](../tests/test_multinomial_gof.html#ts_multinomial_gof) for Multinomial Goodness-of-Fit Test
    * [ts_neyman_gof](../tests/test_neyman_gof.html#ts_neyman_gof) for Neyman Test of Goodness-of-Fit
    * [ts_powerdivergence_gof](../tests/test_powerdivergence_gof.html#ts_powerdivergence_gof) for Power Divergence GoF Test
    
    Alternatives for this visualisation could be:
    * [vi_bar_simple](../visualisations/vis_bar_simple.html#vi_bar_simple) for Simple Bar Chart
    * [vi_cleveland_dot_plot](../visualisations/vis_cleveland_dot_plot.html#vi_cleveland_dot_plot) for Cleveland Dot Plot
    * [vi_pareto_chart](../visualisations/vis_pareto_chart.html#vi_pareto_chart) for Pareto Chart
    * [vi_pie](../visualisations/vis_pie.html#vi_pie) for Pie Chart
    
    References 
    ----------
    Everitt, B. (2004). *The Cambridge dictionary of statistics* (2nd ed). Cambridge University Press.
    
    Upton, G. J. G., & Cook, I. (2014). *Dictionary of statistics* (3rd ed.). Oxford University Press.
    
    Zedeck, S. (Ed.). (2014). *APA dictionary of statistics and research methods*. American Psychological Association.
    
    Author
    ------
    Made by P. Stikker
    
    Companion website: https://PeterStatistics.com  
    YouTube channel: https://www.youtube.com/stikpet  
    Donations: https://www.patreon.com/bePatron?u=19398076
    
    Examples
    --------
    Example 1: pandas series
    >>> df1 = pd.read_csv('https://peterstatistics.com/Packages/ExampleData/GSS2012a.csv', sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
    >>> ex1 = df1['mar1']
    >>> vi_dot_plot(ex1);
    >>> vi_dot_plot(ex1, dotSize=50);
    
    Example 2: a list
    >>> ex2 = ["MARRIED", "DIVORCED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "NEVER MARRIED", "MARRIED", "MARRIED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "MARRIED"]
    >>> vi_dot_plot(ex2);

    '''
    if type(data) == list:
        data = pd.Series(data)
    
    freq = data.value_counts()
    
    # divide if needed by number of dots each should represent
    n_dots = (freq/dotSize).astype(np.int64)
    
    # create an array for each categorie that has the label, and 
    # a consecutive number going from the first to the last case in that category
    a = []
    for i in range(len(n_dots)):
        a.append(np.vstack((np.full(n_dots.values[i], n_dots.keys()[i]), np.arange(1,n_dots.values[i]+1))).T)
    
    # now combine all the arrays from the categories
    my_data = np.concatenate((a[0],a[1]),axis=0)
    for i in range(2, len(n_dots)):
        my_data = np.concatenate((my_data,a[i]),axis=0)
    
    plt.scatter(x=my_data[:,0], y=my_data[:,1])
    plt.ylabel("x " + str(dotSize))
    plt.show()
    
    return