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episodes/07-visualization-ggplot-python.md

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)
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```
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![](fig/06_color_plot.png){alt='scatter plot of Hindfoot length vs weight with colors coordinating to specfic species, showing abundance in the mid to lower left side of the plot'}
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![](fig/06_color_plot.png){alt='scatter plot of Hindfoot length vs weight with colors coordinating to specific species, showing abundance in the mid to lower left side of the plot'}
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Apart from the adaptations of the arguments and settings of the `data`, `aes`
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and `geom-*` elements, additional elements can be added as well, using the `+`
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)
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```
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![](fig/06_color_label_plot.png){alt='scatter plot of Hindfoot length vs weight (g) with colors coordinating to specfic species, showing abundance in the mid to lower left side of the plot'}
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![](fig/06_color_label_plot.png){alt='scatter plot of Hindfoot length vs weight (g) with colors coordinating to specific species, showing abundance in the mid to lower left side of the plot'}
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- Defining scale for colors, axes,... For example, a log-version of the x-axis
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could support the interpretation of the lower numbers:
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## Plotting time series data
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Let's calculate number of counts per year for each species. To do that we need
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Let's calculate the number of counts per year for each species. To do that we need
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to group data first and count the species (`species_id`) within each group.
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```python
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)
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```
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![](fig/06_overlap_bars.png){alt='Bar graph of count per year showing ovelapping x-axis labels'}
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![](fig/06_overlap_bars.png){alt='Bar graph of count per year showing overlapping x-axis labels'}
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Notice that we use the `year` here as a categorical variable by using the
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`factor` functionality. However, by doing so, we have the individual year

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