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Displaying posts with the tag: ggplot2 [Clear Filter]

Make a difference: the alternative for p-values

Posted by on October 8th, 2018

Calculation and reporting of p-values is common in scientific publications and presentations (Cristea and Ioannidis, 2018). Usually, the p-value is calculated to decide whether two conditions, e.g. control and treatment, are different. Although a p-value can flag differences, it cannot quantify the difference itself (footnote 1). Therefore, p-values fail to answer a very relevant question:[…]

Visualizing data with R/ggplot2 – One more time

Posted by on June 26th, 2018

Experiments are rarely performed in isolation. Usually, several conditions are compared in parallel or sequential experiments. This experimental strategy also applies to time-dependent data, e.g. from timelapse imaging. So, naturally, after I published a ‘walk-through for plotting temporal data using R and ggplot2, I was immediately asked how to plot two (or more) sets of[…]

Visualizing data with R/ggplot2 – It’s about time

Posted by on May 31st, 2018

The visualization of temporal data by line graphs has been documented and popularized by William Playfair in the 18th century (Aigner et al, 2011; Beniger and Robyn, 1978). Today, time-dependent changes are still depicted by line graphs and ideally accompanied by a measure of uncertainty (Marx, 2013). Below, I provide a ‘walk-through’ for generating such a[…]

Converting excellent spreadsheets to tidy data

Posted by on October 6th, 2017

Structuring data according to the ‘tidy data‘ standard simplifies data analysis and data visualisation. But understanding the structure of tidy data does not come naturally (in my experience), since it is quite different from the structure of data in spreadsheets or tables. Here, I explain how to convert typical spreadsheet data to tidy data to[…]