The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 10.1067/mem.2001.111570.Ĭooper RJ, Schriger DL, Tashman DA: An evaluation of the graphical literacy of annals of emergency medicine. Schriger DL, Cooper RJ: Achieving graphical excellence: suggestions and methods for creating high-quality visual displays of experimental data. Zhang F, Fan H, Xu Y, Zhang K, Huang X, Zhu Y, Sui M, Sun G, Feng K, Xu B, Zhang X, Su Z, Peng C, Liu P: Converging evidence implicates the dopamine D3 receptor gene in vulnerability to schizophrenia. Wickham H: ggplot2: elegant graphics for data analysis. Newman GE, Scholl BJ: Bar graphs depicting averages are perceptually misinterpreted: the within-the-bar bias. In the future, we will extend it to multiple groups of data. One limitation is that the package applies to only two groups of values. It overcomes the overlapping issue in a scatterplot for two groups of data, and incorporates some key properties of the data, including the P value and the average. It integrates statistical analysis and plotting function together to produce a graph for two group values. The plot2groups package provide easy-to-use functions to plot scatter points for two groups of values. Showing as much of the relevant underlying data as possible in the most meaningful, unbiased way, is a principle in data visualization. However, many graphics fail to portray data at an appropriate level of details, presenting summary statistics rather than underlying distributions. Graphics are an important vehicle of communicating experimental data and results. Plot2f is a similar function which takes a local data file as its first parameter. At the same, the package adds an average bar and the two-sample t-test P value into the graph. > plot2(drd3)As can be seen in Figure 1b, the package automatically lays adjacent points side by side, thus overcoming the overlapping amount the points. To illustrate the functionality of plot2groups, we used the function ‘plot2’ to produce another graph on the drd3 data. In the plot2 function, parameter ‘df’ is a two-column data frame, the first column is numeric values, the second column is character or numeric vectors indicating two groups parameter ‘size’ controls the size of the dots and parameter ‘color’ is a two-string vector defining the color of the two groups. First, the function carries out a two sample t- or rank-test to yield a P value then, it plots a scatterplot for the data by calling the ggplot2 plotting system, incorporating the P value into the text of X label and an average bar into each group. The plot2groups package contains two functions, plot2 and plot2f. Thus, we built user-friendly functions to create such a plot by calling ggplot2. However, it is not easy for common users to master its distinctive grammar. R packages ggplot2 can jitter the position of overlapped points when plotting categorical data. To address this issue, it is desirable to stagger the overlapping values side by side on the X axis. This makes it difficult to observe the full quantity of values in the dataset. Plotting this kind of data can cause over-plotting problems so there are many similar values all stacked on top of each other. For groups of numeric values, one axis (typically the X axis) is discrete values representing categories. Scatterplot typically requires that data on both axes should be continuous. The advantages of scatterplot include retaining exact data values and sample size, showing minimum/maximum and outliers of the data. Scatterplot is one of most commonly used strategy for visual representation of the relationship between two factors of the experiment. Thus bar graphs can be easily manipulated to yield false impressions. In addition, they fail to reveal key properties of the data, such as the exact number of observations, the outliers, and the distribution of the data. Currently, bar graph is one of the most common methods of communicating statistical information-particularly, measures of central tendency, such as the mean, however, graphical asymmetry of bar graph gives rise to a corresponding cognitive asymmetry. Visualizing these data in a graph may provide a clear and intuitive impression for the reader. Comparing two groups of values is one of most common task faced by researchers.
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