How to use plot function in R?
a function (or name of one) to be applied to each subset. It must return one number for a numeric (vector) input. data. data frame containing the variables referenced by x when that is formula-like. …. graphical parameters such as col , see par. ylim. range of y values, as in plot.default. xlab.
How do you add a title to an R plot?
· By default, the estimates are sorted in the same order as they were introduced into the model. Use sort.est = TRUE to sort estimates in descending order, from highest to lowest value. plot_model(m1, sort.est = TRUE) Another way to sort estimates is to use the order.terms -argument. This is a numeric vector, indicating the order of estimates in ...
How to create a probability density plot in R?
Note about normed means. The summarySEWithin function returns both normed and un-normed means. The un-normed means are simply the mean of each group. The normed means are calculated so that means of each between-subject group are the same. These values can diverge when there are between-subject variables. For example:
How do I plot a scatterplot of two vectors in R?
Overlaying Plots Using legend () function. Calling plot () multiple times will have the effect of plotting the current graph on the same window replacing the previous one. However, sometimes we wish to overlay the plots in order to compare the results. This is made possible with the functions lines () and points () to add lines and points ...
Pleleminary tasks
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.
Infos
This analysis has been performed using R statistical software (ver. 3.2.4).
Creating Example Data
First, we need to create some example data that we can use in the examples later on:
Example 1: Basic Application of plot () Function in R
In the first example, we’ll create a graphic with default specifications of the plot function. We simply need to specify our x- and y-values separated by a comma:
Example 2: Add Regression Line to Scatterplot
We can also draw a regression line to our scatterplot by using the abline and lm R functions:
Example 3: Draw a Density Plot in R
In combination with the density () function, the plot function can be used to create a probability density plot in R:
Example 4: Plot Multiple Densities in Same Plot
If we replace the plot () function with the lines () function, we can add a second density to our previously created kernel density plot. Have a look at the following R code:
Example 5: Modify Main Title & Axis Labels
The plot function provides many arguments for the modification of its parameters. For instance, we can change the main title and the axis labels of our plot as follows:
Example 6: Plot with Colors & PCH According to Group
We can also represent different groups in our graph. First, we need to create a group indicator:
Description
Plot univariate effects of one or more factor s, typically for a designed experiment as analyzed by aov ().
Arguments
either a data frame containing the design factors and optionally the response, or a formula or terms object.
Details
The supplied function will be called once for each level of each factor in the design and the plot will show these summary values. The levels of a particular factor are shown along a vertical line, and the overall value of fun () for the response is drawn as a horizontal line.
Fitting a logistic regression model
First, we fit a model that will be used in the following examples. The examples work in the same way for any other model as well.
Plotting estimates of generalized linear models
The simplest function call is just passing the model object as argument. By default, estimates are sorted in descending order, with the highest effect at the top.
Sorting estimates
By default, the estimates are sorted in the same order as they were introduced into the model. Use sort.est = TRUE to sort estimates in descending order, from highest to lowest value.
Estimates on the untransformed scale
By default, plot_model () automatically exponentiates coefficients, if appropriate (e.g. for models with log or logit link). You can explicitley prevent transformation by setting the transform -argument to NULL, or apply any transformation by using a character vector with the function name.
Showing value labels
By default, just the dots and error bars are plotted. Use show.values = TRUE to show the value labels with the estimates values, and use show.p = FALSE to suppress the asterisks that indicate the significance level of the p-values. Use value.offset to adjust the relative positioning of value labels to the dots and lines.
Labelling the plot
As seen in the above examples, by default, the plotting-functions of sjPlot retrieve value and variable labels if the data is labelled, using the sjlabelled-package. If the data is not labelled, the variable names are used. In such cases, use the arguments title, axis.labels and axis.title to annotate the plot title and axes.
Pick or remove specific terms from plot
Use terms resp. rm.terms to select specific terms that should (not) be plotted.
Adding Titles and Labeling Axes
We can add a title to our plot with the parameter main. Similarly, xlab and ylab can be used to label the x-axis and y-axis respectively.
Changing Color and Plot Type
We can see above that the plot is of circular points and black in color. This is the default color.
Overlaying Plots Using legend () function
Calling plot () multiple times will have the effect of plotting the current graph on the same window replacing the previous one.
Basic box plots with add summary statistics
In the following R code, possible values for the argument ggfunc are the ggpubr R package functions, including: ggboxplot, ggviolin, ggdotplot, ggbarplot, ggline, etc. It can be also any other ggplot function that accepts the following arguments: data, x, color, fill, palette, ggtheme, facet.by.
Multipanel plots with summary table
facet.by: character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.
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Creating Example Data
In the examples of this R tutorial, we’ll use the following example data:
Example 1: Basic Creation of Line Graph in R
If we want to draw a basic line plot in R, we can use the plot function with the specification type = “l”. Have a look at the following R code:
Example 2: Add Main Title & Change Axis Labels
In Example 2, you’ll learn how to change the main title and the axis labels of our plot with the main, xlab, and ylab arguments of the plot function:
Example 3: Change Color of Line
We can also adjust the color of our line by using the col argument of the plot command:
Example 4: Modify Thickness of Line
We can increase or decrease the thickness of the lines of a line graphic with the lwd option as follows:
Example 5: Add Points to Line Graph
It is possible to add points to visualize the underlying data of our line plot even better. We simply need to replace the type of our graph from “l” to “b”:
Example 6: Plot Multiple Lines to One Graph
In this example I want to show you how to plot multiple lines to a graph in R. First, we need to create further variables for our plot: