Today we’ll see how R and Python compare in basic data visualization. We’ll compare their standard plotting libraries – Matplotlib and ggplot to see which one is easier to use and which looks better at the end. We’ll also show you how to include Matplotlib charts in R Shiny dashboards, as that’s been a common pain point for Python users.
We can use the following code to create a stacked barplot that displays the points scored by each player, stacked by team and position: library (ggplot2) ggplot(df, aes (fill=position, y=points, x=team)) + geom_bar(position=' stack ', stat=' identity ')
You can quickly add vertical lines to ggplot2 plots using the geom_vline () function, which uses the following syntax: xintercept: Location to add line on the x-intercept. This can be one value or multiple values. linetype: Line style. Default is ‘solid’ but you can specify ‘twodash’, ‘longdash’, ‘dotted’, ‘dotdash
but still it doesn't reflect the probabilities; the points should be stacked as a sort of histogram to reflect the probabilities. A different approach is using the density function, but it can messy things if I have many samples categories to plot out. ggplot (h.melt, aes (x=value, fill=Var2)) + geom_density (alpha=.5, position="identity") Share.
R Map 3: Incident occurrences + layer of "most dangerous neighborhood" location markers. In this map, we are going to use the excellent ggplot feature of layering. We will take the map above and layer on the data points from the "n" dataset which outlines the "most dangerous neighborhoods" in Seattle as determined by housely.com
Fortunately this is simple to do using the scale_color_manual() function and the following example shows how to do so. Example: Create Manual Legend in ggplot2. The following code shows how to plot three fitted regression lines in a plot in ggplot2 with a custom manual legend:
Another way to create a normal distribution plot in R is by using the ggplot2 package. Here are two examples of how to create a normal distribution plot using ggplot2. Example 1: Normal Distribution with mean = 0 and standard deviation = 1. To create a normal distribution plot with mean = 0 and standard deviation = 1, we can use the following code:
This tutorial shows how to use ggplot2 to plot multiple columns of a data frame on the same graph and on different graphs. Example 1: Plot Multiple Columns on the Same Graph. The following code shows how to generate a data frame, then “melt” the data frame into a long format, then use ggplot2 to create a line plot for each column in the
gg3D is a package created to extend ggplot2 to produce 3D plots. It does exactly what you are asking for: it adds a third axis to a ggplot. I find it quite good and easy to use and that is what I use for my limited needs. An example taken from the vignette to produce a basic plot
This R tutorial describes how to create line plots using R software and ggplot2 package. In a line graph, observations are ordered by x value and connected. The functions geom_line(), geom_step(), or geom_path() can be used. x value (for x axis) can be : date : for a time series data; texts; discrete numeric values; continuous numeric values
3 Answers. Sorted by: 3. In your example gg_pets is just a vector of strings. You need to concatenate the data frames in order to iterated over them in the for-loop. You can do it with a list. As follows. You can use the names of the items as a title.
Example: ROC Curve Using ggplot2. Suppose we fit the following logistic regression model in R: #load Default dataset from ISLR book data
Inside of the ggplot2 () function, we're telling ggplot that we'll be plotting data in the scatter_data dataframe. We do this with the syntax data = scatter_data. Next, inside the ggplot2 () function, we're calling the aes () function. Remember, the aes () function enables us to specify the "variable mappings."
Example 1: Use ggsave() to Save Plot with Default Settings. We can use the following syntax with ggsave() to save this scatter plot to a PDF file called my_plot.pdf with all of the default settings: library (ggplot2) #save scatter plot as PDF file ggsave(' my_plot.pdf ')
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how to use ggplot in r
