Bubble chartĪ trend line is a line that is superimposed on a chart to reveal the overall direction of the data. In addition to the basic scatter chart, you can also add a preset bubble chart from the toolbar. (You might want to investigate your own chart outliers to see if you can discover why they don't fit the norm.) The right hand chart also shows the presence of 2 outliers: the course in the bottom left had few hours of homework but also had a low average grade, while the course at the top of the graph had the highest amount of homework, yet still had close to a 3.0 grade average. The trend line in this chart slopes downwards from left to right, indicating there is a negative correlation between the metrics: the less homework assigned, the better the average grade. The chart on the right compares the average student grade with the number of hours of homework for each course. I.e., the more engaged the student, the more likely they are to complete the course. The left-hand chart compares the average course completion rate with the average activity rate (a measurement of how engaged the students were, in terms of forum posts, class activities completed, etc.) The linear trend line in this chart slopes upwards from left to right, indicating that there is a positive relationship between activity rate and completion rate. The scatter charts below give you 2 different views of the performance for a fictional online university. Data points nearer the trend line are more closely correlated than those farther away from the line. Lack of slope can mean there is little or no correlation between the variables. A slope downwards from upper left to lower right can mean a negative correlation: the more X, the less Y. In other words, the more X, then the more Y. The general direction of slope of the trend line shows the type of relationship ("correlation") between the variables: a slope upwards from left to right indicates a positive correlation. To find trends and patterns, and to identify outliers in your data, you can include a trend line. This could help you answer questions such as "Do more expensive ads result in better conversions in all locales?" For example, you could use a scatter chart to see if there's a correlation between ad spend and conversion rate for each country, broken down by region and ad campaign. You can group the data by adding up to 3 dimensions to the chart. To configure a scatter chart in Looker Studio, you select metrics for the horizontal (X) and vertical (Y) axes of the chart. r <- function(x, y, digits = 2, prefix = "", cex.cor. # Function to add correlation coefficients Note that you can add smoothed regression lines passing the panel.smooth function to the lower.panel argument. On the other hand, you can add the correlation coefficients in absolute terms, resized by the level of correlation, with the code of the following block. Upper.panel = NULL, # Disabling the upper panelĭiag.panel = panel.hist) # Adding the histograms # lines(density(x), col = 2, lwd = 2) # Uncomment to add density lines On the one hand, you can add histograms and density lines to the diagonal with the following code: # Function to add histograms Note that if you want to delete some panels you can set them to NULL. The pairs function also allows you to specify custom functions on the upper.panel, lower.panel and diag.panel arguments. Row1attop = TRUE, # If FALSE, changes the direction of the diagonalĬex.labels = NULL, # Size of the diagonal textįont.labels = 1) # Font style of the diagonal text Main = "Iris dataset", # Title of the plot Labels = colnames(data), # Variable namesīg = rainbow(3), # Background color of the symbol (pch 21 to 25)Ĭol = rainbow(3), # Border color of the symbol In the following example we show you how to fully customize the scatter matrix plot, coloring the data points by group. The function can be customized with several arguments. Pairs(~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris) Note that you can also specify a formula if preferred. With the pairs function you can create a pairs or correlation plot from a data frame. Groups <- iris # Factor variable (groups) For explanation purposes we are going to use the well-known iris dataset. The most common function to create a matrix of scatter plots is the pairs function. Plot pairwise correlation: pairs and cpairs functions On the other hand, if you have more than two variables, there are several functions to visualize correlation matrices in R, which we will review in the following sections. You can also calculate Kendall and Spearman correlation with the cor function, setting the method argument to "kendall" or "spearman".
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