In one word. Random.

Use different plots and maybe group your variables into segmented groups for better visuals.

A scatter plot alone is almost useless with that many points because you cant eyeball the density of points to tell if there is any trend. At least add a spline or do a regression

I'd look beyond bivariate correlations. Right now, I'm not seeing anything meaningful in any of those plots.

From a purely visual perspective, make the dots a bit smaller and set the transparency/alpha to about 0.3. You might see a pattern that's hidden by the high volume of overlapping points.

A) Facet by classification B) subsample or use density smooths to get a better of the bivariate distribution within facets.

Duration looks positive and long tailed. Try a log transformation to bring out some me details.

Slightly better than I understand the Waterfall chart.

nice

The best feature of the scatter chat in power bi is the time series, which moves the plotted data points as you watch it. Selecting a single point also tracks it's trajectory.

It's showing the correlation between the observations. If the points form a line, that's a positive or negative correlation. As one measurement increases or decreases, the other increases or decreases.

You can use this kind of plot in an exploratory analysis to spot measurements with strong correlation.

These are scatterplots for each combination of your variables.

Top middle is mpg.city on the y axis and price on the x axis. Top right is mpg.city on the y and horse power on the x. You are visualising scatter plots for all combinations of variables. See how top middle and middle left are the same plot but inverted? It’s the same combination of variables, just a different x and y combination.

Also. You have windows. Go to the search bar. Type in "snip." Use this handy feature to take shots of only portions of the screen you want. Please never take a pic with your phone of a screen unless absolutely necessary. It makes it difficult to read much of the time.

You will need to search how to interpret a scatter plot, it's not really an RStudio problem.

I’ve made a career out of scatterplots. Although i would add that they are deceptively simple.

I usually use a scatter plot when i have 11 or more data points, continuous variables only. Be careful you don’t overinterpret.

It depends. As you've said, it helps to get an overview on a) the distribution and positioning of two variables (more variables get more difficult to visualize and interpret) and b) potential relationships between the two.

There are many things you can learn: Is variable X evenly distributed or clustered, is there lots of variance in the data or not, is the distribution rather on the left, center or right side of the scale? Are there many outliers? For X and Y, is there a linear relationship between both, or even something non-linear, nothing at all?

How many? I guess it depends on the domain. I usually need 20+ points, the more the better. As you can easily visualize many points, there is really no upper limit.

You can learn a lot if you care about the structure of the data. My PhD was in part born out of that necessity.. in particular I found PCA was really over used in life sciences and that graph based projections are more useful for capturing the structure of data. One big issue though is you lose the ability to interpret what the axes mean. I found some simple solutions to this and am working on developing this further so that higher order information can be summarized. Bottom line: it’s a tool that does a specific job so use it where and when it’s appropriate, and interpret it carefully.

I think a lot of people underestimate the power of the eye test when done correctly.

This is exploratory data analysis. It’s more art than science, so you have to just start “looking” at data and you’ll get a feel for it

In general, I have switched from doing a simple scatter diagram to doing a "6 Plot" (https://www.itl.nist.gov/div898/handbook/eda/section3/6plot.htm) if I'm reasonably sure there is a correlation between two variables. (I modify the 6 Plot in the reference to use a Stabilized Normal Probability Plot—https://www.tandfonline.com/doi/pdf/10.1080/00224065.1989.11979171).

If I have a group of variables with data and I just want to see if there's a pattern or correlation, I'll do a matrix plot. (I tend to avoid correlation plots because I would rather see the data trends rather than an index number.)

I would never plot multivariate data like this. Use a scatter plot matrix as efrique suggests.

1. I wouldn't be trying to interpret plots without understanding (i) what all the variables are, how they are measured, what sorts of values should be possible for them, as well as any theory / common understanding about how they should be related, and (ii) what the purpose of the analysis is.

   I realize this is synthetic, but a good synthetic example still shouldn't be treating this as a perfect black box; you're essentially never in the position of knowing nothing whatever about the variables nor what you're trying to learn

   > There are 4 input variables, an input group and an output variable

   This is a little ambiguous. Can you clarify exactly what all the "input" variables are? Is this 4 IVs plus a group factor?

2. You can't necessarily interpret a multivariate relationship from the marginal bivariate ones unless some pretty strong conditions hold. Even if the relationships between the predictors are linear omitted variable bias means you can get distinctly misleading impressions about what's related to what, how strongly and even in which direction.

3. Nevertheless, if the aim here is exploratory rather than inferential, you might consider a scatter plot matrix to begin with so you can see not only how the predictors relate to the response but also to each other (at least in terms of their bivariate margins -- though as noted this is not necessarily sufficient, since higher-order relationships can exist that you cant discern from the margins that may impact things in a substantive way).

4. In an exploratory analysis I'd also be investigating the multivariate structure of the data in other ways.

Would it make more sense to flip your X and Y axes and make a histogram of this data?

Maybe use something like a box plot on the discrete values. You can also group items in some sort of meaningful way (i.e. look at those between 0 and 3).

I'm guessing injury duration occurs in discrete values (1,2,3,.. days). Each horizontal line is formed by a set of observations that have the same injury score.

Try either ln(F) vs ln(x) or F vs 1/x^(3)

I don’t think it’s useful to cross plot all three inputs. Generally, for a 3D scatter plot, you’d plot output for one of the dimensions.

I’ll add that the two outliers either indicate something very important or bad data, but you likely need more data to determine that.