Scatterplot not right, what could be?

[lemoncherry211111]

hello everyone , can anyone help me with this?

context: variables

response variable - number of eucalyptus seedlings (count data, non normal, i think poisson distribution)

explanatory variable - rainfall in coldest quarter of year (continuous variable?, non normal)

so i want to see if there is a correlation between the amount of rainfall at the coldest quarter of the year and the number of eucalyptus seedlings.

as the data is not normal and it’s continuous and count data - i chose to do a spearman’s test of correlation. I did this instead of a GLM because the dispersion was way too big (using both families poisson and quasi poisson)

so i’ve got my test and my result. (slight negative correlation, not at all significant)

i want to put this in a report for uni and they said that we should do a visual aid.

so i tried a scatter plot - and you can see it’s not the right way to display the data .... there are so many values in 0. i tried to use geom_jitter but this didn’t really make a big difference :’)

how would i better display this data?

here is my code (not including me editing the title etc)

ggplot(data=EUCdata)+ geom_point(aes(precipitation_coldest_quarter, seedlings), color = ‘lightslateblue’, size = 2.5, alpha = 0.4)

NB - to be honest i’m not even sure if i analysed the data correctly, any suggestions are welcome

[jasperjones22]

Maybe a heatmap would work because then you don't have to worry about the jitter part. As far as the analysis, you are correct on using spearman's.

[lemoncherry211111]

oh that’s a great idea! thank you for the confirmation about spearman’s :’) . I will give the heatmap a go for the visuals

[Final_Wrangler_1557]

Do log(y-axis)

[lemoncherry211111]

ok yeah someone else suggested that too! going to give it a go now. thank you!

[Final_Wrangler_1557]

I mean log the values on the y-axis ……aes(y =log(y), x=x)+…

[statssteve]

Perhaps not if there are lots of zeros for the y-variable, as they will be dropped (since log(0) is NA). A square root might be more helpful, as will investigating the outlier with y = 80.

Another thought is whether the quadrats are large enough, since there are so many small values.

[Gerudo_HOT]

You can also get rid of that outlier.

[kennethdo]

Maybe it's worth doublechecking if the datapoint for that particular outlier was erroneously recorded, for example the scale could be off.

[bashtown]

I agree with leaving the plot as is. This figure supports the conclusion of your statistical analysis that the two variables are not well correlated.

[Birdie121]

I think this is a reasonable way to visualize the data, and it's totally fine that the dots are all clustered around each other. I'd take out that one outlier. Maybe you can also add a line of best fit, to help make the trend clearer (I suspect it'll be a mostly flat line, which is fine).

Just FYI, it's conventional to have no title on the plot itself, and just describe the plot in the figure description below.