How can I make ggplot plot geom_smooth(method="lm"), but only if it fits some criteria? For instance, if I only want to draw lines if the slope is statistically significant (i.e. *p* from the `lm`

fit is less than 0.01).

EDIT: Updated to a more complex example involving facets. Instead of generating the data from scratch, I modified the `diamonds`

data set.

```
library(ggplot2)
library(data.table)
data(diamonds)
set.seed(777)
d <- data.table(diamonds)
d[color %in% c("D","E"), c("x","y") := list(x + runif(1000, -5, 5),
y + runif(1000, -5, 5))]
plt <- ggplot(d) + aes(x=x, y=y, color=color) +
geom_point() + facet_grid(clarity ~ cut, scales="free")
plt + geom_smooth(method="lm")
```

What I would like is a way to plot all lines except those which do not have statistically significant slopes (i.e. D and E).

# Best How To :

You can calculate the p-values by group and then subset in `geom_smooth`

(per the commenters):

```
# Determine p-values of regression
p.vals = sapply(unique(d$z), function(i) {
coef(summary(lm(y ~ x, data=d[z==i, ])))[2,4]
})
plt <- ggplot(d) + aes(x=x, y=y, color=z) + geom_point()
# Select only values of z for which regression p-value is < 0.05
plt + geom_smooth(data=d[d$z %in% names(p.vals)[p.vals < 0.05],],
aes(x, y, colour=z), method='lm')
```

**UPDATE:** Per your comment, try this, for example:

```
p1 = ggplot(mtcars, aes(wt, mpg)) +
geom_point() + facet_grid(am ~ carb)
dat = data.frame(x=1:5, y=26:30, carb=1:5)
p1 + geom_point(data=dat, aes(x,y), colour="red", size=5)
```

Note that since `dat`

has no `am`

column, `ggplot`

just plots the same values in `dat`

for each value of `am`

. Of course you can add values for `am`

and control what's plotted facet by facet.

**UPDATE2:** I think this will take care of the faceting case. Note, however, that most of the regressions have p-values less than 0.05, probably because when you have lots of data, even tiny coefficients will be statistically significant.

```
## Create a list holing the p-values for regressions on each
## combination of color, cut, and clarity
pvals = lapply(levels(d$color), function(i) {
lapply(levels(d$cut), function(j) {
lapply(levels(d$clarity), function(k) {
if(nrow(d[color==i & cut==j & clarity==k, ]) > 1) {
data.frame(color=i, cut=j, clarity=k,
p.val=coef(summary(lm(y ~ x, data = d[color==i & cut==j & clarity==k, ])))[2,4])
}
})
})
})
# Flatten pvals to a single list level
pvals = unlist(unlist(pvals, recursive=FALSE), recursive=FALSE)
# Turn pvals into a data frame
pvals = do.call(rbind, pvals)
# Keep only rows with p.val < 0.05
pvals = pvals[pvals$p.val < 0.05, ]
plt <- ggplot(d) + aes(x=x, y=y, color=color) +
geom_point() + facet_grid(clarity ~ cut, scales="free")
# Create a subset of data frame d containing only combinations of
# color, cut, and clarity for which we want to plot regression lines
# (you could subset right in the call to geom_smooth, but I thought this would be more clear)
d.subset = d[color %in% pvals$color &
cut %in% pvals$cut &
clarity %in% pvals$clarity, ]
# Plot regression lines only for groups in d.subset
plt + geom_smooth(data=d.subset, method="lm")
```