How to apply a separate coord_cartesian () function to “zoom in” in individual facet_grid () panels?

Question

Inspired by Q Elbow / Knee Search on Curve I started playing with smooth.spline().

In particular, I want to imagine how a parameter df(degree of freedom) affects the approximation and the first and second derivatives. Note that this Q is not about approximation, but about a specific problem (or edge case) when visualizing with ggplot2.

First try: simple facet_grid()

library(ggplot2)
ggplot(ap, aes(x, y)) +
  geom_point(data = dp, alpha = 0.2) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw()

dp- this is a data table containing data points for which approximation is requested, and ap- a data table with approximated data and derivatives (data are given below).

facet_grid() scales = "free_y" , . , "", . , " ".

"" coord_cartesian()

ggplot(ap, aes(x, y)) +
  geom_point(data = dp, alpha = 0.2) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw() +
  coord_cartesian(ylim = c(-200, 50))

3- . . , 1 .

coord_cartesian() ( , , rollise) . , ggplot?

: cowplot

cowplot:

g0 <- ggplot(ap[deriv == 0], aes(x, y)) +
  geom_point(data = dp, alpha = 0.2) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw()

g1 <- ggplot(ap[deriv == 1], aes(x, y)) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw() +
  coord_cartesian(ylim = c(-50, 50))

g2 <- ggplot(ap[deriv == 2], aes(x, y)) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw() +
  coord_cartesian(ylim = c(-200, 100))

cowplot::plot_grid(g0, g1, g2, ncol = 1, align = "v")

,

• ,
• , .

facet_wrap() ?

facet_wrap() facet_grid():

ggplot(ap, aes(x, y)) +
  # geom_point(data = dp, alpha = 0.2) + # this line causes error message
  geom_line() + 
  facet_wrap(~ deriv + df, scales = "free_y", labeller = label_both, nrow = 3) + 
  theme_bw()

y , . , " " , coord_cartesian() .

,

geom_point(data = dp, alpha = 0.2)

gList ( (x = 0,5, y = 0,5, width = 1, height = 1, just = "center",:    "grobs" "gList"

, , , .

library(data.table)
# data points
dp <- data.table(
  x = c(6.6260, 6.6234, 6.6206, 6.6008, 6.5568, 6.4953, 6.4441, 6.2186,
        6.0942, 5.8833, 5.7020, 5.4361, 5.0501, 4.7440, 4.1598, 3.9318,
        3.4479, 3.3462, 3.1080, 2.8468, 2.3365, 2.1574, 1.8990, 1.5644,
        1.3072, 1.1579, 0.95783, 0.82376, 0.67734, 0.34578, 0.27116, 0.058285),
  y = 1:32,
  deriv = 0)
# approximated data points and derivatives
ap <- rbindlist(
  lapply(seq(2, length(dp$x), length.out = 4),
         function(df) {
           rbindlist(
             lapply(0:2, 
                    function(deriv) {
                      result <- as.data.table(
                        predict(smooth.spline(dp$x, dp$y, df = df), deriv = deriv))
                      result[, c("df", "deriv") := list(df, deriv)]
                    })
           )
         })
)  

Answer 1

, . ?

1 , y, scales = "free_y" . :

library(ggplot2)
library(dplyr)

# alternate plot version with truncated data range
p.alt <- ap %>%
  group_by(deriv) %>%
  mutate(upper = quantile(y, 0.75),
         lower = quantile(y, 0.25),
         IQR.multiplier = (upper - lower) * 10) %>%
  ungroup() %>%
  mutate(is.outlier = y < lower - IQR.multiplier | y > upper + IQR.multiplier) %>%
  mutate(y = ifelse(is.outlier, NA, y)) %>%

  ggplot(aes(x, y)) +
  geom_point(data = dp, alpha = 0.2) +
  geom_line() + 
  facet_grid(deriv ~ df, scales = "free_y", labeller = label_both) + 
  theme_bw()

# intended plot version with full data range
p <- p.alt %+% ap

2 ggplot_build() ggplot. :

p <- ggplot_build(p)
p.alt <- ggplot_build(p.alt)

p$layout$panel_params <- p.alt$layout$panel_params
rm(p.alt)

3 :

p <- ggplot_gtable(p)

grid::grid.draw(p)

: , 10 * IQR / NA. .