Interpolating a spline within dplyr

Question

I am trying interpolate splines for the following example data:

trt    depth    root    carbon
A       2        1        14
A       4        2        18
A       6        3        18
A       8        3        17
A      10        1        12
B       2        3        16
B       4        4        18
B       6        4        17
B       8        2        15
B      10        1        12

in the following way:

new_df<-df%>%
  group_by(trt)%>%
  summarise_each(funs(splinefun(., x=depth, method="natural")))

I get an Error: not a vector, but I don't see why not. Am I not expressing the function in the right way?

wibeasley · Accepted Answer

Do you want a dataset that contains the values interpolated? If so, I've expanded the dataset to contain the desired x locations before the splines are calculated.

The resolution of those points are determined in the second line of the expand.grid() function. Just make sure the original depth points are a subset of the expanded depth points (eg, don't use something uneven like by=.732).

library(magrittr)
ds <- readr::read_csv("trt,depth,root,carbon
A,2,1,14
A,4,2,18
A,6,3,18
A,8,3,17
A,10,1,12
B,2,3,16
B,4,4,18
B,6,4,17
B,8,2,15
B,10,1,12")

ds_depths_possible <- expand.grid(
  depth            = seq(from=min(ds$depth), max(ds$depth), by=.5), #Decide resolution here.
  trt              = c("A", "B"),
  stringsAsFactors = FALSE
  )

ds_intpolated <- ds %>% 
  dplyr::right_join(ds_depths_possible, by=c("trt", "depth")) %>% #Incorporate locations to interpolate
  dplyr::group_by(trt) %>% 
  dplyr::mutate(
    root_interpolated     = spline(x=depth, y=root  , xout=depth)$y,
    carbon_interpolated   = spline(x=depth, y=carbon, xout=depth)$y
  ) %>% 
  dplyr::ungroup()
ds_intpolated

Output:

Source: local data frame [34 x 6]

     trt depth  root carbon root_interpolated carbon_interpolated
   (chr) (dbl) (int)  (int)             (dbl)               (dbl)
1      A   2.0     1     14          1.000000            14.00000
2      A   2.5    NA     NA          1.195312            15.57031
3      A   3.0    NA     NA          1.437500            16.72917
4      A   3.5    NA     NA          1.710938            17.52344
5      A   4.0     2     18          2.000000            18.00000
6      A   4.5    NA     NA          2.289062            18.21094
7      A   5.0    NA     NA          2.562500            18.22917
8      A   5.5    NA     NA          2.804688            18.13281
9      A   6.0     3     18          3.000000            18.00000
10     A   6.5    NA     NA          3.132812            17.88281
..   ...   ...   ...    ...               ...                 ...

In the graphs above, the little points & lines are interpolated. The big fat points are observed.

library(ggplot2)
ggplot(ds_intpolated, aes(x=depth, y=root_interpolated, color=trt)) +
  geom_line() +
  geom_point(shape=1) +
  geom_point(aes(y=root), size=5, alpha=.3, na.rm=T) +
  theme_bw()

ggplot(ds_intpolated, aes(x=depth, y=carbon_interpolated, color=trt)) +
  geom_line() +
  geom_point(shape=1) +
  geom_point(aes(y=carbon), size=5, alpha=.3, na.rm=T) +
  theme_bw()

If you want an additional example, here's some recent code and slides. We needed a rolling median for some missing points, and linear stats::approx() for some others. Another option is also stats::loess(), but it's arguments aren't as similar as approx() and spline().

Interpolating a spline within dplyr

Answers (2)

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