Apply different data to a function in R - r

Question

I have the following data frame:
library(tidyverse)
set.seed(1234)
df <- data.frame(
x = seq(1, 100, 1),
y = rnorm(100)
)
Where I apply a smooth spline using different knots:
nknots <- seq(4, 15, 1)
output <- map(nknots, ~ smooth.spline(x = df$x, y = df$y, nknots = .x))
What I need to do now is to apply the same function using 2-point and 3-point averages:
df_2 <- df %>%
group_by(., x = round(.$x/2)*2) %>%
summarise_all(funs(mean))
df_3 <- df %>%
group_by(., x = round(.$x/3)*3) %>%
summarise_all(funs(mean))
In summary, I need to apply the function I used in output with the following data frames:
df
df_2
df_3
Of course, this is a minimal example, so I am looking for a efficient way of doing it. Preferably with the purrr package.

Answer 1

Using lapply, and the library zoo to calculate the moving average in a more simple and elegant manner:
library(zoo)
lapply(1:3,function(roll){
dftemp <- as.data.frame(rollmean(df,roll))
map(nknots, ~ smooth.spline(x = dftemp$x, y = dftemp$y, nknots = .x))
})

Answer 2

Here's one possible solution:
library(tidyverse)
set.seed(1234)
df <- data.frame(x = seq(1, 100, 1),
y = rnorm(100))
# funtion to get v-point averages
GetAverages = function(v) {
df %>%
group_by(., x = round(.$x/v)*v) %>%
summarise_all(funs(mean)) }
# specify nunber of knots
nknots <- seq(4, 15, 1)
dt_res = tibble(v=1:3) %>% # specify v-point averages
mutate(d = map(v, GetAverages)) %>% # get data for each v-point
crossing(., data.frame(nknots=nknots)) %>% # combine each dataset with a knot
mutate(res = map2(d, nknots, ~smooth.spline(x = .x$x, y = .x$y, nknots = .y))) # apply smooth spline
You can use dt_res$res[dt_res$v == 1] to see all results for your original daatset, dt_res$res[dt_res$v == 2] to see results for your 2-point estimate, etc.