R - group_by utilizing splinefun - r

Question

I am trying to group my data by Year and CountyID then use splinefun (cubic spline interpolation) on the subset data. I am open to ideas, however the splinefun is a must and cannot be changed.
Here is the code I am trying to use:
age <- seq(from = 0, by = 5, length.out = 18)
TOT_POP <- df %.%
group_by(unique(df$Year), unique(df$CountyID) %.%
splinefun(age, c(0, cumsum(df$TOT_POP)), method = "hyman")
Here is a sample of my data Year = 2010 : 2013, Agegrp = 1 : 17 and CountyIDs are equal to all counties in the US.
CountyID Year Agegrp TOT_POP
1001 2010 1 3586
1001 2010 2 3952
1001 2010 3 4282
1001 2010 4 4136
1001 2010 5 3154
What I am doing is taking the Agegrp 1 : 17 and splitting the grouping into individual years 0 - 84. Right now each group is a representation of 5 years. The splinefun allows me to do this while providing a level of mathematical rigour to the process i.e., splinefun allows me provide a population total per each year of age, in each individual county in the US.
Lastly, the splinefun code by itself does work but within the group_by function it does not, it produces:
Error: wrong result size(4), expected 68 or 1.
The splinefun code the way I am using it works like this
TOT_POP <- splinefun(age, c(0, cumsum(df$TOT_POP)),
method = "hyman")
TOT_POP = pmax(0, diff(TOT_POP(c(0:85))))
Which was tested on one CountyID during one Year. I need to iterate this process over "x" amount of years and roughly 3200 counties.

Answer 1

# Reproducible data set
set.seed(22)
df = data.frame( CountyID = rep(1001:1005,each = 100),
Year = rep(2001:2010, each = 10),
Agegrp = sample(1:17, 500, replace=TRUE),
TOT_POP = rnorm(500, 10000, 2000))
# Convert Agegrp to age
df$Agegrp = df$Agegrp*5
colnames(df)[3] = "age"
# Make a spline function for every CountyID-Year combination
split.dfs = split(df, interaction(df$CountyID, df$Year))
spline.funs = lapply(split.dfs, function(x) splinefun(x[,"age"], x[,"TOT_POP"]))
# Use the spline functions to interpolate populations for all years between 0 and 85
new.split.dfs = list()
for( i in 1:length(split.dfs)) {
new.split.dfs[[i]] = data.frame( CountyID=split.dfs[[i]]$CountyID[1],
Year=split.dfs[[i]]$Year[1],
age=0:85,
TOT_POP=spline.funs[[i]](0:85))
}
# Does this do what you want? If so, then it will be
# easier for others to work from here
# > head(new.split.dfs[[1]])
# CountyID Year age TOT_POP
# 1 1001 2001 0 909033.4
# 2 1001 2001 1 833999.8
# 3 1001 2001 2 763181.8
# 4 1001 2001 3 696460.2
# 5 1001 2001 4 633716.0
# 6 1001 2001 5 574829.9
# > tail(new.split.dfs[[2]])
# CountyID Year age TOT_POP
# 81 1002 2001 80 10201.693
# 82 1002 2001 81 9529.030
# 83 1002 2001 82 8768.306
# 84 1002 2001 83 7916.070
# 85 1002 2001 84 6968.874
# 86 1002 2001 85 5923.268

Answer 2

First, I believe I was using the wrong wording in what I was trying to achieve, my apologies; group_by actually wasn't going to solve the issue. However, I was able to solve the problem using two functions and ddply. Here is the code that solved the issue:
interpolate <- function(x, ageVector){
result <- splinefun(ageVector,
c(0, cumsum(x)), method = "hyman")
diff(result(c(0:85)))
}
mainFunc <- function(df){
age <- seq(from = 0, by = 5, length.out = 18)
colNames <- setdiff(colnames(df)
c("Year","CountyID","AgeGrp"))
colWiseSpline <- colwise(interpolate, .cols = true,
age)(df[ , colNames])
cbind(data.frame(
Year = df$Year[1],
County = df$CountyID[1],
Agegrp = 0:84
),
colWiseSpline
)
}
CompleteMainRaw <- ddply(.data = df,
.variables = .(CountyID, Year),
.fun = mainFunc)
The code now takes each county by year and runs the splinefun on that subset of population data. At the same time it creates a data.frame with the results i.e., splits the data from 17 age groups to 85 age groups while factoring it our appropriately; which is what splinefun does.
Thanks!