R: using customised function in dplyr - r

Question

Sample data:
library(tidyverse)
set.seed(123)
dat <- tibble(
year = rep(1980:2015, each = 100),
day = rep(200:299, times = 36),
rain = sample(0:17, size = 100*36,replace = T),
PETc = sample(rnorm(100*36)),
ini.t = rep(10:45, each = 100 ))
I have a function that operates on a DataFrame
my.func <- function(df, initial, thres, upper.limit){
df$paw <- rep(NA, nrow(df))
df$aetc <- rep(NA, nrow(df))
df$sw <- rep(NA, nrow(df))
for(n in 1:nrow(df)){
df$paw[n] <- df$rain[n] + initial
df$aetc[n] <- ifelse(df$paw[n] >= thres, df$PETc[n], (df$paw[n]/thres) * df$PETc[n])
df$aetc[n] <- ifelse(df$aetc[n] > df$paw[n], df$paw[n], df$aetc[n])
df$sw[n] <- initial + df$rain[n] - df$aetc[n]
df$sw[n] <- ifelse(df$sw[n] > upper.limit,upper.limit,ifelse(df$sw[n] < 0, 0,df$sw[n]))
initial <- df$sw[n]
}
return(df)
}
thres <- 110
upper.limit <- 200
Applying the above function for a single year:
dat.1980 <- dat[dat$year == 1980,]
my.func(dat.1980, initial = dat.1980$ini.t[1], thres, upper.limit)
How do I apply this function to each year. I thought of using dplyr
dat %>% group_by(year)%>% run my function on each year.
Also since there are 35 years, there will be 35 dataframes returned. How do I return the bind these data frame row wise?

Answer 1

You were on the right track. do lets you perform functions by group.
dat %>%
group_by(year) %>%
do(my.func(., initial = head(.$ini.t, 1), thres, upper.limit))
# Groups: year [36]
# year day rain PETc ini.t paw aetc sw
# <int> <int> <int> <dbl> <int> <dbl> <dbl> <dbl>
# 1 1980 200 5 0.968 10 15.0 0.132 14.9
# 2 1980 201 14 0.413 10 28.9 0.108 28.8
# 3 1980 202 7 -0.912 10 35.8 -0.296 36.1
# 4 1980 203 15 -0.337 10 51.1 -0.156 51.2
# 5 1980 204 16 0.412 10 67.2 0.252 67.0
# 6 1980 205 0 -0.923 10 67.0 -0.562 67.5
# 7 1980 206 9 1.17 10 76.5 0.813 75.7
# 8 1980 207 16 0.0542 10 91.7 0.0452 91.7
# 9 1980 208 9 -0.293 10 101 -0.268 101
# 10 1980 209 8 0.0788 10 109 0.0781 109
# ... with 3,590 more rows
purrr::map functions are the du jour method but I think in this case it's a stylistic choice

Answer 2

We can split by 'year' and then use map to apply the my.func to each of the split datasets in the list
library(purrr)
dat %>%
split(.$year) %>%
map_df(~my.func(.x, initial = .x$ini.t[1], thres, upper.limit))