Hi,
I don't know about the lazyeval package or what you are trying to do but to
answer the main question "How to create columns dynamically using a
function?" I would do something like that:
# dataset
dem <- structure(list(id = c("L1", "L2", "L3", "M1", "M2", "M3"),
TEST_SET_NAME = c("A",
"A", "A", "B", "B", "B"), YLD_BE_REG1 = c(1467L, 1455L, 1382L,
1463L, 1466L, 1455L), YLD_BE_REG2 = c(1501L, 1441L, 1421L, 1482L,
1457L, 1490L), IS_GG = c("NO", "NO", "YES", "NO", "NO", "YES"
)), .Names = c("id", "TEST_SET_NAME", "YLD_BE_REG1", "YLD_BE_REG2",
"IS_GG"), class = "data.frame", row.names = c(NA, -6L))
demo_fn<- function (data, f, names) {
for (i in names) {
data <- f(data, i)
}
data
}
f <- function(data, name) {
col_work <- paste("YLD_BE", name, sep = "_")
col_name_result <- paste("GG", name, sep = "_")
#do something interesting, here I am simply copying the column
data[col_name_result] <- data[col_work]
data
}
demo_fn(dem, f, c("REG1", "REG2"))
If you are working with large datasets it might not be the best solution as
my understanding is that this method involves a lot of copying.
Hope it helps,
Elie Canonici Merle
2017-07-20 17:55 GMT+02:00 DIGHE, NILESH [AG/2362] <
nilesh.di...@monsanto.com>:
> Hi,
> I am writing a function to dynamically create column names and fill those
> columns with some basic calculations. My function "demo_fn" takes argument
> "blup_datacut" and I like to use the contents of those arguments to
> dynamically create new columns in my dataset. Please note that I have
> another function called "calc_gg" within the function "demo_fn". Both
> functions are pasted below.
> I have a for loop within my function and it appears to only create new
> column for the last value in the argument "blup_datacut" which makes me
> think that I am not storing the values coming out of for_loop correctly. I
> have "expected_results", dataset, & functions pasted below to reproduce my
> problem and expected results.
> Any help will be greatly appreciate.
>
>
> # dataset
> dem<- structure(list(id = c("L1", "L2", "L3", "M1", "M2", "M3"),
> TEST_SET_NAME = c("A",
> "A", "A", "B", "B", "B"), YLD_BE_REG1 = c(1467L, 1455L, 1382L,
> 1463L, 1466L, 1455L), YLD_BE_REG2 = c(1501L, 1441L, 1421L, 1482L,
> 1457L, 1490L), IS_GG = c("NO", "NO", "YES", "NO", "NO", "YES"
> )), .Names = c("id", "TEST_SET_NAME", "YLD_BE_REG1", "YLD_BE_REG2",
> "IS_GG"), class = "data.frame", row.names = c(NA, -6L))
>
> # function demo_fn
>
> demo_fn<- function (dat, blup_datacut = c("REG1", "REG2"))
>
> {
>
> for (i in seq_along(blup_datacut)) {
>
> col_name_gg <- paste("GG", blup_datacut[i], sep = "_")
>
> col_mean_gg <- paste("YLD_BE", blup_datacut[i], sep = "_")
>
> dat2 <- calc_gg(dataset = dat, col = col_mean_gg, col_name =
> col_name_gg)
>
> }
>
> dat2
>
> }
>
>
> # function calc_gg
>
> Calc_gg<- function (dataset, col, col_name)
>
> {
>
> mutate_call = lazyeval::interp(~round(((a - mean(a[IS_GG ==
>
> "YES"], na.rm = TRUE))/mean(a[IS_GG == "YES"], na.rm = TRUE)) *
>
> 100, 1), a = as.name(col))
>
> dataset %>% group_by(TEST_SET_NAME) %>% mutate_(.dots =
> setNames(list(mutate_call),
>
> col_name)) %>% ungroup()
>
> }
>
>
> # run function
> results_demo<- demo_fn(dat = dem)
>
> # expected results
>
> structure(list(id = c("L1", "L2", "L3", "M1", "M2", "M3"), TEST_SET_NAME =
> c("A",
>
> "A", "A", "B", "B", "B"), YLD_BE_REG1 = c(1467L, 1455L, 1382L,
>
> 1463L, 1466L, 1455L), YLD_BE_REG2 = c(1501L, 1441L, 1421L, 1482L,
>
> 1457L, 1490L), IS_GG = c("NO", "NO", "YES", "NO", "NO", "YES"
>
> ), GG_REG1 = c(6.2, 5.3, 0, 0.5, 0.8, 0), GG_REG2 = c(5.6, 1.4,
>
> 0, -0.5, -2.2, 0)), .Names = c("id", "TEST_SET_NAME", "YLD_BE_REG1",
>
> "YLD_BE_REG2", "IS_GG", "GG_REG1", "GG_REG2"), row.names = c(NA,
>
> -6L), class = "data.frame")
>
> Thanks.
> Nilesh
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