[R] dynamically create columns using a function

Fri Jul 21 15:00:40 CEST 2017

Hi Elie,

Thanks for your time and efforts. I plugged in the calculation I wanted to do in the code you provided and got I wanted perfectly. Below is the solution to my original problem.

# 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 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()

}

# function f

f<- function (dat, blup_datacut)

{

    col_name_gg <- paste("GG", blup_datacut, sep = "_")

    col_mean_gg <- paste("YLD_BE", blup_datacut, sep = "_")

    dat2 <- calc_gg(dataset = dat, col = col_mean_gg, col_name = col_name_gg)

    dat2

}

# function demo_fn

demo_fn<- function (dat, f, blup_datacut)

{

    for (i in blup_datacut) {

        dat <- f(dat, i)

    }

    dat

}

# get expected results by applying functions
demo_fn(dem, f, c("REG1", "REG2"))

Best Regards,
Nilesh
From: Elie Canonici Merle [mailto:elie.canonicimerle at gmail.com]
Sent: Friday, July 21, 2017 3:44 AM
To: DIGHE, NILESH [AG/2362] <nilesh.dighe at monsanto.com>
Cc: r-help at r-project.org
Subject: Re: [R] dynamically create columns using a function

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.dighe at monsanto.com<mailto:nilesh.dighe at 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<http://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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