library(distributional)
library(tidyverse)
dat <- tibble(
id = 1:10,
mean = c(1:10),
sd = c(1:10)
) %>%
mutate(dist = dist_normal(
sigma = sd,
mean = mean
))
dat
#> # A tibble: 10 × 4
#> id mean sd dist
#> <int> <int> <int> <dist>
#> 1 1 1 1 N(1, 1)
#> 2 2 2 2 N(2, 4)
#> 3 3 3 3 N(3, 9)
#> 4 4 4 4 N(4, 16)
#> 5 5 5 5 N(5, 25)
#> 6 6 6 6 N(6, 36)
#> 7 7 7 7 N(7, 49)
#> 8 8 8 8 N(8, 64)
#> 9 9 9 9 N(9, 81)
#> 10 10 10 10 N(10, 100)
# using distributional
dat %>%
mutate(
samples = generate(dist, 10)
) %>%
unnest_longer(
col = samples
)
#> # A tibble: 100 × 5
#> id mean sd dist samples
#> <int> <int> <int> <dist> <dbl>
#> 1 1 1 1 N(1, 1) 1.21
#> 2 1 1 1 N(1, 1) 1.80
#> 3 1 1 1 N(1, 1) 0.845
#> 4 1 1 1 N(1, 1) 2.83
#> 5 1 1 1 N(1, 1) 2.18
#> 6 1 1 1 N(1, 1) 0.0218
#> 7 1 1 1 N(1, 1) 1.34
#> 8 1 1 1 N(1, 1) 1.11
#> 9 1 1 1 N(1, 1) 1.99
#> 10 1 1 1 N(1, 1) 0.930
#> # … with 90 more rows
dat_fun <- tibble(
id = 1:10,
mean = c(2:10, 100),
sd = 0,
wat = list(\(x) paste0(mean,"_", sd, "_", x)),
fun = list(\(x) rnorm(mean = mean, sd = 0, n = x)),
fun_map = map2(mean, sd, function(x,y){
function(n){
rnorm(n = n, mean = x, sd = y)
}
})
)
dat_fun
#> # A tibble: 10 × 6
#> id mean sd wat fun fun_map
#> <int> <dbl> <dbl> <list> <list> <list>
#> 1 1 2 0 <fn> <fn> <fn>
#> 2 2 3 0 <fn> <fn> <fn>
#> 3 3 4 0 <fn> <fn> <fn>
#> 4 4 5 0 <fn> <fn> <fn>
#> 5 5 6 0 <fn> <fn> <fn>
#> 6 6 7 0 <fn> <fn> <fn>
#> 7 7 8 0 <fn> <fn> <fn>
#> 8 8 9 0 <fn> <fn> <fn>
#> 9 9 10 0 <fn> <fn> <fn>
#> 10 10 100 0 <fn> <fn> <fn>
dat_fun$wat[[1]]("thing")
#> [1] "2_0_thing" "3_0_thing" "4_0_thing" "5_0_thing" "6_0_thing"
#> [6] "7_0_thing" "8_0_thing" "9_0_thing" "10_0_thing" "100_0_thing"
# rnorm takes vectors?!
rnorm(mean = c(1,10,100), n = 1)
#> [1] 3.034248
rnorm(mean = c(1,10,100), n = 2)
#> [1] 0.815239 11.148280
rnorm(mean = c(1,10,100), n = 3)
#> [1] -0.3813014 10.7340930 98.1872477
dat_fun$fun
#> [[1]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[2]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[3]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[4]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[5]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[6]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[7]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[8]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[9]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
#>
#> [[10]]
#> \(x) rnorm(mean = mean, sd = 0, n = x)
#> <environment: 0x11a8976a0>
dat_fun$fun[[1]](1)
#> [1] 2
dat_fun$fun[[1]](2)
#> [1] 2 3
dat_fun$fun[[1]](3)
#> [1] 2 3 4
dat_fun$fun[[1]](4)
#> [1] 2 3 4 5
dat_fun$fun[[1]](10)
#> [1] 2 3 4 5 6 7 8 9 10 100
# old way
mean(dat_fun$fun[[1]](100))
#> [1] 15.4
mean(dat_fun$fun[[10]](100))
#> [1] 15.4
# new way
mean(dat_fun$fun_map[[1]](1))
#> [1] 2
mean(dat_fun$fun_map[[10]](10))
#> [1] 100
# another way
dat_fun2 <- tibble(
id = 1:10,
mean = c(2:10, 100),
sd = c(2:10, 100)
)
dat_fun2
#> # A tibble: 10 × 3
#> id mean sd
#> <int> <dbl> <dbl>
#> 1 1 2 2
#> 2 2 3 3
#> 3 3 4 4
#> 4 4 5 5
#> 5 5 6 6
#> 6 6 7 7
#> 7 7 8 8
#> 8 8 9 9
#> 9 9 10 10
#> 10 10 100 100
dat_fun2 %>%
mutate(
samples = map2_dbl(
.x = mean,
.y = sd,
.f = ~rnorm(mean = .x,
sd = .y,
n = 1)
)
)
#> # A tibble: 10 × 4
#> id mean sd samples
#> <int> <dbl> <dbl> <dbl>
#> 1 1 2 2 2.90
#> 2 2 3 3 4.87
#> 3 3 4 4 9.74
#> 4 4 5 5 -3.00
#> 5 5 6 6 5.75
#> 6 6 7 7 2.18
#> 7 7 8 8 4.94
#> 8 8 9 9 10.6
#> 9 9 10 10 20.4
#> 10 10 100 100 135.
dat_fun %>%
mutate(
samples = fun(10)
)
#> Error in `mutate()`:
#> ℹ In argument: `samples = fun(10)`.
#> Caused by error in `fun()`:
#> ! could not find function "fun"
#> Backtrace:
#> ▆
#> 1. ├─dat_fun %>% mutate(samples = fun(10))
#> 2. ├─dplyr::mutate(., samples = fun(10))
#> 3. ├─dplyr:::mutate.data.frame(., samples = fun(10))
#> 4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#> 5. │ ├─base::withCallingHandlers(...)
#> 6. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#> 7. │ └─mask$eval_all_mutate(quo)
#> 8. │ └─dplyr (local) eval()
#> 9. └─base::.handleSimpleError(...)
#> 10. └─dplyr (local) h(simpleError(msg, call))
#> 11. └─rlang::abort(message, class = error_class, parent = parent, call = error_call)Created on 2023-02-06 with reprex v2.0.2
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.2 (2022-10-31)
#> os macOS Ventura 13.2
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Australia/Hobart
#> date 2023-02-06
#> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#> ──────────────────────────────────────────────────────────────────────────────