Purrr 安全地创建列表列表
Purrr safely creating lists of lists
我已经使用 safely
来捕获我发出咕噜声时代码中发生的错误。然而,safely
的结果比我预期的要复杂得多。
首先我们创建必要的函数和示例数据。
#base functions.
SI_tall <- function(topheight, age, si ){
paramasi <- 25
parambeta <- 7395.6
paramb2 <- -1.7829
refAge <- 100
d <- parambeta*(paramasi^paramb2)
r <- (((topheight-d)^2)+(4*parambeta*topheight*(age^paramb2)))^0.5
## height at reference age
h2 <- (topheight+d+r)/ (2+(4*parambeta*(refAge^paramb2)) / (topheight-d+r))
return(abs(h2 - si))
}
new.topheight <- function(my.si, my.age){
optim(par = list(topheight = 10), ## this topheight is just an initial value
method = 'L-BFGS-B', fn = SI_tall, si = my.si, age = my.age, lower= 0, upper=100)$par
}
#Creating the function which will display errors.
safe_new.topheight <- safely(new.topheight)
#Creating data
my.age <- seq(0,100, by=0.2)
my.si <- c(15)
si.crossing <- tidyr::crossing(my.si, my.age) %>% data.frame()
#Creating the column to be unnested.
si.crossing2<- si.crossing %>%
mutate(height=map2(my.si,my.age, safe_new.topheight))
但是,结果变得复杂到我无法取消嵌套 - 我什至不知道“高度”列中的列表嵌套到底有多深。这是我数据中前 5 行的输入:
structure(list(my.si = c(15, 15, 15, 15, 15), my.age = c(0, 0.2,
0.4, 0.6, 0.8), height = list(list(result = NULL, error = structure(list(
message = "L-BFGS-B needs finite values of 'fn'", call = optim(par = list(topheight = 10),
method = "L-BFGS-B", fn = SI_tall, si = my.si, age = my.age,
lower = 0, upper = 100)), class = c("simpleError", "error",
"condition"))), list(result = c(topheight = 0.000693170450744849),
error = NULL), list(result = c(topheight = 0.00205917508142004),
error = NULL), list(result = c(topheight = 0.00390099534708239),
error = NULL), list(result = c(topheight = 0.00639475141226834),
error = NULL))), row.names = c(NA, 5L), class = "data.frame")
有什么方法可以将其压平到列中:
my.si , my.age , topheight, error.
非常感谢!
区分 safely
和 possibly
可能会有所帮助。 safely
很高兴看到发生了什么错误(以及发生在哪里)。它最好与 transpose
一起使用,而不是在 tibble
内。 possibly
用于 运行 一个 map
函数,即使它通过错误。如果抛出错误,它允许您选择替代值 otherwise
。
# use `transpose` on the height column to turn the list inside out
# which results in two lists `result` and `error`
# first lets have a look at the structure
si.crossing2$height %>%
transpose %>%
str
# then `pluck` the `error` list and remove all elements which are NULL
# with `compact` - here you can see the error that occurred
si.crossing2$height %>%
transpose %>%
pluck("error") %>%
compact
# `safely` is a great function to see what went wrong
# but its not very useful inside a tibbles list-column
# what you actually want to use is `possibly`
possib_new.topheight <- possibly(new.topheight, otherwise = NA)
# this will not tell you what went wrong, but instead yield `NA`
# when an error is thrown - important to use `otherwise = NA`,
# the default is NULL, which makes the output list shorter and
# won't fit to your tibble
si.crossing3 <- si.crossing %>%
mutate(height = map2_dbl(my.si,my.age, possib_new.topheight))
si.crossing3
#> # A tibble: 501 x 3
#> my.si my.age height
#> <dbl> <dbl> <dbl>
#> 1 15 0 NA
#> 2 15 0.2 0.000693
#> 3 15 0.4 0.00206
#> 4 15 0.6 0.00390
#> 5 15 0.8 0.00639
#> 6 15 1 0.00947
#> 7 15 1.2 0.0131
#> 8 15 1.4 0.0172
#> 9 15 1.6 0.0218
#> 10 15 1.8 0.0269
#> # … with 491 more rows
我已经使用 safely
来捕获我发出咕噜声时代码中发生的错误。然而,safely
的结果比我预期的要复杂得多。
首先我们创建必要的函数和示例数据。
#base functions.
SI_tall <- function(topheight, age, si ){
paramasi <- 25
parambeta <- 7395.6
paramb2 <- -1.7829
refAge <- 100
d <- parambeta*(paramasi^paramb2)
r <- (((topheight-d)^2)+(4*parambeta*topheight*(age^paramb2)))^0.5
## height at reference age
h2 <- (topheight+d+r)/ (2+(4*parambeta*(refAge^paramb2)) / (topheight-d+r))
return(abs(h2 - si))
}
new.topheight <- function(my.si, my.age){
optim(par = list(topheight = 10), ## this topheight is just an initial value
method = 'L-BFGS-B', fn = SI_tall, si = my.si, age = my.age, lower= 0, upper=100)$par
}
#Creating the function which will display errors.
safe_new.topheight <- safely(new.topheight)
#Creating data
my.age <- seq(0,100, by=0.2)
my.si <- c(15)
si.crossing <- tidyr::crossing(my.si, my.age) %>% data.frame()
#Creating the column to be unnested.
si.crossing2<- si.crossing %>%
mutate(height=map2(my.si,my.age, safe_new.topheight))
但是,结果变得复杂到我无法取消嵌套 - 我什至不知道“高度”列中的列表嵌套到底有多深。这是我数据中前 5 行的输入:
structure(list(my.si = c(15, 15, 15, 15, 15), my.age = c(0, 0.2,
0.4, 0.6, 0.8), height = list(list(result = NULL, error = structure(list(
message = "L-BFGS-B needs finite values of 'fn'", call = optim(par = list(topheight = 10),
method = "L-BFGS-B", fn = SI_tall, si = my.si, age = my.age,
lower = 0, upper = 100)), class = c("simpleError", "error",
"condition"))), list(result = c(topheight = 0.000693170450744849),
error = NULL), list(result = c(topheight = 0.00205917508142004),
error = NULL), list(result = c(topheight = 0.00390099534708239),
error = NULL), list(result = c(topheight = 0.00639475141226834),
error = NULL))), row.names = c(NA, 5L), class = "data.frame")
有什么方法可以将其压平到列中:
my.si , my.age , topheight, error.
非常感谢!
区分 safely
和 possibly
可能会有所帮助。 safely
很高兴看到发生了什么错误(以及发生在哪里)。它最好与 transpose
一起使用,而不是在 tibble
内。 possibly
用于 运行 一个 map
函数,即使它通过错误。如果抛出错误,它允许您选择替代值 otherwise
。
# use `transpose` on the height column to turn the list inside out
# which results in two lists `result` and `error`
# first lets have a look at the structure
si.crossing2$height %>%
transpose %>%
str
# then `pluck` the `error` list and remove all elements which are NULL
# with `compact` - here you can see the error that occurred
si.crossing2$height %>%
transpose %>%
pluck("error") %>%
compact
# `safely` is a great function to see what went wrong
# but its not very useful inside a tibbles list-column
# what you actually want to use is `possibly`
possib_new.topheight <- possibly(new.topheight, otherwise = NA)
# this will not tell you what went wrong, but instead yield `NA`
# when an error is thrown - important to use `otherwise = NA`,
# the default is NULL, which makes the output list shorter and
# won't fit to your tibble
si.crossing3 <- si.crossing %>%
mutate(height = map2_dbl(my.si,my.age, possib_new.topheight))
si.crossing3
#> # A tibble: 501 x 3
#> my.si my.age height
#> <dbl> <dbl> <dbl>
#> 1 15 0 NA
#> 2 15 0.2 0.000693
#> 3 15 0.4 0.00206
#> 4 15 0.6 0.00390
#> 5 15 0.8 0.00639
#> 6 15 1 0.00947
#> 7 15 1.2 0.0131
#> 8 15 1.4 0.0172
#> 9 15 1.6 0.0218
#> 10 15 1.8 0.0269
#> # … with 491 more rows