小编Sau*_*abh的帖子

我正在stats::unirootdata.table 中的一百万行上运行函数。这是一个玩具示例 -

library(data.table)
cumhaz <- function(t, a, b) b * (t/b)^a
froot <- function(x, u, a, b) cumhaz(x, a, b) - u

n <- 50000
u <- -log(runif(n))
a <- 1/2
b <- 1
dt = data.table(u = u, a = a, b = b)

print(system.time(
dt[, c := uniroot(froot, u=u, a=a, b=b, interval= c(0.01, 10), extendInt="yes")$root, by = u]
))

在上面的代码中，50,000 行所花费的时间接近 8 秒。

有没有更快的替代函数uniroot可以大大减少这个时间？

在过去的 5 天里，我试图让 Keras/Tensorflow 包在 R 中工作。我使用 RStudio 进行安装并使用了conda, miniconda，virtualenv但最终每次都会崩溃。安装库不应该是一场噩梦，尤其是当我们谈论 R（最好的统计语言之一）和 TensorFlow（最好的深度学习库之一）时。有人可以分享在 CentOS 7 上安装 Keras/Tensorflow 的可靠方法吗？

以下是我在 RStudio 中安装的步骤tensorflow。

由于 RStudio 每次运行时都会崩溃，因此tensorflow::tf_config()我无法检查出了什么问题。

devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/keras") # This package also installs tensorflow
library(reticulate)
reticulate::install_miniconda()
reticulate::use_miniconda("r-reticulate")
library(tensorflow)
tensorflow::tf_config() **# Crashes at this point**

sessionInfo()


R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8 …

我正在努力将上一组的价值转移到下一组。我尝试使用它来解决它rleid，但无法获得所需的结果。

df <- data.frame(signal = c(1,1,5,5,5,2,3,3,3,4,4,5,5,5,5,6,7,7,8,9,9,9,10), 
                 desired_outcome = c(NA, NA, 1, 1, 1, 5, 2, 2, 2, 3, 3, 4, 4,4,4,5,6,6,7,8,8,8,9))

# outcome column has the expected result -
   signal desired_outcome
1       1              NA
2       1              NA
3       5               1
4       5               1
5       5               1
6       2               5
7       3               2
8       3               2
9       3               2
10      4               3
11      4               3
12      5               4
13      5               4
14      5               4
15      5               4
16      6 …

我有一个简单的 data.table 如下 -

  ID = c(rep("A", 1000), rep("B", 1000), rep("C", 1000), rep("D", 1000))
  val = c("a", "a", "a", "b", "b", "c", "c","d","d","d","d","e","e","f","f","g","g","g","g","g")

  dt = data.table(ID, val)

val我想向此 data.table 添加一个新列，该列将具有by group的滞后ID。

这是预期的输出

> head(dt, 20)
     ID val val_lag
 1:  A   a    <NA>
 2:  A   a    <NA>
 3:  A   a    <NA>
 4:  A   b       a
 5:  A   b       a
 6:  A   c       b
 7:  A   c       b
 8:  A   d       c
 9:  A   d       c
10:  A   d …

我有一个 data.table，我想向其中添加倒计时，直到列中出现值 1 flag。

dt = structure(list(date = structure(19309:19318, class = c("IDate", 
"Date")), flag = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1)), class = c("data.table", 
"data.frame"), row.names = c(NA, -10L), .internal.selfref = <pointer: 0x55af7de49cb0>)
> dt
          date flag
 1: 2022-11-13    0
 2: 2022-11-14    0
 3: 2022-11-15    0
 4: 2022-11-16    0
 5: 2022-11-17    0
 6: 2022-11-18    1
 7: 2022-11-19    0
 8: 2022-11-20    0
 9: 2022-11-21    0
10: 2022-11-22    1

这是预期的输出

          date flag countdown
 1: 2022-11-13    0 5 …

在下面的 data.table 中，我想按每个组标记第一行。

  temp_dt <- data.table(date = as.Date(c("2000-01-01","2000-03-31","2000-07-01","2000-09-30", 
                                     "2001-01-01","2001-03-31","2001-07-01","2001-09-30",
                                     "2000-01-01","2000-03-31","2000-07-01","2000-09-30", 
                                     "2001-01-01","2001-03-31","2001-07-01","2001-09-30",
                                     "2000-01-01","2000-03-31","2000-07-01","2000-09-30", 
                                     "2001-01-01","2001-03-31","2001-07-01","2001-09-30")),
                    group = c(1,1,1,1,1,1,1,1,
                              2,2,6,6,6,8,8,8,
                              3,3,3,3,4,4,4,4))

以下是添加标志后的预期结果。

> temp_dt
          date group flag
 1: 2000-01-01     1    1
 2: 2000-03-31     1    0
 3: 2000-07-01     1    0
 4: 2000-09-30     1    0
 5: 2001-01-01     1    0
 6: 2001-03-31     1    0
 7: 2001-07-01     1    0
 8: 2001-09-30     1    0
 9: 2000-01-01     2    1
10: 2000-03-31     2    0
11: 2000-07-01     6    1
12: 2000-09-30     6    0
13: 2001-01-01     6    0
14: 2001-03-31     8    1 …