Error In Eval(predvars, Data, Env) : Object '...future.X_jj' Not Found

by ADMIN 71 views

Introduction

The future.apply package in R is designed to facilitate parallel processing of data using the future framework. However, users have reported encountering an error when using the future_lapply function, specifically when trying to perform linear regression on a dataset. This error is characterized by the message "Error in eval(predvars, data, env): object '...future.X_jj' not found." In this article, we will delve into the root cause of this issue and provide a solution to resolve it.

Describe the bug

The bug occurs when using the future_lapply function to perform linear regression on a dataset. The error message indicates that the object '...future.X_jj' is not found, which suggests that there is an issue with the way the future framework is handling the data.

Reproduce example

Here is a reproducible example of the code that triggers the error:

ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)
data <- data.frame(weight = weight, group = group)

future.apply::future_lapply("weight", function(x) {
    formula <- rlang::new_formula(rlang::ensym(x), quote(group))
    lm(formula, data = data)
}, future.globals = list(data = data))
#> Error in eval(predvars, data, env): object '...future.X_jj' not found

Expected behavior

The expected behavior is that the future_lapply function should perform linear regression on the dataset without encountering any errors.

Session information

Here is the session information for the R environment:

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.2 (2024-10-31)
#>  os       Ubuntu 24.04.1 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language en
#>  collate  C.UTF-8
#>  ctype    C.UTF-8
#>  tz       Asia/Shanghai
#>  date     2025-03-10
#>  pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version date (UTC) lib source
#>  cli            3.6.3   2024-06-21 [1] CRAN (R 4.4.0)
#>  codetools      0.2-20  2024-03-31 [4] CRAN (R 4.4.0)
#>  digest         0.6.37  2024-08-19 [1] CRAN (R 4.4.2)
#>  evaluate       1.0.1   2024-10-10 [1] CRAN (R 4.4.2)
#>  fastmap        1.2.0   2024-05-15 [1] CRAN (R 4.4.0)
#>  fs             1.6.5   2024-10-30 [1] CRAN (R 4.4.2)
#>  future         1.33.2  2024-03-26 [1] CRAN (R 4.4.0)
#>  future.apply   1.11.2  2024-03-28 [1] CRAN (R 4.4.0)
#>  globals        0.16.3  2024-03-08 [1] CRAN (R 4.4.0)
#>  glue           1.8.0   2024-09-30 [1] CRAN (R 4.4.2)
#>  htmltools      0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
#>  knitr          1.49    2024-11-08 [1] CRAN (R 4.4.2)
#>  lifecycle      1.0.4   2023-11-07 [1] CRAN (R 4.4.0)
#>  listenv        0.9.1   2024-01-29 [1] CRAN (R 4.4.0)
#>  magrittr       2.0.3   2022-03-30 [1] CRAN (R 4.4.0)
#>  parallelly     1.37.1  2024-02-29 [1] CRAN (R 4.4.0)
#>  purrr          1.0.2   2023-08-10 [1] CRAN (R 4.4.0)
#>  R.cache        0.16.0  2022-07-21 [1] CRAN (R 4.4.0)
#>  R.methodsS3    1.8.2   2022-06-13 [1] CRAN (R 4.4.0)
#>  R.oo           1.26.0  2024-01-24 [1] CRAN (R 4.4.0)
#>  R.utils        2.12.3  2023-11-18 [1] CRAN (R 4.4.0)
#>  reprex         2.1.0   2024-01-11 [1] CRAN (R 4.4.0)
#>  rlang          1.1.4   2024-06-04 [1] CRAN (R 4.4.0)
#>  rmarkdown      2.29    2024-11-04 [1] CRAN (R 4.4.2)
#>  sessioninfo    1.2.2   2021-12-06 [1] CRAN (R 4.4.0)
#>  styler         1.10.3  2024-04-07 [1] CRAN (R 4.4.0)
#>  vctrs          0.6.5   2023-12-01 [1] CRAN (R 4.4.0)
#>  withr          3.0.2   2024-10-28 [1] CRAN (R 4.4.2)
#>  xfun           0.49    2024-10-31 [1] CRAN (R 4.4.2)
#>  yaml           2.3.10  2024-07-26 [1] CRAN (R 4.4.2)
#> 
#>  [1] /home/yun/Rlibrary/4.4
#>  [2] /usr/local/lib/R/site-library
#>  [3] /usr/lib/R/site-library
#>  [4] /usr/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

Solution

The issue arises from the way the future framework is handling the data. Specifically, the future_lapply function is not properly handling the data argument. To resolve this issue, we can modify the code to use the future_lapply function with the future.globals argument set to NULL. This will ensure that the data argument is not passed to the future_lapply function, and the error should be resolved.

Here is the modified code:

future.apply::future_lapply("weight", function(x) {
    formula <- rlang::new_formula(rlang::ensym(x), quote(group))
    lm(formula, data = data)
}, future.globals = NULL)

Conclusion

In conclusion, the error "Error in eval(predvars, data, env): object '...future.X_jj' not found" is caused by the way the future framework is handling the data. By modifying the code to use the future_lapply function with the future.globals argument set to NULL, we can resolve this issue and perform linear regression on the dataset without encountering any errors.

Additional information

Q: What is the error "Error in eval(predvars, data, env): object '...future.X_jj' not found" in R?

A: The error "Error in eval(predvars, data, env): object '...future.X_jj' not found" is a common issue that occurs when using the future package in R. This error is typically caused by a problem with the way the future framework is handling the data.

Q: What is the cause of this error?

A: The cause of this error is usually related to the way the future package is handling the data argument. Specifically, the future_lapply function is not properly handling the data argument, which can lead to this error.

Q: How can I resolve this error?

A: To resolve this error, you can try modifying the code to use the future_lapply function with the future.globals argument set to NULL. This will ensure that the data argument is not passed to the future_lapply function, and the error should be resolved.

Q: What are some common mistakes that can lead to this error?

A: Some common mistakes that can lead to this error include:

  • Not properly setting the future.globals argument in the future_lapply function.
  • Passing the data argument to the future_lapply function without properly handling it.
  • Using the future_lapply function with an incorrect or incomplete formula argument.

Q: How can I prevent this error from occurring in the future?

A: To prevent this error from occurring in the future, you can follow these best practices:

  • Always properly set the future.globals argument in the future_lapply function.
  • Ensure that the data argument is properly handled and passed to the future_lapply function.
  • Use the future_lapply function with a complete and correct formula argument.

Q: What are some additional resources for learning more about the future package and resolving this error?

A: Some additional resources for learning more about the future package and resolving this error include:

Q: Can I get help with resolving this error?

A: Yes, you can get help with resolving this error by: