CRAN Package Check Results for Package healthcareai

Last updated on 2020-06-07 04:47:38 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.4.0 18.21 150.92 169.13 ERROR
r-devel-linux-x86_64-debian-gcc 2.4.0 14.88 115.61 130.49 ERROR
r-devel-linux-x86_64-fedora-clang 2.4.0 205.52 ERROR
r-devel-linux-x86_64-fedora-gcc 2.4.0 201.97 ERROR
r-devel-windows-ix86+x86_64 2.4.0 52.00 208.00 260.00 ERROR
r-patched-linux-x86_64 2.4.0 16.55 150.48 167.03 ERROR
r-patched-solaris-x86 2.4.0 301.80 ERROR
r-release-linux-x86_64 2.4.0 17.06 146.26 163.32 ERROR
r-release-osx-x86_64 2.4.0 OK
r-release-windows-ix86+x86_64 2.4.0 50.00 213.00 263.00 ERROR
r-oldrel-osx-x86_64 2.4.0 OK
r-oldrel-windows-ix86+x86_64 2.4.0 53.00 196.00 249.00 ERROR

Check Details

Version: 2.4.0
Check: examples
Result: ERROR
    Running examples in 'healthcareai-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: add_best_levels
    > ### Title: Build efficient features from high-cardinality,
    > ### multiple-membership factors
    > ### Aliases: add_best_levels get_best_levels
    >
    > ### ** Examples
    >
    > set.seed(45796)
    >
    > # We have two tables we want to use in our models:
    > # - df is the model table. It has the outcomes (survived), and we want one
    > # prediction for each row in df
    > # - meds has detailed information on each row (patient) in df. Each patient
    > # may have zero, one, or more observations (drugs) in meds, and meds may
    > # have associated values (doses).
    >
    > df <- tibble::tibble(
    + patient = paste0("Z", sample(10, 5)),
    + age = sample(20:80, 5),
    + survived = sample(c("N", "Y"), 5, replace = TRUE, prob = c(1, 2))
    + )
    >
    > meds <- tibble::tibble(
    + patient = sample(df$patient, 10, replace = TRUE),
    + drug = sample(c("Quinapril", "Vancomycin", "Ibuprofen",
    + "Paclitaxel", "Epinephrine", "Dexamethasone"),
    + 10, replace = TRUE),
    + dose = sample(c(100, 250), 10, replace = TRUE)
    + )
    >
    > # Identify three drugs likely to be good predictors of survival
    >
    > get_best_levels(d = df,
    + longsheet = meds,
    + id = patient,
    + groups = drug,
    + outcome = survived,
    + n_levels = 3)
    Error: Problem with `filter()` input `..1`.
    x object 'patient' not found
    i Input `..1` is `n_distinct(patient) >= min_obs`.
    i The error occured in group 1: drug = "Dexamethasone".
    Backtrace:
     x
     1. \-healthcareai::get_best_levels(...)
     2. \-`%>%`(...)
     3. +-base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     4. \-base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. \-base::eval(quote(`_fseq`(`_lhs`)), env, env)
     6. \-healthcareai:::`_fseq`(`_lhs`)
     7. \-magrittr::freduce(value, `_function_list`)
     8. \-function_list[[i]](value)
     9. +-dplyr::filter(., n_distinct(!!id) >= min_obs)
     10. \-dplyr:::filter.data.frame(., n_distinct(!!id) >= min_obs)
     11. \-dplyr:::filter_rows(.data, ...)
     12. \-base::tryCatch(...)
     13. \-base:::tryCatchList(expr, classes, parentenv, handlers)
     14. \-base:::tryCatchOne(expr, names, parentenv, hand
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 2.4.0
Check: examples
Result: ERROR
    Running examples in ‘healthcareai-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: add_best_levels
    > ### Title: Build efficient features from high-cardinality,
    > ### multiple-membership factors
    > ### Aliases: add_best_levels get_best_levels
    >
    > ### ** Examples
    >
    > set.seed(45796)
    >
    > # We have two tables we want to use in our models:
    > # - df is the model table. It has the outcomes (survived), and we want one
    > # prediction for each row in df
    > # - meds has detailed information on each row (patient) in df. Each patient
    > # may have zero, one, or more observations (drugs) in meds, and meds may
    > # have associated values (doses).
    >
    > df <- tibble::tibble(
    + patient = paste0("Z", sample(10, 5)),
    + age = sample(20:80, 5),
    + survived = sample(c("N", "Y"), 5, replace = TRUE, prob = c(1, 2))
    + )
    >
    > meds <- tibble::tibble(
    + patient = sample(df$patient, 10, replace = TRUE),
    + drug = sample(c("Quinapril", "Vancomycin", "Ibuprofen",
    + "Paclitaxel", "Epinephrine", "Dexamethasone"),
    + 10, replace = TRUE),
    + dose = sample(c(100, 250), 10, replace = TRUE)
    + )
    >
    > # Identify three drugs likely to be good predictors of survival
    >
    > get_best_levels(d = df,
    + longsheet = meds,
    + id = patient,
    + groups = drug,
    + outcome = survived,
    + n_levels = 3)
    Error: Problem with `filter()` input `..1`.
    ✖ object 'patient' not found
    ℹ Input `..1` is `n_distinct(patient) >= min_obs`.
    ℹ The error occured in group 1: drug = "Dexamethasone".
    Backtrace:
     █
     1. └─healthcareai::get_best_levels(...)
     2. └─`%>%`(...)
     3. ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     4. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     6. └─healthcareai:::`_fseq`(`_lhs`)
     7. └─magrittr::freduce(value, `_function_list`)
     8. └─function_list[[i]](value)
     9. ├─dplyr::filter(., n_distinct(!!id) >= min_obs)
     10. └─dplyr:::filter.data.frame(., n_distinct(!!id) >= min_obs)
     11. └─dplyr:::filter_rows(.data, ...)
     12. └─base::tryCatch(...)
     13. └─base:::tryCatchList(expr, classes, parentenv, handlers)
     14.
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 2.4.0
Check: examples
Result: ERROR
    Running examples in ‘healthcareai-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: add_best_levels
    > ### Title: Build efficient features from high-cardinality,
    > ### multiple-membership factors
    > ### Aliases: add_best_levels get_best_levels
    >
    > ### ** Examples
    >
    > set.seed(45796)
    >
    > # We have two tables we want to use in our models:
    > # - df is the model table. It has the outcomes (survived), and we want one
    > # prediction for each row in df
    > # - meds has detailed information on each row (patient) in df. Each patient
    > # may have zero, one, or more observations (drugs) in meds, and meds may
    > # have associated values (doses).
    >
    > df <- tibble::tibble(
    + patient = paste0("Z", sample(10, 5)),
    + age = sample(20:80, 5),
    + survived = sample(c("N", "Y"), 5, replace = TRUE, prob = c(1, 2))
    + )
    >
    > meds <- tibble::tibble(
    + patient = sample(df$patient, 10, replace = TRUE),
    + drug = sample(c("Quinapril", "Vancomycin", "Ibuprofen",
    + "Paclitaxel", "Epinephrine", "Dexamethasone"),
    + 10, replace = TRUE),
    + dose = sample(c(100, 250), 10, replace = TRUE)
    + )
    >
    > # Identify three drugs likely to be good predictors of survival
    >
    > get_best_levels(d = df,
    + longsheet = meds,
    + id = patient,
    + groups = drug,
    + outcome = survived,
    + n_levels = 3)
    Error: Problem with `filter()` input `..1`.
    ✖ object 'patient' not found
    ℹ Input `..1` is `n_distinct(patient) >= min_obs`.
    ℹ The error occured in group 1: drug = "Dexamethasone".
    Backtrace:
     █
     1. └─healthcareai::get_best_levels(...)
     2. └─`%>%`(...)
     3. ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     4. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     6. └─healthcareai:::`_fseq`(`_lhs`)
     7. └─magrittr::freduce(value, `_function_list`)
     8. └─function_list[[i]](value)
     9. ├─dplyr::filter(., n_distinct(!!id) >= min_obs)
     10. └─dplyr:::filter.data.frame(., n_distinct(!!id) >= min_obs)
     11. └─dplyr:::filter_rows(.data, ...)
     12. └─base::tryCatch(...)
     13. └─base:::tryCatchList(expr, classes, parentenv, handlers)
     14.
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 2.4.0
Check: examples
Result: ERROR
    Running examples in 'healthcareai-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: add_best_levels
    > ### Title: Build efficient features from high-cardinality,
    > ### multiple-membership factors
    > ### Aliases: add_best_levels get_best_levels
    >
    > ### ** Examples
    >
    > set.seed(45796)
    >
    > # We have two tables we want to use in our models:
    > # - df is the model table. It has the outcomes (survived), and we want one
    > # prediction for each row in df
    > # - meds has detailed information on each row (patient) in df. Each patient
    > # may have zero, one, or more observations (drugs) in meds, and meds may
    > # have associated values (doses).
    >
    > df <- tibble::tibble(
    + patient = paste0("Z", sample(10, 5)),
    + age = sample(20:80, 5),
    + survived = sample(c("N", "Y"), 5, replace = TRUE, prob = c(1, 2))
    + )
    >
    > meds <- tibble::tibble(
    + patient = sample(df$patient, 10, replace = TRUE),
    + drug = sample(c("Quinapril", "Vancomycin", "Ibuprofen",
    + "Paclitaxel", "Epinephrine", "Dexamethasone"),
    + 10, replace = TRUE),
    + dose = sample(c(100, 250), 10, replace = TRUE)
    + )
    >
    > # Identify three drugs likely to be good predictors of survival
    >
    > get_best_levels(d = df,
    + longsheet = meds,
    + id = patient,
    + groups = drug,
    + outcome = survived,
    + n_levels = 3)
    Error: Problem with `filter()` input `..1`.
    x object 'patient' not found
    i Input `..1` is `n_distinct(patient) >= min_obs`.
    i The error occured in group 1: drug = "Dexamethasone".
    Backtrace:
     x
     1. \-healthcareai::get_best_levels(...)
     2. \-`%>%`(...)
     3. +-base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     4. \-base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. \-base::eval(quote(`_fseq`(`_lhs`)), env, env)
     6. \-healthcareai:::`_fseq`(`_lhs`)
     7. \-magrittr::freduce(value, `_function_list`)
     8. \-function_list[[i]](value)
     9. +-dplyr::filter(., n_distinct(!!id) >= min_obs)
     10. \-dplyr:::filter.data.frame(., n_distinct(!!id) >= min_obs)
     11. \-dplyr:::filter_rows(.data, ...)
     12. \-base::tryCatch(...)
     13. \-base:::tryCatchList(expr, classes, parentenv, handlers)
     14. \-base:::tryCatchOne(expr, names, parentenv, hand
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64