问题
I am working with the expss package to produce banner tables for survey data, but I keep getting an error that doesn't come up a lot on Google: Error in data.table(cell_var, col_var, row_var) : object '.R.listCopiesNamed' not found.
I've created a reproducible example below. It's unclear to me if it's an error from expss or from data.table, or from the combination of the two. In any case there a way to override the need for '.R.listCopiesNamed', or some other way to resolve the error?
I'm working in this environment:R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Reproducible example:
# load packages
library(expss)
library(tidyverse)
# generate some data
set.seed(369)
age <- base::sample(c("18-24", "25-24", "35-44", "45-54", "55-64", "65+"),
100, replace = TRUE)
sex <- base::sample(c("Male", "Female"),
100, replace = TRUE)
likelihood <- base::sample(c("Much more likely", "Somewhat more likely",
"Equally likely", "Somewhat less likely",
"Much less likely"), 100, replace = TRUE)
importance <- base::sample(c("Extremely important", "Somewhat important",
"Neutral", "Somewhat unimportant",
"Extremely unimportant"), 100, replace = TRUE)
relevance <- base::sample(c("Extremely relevant", "Somewhat relevant",
"Neutral", "Somewhat irrelevant",
"Extremely irrelevant"), 100, replace = TRUE)
data <- data.frame(age, sex, likelihood, importance, relevance)
# make a simple banner table with significance testing
myTable <- data %>%
tab_cells(likelihood, importance, relevance) %>%
tab_cols(total(), age, sex) %>%
tab_stat_cpct() %>%
tab_last_sig_cpct() %>%
tab_pivot()
At this point, I get the error:Error in data.table(cell_var, col_var, row_var) :
object '.R.listCopiesNamed' not found
~~~~~~~
Edited to add traceback() and sessionInfo():
> traceback()
19: data.table(cell_var, col_var, row_var)
18: make_datatable_for_cro(cell_var = cell_var, col_var = col_var,
row_var = row_var, weight = weight, subgroup = subgroup)
17: elementary_cro(cell_var = each_cell_var, col_var = each_col_var,
row_var = each_row_var, weight = weight, subgroup = subgroup,
total_label = total_label, total_statistic = total_statistic,
total_row_position = total_row_position, stat_type = stat_type)
16: FUN(X[[i]], ...)
15: lapply(col_vars, function(each_col_var) {
elementary_cro(cell_var = each_cell_var, col_var = each_col_var,
row_var = each_row_var, weight = weight, subgroup = subgroup,
total_label = total_label, total_statistic = total_statistic,
total_row_position = total_row_position, stat_type = stat_type)
})
14: FUN(X[[i]], ...)
13: lapply(cell_vars, function(each_cell_var) {
all_col_vars = lapply(col_vars, function(each_col_var) {
elementary_cro(cell_var = each_cell_var, col_var = each_col_var,
row_var = each_row_var, weight = weight, subgroup = subgroup,
total_label = total_label, total_statistic = total_statistic,
total_row_position = total_row_position, stat_type = stat_type)
})
Reduce(merge, all_col_vars)
})
12: FUN(X[[i]], ...)
11: lapply(row_vars, function(each_row_var) {
res = lapply(cell_vars, function(each_cell_var) {
all_col_vars = lapply(col_vars, function(each_col_var) {
elementary_cro(cell_var = each_cell_var, col_var = each_col_var,
row_var = each_row_var, weight = weight, subgroup = subgroup,
total_label = total_label, total_statistic = total_statistic,
total_row_position = total_row_position, stat_type = stat_type)
})
Reduce(merge, all_col_vars)
})
res = do.call(add_rows, res)
})
10: multi_cro(cell_vars = cell_vars, col_vars = col_vars, row_vars = row_vars,
weight = weight, subgroup = subgroup, total_label = total_label,
total_statistic = total_statistic, total_row_position = total_row_position,
stat_type = "cpct")
9: cro_cpct(cell_vars = get_cells(data), col_vars = data[[COL_VAR]],
row_vars = data[[ROW_VAR]], weight = data[[WEIGHT]], subgroup =
data[[SUBGROUP]],
total_label = total_label, total_statistic = total_statistic,
total_row_position = total_row_position)
8: tab_stat_cpct(.)
7: function_list[[i]](value)
6: freduce(value, `_function_list`)
5: `_fseq`(`_lhs`)
4: eval(quote(`_fseq`(`_lhs`)), env, env)
3: eval(quote(`_fseq`(`_lhs`)), env, env)
2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
1: data %>% tab_cells(likelihood, importance, relevance) %>% tab_cols(total(),
age, sex) %>% tab_stat_cpct() %>% tab_last_sig_cpct() %>%
tab_pivot()
> sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK:
/Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] expss_0.8.6
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 matrixStats_0.53.1 digest_0.6.15 backports_1.1.2
[5] magrittr_1.5 stringi_1.1.6 data.table_1.11.4 rstudioapi_0.7
[9] checkmate_1.8.5 tools_3.4.4 stringr_1.3.0 foreign_0.8-69
[13] htmlwidgets_1.2 yaml_2.1.17 compiler_3.4.4 htmltools_0.3.6
[17] knitr_1.20 htmlTable_1.11.2
回答1:
The solution: Update the expss package. Thanks to @MichaelChirico for the suggestion!
来源：https://stackoverflow.com/questions/51084894/expss-and-data-table-not-playing-well-together