R 如何在 PCA 期间修复数据资源管理器错误："Item 2 has no length"

我有一个包含102个变量的数据集df：16 int，80 factors，8 logi。没有NA值。

我以前使用过DataExplorer，但当我在这个数据集上运行它时。。。

library(DataExplorer)
create_report(df)

它轻快地前进着，输出着它的进步。。。

# label: correlation_analysis
#   |................................................                 |  74%
#   ordinary text without R code

直到它到达PCA部分时，它产生了这个错误：

#  |..................................................               |  76%
# label: principle_component_analysis
# Quitting from lines 208-221 (report.rmd) 
#
# Error in data.table(pc = paste0("PC", seq_along(pca$sdev)), var = var_exp,  : 
#  Item 2 has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the 1 row in the longest column. Or, all columns can be 0 length, for insert()ing rows into. 

我在谷歌上搜索了这个错误，但只找到了解释PCA的页面，而没有找到这个错误。有什么建议吗？

回溯是

26. stop("Item ", i, " has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the ", 
nr, " row", if (nr > 1L) "s", " in the longest column. Or, all columns can be 0 length, for insert()ing rows into.") 
25. data.table(pc = paste0("PC", seq_along(pca$sdev)), var = var_exp, 
pct = var_exp/sum(var_exp), cum_pct = cumsum(var_exp)/sum(var_exp)) 
24. plot_prcomp(data = structure(list(EnrollmentID = c(4603L, 8457L, 
3290L, 3323L, 6186L, 6501L, 3084L, 8662L, 7676L, 3229L, 6005L, 
3387L, 8204L, 9018L, 4517L, 3320L, 8840L, 7729L, 8835L, 5148L, 
7560L, 1239L, 5874L, 4963L, 3755L, 3397L, 9877L, 8609L, 6584L,  ... 
23. do.call(fun_name, c(list(data = data), report_config[[fun_name]])) at <text>#9
22. do_call("plot_prcomp", na_omit = TRUE) at <text>#8
21. eval(expr, envir, enclos) 
20. eval(expr, envir, enclos) 
19. withVisible(eval(expr, envir, enclos)) 
18. withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler, 
error = eHandler, message = mHandler) 
17. handle(ev <- withCallingHandlers(withVisible(eval(expr, envir, 
enclos)), warning = wHandler, error = eHandler, message = mHandler)) 
16. timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr, 
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler))) 
15. valuate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, 
debug = debug, last = i == length(out), use_try = stop_on_error != 
2L, keep_warning = keep_warning, keep_message = keep_message, 
output_handler = output_handler, include_timing = include_timing) 
14. evaluate::evaluate(...) 
13. evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning), 
keep_message = !isFALSE(options$message), stop_on_error = if (options$error && 
options$include) 0L else 2L, output_handler = knit_handlers(options$render, 
options)) 
12. in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE, 
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message), 
stop_on_error = if (options$error && options$include) 0L else 2L, 
output_handler = knit_handlers(options$render, options))) 
11. block_exec(params) 
10. call_block(x) 
9. process_group.block(group) 
8. process_group(group) 
7. withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), 
error = function(e) {
setwd(wd)
cat(res, sep = "n", file = output %n% "") ... 
6. process_file(text, output) 
5. knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet, 
encoding = encoding) 
4. render(input = report_dir, output_file = output_file, output_dir = output_dir, 
intermediates_dir = output_dir, params = list(data = data, 
report_config = config, response = y), ...) 
3. withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning")) 
2. suppressWarnings(render(input = report_dir, output_file = output_file, 
output_dir = output_dir, intermediates_dir = output_dir, 
params = list(data = data, report_config = config, response = y), 
...)) 
1. create_report(df) 

以下是会话信息：

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    
attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     
other attached packages:
[1] car_3.0-2          knitr_1.20         rmarkdown_1.10     data.table_1.11.8 
[5] DataExplorer_0.7.0 mosaic_1.4.0       Matrix_1.2-14      mosaicData_0.17.0 
[9] ggformula_0.9.0    ggstance_0.3.1     mdsr_0.1.6         Lahman_6.0-0      
[13] ISLR_1.2           forcats_0.3.0      stringr_1.3.1      dplyr_0.7.8       
[17] purrr_0.2.5        readr_1.1.1        tidyr_0.8.2        tibble_1.4.2      
[21] ggplot2_3.1.0      tidyverse_1.2.1    lattice_0.20-35    carData_3.0-2     
loaded via a namespace (and not attached):
[1] ggdendro_0.1-20  httr_1.3.1       RMySQL_0.10.15   jsonlite_1.5     splines_3.5.1   
[6] modelr_0.1.2     assertthat_0.2.0 highr_0.7        cellranger_1.1.0 yaml_2.2.0      
[11] ggrepel_0.8.0    pillar_1.3.0     backports_1.1.2  glue_1.3.0       downloader_0.4  
[16] digest_0.6.18    rvest_0.3.2      colorspace_1.3-2 htmltools_0.3.6  plyr_1.8.4      
[21] pkgconfig_2.0.2  broom_0.5.0      haven_1.1.2      scales_1.0.0     openxlsx_4.1.0  
[26] rio_0.5.10       withr_2.1.2      lazyeval_0.2.1   cli_1.0.1        magrittr_1.5    
[31] crayon_1.3.4     readxl_1.1.0     evaluate_0.12    nlme_3.1-137     MASS_7.3-50     
[36] xml2_1.2.0       foreign_0.8-71   tools_3.5.1      hms_0.4.2        munsell_0.5.0   
[41] babynames_0.3.0  zip_1.0.0        bindrcpp_0.2.2   networkD3_0.4    compiler_3.5.1  
[46] rlang_0.3.0.1    grid_3.5.1       rstudioapi_0.8   htmlwidgets_1.3  igraph_1.2.2    
[51] labeling_0.3     mosaicCore_0.6.0 gtable_0.2.0     abind_1.4-5      DBI_1.0.0       
[56] curl_3.2         reshape2_1.4.3   R6_2.3.0         gridExtra_2.3    lubridate_1.7.4 
[61] rprojroot_1.3-2  bindr_0.1.1      stringi_1.2.4    parallel_3.5.1   Rcpp_1.0.0      
[66] dbplyr_1.2.2     tidyselect_0.2.5

以下是根据以下评论中的要求介绍(df_dummized(的输出：

A tibble: 1 x 9  
rows columns discrete_columns continuous_columns  
<int>   <int>            <int>              <int>  
9527     489                2                487  
all_missing_columns total_missing_values  
<int>                <int>  
0                 7826  
complete_rows total_observations memory_usage  
<int>              <int>        <dbl>  
6889            4658703     18919440