R How can I fix DataExplorer error during PCA: “Item 2 has no length”
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I have a data set df with 102 variables: 16 int, 80 factors, 8 logi. There are no NA values.
I've used DataExplorer before without issue, but when I ran it on this data set ...
library(DataExplorer)
create_report(df)
... it chugged along fine, outputting its progress ...
# label: correlation_analysis
# |................................................ | 74%
# ordinary text without R code
... until it got to the PCA section when it produced this error:
# |.................................................. | 76%
# label: principle_component_analysis
# Quitting from lines 208-221 (report.rmd)
#
# Error in data.table(pc = paste0("PC", seq_along(pca$sdev)), var = var_exp, :
# Item 2 has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the 1 row in the longest column. Or, all columns can be 0 length, for insert()ing rows into.
I googled on this error but found only pages explaining PCA and not this error. Any suggestions?
The traceback is
26. stop("Item ", i, " has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the ",
nr, " row", if (nr > 1L) "s", " in the longest column. Or, all columns can be 0 length, for insert()ing rows into.")
25. data.table(pc = paste0("PC", seq_along(pca$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)
Here's the session info:
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
Here's the output of introduce(df_dummified) as requested in comments below:
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
r
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
add a comment |
0
I have a data set df with 102 variables: 16 int, 80 factors, 8 logi. There are no NA values.
I've used DataExplorer before without issue, but when I ran it on this data set ...
library(DataExplorer)
create_report(df)
... it chugged along fine, outputting its progress ...
# label: correlation_analysis
# |................................................ | 74%
# ordinary text without R code
... until it got to the PCA section when it produced this error:
# |.................................................. | 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.
I googled on this error but found only pages explaining PCA and not this error. Any suggestions?
The traceback is
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)
Here's the session info:
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
Here's the output of introduce(df_dummified) as requested in comments below:
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
r
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
add a comment |
0
0
0
I have a data set df with 102 variables: 16 int, 80 factors, 8 logi. There are no NA values.
I've used DataExplorer before without issue, but when I ran it on this data set ...
library(DataExplorer)
create_report(df)
... it chugged along fine, outputting its progress ...
# label: correlation_analysis
# |................................................ | 74%
# ordinary text without R code
... until it got to the PCA section when it produced this error:
# |.................................................. | 76%
# label: principle_component_analysis
# Quitting from lines 208-221 (report.rmd)
#
# Error in data.table(pc = paste0("PC", seq_along(pca$sdev)), var = var_exp, :
# Item 2 has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the 1 row in the longest column. Or, all columns can be 0 length, for insert()ing rows into.
I googled on this error but found only pages explaining PCA and not this error. Any suggestions?
The traceback is
26. stop("Item ", i, " has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the ",
nr, " row", if (nr > 1L) "s", " in the longest column. Or, all columns can be 0 length, for insert()ing rows into.")
25. data.table(pc = paste0("PC", seq_along(pca$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)
Here's the session info:
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
Here's the output of introduce(df_dummified) as requested in comments below:
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
r
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
I have a data set df with 102 variables: 16 int, 80 factors, 8 logi. There are no NA values.
I've used DataExplorer before without issue, but when I ran it on this data set ...
library(DataExplorer)
create_report(df)
... it chugged along fine, outputting its progress ...
# label: correlation_analysis
# |................................................ | 74%
# ordinary text without R code
... until it got to the PCA section when it produced this error:
# |.................................................. | 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.
I googled on this error but found only pages explaining PCA and not this error. Any suggestions?
The traceback is
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)
Here's the session info:
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
Here's the output of introduce(df_dummified) as requested in comments below:
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
r
r
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
edited Nov 29 '18 at 16:33
Karl Baker
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
asked Nov 24 '18 at 23:28
Karl BakerKarl Baker
371215
371215
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
1
You might also consider skipping the PCA part of the report, by removing "plot_prcomp" from the create_report() config.
I had the same issue and this still created the rest of the report for me:
library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
add a comment |
1
PCA can be applied only on numerical data. Consider only numeric columns for PCA, remove columns other than numeric.
nums <- unlist(lapply(df, is.numeric))
df_new <- df[, nums]
Remove all the columns which have a constant variance.
df_new <- df_new[, apply(df_new, 2, var) != 0]
Reference: How to solve prcomp.default(): cannot rescale a constant/zero column to unit variance
Now, run this. This should create a nice html report for you.
create_report(df_new)
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you triedplot_prcomp(dummify(df))?
– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance 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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).
– Karl Baker
Nov 28 '18 at 23:43
|
show 3 more comments
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2 Answers
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active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
You might also consider skipping the PCA part of the report, by removing "plot_prcomp" from the create_report() config.
I had the same issue and this still created the rest of the report for me:
library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
add a comment |
1
You might also consider skipping the PCA part of the report, by removing "plot_prcomp" from the create_report() config.
I had the same issue and this still created the rest of the report for me:
library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
add a comment |
1
1
1
You might also consider skipping the PCA part of the report, by removing "plot_prcomp" from the create_report() config.
I had the same issue and this still created the rest of the report for me:
library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
You might also consider skipping the PCA part of the report, by removing "plot_prcomp" from the create_report() config.
I had the same issue and this still created the rest of the report for me:
library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)library(DataExplorer)
config <- list(
"introduce" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
# "plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
create_report(df, config = config)answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
answered Nov 28 '18 at 13:03
Dennis van den BergDennis van den Berg
262
262
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
add a comment |
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
Great suggestion, that's exactly what I did so that I could at least see the rest of the DataExplorer output. Then I began combining and converting factors to logical variables. Haven't finished but hopefully this will allow me to run the PCA.
– Karl Baker
Nov 28 '18 at 23:22
add a comment |
1
PCA can be applied only on numerical data. Consider only numeric columns for PCA, remove columns other than numeric.
nums <- unlist(lapply(df, is.numeric))
df_new <- df[, nums]
Remove all the columns which have a constant variance.
df_new <- df_new[, apply(df_new, 2, var) != 0]
Reference: How to solve prcomp.default(): cannot rescale a constant/zero column to unit variance
Now, run this. This should create a nice html report for you.
create_report(df_new)
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you triedplot_prcomp(dummify(df))?
– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance 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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).
– Karl Baker
Nov 28 '18 at 23:43
|
show 3 more comments
1
PCA can be applied only on numerical data. Consider only numeric columns for PCA, remove columns other than numeric.
nums <- unlist(lapply(df, is.numeric))
df_new <- df[, nums]
Remove all the columns which have a constant variance.
df_new <- df_new[, apply(df_new, 2, var) != 0]
Reference: How to solve prcomp.default(): cannot rescale a constant/zero column to unit variance
Now, run this. This should create a nice html report for you.
create_report(df_new)
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you triedplot_prcomp(dummify(df))?
– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance 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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).
– Karl Baker
Nov 28 '18 at 23:43
|
show 3 more comments
1
1
1
PCA can be applied only on numerical data. Consider only numeric columns for PCA, remove columns other than numeric.
nums <- unlist(lapply(df, is.numeric))
df_new <- df[, nums]
Remove all the columns which have a constant variance.
df_new <- df_new[, apply(df_new, 2, var) != 0]
Reference: How to solve prcomp.default(): cannot rescale a constant/zero column to unit variance
Now, run this. This should create a nice html report for you.
create_report(df_new)
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
PCA can be applied only on numerical data. Consider only numeric columns for PCA, remove columns other than numeric.
nums <- unlist(lapply(df, is.numeric))
df_new <- df[, nums]
Remove all the columns which have a constant variance.
df_new <- df_new[, apply(df_new, 2, var) != 0]
Reference: How to solve prcomp.default(): cannot rescale a constant/zero column to unit variance
Now, run this. This should create a nice html report for you.
create_report(df_new)
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
edited Dec 1 '18 at 0:18
Boxuan
1,95122358
1,95122358
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
answered Nov 25 '18 at 20:12
RAJKRAJK
1067
1067
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you triedplot_prcomp(dummify(df))?
– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance 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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).
– Karl Baker
Nov 28 '18 at 23:43
|
show 3 more comments
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you triedplot_prcomp(dummify(df))?
– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance Error in data.table(pc = paste0("PC", seq_along(pca$sdev)), var = var_exp, : Item 2 has no length. Provide at least one item (such as NA, NA_integer_ etc) to be repeated to match the 1 row in the longest column. Or, all columns can be 0 length, for insert()ing rows into."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).
– Karl Baker
Nov 28 '18 at 23:43
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
thanks for the explanation and the link. Since my df contains most of the interesting information in the factors, I'll need to do somehow convert them to numeric data for PCA. Is there a standard approach to convert categorical columns to numeric?
– Karl Baker
Nov 26 '18 at 19:22
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
You might also use dummy variables to convert categorical data to numeric. That might be an option.
– RAJK
Nov 26 '18 at 22:00
@KarlBaker Have you tried
plot_prcomp(dummify(df))?– Boxuan
Nov 28 '18 at 20:05
@KarlBaker Have you tried
plot_prcomp(dummify(df))?– Boxuan
Nov 28 '18 at 20:05
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance Error in data.table(pc = paste0("PC", seq_along(pca$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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, that's new to me, sounds promising, but how do run it? I tried running it after loading DataExplorer but got a similar error as before: "cannot rescale a constant/zero column to unit variance 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."
– Karl Baker
Nov 28 '18 at 23:32
@Boxuan, I should note I was able to dummify the data (
df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).– Karl Baker
Nov 28 '18 at 23:43
@Boxuan, I should note I was able to dummify the data (
df_dummified <- dummify(df)), I just wasn't able to run the PCA (plot_prcomp(df_dummified)).– Karl Baker
Nov 28 '18 at 23:43
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