CRAN Package Check Results for Maintainer ‘Matt Dancho <mdancho at business-science.io>’

Last updated on 2020-05-31 00:47:39 CEST.

Package ERROR WARN NOTE OK
alphavantager 12
anomalize 5 1 6
correlationfunnel 5 5 2
sweep 5 7
tidyquant 8 4
timetk 4 6 2

Package alphavantager

Current CRAN status: OK: 12

Package anomalize

Current CRAN status: ERROR: 5, WARN: 1, OK: 6

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in ‘anomalize-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: decompose_methods
    > ### Title: Methods that power time_decompose()
    > ### Aliases: decompose_methods decompose_twitter decompose_stl
    >
    > ### ** Examples
    >
    >
    > library(dplyr)
    
    Attaching package: ‘dplyr’
    
    The following objects are masked from ‘package:stats’:
    
     filter, lag
    
    The following objects are masked from ‘package:base’:
    
     intersect, setdiff, setequal, union
    
    >
    > tidyverse_cran_downloads %>%
    + ungroup() %>%
    + filter(package == "tidyquant") %>%
    + decompose_stl(count)
    Error in time_frequency(data, period = frequency, message = message) :
     Error time_frequency(): Cannot use on a grouped data frame.
    Frequency should be performed on a single time series.
    Calls: %>% ... withVisible -> <Anonymous> -> decompose_stl -> time_frequency
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64

Version: 0.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [43s/60s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(anomalize)
     ══ Use anomalize to improve your Forecasts by 50%! ═════════════════════════════
     Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
     </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(ggplot2)
     > library(tibble)
     > library(stringr)
     >
     > # set_time_scale_template(time_scale_template())
     >
     > test_check("anomalize")
     ── 1. Error: returns a ggplot (@test-plot_anomalies.R#8) ──────────────────────
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     8. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10) ─────────
     Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::plot_anomaly_decomposition(.)
    
     ── 3. Error: grouped_tbl_time works (@test-time_apply.R#11) ───────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "broom".
     Backtrace:
     9. anomalize::time_apply(...)
     10. anomalize:::grouped_mapper(...)
     3. tidyr::nest(.)
     10. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 4. Error: tbl_time works (@test-time_apply.R#17) ───────────────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "tidyquant".
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     9. anomalize::time_apply(...)
     12. anomalize:::grouped_mapper(...)
     5. tidyr::nest(.)
     12. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 5. Failure: single tbl_df (@test-time_decompose.R#20) ──────────────────────
     ncol(stl_tbl_time) not equal to 5.
     1/1 mismatches
     [1] 6 - 5 == 1
    
     ── 6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(.)
    
     ── 7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = "1 month")
    
     ── 8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44) ─────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = 5)
    
     ── 9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55) ────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 10. Error: time_trend works: period = '90 days' (@test-time_frequency.R#64)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = "30 days")
    
     ── 11. Error: time_trend works: period = 90 (@test-time_frequency.R#73) ───────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = 90)
    
     ── 12. Error: time_trend works with small data: period = 'auto' (@test-time_freq
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 13. Error: time_recompose works on grouped_tbl_time (@test-time_recompose.R#9
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 14. Error: time_recompose works on tbl_time (@test-time_recompose.R#17) ────
     invalid 'type' (character) of argument
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     6. dplyr::ungroup(.)
     6. dplyr::select(., observed:remainder, contains("_l1"))
     6. dplyr::select(., -c(observed, remainder))
     14. base::apply(., MARGIN = 1, FUN = sum)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 53 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 14 ]
     1. Error: returns a ggplot (@test-plot_anomalies.R#8)
     2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10)
     3. Error: grouped_tbl_time works (@test-time_apply.R#11)
     4. Error: tbl_time works (@test-time_apply.R#17)
     5. Failure: single tbl_df (@test-time_decompose.R#20)
     6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35)
     8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44)
     9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.2.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘anomalize_methods.Rmd’ using rmarkdown
    Quitting from lines 87-110 (anomalize_methods.Rmd)
    Error: processing vignette 'anomalize_methods.Rmd' failed with diagnostics:
    Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
    --- failed re-building ‘anomalize_methods.Rmd’
    
    --- re-building ‘anomalize_quick_start_guide.Rmd’ using rmarkdown
    Quitting from lines 68-74 (anomalize_quick_start_guide.Rmd)
    Error: processing vignette 'anomalize_quick_start_guide.Rmd' failed with diagnostics:
    invalid ‘type’ (character) of argument
    --- failed re-building ‘anomalize_quick_start_guide.Rmd’
    
    --- re-building ‘forecasting_with_cleaned_anomalies.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ purrr::is_null() masks testthat::is_null()
    ✖ dplyr::lag() masks stats::lag()
    ✖ dplyr::matches() masks tidyr::matches(), testthat::matches()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'tidyquant'
    
    The following objects are masked from 'package:anomalize':
    
     palette_light, theme_tq
    
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘forecasting_with_cleaned_anomalies.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘anomalize_methods.Rmd’ ‘anomalize_quick_start_guide.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64

Version: 0.2.0
Check: examples
Result: ERROR
    Running examples in ‘anomalize-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: decompose_methods
    > ### Title: Methods that power time_decompose()
    > ### Aliases: decompose_methods decompose_twitter decompose_stl
    >
    > ### ** Examples
    >
    >
    > library(dplyr)
    
    Attaching package: ‘dplyr’
    
    The following objects are masked from ‘package:stats’:
    
     filter, lag
    
    The following objects are masked from ‘package:base’:
    
     intersect, setdiff, setequal, union
    
    >
    > tidyverse_cran_downloads %>%
    + ungroup() %>%
    + filter(package == "tidyquant") %>%
    + decompose_stl(count)
    Error in time_frequency(data, period = frequency, message = message) :
     Error time_frequency(): Cannot use on a grouped data frame.
    Frequency should be performed on a single time series.
    Calls: %>% ... withVisible -> <Anonymous> -> decompose_stl -> time_frequency
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 0.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [70s/185s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(anomalize)
     ══ Use anomalize to improve your Forecasts by 50%! ═════════════════════════════
     Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
     </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(ggplot2)
     > library(tibble)
     > library(stringr)
     >
     > # set_time_scale_template(time_scale_template())
     >
     > test_check("anomalize")
     ── 1. Error: returns a ggplot (@test-plot_anomalies.R#8) ──────────────────────
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     8. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10) ─────────
     Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::plot_anomaly_decomposition(.)
    
     ── 3. Error: grouped_tbl_time works (@test-time_apply.R#11) ───────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "broom".
     Backtrace:
     9. anomalize::time_apply(...)
     10. anomalize:::grouped_mapper(...)
     3. tidyr::nest(.)
     10. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 4. Error: tbl_time works (@test-time_apply.R#17) ───────────────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "tidyquant".
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     9. anomalize::time_apply(...)
     12. anomalize:::grouped_mapper(...)
     5. tidyr::nest(.)
     12. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 5. Failure: single tbl_df (@test-time_decompose.R#20) ──────────────────────
     ncol(stl_tbl_time) not equal to 5.
     1/1 mismatches
     [1] 6 - 5 == 1
    
     ── 6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(.)
    
     ── 7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = "1 month")
    
     ── 8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44) ─────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = 5)
    
     ── 9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55) ────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 10. Error: time_trend works: period = '90 days' (@test-time_frequency.R#64)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = "30 days")
    
     ── 11. Error: time_trend works: period = 90 (@test-time_frequency.R#73) ───────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = 90)
    
     ── 12. Error: time_trend works with small data: period = 'auto' (@test-time_freq
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 13. Error: time_recompose works on grouped_tbl_time (@test-time_recompose.R#9
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 14. Error: time_recompose works on tbl_time (@test-time_recompose.R#17) ────
     invalid 'type' (character) of argument
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     6. dplyr::ungroup(.)
     6. dplyr::select(., observed:remainder, contains("_l1"))
     6. dplyr::select(., -c(observed, remainder))
     14. base::apply(., MARGIN = 1, FUN = sum)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 53 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 14 ]
     1. Error: returns a ggplot (@test-plot_anomalies.R#8)
     2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10)
     3. Error: grouped_tbl_time works (@test-time_apply.R#11)
     4. Error: tbl_time works (@test-time_apply.R#17)
     5. Failure: single tbl_df (@test-time_decompose.R#20)
     6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35)
     8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44)
     9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.2.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘anomalize_methods.Rmd’ using rmarkdown
    Quitting from lines 87-110 (anomalize_methods.Rmd)
    Error: processing vignette 'anomalize_methods.Rmd' failed with diagnostics:
    Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
    --- failed re-building ‘anomalize_methods.Rmd’
    
    --- re-building ‘anomalize_quick_start_guide.Rmd’ using rmarkdown
    Quitting from lines 68-74 (anomalize_quick_start_guide.Rmd)
    Error: processing vignette 'anomalize_quick_start_guide.Rmd' failed with diagnostics:
    invalid 'type' (character) of argument
    --- failed re-building ‘anomalize_quick_start_guide.Rmd’
    
    --- re-building ‘forecasting_with_cleaned_anomalies.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ purrr::is_null() masks testthat::is_null()
    ✖ dplyr::lag() masks stats::lag()
    ✖ dplyr::matches() masks tidyr::matches(), testthat::matches()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'tidyquant'
    
    The following objects are masked from 'package:anomalize':
    
     palette_light, theme_tq
    
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘forecasting_with_cleaned_anomalies.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘anomalize_methods.Rmd’ ‘anomalize_quick_start_guide.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 0.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [71s/182s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(anomalize)
     ══ Use anomalize to improve your Forecasts by 50%! ═════════════════════════════
     Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
     </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(ggplot2)
     > library(tibble)
     > library(stringr)
     >
     > # set_time_scale_template(time_scale_template())
     >
     > test_check("anomalize")
     ── 1. Error: returns a ggplot (@test-plot_anomalies.R#8) ──────────────────────
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     8. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10) ─────────
     Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::plot_anomaly_decomposition(.)
    
     ── 3. Error: grouped_tbl_time works (@test-time_apply.R#11) ───────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "broom".
     Backtrace:
     9. anomalize::time_apply(...)
     10. anomalize:::grouped_mapper(...)
     3. tidyr::nest(.)
     10. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 4. Error: tbl_time works (@test-time_apply.R#17) ───────────────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "tidyquant".
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     9. anomalize::time_apply(...)
     12. anomalize:::grouped_mapper(...)
     5. tidyr::nest(.)
     12. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 5. Failure: single tbl_df (@test-time_decompose.R#20) ──────────────────────
     ncol(stl_tbl_time) not equal to 5.
     1/1 mismatches
     [1] 6 - 5 == 1
    
     ── 6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(.)
    
     ── 7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = "1 month")
    
     ── 8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44) ─────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = 5)
    
     ── 9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55) ────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 10. Error: time_trend works: period = '90 days' (@test-time_frequency.R#64)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = "30 days")
    
     ── 11. Error: time_trend works: period = 90 (@test-time_frequency.R#73) ───────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = 90)
    
     ── 12. Error: time_trend works with small data: period = 'auto' (@test-time_freq
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 13. Error: time_recompose works on grouped_tbl_time (@test-time_recompose.R#9
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 14. Error: time_recompose works on tbl_time (@test-time_recompose.R#17) ────
     invalid 'type' (character) of argument
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     6. dplyr::ungroup(.)
     6. dplyr::select(., observed:remainder, contains("_l1"))
     6. dplyr::select(., -c(observed, remainder))
     14. base::apply(., MARGIN = 1, FUN = sum)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 53 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 14 ]
     1. Error: returns a ggplot (@test-plot_anomalies.R#8)
     2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10)
     3. Error: grouped_tbl_time works (@test-time_apply.R#11)
     4. Error: tbl_time works (@test-time_apply.R#17)
     5. Failure: single tbl_df (@test-time_decompose.R#20)
     6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35)
     8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44)
     9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [81s/96s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(anomalize)
     ══ Use anomalize to improve your Forecasts by 50%! ═════════════════════════════
     Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
     </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(ggplot2)
     > library(tibble)
     > library(stringr)
     >
     > # set_time_scale_template(time_scale_template())
     >
     > test_check("anomalize")
     ── 1. Error: returns a ggplot (@test-plot_anomalies.R#8) ──────────────────────
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     8. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10) ─────────
     Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::plot_anomaly_decomposition(.)
    
     ── 3. Error: grouped_tbl_time works (@test-time_apply.R#11) ───────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "broom".
     Backtrace:
     9. anomalize::time_apply(...)
     10. anomalize:::grouped_mapper(...)
     3. tidyr::nest(.)
     10. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 4. Error: tbl_time works (@test-time_apply.R#17) ───────────────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "tidyquant".
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     9. anomalize::time_apply(...)
     12. anomalize:::grouped_mapper(...)
     5. tidyr::nest(.)
     12. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 5. Failure: single tbl_df (@test-time_decompose.R#20) ──────────────────────
     ncol(stl_tbl_time) not equal to 5.
     1/1 mismatches
     [1] 6 - 5 == 1
    
     ── 6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(.)
    
     ── 7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = "1 month")
    
     ── 8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44) ─────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = 5)
    
     ── 9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55) ────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 10. Error: time_trend works: period = '90 days' (@test-time_frequency.R#64)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = "30 days")
    
     ── 11. Error: time_trend works: period = 90 (@test-time_frequency.R#73) ───────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = 90)
    
     ── 12. Error: time_trend works with small data: period = 'auto' (@test-time_freq
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 13. Error: time_recompose works on grouped_tbl_time (@test-time_recompose.R#9
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 14. Error: time_recompose works on tbl_time (@test-time_recompose.R#17) ────
     invalid 'type' (character) of argument
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     6. dplyr::ungroup(.)
     6. dplyr::select(., observed:remainder, contains("_l1"))
     6. dplyr::select(., -c(observed, remainder))
     14. base::apply(., MARGIN = 1, FUN = sum)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 53 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 14 ]
     1. Error: returns a ggplot (@test-plot_anomalies.R#8)
     2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10)
     3. Error: grouped_tbl_time works (@test-time_apply.R#11)
     4. Error: tbl_time works (@test-time_apply.R#17)
     5. Failure: single tbl_df (@test-time_decompose.R#20)
     6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35)
     8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44)
     9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86

Version: 0.2.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘anomalize_methods.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    Quitting from lines 87-110 (anomalize_methods.Rmd)
    Error: processing vignette 'anomalize_methods.Rmd' failed with diagnostics:
    Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
    --- failed re-building ‘anomalize_methods.Rmd’
    
    --- re-building ‘anomalize_quick_start_guide.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    Quitting from lines 68-74 (anomalize_quick_start_guide.Rmd)
    Error: processing vignette 'anomalize_quick_start_guide.Rmd' failed with diagnostics:
    invalid 'type' (character) of argument
    --- failed re-building ‘anomalize_quick_start_guide.Rmd’
    
    --- re-building ‘forecasting_with_cleaned_anomalies.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ purrr::is_null() masks testthat::is_null()
    ✖ dplyr::lag() masks stats::lag()
    ✖ dplyr::matches() masks tidyr::matches(), testthat::matches()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'tidyquant'
    
    The following objects are masked from 'package:anomalize':
    
     palette_light, theme_tq
    
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘forecasting_with_cleaned_anomalies.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘anomalize_methods.Rmd’ ‘anomalize_quick_start_guide.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-patched-solaris-x86

Version: 0.2.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [56s/62s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(anomalize)
     ══ Use anomalize to improve your Forecasts by 50%! ═════════════════════════════
     Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
     </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(ggplot2)
     > library(tibble)
     > library(stringr)
     >
     > # set_time_scale_template(time_scale_template())
     >
     > test_check("anomalize")
     ── 1. Error: returns a ggplot (@test-plot_anomalies.R#8) ──────────────────────
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     8. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10) ─────────
     Object cannot be grouped. Select a single time series for evaluation, and use `dplyr::ungroup()`.
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::plot_anomaly_decomposition(.)
    
     ── 3. Error: grouped_tbl_time works (@test-time_apply.R#11) ───────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "broom".
     Backtrace:
     9. anomalize::time_apply(...)
     10. anomalize:::grouped_mapper(...)
     3. tidyr::nest(.)
     10. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 4. Error: tbl_time works (@test-time_apply.R#17) ───────────────────────────
     Problem with `mutate()` input `nested.col`.
     ✖ Problem with `mutate()` input `date`.
     ✖ Input `date` can't be recycled to size 7.
     ℹ Input `date` is `data %>% dplyr::pull(date)`.
     ℹ Input `date` must be size 7 or 1, not 425.
     ℹ The error occured in group 1: date = 2017-01-07.
     ℹ Input `nested.col` is `purrr::map(.x = data, .f = .f, target = count, ...)`.
     ℹ The error occured in group 1: package = "tidyquant".
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     9. anomalize::time_apply(...)
     12. anomalize:::grouped_mapper(...)
     5. tidyr::nest(.)
     12. dplyr::mutate(...)
     29. dplyr:::mutate_cols(.data, ...)
    
     ── 5. Failure: single tbl_df (@test-time_decompose.R#20) ──────────────────────
     ncol(stl_tbl_time) not equal to 5.
     1/1 mismatches
     [1] 6 - 5 == 1
    
     ── 6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(.)
    
     ── 7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = "1 month")
    
     ── 8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44) ─────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_frequency(., period = 5)
    
     ── 9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55) ────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 10. Error: time_trend works: period = '90 days' (@test-time_frequency.R#64)
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = "30 days")
    
     ── 11. Error: time_trend works: period = 90 (@test-time_frequency.R#73) ───────
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(., period = 90)
    
     ── 12. Error: time_trend works with small data: period = 'auto' (@test-time_freq
     Cannot use on a grouped data frame.
     Frequency should be performed on a single time series.
     Backtrace:
     9. anomalize::time_trend(.)
    
     ── 13. Error: time_recompose works on grouped_tbl_time (@test-time_recompose.R#9
     invalid 'type' (character) of argument
     Backtrace:
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     4. dplyr::ungroup(.)
     4. dplyr::select(., observed:remainder, contains("_l1"))
     4. dplyr::select(., -c(observed, remainder))
     12. base::apply(., MARGIN = 1, FUN = sum)
    
     ── 14. Error: time_recompose works on tbl_time (@test-time_recompose.R#17) ────
     invalid 'type' (character) of argument
     Backtrace:
     1. dplyr::filter(., package == "tidyquant")
     1. dplyr::ungroup(.)
     1. anomalize::time_decompose(., count, method = "stl")
     1. anomalize::anomalize(., remainder, method = "iqr")
     9. anomalize::time_recompose(.)
     6. dplyr::ungroup(.)
     6. dplyr::select(., observed:remainder, contains("_l1"))
     6. dplyr::select(., -c(observed, remainder))
     14. base::apply(., MARGIN = 1, FUN = sum)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 53 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 14 ]
     1. Error: returns a ggplot (@test-plot_anomalies.R#8)
     2. Error: returns a ggplot (@test-plot_anomaly_decomposition.R#10)
     3. Error: grouped_tbl_time works (@test-time_apply.R#11)
     4. Error: tbl_time works (@test-time_apply.R#17)
     5. Failure: single tbl_df (@test-time_decompose.R#20)
     6. Error: time_frequency works: period = 'auto' (@test-time_frequency.R#26)
     7. Error: time_frequency works: period = '1 month' (@test-time_frequency.R#35)
     8. Error: time_frequency works: period = 5 (@test-time_frequency.R#44)
     9. Error: time_trend works: period = 'auto' (@test-time_frequency.R#55)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-linux-x86_64

Version: 0.2.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘anomalize_methods.Rmd’ using rmarkdown
    dyld: lazy symbol binding failed: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    dyld: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    Error: processing vignette 'anomalize_methods.Rmd' failed with diagnostics:
    pandoc document conversion failed with error 6
    --- failed re-building ‘anomalize_methods.Rmd’
    
    --- re-building ‘anomalize_quick_start_guide.Rmd’ using rmarkdown
    Warning in lubridate::floor_date(x, unit) :
     Multi-unit not supported for weeks. Ignoring.
    Warning in lubridate::ceiling_date(x, unit) :
     Multi-unit not supported for weeks. Ignoring.
    Warning in lubridate::floor_date(x, unit) :
     Multi-unit not supported for weeks. Ignoring.
    Warning in lubridate::ceiling_date(x, unit) :
     Multi-unit not supported for weeks. Ignoring.
    dyld: lazy symbol binding failed: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    dyld: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    Error: processing vignette 'anomalize_quick_start_guide.Rmd' failed with diagnostics:
    pandoc document conversion failed with error 6
    --- failed re-building ‘anomalize_quick_start_guide.Rmd’
    
    --- re-building ‘forecasting_with_cleaned_anomalies.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.0 ✔ purrr 0.3.3
    ✔ tibble 2.1.3 ✔ dplyr 0.8.5
    ✔ tidyr 1.0.2 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ purrr::is_null() masks testthat::is_null()
    ✖ dplyr::lag() masks stats::lag()
    ✖ dplyr::matches() masks tidyr::matches(), testthat::matches()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following object is masked from 'package:base':
    
     date
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'tidyquant'
    
    The following objects are masked from 'package:anomalize':
    
     palette_light, theme_tq
    
    Loading required package: recipes
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stringr':
    
     fixed
    
    The following object is masked from 'package:stats':
    
     step
    
    dyld: lazy symbol binding failed: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    dyld: Symbol not found: ____chkstk_darwin
     Referenced from: /usr/local/bin/pandoc (which was built for Mac OS X 10.15)
     Expected in: /usr/lib/libSystem.B.dylib
    
    Error: processing vignette 'forecasting_with_cleaned_anomalies.Rmd' failed with diagnostics:
    pandoc document conversion failed with error 6
    --- failed re-building ‘forecasting_with_cleaned_anomalies.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘anomalize_methods.Rmd’ ‘anomalize_quick_start_guide.Rmd’
     ‘forecasting_with_cleaned_anomalies.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-release-osx-x86_64

Package correlationfunnel

Current CRAN status: ERROR: 5, NOTE: 5, OK: 2

Version: 0.1.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [10s/16s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(lubridate)
    
     Attaching package: 'lubridate'
    
     The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
     > library(stringr)
     > library(correlationfunnel)
     ══ correlationfunnel Tip #2 ════════════════════════════════════════════════════
     Clean your NA's prior to using `binarize()`.
     Missing values and cleaning data are critical to getting great correlations. :)
     >
     > test_check("correlationfunnel")
     ── 3. Error: (unknown) (@test-binarize.R#45) ──────────────────────────────────
     Internal error: Trace data is not square.
     Backtrace:
     1. testthat::test_check("correlationfunnel")
     2. testthat:::test_package_dir(...)
     3. testthat::test_dir(...)
     4. testthat:::test_files(...)
     10. base::lapply(...)
     11. testthat:::FUN(X[[i]], ...)
     17. testthat::source_file(...)
     18. testthat:::test_code(NULL, exprs, env)
     27. [ base::eval(...) ] with 1 more call
     29. testthat::test_that(...)
     ...
     40. testthat:::o_apply(...)
     41. base::lapply(objects, function(x) x[[method]](...))
     42. testthat:::FUN(X[[i]], ...)
     43. x[[method]](...)
     47. testthat:::failure_summary(result, self$n_fail)
     50. testthat:::format.expectation(x)
     51. testthat:::format_with_trace(x)
     53. rlang:::format.rlang_trace(...)
     54. rlang:::trace_format_branch(x, max_frames, dir, srcrefs)
     55. rlang:::branch_uncollapse_pipe(trace)
    
     ── 4. Failure: Check correlation (@test-correlate.R#61) ───────────────────────
     nrow(marketing_correlated_tbl) not equal to 74.
     1/1 mismatches
     [1] 65 - 74 == -9
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 12 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 4 ]
     1. Error: Check binarize - numeric (@test-binarize.R#47)
     2. Error: Check binarize - numeric (@test-binarize.R#45)
     3. Error: (unknown) (@test-binarize.R#45)
     4. Failure: Check correlation (@test-correlate.R#61)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘introducing_correlation_funnel.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
    Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
    Try subsetting your data column-wise or row-wise to avoid creating too many columns.
    You can always make a big problem smaller by sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    --- finished re-building ‘introducing_correlation_funnel.Rmd’
    
    --- re-building ‘key_considerations.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #1 ════════════════════════════════════════════════════
    Make sure your data is not overly imbalanced prior to using `correlate()`.
    If less than 5% imbalance, consider sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    `geom_smooth()` using formula 'y ~ x'
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
    Quitting from lines 143-148 (key_considerations.Rmd)
    Error: processing vignette 'key_considerations.Rmd' failed with diagnostics:
    Can't subset columns that don't exist.
    ✖ Columns `0%`, `25%`, `50%`, `75%`, and `100%` don't exist.
    --- failed re-building ‘key_considerations.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘key_considerations.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1.0
Check: installed package size
Result: NOTE
     installed size is 5.4Mb
     sub-directories of 1Mb or more:
     doc 3.0Mb
     help 1.6Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-osx-x86_64, r-release-windows-ix86+x86_64, r-oldrel-osx-x86_64, r-oldrel-windows-ix86+x86_64

Version: 0.1.0
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘utils’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.1.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [15s/42s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(lubridate)
    
     Attaching package: 'lubridate'
    
     The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
     > library(stringr)
     > library(correlationfunnel)
     ══ correlationfunnel Tip #1 ════════════════════════════════════════════════════
     Make sure your data is not overly imbalanced prior to using `correlate()`.
     If less than 5% imbalance, consider sampling. :)
     >
     > test_check("correlationfunnel")
     ── 3. Error: (unknown) (@test-binarize.R#45) ──────────────────────────────────
     Internal error: Trace data is not square.
     Backtrace:
     1. testthat::test_check("correlationfunnel")
     2. testthat:::test_package_dir(...)
     3. testthat::test_dir(...)
     4. testthat:::test_files(...)
     10. base::lapply(...)
     11. testthat:::FUN(X[[i]], ...)
     17. testthat::source_file(...)
     18. testthat:::test_code(NULL, exprs, env)
     27. [ base::eval(...) ] with 1 more call
     29. testthat::test_that(...)
     ...
     40. testthat:::o_apply(...)
     41. base::lapply(objects, function(x) x[[method]](...))
     42. testthat:::FUN(X[[i]], ...)
     43. x[[method]](...)
     47. testthat:::failure_summary(result, self$n_fail)
     50. testthat:::format.expectation(x)
     51. testthat:::format_with_trace(x)
     53. rlang:::format.rlang_trace(...)
     54. rlang:::trace_format_branch(x, max_frames, dir, srcrefs)
     55. rlang:::branch_uncollapse_pipe(trace)
    
     ── 4. Failure: Check correlation (@test-correlate.R#61) ───────────────────────
     nrow(marketing_correlated_tbl) not equal to 74.
     1/1 mismatches
     [1] 65 - 74 == -9
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 12 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 4 ]
     1. Error: Check binarize - numeric (@test-binarize.R#47)
     2. Error: Check binarize - numeric (@test-binarize.R#45)
     3. Error: (unknown) (@test-binarize.R#45)
     4. Failure: Check correlation (@test-correlate.R#61)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘introducing_correlation_funnel.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
    Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
    Try subsetting your data column-wise or row-wise to avoid creating too many columns.
    You can always make a big problem smaller by sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    --- finished re-building ‘introducing_correlation_funnel.Rmd’
    
    --- re-building ‘key_considerations.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #2 ════════════════════════════════════════════════════
    Clean your NA's prior to using `binarize()`.
    Missing values and cleaning data are critical to getting great correlations. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    `geom_smooth()` using formula 'y ~ x'
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
    Quitting from lines 143-148 (key_considerations.Rmd)
    Error: processing vignette 'key_considerations.Rmd' failed with diagnostics:
    Can't subset columns that don't exist.
    ✖ Columns `0%`, `25%`, `50%`, `75%`, and `100%` don't exist.
    --- failed re-building ‘key_considerations.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘key_considerations.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [15s/40s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(lubridate)
    
     Attaching package: 'lubridate'
    
     The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
     > library(stringr)
     > library(correlationfunnel)
     ══ correlationfunnel Tip #2 ════════════════════════════════════════════════════
     Clean your NA's prior to using `binarize()`.
     Missing values and cleaning data are critical to getting great correlations. :)
     >
     > test_check("correlationfunnel")
     ── 3. Error: (unknown) (@test-binarize.R#45) ──────────────────────────────────
     Internal error: Trace data is not square.
     Backtrace:
     1. testthat::test_check("correlationfunnel")
     2. testthat:::test_package_dir(...)
     3. testthat::test_dir(...)
     4. testthat:::test_files(...)
     10. base::lapply(...)
     11. testthat:::FUN(X[[i]], ...)
     17. testthat::source_file(...)
     18. testthat:::test_code(NULL, exprs, env)
     27. [ base::eval(...) ] with 1 more call
     29. testthat::test_that(...)
     ...
     40. testthat:::o_apply(...)
     41. base::lapply(objects, function(x) x[[method]](...))
     42. testthat:::FUN(X[[i]], ...)
     43. x[[method]](...)
     47. testthat:::failure_summary(result, self$n_fail)
     50. testthat:::format.expectation(x)
     51. testthat:::format_with_trace(x)
     53. rlang:::format.rlang_trace(...)
     54. rlang:::trace_format_branch(x, max_frames, dir, srcrefs)
     55. rlang:::branch_uncollapse_pipe(trace)
    
     ── 4. Failure: Check correlation (@test-correlate.R#61) ───────────────────────
     nrow(marketing_correlated_tbl) not equal to 74.
     1/1 mismatches
     [1] 65 - 74 == -9
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 12 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 4 ]
     1. Error: Check binarize - numeric (@test-binarize.R#47)
     2. Error: Check binarize - numeric (@test-binarize.R#45)
     3. Error: (unknown) (@test-binarize.R#45)
     4. Failure: Check correlation (@test-correlate.R#61)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘introducing_correlation_funnel.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #2 ════════════════════════════════════════════════════
    Clean your NA's prior to using `binarize()`.
    Missing values and cleaning data are critical to getting great correlations. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    --- finished re-building ‘introducing_correlation_funnel.Rmd’
    
    --- re-building ‘key_considerations.Rmd’ using rmarkdown
    ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
    Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
    Try subsetting your data column-wise or row-wise to avoid creating too many columns.
    You can always make a big problem smaller by sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    `geom_smooth()` using formula 'y ~ x'
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
    Quitting from lines 143-148 (key_considerations.Rmd)
    Error: processing vignette 'key_considerations.Rmd' failed with diagnostics:
    Can't subset columns that don't exist.
    ✖ Columns `0%`, `25%`, `50%`, `75%`, and `100%` don't exist.
    --- failed re-building ‘key_considerations.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘key_considerations.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [22s/24s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(lubridate)
    
     Attaching package: 'lubridate'
    
     The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
     > library(stringr)
     > library(correlationfunnel)
     ══ Using correlationfunnel? ════════════════════════════════════════════════════
     You might also be interested in applied data science training for business.
     </> Learn more at - www.business-science.io </>
     >
     > test_check("correlationfunnel")
     ── 3. Error: (unknown) (@test-binarize.R#45) ──────────────────────────────────
     Internal error: Trace data is not square.
     Backtrace:
     1. testthat::test_check("correlationfunnel")
     2. testthat:::test_package_dir(...)
     3. testthat::test_dir(...)
     4. testthat:::test_files(...)
     10. base::lapply(...)
     11. testthat:::FUN(X[[i]], ...)
     17. testthat::source_file(...)
     18. testthat:::test_code(NULL, exprs, env)
     27. [ base::eval(...) ] with 1 more call
     29. testthat::test_that(...)
     ...
     40. testthat:::o_apply(...)
     41. base::lapply(objects, function(x) x[[method]](...))
     42. testthat:::FUN(X[[i]], ...)
     43. x[[method]](...)
     47. testthat:::failure_summary(result, self$n_fail)
     50. testthat:::format.expectation(x)
     51. testthat:::format_with_trace(x)
     53. rlang:::format.rlang_trace(...)
     54. rlang:::trace_format_branch(x, max_frames, dir, srcrefs)
     55. rlang:::branch_uncollapse_pipe(trace)
    
     ── 4. Failure: Check correlation (@test-correlate.R#61) ───────────────────────
     nrow(marketing_correlated_tbl) not equal to 74.
     1/1 mismatches
     [1] 65 - 74 == -9
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 12 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 4 ]
     1. Error: Check binarize - numeric (@test-binarize.R#47)
     2. Error: Check binarize - numeric (@test-binarize.R#45)
     3. Error: (unknown) (@test-binarize.R#45)
     4. Failure: Check correlation (@test-correlate.R#61)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86

Version: 0.1.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘introducing_correlation_funnel.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ══ correlationfunnel Tip #3 ════════════════════════════════════════════════════
    Using `binarize()` with data containing many columns or many rows can increase dimensionality substantially.
    Try subsetting your data column-wise or row-wise to avoid creating too many columns.
    You can always make a big problem smaller by sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    --- finished re-building ‘introducing_correlation_funnel.Rmd’
    
    --- re-building ‘key_considerations.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ══ correlationfunnel Tip #1 ════════════════════════════════════════════════════
    Make sure your data is not overly imbalanced prior to using `correlate()`.
    If less than 5% imbalance, consider sampling. :)
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    `geom_smooth()` using formula 'y ~ x'
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
    Quitting from lines 143-148 (key_considerations.Rmd)
    Error: processing vignette 'key_considerations.Rmd' failed with diagnostics:
    Can't subset columns that don't exist.
    ✖ Columns `0%`, `25%`, `50%`, `75%`, and `100%` don't exist.
    --- failed re-building ‘key_considerations.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘key_considerations.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-patched-solaris-x86

Version: 0.1.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [13s/14s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(dplyr)
    
     Attaching package: 'dplyr'
    
     The following object is masked from 'package:testthat':
    
     matches
    
     The following objects are masked from 'package:stats':
    
     filter, lag
    
     The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
     > library(lubridate)
    
     Attaching package: 'lubridate'
    
     The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
     > library(stringr)
     > library(correlationfunnel)
     ══ correlationfunnel Tip #1 ════════════════════════════════════════════════════
     Make sure your data is not overly imbalanced prior to using `correlate()`.
     If less than 5% imbalance, consider sampling. :)
     >
     > test_check("correlationfunnel")
     ── 3. Error: (unknown) (@test-binarize.R#45) ──────────────────────────────────
     Internal error: Trace data is not square.
     Backtrace:
     1. testthat::test_check("correlationfunnel")
     2. testthat:::test_package_dir(...)
     3. testthat::test_dir(...)
     4. testthat:::test_files(...)
     10. base::lapply(...)
     11. testthat:::FUN(X[[i]], ...)
     17. testthat::source_file(...)
     18. testthat:::test_code(NULL, exprs, env)
     27. [ base::eval(...) ] with 1 more call
     29. testthat::test_that(...)
     ...
     40. testthat:::o_apply(...)
     41. base::lapply(objects, function(x) x[[method]](...))
     42. testthat:::FUN(X[[i]], ...)
     43. x[[method]](...)
     47. testthat:::failure_summary(result, self$n_fail)
     50. testthat:::format.expectation(x)
     51. testthat:::format_with_trace(x)
     53. rlang:::format.rlang_trace(...)
     54. rlang:::trace_format_branch(x, max_frames, dir, srcrefs)
     55. rlang:::branch_uncollapse_pipe(trace)
    
     ── 4. Failure: Check correlation (@test-correlate.R#61) ───────────────────────
     nrow(marketing_correlated_tbl) not equal to 74.
     1/1 mismatches
     [1] 65 - 74 == -9
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 12 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 4 ]
     1. Error: Check binarize - numeric (@test-binarize.R#47)
     2. Error: Check binarize - numeric (@test-binarize.R#45)
     3. Error: (unknown) (@test-binarize.R#45)
     4. Failure: Check correlation (@test-correlate.R#61)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-linux-x86_64

Version: 0.1.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘introducing_correlation_funnel.Rmd’ using rmarkdown
    ══ Using correlationfunnel? ════════════════════════════════════════════════════
    You might also be interested in applied data science training for business.
    </> Learn more at - www.business-science.io </>
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    --- finished re-building ‘introducing_correlation_funnel.Rmd’
    
    --- re-building ‘key_considerations.Rmd’ using rmarkdown
    ══ Using correlationfunnel? ════════════════════════════════════════════════════
    You might also be interested in applied data science training for business.
    </> Learn more at - www.business-science.io </>
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    `geom_smooth()` using formula 'y ~ x'
    Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
    Using compatibility `.name_repair`.
    This warning is displayed once every 8 hours.
    Call `lifecycle::last_warnings()` to see where this warning was generated.
    Warning: Expected 2 pieces. Missing pieces filled with `NA` in 2 rows [1, 2].
    Quitting from lines 143-148 (key_considerations.Rmd)
    Error: processing vignette 'key_considerations.Rmd' failed with diagnostics:
    Can't subset columns that don't exist.
    ✖ Columns `0%`, `25%`, `50%`, `75%`, and `100%` don't exist.
    --- failed re-building ‘key_considerations.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘key_considerations.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-release-linux-x86_64

Package sweep

Current CRAN status: NOTE: 5, OK: 7

Version: 0.2.2
Check: dependencies in R code
Result: NOTE
    Namespaces in Imports field not imported from:
     ‘lazyeval’ ‘lubridate’ ‘tidyr’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Package tidyquant

Current CRAN status: NOTE: 8, OK: 4

Version: 1.0.0
Check: package dependencies
Result: NOTE
    Imports includes 22 non-default packages.
    Importing from so many packages makes the package vulnerable to any of
    them becoming unavailable. Move as many as possible to Suggests and
    use conditionally.
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0.0
Check: installed package size
Result: NOTE
     installed size is 5.3Mb
     sub-directories of 1Mb or more:
     doc 4.4Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-osx-x86_64, r-release-windows-ix86+x86_64, r-oldrel-osx-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0.0
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘xml2’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.0.0
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘Rblpapi’
Flavor: r-patched-solaris-x86

Version: 1.0.0
Check: Rd cross-references
Result: NOTE
    Package unavailable to check Rd xrefs: ‘Rblpapi’
Flavor: r-patched-solaris-x86

Package timetk

Current CRAN status: ERROR: 4, NOTE: 6, OK: 2

Version: 1.0.0
Check: examples
Result: ERROR
    Running examples in ‘timetk-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: plot_acf_diagnostics
    > ### Title: Visualize the ACF, PACF, and CCFs for One or More Time Series
    > ### Aliases: plot_acf_diagnostics
    >
    > ### ** Examples
    >
    > library(tidyverse)
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    > library(timetk)
    >
    >
    > # Apply Transformations
    > # - Differencing transformation to identify ARIMA & SARIMA Orders
    > m4_hourly %>%
    + group_by(id) %>%
    + plot_acf_diagnostics(
    + date, value, # ACF & PACF
    + .lags = 0:(24*7), # 7-Days of hourly lags
    + .interactive = FALSE
    + )
    Error: Problem with `mutate()` input `nested.col`.
    ✖ Can't recycle `..1` (size 169) to match `..2` (size 0).
    ℹ Input `nested.col` is `purrr::map(...)`.
    ℹ The error occured in group 1: id = "H10".
    Backtrace:
     █
     1. └─`%>%`(...)
     2. ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     3. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     4. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. └─`_fseq`(`_lhs`)
     6. └─magrittr::freduce(value, `_function_list`)
     7. ├─base::withVisible(function_list[[k]](value))
     8. └─function_list[[k]](value)
     9. ├─timetk::plot_acf_diagnostics(...)
     10. └─timetk:::plot_acf_diagnostics.grouped_df(...)
     11. ├─timetk::tk_acf_diagnostics(...)
     12. └─timetk:::tk_acf_diagnostics.grouped_df(...)
     13. └─`%>%`(...)
     14. ├─base::withVisible(eval(quote(`_fseq`(`_
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64

Version: 1.0.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’ using rmarkdown
    Loading required package: dplyr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
     step
    
    For binary classification, the first factor level is assumed to be the event.
    Set the global option `yardstick.event_first` to `FALSE` to change this.
    
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ stringr 1.4.0
    ✔ tidyr 1.1.0 ✔ forcats 0.5.0
    ✔ readr 1.3.1
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ stringr::fixed() masks recipes::fixed()
    ✖ dplyr::lag() masks stats::lag()
    ✖ readr::spec() masks yardstick::spec()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using method = 'loess' and formula 'y ~ x'
    Warning: Removed 2 rows containing non-finite values (stat_smooth).
    Warning: Removed 2 rows containing missing values (geom_point).
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using formula 'y ~ x'
    `geom_smooth()` using formula 'y ~ x'
    --- finished re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’
    
    --- re-building ‘TK04_Plotting_Time_Series.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    --- finished re-building ‘TK04_Plotting_Time_Series.Rmd’
    
    --- re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Quitting from lines 49-56 (TK05_Plotting_Seasonality_and_Correlation.Rmd)
    Error: processing vignette 'TK05_Plotting_Seasonality_and_Correlation.Rmd' failed with diagnostics:
    Problem with `mutate()` input `nested.col`.
    ✖ Can't recycle `..1` (size 169) to match `..2` (size 0).
    ℹ Input `nested.col` is `purrr::map(...)`.
    ℹ The error occured in group 1: id = "H10".
    --- failed re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    --- re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64

Version: 1.0.0
Check: package dependencies
Result: NOTE
    Imports includes 24 non-default packages.
    Importing from so many packages makes the package vulnerable to any of
    them becoming unavailable. Move as many as possible to Suggests and
    use conditionally.
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0.0
Check: installed package size
Result: NOTE
     installed size is 6.5Mb
     sub-directories of 1Mb or more:
     doc 5.1Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-osx-x86_64, r-release-windows-ix86+x86_64, r-oldrel-osx-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0.0
Check: data for non-ASCII characters
Result: NOTE
     Note: found 2750 marked UTF-8 strings
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.0.0
Check: examples
Result: ERROR
    Running examples in ‘timetk-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: plot_acf_diagnostics
    > ### Title: Visualize the ACF, PACF, and CCFs for One or More Time Series
    > ### Aliases: plot_acf_diagnostics
    >
    > ### ** Examples
    >
    > library(tidyverse)
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    > library(timetk)
    >
    >
    > # Apply Transformations
    > # - Differencing transformation to identify ARIMA & SARIMA Orders
    > m4_hourly %>%
    + group_by(id) %>%
    + plot_acf_diagnostics(
    + date, value, # ACF & PACF
    + .lags = 0:(24*7), # 7-Days of hourly lags
    + .interactive = FALSE
    + )
    Error: Problem with `mutate()` input `nested.col`.
    ✖ Can't recycle `..1` (size 169) to match `..2` (size 0).
    ℹ Input `nested.col` is `purrr::map(...)`.
    ℹ The error occured in group 1: id = "H10".
    Backtrace:
     █
     1. └─`%>%`(...)
     2. ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
     3. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     4. └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
     5. └─`_fseq`(`_lhs`)
     6. └─magrittr::freduce(value, `_function_list`)
     7. ├─base::withVisible(function_list[[k]](value))
     8. └─function_list[[k]](value)
     9. ├─timetk::plot_acf_diagnostics(...)
     10. └─timetk:::plot_acf_diagnostics.grouped_df(...)
     11. ├─timetk::tk_acf_diagnostics(...)
     12. └─timetk:::tk_acf_diagnostics.grouped_df(...)
     13. └─`%>%`(...)
     14. ├─base::withVisible(eval(quote(`_fseq`(`_
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 1.0.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’ using rmarkdown
    Loading required package: dplyr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
     step
    
    For binary classification, the first factor level is assumed to be the event.
    Set the global option `yardstick.event_first` to `FALSE` to change this.
    
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ stringr 1.4.0
    ✔ tidyr 1.1.0 ✔ forcats 0.5.0
    ✔ readr 1.3.1
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ stringr::fixed() masks recipes::fixed()
    ✖ dplyr::lag() masks stats::lag()
    ✖ readr::spec() masks yardstick::spec()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using method = 'loess' and formula 'y ~ x'
    Warning: Removed 2 rows containing non-finite values (stat_smooth).
    Warning: Removed 2 rows containing missing values (geom_point).
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using formula 'y ~ x'
    `geom_smooth()` using formula 'y ~ x'
    --- finished re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’
    
    --- re-building ‘TK04_Plotting_Time_Series.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    --- finished re-building ‘TK04_Plotting_Time_Series.Rmd’
    
    --- re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Quitting from lines 49-56 (TK05_Plotting_Seasonality_and_Correlation.Rmd)
    Error: processing vignette 'TK05_Plotting_Seasonality_and_Correlation.Rmd' failed with diagnostics:
    Problem with `mutate()` input `nested.col`.
    ✖ Can't recycle `..1` (size 169) to match `..2` (size 0).
    ℹ Input `nested.col` is `purrr::map(...)`.
    ℹ The error occured in group 1: id = "H10".
    --- failed re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    --- re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’ using rmarkdown
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.0.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    Loading required package: dplyr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'recipes'
    
    The following object is masked from 'package:stats':
    
     step
    
    For binary classification, the first factor level is assumed to be the event.
    Set the global option `yardstick.event_first` to `FALSE` to change this.
    
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ stringr 1.4.0
    ✔ tidyr 1.1.0 ✔ forcats 0.5.0
    ✔ readr 1.3.1
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ stringr::fixed() masks recipes::fixed()
    ✖ dplyr::lag() masks stats::lag()
    ✖ readr::spec() masks yardstick::spec()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using method = 'loess' and formula 'y ~ x'
    Warning: Removed 2 rows containing non-finite values (stat_smooth).
    Warning: Removed 2 rows containing missing values (geom_point).
    Warning in predict.lm(object = object$fit, newdata = new_data, type = "response") :
     prediction from a rank-deficient fit may be misleading
    `geom_smooth()` using formula 'y ~ x'
    `geom_smooth()` using formula 'y ~ x'
    --- finished re-building ‘TK03_Forecasting_Using_Time_Series_Signature.Rmd’
    
    --- re-building ‘TK04_Plotting_Time_Series.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    --- finished re-building ‘TK04_Plotting_Time_Series.Rmd’
    
    --- re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Quitting from lines 49-56 (TK05_Plotting_Seasonality_and_Correlation.Rmd)
    Error: processing vignette 'TK05_Plotting_Seasonality_and_Correlation.Rmd' failed with diagnostics:
    Problem with `mutate()` input `nested.col`.
    ✖ Can't recycle `..1` (size 169) to match `..2` (size 0).
    ℹ Input `nested.col` is `purrr::map(...)`.
    ℹ The error occured in group 1: id = "H10".
    --- failed re-building ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    --- re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
    ✔ ggplot2 3.3.1 ✔ purrr 0.3.4
    ✔ tibble 3.0.1 ✔ dplyr 1.0.0
    ✔ tidyr 1.1.0 ✔ stringr 1.4.0
    ✔ readr 1.3.1 ✔ forcats 0.5.0
    ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
    ✖ dplyr::filter() masks stats::filter()
    ✖ dplyr::lag() masks stats::lag()
    Loading required package: lubridate
    
    Attaching package: 'lubridate'
    
    The following objects are masked from 'package:base':
    
     date, intersect, setdiff, union
    
    Loading required package: PerformanceAnalytics
    Loading required package: xts
    Loading required package: zoo
    
    Attaching package: 'zoo'
    
    The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
    
    Attaching package: 'xts'
    
    The following objects are masked from 'package:dplyr':
    
     first, last
    
    
    Attaching package: 'PerformanceAnalytics'
    
    The following object is masked from 'package:graphics':
    
     legend
    
    Loading required package: quantmod
    Loading required package: TTR
    Version 0.4-0 included new data defaults. See ?getSymbols.
    ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
    Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
    </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>
    
    Attaching package: 'timetk'
    
    The following objects are masked from 'package:tidyquant':
    
     summarise_by_time, summarize_by_time
    
    --- finished re-building ‘TK07_Time_Series_Data_Wrangling.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘TK05_Plotting_Seasonality_and_Correlation.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-patched-solaris-x86