sessionInfo()
#> R version 4.0.4 (2021-02-15)
#> Platform: x86_64-apple-darwin19.6.0 (64-bit)
#> Running under: macOS Catalina 10.15.7
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /usr/local/Cellar/r/4.0.4/lib/R/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] timeDate_3043.102 htmltools_0.5.1.1 rvest_1.0.0
#> [4] gganonymize_0.0.0.9000 data.table_1.14.0 digest_0.6.27
#> [7] gganimate_1.0.7 yaml_2.2.1 xtable_1.8-4
#> [10] tseries_0.10-48 tidyselect_1.1.0 sessioninfo_1.1.1
#> [13] sass_0.3.1 rmarkdown_2.7 rbokeh_0.5.1
#> [16] pander_0.6.3 scales_1.1.1 Hmisc_4.5-0
#> [19] Formula_1.2-4 survival_3.2-7 lattice_0.20-41
#> [22] knitr_1.31 summarytools_0.9.8 expsmooth_2.3
#> [25] fma_2.4 forecast_8.13 fpp2_2.4
#> [28] viridis_0.5.1 viridisLite_0.3.0 hrbrthemes_0.8.0
#> [31] rcmdcheck_1.3.3 httr_1.4.2 htmlwidgets_1.5.3
#> [34] dygraphs_1.1.1.6 xts_0.12.1 zoo_1.8-8
#> [37] av_0.5.1 ggthemes_4.2.4 png_0.1-7
#> [40] gifski_0.8.7.1 ggplot2_3.3.3 tidyr_1.1.3
#> [43] lubridate_1.7.10 readr_1.4.0 dplyr_1.0.5
#> [46] DT_0.17 ggridges_0.5.3 devtools_2.3.2
#> [49] usethis_2.0.1
#>
#> loaded via a namespace (and not attached):
#> [1] backports_1.2.1 systemfonts_1.0.1 plyr_1.8.6 lazyeval_0.2.2
#> [5] splines_4.0.4 crosstalk_1.1.1 pryr_0.1.4 magick_2.7.0
#> [9] fansi_0.4.2 magrittr_2.0.1 checkmate_2.0.0 memoise_2.0.0
#> [13] cluster_2.1.1 remotes_2.2.0 extrafont_0.17 matrixStats_0.58.0
#> [17] extrafontdb_1.0 prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_2.0-0
#> [21] xfun_0.21 tcltk_4.0.4 callr_3.5.1 crayon_1.4.1
#> [25] jsonlite_1.7.2 hexbin_1.28.2 glue_1.4.2 gtable_0.3.0
#> [29] pkgbuild_1.2.0 Rttf2pt1_1.3.8 quantmod_0.4.18 maps_3.3.0
#> [33] rapportools_1.0 Rcpp_1.0.6 progress_1.2.2 htmlTable_2.1.0
#> [37] foreign_0.8-81 xopen_1.0.0 RColorBrewer_1.1-2 ellipsis_0.3.1
#> [41] pkgconfig_2.0.3 farver_2.1.0 nnet_7.3-15 utf8_1.1.4
#> [45] crul_1.1.0 labeling_0.4.2 rlang_0.4.10 munsell_0.5.0
#> [49] tools_4.0.4 cachem_1.0.4 cli_2.3.1 generics_0.1.0
#> [53] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0 processx_3.4.5
#> [57] fs_1.5.0 purrr_0.3.4 nlme_3.1-152 xml2_1.3.2
#> [61] compiler_4.0.4 rstudioapi_0.13 curl_4.3 testthat_3.0.2
#> [65] tibble_3.1.0 tweenr_1.0.1 bslib_0.2.4 stringi_1.5.3
#> [69] highr_0.8 gistr_0.9.0 ps_1.6.0 desc_1.3.0
#> [73] gdtools_0.2.3 Matrix_1.3-2 urca_1.3-0 vctrs_0.3.6
#> [77] pillar_1.5.1 lifecycle_1.0.0 lmtest_0.9-38 jquerylib_0.1.3
#> [81] R6_2.5.0 latticeExtra_0.6-29 gridExtra_2.3 codetools_0.2-18
#> [85] assertthat_0.2.1 pkgload_1.2.0 rprojroot_2.0.2 withr_2.4.1
#> [89] httpcode_0.3.0 fracdiff_1.5-1 parallel_4.0.4 hms_1.0.0
#> [93] quadprog_1.5-8 grid_4.0.4 rpart_4.1-15 TTR_0.24.2
#> [97] base64enc_0.1-3
A custom-defined function transforms over 12 million data points into 2309 data points giving summaries for each day in the range.
helix.daily = generate_daily(helix.full)
datatable(helix.daily, options = list(pageLength = 10))
cadillac.daily = generate_daily(cadillac.full)
datatable(cadillac.daily, options = list(pageLength = 10))
##{-}
In addition, we can visualize the changing distribution of daily job count by year using a ridgeplot.
helix.daily %>% mutate(year = floor_date(Date,"year")) %>% ggplot(aes(x=total.jobs, y=as.factor(format(year,"%Y")), fill = stat(x))) + geom_density_ridges_gradient(scale=2, rel_min_height = 0.01) + scale_fill_viridis(name = "Number of Jobs", option="C") + theme_ipsum() + theme(legend.position="none", panel.spacing = unit(0.1, "lines"), strip.text.x = element_text(size = 8)) + scale_x_continuous(limits = c(0,20000)) + ylab("Year") + xlab("Total Jobs Run per Day") + labs(title= "Single Day Job Count by Year - Helix")
cadillac.daily %>% mutate(year = floor_date(Date,"year")) %>% ggplot(aes(x=total.jobs, y=as.factor(format(year,"%Y")), fill = stat(x))) + geom_density_ridges_gradient(scale=2, rel_min_height = 0.01) + scale_fill_viridis(name = "Number of Jobs", option="C") + theme_ipsum() + theme(legend.position="none", panel.spacing = unit(0.1, "lines"), strip.text.x = element_text(size = 8)) + scale_x_continuous(limits = c(0,20000)) + ylab("Year") + xlab("Total Jobs Run per Day") + labs(title= "Single Day Job Count by Year - Cadillac")
## {-}