UK Electricity Generation
UK ESO grid data
Ben Anderson
<<<<<<< HEADLast run at: 2020-07-03 14:49:17
=======Last run at: 2020-07-09 15:58:19
>>>>>>> 9c8efd5bc4ce0204cb88234db1734f4656a076331 A basic .Rmd template to illustrate our workflow
1.1 Data
The following uses skimr::skim() to describe the data. Remember we created the skim output object in the R script. We just report it here.
| Name | esoData |
| Number of rows | 201790 |
| Number of columns | 34 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 1 |
| numeric | 31 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| DATETIME | 0 | 1 | 19 | 19 | 0 | 201790 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| weekDay | 0 | 1 | TRUE | 7 | Thu: 28848, Fri: 28848, Sat: 28848, Sun: 28846 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| GAS | 0 | 1 | 12739.94 | 5272.46 | 1556.0 | 8609.0 | 12867.0 | 16841.00 | 27472.0 | ▃▆▇▅▁ |
| COAL | 0 | 1 | 8051.56 | 6596.52 | 0.0 | 1565.0 | 7126.0 | 13398.75 | 26044.0 | ▇▃▅▂▁ |
| NUCLEAR | 0 | 1 | 7093.11 | 1048.96 | 3705.0 | 6398.0 | 7250.0 | 7894.00 | 9342.0 | ▁▂▆▇▃ |
| WIND | 0 | 1 | 3237.05 | 3193.43 | 0.0 | 812.0 | 2188.0 | 4750.00 | 17129.0 | ▇▃▁▁▁ |
| HYDRO | 0 | 1 | 396.56 | 245.98 | 0.0 | 193.0 | 366.0 | 565.00 | 1403.0 | ▇▇▅▁▁ |
| IMPORTS | 0 | 1 | 1978.97 | 1100.63 | 0.0 | 1160.0 | 2014.0 | 2942.00 | 4884.0 | ▅▅▇▆▁ |
| BIOMASS | 0 | 1 | 447.68 | 867.07 | 0.0 | 0.0 | 0.0 | 0.00 | 3204.0 | ▇▁▁▁▁ |
| OTHER | 0 | 1 | 528.53 | 681.31 | 0.0 | 0.0 | 121.0 | 830.00 | 2456.0 | ▇▂▂▁▁ |
| SOLAR | 0 | 1 | 648.10 | 1465.86 | 0.0 | 0.0 | 0.0 | 377.00 | 9680.0 | ▇▁▁▁▁ |
| STORAGE | 0 | 1 | 309.39 | 348.68 | 0.0 | 0.0 | 294.0 | 452.00 | 2660.0 | ▇▂▁▁▁ |
| GENERATION | 0 | 1 | 35430.89 | 7453.15 | 18287.0 | 29669.0 | 35346.0 | 40604.00 | 59500.0 | ▃▇▇▃▁ |
| CARBON_INTENSITY | 0 | 1 | 387.85 | 140.17 | 54.0 | 263.0 | 403.0 | 508.00 | 695.0 | ▂▆▆▇▂ |
| LOW_CARBON | 0 | 1 | 11822.50 | 4095.75 | 4626.0 | 8808.0 | 10752.0 | 14092.00 | 30746.0 | ▇▇▃▁▁ |
| ZERO_CARBON | 0 | 1 | 11374.82 | 3644.35 | 4626.0 | 8702.0 | 10477.0 | 13484.00 | 28473.0 | ▇▇▃▁▁ |
| RENEWABLE | 0 | 1 | 4281.71 | 3759.08 | 0.0 | 1288.0 | 3154.0 | 6304.00 | 23118.0 | ▇▃▁▁▁ |
| FOSSIL | 0 | 1 | 20791.50 | 8826.64 | 1899.0 | 14075.0 | 20080.5 | 27050.75 | 49096.0 | ▃▇▇▃▁ |
| GAS_perc | 0 | 1 | 35.52 | 12.24 | 5.0 | 26.4 | 36.4 | 45.00 | 70.1 | ▂▆▇▆▁ |
| COAL_perc | 0 | 1 | 21.31 | 15.93 | 0.0 | 4.6 | 20.9 | 35.50 | 60.6 | ▇▅▅▅▁ |
| NUCLEAR_perc | 0 | 1 | 20.87 | 5.24 | 9.2 | 17.0 | 20.1 | 24.00 | 43.1 | ▃▇▃▁▁ |
| WIND_perc | 0 | 1 | 9.64 | 9.86 | 0.0 | 2.3 | 6.3 | 13.90 | 60.3 | ▇▂▁▁▁ |
| HYDRO_perc | 0 | 1 | 1.10 | 0.64 | 0.0 | 0.6 | 1.0 | 1.50 | 4.2 | ▇▇▃▁▁ |
| IMPORTS_perc | 0 | 1 | 6.00 | 3.65 | 0.0 | 3.2 | 6.2 | 8.60 | 18.9 | ▆▇▆▂▁ |
| BIOMASS_perc | 0 | 1 | 1.39 | 2.73 | 0.0 | 0.0 | 0.0 | 0.00 | 16.1 | ▇▁▁▁▁ |
| OTHER_perc | 0 | 1 | 1.58 | 2.10 | 0.0 | 0.0 | 0.4 | 2.60 | 10.5 | ▇▂▁▁▁ |
| SOLAR_perc | 0 | 1 | 1.81 | 4.19 | 0.0 | 0.0 | 0.0 | 1.00 | 32.8 | ▇▁▁▁▁ |
| STORAGE_perc | 0 | 1 | 0.78 | 0.84 | 0.0 | 0.0 | 0.7 | 1.20 | 7.9 | ▇▁▁▁▁ |
| GENERATION_perc | 0 | 1 | 100.00 | 0.00 | 100.0 | 100.0 | 100.0 | 100.00 | 100.0 | ▁▁▇▁▁ |
| LOW_CARBON_perc | 0 | 1 | 34.80 | 13.79 | 10.7 | 24.2 | 31.9 | 42.60 | 87.9 | ▆▇▃▁▁ |
| ZERO_CARBON_perc | 0 | 1 | 33.42 | 12.35 | 10.7 | 24.0 | 31.1 | 40.40 | 85.1 | ▆▇▃▁▁ |
| RENEWABLE_perc | 0 | 1 | 12.55 | 11.27 | 0.0 | 3.7 | 9.1 | 18.40 | 66.2 | ▇▃▁▁▁ |
| FOSSIL_perc | 0 | 1 | 56.84 | 16.25 | 7.6 | 45.7 | 58.6 | 70.10 | 88.0 | ▁▃▆▇▅ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| rDateTime | 0 | 1 | 2009-01-01 | 2020-07-05 22:30:00 | 2014-10-03 23:15:00 | 201790 |
There's quite a lot of data...
1.2 Plot
1.1 plots every data point in the data (!). Remember we created the plot output object in the R script. We just print it here.
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>>>>>>> 9c8efd5bc4ce0204cb88234db1734f4656a07633
Figure 1.1: Half-hourly generation (GW)
2 Runtime
Report generated in 33.71 seconds ( 0.56 minutes) using knitr in RStudio with R version 3.6.0 (2019-04-26) running on x86_64-redhat-linux-gnu.
2 R environment
Report generated in 16.89 seconds ( 0.28 minutes) using knitr in RStudio with R version 3.6.3 (2020-02-29) running on x86_64-apple-darwin15.6.0.
2.1 R packages used
- base R (R Core Team 2016)
- bookdown (Xie 2018)
- data.table (Dowle et al. 2015)
- drake (Landau 2018)
- ggplot2 (Wickham 2009)
- here (Müller 2017)
- knitr (Xie 2016)
- lubridate (Grolemund and Wickham 2011)
- rmarkdown (J. Allaire et al. 2020,Xie, Allaire, and Grolemund (2018))
3.2 Session info
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
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2.2 Session info
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
>>>>>>> 9c8efd5bc4ce0204cb88234db1734f4656a07633
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
## [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] skimr_2.1.1 ggplot2_3.3.1 lubridate_1.7.9 here_0.1 drake_7.12.2
## [6] data.table_1.12.0 woRkflow_0.1.0
##
## loaded via a namespace (and not attached):
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## [1] storr_1.2.1 progress_1.2.2 tidyselect_1.1.0 xfun_0.14 repr_1.1.0
## [6] purrr_0.3.4 colorspace_1.4-0 vctrs_0.3.1 generics_0.0.2 viridisLite_0.3.0
## [11] htmltools_0.3.6 yaml_2.2.0 base64enc_0.1-3 utf8_1.1.4 rlang_0.4.6
## [16] pillar_1.4.4 txtq_0.2.0 glue_1.4.1 withr_2.1.2 lifecycle_0.2.0
## [21] stringr_1.4.0 munsell_0.5.0 gtable_0.2.0 evaluate_0.14 labeling_0.3
## [26] knitr_1.28 parallel_3.6.0 fansi_0.4.0 highr_0.7 Rcpp_1.0.1
## [31] scales_1.0.0 backports_1.1.3 filelock_1.0.2 jsonlite_1.6 hms_0.5.3
## [36] digest_0.6.25 stringi_1.2.4 bookdown_0.19 dplyr_1.0.0 grid_3.6.0
## [41] rprojroot_1.3-2 cli_2.0.2 tools_3.6.0 magrittr_1.5 base64url_1.4
## [46] tibble_3.0.1 tidyr_1.1.0 crayon_1.3.4 pkgconfig_2.0.2 ellipsis_0.3.1
## [51] prettyunits_1.0.2 assertthat_0.2.0 rmarkdown_2.2 rstudioapi_0.11 R6_2.3.0
## [56] igraph_1.2.2 compiler_3.6.0
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## [1] storr_1.2.1 progress_1.2.2 tidyselect_1.0.0 xfun_0.12
## [5] repr_1.1.0 purrr_0.3.3 colorspace_1.4-1 vctrs_0.2.4
## [9] viridisLite_0.3.0 htmltools_0.4.0 yaml_2.2.1 base64enc_0.1-3
## [13] rlang_0.4.5 pillar_1.4.3 txtq_0.2.0 glue_1.3.2
## [17] withr_2.1.2 lifecycle_0.2.0 stringr_1.4.0 munsell_0.5.0
## [21] gtable_0.3.0 evaluate_0.14 labeling_0.3 knitr_1.28
## [25] parallel_3.6.3 fansi_0.4.1 highr_0.8 Rcpp_1.0.4
## [29] scales_1.1.0 backports_1.1.5 filelock_1.0.2 jsonlite_1.6.1
## [33] farver_2.0.3 hms_0.5.3 digest_0.6.25 stringi_1.4.6
## [37] bookdown_0.18 dplyr_0.8.5 grid_3.6.3 rprojroot_1.3-2
## [41] cli_2.0.2 tools_3.6.3 magrittr_1.5 base64url_1.4
## [45] tibble_2.1.3 crayon_1.3.4 pkgconfig_2.0.3 prettyunits_1.1.1
## [49] assertthat_0.2.1 rmarkdown_2.1 rstudioapi_0.11 R6_2.4.1
## [53] igraph_1.2.5 compiler_3.6.3References
Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2020. Rmarkdown: Dynamic Documents for R. https://github.com/rstudio/rmarkdown.
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Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.
Landau, William Michael. 2018. “The Drake R Package: A Pipeline Toolkit for Reproducibility and High-Performance Computing.” Journal of Open Source Software 3 (21). https://doi.org/10.21105/joss.00550.
Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.
———. 2018. Bookdown: Authoring Books and Technical Documents with R Markdown. https://github.com/rstudio/bookdown.
Xie, Yihui, J.J. Allaire, and Garrett Grolemund. 2018. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown.