UK Electricity Generation
UK ESO grid data
Ben Anderson
Last run at: 2020-06-18 21:21:42
1 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 | 199102 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 31 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| DATETIME | 0 | 1 | 19 | 19 | 0 | 199102 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| GAS | 0 | 1 | 12791.82 | 5264.01 | 1556.0 | 8704.0 | 12930.0 | 16876.75 | 27472.0 | ▃▆▇▅▁ |
| COAL | 0 | 1 | 8160.30 | 6573.91 | 0.0 | 1745.0 | 7285.0 | 13473.00 | 26044.0 | ▇▅▅▂▁ |
| NUCLEAR | 0 | 1 | 7121.63 | 1025.01 | 3705.0 | 6430.0 | 7267.0 | 7903.00 | 9342.0 | ▁▂▆▇▃ |
| WIND | 0 | 1 | 3200.58 | 3170.14 | 0.0 | 802.0 | 2154.0 | 4689.00 | 17129.0 | ▇▃▁▁▁ |
| HYDRO | 0 | 1 | 397.71 | 246.55 | 0.0 | 193.0 | 368.0 | 567.00 | 1403.0 | ▇▇▅▁▁ |
| IMPORTS | 0 | 1 | 1975.82 | 1098.15 | 0.0 | 1162.0 | 2010.0 | 2938.00 | 4884.0 | ▅▅▇▆▁ |
| BIOMASS | 0 | 1 | 425.03 | 845.76 | 0.0 | 0.0 | 0.0 | 0.00 | 3204.0 | ▇▁▁▁▁ |
| OTHER | 0 | 1 | 533.64 | 684.44 | 0.0 | 0.0 | 121.0 | 843.00 | 2456.0 | ▇▂▂▁▁ |
| SOLAR | 0 | 1 | 625.91 | 1429.94 | 0.0 | 0.0 | 0.0 | 342.00 | 9680.0 | ▇▁▁▁▁ |
| STORAGE | 0 | 1 | 311.84 | 349.13 | 0.0 | 0.0 | 294.0 | 454.00 | 2660.0 | ▇▂▁▁▁ |
| GENERATION | 0 | 1 | 35544.26 | 7419.22 | 18708.0 | 29811.0 | 35486.0 | 40674.00 | 59500.0 | ▃▇▇▃▁ |
| CARBON_INTENSITY | 0 | 1 | 390.74 | 138.72 | 57.0 | 267.0 | 406.0 | 509.00 | 695.0 | ▂▆▆▇▂ |
| LOW_CARBON | 0 | 1 | 11770.86 | 4070.68 | 4626.0 | 8790.0 | 10706.0 | 14011.00 | 30746.0 | ▇▇▃▁▁ |
| ZERO_CARBON | 0 | 1 | 11345.83 | 3622.82 | 4626.0 | 8694.0 | 10451.0 | 13434.00 | 28473.0 | ▇▇▃▁▁ |
| RENEWABLE | 0 | 1 | 4224.20 | 3716.39 | 0.0 | 1275.0 | 3103.0 | 6221.75 | 23118.0 | ▇▃▁▁▁ |
| FOSSIL | 0 | 1 | 20952.11 | 8761.39 | 2187.0 | 14292.0 | 20244.0 | 27168.00 | 49096.0 | ▃▇▆▃▁ |
| GAS_perc | 0 | 1 | 35.57 | 12.22 | 5.0 | 26.5 | 36.5 | 45.00 | 66.8 | ▂▆▇▇▁ |
| COAL_perc | 0 | 1 | 21.60 | 15.84 | 0.0 | 5.1 | 21.3 | 35.70 | 60.6 | ▇▅▅▅▁ |
| NUCLEAR_perc | 0 | 1 | 20.89 | 5.25 | 9.2 | 17.0 | 20.1 | 24.00 | 43.1 | ▃▇▃▁▁ |
| WIND_perc | 0 | 1 | 9.47 | 9.67 | 0.0 | 2.3 | 6.2 | 13.70 | 58.3 | ▇▂▁▁▁ |
| HYDRO_perc | 0 | 1 | 1.10 | 0.64 | 0.0 | 0.6 | 1.0 | 1.50 | 4.2 | ▇▇▃▁▁ |
| IMPORTS_perc | 0 | 1 | 5.97 | 3.62 | 0.0 | 3.2 | 6.1 | 8.60 | 18.9 | ▆▇▆▂▁ |
| BIOMASS_perc | 0 | 1 | 1.30 | 2.61 | 0.0 | 0.0 | 0.0 | 0.00 | 15.9 | ▇▁▁▁▁ |
| OTHER_perc | 0 | 1 | 1.59 | 2.11 | 0.0 | 0.0 | 0.4 | 2.60 | 10.5 | ▇▂▁▁▁ |
| SOLAR_perc | 0 | 1 | 1.73 | 4.04 | 0.0 | 0.0 | 0.0 | 0.90 | 31.7 | ▇▁▁▁▁ |
| STORAGE_perc | 0 | 1 | 0.78 | 0.84 | 0.0 | 0.0 | 0.7 | 1.20 | 6.1 | ▇▂▁▁▁ |
| GENERATION_perc | 0 | 1 | 100.00 | 0.00 | 100.0 | 100.0 | 100.0 | 100.00 | 100.0 | ▁▁▇▁▁ |
| LOW_CARBON_perc | 0 | 1 | 34.48 | 13.51 | 10.7 | 24.1 | 31.7 | 42.20 | 87.9 | ▆▇▃▁▁ |
| ZERO_CARBON_perc | 0 | 1 | 33.19 | 12.14 | 10.7 | 24.0 | 31.0 | 40.10 | 85.1 | ▆▇▃▁▁ |
| RENEWABLE_perc | 0 | 1 | 12.30 | 11.00 | 0.0 | 3.6 | 8.9 | 18.00 | 66.2 | ▇▃▁▁▁ |
| FOSSIL_perc | 0 | 1 | 57.17 | 16.03 | 7.7 | 46.2 | 58.9 | 70.20 | 88.0 | ▁▃▆▇▅ |
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.
Figure 1.1: Half-hourly generation (GW)
2 Runtime
Report generated in 20.42 seconds ( 0.34 minutes) using knitr in RStudio with R version 3.6.0 (2019-04-26) running on x86_64-redhat-linux-gnu.
3 R environment
3.1 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
##
## 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 data.table_1.12.0
## [7] woRkflow_0.1.0
##
## loaded via a namespace (and not attached):
## [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] backports_1.1.3 scales_1.0.0 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 crayon_1.3.4 pkgconfig_2.0.2 ellipsis_0.3.1 prettyunits_1.0.2
## [51] assertthat_0.2.0 rmarkdown_2.2 rstudioapi_0.11 R6_2.3.0 igraph_1.2.2
## [56] compiler_3.6.0References
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