In austl001/MOSLR: MOSL Reports and Utility Functions

Additional Performance Indicators

Performance Overview

As of r format(data.period, "%B-%Y"), MOSL were monitoring r SHORT.NAME's performance against r nrow(perf_status_api_temp) standards. The performance of Trading Parties against these standards is measured according to:

r paste(unique(perf_status_api_temp$PerformanceMeasure), collapse = " and ")

The current APIs are

\begin{enumerate} \item GIS Issues: The proportion of meters that have no verifiable issues with their GIS coordinates. The issues currently measured are: \begin{itemize} \item >20 meters stacked: Marked as an issue when 20 or more meters are marked to be in a $10cm\times 10cm$ area \item UPRN/Postocde centre: Marked as an issue when the GIS coordinates match exactly with the UPRN/Postcode centre coordinates \item Far from UPRN/Postcode centre: Marked as an issue when the GIS coordinates are more than 1km away from the UPRN/Postcode centre coordinates \end{itemize} \item UPRN Completeness: The proportion of premises that have a Unique Property Reference Number (UPRN) \item VOA Completeness: The proportion of premises that have a Valuation Office Agency (VOA) Billing Authority Reference Number \end{enumerate}

For more information on the data items mentioned, see $\color{blue}{\text{\bf here}}$

tryCatch(
  expr = {
  agg_perf <- api_aggregate_perf %>%
    dplyr::filter(Trading.Party.ID == TRADING.PARTY) %>%
    dplyr::select(Period, Agg_Perf_roll) %>%
    dplyr::rename(GroupPerf = Agg_Perf_roll) %>%
    dplyr::mutate(Group = "Aggregate Performance")

  api_data_clean_temp %>%
    dplyr::group_by(Period, Group) %>%
    dplyr::summarise(
      GroupPerf = sum(OnTimeTasks) / sum(TaskVolume)
      ) %>%
    dplyr::ungroup() %>%
    dplyr::group_by(Group) %>%
    dplyr::mutate(
      GroupPerf = zoo::rollapply(GroupPerf, 6, mean, align = "right", fill = NA)
      ) %>%
    dplyr::ungroup() %>%
    base::rbind(., agg_perf) %>%
    droplevels() %>%
    tidyr::drop_na() %>%
    ggplot2::ggplot() + 
    ggplot2::geom_line(
      ggplot2::aes(
        x = Period, 
        y = GroupPerf, 
        colour = Group,
        linetype = Group
        )
      ) +
    ggplot2::geom_point(
      ggplot2::aes(
        x = Period, 
        y = GroupPerf,
        colour = Group,
        fill = Group,
        shape = Group
        )
      ) +
    ggplot2::theme(
      legend.title = ggplot2::element_blank(),
      text = ggplot2::element_text(size = 15)
      ) +
    ggplot2::scale_y_continuous(
      labels = scales::percent_format(accuracy = 1),
      breaks = scales::pretty_breaks(4)
      ) +
    ggplot2::theme_bw() + 
    ggplot2::scale_colour_manual(
      values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")
      ) +
    ggplot2::scale_fill_manual(
      values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")
      ) +
    ggplot2::labs(
      title = paste0("API Performance: ", SHORT.NAME),
      subtitle = "Breakdown by month and standard",
      caption = "Source: MOSL\nNote: Aggregate performance corresponds to the 6-month average of aggregate API performance"
      ) +
    ggplot2::ylab("") + ggplot2::xlab("")
  }, 
  error = function(e){}
)

tryCatch(
  expr = {
  api_data_clean_temp %>% 
    dplyr::filter(
      Period >= max(api_data_clean_temp$Period) %m-% months(11)
      ) %>%
    dplyr::mutate(
      Performance = scales::percent(Performance, accuracy = 1),
      Period = format(Period, "%Y-%m")
      ) %>%
    dplyr::select(
      Period, Standard, Performance
      ) %>%
    tidyr::spread(key = Standard, value = Performance) %>%
    kableExtra::kable(
      format = "latex", 
      caption = "API Performance by Month",
      linesep = "",
      format.args = list(big.mark = ","),
      booktabs = TRUE,
      align = "c"
      ) %>%
    kableExtra::kable_styling(
      font_size = 10,
      latex_options = c(
        "repeat_header",
        "HOLD_position",
        "striped",
        position = "center", 
        full_width = FALSE,
        "scale_down"
        )
      ) %>%
    kableExtra::row_spec(0, bold = TRUE)
  }, error = function(e){}
)

r SHORT.NAME's 6-month aggregate API performance is r scales::percent(api_aggregate_perf_temp$Agg_Perf_roll, accuracy = 1), which currently ranks as:

r scales::ordinal(api_aggregate_perf_temp$rank_all) out of r api_aggregate_perf_temp$n_all for all Trading Parties
r scales::ordinal(api_aggregate_perf_temp$rank_type) out of r paste0(api_aggregate_perf_temp$n_type, " ", api_aggregate_perf_temp$tp_type)s
r scales::ordinal(api_aggregate_perf_temp$rank_league) out of r paste(api_aggregate_perf_temp$n_league, api_aggregate_perf_temp$LeagueGroup)

tryCatch(
  expr = {
  api_aggregate_perf %>%
    dplyr::filter(Period == data.period) %>%
    ggplot2::ggplot() +
    ggplot2::geom_bar(
      ggplot2::aes(
        x = reorder(ShortName, -rank_league), 
        y = Agg_Perf_roll,
        fill = dplyr::if_else(Trading.Party.ID == TRADING.PARTY, "highlight", "normal")
        ), 
      stat = "identity",
      show.legend = FALSE
      ) +
    ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    ggplot2::scale_fill_manual(values = c("#425563", "azure3")) +
    ggplot2::coord_flip() +
    ggplot2::theme_bw() +
    ggplot2::theme(text = ggplot2::element_text(size = 15)) +
    ggplot2::labs(
      title = "Peer Comparison Tables: Additional Performance Indicators",
      subtitle = "Six-month rolling aggregate performance",
      caption = "Source: MOSL", x = "", y = ""
      ) +
    ggplot2::facet_wrap(~LeagueGroup, scales = "free_y", ncol = 2)
  }, 
  error = function(e){}
)

``` {r message = FALSE, echo = FALSE, error = FALSE, warning = FALSE, results = "asis", eval=FALSE}

tryCatch( expr = { perf_status_api_temp %>% dplyr::select(Standard, SecondaryCategory, TaskVolume, Performance, Status, Action) %>% dplyr::mutate( Status = tidyr::replace_na(Status, ""), TaskVolume = scales::comma(TaskVolume, accuracy = 1), Performance = scales::percent(Performance, accuracy = 1) ) %>% kableExtra::kable( format = "latex", caption = "Current Status of API", linesep = "", booktabs = TRUE, col.names = c("API", "Category", "Task\nVolume", "Performance", "Rectification\nStatus", "MOSL\nAction") ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "HOLD_position", "striped", position = "center", full_width = FALSE, "scale_down" ) ) %>% kableExtra::row_spec(0, bold = TRUE) }, error = function(e){} )

<!-- See Appendix for an explanation of how MOSL evaluates Trading Party performance against the standards. -->

```r

tryCatch(
  expr = {
  perf_status_api_temp %>%
    ggplot2::ggplot() +
    ggrepel::geom_label_repel(
      ggplot2::aes(
        x = rolling.sd,
        y = rolling.mean,
        label = Standard,
        fill = Group
        ), 
      alpha = 0.8,
      colour = "white"
      ) +
    ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    ggplot2::scale_x_reverse(labels = scales::percent_format(accuracy = 1)) +
    ggplot2::theme_bw() +
    ggplot2::scale_fill_manual(
      values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")
      ) +
    ggplot2::theme(
      text = ggplot2::element_text(size = 15)
      ) +
    ggplot2::labs(
      title = paste0("API Performance and Consistency as of ", format(as.Date(data.period), '%B %Y')),
      subtitle = "Rolling 6-month mean and standard deviation of API performance",
      caption = "Source: MOSL"
      ) +
    ggplot2::ylab("Performance") + ggplot2::xlab("Consistency")
  }, error = function(e){}
)

Detailed Analysis

api_list <- unique(as.character(perf_status_api_temp$Standard))

res <- lapply(api_list, function (API_CHUNK) {

  knitr::knit_child(
    "pfm_report_api_template.Rmd", envir = environment(), quiet = TRUE
    )
  }
  )

cat(unlist(res), sep = "\n")

\newpage

Market Aggregates

metrics.list <- c("MarketMean", "MarketTaskVolume")

for (METRIC in metrics.list) {

  api_summary %>% 
    dplyr::select(
      Period, Standard, METRIC
      ) %>% 
    dplyr::filter(
      Period >= data.period %m-% months(11)
      ) %>% 
    dplyr::mutate(Period = format(Period, "%Y-%m")) %>%
    tidyr::spread(Period, METRIC) %>%
    dplyr::mutate_if(
      is.numeric, 
      ifelse(
        METRIC != "MarketTaskVolume", 
        scales::percent_format(accuracy = 1), 
        scales::number_format(big.mark = ",", accuracy = 1)
        )
      ) %>%
    kableExtra::kable(
      format = "latex", 
      caption = paste0("Market ", base::gsub("Market", x = METRIC, replacement = " ")), 
      linesep = "",
      digits = 1,
      booktabs = TRUE,
      align = "c"
      ) %>%
    kableExtra::kable_styling(
      latex_options = c(
        "repeat_header",
        "HOLD_position",
        "striped",
        "scale_down"
        )
      ) %>%
    kableExtra::row_spec(0, bold = TRUE) %>%
    print()

  cat("\n")

}