In austl001/MOSLR: MOSL Reports and Utility Functions
Operational Performance Standards
Performance Charges
tryCatch(
expr = {
ops_data_clean_temp %>%
dplyr::filter(
Charges > 0
) %>%
tidyr::drop_na(Charges) %>%
base::droplevels() %>%
ggplot2::ggplot(
ggplot2::aes(x = Period, y = Charges, fill = Group)
) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::theme(
text = ggplot2::element_text(size = 15)
) +
ggplot2::scale_y_continuous(
labels = scales::dollar_format(prefix = "£"),
breaks = scales::pretty_breaks(4)
) +
ggplot2::theme_bw() +
ggplot2::scale_fill_manual(values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")) +
ggplot2::labs(
title = "Performance Charges by OPS Group",
subtitle = "Breakdown by month and standard grouping",
caption = "Source: MOSL",
fill = "OPS Group"
) +
ggplot2::ylab("") + ggplot2::xlab("")
}, error = function(e){}
)
OPS_grouping <- dplyr::tibble(
"Metering" = c("OPS B1a", "OPS B3a", "OPS B3b", "OPS B5a", rep("", 4)),
"Supply Check" = c("OPS C1a", "OPS C1b", "OPS C2a", "OPS C3a", "OPS C4a", "OPS C4b", "OPS C5a", "OPS C6a"),
"Complaints" = c("OPS F5a", "OPS F5b", rep("", 6)),
"Trade Effluent" = c("OPS G2a", "OPS G4a", "OPS G4b", rep("", 5)),
"Allowances" = c("OPS H1a", rep("", 7)),
"Disconnection/ Reconnection" = c("OPS I1a", "OPS I1b", "OPS I8a", "OPS I8b", rep("", 4))
)
OPS_grouping %>%
kableExtra::kable(
format = "latex",
caption = "Details of OPS groupings",
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
)
) %>%
kableExtra::row_spec(0, bold = TRUE) %>%
kableExtra::column_spec(1:ncol(OPS_grouping), width = "2.4cm")
# Creating table of charges by OPS ----------------
tryCatch(
expr = {
ops_data_clean_temp %>%
dplyr::filter(
Period >= data.period %m-% months(11),
PerformanceMeasure == "Completed",
Charges > 0
) %>%
base::droplevels() %>%
dplyr::select(Period, Standard, Charges) %>%
dplyr::mutate(
Period = format(Period, "%Y-%m"),
Charges = tidyr::replace_na(Charges, 0)
) %>%
tidyr::drop_na(Charges) %>%
base::droplevels() %>%
tidyr::spread(Standard, Charges) %>%
dplyr::mutate(Total = rowSums(dplyr::select(., -Period), na.rm = TRUE)) %>%
dplyr::mutate_if(~ any(is.na(.)), ~ ifelse(is.na(.), 0, .)) %>%
dplyr::mutate_if(is.numeric, scales::dollar_format(prefix = "£")) %>%
tidyr::gather("Standard", "Charges", -Period) %>%
tidyr::spread(Period, Charges) %>%
kableExtra::kable(
format = "latex",
caption = "Breakdown of Charges by Standard",
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){}
)
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring r SHORT.NAME's performance against r nrow(perf_status_ops_temp) standards. The performance of Trading Parties against these standards is measured according to:
r paste(unique(perf_status_ops$PerformanceMeasure), collapse = " and ")
tryCatch(
expr = {
agg_perf <- ops_aggregate_perf %>%
dplyr::filter(Trading.Party.ID == TRADING.PARTY) %>%
dplyr::select(Period, PerformanceMeasure, Agg_Perf_roll) %>%
dplyr::rename(GroupPerf = Agg_Perf_roll) %>%
dplyr::mutate(Group = "Aggregate Performance")
ops_data_clean_temp %>%
dplyr::group_by(Period, PerformanceMeasure, Group) %>%
dplyr::summarise(
GroupPerf = stats::weighted.mean(Performance, TaskVolume)
) %>%
dplyr::ungroup() %>%
dplyr::group_by(PerformanceMeasure, Group) %>%
dplyr::mutate(
GroupPerf = zoo::rollapply(GroupPerf, 6, mean, align = "right", fill = NA)
) %>%
dplyr::ungroup() %>%
base::rbind(., agg_perf) %>%
tidyr::drop_na(GroupPerf) %>%
droplevels() %>%
tidyr::drop_na() %>%
ggplot2::ggplot() +
ggplot2::geom_line(
ggplot2::aes(
x = Period,
y = GroupPerf,
colour = Group,
linetype = Group
)
) +
ggplot2::geom_point(
ggplot2::aes(
x = Period,
y = GroupPerf,
colour = Group,
fill = Group,
shape = Group
)
) +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
text = ggplot2::element_text(size = 15)
) +
ggplot2::scale_y_continuous(
labels = scales::percent_format(accuracy = 1),
breaks = scales::pretty_breaks(4)
) +
ggplot2::theme_bw() +
ggplot2::scale_colour_manual(
values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")
) +
ggplot2::scale_fill_manual(
values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")
) +
ggplot2::labs(
title = paste0("Completed and Outstanding Tasks OPS Performance: ", SHORT.NAME),
subtitle = "Breakdown by month and OPS standard grouping",
caption = "Note: Aggregate performance corresponds to the 6-month average of aggregate OPS performance\nSource: MOSL"
) +
ggplot2::ylab("") + ggplot2::xlab("") +
ggplot2::facet_wrap(~PerformanceMeasure)
}, error = function(e){}
)
tryCatch(
expr = {
ops_data_clean_temp %>%
dplyr::filter(
Period >= max(ops_data_clean_temp$Period) %m-% months(11),
PerformanceMeasure == "Completed",
TaskVolume > 0
) %>%
tidyr::drop_na(TaskVolume) %>%
base::droplevels() %>%
dplyr::mutate(
Performance = scales::percent(Performance, accuracy = 1),
Period = format(Period, "%Y-%m")
) %>%
dplyr::select(
Period, Standard, Performance
) %>%
tidyr::drop_na(Performance) %>%
base::droplevels() %>%
tidyr::spread(key = Period, value = Performance) %>%
kableExtra::kable(
format = "latex",
caption = "OPS On Time Completed Tasks 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){}
)
tryCatch(
expr = {
ops_data_clean_temp %>%
dplyr::filter(
Period >= max(ops_data_clean_temp$Period) %m-% months(11),
PerformanceMeasure == "Outstanding",
TaskVolume > 0
) %>%
tidyr::drop_na(TaskVolume) %>%
base::droplevels() %>%
dplyr::mutate(
Performance = scales::percent(Performance, accuracy = 1),
Period = format(Period, "%Y-%m")
) %>%
dplyr::select(
Period, Standard, Performance
) %>%
tidyr::drop_na(Performance) %>%
base::droplevels() %>%
tidyr::spread(key = Period, value = Performance) %>%
kableExtra::kable(
format = "latex",
caption = "OPS Outstanding Tasks Within Time by Month",
linesep = "",
format.args = list(big.mark = ","),
booktabs = TRUE,
align = "c"
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"HOLD_position",
"striped",
position = "center",
full_width = FALSE,
"scale_down"
)
) %>%
kableExtra::row_spec(0, bold = TRUE)
}, error = function(e){}
)
ops_perf_comp <- ops_aggregate_perf_temp %>%
dplyr::filter(PerformanceMeasure == "Completed")
ops_perf_outs <- ops_aggregate_perf_temp %>%
dplyr::filter(PerformanceMeasure == "Outstanding")
r SHORT.NAME's 6-month aggregate OPS performance for completed tasks is r scales::percent(ops_perf_comp$Agg_Perf_roll, accuracy = 1), which currently ranks as:
r scales::ordinal(ops_perf_comp$rank_type) out of r paste0(ops_perf_comp$n_type, " ", ops_perf_comp$tp_type)sr scales::ordinal(ops_perf_comp$rank_league) out of r paste(ops_perf_comp$n_league, ops_perf_comp$LeagueGroup)
tryCatch(
expr = {
ops_aggregate_perf %>%
dplyr::filter(
Period == data.period,
PerformanceMeasure == "Completed"
) %>%
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: Operational Performance Standards (Completed)",
subtitle = "Six-month rolling aggregate performance",
caption = "Source: MOSL", x = "", y = ""
) +
ggplot2::facet_wrap(~LeagueGroup, scales = "free", ncol = 2)
},
error = function(e){}
)
r SHORT.NAME's 6-month aggregate OPS performance for outstanding tasks is r scales::percent(ops_perf_outs$Agg_Perf_roll, accuracy = 1), which currently ranks as:
r scales::ordinal(ops_perf_outs$rank_type) out of r paste0(ops_perf_outs$n_type, " ", ops_perf_outs$tp_type)sr scales::ordinal(ops_perf_outs$rank_league) out of r paste(ops_perf_outs$n_league, ops_perf_outs$LeagueGroup)
tryCatch(
expr = {
ops_aggregate_perf %>%
dplyr::filter(
Period == data.period,
PerformanceMeasure == "Outstanding"
) %>%
ggplot2::ggplot() +
ggplot2::geom_bar(
ggplot2::aes(
x = reorder(ShortName, -rank_league),
y = Agg_Perf_roll,
fill = dplyr::if_else(Trading.Party.ID == TRADING.PARTY, "highlight", "normal")
),
stat = "identity",
show.legend = FALSE) +
ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ggplot2::scale_fill_manual(values = c("#425563", "azure3")) +
ggplot2::coord_flip() +
ggplot2::theme_bw() +
ggplot2::theme(text = ggplot2::element_text(size = 15)) +
ggplot2::labs(
title = "Peer Comparison Tables: Operational Performance Standards (Outstanding)",
subtitle = "Six-month rolling aggregate performance",
caption = "Source: MOSL", x = "", y = ""
) +
ggplot2::facet_wrap(~LeagueGroup, scales = "free_y", ncol = 2)
},
error = function(e){}
)
The current status of r SHORT.NAME's performance against each standard and performance measure as of r format(data.period, "%B-%Y") is summarised in the table(s) below.
``` {r message = FALSE, echo = FALSE, error = FALSE, warning = FALSE, results = "asis"}
for (MEASURE in unique(perf_status_ops_temp$PerformanceMeasure)) {
tryCatch(
expr = {
perf_status_ops_temp %>%
dplyr::filter(PerformanceMeasure == MEASURE) %>%
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 = paste0("Current Status of OPS (", MEASURE, ")"),
linesep = "",
booktabs = TRUE,
col.names = c("OPS", "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) %>%
print()
cat("\n")
}, error = function(e){}
)
}
See Appendix for an explanation of how MOSL evaluates Trading Party performance against the standards.
```r
tryCatch(
expr = {
perf_status_ops_temp %>%
ggplot2::ggplot() +
ggrepel::geom_label_repel(
ggplot2::aes(
x = rolling.sd,
y = rolling.mean,
label = Standard,
fill= Group
), alpha = 0.8
) +
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("OPS Performance Rating and Consistency as of ", format(as.Date(data.period), '%B %Y')),
subtitle = "Rolling 6-month mean and standard deviation of OPS performance",
caption = "Source: MOSL"
) +
ggplot2::ylab("Performance") + ggplot2::xlab("Consistency") +
ggplot2::facet_wrap(~ PerformanceMeasure)
}, error = function(e){}
)
Detailed Analysis
ops_list <- unique(as.character(perf_status_ops$Standard[perf_status_ops$Trading.Party.ID == TRADING.PARTY & perf_status_ops$TaskVolume > 0]))
res <- lapply(ops_list, function (OPS_CHUNK) {
knitr::knit_child(
"pfm_report_ops_template.Rmd", envir = environment(), quiet = TRUE
)
}
)
cat(unlist(res), sep = "\n")
Market Aggregates
metrics.list <- c("MarketMean", "MarketTaskVolume")
for (METRIC in metrics.list) {
for (MEASURE in unique(ops_summary$PerformanceMeasure)) {
ops_summary %>%
dplyr::filter(
PerformanceMeasure == MEASURE
) %>%
tidyr::drop_na() %>%
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 = " "), " (", MEASURE, ")"),
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")
}
}
austl001/MOSLR documentation built on Aug. 17, 2022, 12:07 a.m.
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Operational Performance Standards
Performance Charges
tryCatch( expr = { ops_data_clean_temp %>% dplyr::filter( Charges > 0 ) %>% tidyr::drop_na(Charges) %>% base::droplevels() %>% ggplot2::ggplot( ggplot2::aes(x = Period, y = Charges, fill = Group) ) + ggplot2::geom_bar(stat = "identity") + ggplot2::theme( text = ggplot2::element_text(size = 15) ) + ggplot2::scale_y_continuous( labels = scales::dollar_format(prefix = "£"), breaks = scales::pretty_breaks(4) ) + ggplot2::theme_bw() + ggplot2::scale_fill_manual(values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC")) + ggplot2::labs( title = "Performance Charges by OPS Group", subtitle = "Breakdown by month and standard grouping", caption = "Source: MOSL", fill = "OPS Group" ) + ggplot2::ylab("") + ggplot2::xlab("") }, error = function(e){} )
OPS_grouping <- dplyr::tibble( "Metering" = c("OPS B1a", "OPS B3a", "OPS B3b", "OPS B5a", rep("", 4)), "Supply Check" = c("OPS C1a", "OPS C1b", "OPS C2a", "OPS C3a", "OPS C4a", "OPS C4b", "OPS C5a", "OPS C6a"), "Complaints" = c("OPS F5a", "OPS F5b", rep("", 6)), "Trade Effluent" = c("OPS G2a", "OPS G4a", "OPS G4b", rep("", 5)), "Allowances" = c("OPS H1a", rep("", 7)), "Disconnection/ Reconnection" = c("OPS I1a", "OPS I1b", "OPS I8a", "OPS I8b", rep("", 4)) ) OPS_grouping %>% kableExtra::kable( format = "latex", caption = "Details of OPS groupings", 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 ) ) %>% kableExtra::row_spec(0, bold = TRUE) %>% kableExtra::column_spec(1:ncol(OPS_grouping), width = "2.4cm")
# Creating table of charges by OPS ---------------- tryCatch( expr = { ops_data_clean_temp %>% dplyr::filter( Period >= data.period %m-% months(11), PerformanceMeasure == "Completed", Charges > 0 ) %>% base::droplevels() %>% dplyr::select(Period, Standard, Charges) %>% dplyr::mutate( Period = format(Period, "%Y-%m"), Charges = tidyr::replace_na(Charges, 0) ) %>% tidyr::drop_na(Charges) %>% base::droplevels() %>% tidyr::spread(Standard, Charges) %>% dplyr::mutate(Total = rowSums(dplyr::select(., -Period), na.rm = TRUE)) %>% dplyr::mutate_if(~ any(is.na(.)), ~ ifelse(is.na(.), 0, .)) %>% dplyr::mutate_if(is.numeric, scales::dollar_format(prefix = "£")) %>% tidyr::gather("Standard", "Charges", -Period) %>% tidyr::spread(Period, Charges) %>% kableExtra::kable( format = "latex", caption = "Breakdown of Charges by Standard", 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){} )
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring r SHORT.NAME's performance against r nrow(perf_status_ops_temp) standards. The performance of Trading Parties against these standards is measured according to:
r paste(unique(perf_status_ops$PerformanceMeasure), collapse = " and ")
tryCatch( expr = { agg_perf <- ops_aggregate_perf %>% dplyr::filter(Trading.Party.ID == TRADING.PARTY) %>% dplyr::select(Period, PerformanceMeasure, Agg_Perf_roll) %>% dplyr::rename(GroupPerf = Agg_Perf_roll) %>% dplyr::mutate(Group = "Aggregate Performance") ops_data_clean_temp %>% dplyr::group_by(Period, PerformanceMeasure, Group) %>% dplyr::summarise( GroupPerf = stats::weighted.mean(Performance, TaskVolume) ) %>% dplyr::ungroup() %>% dplyr::group_by(PerformanceMeasure, Group) %>% dplyr::mutate( GroupPerf = zoo::rollapply(GroupPerf, 6, mean, align = "right", fill = NA) ) %>% dplyr::ungroup() %>% base::rbind(., agg_perf) %>% tidyr::drop_na(GroupPerf) %>% droplevels() %>% tidyr::drop_na() %>% ggplot2::ggplot() + ggplot2::geom_line( ggplot2::aes( x = Period, y = GroupPerf, colour = Group, linetype = Group ) ) + ggplot2::geom_point( ggplot2::aes( x = Period, y = GroupPerf, colour = Group, fill = Group, shape = Group ) ) + ggplot2::theme( legend.title = ggplot2::element_blank(), text = ggplot2::element_text(size = 15) ) + ggplot2::scale_y_continuous( labels = scales::percent_format(accuracy = 1), breaks = scales::pretty_breaks(4) ) + ggplot2::theme_bw() + ggplot2::scale_colour_manual( values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC") ) + ggplot2::scale_fill_manual( values = c("#425563", "#05C3DE", "#005F83", "#00A499", "#FFAA4D", "#F9E547", "#8866BC") ) + ggplot2::labs( title = paste0("Completed and Outstanding Tasks OPS Performance: ", SHORT.NAME), subtitle = "Breakdown by month and OPS standard grouping", caption = "Note: Aggregate performance corresponds to the 6-month average of aggregate OPS performance\nSource: MOSL" ) + ggplot2::ylab("") + ggplot2::xlab("") + ggplot2::facet_wrap(~PerformanceMeasure) }, error = function(e){} )
tryCatch( expr = { ops_data_clean_temp %>% dplyr::filter( Period >= max(ops_data_clean_temp$Period) %m-% months(11), PerformanceMeasure == "Completed", TaskVolume > 0 ) %>% tidyr::drop_na(TaskVolume) %>% base::droplevels() %>% dplyr::mutate( Performance = scales::percent(Performance, accuracy = 1), Period = format(Period, "%Y-%m") ) %>% dplyr::select( Period, Standard, Performance ) %>% tidyr::drop_na(Performance) %>% base::droplevels() %>% tidyr::spread(key = Period, value = Performance) %>% kableExtra::kable( format = "latex", caption = "OPS On Time Completed Tasks 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){} )
tryCatch( expr = { ops_data_clean_temp %>% dplyr::filter( Period >= max(ops_data_clean_temp$Period) %m-% months(11), PerformanceMeasure == "Outstanding", TaskVolume > 0 ) %>% tidyr::drop_na(TaskVolume) %>% base::droplevels() %>% dplyr::mutate( Performance = scales::percent(Performance, accuracy = 1), Period = format(Period, "%Y-%m") ) %>% dplyr::select( Period, Standard, Performance ) %>% tidyr::drop_na(Performance) %>% base::droplevels() %>% tidyr::spread(key = Period, value = Performance) %>% kableExtra::kable( format = "latex", caption = "OPS Outstanding Tasks Within Time by Month", linesep = "", format.args = list(big.mark = ","), booktabs = TRUE, align = "c" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "HOLD_position", "striped", position = "center", full_width = FALSE, "scale_down" ) ) %>% kableExtra::row_spec(0, bold = TRUE) }, error = function(e){} )
ops_perf_comp <- ops_aggregate_perf_temp %>% dplyr::filter(PerformanceMeasure == "Completed") ops_perf_outs <- ops_aggregate_perf_temp %>% dplyr::filter(PerformanceMeasure == "Outstanding")
r SHORT.NAME's 6-month aggregate OPS performance for completed tasks is r scales::percent(ops_perf_comp$Agg_Perf_roll, accuracy = 1), which currently ranks as:
r scales::ordinal(ops_perf_comp$rank_type)out ofr paste0(ops_perf_comp$n_type, " ", ops_perf_comp$tp_type)sr scales::ordinal(ops_perf_comp$rank_league)out ofr paste(ops_perf_comp$n_league, ops_perf_comp$LeagueGroup)
tryCatch( expr = { ops_aggregate_perf %>% dplyr::filter( Period == data.period, PerformanceMeasure == "Completed" ) %>% 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: Operational Performance Standards (Completed)", subtitle = "Six-month rolling aggregate performance", caption = "Source: MOSL", x = "", y = "" ) + ggplot2::facet_wrap(~LeagueGroup, scales = "free", ncol = 2) }, error = function(e){} )
r SHORT.NAME's 6-month aggregate OPS performance for outstanding tasks is r scales::percent(ops_perf_outs$Agg_Perf_roll, accuracy = 1), which currently ranks as:
r scales::ordinal(ops_perf_outs$rank_type)out ofr paste0(ops_perf_outs$n_type, " ", ops_perf_outs$tp_type)sr scales::ordinal(ops_perf_outs$rank_league)out ofr paste(ops_perf_outs$n_league, ops_perf_outs$LeagueGroup)
tryCatch( expr = { ops_aggregate_perf %>% dplyr::filter( Period == data.period, PerformanceMeasure == "Outstanding" ) %>% ggplot2::ggplot() + ggplot2::geom_bar( ggplot2::aes( x = reorder(ShortName, -rank_league), y = Agg_Perf_roll, fill = dplyr::if_else(Trading.Party.ID == TRADING.PARTY, "highlight", "normal") ), stat = "identity", show.legend = FALSE) + ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + ggplot2::scale_fill_manual(values = c("#425563", "azure3")) + ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::theme(text = ggplot2::element_text(size = 15)) + ggplot2::labs( title = "Peer Comparison Tables: Operational Performance Standards (Outstanding)", subtitle = "Six-month rolling aggregate performance", caption = "Source: MOSL", x = "", y = "" ) + ggplot2::facet_wrap(~LeagueGroup, scales = "free_y", ncol = 2) }, error = function(e){} )
The current status of r SHORT.NAME's performance against each standard and performance measure as of r format(data.period, "%B-%Y") is summarised in the table(s) below.
``` {r message = FALSE, echo = FALSE, error = FALSE, warning = FALSE, results = "asis"}
for (MEASURE in unique(perf_status_ops_temp$PerformanceMeasure)) {
tryCatch( expr = { perf_status_ops_temp %>% dplyr::filter(PerformanceMeasure == MEASURE) %>% 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 = paste0("Current Status of OPS (", MEASURE, ")"), linesep = "", booktabs = TRUE, col.names = c("OPS", "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) %>% print()
cat("\n")
}, error = function(e){} )
}
See Appendix for an explanation of how MOSL evaluates Trading Party performance against the standards.
```r
tryCatch(
expr = {
perf_status_ops_temp %>%
ggplot2::ggplot() +
ggrepel::geom_label_repel(
ggplot2::aes(
x = rolling.sd,
y = rolling.mean,
label = Standard,
fill= Group
), alpha = 0.8
) +
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("OPS Performance Rating and Consistency as of ", format(as.Date(data.period), '%B %Y')),
subtitle = "Rolling 6-month mean and standard deviation of OPS performance",
caption = "Source: MOSL"
) +
ggplot2::ylab("Performance") + ggplot2::xlab("Consistency") +
ggplot2::facet_wrap(~ PerformanceMeasure)
}, error = function(e){}
)
Detailed Analysis
ops_list <- unique(as.character(perf_status_ops$Standard[perf_status_ops$Trading.Party.ID == TRADING.PARTY & perf_status_ops$TaskVolume > 0])) res <- lapply(ops_list, function (OPS_CHUNK) { knitr::knit_child( "pfm_report_ops_template.Rmd", envir = environment(), quiet = TRUE ) } ) cat(unlist(res), sep = "\n")
Market Aggregates
metrics.list <- c("MarketMean", "MarketTaskVolume") for (METRIC in metrics.list) { for (MEASURE in unique(ops_summary$PerformanceMeasure)) { ops_summary %>% dplyr::filter( PerformanceMeasure == MEASURE ) %>% tidyr::drop_na() %>% 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 = " "), " (", MEASURE, ")"), 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") } }
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