In austl001/MOSLR: MOSL Reports and Utility Functions
Market Performance Standards
Performance Charges
mps_data_clean %>%
dplyr::filter(
Period <= data.period
) %>%
dplyr::select(
Period, Charges, Group
) %>%
dplyr::group_by(Period, Group) %>%
dplyr::summarise(
Charges = sum(Charges)
) %>%
ggplot2::ggplot(
ggplot2::aes(x = Period, y = Charges, fill = Group)
) +
ggplot2::geom_bar(stat = "identity") +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
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",
subtitle = "Breakdown by month and standard grouping",
caption = "Source: MOSL"
) +
ggplot2::ylab("") + ggplot2::xlab("")
# Creating table of charges by MPS ----------------
mps_data_clean %>%
dplyr::filter(
Period >= data.period %m-% months(11),
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() %>%
dplyr::group_by(Period, Standard) %>%
dplyr::summarise(
Charges = sum(Charges, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
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)
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring Trading Party performance against r length(unique(perf_status_mps$Standard)) standards. Performance for these standards is measured according to:
r paste(unique(perf_status_mps$PerformanceMeasure), collapse = " and ")
agg_perf <- mps_data_clean %>%
dplyr::group_by(Period) %>%
dplyr::summarise(
Perf = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(
Agg_Perf = zoo::rollapply(Perf, 6, mean, align = "right", fill = NA)
) %>%
dplyr::rename(GroupPerf = Agg_Perf) %>%
dplyr::mutate(Group = "Aggregate Performance")
mps_data_clean %>%
dplyr::group_by(Period, Group) %>%
dplyr::summarise(
Perf = sum(OnTimeTasks) / sum(TaskVolume),
) %>%
dplyr::ungroup() %>%
dplyr::group_by(Group) %>%
dplyr::mutate(
GroupPerf = zoo::rollapply(Perf, 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("MPS On-Time Task Completion"),
subtitle = "Breakdown by month and standard",
caption = "Source: MOSL\nNote: Aggregate performance corresponds to the 6-month average of aggregate MPS performance"
) +
ggplot2::ylab("") + ggplot2::xlab("")
mps_data_clean %>%
dplyr::filter(
Period >= max(mps_data_clean$Period) %m-% months(11)
) %>%
dplyr::group_by(Period, Standard) %>%
dplyr::summarise(
Performance = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(
Performance = scales::percent(Performance, accuracy = 1),
Period = format(Period, "%Y-%m")
) %>%
tidyr::spread(Period, Performance) %>%
kableExtra::kable(
format = "latex",
caption = "MPS Performance by Month",
linesep = "",
format.args = list(big.mark = ","),
booktabs = TRUE,
align = "c"
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE,
"scale_down"
)
) %>%
kableExtra::row_spec(0, bold = TRUE)
for(sub.type in unique(mps_aggregate_perf$sub_type[!is.na(mps_aggregate_perf$sub_type)])){
tryCatch(
expr = {
plot(mps_aggregate_perf %>%
dplyr::filter(
sub_type == sub.type,
Period == data.period
) %>%
ggplot2::ggplot() +
ggplot2::geom_bar(
ggplot2::aes(
x = reorder(ShortName, Agg_Perf_roll),
y = Agg_Perf_roll
),
stat = "identity",
show.legend = FALSE,
fill = "azure3",
col = "black") +
ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ggplot2::coord_flip() +
ggplot2::theme_bw() +
ggplot2::theme(
text = ggplot2::element_text(size = 15)
) +
ggplot2::labs(
title = "Ranking of Overall MPS Performance",
subtitle =
paste0(
"Rolling 6-month mean of aggregate MPS performance by Trading Party (", sub.type, ")"
),
caption = "Source: MOSL"
) +
ggplot2::ylab("") + ggplot2::xlab(""))
}, error = function(e){}
)
}
Performance Monitoring and Rectification
Status Summary
There are currently r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, ActiveIPRP == T)) Active IPRPs and
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, ActivePRP == T)) Active PRPs for
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Trading.Party.ID)) Trading Parties and
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard)) different MPS standards.
Rectification_sum <- perf_status_mps%>%
dplyr::filter(
Period == data.period,
ActiveIPRP == T | ActivePRP == T)%>%
dplyr::count(Standard, Status)%>%
tidyr::pivot_wider(names_from = Status, values_from = n,values_fill = list(n = 0))%>%
dplyr::mutate(
Total = rowSums(
dplyr::select(., -Standard),
na.rm = T)
)%>%
dplyr::select(
Standard,
sort(names(.))
)
tryCatch(
expr = {
kableExtra::kable(
Rectification_sum,
format = "latex",
caption = "Number of Active Rectification plans by Standard",
booktabs=TRUE,
linesep=""
) %>%
kableExtra::kable_styling(
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE,
"scale_down")
) %>%
kableExtra::row_spec(0, bold = TRUE)%>%
kableExtra::column_spec(ncol(Rectification_sum)-1, border_right = T)
}, error = function(e){}
)
Retification count & matrix
tryCatch(
expr = {
perf_status_mps%>%
dplyr::filter(
Period == data.period,
ActiveIPRP == T | ActivePRP == T)%>%
tidyr::pivot_wider(id_cols = Trading.Party.ID, names_from = Standard, values_from = Status, values_fill = list(Status = ""))%>%
dplyr::rename("Trading Party" = Trading.Party.ID)%>%
dplyr::select("Trading Party", sort(names(.)))%>%
kableExtra::kable(
format = "latex",
caption = "Number of Active Rectification plans by Trading Party",
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){}
)
Watch-list and areas of concern
There are currently r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, Action == "Watch")) Trading Parties on watch for MPS performance.
tryCatch(
expr = {
perf_status_mps%>%
dplyr::filter(Period == data.period,
Action == "Watch")%>%
dplyr::select(Trading.Party.ID, Standard)%>%
kableExtra::kable(
format = "latex",
caption = "Trading Parties on watch for MPS performance",
col.names = c(
"Trading Party", "MPS"),
longtable = TRUE,
booktabs=TRUE,
linesep=""
) %>%
kableExtra::kable_styling(
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::row_spec(0, bold = TRUE)
}, error = function(e){}
)
Detailed Rectification Analysis
Below are the Performance graphs of Rectification plans that are either Off-track or under review
IPRP_details_list <- perf_status_mps%>%
dplyr::filter(
Period == data.period,
(Status == "IPRP: Below plan" | Status == "PRP: Below plan" | UnderReview == T)
)
if(nrow(IPRP_details_list)>0){
for(i in 1:nrow(IPRP_details_list)){
plot(MOSLR::plot_perf_graph(
df = perf_status_mps,
trading.party = IPRP_details_list$Trading.Party.ID[i],
standard = IPRP_details_list$Standard[i],
include.iprp = T,
graph.title = paste(IPRP_details_list$Trading.Party.ID[i], IPRP_details_list$Standard[i])
))
}
}
Market Aggregates
metrics.list <- c("MarketMean", "MarketMedian", "MarketTaskVolume")
for (METRIC in metrics.list) {
mps_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")
}
\newpage
Operational Performance Standards
Performance Charges
ops_data_clean %>%
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("")
# Creating table of charges by OPS ----------------
ops_data_clean %>%
dplyr::filter(
Period >= data.period %m-% months(11),
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() %>%
dplyr::group_by(Period, Standard) %>%
dplyr::summarise(
Charges = sum(Charges, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
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)
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring Trading Party performance against r length(unique(perf_status_ops$Standard[!is.na(perf_status_ops$Standard)])) standards. Performance for these standards is measured according to:
r paste(unique(perf_status_ops$PerformanceMeasure), collapse = " and ")
agg_perf <- ops_data_clean %>%
dplyr::group_by(Period, PerformanceMeasure) %>%
dplyr::summarise(
Perf = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(
Agg_Perf = zoo::rollapply(Perf, 6, mean, align = "right", fill = NA)
) %>%
dplyr::rename(GroupPerf = Agg_Perf) %>%
dplyr::mutate(Group = "Aggregate Performance")
ops_data_clean %>%
dplyr::group_by(Period,PerformanceMeasure, Group) %>%
dplyr::summarise(
Perf = sum(OnTimeTasks) / sum(TaskVolume),
) %>%
dplyr::ungroup() %>%
dplyr::group_by(Group) %>%
dplyr::mutate(
GroupPerf = zoo::rollapply(Perf, 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("Completed and Outstanding Tasks OPS Performance"),
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)
ops_data_clean %>%
dplyr::filter(
Period >= max(ops_data_clean$Period, na.rm = T) %m-% months(11),
PerformanceMeasure == "Completed"
) %>%
dplyr::group_by(Period, Standard) %>%
dplyr::summarise(
Performance = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
dplyr::mutate(
Performance = scales::percent(Performance, accuracy = 1),
Period = format(Period, "%Y-%m")
) %>%
tidyr::spread(Period, Performance) %>%
kableExtra::kable(
format = "latex",
caption = "OPS Performance by Month",
linesep = "",
format.args = list(big.mark = ","),
booktabs = TRUE,
align = "c"
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE,
"scale_down"
)
) %>%
kableExtra::row_spec(0, bold = TRUE)
for(sub.type in unique(ops_aggregate_perf$sub_type[!is.na(ops_aggregate_perf$sub_type)])){
tryCatch(
expr = {
plot(ops_aggregate_perf %>%
dplyr::filter(
sub_type == sub.type,
Period == data.period,
PerformanceMeasure == "Completed"
) %>%
ggplot2::ggplot() +
ggplot2::geom_bar(
ggplot2::aes(
x = reorder(ShortName, Agg_Perf_roll),
y = Agg_Perf_roll
),
stat = "identity",
show.legend = FALSE,
fill = "azure3",
col = "black") +
ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ggplot2::coord_flip() +
ggplot2::theme_bw() +
ggplot2::theme(
text = ggplot2::element_text(size = 15)
) +
ggplot2::labs(
title = "Ranking of Overall OPS Performance",
subtitle =
paste0(
"Rolling 6-month mean of aggregate OPS performance by Trading Party (", sub.type, ")"
),
caption = "Source: MOSL"
) +
ggplot2::ylab("") + ggplot2::xlab(""))
}, error = function(e){}
)
}
Performance Monitoring and Rectification
Status Summary
There are currently r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, ActiveIPRP == T)) Active IPRPs and r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, ActivePRP == T)) Active PRPs for
r nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Trading.Party.ID)) Trading Parties and
r nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard, PerformanceMeasure)) different OPS standards.
perf_status_ops%>%
dplyr::filter(
Period == data.period,
ActiveIPRP == T | ActivePRP == T)%>%
dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>%
dplyr::count(Standard, Status)%>%
tidyr::pivot_wider(names_from = Status, values_from = n,values_fill = list(n = 0))%>%
dplyr::mutate(
Total = rowSums(
dplyr::select(., -Standard),
na.rm = T)
)%>%
kableExtra::kable(
format = "latex",
caption = "Number of Active Rectifications by Standard",
longtable = TRUE,
booktabs=TRUE,
linesep=""
) %>%
kableExtra::kable_styling(
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::row_spec(0, bold = TRUE)%>%
kableExtra::column_spec(4, border_right = T)
Rectification count & matrix
perf_status_ops%>%
dplyr::filter(
Period == data.period,
ActiveIPRP == T | ActivePRP == T)%>%
dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>%
tidyr::pivot_wider(id_cols = Trading.Party.ID, names_from = Standard, values_from = Status, values_fill = list(Status = ""))%>%
dplyr::rename("Trading Party" = Trading.Party.ID)%>%
dplyr::select("Trading Party", sort(names(.)))%>%
kableExtra::kable(
format = "latex",
caption = "Number of Active Rectification plans by Trading Party",
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(2:(nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard, PerformanceMeasure))+1),width = "2.5cm")
Watch-list and areas of concern
There are currently r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, Action == "Watch")) Trading Parties on watch for OPS performance.
perf_status_ops%>%
dplyr::filter(Period == data.period,
Action == "Watch")%>%
dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>%
dplyr::select(Trading.Party.ID, Standard)%>%
kableExtra::kable(
format = "latex",
caption = "Trading Parties on watch for OPS performance",
col.names = c(
"Trading Party", "OPS"),
longtable = TRUE,
booktabs=TRUE,
linesep=""
) %>%
kableExtra::kable_styling(
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::row_spec(0, bold = TRUE)
Detailed Rectification Analysis
Below are the Performance graphs of IPRPs that are either Off-track or under review
IPRP_details_list <- perf_status_ops%>%
dplyr::filter(
Period == data.period,
(Status == "IPRP: Below plan" | Status == "PRP: Below plan" | UnderReview == T)
)
if(nrow(IPRP_details_list)>0){
for(i in 1:nrow(IPRP_details_list)){
plot(MOSLR::plot_perf_graph(
df = perf_status_ops,
trading.party = IPRP_details_list$Trading.Party.ID[i],
standard = IPRP_details_list$Standard[i],
include.iprp = T,
graph.title = paste(IPRP_details_list$Trading.Party.ID[i], IPRP_details_list$Standard[i], IPRP_details_list$PerformanceMeasure[i]),
performance.measure = IPRP_details_list$PerformanceMeasure[i]
))
}
}
Market Aggregates
metrics.list <- c("MarketMean", "MarketMedian", "MarketTaskVolume")
for (METRIC in metrics.list) {
for (MEASURE in unique(ops_summary$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")
}
}
\newpage
MPOP Reporting
res <- knitr::knit_child(
"MPOP.Rmd", envir = environment(), quiet = TRUE
)
cat(res, sep = '\n')
austl001/MOSLR documentation built on Aug. 17, 2022, 12:07 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Market Performance Standards
Performance Charges
mps_data_clean %>% dplyr::filter( Period <= data.period ) %>% dplyr::select( Period, Charges, Group ) %>% dplyr::group_by(Period, Group) %>% dplyr::summarise( Charges = sum(Charges) ) %>% ggplot2::ggplot( ggplot2::aes(x = Period, y = Charges, fill = Group) ) + ggplot2::geom_bar(stat = "identity") + ggplot2::theme( legend.title = ggplot2::element_blank(), 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", subtitle = "Breakdown by month and standard grouping", caption = "Source: MOSL" ) + ggplot2::ylab("") + ggplot2::xlab("")
# Creating table of charges by MPS ---------------- mps_data_clean %>% dplyr::filter( Period >= data.period %m-% months(11), 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() %>% dplyr::group_by(Period, Standard) %>% dplyr::summarise( Charges = sum(Charges, na.rm = TRUE) ) %>% dplyr::ungroup() %>% 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)
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring Trading Party performance against r length(unique(perf_status_mps$Standard)) standards. Performance for these standards is measured according to:
r paste(unique(perf_status_mps$PerformanceMeasure), collapse = " and ")
agg_perf <- mps_data_clean %>% dplyr::group_by(Period) %>% dplyr::summarise( Perf = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE) ) %>% dplyr::ungroup() %>% dplyr::mutate( Agg_Perf = zoo::rollapply(Perf, 6, mean, align = "right", fill = NA) ) %>% dplyr::rename(GroupPerf = Agg_Perf) %>% dplyr::mutate(Group = "Aggregate Performance") mps_data_clean %>% dplyr::group_by(Period, Group) %>% dplyr::summarise( Perf = sum(OnTimeTasks) / sum(TaskVolume), ) %>% dplyr::ungroup() %>% dplyr::group_by(Group) %>% dplyr::mutate( GroupPerf = zoo::rollapply(Perf, 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("MPS On-Time Task Completion"), subtitle = "Breakdown by month and standard", caption = "Source: MOSL\nNote: Aggregate performance corresponds to the 6-month average of aggregate MPS performance" ) + ggplot2::ylab("") + ggplot2::xlab("")
mps_data_clean %>% dplyr::filter( Period >= max(mps_data_clean$Period) %m-% months(11) ) %>% dplyr::group_by(Period, Standard) %>% dplyr::summarise( Performance = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE) ) %>% dplyr::ungroup() %>% dplyr::mutate( Performance = scales::percent(Performance, accuracy = 1), Period = format(Period, "%Y-%m") ) %>% tidyr::spread(Period, Performance) %>% kableExtra::kable( format = "latex", caption = "MPS Performance by Month", linesep = "", format.args = list(big.mark = ","), booktabs = TRUE, align = "c" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE, "scale_down" ) ) %>% kableExtra::row_spec(0, bold = TRUE)
for(sub.type in unique(mps_aggregate_perf$sub_type[!is.na(mps_aggregate_perf$sub_type)])){ tryCatch( expr = { plot(mps_aggregate_perf %>% dplyr::filter( sub_type == sub.type, Period == data.period ) %>% ggplot2::ggplot() + ggplot2::geom_bar( ggplot2::aes( x = reorder(ShortName, Agg_Perf_roll), y = Agg_Perf_roll ), stat = "identity", show.legend = FALSE, fill = "azure3", col = "black") + ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::theme( text = ggplot2::element_text(size = 15) ) + ggplot2::labs( title = "Ranking of Overall MPS Performance", subtitle = paste0( "Rolling 6-month mean of aggregate MPS performance by Trading Party (", sub.type, ")" ), caption = "Source: MOSL" ) + ggplot2::ylab("") + ggplot2::xlab("")) }, error = function(e){} ) }
Performance Monitoring and Rectification
Status Summary
There are currently r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, ActiveIPRP == T)) Active IPRPs and
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, ActivePRP == T)) Active PRPs for
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Trading.Party.ID)) Trading Parties and
r nrow(perf_status_mps%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard)) different MPS standards.
Rectification_sum <- perf_status_mps%>% dplyr::filter( Period == data.period, ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard, Status)%>% tidyr::pivot_wider(names_from = Status, values_from = n,values_fill = list(n = 0))%>% dplyr::mutate( Total = rowSums( dplyr::select(., -Standard), na.rm = T) )%>% dplyr::select( Standard, sort(names(.)) ) tryCatch( expr = { kableExtra::kable( Rectification_sum, format = "latex", caption = "Number of Active Rectification plans by Standard", booktabs=TRUE, linesep="" ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE, "scale_down") ) %>% kableExtra::row_spec(0, bold = TRUE)%>% kableExtra::column_spec(ncol(Rectification_sum)-1, border_right = T) }, error = function(e){} )
Retification count & matrix
tryCatch( expr = { perf_status_mps%>% dplyr::filter( Period == data.period, ActiveIPRP == T | ActivePRP == T)%>% tidyr::pivot_wider(id_cols = Trading.Party.ID, names_from = Standard, values_from = Status, values_fill = list(Status = ""))%>% dplyr::rename("Trading Party" = Trading.Party.ID)%>% dplyr::select("Trading Party", sort(names(.)))%>% kableExtra::kable( format = "latex", caption = "Number of Active Rectification plans by Trading Party", 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){} )
Watch-list and areas of concern
There are currently r nrow(perf_status_mps%>%dplyr::filter(Period == data.period, Action == "Watch")) Trading Parties on watch for MPS performance.
tryCatch( expr = { perf_status_mps%>% dplyr::filter(Period == data.period, Action == "Watch")%>% dplyr::select(Trading.Party.ID, Standard)%>% kableExtra::kable( format = "latex", caption = "Trading Parties on watch for MPS performance", col.names = c( "Trading Party", "MPS"), longtable = TRUE, booktabs=TRUE, linesep="" ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::row_spec(0, bold = TRUE) }, error = function(e){} )
Detailed Rectification Analysis
Below are the Performance graphs of Rectification plans that are either Off-track or under review
IPRP_details_list <- perf_status_mps%>% dplyr::filter( Period == data.period, (Status == "IPRP: Below plan" | Status == "PRP: Below plan" | UnderReview == T) ) if(nrow(IPRP_details_list)>0){ for(i in 1:nrow(IPRP_details_list)){ plot(MOSLR::plot_perf_graph( df = perf_status_mps, trading.party = IPRP_details_list$Trading.Party.ID[i], standard = IPRP_details_list$Standard[i], include.iprp = T, graph.title = paste(IPRP_details_list$Trading.Party.ID[i], IPRP_details_list$Standard[i]) )) } }
Market Aggregates
metrics.list <- c("MarketMean", "MarketMedian", "MarketTaskVolume") for (METRIC in metrics.list) { mps_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") }
\newpage
Operational Performance Standards
Performance Charges
ops_data_clean %>% 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("")
# Creating table of charges by OPS ---------------- ops_data_clean %>% dplyr::filter( Period >= data.period %m-% months(11), 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() %>% dplyr::group_by(Period, Standard) %>% dplyr::summarise( Charges = sum(Charges, na.rm = TRUE) ) %>% dplyr::ungroup() %>% 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)
Performance Overview
As of r format(data.period, "%B-%Y"), MOSL were monitoring Trading Party performance against r length(unique(perf_status_ops$Standard[!is.na(perf_status_ops$Standard)])) standards. Performance for these standards is measured according to:
r paste(unique(perf_status_ops$PerformanceMeasure), collapse = " and ")
agg_perf <- ops_data_clean %>% dplyr::group_by(Period, PerformanceMeasure) %>% dplyr::summarise( Perf = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE) ) %>% dplyr::ungroup() %>% dplyr::mutate( Agg_Perf = zoo::rollapply(Perf, 6, mean, align = "right", fill = NA) ) %>% dplyr::rename(GroupPerf = Agg_Perf) %>% dplyr::mutate(Group = "Aggregate Performance") ops_data_clean %>% dplyr::group_by(Period,PerformanceMeasure, Group) %>% dplyr::summarise( Perf = sum(OnTimeTasks) / sum(TaskVolume), ) %>% dplyr::ungroup() %>% dplyr::group_by(Group) %>% dplyr::mutate( GroupPerf = zoo::rollapply(Perf, 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("Completed and Outstanding Tasks OPS Performance"), 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)
ops_data_clean %>% dplyr::filter( Period >= max(ops_data_clean$Period, na.rm = T) %m-% months(11), PerformanceMeasure == "Completed" ) %>% dplyr::group_by(Period, Standard) %>% dplyr::summarise( Performance = sum(OnTimeTasks, na.rm = TRUE) / sum(TaskVolume, na.rm = TRUE) ) %>% dplyr::ungroup() %>% dplyr::mutate( Performance = scales::percent(Performance, accuracy = 1), Period = format(Period, "%Y-%m") ) %>% tidyr::spread(Period, Performance) %>% kableExtra::kable( format = "latex", caption = "OPS Performance by Month", linesep = "", format.args = list(big.mark = ","), booktabs = TRUE, align = "c" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE, "scale_down" ) ) %>% kableExtra::row_spec(0, bold = TRUE)
for(sub.type in unique(ops_aggregate_perf$sub_type[!is.na(ops_aggregate_perf$sub_type)])){ tryCatch( expr = { plot(ops_aggregate_perf %>% dplyr::filter( sub_type == sub.type, Period == data.period, PerformanceMeasure == "Completed" ) %>% ggplot2::ggplot() + ggplot2::geom_bar( ggplot2::aes( x = reorder(ShortName, Agg_Perf_roll), y = Agg_Perf_roll ), stat = "identity", show.legend = FALSE, fill = "azure3", col = "black") + ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::theme( text = ggplot2::element_text(size = 15) ) + ggplot2::labs( title = "Ranking of Overall OPS Performance", subtitle = paste0( "Rolling 6-month mean of aggregate OPS performance by Trading Party (", sub.type, ")" ), caption = "Source: MOSL" ) + ggplot2::ylab("") + ggplot2::xlab("")) }, error = function(e){} ) }
Performance Monitoring and Rectification
Status Summary
There are currently r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, ActiveIPRP == T)) Active IPRPs and r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, ActivePRP == T)) Active PRPs for
r nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Trading.Party.ID)) Trading Parties and
r nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard, PerformanceMeasure)) different OPS standards.
perf_status_ops%>% dplyr::filter( Period == data.period, ActiveIPRP == T | ActivePRP == T)%>% dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>% dplyr::count(Standard, Status)%>% tidyr::pivot_wider(names_from = Status, values_from = n,values_fill = list(n = 0))%>% dplyr::mutate( Total = rowSums( dplyr::select(., -Standard), na.rm = T) )%>% kableExtra::kable( format = "latex", caption = "Number of Active Rectifications by Standard", longtable = TRUE, booktabs=TRUE, linesep="" ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::row_spec(0, bold = TRUE)%>% kableExtra::column_spec(4, border_right = T)
Rectification count & matrix
perf_status_ops%>% dplyr::filter( Period == data.period, ActiveIPRP == T | ActivePRP == T)%>% dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>% tidyr::pivot_wider(id_cols = Trading.Party.ID, names_from = Standard, values_from = Status, values_fill = list(Status = ""))%>% dplyr::rename("Trading Party" = Trading.Party.ID)%>% dplyr::select("Trading Party", sort(names(.)))%>% kableExtra::kable( format = "latex", caption = "Number of Active Rectification plans by Trading Party", 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(2:(nrow(perf_status_ops%>%dplyr::filter(Period == data.period,ActiveIPRP == T | ActivePRP == T)%>% dplyr::count(Standard, PerformanceMeasure))+1),width = "2.5cm")
Watch-list and areas of concern
There are currently r nrow(perf_status_ops%>%dplyr::filter(Period == data.period, Action == "Watch")) Trading Parties on watch for OPS performance.
perf_status_ops%>% dplyr::filter(Period == data.period, Action == "Watch")%>% dplyr::mutate(Standard = paste(Standard, PerformanceMeasure))%>% dplyr::select(Trading.Party.ID, Standard)%>% kableExtra::kable( format = "latex", caption = "Trading Parties on watch for OPS performance", col.names = c( "Trading Party", "OPS"), longtable = TRUE, booktabs=TRUE, linesep="" ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::row_spec(0, bold = TRUE)
Detailed Rectification Analysis
Below are the Performance graphs of IPRPs that are either Off-track or under review
IPRP_details_list <- perf_status_ops%>% dplyr::filter( Period == data.period, (Status == "IPRP: Below plan" | Status == "PRP: Below plan" | UnderReview == T) ) if(nrow(IPRP_details_list)>0){ for(i in 1:nrow(IPRP_details_list)){ plot(MOSLR::plot_perf_graph( df = perf_status_ops, trading.party = IPRP_details_list$Trading.Party.ID[i], standard = IPRP_details_list$Standard[i], include.iprp = T, graph.title = paste(IPRP_details_list$Trading.Party.ID[i], IPRP_details_list$Standard[i], IPRP_details_list$PerformanceMeasure[i]), performance.measure = IPRP_details_list$PerformanceMeasure[i] )) } }
Market Aggregates
metrics.list <- c("MarketMean", "MarketMedian", "MarketTaskVolume") for (METRIC in metrics.list) { for (MEASURE in unique(ops_summary$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") } }
\newpage
MPOP Reporting
res <- knitr::knit_child( "MPOP.Rmd", envir = environment(), quiet = TRUE ) cat(res, sep = '\n')
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