In austl001/MOSLR: MOSL Reports and Utility Functions
flagged_mps_table <- readRDS(file = paste0(dir, "/data/rdata/flagged_mps_table.Rda"))
flagged_milestones_mps <- readRDS(file = paste0(dir, "/data/rdata/flagged_milestones_mps.Rda"))
flagged_iprp_end_mps <- readRDS(file = paste0(dir, "/data/rdata/flagged_iprp_end_mps.Rda"))
watch_mps <- readRDS(file = paste0(dir, "/data/rdata/watch_mps.Rda"))
flagged_mps_data_melt <- readRDS(file = paste0(dir, "/data/rdata/flagged_mps_data_melt.Rda"))
IPRP_plans_melt_mps <- readRDS(file = paste0(dir, "/data/rdata/IPRP_plans_melt_mps.Rda"))
watch_mps_melt <- readRDS(file = paste0(dir, "/data/rdata/watch_mps_melt.Rda"))
mps_summary_melt <- readRDS(file = paste0(dir, "/data/rdata/mps_summary_melt.Rda"))
iprp_plan_comparison_mps <- readRDS(file = paste0(dir, "/data/rdata/iprp_plan_comparison_mps.Rda"))
iprp_status_mps <- readRDS(file = paste0(dir, "/data/rdata/iprp_status_mps.Rda"))
\newpage
Summary of Performance and MOSL Actions
Overview
There were r nrow(flagged_mps_table) flags for performance issues this month, including r nrow(flagged_mps_table[flagged_mps_table$Watch_list == "Yes",]) issues that were flagged because they were on the Watch-list. These flags cover r NROW(unique(flagged_mps_table$Trading.Party.ID)) Trading Parties and the following Market Performance Standards: r unique(flagged_mps_table$MPS). Of the r nrow(flagged_mps_table[flagged_mps_table$Watch_list == "No",]) performance issues not already on the watch-list:
r nrow(flagged_mps_table[flagged_mps_table$Action == "IPRP" & flagged_mps_table$Watch_list == "No",]) have been escalated to an IPRPr nrow(flagged_mps_table[flagged_mps_table$Action == "Watch" & flagged_mps_table$Watch_list == "No",]) have been placed on the watch-list
Of the r nrow(unique(flagged_mps_table[flagged_mps_table$Watch_list == "Yes",])) performance issues on the MPS Watch-list this month:
r nrow(flagged_mps_table[flagged_mps_table$Action == "IPRP" & flagged_mps_table$Watch_list == "Yes",]) have been escalated to an IPRPr nrow(flagged_mps_table[flagged_mps_table$Action == "Watch" & flagged_mps_table$Watch_list == "Yes",]) remain on the watch-listr nrow(flagged_mps_table[tolower(flagged_mps_table$Action) == "none" & flagged_mps_table$Watch_list == "Yes",]) were removed from the watch-list
There were r nrow(flagged_milestones_mps[flagged_milestones_mps$Watch_list == "No",]) IPRPs flagged for performance being more than 5% below the planned milestone. Of these:
r nrow(flagged_milestones_mps %>% dplyr::filter(tolower(Action) == "none" & Watch_list == "No")) have had no action taken against themr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Resubmit" & Watch_list == "No")) have been asked to re-submit an IPRPr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Escalate_MPC" & Watch_list == "No")) have been escalated to MPC / Panel by MOSLr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Watch" & Watch_list == "No")) have been placed on watch with increased scrutiny
There were r nrow(flagged_milestones_mps[flagged_milestones_mps$Watch_list == "Yes",]) IPRP(s) on the Watch-list. Of these:
r nrow(flagged_milestones_mps %>% dplyr::filter(tolower(Action) == "none" & Watch_list == "Yes")) have had no action taken against themr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Resubmit" & Watch_list == "Yes")) have been asked to re-submit an IPRPr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Escalate_MPC" & Watch_list == "Yes")) have been escalated to MPC / Panel by MOSLr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Watch" & Watch_list == "Yes")) have been placed on watch with increased scrutiny
There were r nrow(flagged_iprp_end_mps) IPRP(s) that reached the end of their plans this month. Of these:
r nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "De-escalate")) have been de-escalated to temporary watch statusr nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "Extend")) have been extended for a further periodr nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "Watch")) have been placed on watch and are under review
The tables below summarise the monthly performance triggers and the IPRP milestone flags with the corresponding MOSL actions and rationale.
Monthly performance flags and watch-list
kableExtra::kable(
flagged_mps_table,
format = "latex",
caption = "Trading Parties flagged for below peer performance",
col.names = c(
"Trading Party",
"MPS",
"Action",
"Rationale",
"Watch"
),
longtable = TRUE,
booktabs = TRUE,
linesep = ""
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::column_spec(4, width = "8.5cm") %>%
kableExtra::row_spec(0, bold = TRUE)
IPRP milestone triggers and watch-list
kableExtra::kable(
flagged_milestones_mps %>%
dplyr::select(Trading.Party.ID, MPS, Batch, Action, Rationale),
format = "latex",
caption = "IPRPs flagged for performance below monthly milestone",
col.names = c(
"Trading Party",
"MPS",
"Batch",
"Action",
"Rationale"
),
longtable = TRUE,
booktabs = TRUE,
linesep = ""
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::column_spec(5, width = "7cm")%>%
kableExtra::row_spec(0, bold = TRUE)
End of IPRPs
kableExtra::kable(
flagged_iprp_end_mps %>%
dplyr::select(Trading.Party.ID, MPS, Batch, Action, Rationale),
format = "latex",
caption = "IPRPs that have reached the end of their plans",
col.names = c(
"Trading Party",
"MPS",
"Batch",
"Action",
"Rationale"
),
longtable = TRUE,
booktabs = TRUE,
linesep = ""
) %>%
kableExtra::kable_styling(
font_size = 10,
latex_options = c(
"repeat_header",
"hold_position",
"striped",
position = "center",
full_width = FALSE)
) %>%
kableExtra::column_spec(5,width = "7cm")%>%
kableExtra::row_spec(0, bold = TRUE)
Watch-list
The two tables below show the Trading Parties that were on watch prior to this month's actions.
MPS
There are currently r nrow(watch_mps) Trading Parties on watch for MPS performance.
if (nrow(watch_mps) > 0) {
kableExtra::kable(
watch_mps,
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)
}
\pagebreak
Performance Graphs
Monthly Performance Triggers (flagged Trading Parties only)
Below are graphs for those Trading Parties flagged for below-peer performance by MPS, as indicated in Table 1 above.
flagged_labels <- c("Task Completion (RHS)","Mean (RHS)","Median (RHS)","Task Share (RHS)")
##producing the MPS flagged graphs as list then using marrangeGrob
plist_flagged = lapply(split(flagged_mps_data_melt, flagged_mps_data_melt$key), function(z) {
data <- z
plot <- ggplot2::ggplot(data) +
ggplot2::geom_bar(
ggplot2::aes(
x = Date,
y = TaskVolume,
fill = "Task Volume (LHS)"
),
stat = "identity",
position = "dodge",
inherit.aes = FALSE
) +
ggplot2::geom_line(
ggplot2::aes(
x = Date,
y = value * max(data$TaskVolume),
colour = variable,
linetype = variable,
size = variable
)
) +
ggplot2::scale_y_continuous(
sec.axis = ggplot2::sec_axis(~ . / max(data$TaskVolume), name = "Proportion")
) +
ggplot2::scale_fill_manual(
NULL, values = "azure3", na.value = "red"
) +
ggplot2::scale_size_manual(
values = c(1,0.5,0.5,0.5),
na.value = "1",
labels = flagged_labels
) +
ggplot2::scale_linetype_manual(
values = c(1,2,1,3),
na.value = "1",
labels = flagged_labels
) +
ggplot2::scale_colour_manual(
values = c("darkorange", "azure4", "dodgerblue4","grey3"),
na.value = "red",
labels = flagged_labels
) +
ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
legend.position = "right") +
ggplot2::ggtitle(paste(Trading.Party.ID, " (", MPS, ")", sep = ""))
}
)
gridExtra::marrangeGrob(plist_flagged,top=NULL,nrow=1,ncol=1)
\pagebreak
IPRP Performance Comparison (all plans)
The graphs below depict the performance of each IPRP against its planned milestones, including--but not limited to--those flagged for below-planned performance (as indicated in Table 2).
# Producing the graphs as list then using marrangeGrob to plot them separately --------------------------------------------------------------------------------------------------------------------------------------------------------------------
iprp_labels <- c("Task Completion (RHS)", "Mean (RHS)", "Median (RHS)", "Task Share (RHS)", "Planned Perf (RHS)")
plist_IPRP = lapply(split(IPRP_plans_melt_mps, IPRP_plans_melt_mps$key), function(data) {
plot <- ggplot2::ggplot(data) +
ggplot2::geom_bar(
ggplot2::aes(
x = Date,
y = TaskVolume,
fill = "Task Volume (LHS)"),
stat = "identity",
position = "dodge",
inherit.aes = FALSE
) +
ggplot2::geom_line(
ggplot2::aes(
x = Date,
y = value * max(data$TaskVolume),
colour = variable,
linetype = variable,
size = variable
)
) +
ggplot2::geom_point(
ggplot2::aes(
x = Date,
y = value * max(data$TaskVolume),
shape = variable,
alpha = variable
)
) +
ggplot2::scale_y_continuous(
sec.axis = ggplot2::sec_axis(~. / max(data$TaskVolume), name = "Proportion")
) +
ggplot2::scale_fill_manual(
NULL,
values = "azure3",
na.value = "red"
) +
ggplot2::scale_size_manual(
values = c(1, 0.5, 0.5, 0.5, 1),
na.value = "1",
labels = iprp_labels
) +
ggplot2::scale_linetype_manual(
values = c(1, 2, 1, 3, 1),
na.value = "1",
labels = iprp_labels
) +
ggplot2::scale_colour_manual(
values = c("darkorange", "azure4", "dodgerblue4", "grey3", "red"),
na.value="red",
labels = iprp_labels
) +
ggplot2::scale_shape_manual(
values = c(0, 0, 0, 0, 1),
na.value = 0,
labels = iprp_labels
) +
ggplot2::scale_alpha_manual(
values = c(0, 0, 0, 0, 1),
na.value = 0,
labels = iprp_labels
) +
ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
legend.position = "right"
) +
ggplot2::ggtitle(
paste0(
"Batch ",
max(as.numeric(data$Batch), na.rm = TRUE), ": ", data$Trading.Party.ID, " (", data$MPS, ")")
)
}
)
gridExtra::marrangeGrob(plist_IPRP, top = NULL, nrow = 1, ncol = 1)
Watch-list
The graphs below depict the performance of issue(s) on the watch-list (not including IPRPs).
watch_labels <- c("Task Completion (RHS)", "Mean (RHS)", "Median (RHS)", "Task Share (RHS)")
plist_watch = lapply(split(watch_mps_melt, watch_mps_melt$key), function(data) {
plot <- ggplot2::ggplot(data) +
ggplot2::geom_bar(
ggplot2::aes(
x = Date,
y = TaskVolume,
fill = "Task Volume (LHS)"
),
stat = "identity",
position = "dodge",
inherit.aes = FALSE
) +
ggplot2::geom_line(
ggplot2::aes(
x = Date,
y = value * max(data$TaskVolume),
colour = variable,
linetype = variable,
size = variable)
) +
ggplot2::geom_point(
ggplot2::aes(
x = Date,
y = value * max(data$TaskVolume),
shape = variable,
alpha = variable)
) +
ggplot2::scale_y_continuous(
sec.axis = ggplot2::sec_axis(~. / max(data$TaskVolume), name = "Proportion")
) +
ggplot2::scale_fill_manual(
NULL,
values = "azure3",
na.value = "red"
) +
ggplot2::scale_size_manual(
values = c(1, 0.5, 0.5, 0.5, 1),
na.value = "1",
labels = iprp_labels
) +
ggplot2::scale_linetype_manual(
values = c(1, 2, 1, 3, 1),
na.value = "1",
labels = iprp_labels
) +
ggplot2::scale_colour_manual(
values = c("darkorange", "azure4", "dodgerblue4", "grey3", "red"),
na.value = "red",
labels = iprp_labels
) +
ggplot2::scale_shape_manual(
values = c(0, 0, 0, 0, 1),
na.value = 0,
labels = iprp_labels
) +
ggplot2::scale_alpha_manual(
values = c(0, 0, 0, 0, 1),
na.value = 0,
labels = iprp_labels
) +
ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
legend.position = "right"
) +
ggplot2::ggtitle(paste0(data$Trading.Party.ID," (", data$MPS, ")"))
}
)
gridExtra::marrangeGrob(plist_watch, top = NULL, nrow = 1, ncol = 1)
Aggregated MPS
The graphs below show the aggregated performance for each MPS.
label_agg <- c("Mean (RHS)","Median (RHS)")
plist_mps_agg = lapply(split(mps_summary_melt, mps_summary_melt$MPS), function(data) {
ggplot2::ggplot(data) +
ggplot2::geom_bar(
ggplot2::aes(
x = Date,
y = TotalTaskVolume,
fill = "Task Volume (LHS)"),
stat = "identity",
position = "dodge",
inherit.aes = FALSE
) +
ggplot2::geom_line(
ggplot2::aes(
x = Date,
y = value * max(data$TotalTaskVolume),
colour = variable,
linetype = variable)
) +
ggplot2::scale_y_continuous(
sec.axis = ggplot2::sec_axis(~. / max(data$TotalTaskVolume), name = "Proportion")
) +
ggplot2::scale_fill_manual(
NULL, values = "azure3"
) +
ggplot2::scale_linetype_manual(
values = c(2,1),
labels = label_agg
) +
ggplot2::scale_colour_manual(
values = c("azure4", "dodgerblue4"),
labels = label_agg
) +
ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") +
ggplot2::theme(
legend.title = ggplot2::element_blank(),
legend.position = "right"
) +
ggplot2::ggtitle(paste(data$MPS))
}
)
gridExtra::marrangeGrob(plist_mps_agg, top = NULL, nrow = 1, ncol = 1)
\newpage
IPRP Analytics
IPRP Performance: Delta to Plan and Market Level
The graphs below give an indication of whether, overall, Trading Parties are meeting or exceeding their IPRP milestone targets (left-hand side graph); and an indication of whether performance, overall, is converging with peer-level performance. The graphs are a general guide only, as the data is aggregated across all plans.
plott1 <- iprp_plan_comparison_mps %>%
dplyr::filter(Date <= Sys.Date()) %>%
dplyr::group_by(Date) %>%
dplyr::summarise(mean_delta = mean(Delta, na.rm = TRUE)) %>%
ggplot2::ggplot(
ggplot2::aes(
x = Date,
y = mean_delta)
) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::ylab("Mean Difference") + ggplot2::xlab("Date") +
geom_label(
ggplot2::aes(
label = format(round(mean_delta, 2), nsmall = 2))
) +
ggplot2::ggtitle("All batches: mean difference between \n actual and planned performance")
plott2 <- iprp_plan_comparison_mps %>%
dplyr::filter(Date <= Sys.Date()) %>%
dplyr::mutate(delta_to_mean = TaskCompletion - MPS_Mean) %>%
dplyr::group_by(Date) %>%
dplyr::summarise(mean_delta_to_mean = mean(delta_to_mean, na.rm = TRUE)) %>%
ggplot2::ggplot(
ggplot2::aes(
x = Date,
y = mean_delta_to_mean)
) +
ggplot2::geom_line() +
ggplot2::geom_point() +
ggplot2::ylab("Delta to the Market MPS level") + ggplot2::xlab("Date") +
geom_label(
ggplot2::aes(
label = format(round(mean_delta_to_mean, 2 ), nsmall = 2))
) +
ggplot2::ggtitle("All batches: mean difference between \n performance and market MPS level")
plot_list = c(plott1,plott2)
g <- gridExtra::arrangeGrob(plott1, plott2, nrow = 1, ncol = 2)
plot(g)
\newpage
Current Status of IPRPs
The current status of each IPRP is shown in the table below.
kableExtra::kable(
iprp_status_mps,
format = "latex",
caption = "Current status of IPRP(s)",
col.names = c(
"Trading Party",
"MPS",
"Batch",
"Actual Perf.",
"Planned Perf.",
"Status"),
longtable = TRUE,
booktabs = TRUE,
linesep = ""
) %>%
kableExtra::kable_styling(
latex_options =
c(
"repeat_header",
"hold_position",
"striped",
position= "center",
full_width = TRUE
)
) %>%
kableExtra::row_spec(0, bold = TRUE)
The graphs below depict the changes in status of IPRPs (on-track, close and off-track).
ploty <- iprp_plan_comparison_mps %>%
dplyr::select(
Date, OnTrack, Close, OffTrack, Batch
) %>%
tidyr::gather(key = "variable", value = "value", OnTrack, Close, OffTrack) %>%
dplyr::mutate(
variable2 = factor(variable, levels = c("OffTrack", "Close", "OnTrack"))
) %>%
ggplot2::ggplot(
ggplot2::aes(
x = Date,
y = value,
fill = variable2)
) +
ggplot2::geom_bar(stat = "identity") +
labs(
title = "IPRP Performance",
subtitle = "Count of active plans by month and status"
) +
ggplot2::ylab("Count of plans by status") + ggplot2::xlab("") +
ggplot2::theme_minimal(base_size = 11) +
ggplot2::scale_fill_manual(values = c("darkorange", "azure3", "dodgerblue4")) +
ggplot2::theme(
legend.position = "top",
legend.title = ggplot2::element_blank()
)
plot(ploty)
austl001/MOSLR documentation built on Aug. 17, 2022, 12:07 a.m.
R Package Documentation
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flagged_mps_table <- readRDS(file = paste0(dir, "/data/rdata/flagged_mps_table.Rda")) flagged_milestones_mps <- readRDS(file = paste0(dir, "/data/rdata/flagged_milestones_mps.Rda")) flagged_iprp_end_mps <- readRDS(file = paste0(dir, "/data/rdata/flagged_iprp_end_mps.Rda")) watch_mps <- readRDS(file = paste0(dir, "/data/rdata/watch_mps.Rda")) flagged_mps_data_melt <- readRDS(file = paste0(dir, "/data/rdata/flagged_mps_data_melt.Rda")) IPRP_plans_melt_mps <- readRDS(file = paste0(dir, "/data/rdata/IPRP_plans_melt_mps.Rda")) watch_mps_melt <- readRDS(file = paste0(dir, "/data/rdata/watch_mps_melt.Rda")) mps_summary_melt <- readRDS(file = paste0(dir, "/data/rdata/mps_summary_melt.Rda")) iprp_plan_comparison_mps <- readRDS(file = paste0(dir, "/data/rdata/iprp_plan_comparison_mps.Rda")) iprp_status_mps <- readRDS(file = paste0(dir, "/data/rdata/iprp_status_mps.Rda"))
\newpage
Summary of Performance and MOSL Actions
Overview
There were r nrow(flagged_mps_table) flags for performance issues this month, including r nrow(flagged_mps_table[flagged_mps_table$Watch_list == "Yes",]) issues that were flagged because they were on the Watch-list. These flags cover r NROW(unique(flagged_mps_table$Trading.Party.ID)) Trading Parties and the following Market Performance Standards: r unique(flagged_mps_table$MPS). Of the r nrow(flagged_mps_table[flagged_mps_table$Watch_list == "No",]) performance issues not already on the watch-list:
r nrow(flagged_mps_table[flagged_mps_table$Action == "IPRP" & flagged_mps_table$Watch_list == "No",])have been escalated to an IPRPr nrow(flagged_mps_table[flagged_mps_table$Action == "Watch" & flagged_mps_table$Watch_list == "No",])have been placed on the watch-list
Of the r nrow(unique(flagged_mps_table[flagged_mps_table$Watch_list == "Yes",])) performance issues on the MPS Watch-list this month:
r nrow(flagged_mps_table[flagged_mps_table$Action == "IPRP" & flagged_mps_table$Watch_list == "Yes",])have been escalated to an IPRPr nrow(flagged_mps_table[flagged_mps_table$Action == "Watch" & flagged_mps_table$Watch_list == "Yes",])remain on the watch-listr nrow(flagged_mps_table[tolower(flagged_mps_table$Action) == "none" & flagged_mps_table$Watch_list == "Yes",])were removed from the watch-list
There were r nrow(flagged_milestones_mps[flagged_milestones_mps$Watch_list == "No",]) IPRPs flagged for performance being more than 5% below the planned milestone. Of these:
r nrow(flagged_milestones_mps %>% dplyr::filter(tolower(Action) == "none" & Watch_list == "No"))have had no action taken against themr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Resubmit" & Watch_list == "No"))have been asked to re-submit an IPRPr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Escalate_MPC" & Watch_list == "No"))have been escalated to MPC / Panel by MOSLr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Watch" & Watch_list == "No"))have been placed on watch with increased scrutiny
There were r nrow(flagged_milestones_mps[flagged_milestones_mps$Watch_list == "Yes",]) IPRP(s) on the Watch-list. Of these:
r nrow(flagged_milestones_mps %>% dplyr::filter(tolower(Action) == "none" & Watch_list == "Yes"))have had no action taken against themr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Resubmit" & Watch_list == "Yes"))have been asked to re-submit an IPRPr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Escalate_MPC" & Watch_list == "Yes"))have been escalated to MPC / Panel by MOSLr nrow(flagged_milestones_mps %>% dplyr::filter(Action == "Watch" & Watch_list == "Yes"))have been placed on watch with increased scrutiny
There were r nrow(flagged_iprp_end_mps) IPRP(s) that reached the end of their plans this month. Of these:
r nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "De-escalate"))have been de-escalated to temporary watch statusr nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "Extend"))have been extended for a further periodr nrow(flagged_iprp_end_mps %>% dplyr::filter(Action == "Watch"))have been placed on watch and are under review
The tables below summarise the monthly performance triggers and the IPRP milestone flags with the corresponding MOSL actions and rationale.
Monthly performance flags and watch-list
kableExtra::kable( flagged_mps_table, format = "latex", caption = "Trading Parties flagged for below peer performance", col.names = c( "Trading Party", "MPS", "Action", "Rationale", "Watch" ), longtable = TRUE, booktabs = TRUE, linesep = "" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::column_spec(4, width = "8.5cm") %>% kableExtra::row_spec(0, bold = TRUE)
IPRP milestone triggers and watch-list
kableExtra::kable( flagged_milestones_mps %>% dplyr::select(Trading.Party.ID, MPS, Batch, Action, Rationale), format = "latex", caption = "IPRPs flagged for performance below monthly milestone", col.names = c( "Trading Party", "MPS", "Batch", "Action", "Rationale" ), longtable = TRUE, booktabs = TRUE, linesep = "" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::column_spec(5, width = "7cm")%>% kableExtra::row_spec(0, bold = TRUE)
End of IPRPs
kableExtra::kable( flagged_iprp_end_mps %>% dplyr::select(Trading.Party.ID, MPS, Batch, Action, Rationale), format = "latex", caption = "IPRPs that have reached the end of their plans", col.names = c( "Trading Party", "MPS", "Batch", "Action", "Rationale" ), longtable = TRUE, booktabs = TRUE, linesep = "" ) %>% kableExtra::kable_styling( font_size = 10, latex_options = c( "repeat_header", "hold_position", "striped", position = "center", full_width = FALSE) ) %>% kableExtra::column_spec(5,width = "7cm")%>% kableExtra::row_spec(0, bold = TRUE)
Watch-list
The two tables below show the Trading Parties that were on watch prior to this month's actions.
MPS
There are currently r nrow(watch_mps) Trading Parties on watch for MPS performance.
if (nrow(watch_mps) > 0) { kableExtra::kable( watch_mps, 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) }
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Performance Graphs
Monthly Performance Triggers (flagged Trading Parties only)
Below are graphs for those Trading Parties flagged for below-peer performance by MPS, as indicated in Table 1 above.
flagged_labels <- c("Task Completion (RHS)","Mean (RHS)","Median (RHS)","Task Share (RHS)") ##producing the MPS flagged graphs as list then using marrangeGrob plist_flagged = lapply(split(flagged_mps_data_melt, flagged_mps_data_melt$key), function(z) { data <- z plot <- ggplot2::ggplot(data) + ggplot2::geom_bar( ggplot2::aes( x = Date, y = TaskVolume, fill = "Task Volume (LHS)" ), stat = "identity", position = "dodge", inherit.aes = FALSE ) + ggplot2::geom_line( ggplot2::aes( x = Date, y = value * max(data$TaskVolume), colour = variable, linetype = variable, size = variable ) ) + ggplot2::scale_y_continuous( sec.axis = ggplot2::sec_axis(~ . / max(data$TaskVolume), name = "Proportion") ) + ggplot2::scale_fill_manual( NULL, values = "azure3", na.value = "red" ) + ggplot2::scale_size_manual( values = c(1,0.5,0.5,0.5), na.value = "1", labels = flagged_labels ) + ggplot2::scale_linetype_manual( values = c(1,2,1,3), na.value = "1", labels = flagged_labels ) + ggplot2::scale_colour_manual( values = c("darkorange", "azure4", "dodgerblue4","grey3"), na.value = "red", labels = flagged_labels ) + ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") + ggplot2::theme( legend.title = ggplot2::element_blank(), legend.position = "right") + ggplot2::ggtitle(paste(Trading.Party.ID, " (", MPS, ")", sep = "")) } ) gridExtra::marrangeGrob(plist_flagged,top=NULL,nrow=1,ncol=1)
\pagebreak
IPRP Performance Comparison (all plans)
The graphs below depict the performance of each IPRP against its planned milestones, including--but not limited to--those flagged for below-planned performance (as indicated in Table 2).
# Producing the graphs as list then using marrangeGrob to plot them separately -------------------------------------------------------------------------------------------------------------------------------------------------------------------- iprp_labels <- c("Task Completion (RHS)", "Mean (RHS)", "Median (RHS)", "Task Share (RHS)", "Planned Perf (RHS)") plist_IPRP = lapply(split(IPRP_plans_melt_mps, IPRP_plans_melt_mps$key), function(data) { plot <- ggplot2::ggplot(data) + ggplot2::geom_bar( ggplot2::aes( x = Date, y = TaskVolume, fill = "Task Volume (LHS)"), stat = "identity", position = "dodge", inherit.aes = FALSE ) + ggplot2::geom_line( ggplot2::aes( x = Date, y = value * max(data$TaskVolume), colour = variable, linetype = variable, size = variable ) ) + ggplot2::geom_point( ggplot2::aes( x = Date, y = value * max(data$TaskVolume), shape = variable, alpha = variable ) ) + ggplot2::scale_y_continuous( sec.axis = ggplot2::sec_axis(~. / max(data$TaskVolume), name = "Proportion") ) + ggplot2::scale_fill_manual( NULL, values = "azure3", na.value = "red" ) + ggplot2::scale_size_manual( values = c(1, 0.5, 0.5, 0.5, 1), na.value = "1", labels = iprp_labels ) + ggplot2::scale_linetype_manual( values = c(1, 2, 1, 3, 1), na.value = "1", labels = iprp_labels ) + ggplot2::scale_colour_manual( values = c("darkorange", "azure4", "dodgerblue4", "grey3", "red"), na.value="red", labels = iprp_labels ) + ggplot2::scale_shape_manual( values = c(0, 0, 0, 0, 1), na.value = 0, labels = iprp_labels ) + ggplot2::scale_alpha_manual( values = c(0, 0, 0, 0, 1), na.value = 0, labels = iprp_labels ) + ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") + ggplot2::theme( legend.title = ggplot2::element_blank(), legend.position = "right" ) + ggplot2::ggtitle( paste0( "Batch ", max(as.numeric(data$Batch), na.rm = TRUE), ": ", data$Trading.Party.ID, " (", data$MPS, ")") ) } ) gridExtra::marrangeGrob(plist_IPRP, top = NULL, nrow = 1, ncol = 1)
Watch-list
The graphs below depict the performance of issue(s) on the watch-list (not including IPRPs).
watch_labels <- c("Task Completion (RHS)", "Mean (RHS)", "Median (RHS)", "Task Share (RHS)") plist_watch = lapply(split(watch_mps_melt, watch_mps_melt$key), function(data) { plot <- ggplot2::ggplot(data) + ggplot2::geom_bar( ggplot2::aes( x = Date, y = TaskVolume, fill = "Task Volume (LHS)" ), stat = "identity", position = "dodge", inherit.aes = FALSE ) + ggplot2::geom_line( ggplot2::aes( x = Date, y = value * max(data$TaskVolume), colour = variable, linetype = variable, size = variable) ) + ggplot2::geom_point( ggplot2::aes( x = Date, y = value * max(data$TaskVolume), shape = variable, alpha = variable) ) + ggplot2::scale_y_continuous( sec.axis = ggplot2::sec_axis(~. / max(data$TaskVolume), name = "Proportion") ) + ggplot2::scale_fill_manual( NULL, values = "azure3", na.value = "red" ) + ggplot2::scale_size_manual( values = c(1, 0.5, 0.5, 0.5, 1), na.value = "1", labels = iprp_labels ) + ggplot2::scale_linetype_manual( values = c(1, 2, 1, 3, 1), na.value = "1", labels = iprp_labels ) + ggplot2::scale_colour_manual( values = c("darkorange", "azure4", "dodgerblue4", "grey3", "red"), na.value = "red", labels = iprp_labels ) + ggplot2::scale_shape_manual( values = c(0, 0, 0, 0, 1), na.value = 0, labels = iprp_labels ) + ggplot2::scale_alpha_manual( values = c(0, 0, 0, 0, 1), na.value = 0, labels = iprp_labels ) + ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") + ggplot2::theme( legend.title = ggplot2::element_blank(), legend.position = "right" ) + ggplot2::ggtitle(paste0(data$Trading.Party.ID," (", data$MPS, ")")) } ) gridExtra::marrangeGrob(plist_watch, top = NULL, nrow = 1, ncol = 1)
Aggregated MPS
The graphs below show the aggregated performance for each MPS.
label_agg <- c("Mean (RHS)","Median (RHS)") plist_mps_agg = lapply(split(mps_summary_melt, mps_summary_melt$MPS), function(data) { ggplot2::ggplot(data) + ggplot2::geom_bar( ggplot2::aes( x = Date, y = TotalTaskVolume, fill = "Task Volume (LHS)"), stat = "identity", position = "dodge", inherit.aes = FALSE ) + ggplot2::geom_line( ggplot2::aes( x = Date, y = value * max(data$TotalTaskVolume), colour = variable, linetype = variable) ) + ggplot2::scale_y_continuous( sec.axis = ggplot2::sec_axis(~. / max(data$TotalTaskVolume), name = "Proportion") ) + ggplot2::scale_fill_manual( NULL, values = "azure3" ) + ggplot2::scale_linetype_manual( values = c(2,1), labels = label_agg ) + ggplot2::scale_colour_manual( values = c("azure4", "dodgerblue4"), labels = label_agg ) + ggplot2::ylab("Volume of tasks") + ggplot2::xlab("Date") + ggplot2::theme( legend.title = ggplot2::element_blank(), legend.position = "right" ) + ggplot2::ggtitle(paste(data$MPS)) } ) gridExtra::marrangeGrob(plist_mps_agg, top = NULL, nrow = 1, ncol = 1)
\newpage
IPRP Analytics
IPRP Performance: Delta to Plan and Market Level
The graphs below give an indication of whether, overall, Trading Parties are meeting or exceeding their IPRP milestone targets (left-hand side graph); and an indication of whether performance, overall, is converging with peer-level performance. The graphs are a general guide only, as the data is aggregated across all plans.
plott1 <- iprp_plan_comparison_mps %>% dplyr::filter(Date <= Sys.Date()) %>% dplyr::group_by(Date) %>% dplyr::summarise(mean_delta = mean(Delta, na.rm = TRUE)) %>% ggplot2::ggplot( ggplot2::aes( x = Date, y = mean_delta) ) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab("Mean Difference") + ggplot2::xlab("Date") + geom_label( ggplot2::aes( label = format(round(mean_delta, 2), nsmall = 2)) ) + ggplot2::ggtitle("All batches: mean difference between \n actual and planned performance") plott2 <- iprp_plan_comparison_mps %>% dplyr::filter(Date <= Sys.Date()) %>% dplyr::mutate(delta_to_mean = TaskCompletion - MPS_Mean) %>% dplyr::group_by(Date) %>% dplyr::summarise(mean_delta_to_mean = mean(delta_to_mean, na.rm = TRUE)) %>% ggplot2::ggplot( ggplot2::aes( x = Date, y = mean_delta_to_mean) ) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ylab("Delta to the Market MPS level") + ggplot2::xlab("Date") + geom_label( ggplot2::aes( label = format(round(mean_delta_to_mean, 2 ), nsmall = 2)) ) + ggplot2::ggtitle("All batches: mean difference between \n performance and market MPS level") plot_list = c(plott1,plott2) g <- gridExtra::arrangeGrob(plott1, plott2, nrow = 1, ncol = 2) plot(g)
\newpage
Current Status of IPRPs
The current status of each IPRP is shown in the table below.
kableExtra::kable( iprp_status_mps, format = "latex", caption = "Current status of IPRP(s)", col.names = c( "Trading Party", "MPS", "Batch", "Actual Perf.", "Planned Perf.", "Status"), longtable = TRUE, booktabs = TRUE, linesep = "" ) %>% kableExtra::kable_styling( latex_options = c( "repeat_header", "hold_position", "striped", position= "center", full_width = TRUE ) ) %>% kableExtra::row_spec(0, bold = TRUE)
The graphs below depict the changes in status of IPRPs (on-track, close and off-track).
ploty <- iprp_plan_comparison_mps %>% dplyr::select( Date, OnTrack, Close, OffTrack, Batch ) %>% tidyr::gather(key = "variable", value = "value", OnTrack, Close, OffTrack) %>% dplyr::mutate( variable2 = factor(variable, levels = c("OffTrack", "Close", "OnTrack")) ) %>% ggplot2::ggplot( ggplot2::aes( x = Date, y = value, fill = variable2) ) + ggplot2::geom_bar(stat = "identity") + labs( title = "IPRP Performance", subtitle = "Count of active plans by month and status" ) + ggplot2::ylab("Count of plans by status") + ggplot2::xlab("") + ggplot2::theme_minimal(base_size = 11) + ggplot2::scale_fill_manual(values = c("darkorange", "azure3", "dodgerblue4")) + ggplot2::theme( legend.position = "top", legend.title = ggplot2::element_blank() ) plot(ploty)
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