In austl001/MOSLR: MOSL Reports and Utility Functions
A. High Quality Consumption & Asset Data
Vacancy - Market Level Reporting
history_vac <- vacant %>%
dplyr::group_by(Period) %>%
dplyr::summarise(
VacantPremises = sum(VacantPremises),
total = sum(Premises),
percent_vacant = sum(VacantPremises) / sum(Premises)
) %>%
dplyr::filter(Period > "2019-03-01")
vac_bar_plot <- history_vac %>%
ggplot2::ggplot(ggplot2::aes(x = Period, y = VacantPremises, label = scales::comma(VacantPremises, accuracy = 1))) +
ggplot2::geom_col(ggplot2::aes(x = Period, y = VacantPremises, fill = "MOSLR"), show.legend = FALSE) +
MOSLR::scale_fill_MOSL() +
ggplot2::labs(title = "Volume of Vacant Premises", y = "", x = "", caption = "Source: MOSL") +
ggplot2::theme_bw() +
ggplot2::scale_y_continuous(labels = scales::comma) +
ggplot2::geom_text(nudge_y = 50000, check_overlap = TRUE)
vac_line_plot <- history_vac %>%
ggplot2::ggplot(ggplot2::aes(x = Period, y = percent_vacant, label = scales::percent(percent_vacant, accuracy = 0.1))) +
ggplot2::geom_line(ggplot2::aes(x = Period, y = percent_vacant, colour = "MOSLR"), show.legend = FALSE) +
ggplot2::geom_point(ggplot2::aes(x = Period, y = percent_vacant, fill = "MOSLR"), show.legend = FALSE) +
MOSLR::scale_colour_MOSL() +
MOSLR::scale_fill_MOSL() +
ggplot2::labs(title = "Vacancy Rate", y = "", x = "", caption = "Source: MOSL") +
ggplot2::theme_bw() +
ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
ggplot2::geom_text(nudge_y = 0.01, check_overlap = TRUE)
combined_plot <- gridExtra::arrangeGrob(vac_bar_plot, vac_line_plot)
plot(combined_plot)
This chart shows the change in volume (bars) and % (line) of vacant premises, with a forecast for the next 3 months
\newpage
Vacancy - Trading Party Level Reporting
vac_MO_avg <- vacant %>%
dplyr::filter(Period == "2017-04-01") %>%
dplyr::summarise(Total = sum(VacantPremises) / sum(Premises))
graph_data <- MOSLR::MPOP_data_prep(
df = vacant,
df.tp = vacant_TP,
output.table = "vacancy",
by = "Retailer"
)
MOSLR::bubble_chart(
df = graph_data,
vertical.sep = vac_MO_avg$Total,
title = "Vacant premises change in the last 12 months by wholesaler-retailer pairing",
subtitle = NULL,
caption = "Source: MOSL",
x_lab = "Total % of vacant premises",
y_lab = "Change in % of vacant premises in the last 12 months"
)
This chart shows relative volumes of Vacant Premises by Retailer and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however theirpercentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage
\newpage
top10_vac_ret <- graph_data %>%
dplyr::arrange(dplyr::desc(percent)) %>%
dplyr::mutate(
percent_temp = ifelse(
percent - last_percent > 0,
paste0(round(percent * 100, 1), "\\% $\\uparrow$"),
paste0(round(percent * 100, 1), "\\% $\\downarrow$")
),
percent_temp = kableExtra::cell_spec(
percent_temp,
format = "latex",
background = dplyr::case_when(
percent > vac_MO_avg$Total + 0.02 ~ "#F08080",
percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699",
percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4",
TRUE ~ "#FFFFFF"),
escape = FALSE
),
TotalPremises = scales::comma(TotalPremises, digits = 0),
VacantPremises = scales::comma(VacantPremises, digits = 0)
)
top10_vac_ret %>%
dplyr::select(Retailer, TotalPremises, VacantPremises, percent_temp) %>%
knitr::kable(
booktabs = TRUE, format = "latex",
col.names = c("Retailer", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"),
linesep = "", escape = FALSE,
caption = "Retailer Vacancy ranking",
align = c("l", rep("r", ncol(top10_vac_ret) - 1))
) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::row_spec(1:nrow(top10_vac_ret), hline_after = TRUE)
\arrayrulecolor{white}
knitr::kable(
table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "),
linesep = "", escape = FALSE
) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Retailers with more than 200 Premises are included here. The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
graph_data <- MOSLR::MPOP_data_prep(
df = vacant,
df.tp = vacant_TP,
output.table = "vacancy",
by = "Wholesaler"
)
MOSLR::bubble_chart(
df = graph_data,
vertical.sep = vac_MO_avg$Total,
title = "Wholesaler area vacant premises change in the last 12 months",
subtitle = NULL,
caption = "Source: MOSL",
x_lab = "Total % of vacant premises",
y_lab = "Change in % of vacant premises in the last 12 months"
)
This chart shows relative volumes of Vacant Premises by Wholesaler area and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however their percentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage
\newpage
top10_vac_whole <- graph_data %>%
dplyr::mutate(
percent_temp = ifelse(
percent - last_percent > 0,
paste0(round(percent * 100, 1), "\\% $\\uparrow$"),
paste0(round(percent * 100, 1), "\\% $\\downarrow$")
),
percent_temp = kableExtra::cell_spec(
percent_temp,
format = table_format,
background = dplyr::case_when(
percent > vac_MO_avg$Total + 0.02 ~ "#F08080",
percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699",
percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4",
TRUE ~ "#FFFFFF"
),
escape = FALSE),
TotalPremises = scales::comma(TotalPremises, digits = 0),
VacantPremises = scales::comma(VacantPremises, digits = 0)
) %>%
dplyr::arrange(dplyr::desc(percent)) %>%
dplyr::mutate(percent = paste0(round(percent * 100, 1), "\\%")) %>%
dplyr::filter(!is.na(Total_Premises_april)) %>%
dplyr::select(Wholesaler, TotalPremises, VacantPremises, percent_temp)
knitr::kable(
top10_vac_whole,
booktabs = TRUE, format = table_format,
col.names = c("Wholesaler", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"),
linesep = "", escape = FALSE, caption = "Wholesaler area Vacancy ranking", align = c("l", rep("r", ncol(top10_vac_whole) - 1))
) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::row_spec(1:nrow(top10_vac_whole), hline_after = TRUE)
\arrayrulecolor{white}
knitr::kable(
table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "), linesep = "", escape = FALSE
) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Wholesalers with more than 200 Premises are included here.
The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
graph_data <- MOSLR::MPOP_data_prep(
df = vacant,
df.tp = vacant_TP,
output.table = "vacancy",
by = "Pair"
)
MOSLR::bubble_chart(
df = graph_data,
vertical.sep = vac_MO_avg$Total,
title = "Vacant premises change in the last 12 months per Pairing",
subtitle = NULL,
caption = "Source: MOSL",
x_lab = "Total % of vacant premises",
y_lab = "Change in % of vacant premises in the last 12 months"
)
This chart shows relative volumes of Vacant Premises by Wholesaler-Retailer Pairing and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however theirpercentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage
\newpage
top10_vac_pair <- graph_data %>%
dplyr::arrange(dplyr::desc(percent)) %>%
dplyr::mutate(
percent_temp = ifelse(
percent - last_percent > 0,
paste0(round(percent*100, 1), "\\% $\\uparrow$"),
paste0(round(percent*100, 1), "\\% $\\downarrow$")
),
percent_temp = kableExtra::cell_spec(
percent_temp, format = table_format,
background = dplyr::case_when(
percent > vac_MO_avg$Total + 0.02 ~ "#F08080",
percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699",
percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4",
TRUE ~ "#FFFFFF"
),
escape = FALSE),
TotalPremises = scales::comma(TotalPremises, digits = 0),
VacantPremises = scales::comma(VacantPremises, digits = 0)
) %>%
dplyr::select(Pair, TotalPremises, VacantPremises, percent_temp)
knitr::kable(
top10_vac_pair, booktabs = TRUE, format = table_format,
col.names = c("Pairing", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"),
linesep = "", escape = FALSE, caption = "Pairing Vacancy ranking",
align = c("l", rep("r", ncol(top10_vac_pair) - 1))
) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::row_spec(1:nrow(top10_vac_pair), hline_after = TRUE) %>%
kableExtra::column_spec(1, width = "6.5cm")
\arrayrulecolor{white}
knitr::kable(
table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "), linesep = "", escape = FALSE) %>%
kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>%
kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Pairings with more than 6000 Premises are included here.
The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
vacant_current <- vacant %>%
dplyr::filter(Period == max(vacant$Period)) %>%
dplyr::filter(!is.na(VacantPremises)) %>%
dplyr::arrange(RetailerID, WholesalerID)
vacancy_pivot <- vacant_current %>%
dplyr::select(RetailerID, WholesalerID, VacantPremises) %>%
tidyr::pivot_wider(names_from = WholesalerID, values_from = VacantPremises) %>%
janitor::adorn_totals(where = c("row", "col")) %>%
dplyr::mutate_if(is.numeric, ~scales::comma(., accuracy = 1)) %>%
dplyr::mutate_all(~replace(., is.na(.), ""))
knitr::kable(
vacancy_pivot,
format = "latex", booktabs = TRUE, linesep = "",
caption = "Total number of vacant premises",
align = c("l", rep("c", ncol(vacancy_pivot) - 1)),
col.names = c("", colnames(vacancy_pivot)[2:ncol(vacancy_pivot)])
) %>%
kableExtra::kable_styling(
bootstrap_options = "bordered",
latex_options = c("hold_position", "scale_down", "striped"),
stripe_color = "blizzardblue"
) %>%
kableExtra::column_spec(ncol(vacancy_pivot) - 1, border_right = TRUE) %>%
kableExtra::row_spec(0, angle = 90) %>%
kableExtra::row_spec(nrow(vacancy_pivot) - 1, hline_after = TRUE) %>%
kableExtra::row_spec(nrow(vacancy_pivot), bold = TRUE) %>%
kableExtra::column_spec(ncol(vacancy_pivot), bold = TRUE)
retailer_vacancy_total <- vacant_current %>%
dplyr::group_by(RetailerID) %>%
dplyr::summarise(Total = sum(VacantPremises) / sum(Premises))
wholesaler_vacancy_total <- vacant_current %>%
dplyr::group_by(WholesalerID) %>%
dplyr::summarise(Total = sum(VacantPremises) / sum(Premises)) %>%
tidyr::pivot_wider(names_from = WholesalerID, values_from = Total)
vacancy_pivot <- vacant_current %>%
dplyr::mutate(percent = VacantPremises / Premises) %>%
dplyr::select(RetailerID, WholesalerID, percent) %>%
tidyr::pivot_wider(names_from = WholesalerID, values_from = percent) %>%
dplyr::bind_rows(wholesaler_vacancy_total) %>%
dplyr::left_join(retailer_vacancy_total, by = "RetailerID") %>%
tidyr::replace_na(list(RetailerID = "Total")) %>%
janitor::adorn_pct_formatting()
knitr::kable(
vacancy_pivot,
format = table_format, booktabs = TRUE, linesep = "",
caption = "Percentage of premises that are vacant",
align = c("l", rep("c", ncol(vacancy_pivot) - 1)),
col.names = c("", colnames(vacancy_pivot)[2:ncol(vacancy_pivot)])
) %>%
kableExtra::kable_styling(
latex_options = c("hold_position", "scale_down", "striped"),
stripe_color = "blizzardblue"
) %>%
kableExtra::column_spec(ncol(vacancy_pivot) - 1, border_right = TRUE) %>%
kableExtra::row_spec(0, angle = 90) %>%
kableExtra::row_spec(nrow(vacancy_pivot) - 1, hline_after = TRUE) %>%
kableExtra::row_spec(nrow(vacancy_pivot), bold = TRUE) %>%
kableExtra::column_spec(ncol(vacancy_pivot), bold = TRUE)
\clearpage
austl001/MOSLR documentation built on Aug. 17, 2022, 12:07 a.m.
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A. High Quality Consumption & Asset Data
Vacancy - Market Level Reporting
history_vac <- vacant %>% dplyr::group_by(Period) %>% dplyr::summarise( VacantPremises = sum(VacantPremises), total = sum(Premises), percent_vacant = sum(VacantPremises) / sum(Premises) ) %>% dplyr::filter(Period > "2019-03-01") vac_bar_plot <- history_vac %>% ggplot2::ggplot(ggplot2::aes(x = Period, y = VacantPremises, label = scales::comma(VacantPremises, accuracy = 1))) + ggplot2::geom_col(ggplot2::aes(x = Period, y = VacantPremises, fill = "MOSLR"), show.legend = FALSE) + MOSLR::scale_fill_MOSL() + ggplot2::labs(title = "Volume of Vacant Premises", y = "", x = "", caption = "Source: MOSL") + ggplot2::theme_bw() + ggplot2::scale_y_continuous(labels = scales::comma) + ggplot2::geom_text(nudge_y = 50000, check_overlap = TRUE) vac_line_plot <- history_vac %>% ggplot2::ggplot(ggplot2::aes(x = Period, y = percent_vacant, label = scales::percent(percent_vacant, accuracy = 0.1))) + ggplot2::geom_line(ggplot2::aes(x = Period, y = percent_vacant, colour = "MOSLR"), show.legend = FALSE) + ggplot2::geom_point(ggplot2::aes(x = Period, y = percent_vacant, fill = "MOSLR"), show.legend = FALSE) + MOSLR::scale_colour_MOSL() + MOSLR::scale_fill_MOSL() + ggplot2::labs(title = "Vacancy Rate", y = "", x = "", caption = "Source: MOSL") + ggplot2::theme_bw() + ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + ggplot2::geom_text(nudge_y = 0.01, check_overlap = TRUE) combined_plot <- gridExtra::arrangeGrob(vac_bar_plot, vac_line_plot) plot(combined_plot)
This chart shows the change in volume (bars) and % (line) of vacant premises, with a forecast for the next 3 months
\newpage
Vacancy - Trading Party Level Reporting
vac_MO_avg <- vacant %>% dplyr::filter(Period == "2017-04-01") %>% dplyr::summarise(Total = sum(VacantPremises) / sum(Premises)) graph_data <- MOSLR::MPOP_data_prep( df = vacant, df.tp = vacant_TP, output.table = "vacancy", by = "Retailer" ) MOSLR::bubble_chart( df = graph_data, vertical.sep = vac_MO_avg$Total, title = "Vacant premises change in the last 12 months by wholesaler-retailer pairing", subtitle = NULL, caption = "Source: MOSL", x_lab = "Total % of vacant premises", y_lab = "Change in % of vacant premises in the last 12 months" )
This chart shows relative volumes of Vacant Premises by Retailer and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however theirpercentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage \newpage
top10_vac_ret <- graph_data %>% dplyr::arrange(dplyr::desc(percent)) %>% dplyr::mutate( percent_temp = ifelse( percent - last_percent > 0, paste0(round(percent * 100, 1), "\\% $\\uparrow$"), paste0(round(percent * 100, 1), "\\% $\\downarrow$") ), percent_temp = kableExtra::cell_spec( percent_temp, format = "latex", background = dplyr::case_when( percent > vac_MO_avg$Total + 0.02 ~ "#F08080", percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699", percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4", TRUE ~ "#FFFFFF"), escape = FALSE ), TotalPremises = scales::comma(TotalPremises, digits = 0), VacantPremises = scales::comma(VacantPremises, digits = 0) ) top10_vac_ret %>% dplyr::select(Retailer, TotalPremises, VacantPremises, percent_temp) %>% knitr::kable( booktabs = TRUE, format = "latex", col.names = c("Retailer", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"), linesep = "", escape = FALSE, caption = "Retailer Vacancy ranking", align = c("l", rep("r", ncol(top10_vac_ret) - 1)) ) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::row_spec(1:nrow(top10_vac_ret), hline_after = TRUE)
\arrayrulecolor{white}
knitr::kable( table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "), linesep = "", escape = FALSE ) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Retailers with more than 200 Premises are included here. The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
graph_data <- MOSLR::MPOP_data_prep( df = vacant, df.tp = vacant_TP, output.table = "vacancy", by = "Wholesaler" ) MOSLR::bubble_chart( df = graph_data, vertical.sep = vac_MO_avg$Total, title = "Wholesaler area vacant premises change in the last 12 months", subtitle = NULL, caption = "Source: MOSL", x_lab = "Total % of vacant premises", y_lab = "Change in % of vacant premises in the last 12 months" )
This chart shows relative volumes of Vacant Premises by Wholesaler area and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however their percentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage \newpage
top10_vac_whole <- graph_data %>% dplyr::mutate( percent_temp = ifelse( percent - last_percent > 0, paste0(round(percent * 100, 1), "\\% $\\uparrow$"), paste0(round(percent * 100, 1), "\\% $\\downarrow$") ), percent_temp = kableExtra::cell_spec( percent_temp, format = table_format, background = dplyr::case_when( percent > vac_MO_avg$Total + 0.02 ~ "#F08080", percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699", percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4", TRUE ~ "#FFFFFF" ), escape = FALSE), TotalPremises = scales::comma(TotalPremises, digits = 0), VacantPremises = scales::comma(VacantPremises, digits = 0) ) %>% dplyr::arrange(dplyr::desc(percent)) %>% dplyr::mutate(percent = paste0(round(percent * 100, 1), "\\%")) %>% dplyr::filter(!is.na(Total_Premises_april)) %>% dplyr::select(Wholesaler, TotalPremises, VacantPremises, percent_temp) knitr::kable( top10_vac_whole, booktabs = TRUE, format = table_format, col.names = c("Wholesaler", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"), linesep = "", escape = FALSE, caption = "Wholesaler area Vacancy ranking", align = c("l", rep("r", ncol(top10_vac_whole) - 1)) ) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::row_spec(1:nrow(top10_vac_whole), hline_after = TRUE)
\arrayrulecolor{white}
knitr::kable( table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "), linesep = "", escape = FALSE ) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Wholesalers with more than 200 Premises are included here.
The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
graph_data <- MOSLR::MPOP_data_prep( df = vacant, df.tp = vacant_TP, output.table = "vacancy", by = "Pair" ) MOSLR::bubble_chart( df = graph_data, vertical.sep = vac_MO_avg$Total, title = "Vacant premises change in the last 12 months per Pairing", subtitle = NULL, caption = "Source: MOSL", x_lab = "Total % of vacant premises", y_lab = "Change in % of vacant premises in the last 12 months" )
This chart shows relative volumes of Vacant Premises by Wholesaler-Retailer Pairing and their movement over the past 12 months.
- Declining (bottom left quadrant): Retailers are showing an increase in percentage of Vacant premises and their percentage is higher than the Expected level of Vacant premises
- Improving (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, however their percentage is still higher than the Expected level of Vacant premises
- At risk (bottom right quadrant): Retailers are showing an increase in percentage of Vacant premises, however theirpercentage is still lower than the Expected level of Vacant premises
- Leading (top right quadrant): Retailers are showing a decrease in percentage of Vacant premises, and their percentage is lower than the Expected level of Vacant premises
\clearpage \newpage
top10_vac_pair <- graph_data %>% dplyr::arrange(dplyr::desc(percent)) %>% dplyr::mutate( percent_temp = ifelse( percent - last_percent > 0, paste0(round(percent*100, 1), "\\% $\\uparrow$"), paste0(round(percent*100, 1), "\\% $\\downarrow$") ), percent_temp = kableExtra::cell_spec( percent_temp, format = table_format, background = dplyr::case_when( percent > vac_MO_avg$Total + 0.02 ~ "#F08080", percent > vac_MO_avg$Total - 0.02 & percent < vac_MO_avg$Total + 0.02 ~ "#FFE699", percent < vac_MO_avg$Total - 0.02 ~ "#C6E0B4", TRUE ~ "#FFFFFF" ), escape = FALSE), TotalPremises = scales::comma(TotalPremises, digits = 0), VacantPremises = scales::comma(VacantPremises, digits = 0) ) %>% dplyr::select(Pair, TotalPremises, VacantPremises, percent_temp) knitr::kable( top10_vac_pair, booktabs = TRUE, format = table_format, col.names = c("Pairing", "Total number of premises", "Total number of vacant premises", "Percentage of vacant premises"), linesep = "", escape = FALSE, caption = "Pairing Vacancy ranking", align = c("l", rep("r", ncol(top10_vac_pair) - 1)) ) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::row_spec(1:nrow(top10_vac_pair), hline_after = TRUE) %>% kableExtra::column_spec(1, width = "6.5cm")
\arrayrulecolor{white}
knitr::kable( table_legend_vac, booktabs = TRUE, format = table_format, col.names = c("", " ", " "), linesep = "", escape = FALSE) %>% kableExtra::kable_styling(latex_options = c("hold_position", "scale_down")) %>% kableExtra::column_spec(1:3, width = "5cm")
\arrayrulecolor{black}
Only Pairings with more than 6000 Premises are included here.
The arrows indicate the change in percentage of Vacant premises compared to last month
\newpage
vacant_current <- vacant %>% dplyr::filter(Period == max(vacant$Period)) %>% dplyr::filter(!is.na(VacantPremises)) %>% dplyr::arrange(RetailerID, WholesalerID) vacancy_pivot <- vacant_current %>% dplyr::select(RetailerID, WholesalerID, VacantPremises) %>% tidyr::pivot_wider(names_from = WholesalerID, values_from = VacantPremises) %>% janitor::adorn_totals(where = c("row", "col")) %>% dplyr::mutate_if(is.numeric, ~scales::comma(., accuracy = 1)) %>% dplyr::mutate_all(~replace(., is.na(.), "")) knitr::kable( vacancy_pivot, format = "latex", booktabs = TRUE, linesep = "", caption = "Total number of vacant premises", align = c("l", rep("c", ncol(vacancy_pivot) - 1)), col.names = c("", colnames(vacancy_pivot)[2:ncol(vacancy_pivot)]) ) %>% kableExtra::kable_styling( bootstrap_options = "bordered", latex_options = c("hold_position", "scale_down", "striped"), stripe_color = "blizzardblue" ) %>% kableExtra::column_spec(ncol(vacancy_pivot) - 1, border_right = TRUE) %>% kableExtra::row_spec(0, angle = 90) %>% kableExtra::row_spec(nrow(vacancy_pivot) - 1, hline_after = TRUE) %>% kableExtra::row_spec(nrow(vacancy_pivot), bold = TRUE) %>% kableExtra::column_spec(ncol(vacancy_pivot), bold = TRUE)
retailer_vacancy_total <- vacant_current %>% dplyr::group_by(RetailerID) %>% dplyr::summarise(Total = sum(VacantPremises) / sum(Premises)) wholesaler_vacancy_total <- vacant_current %>% dplyr::group_by(WholesalerID) %>% dplyr::summarise(Total = sum(VacantPremises) / sum(Premises)) %>% tidyr::pivot_wider(names_from = WholesalerID, values_from = Total) vacancy_pivot <- vacant_current %>% dplyr::mutate(percent = VacantPremises / Premises) %>% dplyr::select(RetailerID, WholesalerID, percent) %>% tidyr::pivot_wider(names_from = WholesalerID, values_from = percent) %>% dplyr::bind_rows(wholesaler_vacancy_total) %>% dplyr::left_join(retailer_vacancy_total, by = "RetailerID") %>% tidyr::replace_na(list(RetailerID = "Total")) %>% janitor::adorn_pct_formatting() knitr::kable( vacancy_pivot, format = table_format, booktabs = TRUE, linesep = "", caption = "Percentage of premises that are vacant", align = c("l", rep("c", ncol(vacancy_pivot) - 1)), col.names = c("", colnames(vacancy_pivot)[2:ncol(vacancy_pivot)]) ) %>% kableExtra::kable_styling( latex_options = c("hold_position", "scale_down", "striped"), stripe_color = "blizzardblue" ) %>% kableExtra::column_spec(ncol(vacancy_pivot) - 1, border_right = TRUE) %>% kableExtra::row_spec(0, angle = 90) %>% kableExtra::row_spec(nrow(vacancy_pivot) - 1, hline_after = TRUE) %>% kableExtra::row_spec(nrow(vacancy_pivot), bold = TRUE) %>% kableExtra::column_spec(ncol(vacancy_pivot), bold = TRUE)
\clearpage
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