/
simple_dashboard2.R
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simple_dashboard2.R
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#' @param digits_outliers number of digits used in the table of outliers.
#' @param columns_outliers informations about outliers that should be printed in the summary table.
#' Can be either a vector of characters among `c("Estimate", "Std. Error", "T-stat", "Pr(>|t|)")`;
#' or an vector of integer: `1` corresponding to the estimate coefficient (`"Estimate"`),
#' `2` corresponding to the standard deviation error (`"Std. Error"`),
#' `3` corresponding to the t-statistic (`"T-stat"`) or
#' `4` corresponding to the p-value (`"Pr(>|t|)"`).
#' By default only the estimate coefficients and the t-statistics are printed
#' (`columns_outliers = c("Estimate", "T-stat")`).
#' @param n_last_outliers number of last outliers to be printed (by default `n_last_outliers = 4`).
#' @param order_outliers order of the outliers in case of several outliers at the same date.
#'
#'
#' @rdname simple_dashboard
#' @export
simple_dashboard2 <- function(x, digits = 2,
scale_var_decomp = FALSE,
remove_others_contrib = FALSE,
digits_outliers = digits,
columns_outliers = c("Estimate", "T-stat"),
n_last_outliers = 4,
order_outliers = c("AO", "LS", "TC", "SO")) {
if (inherits(x, "TRAMO_SEATS")) {
x <- RJDemetra::jtramoseats(RJDemetra::get_ts(x), RJDemetra::tramoseats_spec(x))
} else if (inherits(x, "X13")) {
x <- RJDemetra::jx13(RJDemetra::get_ts(x), RJDemetra::x13_spec(x))
}
nb_format <- paste0("%.", digits, "f")
if (is.numeric(columns_outliers)) {
columns_outliers <- c("Estimate", "Std. Error", "T-stat", "Pr(>|t|)")[columns_outliers]
} else {
columns_outliers <- match.arg(columns_outliers,
c("Estimate", "Std. Error", "T-stat", "Pr(>|t|)"),
several.ok = TRUE)
}
# Raw, trend, sa
data_plot <- RJDemetra::get_indicators(x, c("y", "t", "sa", "y_f", "t_f", "sa_f"))
last_date <- tail(time_to_date(data_plot[[1]]), 1)
last_date <- format(last_date, format = "%Y-%m")
data_plot <- do.call(ts.union, data_plot)
# add observed data for plots
data_plot[which(is.na(data_plot[,"y"]))[1]-1, c("y_f", "t_f", "sa_f")] <-
data_plot[which(is.na(data_plot[,"y"]))[1]-1, c("y", "t", "sa")]
# Global info on model
arima_ord <- sprintf("ARIMA(%s)(%s)",
paste(unlist(RJDemetra::get_indicators(x, sprintf("preprocessing.arima.%s", c("p", "d", "q")))), collapse = ","),
paste(unlist(RJDemetra::get_indicators(x, sprintf("preprocessing.arima.%s", c("bp", "bd", "bq")))), collapse = ","))
ntd <- RJDemetra::get_indicators(x, "preprocessing.model.ntd")[[1]] # nombre de JO
nmh <- RJDemetra::get_indicators(x, "preprocessing.model.nmh")[[1]]
is_easter <- (! is.null(nmh)) &&
(nmh > 0)
est_span <- sprintf("Estimation span: %s to %s (%s obs)",
RJDemetra::get_indicators(x, "preprocessing.model.espan.start")[[1]],
RJDemetra::get_indicators(x, "preprocessing.model.espan.end")[[1]],
RJDemetra::get_indicators(x, "preprocessing.model.espan.n")[[1]]
)
transform <- ifelse(RJDemetra::get_indicators(x, "preprocessing.model.log")[[1]] == "false",
"Series hasn't been transformed",
"Series has been log-transformed")
tde <- sprintf("%s, %s",
ifelse(ntd==0, "No trading days effect",
sprintf("Trading days effect (%s)", ntd)),
ifelse(is_easter, "easter effect",
"no easter effect"))
# nb outliers
nout <- RJDemetra::get_indicators(x, "preprocessing.model.nout")[[1]]
out <- sprintf("%s detected outliers", nout)
summary_text <- c(est_span, transform, tde, out, arima_ord)
# Stats on quality of decomposition
qstats <- list2DF(RJDemetra::get_indicators(x, "mstats.Q", "mstats.Q-M2"))
colnames(qstats) <- c("Q", "Q-M2")
# Stats on variance decomp
var_decomp <- RJDemetra::get_indicators(x, "diagnostics.variancedecomposition")[[1]]
names(var_decomp) <- c("Cycle", "Seasonal", "Irregular", "TDH", "Others", "Total")
if (remove_others_contrib) {
var_decomp <- var_decomp[-5]
i_total <- length(var_decomp)
var_decomp[i_total] <- sum(var_decomp[-i_total])
}
if (scale_var_decomp) {
i_total <- length(var_decomp)
var_decomp[-i_total] <- var_decomp[-i_total] / sum(var_decomp[-i_total])
var_decomp[i_total] <- 1
}
var_decomp <- as.data.frame(t(data.frame(var_decomp*100)))
var_decomp <- var_decomp
# Tests on linearised series
liste_ind_seas <- c("F-test" = "diagnostics.seas-lin-f",
"QS-test" = "diagnostics.seas-lin-qs",
"Kruskal-Wallis" = "diagnostics.seas-lin-kw",
"Friedman" = "diagnostics.seas-lin-friedman",
"Combined" = "diagnostics.combined.all.summary")
# residuals tests
liste_ind_res_seas <- c("F-test" = "diagnostics.seas-sa-f",
"QS-test" = "diagnostics.seas-sa-qs",
"Kruskal-Wallis" = "diagnostics.seas-sa-kw",
"Friedman" = "diagnostics.seas-sa-friedman",
"Combined" = "diagnostics.seas-sa-combined")
liste_ind_res_jo <-
c("Residual TD" = "diagnostics.td-sa-last")
seas_test <- list2DF(lapply(RJDemetra::get_indicators(x, liste_ind_seas), function(x) {
if(length(x) > 1)
x <- sprintf(nb_format, x[2])
x
}))
seas_res_test <- list2DF(lapply(RJDemetra::get_indicators(x, liste_ind_res_seas), function(x) {
if(length(x) > 1)
x <- sprintf(nb_format, x[2])
x
}))
td_res_test <- data.frame(sprintf(nb_format, RJDemetra::get_indicators(x, liste_ind_res_jo)[[1]][2]),
"", "", "", "" )
names(seas_test) <- names(seas_res_test) <-
names(td_res_test) <- names(liste_ind_seas)
all_tests <- rbind(seas_test, seas_res_test,
td_res_test)
rownames(all_tests) <- c("Seasonality",
"Residual Seasonality",
"Residual TD effect")
# On calcule les couleurs
color_test <- rbind(c(ifelse(seas_test[,-5] < 0.05, "#A0CD63", "red"),
switch(seas_test[,5], "Present" = "#A0CD63",
"None" = "red", "orange")),
c(ifelse(seas_res_test[,-5] < 0.05, "red", "#A0CD63"),
switch(seas_res_test[,5], "Present" = "red",
"None" = "#A0CD63", "orange")),
c(ifelse(td_res_test[,1] < 0.05, "red", "#A0CD63"),
rep("white", 4)))
decomp_stats_color <- c(sapply(qstats, function(x) ifelse(x < 1, "#A0CD63", "red")),
"white",
rep("grey90", ncol(var_decomp)
))
qstats[,] <- lapply(qstats, sprintf, fmt = nb_format)
var_decomp[,] <- lapply(var_decomp, sprintf, fmt = nb_format)
if (nrow(qstats) == 0) {
# TRAMO-SEATS
decomp_stats <- var_decomp
decomp_stats_color <- unlist(decomp_stats_color[-c(1:3)])
} else {
# X-13
decomp_stats <- cbind(qstats, " " , var_decomp)
colnames(decomp_stats)[ncol(qstats)+1] <- " "
}
outliers <- outliers_color <- NULL
if (nout > 0) {
outliers <- do.call(rbind, RJDemetra::get_indicators(x, sprintf("preprocessing.model.out(%i)", seq_len(nout))))
# sort outliers by dates
dates_out <- outliers_to_dates(rownames(outliers))
dates_out$type <- factor(dates_out$type, levels = order_outliers, ordered = TRUE)
outliers <- outliers[order(dates_out$year, dates_out$period, dates_out$type, decreasing = TRUE), , drop = FALSE]
outliers <- outliers[seq_len(min(n_last_outliers, nrow(outliers))), columns_outliers, drop = FALSE]
outliers <- round(outliers, digits_outliers)
outliers <- data.frame(rownames(outliers),
outliers)
colnames(outliers)[1] <- sprintf("Last %i outliers", n_last_outliers)
rownames(outliers) <- NULL
outliers_color <-
cbind(rep("grey90", nrow(outliers)),
matrix("white", ncol = ncol(outliers) - 1, nrow = nrow(outliers)))
}
res <- list(main_plot = data_plot,
siratio_plot = ggdemetra::siratio(x),
summary_text = summary_text,
decomp_stats = list(table = decomp_stats,
colors = decomp_stats_color),
residuals_tests = list(table = all_tests,
colors = color_test),
last_date = last_date,
outliers = list(table = outliers,
colors = outliers_color))
class(res) <- c("simple_dashboard2")
res
}
outliers_to_dates <- function(name_out){
dates_out <- gsub("\\w. \\((.*)\\)", "\\1", name_out)
types <- gsub(" .*", "", name_out)
dates <- do.call(rbind, strsplit(dates_out, "-"))
periods <- as.numeric(as.roman(dates[,1]))
years <- as.numeric(dates[,2])
data.frame(year = years, period = periods, type = types)
}
#' @rdname plot.simple_dashboard
#' @export
plot.simple_dashboard2 <- function(x, main = "Simple Dashboard with outliers",
subtitle = NULL,
color_series = c(y = "#F0B400", t = "#1E6C0B", sa = "#155692"),
reference_date = TRUE,...){
main_plot = x$main_plot
siratio_plot = x$siratio_plot
summary_text = x$summary_text
decomp_stats = x$decomp_stats
residuals_tests = x$residuals_tests
last_date = x$last_date
outliers = x$outliers
def.par <- par(no.readonly = TRUE)
nf <- layout(matrix(c(rep(1,8),
rep(2,4), rep(3,4),
rep(4,3), rep(5,5),
rep(4,3), rep(6,5),
rep(7,3), rep(8,5)),ncol = 8,byrow = T),
heights = c(0.2,2.2,0.5,0.2,0.7))
# layout.show(nf)
on.exit({
par(def.par)
})
oma.saved <- par("oma")
par(oma = rep.int(0, 4))
# par(oma = oma.saved)
o.par <- par(mar = rep.int(0, 4))
plot.new()
box(which = "inner")
box()
text(0.5, 0.5, main, font = 2,cex = 1.2)
par(o.par)
par(mai = c(0, 0.4, 0.2, 0.1))
stats::plot.ts(main_plot,plot.type = "single",
col = rep(color_series, 2),
lty = rep(c(1,2), each = 3),
xlab = NULL,
ylab = NULL,
main = NULL
)
legend("bottomright", legend = names(color_series),
col = color_series, lty = 1,
pch = NA_integer_,
inset = c(0,1), xpd = TRUE, horiz=TRUE, bty = "n")
par(mai = c(0.0, 0.2, 0.2, 0.4))
ggdemetra::siratioplot(siratio_plot, main = NULL)
par(mai = c(0.4, 0.2, 0.2, 0))
par(mar = rep.int(0.4, 4))
plot.new()
# box()
legend("topleft", legend = c(NA, summary_text),
bty = "n", text.font = 2, inset = c(0),
cex = 0.95,
xpd = TRUE)
# plot.new()
# # box()
# legend("right", legend = c(arima_ord),
# bty = "n", text.font = 2, inset = c(0),
# cex = 0.8)
par(mar = rep(rep(2, 4)))
par(mai = c(0, 0.2, 0, 0.2))
plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(0, 1),
axes = FALSE)
plotrix::addtable2plot(0.5, 0,
decomp_stats$table, bty = "o", display.rownames = FALSE, hlines = TRUE,
vlines = TRUE,bg = decomp_stats$colors, xjust = 0.5, yjust = 1)
# Empty plot
plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(0, 1),
axes = FALSE)
plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(0, 1),
axes = FALSE)
par(mai = c(0, 0.2, 0.2, 0.2))
if(! is.null(outliers$table)) {
plotrix::addtable2plot(0.5, 0.7,
outliers$table,
bg = outliers$colors,
bty = "o",
display.rownames = FALSE, hlines = TRUE,
vlines = TRUE,
xjust = 0.5, yjust = 0.5)
}
plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(0, 1),
axes = FALSE)
par(mai = c(0, 0.2, 0.2, 0.2))
plotrix::addtable2plot(0.5, 0.8,
residuals_tests$table, bty = "o",
display.rownames = TRUE, hlines = TRUE,
vlines = TRUE,
bg = residuals_tests$colors,
xjust = 0.5, yjust = 0.5)
if (reference_date)
mtext(sprintf("Reference Date: %s",last_date), side = 3, line = -3,
outer = TRUE,font = 3,cex = 0.7,at = 0.95, adj = 1)
mtext(subtitle, side = 3, line = -3,
outer = TRUE,font = 3,cex = 0.7,at = 0.1, adj = 1)
invisible()
}