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tidy_autoplot() #97
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2 tasks
tidy_autoplot <- function(.data, .plot_type = "density", .line_size = .5,
.geom_point = FALSE, .point_size = 1,
.geom_rug = FALSE, .geom_smooth = FALSE,
.geom_jitter = FALSE, .interactive = FALSE) {
# Plot type ----
plot_type <- tolower(as.character(.plot_type))
line_size <- as.numeric(.line_size)
point_size <- as.numeric(.point_size)
# Get the data attributes
atb <- attributes(.data)
ns <- atb$.num_sims
ps <- attributes(.data)$ps
ps <- rep(ps, ns)
qs <- attributes(.data)$qs
qs <- rep(qs, ns)
# Checks on data ---
if (!is.data.frame(.data)) {
rlang::abort("The .data parameter must be a valid data.frame from a `tidy_`
distribution function. ")
}
if (!"tibble_type" %in% names(atb)) {
rlang::abort("The data passed must come from a `tidy_` distribution function.")
}
if (!attributes(.data)$tibble_type %in% c(
"tidy_gaussian", "tidy_poisson", "tidy_gamma", "tidy_beta", "tidy_f",
"tidy_hypergeometric", "tidy_lognormal", "tidy_cauchy", "tidy_chisquare",
"tidy_weibull", "tidy_uniform", "tidy_logistic", "tidy_exponential",
"tidy_empirical", "tidy_binomial", "tidy_geometric", "tidy_negative_binomial",
"tidy_zero_truncated_poisson", "tidy_zero_truncated_geometric",
"tidy_zero_truncated_binomial", "tidy_zero_truncated_negative_binomial",
"tidy_pareto_single_parameter", "tidy_pareto", "tidy_inverse_pareto",
"tidy_generalized_pareto","tidy_paralogistic", "tidy_inverse_exponential",
"tidy_inverse_gamma","tidy_inverse_weibull","tidy_burr","tidy_inverse_burr",
"tidy_inverse_gaussian","tidy_generalized_beta"
)) {
rlang::abort("The data passed must come from a `tidy_` distribution function.")
}
if (!is.numeric(.line_size) | !is.numeric(.point_size) | .line_size < 0 | .point_size < 0) {
rlang::abort("The parameters .line_size and .point_size must be numeric and
greater than 0.")
}
if (!plot_type %in% c("density", "quantile", "probability", "qq")) {
rlang::abort("You have chose an unsupported plot type.")
}
# Get .data parameters from the tidy_ function to construct subtitle
# for ggplot
n <- atb$.n
sims <- atb$.num_sims
dist_type <- stringr::str_remove(atb$tibble_type, "tidy_") %>%
stringr::str_replace_all(pattern = "_", " ") %>%
stringr::str_to_title()
sub_title <- paste0(
"Data Points: ", n, " - ",
"Simulations: ", sims, "\n",
"Distribution Family: ", dist_type, "\n",
"Parameters: ", if (atb$tibble_type == "tidy_gaussian") {
paste0("Mean: ", atb$.mean, " - SD: ", atb$.sd)
} else if (atb$tibble_type == "tidy_gamma") {
paste0("Shape: ", atb$.shape, " - Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_beta") {
paste0("Shape1: ", atb$.shape1, " - Shape2: ", atb$.shape2, " - NCP: ", atb$.ncp)
} else if (atb$tibble_type %in% c("tidy_poisson", "tidy_zero_truncated_poisson")) {
paste0("Lambda: ", atb$.lambda)
} else if (atb$tibble_type == "tidy_f") {
paste0("DF1: ", atb$.df1, " - DF2: ", atb$.df2, " - NCP: ", atb$.ncp)
} else if (atb$tibble_type == "tidy_hypergeometric") {
paste0("M: ", atb$.m, " - NN: ", atb$.nn, " - K: ", atb$.k)
} else if (atb$tibble_type == "tidy_lognormal") {
paste0("Mean Log: ", atb$.meanlog, " - SD Log: ", atb$.sdlog)
} else if (atb$tibble_type == "tidy_cauchy") {
paste0("Location: ", atb$.location, " - Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_chisquare") {
paste0("DF: ", atb$.df, " - NPC: ", atb$.ncp)
} else if (atb$tibble_type == "tidy_weibull") {
paste0("Shape: ", atb$.schape, " - Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_uniform") {
paste0("Max: ", atb$.max, " - Min: ", atb$.min)
} else if (atb$tibble_type == "tidy_logistic") {
paste0("Location: ", atb$.location, " - Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_exponential") {
paste0("Rate: ", atb$.rate)
} else if (atb$tibble_type == "tidy_empirical") {
paste0("Empirical - No params")
} else if (atb$tibble_type %in% c(
"tidy_binomial", "tidy_negative_binomial",
"tidy_zero_truncated_binomial",
"tidy_zero_truncated_negative_binomial"
)) {
paste0("Size: ", atb$.size, " - Prob: ", atb$.prob)
} else if (atb$tibble_type %in% c("tidy_geometric", "tidy_zero_truncated_geometric")) {
paste0("Prob: ", atb$.prob)
} else if (atb$tibble_type %in% c("tidy_pareto_single_parameter")) {
paste0("Shape: ", atb$.shape, " - Min: ", atb$.min)
} else if (atb$tibble_type %in% c("tidy_pareto", "tidy_inverse_pareto")) {
paste0("Shape: ", atb$.shape, " - Scale: ", atb$.scale)
} else if (atb$tibble_type %in% c("tidy_generalized_pareto",
"tidy_burr","tidy_inverse_burr")){
paste0(
"Shape1: ", atb$.shape1, " - ",
"Shape2: ", atb$.shape2, " - ",
"Rate: ", atb$.rate, " - ",
"Scale: ", atb$.scale
)
} else if (atb$tibble_type %in% c(
"tidy_paralogistic",
"tidy_inverse_gamma",
"tidy_inverse_weibull"
)
){
paste0("Shape: ", atb$.shape, " - ",
"Rate: ", atb$.rate, " - ",
"Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_inverse_exponential"){
paste0("Rate: ", atb$.rate, " - Scale: ", atb$.scale)
} else if (atb$tibble_type == "tidy_inverse_gaussian"){
paste0("Mean: ", atb$.mean, " - ",
"Shape: ", atb$.shape, " - ",
"Dispersion: ", atb$.dispersion)
} else if (atb$tibble_type == "tidy_generalized_beta"){
paste0(
"Shape1: ", atb$.shape1, " - ",
"Shape2: ", atb$.shape2, " - ",
"Shape3: ", atb$.shape3, " - ",
"Scale: ", atb$.scale, " - ",
"Rate: ", atb$.rate
)
}
)
# Data ----
data_tbl <- dplyr::as_tibble(.data)
# Plot logic ----
leg_pos <- if (atb$tibble_type == "tidy_empirical") {
"none"
} else if (sims > 9) {
"none"
} else {
"bottom"
}
if (plot_type == "density" & atb$distribution_family_type == "continuous") {
plt <- data_tbl %>%
ggplot2::ggplot(
ggplot2::aes(x = dx, y = dy, group = sim_number, color = sim_number)
) +
ggplot2::geom_line(size = line_size) +
ggplot2::theme_minimal() +
ggplot2::labs(
title = "Density Plot",
subtitle = sub_title,
color = "Simulation"
) +
ggplot2::theme(legend.position = leg_pos)
} else if (plot_type == "density" & atb$distribution_family_type == "discrete"){
plt <- data_tbl %>%
ggplot2::ggplot(ggplot2::aes(x = y, group = sim_number, fill = sim_number)) +
ggplot2::geom_histogram(alpha = 0.318, color = "#e9ecef",
bins = max(unique(data_tbl$y)) + 1,
position = "identity") +
ggplot2::theme_minimal() +
ggplot2::labs(
title = "Histogram Plot",
subtitle = sub_title,
fill = "Simulation"
) +
ggplot2::theme(legend.position = leg_pos)
} else if (plot_type == "quantile") {
plt <- data_tbl %>%
ggplot2::ggplot(
ggplot2::aes(
x = qs, y = q, group = sim_number, color = sim_number
)
) +
ggplot2::geom_line(size = line_size) +
ggplot2::theme_minimal() +
ggplot2::labs(
title = "Qantile Plot",
subtitle = sub_title,
color = "Simulation"
) +
ggplot2::theme(legend.position = leg_pos)
} else if (plot_type == "probability") {
plt <- data_tbl %>%
ggplot2::ggplot(
ggplot2::aes(
x = ps, y = p, color = sim_number, group = sim_number
)
) +
ggplot2::geom_line(size = line_size) +
ggplot2::theme_minimal() +
ggplot2::labs(
title = "Probabilty Plot",
subtitle = sub_title,
color = "Simulation"
) +
ggplot2::theme(legend.position = leg_pos)
} else if (plot_type == "qq") {
plt <- data_tbl %>%
ggplot2::ggplot(
ggplot2::aes(
sample = y, color = sim_number, group = sim_number
)
) +
ggplot2::stat_qq(size = point_size) +
ggplot2::stat_qq_line(size = line_size) +
ggplot2::theme_minimal() +
ggplot2::labs(
title = "QQ Plot",
subtitle = sub_title,
color = "Simulation"
) +
ggplot2::theme(legend.position = leg_pos)
}
if (.geom_rug) {
plt <- plt +
ggplot2::geom_rug()
}
if ((.geom_point) & (!plot_type == "qq")) {
plt <- plt +
ggplot2::geom_point(size = point_size)
}
if (.geom_smooth) {
max_dy <- max(data_tbl$dy)
plt <- plt +
ggplot2::geom_smooth(
ggplot2::aes(
group = FALSE
),
se = FALSE,
color = "black",
linetype = "dashed"
) +
ggplot2::xlim(0, max_dy)
}
if (.geom_jitter) {
plt <- plt +
ggplot2::geom_jitter()
}
if (.interactive) {
plt <- plotly::ggplotly(plt)
}
# Return ----
return(plt)
} Example discrete distribution using |
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