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plot_metrics.R
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# # Carregue os pacotes
# library(readxl)
# library(dplyr)
# library(ggplot2)
#
# # Caminhos dos arquivos para V001, V002 e V003
# files <- list(
# V001 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v001.xlsx",
# V002 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v002.xlsx",
# V003 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v003.xlsx"
# )
#
# # Função para ler e processar cada arquivo
# read_and_process_file <- function(file_path, version) {
# # Leia o arquivo XLSX
# data <- read_excel(file_path)
#
# # Adicione a coluna de versão
# data <- data %>%
# mutate(version = version)
#
# return(data)
# }
#
# # Processar arquivos e combinar os dados
# metrics_combined <- bind_rows(
# lapply(names(files), function(version) {
# read_and_process_file(files[[version]], version)
# })
# )
#
# # Plotar métricas
# plot_metrics <- function(metric_name) {
# ggplot(metrics_combined, aes_string(x = "PARTICULA", y = metric_name, fill = "VERSAO")) +
# geom_bar(stat = "identity", position = "dodge") +
# labs(title = paste("Granulometria", metric_name),
# x = "",
# y = metric_name) +
# theme_minimal(base_size = 15) +
# theme(
# panel.background = element_rect(fill = "white", colour = "white"),
# plot.background = element_rect(fill = "white", colour = "white"),
# legend.background = element_rect(fill = "white", colour = "white"),
# legend.key = element_rect(fill = "white", colour = "white"),
# plot.title = element_text(size = 20, face = "bold"),
# axis.title.x = element_text(size = 16),
# axis.title.y = element_text(size = 16),
# axis.text.x = element_text(size = 14, angle = 45, hjust = 1),
# axis.text.y = element_text(size = 14),
# legend.title = element_text(size = 16),
# legend.text = element_text(size = 14)
# )
# }
#
# # Lista de métricas
# metrics_names <- c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC")
#
# # Gerar gráficos para todas as métricas e salvar em PNG
# for (metric in metrics_names) {
# plot <- plot_metrics(metric)
# print(plot)
# ggsave(filename = paste0("C:/Users/marco/OneDrive/Área de Trabalho/Externa/", metric, "_comparison.png"), plot = plot, bg = "white", width = 10, height = 6)
# }
# # Carregue os pacotes
# library(readxl)
# library(dplyr)
# library(ggplot2)
#
# # Caminhos dos arquivos para V000, V001, V002 e V003
# files <- list(
# V000 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v000.xlsx",
# V001 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v001.xlsx",
# V002 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v002.xlsx",
# V003 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v003.xlsx"
# )
#
# # Função para ler e processar cada arquivo
# read_and_process_file <- function(file_path, version) {
# # Leia o arquivo XLSX
# data <- read_excel(file_path)
#
# # Adicione a coluna de versão
# data <- data %>%
# mutate(version = version)
#
# return(data)
# }
#
# # Processar arquivos e combinar os dados
# metrics_combined <- bind_rows(
# lapply(names(files), function(version) {
# read_and_process_file(files[[version]], version)
# })
# )
#
# # Converter valores das métricas para numéricos
# metrics_combined <- metrics_combined %>%
# mutate(across(c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC"), as.numeric))
#
# # Plotar métricas
# plot_metrics <- function(metric_name) {
# min_y <- min(metrics_combined[[metric_name]], na.rm = TRUE)
# max_y <- max(metrics_combined[[metric_name]], na.rm = TRUE)
#
# ggplot(metrics_combined, aes(x = PARTICULA, y = .data[[metric_name]], color = version)) +
# geom_point(size = 3) +
# labs(title = paste("Granulometria", metric_name),
# x = "",
# y = metric_name) +
# scale_y_continuous(limits = c(min(0, min_y), max_y)) +
# theme_minimal(base_size = 15) +
# theme(
# panel.background = element_rect(fill = "white", colour = "white"),
# plot.background = element_rect(fill = "white", colour = "white"),
# legend.background = element_rect(fill = "white", colour = "white"),
# legend.key = element_rect(fill = "white", colour = "white"),
# plot.title = element_text(size = 20, face = "bold"),
# axis.title.x = element_text(size = 16),
# axis.title.y = element_text(size = 16),
# axis.text.x = element_text(size = 14, angle = 45, hjust = 1),
# axis.text.y = element_text(size = 14),
# legend.title = element_text(size = 16),
# legend.text = element_text(size = 14)
# )
# }
#
# # Lista de métricas
# metrics_names <- c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC")
#
# # Gerar gráficos para todas as métricas e salvar em PNG
# for (metric in metrics_names) {
# plot <- plot_metrics(metric)
# print(plot)
# ggsave(filename = paste0("C:/Users/marco/OneDrive/Área de Trabalho/Externa/", metric, "_comparison_eixo.png"), plot = plot, bg = "white", width = 10, height = 6)
# }
# Carregue os pacotes
library(readxl)
library(dplyr)
library(ggplot2)
# Caminhos dos arquivos para V000, V001, V002 e V003
files <- list(
V000 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v000.xlsx",
V001 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v001.xlsx",
V002 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v002.xlsx",
V003 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v003.xlsx"
)
# Função para ler e processar cada arquivo
read_and_process_file <- function(file_path, version) {
# Leia o arquivo XLSX
data <- read_excel(file_path)
# Adicione a coluna de versão
data <- data %>%
mutate(version = version)
return(data)
}
# Processar arquivos e combinar os dados
metrics_combined <- bind_rows(
lapply(names(files), function(version) {
read_and_process_file(files[[version]], version)
})
)
# Converter valores das métricas para numéricos
metrics_combined <- metrics_combined %>%
mutate(across(c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC"), as.numeric))
# Função para definir limites do eixo y de acordo com a métrica
get_y_limits <- function(metric_name) {
min_y <- min(metrics_combined[[metric_name]], na.rm = TRUE)
max_y <- max(metrics_combined[[metric_name]], na.rm = TRUE)
c(floor(min_y), ceiling(max_y))
}
# Plotar métricas
plot_metrics <- function(metric_name) {
y_limits <- get_y_limits(metric_name)
ggplot(metrics_combined, aes(x = PARTICULA, y = .data[[metric_name]], fill = version)) +
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = paste("Granulometria", metric_name),
x = "",
y = metric_name) +
scale_y_continuous(limits = y_limits, breaks = seq(y_limits[1], y_limits[2], length.out = 6)) +
theme_minimal(base_size = 15) +
theme(
panel.background = element_rect(fill = "white", colour = "white"),
plot.background = element_rect(fill = "white", colour = "white"),
legend.background = element_rect(fill = "white", colour = "white"),
legend.key = element_rect(fill = "white", colour = "white"),
plot.title = element_text(size = 20, face = "bold"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 14, angle = 45, hjust = 1),
axis.text.y = element_text(size = 14),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14)
)
}
# Lista de métricas
metrics_names <- c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC")
# Gerar gráficos para todas as métricas e salvar em PNG
for (metric in metrics_names) {
plot <- plot_metrics(metric)
print(plot)
ggsave(filename = paste0("C:/Users/marco/OneDrive/Área de Trabalho/Externa/", metric, "_comparison_dnv.png"), plot = plot, bg = "white", width = 10, height = 6)
}
# # Carregue os pacotes
# library(readxl)
# library(dplyr)
# library(ggplot2)
#
# # Caminhos dos arquivos para V001, V002 e V003
# files <- list(
# V001 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v001.xlsx",
# V002 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v002.xlsx",
# V003 = "C:/Users/marco/OneDrive/Área de Trabalho/Externa/metrics_v003.xlsx"
# )
#
# # Função para ler e processar cada arquivo
# read_and_process_file <- function(file_path, version) {
# # Leia o arquivo XLSX
# data <- read_excel(file_path)
#
# # Adicione a coluna de versão
# data <- data %>%
# mutate(version = version)
#
# return(data)
# }
#
# # Processar arquivos e combinar os dados
# metrics_combined <- bind_rows(
# lapply(names(files), function(version) {
# read_and_process_file(files[[version]], version)
# })
# )
#
# # Plotar métricas
# plot_metrics <- function(metric_name) {
# ggplot(metrics_combined, aes_string(x = "PARTICULA", y = metric_name, color = "VERSAO")) +
# geom_point(size = 3) +
# labs(title = paste("Granulometria", metric_name),
# x = "",
# y = metric_name) +
# theme_minimal(base_size = 15) +
# theme(
# panel.background = element_rect(fill = "white", colour = "white"),
# plot.background = element_rect(fill = "white", colour = "white"),
# legend.background = element_rect(fill = "white", colour = "white"),
# legend.key = element_rect(fill = "white", colour = "white"),
# plot.title = element_text(size = 20, face = "bold"),
# axis.title.x = element_text(size = 16),
# axis.title.y = element_text(size = 16),
# axis.text.x = element_text(size = 14, angle = 45, hjust = 1),
# axis.text.y = element_text(size = 14),
# legend.title = element_text(size = 16),
# legend.text = element_text(size = 14)
# )
# }
#
# # Lista de métricas
# metrics_names <- c("ME", "MSE", "MAE", "RMSE", "NSE", "Rsquared", "CCC")
#
# # Gerar gráficos para todas as métricas e salvar em PNG
# for (metric in metrics_names) {
# plot <- plot_metrics(metric)
# print(plot)
# ggsave(filename = paste0("C:/Users/marco/OneDrive/Área de Trabalho/Externa/", metric, "_comparison_point.png"), plot = plot, bg = "white", width = 10, height = 6)
# }