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indicadores_ACES_sample.R
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indicadores_ACES_sample.R
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# 1 - Set up
## Define required packages
packages <- c("lubridate", "tidyverse", "data.table", "ggthemes", "scales", "rio", "readxl", "rvest")
## Check which packages are not installed
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
## Install missing packages
if(length(new_packages) > 0) {
install.packages(new_packages)
}
## Load all packages
lapply(packages, library, character.only = TRUE)
rm(new_packages, packages)
## Get the Tableau palettes for 20 colors
lista_tableau <- ggthemes_data[["tableau"]][["color-palettes"]][["regular"]]$`Tableau 20`$value
color_palette <- c("#1F77B4", "#AEC7E8", "#FF7F0E", "#FFBB78", "#2CA02C", "#98DF8A", "#D62728", "#FF9896", "#9467BD", "#C5B0D5", "#8C564B", "#C49C94", "#E377C2", "#F7B6D2", "#7F7F7F", "#C7C7C7", "#BCBD22", "#DBDB8D", "#17BECF", "#9EDAE5", "#1C9B89", "#F7941E", "#7A3B3F", "#A57C52", "#4E4D8B", "#E6437A", "#4682B4", "#D896A3", "#7B8BAF", "#69A68C")
## Define the Primary Care Grouping
aces_name <- "ACES Lisboa Central"
########################################
# 2 - Loading Data
## Define the base path of data files (to be replaced)
path <- paste(wd(),"SIARS - ACes Lisboa Central/UF - Export - P01_02_01_R01_ Relatório de Indicadores - Comparação de Indicadores entre UF_s_ no Pe.csv")
df_raw <- read.csv(path, header = FALSE, fileEncoding = "UTF-8")
# 3 - Processing Data
## 3.1 - Rows Manipulation
df_clean <- df_raw[-c(1:8,10),-c(1,4)]
df_clean <- replace(df_clean, df_clean == '', NA)
df_clean <- as.data.frame(t(df_clean))
df_clean <- df_clean %>%
fill(c("9","11"), .direction = "down")
df_clean <- as.data.frame(t(df_clean))
df_key <- df_clean[-c(1:3),c(1,2)]
colnames(df_key) <- c("ID_Indicador", "Nome_Indicador")
df_clean <- df_clean[,-c(1)]
df_clean <- t(df_clean)
colnames(df_clean) <- df_clean[1,]
df_clean <- df_clean[-1,]
colnames(df_clean)[1:3] <- c("Data", "UF", "Tipo_Valor")
uf <- unique(colnames(df_clean)[4:length(colnames(df_clean))])
df_clean <- as.data.frame(df_clean)
df_clean <- df_clean %>% pivot_longer(cols=uf,
names_to='Indicador',
values_to="Valor")
### 3.3.3 - Wide to long
df_clean$Data <- ym(as.character(df_clean$Data))
df_clean$UF <- as.factor(df_clean$UF)
df_clean$Tipo_Valor <- as.factor(df_clean$Tipo_Valor)
levels(df_clean$Tipo_Valor) = c("Denominador", "Numerador", "Valor")
df_clean$Indicador <- as.factor(df_clean$Indicador)
df_clean$Valor <- as.numeric(gsub(",", "",df_clean$Valor))
## 3.4 - Customize to untis of analysis - Only USF and UCSP
selected_columns <- unique(grep("^USF|^UCSP", df_clean$UF, value = TRUE))
df_clean <- df_clean[df_clean$UF %in% selected_columns, ]
lista_usf <- unique(grep("^USF", df_clean$UF, value = TRUE))
lista_ucsp <- unique(grep("^UCSP", df_clean$UF, value = TRUE))
df_USF <- df_clean[df_clean$UF %in% lista_usf,]
df_UCSP <- df_clean[df_clean$UF %in% lista_ucsp,]
df_USF$Tipo_UF <- "USF"
df_UCSP$Tipo_UF <- "UCSP"
df_ready <- rbind(df_USF,df_UCSP)
rownames(df_ready) <- NULL
df_ready$Tipo_UF <- as.factor(df_ready$Tipo_UF)
df_ready$Tipo_UF <- relevel(df_ready$Tipo_UF, "USF")
### 3.5 - Create subgroups
lista_indicadores <- unique(df_ready$Indicador)
lista_indicadores_perc <- unique(grep("^Perc", lista_indicadores, value = TRUE))
lista_indicadores_prop <- unique(grep("^Prop", lista_indicadores, value = TRUE))
lista_indicadores_max <- setdiff(lista_indicadores, c(lista_indicadores_perc, lista_indicadores_prop))
df_ready <- df_ready %>%
mutate(Tipo_Indicador = ifelse(Indicador %in% lista_indicadores_perc, "Percentagem",
ifelse(Indicador %in% lista_indicadores_prop, "Proporção", "Outro")))
df_ready$Tipo_Indicador <- as.factor(df_ready$Tipo_Indicador)
### 3.6 - Extract Data from SDM
base_url = "https://sdm.min-saude.pt/BI.aspx?id="
df_key <- df_key %>%
mutate(ID_Indicador_Simples = result <- substr(ID_Indicador, 6, 8)) %>%
mutate(Link_Indicador = paste0(base_url,ID_Indicador_Simples))
for(link in 1:length(df_key$Link_Indicador)) {
page = read_html(df_key$Link_Indicador[link])
elem <- page %>% html_element("form")
df <- html_table(elem, header = FALSE)
df_key[link,5] <- as.character(df[16,1])
}
colnames(df_ready)[4] <- "Nome_Indicador"
colnames(df_key)[5] <- "Descricao"
df_ready <- right_join(df_ready, df_key[,-c(3)], by = "Nome_Indicador")
### 3.7 - Write Files (CSV)
write.csv(df_ready, "C:/Users/jmdin/Documents/R/Repositories/Indicadores_ACES/Output/Indicadores_Trabalhados_ACES_Lisboa_Central.csv", row.names=TRUE, fileEncoding = "ISO-8859-1")
### 3.8 - Specific Analyses
#### 3.8.1 - Number of registered patients
a <- df_ready[df_ready$Nome_Indicador == 'Proporção utentes c/ "DM não insul. depend."' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Denominador",]
total_rp <- sum(a$Valor)
#### 3.8.2 - Number of DM patients, by need of insulinotherapy
b <- df_ready[df_ready$Nome_Indicador == 'Proporção utentes c/ "DM não insul. depend."' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Numerador",]
total_dm_ni <- sum(b$Valor)
c <- df_ready[df_ready$Nome_Indicador == 'Proporção utentes com \"DM insulino depend.\"' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Numerador",]
total_dm_i <- sum(c$Valor)
#### 3.8.3 - Percentage of quality care in DM
d <- df_ready[df_ready$Nome_Indicador == 'Proporção DM c/ acompanham. adequado' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Numerador",]
#### 3.8.4 - Percentage of quality care in HT
e <- df_ready[df_ready$Nome_Indicador == 'Proporção de hipertensos, c/ acompanh. adequado' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Numerador",]
#### 3.8.5 - Percentage of registered patients with DM risk assessment
f <- df_ready[df_ready$Nome_Indicador == 'Proporção utentes com avaliação risco DM2 (3A)' & df_ready$Data == max(df_ready$Data) & df_ready$Tipo_Valor == "Numerador",]
# 4 - Graphs
## 4.1 - Generate all graphs: UCSP e USF
### Initialize an empty list to store the plots
plot_list <- list()
### Loop through each indicator in lista_indicadores
count <- 1
#df_ready <- df_ready[is.na(df_ready$Valor) == F,]
#df_ready <- df_ready[df_ready$Tipo_Valor == "Valor",]
#lista_indicadores <- unique(df_ready$Nome_Indicador)
for (indicador in lista_indicadores) {
# Filter the data for the current indicator
df_filtered <- subset(df_ready, df_ready$Nome_Indicador == indicador & df_ready$Tipo_Valor == "Valor")
# Create the plot for the current indicator
if (df_filtered$Tipo_Indicador[lista_indicadores[count]] == "Outro") {
p <- ggplot(df_filtered, aes(x = Data, y = Valor, fill = UF, color = UF)) +
geom_bar(stat = "identity", position = 'dodge', colour = NA) +
theme_minimal() +
theme(plot.background = element_rect(fill = "white", colour = NA), axis.text = element_text(size = 7), legend.position = "none") +
labs(x = "\nTempo",
y = "Valor\n",
subtitle = paste0("UCSP e USF - ", aces_name, "\n"),
title = str_wrap(paste0(df_filtered$Descricao, " - ", df_filtered$ID_Indicador), width = 115)) +
scale_fill_manual(values = color_palette) +
xlim(min(df_filtered$Data),max(df_filtered$Data)) +
facet_wrap(vars(UF), ncol = 5)
} else {
p <- ggplot(df_filtered, aes(x = Data, y = Valor, fill = UF, color = UF)) +
geom_bar(stat = "identity", position = 'dodge', colour = NA) +
theme_minimal() +
theme(plot.background = element_rect(fill = "white", colour = NA), axis.text = element_text(size = 7), legend.position = "none") +
labs(x = "\nTempo",
y = "Percentagem (%)\n",
subtitle = paste0("UCSP e USF - ", aces_name, "\n"),
title = str_wrap(paste0(df_filtered$Descricao, " - ", df_filtered$ID_Indicador), width = 115)) +
scale_fill_manual(values = color_palette) +
scale_y_continuous(labels = scales::percent_format(scale = 1)) +
xlim(min(df_filtered$Data),max(df_filtered$Data)) +
facet_wrap(vars(UF), ncol = 5)
}
# Store the plot in the list
plot_list[[count]] <- p
count <- count + 1
}
## 4.3 - Save all graphs: UCSP e USF
lista_indicadores <- unique(df_ready$Descricao)
#Change name to avoid folder creation
lista_indicadores_simples <- gsub(" / ", " ou ", lista_indicadores)
lista_indicadores_simples <- gsub("/", "-", lista_indicadores)
lista_indicadores_simples <- gsub("\"", "", lista_indicadores_simples)
#Initialize a counter
counter <- 1
#Loop through each indicator in lista_indicadores
for(i in lista_indicadores_simples) {
#Build file name
set_filename <- paste0("C:/Users/jmdin/Desktop/Geral - UF & UCSP/", substr(i, 1, 100), ".png")
#Save the plot from plot_list using ggsave
ggsave(filename = set_filename,
device = "png",
plot = plot_list[[counter]],
width = 32,
height = 18,
units = c("cm"),
dpi = "retina")
#Print status
print(set_filename)
#Increment the counter
counter <- counter + 1
}