314 lines
14 KiB
R
Executable File
314 lines
14 KiB
R
Executable File
#setup ----
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library(tidyverse)
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library(influxdbclient)
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library(rmarkdown)
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if(Sys.info()[4] == "pseudotsuga") {
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setwd("~/Documents/dataProjects/laundry_status")
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} else {
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setwd("/laundry_status")
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}
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Sys.setenv(TZ='America/Chicago')
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# parameters needed to make connection to Database
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token <- substr(read_file("data/api_key"), 1, 88)
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org = "home_assistant"
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bucket = "home_assistant"
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## make connection to the influxDB bucket
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home_assistant <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
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token = token,
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org = org)
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update_interval <- 5
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long_term_rounds <- 1440/update_interval
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power_threshold_on <- 10
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power_threshold_wash_door <- 4
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power_threshold_dry_door <- 1.5
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# ---- set variables
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entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("washing_machine_power", "dryer_power"), name_es = c("lavadora", "secadora"))
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colors <- data.frame(value = c("off", "on"), color = c("#bcbcbc", "#008080"))
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# Define a function to calculate the time difference
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calculateTimeAgo <- function(eventTime) {
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current <- Sys.time() # Get the current time
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timeDiff <- as.duration(current - eventTime) # Calculate the time difference
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# Convert the time difference to appropriate units and format the result
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if (timeDiff >= hours(24)) {
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result <- paste(round(timeDiff / dhours(24)), "days ago")
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} else if (timeDiff >= hours(1)) {
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result <- paste(round(timeDiff / dhours(1)), "hours ago")
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} else if (timeDiff >= minutes(1)) {
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result <- paste(round(timeDiff / dminutes(1)), "minutes ago")
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} else {
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result <- "Just now"
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}
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return(result)
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}
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calculateTimeAgo_es <- function(eventTime) {
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current <- Sys.time() # Get the current time
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timeDiff <- as.duration(current - eventTime) # Calculate the time difference
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# Convert the time difference to appropriate units and format the result
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if (timeDiff >= hours(24)) {
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result <- paste("hace", round(timeDiff / dhours(24)), "dias")
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} else if (timeDiff >= hours(1)) {
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result <- paste("hace", round(timeDiff / dhours(1)), "horas")
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} else if (timeDiff >= minutes(1)) {
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result <- paste("hace", round(timeDiff / dminutes(1)), "minutos")
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} else {
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result <- "Ahora"
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}
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return(result)
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}
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# ---- update_data function
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update_data <- function(){
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Sys.setlocale("LC_TIME", "en_US.UTF-8")
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run_time <- Sys.time()
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start_time <- floor_date(run_time - ddays(7), unit = "days")
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values <- home_assistant$query(paste0('from(bucket: "home_assistant") |> range(start: ',
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start_time,
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') |> filter(fn: (r) => r["entity_id"] == "washing_machine_power" or r["entity_id"] == "dryer_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")'),
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POSIXctCol = NULL)
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values <- bind_rows(values)
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values <- values %>%
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rename(value = "_value",
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time = "_time")
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values <- values %>%
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mutate(
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time = as.POSIXct(time, tz = "America/Chicago"),
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status = ifelse(value > power_threshold_on, "on", "off")) %>%
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mutate(door_threshold = ifelse(entity_id == "washing_machine_power", power_threshold_wash_door, power_threshold_dry_door)) %>%
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mutate(door = ifelse(value < door_threshold, "open", "closed")) %>%
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filter(time >= start_time)
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values_by_entity <- as.list(NULL)
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for(entity in entities$entity_id) {
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values_by_entity[[entity]] <- values %>%
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filter(entity_id %in% entity) %>%
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mutate(end_time = c(time[-1], run_time))
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}
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values <- bind_rows(values_by_entity)
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last_change <- values %>%
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group_by(entity_id, status) %>%
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slice_max(n = 1, order_by = time) %>%
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pivot_wider(id_cols = domain, names_from = c(entity_id, status), values_from = time)
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status_door <- values %>%
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group_by(entity_id) %>%
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slice_max(n = 1, order_by = time) %>%
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select(entity_id, door) %>%
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pivot_wider(names_from = entity_id, values_from = door)
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current_status <- as.list(NULL)
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for (entity in entities$entity_id){
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current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > power_threshold_on, "on", "off")
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}
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current_status <- bind_rows(current_status)
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## add spanish translations
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current_status <- current_status %>%
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mutate(dryer_power_es = ifelse(dryer_power == "on", "encendida", "apagada"),
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washing_machine_power_es = ifelse(washing_machine_power == "on", "encendida", "apagada"))
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status_door <- status_door %>%
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mutate(dryer_power_es = ifelse(dryer_power == "open", "abierto", "cerado"),
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washing_machine_power_es = ifelse(washing_machine_power == "open", "abierto", "cerado"))
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# ---- make plots
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plot_1day <- ggplot(data = values %>% filter(time >= max(values$end_time) - hours(24))) +
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geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
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y = entity_id,
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width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
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height = 0.5,
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fill = status)) +
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scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
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scale_x_datetime(breaks = seq(round_date(max(values$end_time), "4 hours") - hours(24), round_date(max(values$end_time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
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scale_fill_manual(values = colors$color) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "Last 24 hours",
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x = NULL,
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y = NULL,
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fill = NULL)
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plot_1week_days <- ggplot(data = values %>%
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filter(as.Date(time, tz = "America/Chicago") == as.Date(end_time, tz = "America/Chicago")) %>%
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mutate(date = as.Date(time, tz = "America/Chicago")) %>%
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left_join(. , entities, by = join_by(entity_id)) %>%
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mutate(name = factor(name, levels = c("washing machine", "dryer")))) +
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geom_rect(aes(xmin = date - hours(8),
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xmax = date + hours(8),
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ymin = ymd_hms(paste("2023-01-01", strftime(time, format = "%H:%M:%S"))),
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ymax = ymd_hms(paste("2023-01-01", strftime(end_time, format = "%H:%M:%S"))),
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fill = status)) +
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facet_grid(name ~ .) +
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scale_y_datetime(date_breaks = "4 hours", date_labels = '%I:%M %p', minor_breaks = "2 hours", expand = expansion(mult = 0)) +
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scale_x_datetime(date_breaks = "1 day", date_labels = '%A', minor_breaks = NULL) +
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scale_fill_manual(values = colors$color) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "The past week",
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x = "Day",
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y = "Time of Day",
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fill = NULL)
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# ---- generate html
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render("laundry_status.Rmd",
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output_dir = "html",
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output_file = "index.html")
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Sys.setlocale("LC_TIME", "es_NI.UTF-8")
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# ---- make plots (es)
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plot_1day_es <- ggplot(data = values %>% filter(time >= max(values$end_time) - hours(24)) %>%
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mutate(status = ifelse(status == "on", "encendida", "apagada"))) +
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geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
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y = entity_id,
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width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
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height = 0.5,
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fill = status)) +
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scale_y_discrete(breaks = entities$entity_id, labels = entities$name_es) +
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scale_x_datetime(breaks = seq(round_date(max(values$end_time), "4 hours") - hours(24), round_date(max(values$end_time), "4 hours"), by = "4 hours"), date_labels = '%H:%M', date_minor_breaks = "1 hours") +
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scale_fill_manual(values = colors$color) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "Hace 24 horas",
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x = NULL,
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y = NULL,
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fill = NULL)
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plot_1week_days_es <- ggplot(data = values %>%
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filter(as.Date(time, tz = "America/Chicago") == as.Date(end_time, tz = "America/Chicago")) %>%
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mutate(date = as.Date(time, tz = "America/Chicago")) %>%
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left_join(. , entities, by = join_by(entity_id)) %>%
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mutate(name = factor(name_es, levels = c("lavadora", "secadora"))) %>%
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mutate(status = ifelse(status == "on", "encendida", "apagada"))) +
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geom_rect(aes(xmin = date - hours(8),
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xmax = date + hours(8),
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ymin = ymd_hms(paste("2023-01-01", strftime(time, format = "%H:%M:%S"))),
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ymax = ymd_hms(paste("2023-01-01", strftime(end_time, format = "%H:%M:%S"))),
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fill = status)) +
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facet_grid(name ~ .) +
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scale_y_datetime(date_breaks = "4 hours", date_labels = '%H:%M', minor_breaks = "2 hours", expand = expansion(mult = 0)) +
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scale_x_datetime(date_breaks = "1 day", date_labels = '%A', minor_breaks = NULL) +
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scale_fill_manual(values = colors$color) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "La semana pasada",
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x = "Día",
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y = "Tiempo",
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fill = NULL)
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render("laundry_status_es.Rmd",
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output_dir = "html",
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output_file = "es.html")
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}
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update_longterm_data <- function(){
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Sys.setlocale("LC_TIME", "en_US.UTF-8")
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run_time <- Sys.time()
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start_time <- floor_date(run_time - ddays(365), unit = "days")
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values <- home_assistant$query(paste0('from(bucket: "home_assistant") |> range(start: ',
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start_time,
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') |> filter(fn: (r) => r["entity_id"] == "washing_machine_power" or r["entity_id"] == "dryer_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")'),
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POSIXctCol = NULL)
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values <- bind_rows(values)
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values <- values %>%
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rename(value = "_value",
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time = "_time")
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values <- values %>%
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mutate(
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time = as.POSIXct(time, tz = "America/Chicago"),
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status = ifelse(value > power_threshold_on, "on", "off")) %>%
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mutate(door_threshold = ifelse(entity_id == "washing_machine_power", power_threshold_wash_door, power_threshold_dry_door)) %>%
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mutate(door = ifelse(value < door_threshold, "open", "closed")) %>%
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filter(time >= start_time)
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values_by_entity <- as.list(NULL)
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for(entity in entities$entity_id) {
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values_by_entity[[entity]] <- values %>%
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filter(entity_id %in% entity) %>%
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mutate(end_time = c(time[-1], run_time))
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}
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values <- bind_rows(values_by_entity)
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values["weekday"] <- wday(values$time, label = TRUE)
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values["hourofday"] <- floor_date(ymd_hms(paste("2023_01_01",strftime(values$time, format="%H:%M:%S"))), unit = "hours")
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long_term <- values %>%
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filter(status =="on") %>%
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group_by(weekday, hourofday, entity_id, status) %>%
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summarise(count = n())
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# ---- make plots
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plot_all_week_days <- ggplot(data = long_term %>%
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left_join(. , entities, by = join_by(entity_id)) %>%
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mutate(name = factor(name, levels = c("washing machine", "dryer")))) +
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geom_tile(aes(x = weekday,
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y = hourofday,
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alpha = count,
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fill = status)) +
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facet_grid(name ~ .) +
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scale_y_datetime(date_breaks = "4 hours", date_labels = '%I:%M %p', minor_breaks = "2 hours", expand = expansion(mult = 0)) +
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scale_x_discrete() +
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scale_fill_manual(values = colors$color[2], guide = NULL) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "Long term trends",
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subtitle = paste0("data from ",strftime(min(values$time), format = "%B %d, %Y"), " to ", strftime(run_time, format = "%B %d, %Y")),
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x = "Weekday",
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y = "Time of Day",
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alpha = "Frequency")
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# ---- generate html
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render("laundry_longterm_data.Rmd",
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output_dir = "html",
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output_file = "longterm_data.html")
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# ---- spanish translation
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Sys.setlocale("LC_TIME", "es_NI.UTF-8")
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values["weekday"] <- wday(values$time, label = TRUE)
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values["hourofday"] <- floor_date(ymd_hms(paste("2023_01_01",strftime(values$time, format="%H:%M:%S"))), unit = "hours")
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long_term <- values %>%
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filter(status =="on") %>%
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group_by(weekday, hourofday, entity_id, status) %>%
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summarise(count = n())
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# ---- make plots
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plot_all_week_days_es <- ggplot(data = long_term %>%
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left_join(. , entities, by = join_by(entity_id)) %>%
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mutate(name = factor(name_es, levels = c("lavadora", "secadora")))) +
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geom_tile(aes(x = weekday,
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y = hourofday,
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alpha = count,
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fill = status)) +
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facet_grid(name ~ .) +
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scale_y_datetime(date_breaks = "4 hours", date_labels = '%H:%M', minor_breaks = "2 hours", expand = expansion(mult = 0)) +
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scale_x_discrete() +
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scale_fill_manual(values = colors$color[2], guide = NULL) +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "Tendencias a largo plazo",
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subtitle = paste0("datos desde el ",strftime(min(values$time), format = "%d de %B de %Y"), " hasta el ", strftime(run_time, format = "%d de %B de %Y")),
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x = "Día de la semana",
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y = "Tiempo",
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alpha = "Frecuencia")
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render("laundry_longterm_data_es.Rmd",
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output_dir = "html",
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output_file = "longterm_data_es.html")
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}
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continue <- TRUE
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i <- 0
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while(continue){
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message(Sys.time())
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update_data()
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if (i %% long_term_rounds == 0) {
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update_longterm_data()
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}
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i <- i + 1
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Sys.sleep(60*update_interval)
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}
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