70 lines
2.7 KiB
R
70 lines
2.7 KiB
R
#setup ----
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library(tidyverse)
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library(influxdbclient)
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library(rmarkdown)
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setwd("/laundry_status")
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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$new(url = "https://influxdb.dendroalsia.net",
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token = token,
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org = org)
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update_interval <- 5
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# ---- set variables
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entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("lamp_a_power", "lamp_b_power"))
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update_data <- function(){
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_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 > 1, "on", "off")) %>%
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mutate(end_time = time + minutes(1))
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# ---- generate html
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current_status <- as.list(NULL)
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for (entity in entities$entity_id){
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current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > 5, "on", "off")
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}
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plot_1week <- ggplot(data = values) +
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geom_rect(aes(xmin = end_time,
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xmax = time,
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ymin = ifelse(entity_id == "lamp_a_power", 0, 2) + 0.5,
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ymax = ifelse(entity_id == "lamp_a_power", 0, 2) + 1.5,
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fill = status)) +
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scale_y_continuous() +
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scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
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theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
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labs(title = "Last week")
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plot_1day <- ggplot(data = values %>% filter(time >= max(values$time) - hours(24))) +
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geom_rect(aes(xmin = end_time,
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xmax = time,
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ymin = ifelse(entity_id == "lamp_a_power", 0, 2) + 0.5,
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ymax = ifelse(entity_id == "lamp_a_power", 0, 2) + 1.5,
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fill = status)) +
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scale_y_continuous() +
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scale_x_datetime(breaks = seq(round_date(max(values$time), "4 hours") - hours(24), round_date(max(values$time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
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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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render("laundry_status.Rmd")
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}
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for(i in 1:8640){
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update_data()
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Sys.sleep(60*update_interval)
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} |