diff --git a/.Rhistory b/.Rhistory
deleted file mode 100644
index 54b77fe..0000000
--- a/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-ggplot() +
-geom_point(aes(x = glencoe_air_monitor_temperature,
-y = living_room_temperature,
-color = entity_id)) +
-# calcs voltage loss per hour
-lm_calc$coefficients[2] * 60 * 60
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value)
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-ggplot() +
-geom_point(aes(x = glencoe_air_monitor_temperature,
-y = living_room_temperature)) +
-# calcs voltage loss per hour
-lm_calc$coefficients[2] * 60 * 60
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-ggplot() +
-geom_point(aes(x = glencoe_air_monitor_temperature,
-y = living_room_temperature))
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-+ 2.6
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-sensors <- c("living_room_humidity", "air_monitor_humidity")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_humidity -13) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_humidity)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_humidity),
-method = "lm")
-sensors <- c("living_room_humidity", "air_monitor_humidity")
-sensors <- c("living_room_humidity", "glencoe_air_monitor_humidity")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = mean(value)) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_humidity -13) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_humidity)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_humidity),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-time_start <- ymd_hms("2023_07_14-12:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-lm_calc <- lm(value ~ time, data = values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value)
-sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
-values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-12:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-lm_calc <- lm(value ~ time, data = values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-lm_calc <- lm(value ~ time, data = values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
-mutate(glencoe_adjusted = air_monitor_temperature) %>%
-ggplot() +
-geom_point(aes(x = glencoe_adjusted,
-y = living_room_temperature)) +
-geom_smooth(aes(x = glencoe_adjusted,
-y = living_room_temperature),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-ggplot() +
-geom_point(aes(x = time_rounded,
-y = value)) +
-geom_smooth(aes(x = time_rounded,
-y = value),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-ggplot() +
-geom_point(aes(x = time_rounded,
-y = value,
-color = entiry_id)) +
-geom_smooth(aes(x = time_rounded,
-y = value),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-ggplot() +
-geom_point(aes(x = time_rounded,
-y = value,
-color = entity_id)) +
-geom_smooth(aes(x = time_rounded,
-y = value),
-method = "lm")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
-values <- bind_rows(values)
-values$value <- values$"_value"
-sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
-values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
-values <- bind_rows(values)
-values$value <- values$"_value"
-sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
-#sensors <- c("living_room_humidity", "office_humidity", "living_room_co2_humidity", "office_co2_humidity", "bedroom_humidity")
-#sensors <- c("air_monitor_air_monitor_battery_voltage")
-values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
-values <- bind_rows(values)
-values$value <- values$"_value"
-sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
-#sensors <- c("living_room_humidity", "office_humidity", "living_room_co2_humidity", "office_co2_humidity", "bedroom_humidity")
-#sensors <- c("air_monitor_air_monitor_battery_voltage")
-values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
-values <- bind_rows(values)
-values$value <- values$"_value"
-sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
-#sensors <- c("living_room_humidity", "office_humidity", "living_room_co2_humidity", "office_co2_humidity", "bedroom_humidity")
-#sensors <- c("air_monitor_air_monitor_battery_voltage")
-values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
-values %>%
-filter(entity_id %in% sensors) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-time_start <- ymd_hms("2023_07_14-13:00:00")
-time_end <- ymd_hms("2023_07_14-16:00:00")
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-ggplot() +
-geom_point(aes(x = time,
-y = value,
-color = entity_id))
-values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end) %>%
-group_by(time_rounded, entity_id) %>%
-summarise(value = (mean(value)-32)/1.8) %>%
-ggplot() +
-geom_point(aes(x = time_rounded,
-y = value,
-color = entity_id)) +
-geom_smooth(aes(x = time_rounded,
-y = value),
-method = "lm")
-lm_calc <- lm(value ~ time, data = values %>%
-filter(entity_id %in% sensors) %>%
-filter(time > time_start,
-time < time_end))
-View(lm_calc)
-#setup ----
-library(tidyverse)
-library(influxdbclient)
-library(rmarkdown)
-if(Sys.info()[4] == "pseudotsuga") {
-setwd("~/Documents/dataProjects/laundry_status")
-} else {
-setwd("/laundry_status")
-}
-Sys.setenv(TZ='America/Chicago')
-# parameters needed to make connection to Database
-token <- substr(read_file("data/api_key"), 1, 88)
-org = "home_assistant"
-bucket = "home_assistant"
-## make connection to the influxDB bucket
-home_assistant <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-update_interval <- 5
-cronjob_interval <- 60
-power_threshhold <- 10
-# ---- set variables
-entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("washing_machine_power", "dryer_power"))
-update_data <- function(){
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "washing_machine_power" or r["entity_id"] == "dryer_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off"))
-values_by_entity <- as.list(NULL)
-for(entity in entities$entity_id) {
-values_by_entity[[entity]] <- values %>%
-filter(entity_id %in% entity) %>%
-mutate(end_time = c(time[-1], run_time))
-}
-values <- bind_rows(values_by_entity)
-washer_last_on <- values %>% filter(entity_id == entities$entity_id[1], value > power_threshhold) %>% tail(1) %>% pull(end_time)
-washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < power_threshhold) %>% tail(1) %>% pull(end_time)
-dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > power_threshhold) %>% tail(1) %>% pull(end_time)
-dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < power_threshhold) %>% tail(1) %>% pull(end_time)
-# ---- generate html
-current_status <- as.list(NULL)
-for (entity in entities$entity_id){
-current_status[[entity]] <- ifelse(values %>% filter(entity_id %in% entity) %>% tail(1) %>% pull(value) > power_threshhold, "on", "off")
-}
-plot_1week <- ggplot(data = values) +
-geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
-y = entity_id,
-width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week",
-x = NULL,
-y = NULL,
-fill = NULL)
-plot_1day <- ggplot(data = values %>% filter(time >= max(values$end_time) - hours(24))) +
-geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
-y = entity_id,
-width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-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") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "Last 24 hours",
-x = NULL,
-y = NULL,
-fill = NULL)
-render("laundry_status.Rmd",
-output_dir = "html",
-output_file = "index.html")
-}
-# for(i in 1:(cronjob_interval/update_interval)){
-#   message(Sys.time())
-#   update_data()
-#   Sys.sleep(60*update_interval)
-# }
-continue <- TRUE
-while(continue){
-message(Sys.time())
-update_data()
-Sys.sleep(60*update_interval)
-}
diff --git a/.gitignore b/.gitignore
index 9c74b78..a436f8b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@ data/*
 figures/*
 
 html/index.html
+.Rhistory