From 64a1dbcb3e1bd61db97a36b18ae3f56e602fdc67 Mon Sep 17 00:00:00 2001
From: Ben Varick <ben@dendroalsia.net>
Date: Thu, 9 Nov 2023 09:58:53 -0600
Subject: [PATCH] made laundry_status.R executable

---
 .Rhistory        | 852 +++++++++++++++++++++++------------------------
 laundry_status.R |   0
 2 files changed, 426 insertions(+), 426 deletions(-)
 mode change 100644 => 100755 laundry_status.R

diff --git a/.Rhistory b/.Rhistory
index e330195..88bdf45 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,342 +1,429 @@
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "Last week")
-View(values)
-ggplot(data = values) +
-geom_tile(aes(x = time,
-y = entity_id,
-fill = status))
-ggplot(data = values) +
-geom_tile(aes(x = time,
-y = entity_id,
-width = 1,
-height = 1
-fill = status)) +
-ggplot(data = values) +
-geom_tile(aes(x = time,
-y = entity_id,
-width = 1,
-height = 1,
-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")
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
+View(values %>%
+filter(entity_id %in% sensors) %>%
+filter(time > time_start,
+time < time_end))
+values %>%
+filter(entity_id %in% sensors) %>%
+filter(time > time_start,
+time < time_end) %>%
+group_by(time_rounded, entity_id) %>%
+summarise(value = mean(value)) %>%
+pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
+ggplot() +
+geom_point(aes(x = glencoe_air_monitor_temperature,
+y = living_room_temperature,
+color = entity_id)) +
+# calcs voltage loss per hour
+lm_calc$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$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
 values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = values$time[-1])
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
+values$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$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
 values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], NA))
-View(values)
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
+values$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$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
 values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], Sys.time()))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
+values$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$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
 values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], run_time))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off"))
-c(values$time[-1], run_time)
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], run_time))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > 1, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], as.POSIXct(run_time)))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], c(run_time)))
-power_threshhold <- 5
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off")) %>%
-mutate(end_time = c(values$time[-1], c(run_time)))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off"))
-values <- values %>%
-mutate(end_time = c(values$time[-1], run_time))
-ggplot(data = values) +
-geom_tile(aes(x = time + minutes(as.numerica(difftime(time, endtime, unit = "mins")))/2,
-y = entity_id,
-width = minutes(as.numerica(difftime(time, endtime, unit = "mins"))),
-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")
-ggplot(data = values) +
-geom_tile(aes(x = time + minutes(as.numeric(difftime(time, endtime, unit = "mins")))/2,
-y = entity_id,
-width = minutes(as.numeric(difftime(time, endtime, unit = "mins"))),
-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")
-ggplot(data = values) +
-geom_tile(aes(x = time + minutes(as.numeric(difftime(time, end_time, unit = "mins")))/2,
-y = entity_id,
-width = minutes(as.numeric(difftime(time, end_time, unit = "mins"))),
-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")
-ggplot(data = values) +
-geom_tile(aes(x = time + minutes(as.numeric(difftime(time, end_time, unit = "mins"))/2),
-y = entity_id,
-width = minutes(as.numeric(difftime(time, end_time, unit = "mins"))),
-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")
-values$time + minutes(as.numeric(difftime(values$time, values$end_time, unit = "mins"))/2)
-difftime(values$time, values$end_time, unit = "mins")
-as.numeric(difftime(values$time, values$end_time, unit = "mins"))
-ggplot(data = values) +
-geom_tile(aes(x = time + minutes(as.numeric(difftime(end_time, time, unit = "mins"))/2),
-y = entity_id,
-width = minutes(as.numeric(difftime(end_time, time, unit = "mins"))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week")
-ggplot(data = values) +
-geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
-y = entity_id,
-width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week")
-run_time <- Sys.time()
-values <- home_assistant$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")',
-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 <- values %>%
-group_by(entity_id) %>%
-arrange(time) %>%
-mutate(end_time = c(values$time[-1], run_time))
-run_time <- Sys.time()
-values <- home_assistant$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")',
-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 <- values %>%
-group_by(entity_id) %>%
-mutate(end_time = c(values$time[-1], run_time))
-run_time <- Sys.time()
-values <- home_assistant$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")',
-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"))
-for(entity in entities$entity_id) {
-values_by_entity[[entity]] <- values %>%
-filter(entity_id %in% entity) %>%
-mutate(end_time = c(values$time[-1], run_time))
-}
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off"))
-for(entity in entities$entity_id) {
-values_by_entity[[entity]] <- values %>%
-filter(entity_id %in% entity) %>%
-mutate(end_time = c(time[-1], run_time))
-}
-values_by_entity[[entity]] <- values %>%
-filter(entity_id %in% entity) %>%
-mutate(end_time = c(time[-1], run_time))
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off"))
-values_by_entity <- as.list(NULL)
-for(entity in entities$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)
-View(values)
-washer_last_on <- values %>% filter(entity_id == entities$entity_id[1], value > power_threshhold) %>% tail(1) %>% pull(time)
-washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < power_threshhold) %>% tail(1) %>% pull(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$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week")
-ggplot(data = values) +
-geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
-y = entity_id,
-width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week")
-ggplot(data = values %>% filter(time >= max(values$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_y_continuous()  +
-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") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "Last 24 hours")
-ggplot(data = values %>% filter(time >= max(values$time) - hours(24))) +
-geom_tile(aes(x = time + seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))/2),
-y = entity_id,
-width = seconds(round(as.numeric(difftime(end_time, time, unit = "secs")))),
-height = 0.5,
-fill = status)) +
-scale_y_discrete(breaks = entities$entity_id, labels = entities$name) +
-scale_x_datetime(breaks = seq(round_date(max(values$time), "4 hours") - hours(24), round_date(max(values$time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "Last 24 hours")
+values$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)
@@ -379,97 +466,10 @@ 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$entity_id[1], value > power_threshhold) %>% tail(1) %>% pull(time)
-washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < power_threshhold) %>% tail(1) %>% pull(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$name) +
-scale_x_datetime(date_breaks = "24 hours", date_labels = '%A', date_minor_breaks = "6 hours") +
-theme(axis.text.x = element_text(angle = 30, vjust = 0.5)) +
-labs(title = "The past week",
-x = NULL,
-y = NULL,
-fill = NULL)
-plot_1day <- ggplot(data = values %>% filter(time >= max(values$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$time), "4 hours") - hours(24), round_date(max(values$time), "4 hours"), by = "4 hours"), date_labels = '%I:%M %p', date_minor_breaks = "1 hours") +
-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)
-}
-round_date(max(values$time), "4 hours")
-#setup ----
-library(tidyverse)
-library(influxdbclient)
-library(rmarkdown)
-if(Sys.info()[4] == "pseudotsuga") {
-setwd("~/Documents/dataProjects/laundry_status")
-} else {
-setwd("/laundry_status")
-}
-Sys.setenv(TZ='America/Chicago')
-# parameters needed to make connection to Database
-token <- substr(read_file("data/api_key"), 1, 88)
-org = "home_assistant"
-bucket = "home_assistant"
-## make connection to the influxDB bucket
-home_assistant <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-update_interval <- 5
-cronjob_interval <- 60
-power_threshhold <- 5
-# ---- set variables
-entities <- data.frame(name = c("washing machine", "dryer"), entity_id = c("lamp_a_power", "lamp_b_power"))
-update_data <- function(){
-run_time <- Sys.time()
-values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
-POSIXctCol = NULL)
-values <- bind_rows(values)
-values <- values %>%
-rename(value = "_value",
-time = "_time")
-values <- values %>%
-mutate(
-time = as.POSIXct(time, tz = "America/Chicago"),
-status = ifelse(value > power_threshhold, "on", "off"))
-values_by_entity <- as.list(NULL)
-for(entity in entities$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$entity_id[1], value > power_threshhold) %>% tail(1) %>% pull(time)
-washer_last_off <- values %>% filter(entity_id == entities$entity_id[1], value < power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_on <- values %>% filter(entity_id == entities$entity_id[2], value > power_threshhold) %>% tail(1) %>% pull(time)
-dryer_last_off <- values %>% filter(entity_id == entities$entity_id[2], value < power_threshhold) %>% tail(1) %>% pull(time)
+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){
diff --git a/laundry_status.R b/laundry_status.R
old mode 100644
new mode 100755