made laundry_status.R executable
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@ -1,342 +1,429 @@
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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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View(values)
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ggplot(data = values) +
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geom_tile(aes(x = time,
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y = entity_id,
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fill = status))
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ggplot(data = values) +
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geom_tile(aes(x = time,
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y = entity_id,
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width = 1,
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height = 1
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fill = status)) +
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ggplot(data = values) +
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geom_tile(aes(x = time,
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y = entity_id,
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width = 1,
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height = 1,
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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(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 = "The past week")
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
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POSIXctCol = NULL)
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View(values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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ggplot() +
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geom_point(aes(x = glencoe_air_monitor_temperature,
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y = living_room_temperature,
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color = entity_id)) +
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# calcs voltage loss per hour
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lm_calc$coefficients[2] * 60 * 60
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value)
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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ggplot() +
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geom_point(aes(x = glencoe_air_monitor_temperature,
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y = living_room_temperature)) +
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# calcs voltage loss per hour
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lm_calc$coefficients[2] * 60 * 60
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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ggplot() +
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geom_point(aes(x = glencoe_air_monitor_temperature,
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y = living_room_temperature))
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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+ 2.6
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature + 2.6) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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sensors <- c("living_room_humidity", "air_monitor_humidity")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_humidity -13) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_humidity)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_humidity),
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method = "lm")
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sensors <- c("living_room_humidity", "air_monitor_humidity")
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sensors <- c("living_room_humidity", "glencoe_air_monitor_humidity")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = mean(value)) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_humidity -13) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_humidity)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_humidity),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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time_start <- ymd_hms("2023_07_14-13:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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time_start <- ymd_hms("2023_07_14-12:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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lm_calc <- lm(value ~ time, data = values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = glencoe_air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value)
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sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
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values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
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values %>%
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filter(entity_id %in% sensors) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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time_start <- ymd_hms("2023_07_14-12:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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lm_calc <- lm(value ~ time, data = values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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time_start <- ymd_hms("2023_07_14-13:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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lm_calc <- lm(value ~ time, data = values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end))
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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pivot_wider(id_cols = time_rounded, names_from = entity_id, values_from = value) %>%
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mutate(glencoe_adjusted = air_monitor_temperature) %>%
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ggplot() +
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geom_point(aes(x = glencoe_adjusted,
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y = living_room_temperature)) +
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geom_smooth(aes(x = glencoe_adjusted,
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y = living_room_temperature),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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ggplot() +
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geom_point(aes(x = time_rounded,
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y = value)) +
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geom_smooth(aes(x = time_rounded,
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y = value),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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ggplot() +
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geom_point(aes(x = time_rounded,
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y = value,
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color = entiry_id)) +
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geom_smooth(aes(x = time_rounded,
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y = value),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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group_by(time_rounded, entity_id) %>%
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summarise(value = (mean(value)-32)/1.8) %>%
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ggplot() +
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geom_point(aes(x = time_rounded,
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y = value,
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color = entity_id)) +
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geom_smooth(aes(x = time_rounded,
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y = value),
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method = "lm")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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values %>%
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filter(entity_id %in% sensors) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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time_start <- ymd_hms("2023_07_14-13:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
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values <- bind_rows(values)
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values <- values %>%
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rename(value = "_value",
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time = "_time")
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values <- values %>%
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mutate(
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time = as.POSIXct(time, tz = "America/Chicago"),
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status = ifelse(value > 1, "on", "off")) %>%
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mutate(end_time = values$time[-1])
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
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POSIXctCol = NULL)
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values$value <- values$"_value"
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sensors <- c("living_room_temperature", "air_monitor_temperature", "glencoe_air_monitor_temperature")
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values['time_rounded'] <- round_date(ymd_hms(values$`_time`), unit = "minute")
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values %>%
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filter(entity_id %in% sensors) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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time_start <- ymd_hms("2023_07_14-13:00:00")
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time_end <- ymd_hms("2023_07_14-16:00:00")
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values %>%
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filter(entity_id %in% sensors) %>%
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filter(time > time_start,
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time < time_end) %>%
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ggplot() +
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geom_point(aes(x = time,
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y = value,
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color = entity_id))
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -24h) |> drop(columns: ["_start", "_stop"]) |> filter(fn: (r) => r._field =="value")')
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values <- bind_rows(values)
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values <- values %>%
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rename(value = "_value",
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time = "_time")
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values <- values %>%
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mutate(
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time = as.POSIXct(time, tz = "America/Chicago"),
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status = ifelse(value > 1, "on", "off")) %>%
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mutate(end_time = c(values$time[-1], NA))
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View(values)
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values <- home_assistant$query('from(bucket: "home_assistant") |> range(start: -7d) |> filter(fn: (r) => r["entity_id"] == "lamp_b_power" or r["entity_id"] == "lamp_a_power") |> filter(fn: (r) => r["_field"] == "value") |> filter(fn: (r) => r["_measurement"] == "W")',
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||||
POSIXctCol = NULL)
|
||||
values$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){
|
||||
|
||||
0
laundry_status.R
Normal file → Executable file
0
laundry_status.R
Normal file → Executable file
Loading…
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Reference in New Issue
Block a user