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