Backup

library(xts)
library(influxdbr)
library(ggplot2)
library(grid)
library(gridExtra)
require(lubridate)
con <- influxdbr::influx_connection(host = "87.190.238.132",
                                    port = 8086,
                                    user = "default",
                                    pass = "password")

dat <- read.csv(paste0(getwd(), "/common.csv"))
one.month <- 30 * 24
periods <- c(
    one.month * 18
)
dat <- dat[dat$Power_On_Hours < periods, ]
dat <- dat[dat$site == "dresden2", ]

dat$node<- droplevels(dat$node)


needed.nodes <- levels(dat$node)
df <- df[df$node %in% needed.nodes, ]

df$node <- as.factor(df$node)

date.collection <- as.Date("16-07-21")

for(node in needed.nodes){
    node = needed.nodes[1]
    curr <- dat[dat$node == node, ]
    pow_on_hours <- curr$Power_On_Hours
    power_on_days <- round(pow_on_hours / 24, 0)

    dateofreplace <- date.collection
    day(dateofreplace) <- day(dateofreplace) - power_on_days
    monthbefore <- dateofreplace
    month(monthbefore) <- month(monthbefore) - 1
    dateofreplace <- format(dateofreplace, format="%Y-%m-%d")
    monthbefore <- format(monthbefore, format="%Y-%m-%d")
    time.bord <- paste0("time > \'20", monthbefore, "\' AND time < \'20", dateofreplace, "\'")
    where <- paste0("node=\'", node, "\' AND ", time.bord)
    print(where)
    node.data <- influx_select(
        con = con,
        db = "yearly",
        value = "time, value",
        from = "HDDTemp_temp_AVERAGE",
        where = where,
        order_desc = TRUE,
        return_xts = TRUE
    )$HDDTemp_temp_AVERAGE

    df <- as.data.frame(node.data)
    df$time = strptime(df$time, format = "%Y-%m-%dT%H:%M:%S")

    p <- ggplot(df, aes(x = time, y = value))
    p <- p + geom_line(size = 0.5)
    grid.draw(p)
}