74 lines
2.0 KiB
R
74 lines
2.0 KiB
R
library(DBI)
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library(tidyr)
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library(dplyr)
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library(lubridate)
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library(R.utils)
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get_freq_df <- function(con, startDate, endDate) {
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startStr <- strftime(startDate, "%Y-%m-%d %H:%M:%S", tz="UTC")
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endStr <- strftime(endDate, "%Y-%m-%d %H:%M:%S", tz="UTC")
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res <-dbSendQuery(con, "select time, location, freq from mainsfrequency where valid=1 and time >= $1 and time < $2")
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dbBind(res, list(startStr, endStr))
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frequencies <- dbFetch(res)
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dbClearResult(res)
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freq_wide <- frequencies %>%
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pivot_wider(names_from = location,
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values_from = freq,
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values_fn = mean)
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THRESHOLD <- 0.5
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for (colIdx in 2:length(freq_wide)) {
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last <- freq_wide[[1, colIdx]]
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for (rowIdx in 1:length(freq_wide[[colIdx]])) {
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current <- freq_wide[[rowIdx, colIdx]]
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if (!is.na(current) && !is.na(last) && (abs(current - last) > THRESHOLD)) {
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freq_wide[[rowIdx, colIdx]] = NA
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}
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last <- current
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}
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}
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return (freq_wide)
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}
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con <- dbConnect(RPostgres::Postgres(),
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dbname='mainscnt',
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host='db.mainscnt.eu',
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user='wn')
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START <- "2021-08-03 07:00:00"
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INTERVAL <- 3600
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for (offset in 0:0) {
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startDate <- ymd_hms(START) + INTERVAL * offset
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endDate <- startDate + INTERVAL
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freq_wide <- get_freq_df(con, startDate, endDate)
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freq_wide_names <- names(freq_wide)
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for (colIdx in 2:length(freq_wide)) {
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colName.mean <- paste("mean.w.o.", freq_wide_names[colIdx], sep="")
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colName.diff <- paste(freq_wide_names[colIdx], ".to.mean", sep="")
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freq_wide <- freq_wide %>%
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rowwise() %>%
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mutate(!!colName.mean := mean(c_across(freq_wide_names[c(-1, - colIdx)]), na.rm=TRUE)) %>%
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mutate(!!colName.diff := abs(eval(as.name(colName.mean)) - eval(as.name(freq_wide_names[colIdx]))) * 100)
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}
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means <- freq_wide %>% select(ends_with(".to.mean"))
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means.means <- apply(means, 2, mean, na.rm=TRUE)
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# print(summary(freq_wide))
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}
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dbDisconnect(con)
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