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Fixed one variable error see issue #3. Added explicid colnames and wr… #4

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Fixed one variable error see issue #3. Added explicid colnames and wr… #4

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18 changes: 10 additions & 8 deletions R/transform_data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
#' @importFrom tsibble as_tsibble
#' @examples
#' data <- data_sandy_anom[,c("Timestamp", "Cond", "Tur", "Level")]
#' data <- data_sandy_anom[,c("Timestamp", "Cond")]
#' data <- tidyr::drop_na(data)
#' trans_data <- oddwater::transform_data(data)
transform_data <- function(data, time_bound = 90, regular = FALSE, time_col = "Timestamp")
Expand All @@ -26,6 +27,7 @@ transform_data <- function(data, time_bound = 90, regular = FALSE, time_col = "

n <- nrow(data)
data_var <- as.matrix(data[ , !(names(data) %in% time_col)])
colnames(data_var) <- colnames(data)[!(colnames(data) %in% time_col)]

# apply log transformation
log_series <- log(data_var)
Expand All @@ -40,42 +42,42 @@ transform_data <- function(data, time_bound = 90, regular = FALSE, time_col = "
# take the derivative of the log series (time bounded)
time <- as.numeric(data$Timestamp[2:n] - data$Timestamp[1:(n-1)])
time_bound <- ifelse( time >= time_bound, time, time_bound) # to reduce the effect coming from the too small time gaps
der_log_bounded <- rbind( rep(NA, ncol(data_var)), diff_log_series[2:n, ] / as.numeric(time_bound))
der_log_bounded <- rbind( rep(NA, ncol(data_var)), as.matrix(diff_log_series[2:n, ] / as.numeric(time_bound)))
colnames(der_log_bounded) <- paste("der_log_bound_", colnames(data_var), sep = "")
data <- cbind(data, der_log_bounded)
time <- c(NA, time)

# take non linear transformation - one sided (negative) log derivatives (time bounded)
neg_der_log_bounded <- ifelse(der_log_bounded <= 0, der_log_bounded, 0)
colnames(neg_der_log_bounded) <- paste("neg_der_log_bound_", colnames(data_var), sep = "")
neg_der_log_bounded <- rbind( rep(NA, ncol(data_var)), neg_der_log_bounded[-1,])
neg_der_log_bounded <- rbind( rep(NA, ncol(data_var)), as.matrix(neg_der_log_bounded[-1,]))
data <- cbind(data, neg_der_log_bounded, time)

# take non linear transformation - one sided (positive) log derivatives (time bounded)
pos_der_log_bounded <- ifelse(der_log_bounded >= 0, der_log_bounded, 0)
colnames(pos_der_log_bounded) <- paste("pos_der_log_bound_", colnames(data_var), sep = "")
pos_der_log_bounded <- rbind(rep(NA, ncol(data_var)), pos_der_log_bounded[-1,])
pos_der_log_bounded <- rbind(rep(NA, ncol(data_var)), as.matrix(pos_der_log_bounded[-1,]))
data <- cbind(data, pos_der_log_bounded)

# rate of change
rc_series <- rbind( rep(NA, ncol(data_var)), (data_var[2:n,] - data_var[1:(n-1),]) / data_var[1:(n-1),])
rc_series <- rbind( rep(NA, ncol(data_var)), as.matrix((data_var[2:n,] - data_var[1:(n-1),]) / data_var[1:(n-1),]))
colnames(rc_series) <- paste("rc_", colnames(data_var), sep = "")
data <- cbind(data, rc_series)

# Ratio
ratio_series <- rbind( rep(NA, ncol(data_var)), data_var[2:n,] / data_var[1:(n-1),])
ratio_series <- rbind( rep(NA, ncol(data_var)), as.matrix(data_var[2:n,] / data_var[1:(n-1),]))
colnames(ratio_series) <- paste("ratio_", colnames(data_var), sep = "")
data <- cbind(data, ratio_series)

# Relative difference (log)
relative_series <- rbind( rep(NA, ncol(data_var)), (log_series[2:(n-1),] - (1/2)*(log_series[1:(n-2),] +log_series[3:n,] )),
relative_series <- rbind( rep(NA, ncol(data_var)), as.matrix((log_series[2:(n-1),] - (1/2)*(log_series[1:(n-2),] +log_series[3:n,] ))),
rep(NA, ncol(data_var)))
colnames(relative_series) <- paste("rdifflog_", colnames(data_var), sep = "")
data <- cbind(data, relative_series)

# Relative difference (original)
relative_series_o <- rbind( rep(NA, ncol(data_var)), (data_var[2:(n-1),] - (1/2)*(data_var[1:(n-2),] +data_var[3:n,] )),
rep(NA, ncol(data_var)))
relative_series_o <- rbind( rep(NA, ncol(data_var)), as.matrix((data_var[2:(n-1),] - (1/2)*(data_var[1:(n-2),] + data_var[3:n,] ))),
rep(NA, ncol(data_var)))
colnames(relative_series_o) <- paste("rdiff_", colnames(data_var), sep = "")
data <- cbind(data, relative_series_o)

Expand Down