R Function looping over new row using previous row data

I am trying to categorize incoming temperature data for early detection of an increasing trend (event =1) or a decreasing trend (event =2). The start of an uptrend is characterized by a 1% increase from the lowest point (Pl). The start of a downtrend is characterized by a 1% decrease from the highest point (Ph)

Dataset is initialized with trend=1, Ph and Pl = Temperature and I would like to loop over each new row, updating Pl/Ph and categorize the event type.

Dataset used

data <- data.frame (Temperature=c(93.37, 93.44, 93.22, 93.28, 93.32, 93.48, 93.32, 92.49, 92.21, 92.16, 91.31, 91.30, 91.37, 91.30, 91.21, 91.37, 91.59, 91.45, 92.07, 92.16, 92.35, 92.52, 92.48, 92.13, 92.46), 
                    event=c(1, rep(NA, 24)), Ph=c(93.37, rep(NA, 24)), Pl=c(93.37, rep(NA, 24)))

Expected result

data <- data.frame (Temperature=c(93.37, 93.44, 93.22, 93.28, 93.32, 93.48, 93.32, 92.49, 92.21, 92.16, 91.31, 91.30, 91.37, 91.30, 91.21, 91.37, 91.59, 91.45, 92.07, 92.16, 92.35, 92.52, 92.48, 92.13, 92.46), 
event=c(1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1), 
Ph=c(93.37,NA,NA,NA,NA,NA,NA,92.49,92.21,92.16,91.31,91.3,91.3,91.3,91.21,91.21,91.21,91.21,91.21,NA,NA,NA,NA,NA,NA),
Pl=c(93.37,93.44,93.44,93.44,93.44,93.48,93.48,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,92.16,92.35,92.52,92.52,92.52,92.52))

I fail to convert the following code into a loop that can perform these commands on an historical dataset. I have tried a function as well as the apply family without success.

data$Ph_lag <- lag(data$Ph, 1)
data$Pl_lag <- lag(data$Pl, 1)

for(i in 2:nrow(data)) {

  if (data$event[i-1] == 1) {

    if (data$Temperature[i]  <= data$Ph_lag[i] * 0.99) { # missing value where TRUE/FALSE needed (=all NA ROWS)
      data$event[i]  <- 2
      data$Pl[i] <- data$Close[i]

    } else if (data$Temperature[i] > data$Ph_lag[i]) { 
      data$Ph[i] <- data$Temperature[i]
      data$event[i] <- 1

    } else {  
      data$Ph[i] <- data$Ph_lag[i]
      data$event[i] <- 1
    }  

  } else if (data$event[i-1] == 2) { 

    if (data$Temperature[i]  >= data$Pl_lag[i]  * 1.01) { 
      data$event[i]  <- 1
      data$Ph[i] <- data$Temperature[i]

    } else if (data$Temperature[i] < data$Pl_lag[i]) { 
      data$Pl[i] <- data$Temperature[i]
      data$event[i]  <- 2

    } else {  
      data$Pl[i] <- data$Pl_lag[i]
      data$event[i]  <- 2
    }}}

In its current state, this code is successful when applied to a single row, but can hardly be used to fill up historical data containing thousands of observations.

Comments appreciated, would be very grateful