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I have this NDVI timeseries dataset where the first column is dates and the next three are NDVI data for three different IDs (59231, 158157, 282302)

 Date X59231 X158157 X282302
1 13149 NA 0.398 NA
2 13157 0.344 0.267 0.327
3 13165 NA 0.431 NA
. ..... ..... ..... ..... 

Here's the dput:

structure(list(Date = c(13149L, 13157L, 13165L, 13173L, 13181L, 
13189L, 13197L, 13205L, 13213L, 13221L, 13229L, 13237L, 13245L, 
13253L, 13261L, 13269L, 13277L, 13285L, 13293L, 13301L, 13309L, 
13317L, 13325L, 13333L, 13341L, 13349L, 13357L, 13365L, 13373L, 
13381L, 13389L, 13397L, 13405L, 13413L, 13421L, 13429L, 13437L, 
13445L, 13453L, 13461L, 13469L, 13477L, 13485L, 13493L, 13501L, 
13509L), X59231 = c(NA, 0.344, NA, 0.398, NA, 0.587, NA, NA, 
0.451, 0.597, 0.593, 0.556, 0.559, 0.375, 0.374, 0.386, 0.425, 
0.383, 0.349, 0.315, 0.282, 0.323, 0.315, 0.359, 0.292, 0.271, 
0.297, 0.307, 0.322, 0.344, 0.297, 0.285, 0.273, 0.282, 0.281, 
0.304, 0.314, NA, 0.391, 0.601, 0.65, NA, 0.653, 0.666, 0.519, 
0.625), X158157 = c(0.398, 0.267, 0.431, NA, 0.36, 0.434, 0.434, 
0.465, 0.447, 0.521, 0.539, 0.563, 0.595, 0.541, 0.553, 0.381, 
0.533, 0.505, 0.551, NA, 0.546, 0.535, 0.523, 0.501, 0.508, 0.51, 
0.506, 0.51, 0.514, 0.526, 0.555, 0.545, 0.53, 0.539, 0.531, 
0.53, NA, 0.585, 0.597, 0.32, 0.569, 0.601, NA, NA, 0.52, 0.532
), X282302 = c(NA, 0.327, NA, 0.282, 0.26, 0.293, 0.25, 0.288, 
0.336, 0.299, 0.29, 0.28, NA, 0.305, 0.319, NA, 0.255, 0.292, 
0.294, NA, NA, 0.367, 0.331, 0.344, 0.283, 0.284, 0.291, 0.273, 
0.239, 0.285, 0.249, 0.285, 0.247, 0.288, 0.276, NA, 0.317, 0.375, 
0.38, 0.417, 0.374, 0.491, NA, NA, NA, 0.471)), class = "data.frame", row.names = c(NA, 
-46L))

I run the following code to smooth the timeseries (get rid of noise) and find the multiple maxs and mins for each ID's NDVI timeseries.

rm(list=ls())

#Read in csv data
df=read.csv("Data.csv", header = TRUE)
date_col = df[,1]

num_cols = length(df[1,]) #count number of columns there are
num_Dcols = num_cols-1 #count the number of columns there are minus the index (first) column


#Function to append columns to a dataframe
cbind.fill <- function(...)
 nm <- list(...) 
 nm <- lapply(nm, as.matrix)
 n <- max(sapply(nm, nrow)) 
 do.call(cbind, lapply(nm, function (x) 
 rbind(x, matrix(, n-nrow(x), ncol(x))))) 


#Create an empty data frame
finalDF = data.frame(matrix(ncol=(0),nrow=0)) #create empty dataframe

#Create an empty vector for column names
CNames = c()

for (i in c(1:num_Dcols))
 df_sub = df[,c(1,i+1)] #create a data frame of the date column and the i+1 column

 df_removeNA = na.omit(df_sub)

 #Append the date column to the final data frame
 df_date = df_removeNA[,1]
 finalDF = cbind.fill(finalDF, df_date)

 #Append the NDVI timeseries column to the final data frame
 df_data = df_removeNA[,2]
 finalDF = cbind.fill(finalDF, df_data)


 stl_1=stl(ts(df_data, frequency=4), "periodic")

 #Function to calculate all the maximums
 ts_max<-function(signal)
 
 points_max=which(diff(sign(diff(signal)))==-2)+1
 return(points_max)
 

 #Function to calculate all the minimums
 ts_min<-function(signal)
 
 points_min=which(diff(sign(diff(-signal)))==-2)+1
 return(points_min)
 

 #Smooth the timeseries
 trend_1=as.numeric(stl_1$time.series[,2])

 #Find max and mins of the smoothed timeseries
 max_1=ts_max(trend_1)
 min_1=ts_min(trend_1)

 #Append max and mins to the final data frame
 finalDF = cbind.fill(finalDF, df_data[max_1])
 finalDF = cbind.fill(finalDF, df_data[min_1])

 #Append column names to the column names vector
 CNames = c(CNames, toString(colnames(df_sub[1])))
 CNames = c(CNames, toString(colnames(df_sub[2])))
 CNames = c(CNames, paste(c(toString(colnames(df_sub[2])), "_Max"), collapse=''))
 CNames = c(CNames, paste(c(toString(colnames(df_sub[2])), "_Min"), collapse=''))

 #Plot final results
 plot(df_date, trend_1, type = 'l')
 abline(v=df_date[max_1], col="red")
 abline(v=df_date[min_1], col="blue")


#Rename final data frame's column names
colnames(finalDF) = CNames

#Export final data frame to CSV
write.csv(finalDF, file = "finalDF_smooth.csv")

Here's an image of all the maxs and mins for the first column of NDVI timeseries data.

What I'm trying to figure out is how to add two new columns into the original (or new) data frame next to each ID column where I can store the maximums and minimums. The maximums and minimums need to be placed in the cell that matches its corresponding date. In other words, I need two duplicated columns of each ID column. Inserted next to each ID column with all values replaced with NA except the maximums and minimums. Both of which were calculated in the smoothing code above. For example, this is what I need the final dataframe to look like :

 Date 59231 59231_Max 59231_Min 158157 158157_Max 158157_Min 282302 282302_Max 282302_Min
13149 NA NA NA 0.398 NA NA NA NA NA
13157 0.344 NA NA 0.267 NA NA 0.327 NA NA
13165 NA NA NA 0.431 NA NA NA NA NA
13173 0.398 NA NA NA NA NA 0.282 NA NA
13181 NA NA NA 0.360 NA NA 0.260 NA NA
13189 0.587 NA NA 0.434 NA NA 0.293 NA 0.293
13197 NA NA NA 0.434 NA NA 0.25 NA NA
13205 NA NA NA 0.465 NA NA 0.288 NA NA
13213 0.451 NA NA 0.447 NA NA 0.336 NA NA
13221 0.597 NA NA 0.521 NA NA 0.299 0.299 NA
 ... ... .. .. ... .. .. ... ... ..

This is what it looks like right now.

 Date 59231 59231_Max 59231_Min Date 158157 158157_Max 158157_Min Date 282302 282302_Max 282302_Min
13157 0.344 0.593 0.386 13149 0.398 0.595 0.533 13157 0.327 0.299 0.293
13173 0.398 0.425 0.282 13157 0.267 0.546 0.508 13173 0.282 0.331 0.255
13189 0.587 0.315 0.297 13165 0.431 0.545 0.539 13181 0.260 NA 0.285
13213 0.451 0.322 0.273 13181 0.360 0.530 0.320 13189 0.293 NA NA
13221 0.597 0.653 NA 13189 0.434 NA NA 13197 0.250 NA NA
13229 0.593 NA NA 13197 0.434 NA NA 13205 0.288 NA NA
13237 0.556 NA NA 13205 0.465 NA NA 13213 0.336 NA NA
13245 0.559 NA NA 13213 0.447 NA NA 13221 0.299 NA NA
13253 0.375 NA NA 13221 0.521 NA NA 13229 0.290 NA NA
13261 0.374 NA NA 13229 0.539 NA NA 13237 0.280 NA NA
..... ... .. .. ..... ..... .. .. ..... ..... ... ..

Note: I had to omit NAs during each loop so the code produces a CSV file with a unique subset date column for each ID. I would love to just have one date column like the ideal table above.

In my code I started to create a new data frame and appending each column after each loop but I can't figure out how to match up the maxs and mins in the right cells. Right now all the max and mins are stacked at the top of their columns. Any ideas? Thanks.

How about this? It adds the min and max columns.

df
df$max <- apply(df[2:4], 1, max, na.rm = TRUE)
df$min <- apply(df[2:4], 1, min, na.rm = TRUE)
head(df)

Which produces:

 ID X59231 X158157 X282302 max min
1 13149 NA 0.398 NA 0.398 0.398
2 13157 0.344 0.267 0.327 0.344 0.267
3 13165 NA 0.431 NA 0.431 0.431
4 13173 0.398 NA 0.282 0.398 0.282
5 13181 NA 0.360 0.260 0.360 0.260
6 13189 0.587 0.434 0.293 0.587 0.293

I have added this based on the clarification that you have provided. You can ignore the bit above:

This will produce what you want. I have only done it for the first column, but you can just change the variables to get the other columns.

library(dplyr)
df2 <- as_tibble(df)
df2 <- df2 %>% 
 mutate(X59231_min = min(X59231, na.rm = TRUE))%>% 
 mutate(X59231_min = ifelse(X59231 == X59231_min, X59231_min, NA)) %>% 
 mutate(X59231_max = max(X59231, na.rm = TRUE))%>% 
 mutate(X59231_max = ifelse(X59231 == X59231_max, X59231_max, NA))

So:

df2 %>% filter(!is.na(X59231_min))

gives us:

# A tibble: 1 x 6
 ID X59231 X158157 X282302 X59231_min X59231_max
 <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 13349 0.271 0.51 0.284 0.271 NA

And:

df2 %>% filter(!is.na(X59231_max))

Shows:

# A tibble: 1 x 6
 ID X59231 X158157 X282302 X59231_min X59231_max
 <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 13493 0.666 NA NA NA 0.666

You should be able to do it for the other columns.