# cast long-format data into wide-format
dcast(df1,  c(startPeriod, endPeriod) ~ AffectedAssetMask, value.var = "UnavailableCapacity", fun.aggregate = mean)

# create monthly hourly sequence
start_period <- as.POSIXct(strptime("01/05/2018 00:00:00", "%d/%m/%Y %H:%M:%S"))
end_period <- as.POSIXct(strptime("30/05/2018 00:00:00", "%d/%m/%Y %H:%M:%S"))

dataseq <- seq(start_period, end_period, by = 3600)

# use expand.grid to create a sequence of hourly dates
hourly_seq <- expand.grid(Date = dataseq)

# merge the hourly sequence with the original data
merged_data <- left_join(hourly_seq, df1, by = "Date")

# fill missing values with 0
merged_data$UnavailableCapacity[is.na(merged_data$UnavailableCapacity)] <- 0

# pivot the data to wide format
wide_format_data <- merged_data[, c("Date", names(df1))]

# set Date as the index
set.seed(123)
wide_format_data$Date <- as.Date(wide_format_data$Date)

# convert UnavailableCapacity column to numeric values
wide_format_data$UnavailableCapacity <- as.numeric(wide_format_data$UnavailableCapacity)

# reset the index
new_df <- wide_format_data[order(wide_format_data$Date),]

# calculate daily mean for each hourly date
daily_mean <- aggregate(new_df$UnavailableCapacity, by.list(new_df), mean)

# create a sequence of dates from start to end period
dates_seq <- seq(start_period, end_period, by = "day")

# merge the data with the hourly dates
final_df <- expand.grid(Date = dates_seq)
final_df$UnavailableCapacity[is.na(final_df$UnavailableCapacity)] <- 0

# merge the daily mean data with the final df
merged_daily_mean <- join(daily_mean, final_df, by.x = "Date", by.y = "Date")

# add the hourly values from the original data
original_df_hourly <- left_join(final_df[!is.na(final_df$UnavailableCapacity),], df1, by = c("Date" = "EventStart"))
original_df_hourly$UnavailableCapacity[is.na(original_df_hourly$UnavailableCapacity)] <- 0

# merge the two data frames
final_df <- merge(merged_daily_mean, original_df_hourly, by.x = "Date", by.y = "Date")

# set Date as the index and UnavailableCapacity as a numeric column
set.seed(123)
final_df$Date <- as.Date(final_df$Date)

# convert UnavailableCapacity column to numeric values
final_df$UnavailableCapacity <- as.numeric(final_df$UnavailableCapacity)

# reset the index
final_answer <- final_df[order(final_df$Date),]

# rename columns
colnames(final_answer) <- c("Date", "A_1", "A_2", "A_3", "A_4", "A_5")

# print the result
print(final_answer)