monthly_from_daily: Convert Daily Data to monthly

In MissingHandle: Handles Missing Dates and Data and Converts into Weekly and Monthly from Daily

Convert Daily Data to monthly

Description

Converts daily data to monthly data. One needs to specify the month format.

Usage

monthly_from_daily(
  my_daily_data,
  starting_date = "2011-01-01",
  ending_date = "2022-12-31",
  year_month_format = "%Y-%m",
  month_ending_format = "%Y-%m-%d",
  month_ending_day = "-1",
  year_month = "year_month",
  month_ending_date = "month_ending_date"
)

Arguments

my_daily_data	A data frame containing first column as dates and others are columns contains daily data
starting_date	From which date data is present
ending_date	Upto which date data is present
year_month_format	specify the year month format
month_ending_format	specify month ending format
month_ending_day	corresponding days of a month
year_month	this is a variable, leave this as it is
month_ending_date	name of the first column of the output data frame

Value

• my_monthly_data: Data frame containing converted data into monthly one

References

• Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.

• Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.

• Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.

• Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.

Examples

# creating example ####

# 1st element ####
# Create a sequence of dates from "2011-01-01" to "2015-12-31"
dates <- seq(as.Date("2011-01-01"), as.Date("2011-03-31"), by="day")

# Generate random prices for each date
price_1 <- runif(length(dates), min=0, max=100)

# Combine the dates and prices into a data frame
df <- data.frame(Dates = dates, Price_a = price_1)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates2 <- seq(as.Date("2011-05-01"), as.Date("2011-12-31"), by="day")

# Generate random prices for each date
price_2 <- runif(length(dates2), min=0, max=100)

# Combine the dates and prices into a data frame
df2 <- data.frame(Dates = dates2, Price_a = price_2)

# Merge the two data frames row-wise
df <- rbind(df, df2)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates3 <- seq(as.Date("2012-02-01"), as.Date("2012-12-31"), by="day")

# Generate random prices for each date
price_3 <- runif(length(dates3), min=0, max=100)

# Combine the dates and prices into a data frame
df3 <- data.frame(Dates = dates3, Price_a = price_3)

# Merge the two data frames row-wise
df <- rbind(df, df3)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates4 <- seq(as.Date("2013-04-01"), as.Date("2022-12-31"), by="day")

# Generate random prices for each date
price_4 <- runif(length(dates4), min=0, max=100)

# Combine the dates and prices into a data frame
df4 <- data.frame(Dates = dates4, Price_a = price_4)

# Merge the two data frames row-wise
df <- rbind(df, df4)

# Specify column data types
df <- data.frame(Dates = as.Date(df$Dates),
                price_a = round(as.numeric(df$Price_a)))
# 2nd element ####
# Create a sequence of dates from "2011-01-01" to "2015-12-31"
dates <- seq(as.Date("2011-01-01"), as.Date("2011-05-31"), by="day")

# Generate random prices for each date
price_1 <- runif(length(dates), min=0, max=100)

# Combine the dates and prices into a data frame
df_second <- data.frame(Dates = dates, Price_b = price_1)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates2 <- seq(as.Date("2011-06-01"), as.Date("2011-10-31"), by="day")

# Generate random prices for each date
price_2 <- runif(length(dates2), min=0, max=100)

# Combine the dates and prices into a data frame
df_second2 <- data.frame(Dates = dates2, Price_b = price_2)

# Merge the two data frames row-wise
df_second <- rbind(df_second, df_second2)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates3 <- seq(as.Date("2012-01-01"), as.Date("2012-12-31"), by="day")

# Generate random prices for each date
price_3 <- runif(length(dates3), min=0, max=100)

# Combine the dates and prices into a data frame
df_second3 <- data.frame(Dates = dates3, Price_b = price_3)

# Merge the two data frames row-wise
df_second <- rbind(df_second, df_second3)

# Create a sequence of dates from "2016-02-01" to "2022-12-31"
dates4 <- seq(as.Date("2013-03-01"), as.Date("2022-12-31"), by="day")

# Generate random prices for each date
price_4 <- runif(length(dates4), min=0, max=100)

# Combine the dates and prices into a data frame
df_second4 <- data.frame(Dates = dates4, Price_b = price_4)

# Merge the two data frames row-wise
df_second <- rbind(df_second, df_second4)

# Specify column data types
df_second <- data.frame(Dates = as.Date(df_second$Dates),
                       price_b = round(as.numeric(df_second$Price_b)))
# my_list ####
# Create a list
my_list <- list()

# Add the data frame to the list
my_list$df <- df

my_list$df_second <- df_second

# getting output ####
my_combined_data <- clean_and_combine(my_list = my_list)
print(head(my_combined_data))
my_imputed_data <- impute_combined(my_combined_data)
print(head(my_imputed_data))
my_monthly_data <- monthly_from_daily(my_imputed_data)
print(head(my_monthly_data))

MissingHandle documentation built on April 30, 2023, 9:08 a.m.