weekly_from_daily {MissingHandle}

Convert Daily Data to Weekly

Description

Converts daily data to weekly data. One needs to specify the week format.

Usage

weekly_from_daily(
  my_daily_data,
  starting_date = "2011-01-01",
  ending_date = "2022-12-31",
  year_week_format = "%Y-%W",
  week_ending_format = "%Y-%W-%u",
  week_ending_day = "-7",
  year_week = "year_week",
  week_ending_date = "week_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_week_format

specify the year week format

week_ending_format

specify week ending format

week_ending_day

corresponding days of a week 7 or 6 days

year_week

this is a variable, leave this as it is

week_ending_date

name of the first column of the output data frame

Value

• my_weekly_data: Data frame containing converted data into weekly 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_weekly_data <- weekly_from_daily(my_imputed_data)
print(head(my_weekly_data))