Business Forecasting essay

Build a predictive model to forecast data with R,’Everything is explained in the word doc! This is a business forecasting course that uses R to build some models. Theres no standard/required method to do the modeling feel free to use any. Please upload the R code file too~ Thanks! data scraping and preparation hints(below) code: #Homework 3: webscraping data understand and preparation step #Import the data by scraping the website library(rvest) #this is a package that contains function to scrape website library(tidyverse) library(ggplot2) library(forecast) #define the URL variable: url=”https://www.census.gov/econ/currentdata/dbsearch?program=MWTS&startYear=1992&endYear=2020&categories%5B%5D=4232&dataType=SM&geoLevel=US¬Adjusted=1&submit=GET+DATA&releaseScheduleId=” #read_html(url) = %>% read_html() furniture= url %>% read_html() %>% html_nodes(xpath=//*[@id=”report0″]/table) %>% html_table() #in what data structure furniture are? class(furniture) #what are the column types in furniture? str(furniture) unlist(furniture) furniture=furniture[[1]] # take/extract first list element which is adata frame and store it as data frame structure #in what data structure furniture are now? class(furniture) # remove first column: furniture = furniture[-1] #check: furniture # remove comma and convert to numeric type: # Explanation why it will not work exactly what we did in class. # One little trick is needed! Let me show how we can break it # “drill down” and identify the issue: furniture # this is a data frame with 12 columns and character type data gsub(“”””furniture) # this will produce a data structure that consist of # a sequence of vectors where each vector is a column from a data frame # to see what the above function produces lets use cat function: ehn output # looks strange( contaiuns strange formats such as \ or / etc. try cat function): cat(gsub(“”””furniture)) # Though visually output looks a bit nicer it is still unclear how to deal # with such output. In addition to data frame (format/data structure for # storing tabular data) R has another data structure for storing tabular data # that are of the same type i.e. all columns are either numerical or all # columns are character or TRUE/FALSE etc. It is matrix. So data frame when # all columns are of the same type could be converted to matrix using as.matrix: as.matrix(furniture) # this is still a table of numbers # you run ?as.matrix and read a bit more # Why this is useful? check this out: gsub(“”””as.matrix(furniture)) # gsub function is applied to all columns! Yippee! # and now can convert to numeric: as.numeric(gsub(“”””as.matrix(furniture))) # Note that as.numeric also changes the data structure the data is stored in: # it changed from matrix(data table) into a vector which conveniently going # to be converted to a time series object. There is little nuance: as.numeric # formed a vector by taking a first column then put second column values etc # It basically ruined the time order of data! The data should follow # chronological order: 1992 jan 1992 feb … 1992 dec 1993 jan 1993 feb …. # to achieve that we can transpose matrix which is an outcome of as.matrix i.e. # switch rows and columns: # example starts a=matrix(1:1025) a t(a) # example ends # lets at t(): as.numeric(gsub(“”””t(as.matrix(furniture)))) # Now convert data into the ts object: y=ts(as.numeric(gsub(“”””t(as.matrix(furniture))))start=c(19921)frequency =12) autoplot(y) # All the code to scrape the data clean and create a ts object can be written # using the pipe oprator %>%. Remember %>%passes output of function # the next functions FIRST parameter. For gsub data is the third parameter. # Using pipe when oput put of one fuction is not the first input for another fucntion # see below gsub function we use . dot notation to tell R to use output of the # previous function). Strange – but thats how it works. We just need to remmeber y = url %>% read_html() %>% html_nodes(xpath=//*[@id=”report0”]/table) %>% html_table() %>% last %>% select(-Year) %>% as.matrix %>% t %>% gsub(“”””.) %>% as.numeric %>% ts(start=c(19921)frequency =12) autoplot(y) Requirements: Answers following doc guidelines and R code file (any forecasting style that you are familiar with) | .doc file ‘

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