## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5, warning = FALSE, message = FALSE, eval=FALSE ) ## ----------------------------------------------------------------------------- # library(PriceIndices) # head(milk) ## ----------------------------------------------------------------------------- # unique(milk$description) ## ----------------------------------------------------------------------------- # dataset<-generate(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02), # qmi=c(3,4,4),qsigma=c(0.1,0.1,0.15), # start="2020-01") # head(dataset) ## ----------------------------------------------------------------------------- # tindex(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02),start="2020-01",ratio=FALSE) ## ----------------------------------------------------------------------------- # #Generating an artificial dataset (the elasticity of substitution is 1.25) # df<-generate_CES(pmi=c(1.02,1.03),psigma=c(0.04,0.03), # elasticity=1.25,start="2020-01",n=100,days=TRUE) # head(df) ## ----------------------------------------------------------------------------- # #Verifying the elasticity of substitution # elasticity(df, start="2020-01",end="2020-02") ## ----------------------------------------------------------------------------- # head(data_preparing(milk, time="time",prices="prices",quantities="quantities")) ## ----------------------------------------------------------------------------- # #Creating a data frame with zero prices (df) # data<-dplyr::filter(milk,time>=as.Date("2018-12-01") & time<=as.Date("2019-03-01")) # sample<-dplyr::sample_n(data, 100) # rest<-setdiff(data, sample) # sample$prices<-0 # df<-rbind(sample, rest) # #The Fisher price index calculated for the original data set # fisher(df, "2018-12","2019-03") # #Zero price imputations: # df2<-data_imputing(df, start="2018-12", end="2019-02", # zero_prices=TRUE, # outlets=TRUE) # #The Fisher price index calculated for the data set with imputed prices: # fisher(df2, "2018-12","2019-03") ## ----------------------------------------------------------------------------- # dataAGGR ## ----------------------------------------------------------------------------- # data_aggregating(dataAGGR) ## ----------------------------------------------------------------------------- # data_unit(dataU,units=c("g|ml|kg|l"),multiplication="x") ## ----------------------------------------------------------------------------- # # Preparing a data set # data<-data_unit(dataU,units=c("g|ml|kg|l"),multiplication="x") # # Normalization of grammage units # data_norm(data, rules=list(c("ml","l",1000),c("g","kg",1000))) ## ----------------------------------------------------------------------------- # subgroup1<-data_selecting(milk, include=c("milk"), must=c("UHT")) # head(subgroup1) ## ----------------------------------------------------------------------------- # unique(subgroup1$description) ## ----------------------------------------------------------------------------- # subgroup2<-data_selecting(milk, must=c("milk"), exclude=c("past","goat")) # head(subgroup2) ## ----------------------------------------------------------------------------- # unique(subgroup2$description) ## ----------------------------------------------------------------------------- # head(dataMATCH) ## ----------------------------------------------------------------------------- # data1<-data_matching(dataMATCH, start="2018-12",end="2019-02", codeIN=TRUE, codeOUT=TRUE, precision=.98, interval=TRUE) # head(data1) ## ----------------------------------------------------------------------------- # data2<-data_matching(dataMATCH, start="2018-12",end="2019-02", # codeIN=FALSE, onlydescription=TRUE, interval=TRUE) # head(data2) ## ----------------------------------------------------------------------------- # fisher(data1, start="2018-12", end="2019-02") # jevons(data2, start="2018-12", end="2019-02") ## ----------------------------------------------------------------------------- # filter1<-data_filtering(milk,start="2018-12",end="2019-03", # filters=c("extremeprices"),pquantiles=c(0.01,0.99)) # filter2<-data_filtering(milk,start="2018-12",end="2019-03", # filters=c("extremeprices"),plimits=c(0.5,2)) # filter3<-data_filtering(milk,start="2018-12",end="2019-03", # filters=c("extremeprices","lowsales"),plimits=c(0.5,2)) # filter4<-data_filtering(milk,start="2018-12",end="2019-03", # filters=c("dumpprices"),dplimits=c(0.9,0.8)) ## ----------------------------------------------------------------------------- # data_without_filters<-data_filtering(milk,start="2018-12",end="2019-03",filters=c()) # nrow(data_without_filters) # nrow(filter1) # nrow(filter2) # nrow(filter3) # nrow(filter4) ## ----------------------------------------------------------------------------- # filter1B<-data_filtering(milk,start="2018-12",end="2019-03", # filters=c("extremeprices"),pquantiles=c(0.01,0.99), # interval=TRUE, outlets=TRUE) # nrow(filter1B) ## ----------------------------------------------------------------------------- # sugar.<-dplyr::filter(sugar, time==as.Date("2018-12-01") | time==as.Date("2019-12-01")) # nrow(sugar.) # sugar_<-data_reducing(sugar., start="2018-12", end="2019-12",by="description", minN=5) # nrow(sugar_) ## ----------------------------------------------------------------------------- # #Data matching over time # df<-data_matching(data=data_DOWN_UP_SIZED, start="2024-01", end="2024-02", # codeIN=TRUE,codeOUT=TRUE,description=TRUE, # onlydescription=FALSE,precision=0.9,interval=FALSE) # # Extraction of information about grammage # df<-data_unit(df,units=c("g|ml|kg|l"),multiplication="x") # # Price standardization # df<-data_norm(df, rules=list(c("ml","l",1000),c("g","kg",1000))) # # Downsized and upsized products detection # result<-shrinkflation(data=df, start="2024-01","2024-02", prec=3, interval=FALSE, type="shrinkflation") # # result$df_changes # result$df_type # result$df_overview # # result$products_detected # # result$df_detected # # result$df_reduced # result$df_summary ## ----------------------------------------------------------------------------- # df<-MARS(data=dataMARS, # start="2025-05", # end="2025-09", # attributes=c("brand","size","fabric"), # strategy="two_months") # #Results: # df$scores ## ----------------------------------------------------------------------------- # available(milk, period1="2018-12", period2="2019-12", type="retID",interval=TRUE) ## ----------------------------------------------------------------------------- # matched(milk, period1="2018-12", period2="2019-12", type="prodID",interval=TRUE) ## ----------------------------------------------------------------------------- # matched_index(milk, period1="2018-12", period2="2019-12", type="prodID",interval=TRUE) ## ----------------------------------------------------------------------------- # matched_fig(milk, start="2018-12", end="2019-12", type="prodID") ## ----------------------------------------------------------------------------- # matched_fig(milk, start="2018-12", end="2019-04", type="prodID", figure=FALSE) ## ----------------------------------------------------------------------------- # list<-products(milk, "2018-12","2019-12") # list$statistics ## ----------------------------------------------------------------------------- # list$figure ## ----------------------------------------------------------------------------- # products_fig(milk, "2018-12","2019-12", # fixed_base=TRUE, contributions=FALSE, # show=c("new","disappearing","matched","available")) # ## ----------------------------------------------------------------------------- # prices(milk, period="2019-06") ## ----------------------------------------------------------------------------- # quantities(milk, period="2019-06", set=c(400032, 71772, 82919), ID=TRUE) ## ----------------------------------------------------------------------------- # sales(milk, period="2019-06", set=c(400032, 71772, 82919)) ## ----------------------------------------------------------------------------- # ctg<-unique(milk$description) # categories<-c(ctg[1],ctg[2],ctg[3]) # milk1<-dplyr::filter(milk, milk$description==categories[1]) # milk2<-dplyr::filter(milk, milk$description==categories[2]) # milk3<-dplyr::filter(milk, milk$description==categories[3]) # sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07") # sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07", shares=TRUE) ## ----------------------------------------------------------------------------- # sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07", # barplot=TRUE, shares=TRUE, names=categories) ## ----------------------------------------------------------------------------- # pqcor(milk, period="2019-05") # pqcor(milk, period="2019-05",figure=TRUE) ## ----------------------------------------------------------------------------- # pqcor_fig(milk, start="2018-12", end="2019-06", figure=FALSE) # pqcor_fig(milk, start="2018-12", end="2019-06") ## ----------------------------------------------------------------------------- # dissimilarity(milk, period1="2018-12",period2="2019-12",type="pq") ## ----------------------------------------------------------------------------- # dissimilarity_fig(milk, start="2018-12",end="2019-12",type="pq",benchmark="start") ## ----------------------------------------------------------------------------- # elasticity(coffee, start = "2018-12", end = "2019-01") ## ----------------------------------------------------------------------------- # elasticity_fig (milk,start="2018-12",end="2019-04",figure=TRUE, # method=c("lm","f","sv"),names=c("LM","Fisher", "SV")) ## ----------------------------------------------------------------------------- # jevons(milk, start="2018-12", end="2020-01") # jevons(milk, start="2018-12", end="2020-01", interval=TRUE) ## ----------------------------------------------------------------------------- # fisher(milk, start="2018-12", end="2020-01") # lloyd_moulton(milk, start="2018-12", end="2020-01", sigma=0.9) # lowe(milk, start="2019-12", end="2020-02", base="2018-12", interval=TRUE) ## ----------------------------------------------------------------------------- # retro_index(milk, start="2018-12", end="2019-12", formula="dhkh") ## ----------------------------------------------------------------------------- # chfisher(milk, start="2018-12", end="2020-01") # chfisher(milk, start="2018-12", end="2020-01", interval=TRUE) ## ----------------------------------------------------------------------------- # geks(milk, start="2019-01", end="2019-04",window=10) # geksl(milk, wstart="2018-12", start="2019-03", end="2019-05") ## ----------------------------------------------------------------------------- # milk.<-milk # milk.$prodID<-milk.$description # m_decomposition(milk., start="2018-12", end="2019-12", # formula=c("geks","ccdi"))$multiplicative ## ----------------------------------------------------------------------------- # prodID<-base::unique(milk$prodID) # values<-stats::runif(length(prodID),1,2) # v<-data.frame(prodID,values) # head(v) # ## ----------------------------------------------------------------------------- # QU(milk, start="2018-12", end="2019-12", v) ## ----------------------------------------------------------------------------- # tpd_splice(milk, start="2018-12", end="2020-02",window=10,splice="half",interval=TRUE) ## ----------------------------------------------------------------------------- # geks_fbew(milk, start="2018-12", end="2020-03") # geks_fbew(milk, start="2018-12", end="2019-12")* # geks_fbew(milk, start="2019-12", end="2020-03") ## ----------------------------------------------------------------------------- # ccdi_fbmw(milk, start="2018-12", end="2020-03") # ccdi_fbmw(milk, start="2018-12", end="2019-12")* # ccdi_fbmw(milk, start="2019-12", end="2020-03") ## ----------------------------------------------------------------------------- # price_indices(milk, # start = "2018-12", end = "2019-12", # formula=c("geks","ccdi","hybrid","fisher", # "QMp","young","geksl_fbew"), # window = c(13, 13), # base = c("2019-03", "2019-03"), # r=c(3), interval=TRUE) ## ----------------------------------------------------------------------------- # price_indices(coffee, # start = "2018-12", end = "2019-12", # formula=c("laspeyres","paasche","fisher"), # interval=FALSE) ## ----------------------------------------------------------------------------- # final_index(milk, start = "2018-12", end = "2019-12", # formula = "fisher", groups = TRUE, outlets = TRUE, # aggr = "laspeyres", by = "description", # interval = TRUE) # ## ----------------------------------------------------------------------------- # resultant_index(w0 = c(0.4, 0.4, 0.2), # wt = c(0.35, 0.5, 0.15), # subindices = c(1.1, 0.95, 1.12), # aggr = "fisher") # ## ----------------------------------------------------------------------------- # df<-price_indices(milk, start = "2018-12", end = "2019-12", # formula=c("laspeyres", "fisher"), interval = TRUE) # compare_indices_df(df) ## ----------------------------------------------------------------------------- # case1<-price_indices(milk, start="2018-12",end="2019-12", # formula="fisher", interval=TRUE) # case2<-final_index(milk, start="2018-12", end="2019-12", # formula="fisher", # outlets=TRUE, # aggr = "laspeyres", # interval=TRUE) # ## ----------------------------------------------------------------------------- # compare_indices_list(data=list(case1, case2), # names=c("Fisher without aggregation", # "Fisher with aggregation")) ## ----------------------------------------------------------------------------- # #Creating a data frame with unweighted bilateral index values # df<-price_indices(milk, # formula=c("jevons","dutot","carli"), # start="2018-12", # end="2019-12", # interval=TRUE) # #Calculating average distances between indices (in p.p) # compare_distances(df) ## ----------------------------------------------------------------------------- # #Creating a data frame with example bilateral indices # df<-price_indices(milk, # formula=c("jevons","laspeyres","paasche","walsh"), # start="2018-12",end="2019-12",interval=TRUE) # #Calculating the target Fisher price index # target_index<-fisher(milk,start="2018-12",end="2019-12",interval=TRUE) # #Calculating average distances between considered indices and the Fisher index (in p.p) # compare_to_target(df,target=target_index) ## ----------------------------------------------------------------------------- # #creating a list with jackknife results # comparison<-compare_indices_jk(milk, # formula=c("jevons","fisher","geks"), # start="2018-12", # end="2019-12", # window=c(13), # names=c("Jevons","Fisher","GEKS"), # by="retID", # title_iterations="Box-plots for iteration values", # title_pseudovalues="Box-plots for pseudovalues") # #displaying a data frame with basic characteristics of the calculated iteration index values # comparison$iterations ## ----------------------------------------------------------------------------- # #displaying a data frame with basic characteristics of the calculated index pseudovalues obtained in the jackknife procedure # comparison$pseudovalues ## ----------------------------------------------------------------------------- # #displaying box-plotes created for the calculated iteration index values # comparison$figure_iterations ## ----------------------------------------------------------------------------- # #displaying box-plotes created for the calculated index pseudovalues obtained in the jackknife procedure # comparison$figure_pseudovalues ## ----------------------------------------------------------------------------- # bennet(milk, start = "2018-12", end = "2019-12", interval=TRUE) ## ----------------------------------------------------------------------------- # milk$prodID<-milk$description # bennet(milk, start = "2018-12", end = "2019-12", contributions = TRUE) ## ----------------------------------------------------------------------------- # montgomery(coffee, start = "2018-12", end = "2019-12", interval=TRUE) ## ----------------------------------------------------------------------------- # coffee$prodID<-coffee$description # montgomery(coffee, start = "2018-12", end = "2019-12", contributions = TRUE)