In bluefoxr/COINr: Composite Indicator Construction and Analysis

Cross checking COINr v0.6 against v1.

This file is in the development project of COINr so we assume it is open and available.

We need to load up old COINr which is now called COINr6.

  library(COINr6)

We will start by checking the old ASEM example against the new COINr.

c6 <- build_ASEM()

Now we check the data sets step by step.

# get iCodes first
iCodes <- c6$Input$IndMeta$IndCode

# RAW
raw6 <- c6$Data$Raw[c("UnitCode", iCodes)]
names(raw6)[1] <- "uCode"

# New: build coin
c1 <- new_coin(ASEM_iData, ASEM_iMeta)
raw1 <- get_dset(c1, "Raw")

# cross-check
compare_df(raw6, raw1, "uCode")

Ok, so far so good. Next thing to check is the denomination function. ASEM example is denominated according to the specs in the metadata.

# v6
den6 <- c6$Data$Denominated[c("UnitCode", iCodes)]
names(raw6)[1] <- "uCode"

# denominate v1
c1 <- Denominate(c1, dset = "Raw")
den1 <- get_dset(c1, "Denominated")

# cross-check
compare_df(raw6, raw1, "uCode")

This also checks out fine. Now we move on to imputation.

NOTE: need to sort out how to pass group col to Impute(), since at the moment would need to pass a vector but we just want to name a grouping variable as input for coin method.

# v6
im6 <- c6$Data$Imputed[c("UnitCode", iCodes)]
names(im6)[1] <- "uCode"

# impute 1
c1 <- Impute(c1, dset = "Denominated", f_i = "i_mean_grp", use_group = "EurAsia_group")
im1 <- get_dset(c1, "Imputed")

# cross-check
compare_df(im6, im1, "uCode")

And this is also OK. Now to the part which is probably most likely to go wrong... the data treatment!

# v6
tr6 <- c6$Data$Treated[c("UnitCode", iCodes)]
names(tr6)[1] <- "uCode"

# treat v1
# note: v6 uses ceiling(nUnit * 0.1) as default for winmax, which is 6 here
c1 <- Treat(c1, dset = "Imputed", global_specs = list(f1_para = list(winmax = 6)))
tr1 <- get_dset(c1, "Treated")

# cross-check
compare_df(tr6, tr1, "uCode")

Unbelievably, this is right first time with no tweaks! Should get a medal for this. Right, next thing is the normalisation.

# v6
n6 <- c6$Data$Normalised[c("UnitCode", iCodes)]
names(n6)[1] <- "uCode"

# normalise v1
c1 <- Normalise(c1, dset = "Treated", global_specs = list(f_n = "n_minmax",
                                                                  f_n_para = list(c(0,100))))
n1 <- get_dset(c1, "Normalised")

# cross-check
compare_df(n6, n1, "uCode")

Ok, had to fix an important bug regarding the direction, but now it works. Last thing is aggregation.

# v6
a6 <- c6$Data$Aggregated[c("UnitCode", iCodes, c6$Input$AggMeta$Code)]
names(a6)[1] <- "uCode"

# normalise v1
c1 <- Aggregate(c1, dset = "Normalised", f_ag = "a_amean")
a1 <- get_dset(c1, "Aggregated")

# cross-check
compare_df(a6, a1, "uCode")