# NOT RUN {
## World Development Panel Data
head(wlddev) # View data
qsu(wlddev, pid = ~ iso3c, cols = 9:12, vlabels = TRUE) # Sumarizing data
str(psmat(wlddev$PCGDP, wlddev$iso3c, wlddev$year)) # Generating matrix of GDP
r <- psmat(wlddev, PCGDP ~ iso3c, ~ year) # Same thing using data.frame method
plot(r, main = vlabels(wlddev)[9], xlab = "Year") # Plot the matrix
str(r) # See srructure
str(psmat(wlddev$PCGDP, wlddev$iso3c)) # The Data is sorted, could omit t
str(psmat(wlddev$PCGDP, 216)) # This panel is also balanced, so
# ..indicating the number of groups would be sufficient to obtain a matrix
ar <- psmat(wlddev, ~ iso3c, ~ year, 9:12) # Get array of transposed matrices
str(ar)
plot(ar)
plot(ar, legend = TRUE)
plot(psmat(collap(wlddev, ~region+year, cols = 9:12), # More legible and fancy plot
~region, ~year), legend = TRUE,
labs = vlabels(wlddev)[9:12])
psml <- psmat(wlddev, ~ iso3c, ~ year, 9:12, array = FALSE) # This gives list of ps-matrices
head(unlist2d(psml, "Variable", "Country", id.factor = TRUE),2) # Using unlist2d, can generate DF
# }
# NOT RUN {
<!-- % No code relying on suggested package -->
## Using plm simplifies things
pwlddev <- plm::pdata.frame(wlddev, index = c("iso3c","year")) # Creating a Panel Data Frame
PCGDP <- pwlddev$PCGDP # A panel-Series of GDP per Capita
head(psmat(PCGDP), 2) # Same as above, more parsimonious
plot(psmat(PCGDP))
plot(psmat(pwlddev[9:12]))
plot(psmat(G(pwlddev[9:12]))) # Here plotting panel- growth rates
# }
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