Read.ftable rcode code#

To order our GDI dataset by GDI in 2016 descending and then by country alphabetically: rowidx <- order(-gdi, gdi)įor non-numeric vectors, we can take advantage of the xtfrm function, which returns a numeric vector which will sort in the same order as the one provided to it. To order by multiple columns in different orders, for numeric vectors we can use a simple -, since negated numeric vector will order in reverse order. To use multiple vectors for ordering is also very simple: rowidx <- order(gdi, gdi) That looks good, but we may want to order the rows that have NA as GDI in 2016 alphabetically by country (or generalize even further). Sorting by multiple vectors with different order To order in descending order, we can use decreasing = TRUE, to see NAs first we can use na.last = FALSE rowidx <- order(gdi, decreasing = TRUE, na.last = FALSE) # Look at the 2 relevant columns of the result Then we simply use rowidx to subset the rows of gdi in the order we wanted: rowidx <- order(gdi) To order the rows (countries) by GDI in 2016, we use the function order, which finds the permutation that rearranges the values into ascending order and save that order into a variable called rowidx.

We also add the drop = FALSE for safety here as we omitted it in the 2 above examples for readability: gdi_reversed <- gdi Or both rows and columns at the same time.

We can take a very similar approach to reverse order the columns: gdi_reversed_cols <- gdi To get the rows of a data frame in order reverse to the current one, we can just subset the rows with an index that goes from the last row to the very first (or safer, zeroth) like so: gdi_reversed_rows <- gdi Sorting a data frame is loosely coupled with subsetting. Subsetting as a mechanism for sorting data The goal of the article is therefore not really in presenting these concrete results, but to focus on the technical aspects and usefulness of the presented methods. This makes comparing the results between countries difficult, since one would need to do a proper time-dependent currency conversion and potentially inflation adjustment to get comparable data. Please note that the figures in the data provided by Eurostat are presented in millions of euros for euro area countries, euro area and EU aggregates and in millions of national currency otherwise.

Read.ftable rcode code#

If you have no time for reading, you can click here to get just the code with commentaryįirst, let’s read in yearly data on gross disposable income of household in the EU countries into R ( click here to download): gdi Assuming the author did not fail miserably, the code will work as-is even with vanilla R, no packages or setup needed - it is a case4base after all!

This article is best used with an R session opened in a window next to it - you can test and play with the code yourself instantly while reading.