Table: Table 1: Data summary
| Name | Piped data |
| Number of rows | 308 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
Table output of R is one of the richest and satisfying to use feature. Rmarkdown format provides loads of package support to create, format, and present tables beautifully. This is on one aspect extremely useful while on the other end it could very well be daunting as to choose between various package options to use while formating your table. I have a bunch of suggestions and enlistments here to help get off that dilemma.
Although not a new topic in finance and portfolio management, as some of the tweets below suggest, it’s application in field of Agricultural Economics and commodity market analysis is not often highlighted. A tweet relating S2F and bitcoin (BTC) trading was posted recently.
In this post, I demonstrate what are the basic ingredients of the cointegration analysis. In particular, I provide some context as to how it came to being and what it’s roots are in theory. While catching along the core applications, I will use example dataset and present its features, in synology.
Incidentally, I ran into outer product function outer today. It is extremely powerful function, in that it computes all combinations of product of two objects. One simplest and obvious demonstration is the multiplication table of numbers. 😄
We can show multiplication table of numbers 1 through 12 each multiplied 1 through 10.
outer(1:10, 1:12, "*")
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] 1 2 3 4 5 6 7 8 9 10 11 12
## [2,] 2 4 6 8 10 12 14 16 18 20 22 24
## [3,] 3 6 9 12 15 18 21 24 27 30 33 36
## [4,] 4 8 12 16 20 24 28 32 36 40 44 48
## [5,] 5 10 15 20 25 30 35 40 45 50 55 60
## [6,] 6 12 18 24 30 36 42 48 54 60 66 72
## [7,] 7 14 21 28 35 42 49 56 63 70 77 84
## [8,] 8 16 24 32 40 48 56 64 72 80 88 96
## [9,] 9 18 27 36 45 54 63 72 81 90 99 108
## [10,] 10 20 30 40 50 60 70 80 90 100 110 120
Another immediate use can be seen in design and combinatorics. For instance,
This post is the first in a series of upcoming blog that tries to describe application of a lesser used technique in econometrics – time series analysis. I make extensive use of datasets available in several R packages – mostly the tsibbledata package. Furthermore, an external package hosted in
github.com/FinYang/tsdl repo will be used.