Robert Schnitman
2018-07-16
Recommended Citation:
       Schnitman, Robert (2018). afpj v0.0.0.1. https://github.com/robertschnitman/afpj

Outline

1. Introduction
2. mapcat()
3. mrchop()
4. mop()
5. Conclusion
6. References
7. See also

0. Installation

    # Julia >= 0.6.2
    Pkg.clone("https://github.com/robertschnitman/afpj.git")

1. Introduction

The afpj package--Applied Functional Programming in Julia--is based on the original R library, afp. Some functions in this library are direct translations, while others cover gaps in Julia functionality. For example, mapcat() is the equivalent of do.bind and mrchop() replicates the process of its R counterpart, whereas mop() attempts to simulate its R equivalent that simplifies sweep() from the same programming language (an attempted replication of the latter function is also in this package).

Thus, the purpose of afpj is to supplement base Julia and its libraries to support efficient and concise programming.

The following sections provide examples for the primary functions: mapcat(), mrchop(), and mop(). Tables were made with the CSV to Markdown Table Generator tool (Donat Studios).

2. mapcat()

After mapping a function to an array of arrays, one may wish to concatenate the results with [v/h]cat(). To streamline this procedure, mapcat() takes the function, array of arrays, and dimension into a single call--the associated parameters are f, a, and d (1 for row-wise concatenation, 2 for column-wise) respectively.

EXAMPLE

 A = [[1:5;] [6:10;] [11:15;]] # Create 3 arrays (A, B, C).
 B = [[16:20;] [21:25;] [26:30;]]
 C = [[31:35;] [36:40;] [41:45;]]
 ABC = [A, B, C] # 3-element Array of Arrays
 mapcat(x -> x.^2, ABC, 1) # Row-wise concatenation (default).

1	36	121
4	49	144
9	64	169
16	81	196
25	100	225
256	441	676
289	484	729
324	529	784
361	576	841
400	625	900
961	1296	1681
1024	1369	1764
1089	1444	1849
1156	1521	1936
1225	1600	2025

 mapcat(x -> x.^2, ABC, 2) # Column-wise concatenation of the same.

1	36	121	256	441	676	961	1296	1681
4	49	144	289	484	729	1024	1369	1764
9	64	169	324	529	784	1089	1444	1849
16	81	196	361	576	841	1156	1521	1936
25	100	225	400	625	900	1225	1600	2025

3. mrchop()

As inspired by mapslices(), mrchop() acts as a dimension-specific version of mapreduce().

The parameters are f, o, x, and d--the function, (binary) operator, collection, and dimension respectively.

EXAMPLE

A = [[1:5;] [6:10;] [11:15;]]
mrchop(x -> x.^2, /, A, 1) # Column-wise reduction of squared elements.

6.94444e-5	1.41723e-6	1.12745e-7

mrchop(x -> x.^2, /, A, 2) # Row-wise of the same.

0.000229568
0.000566893
0.000832101
0.00100781
0.00111111

4. mop()

The function mop() attempts to simulate its R equivalent from the afp library: it operates on an array by a summary statistic function according to a given dimension. In other words, it indexes values by the summary statistic function column-wise or row-wise. The advantages of this function includes (1) the ability to compare individual values to the median of a variable, for example, and (2) the convenience to users of having them avoid writing anonymous functions within mapslices() to accomplish the same task (e.g. mapslices(x -> x./median(x), A, 1)).

The parameters are the same as in mrchop().

EXAMPLE

# Divide each element by the median of the associated column.
A = [1:10 11:20 21:30;]
mop(median, /, A, 1) # == mapslices(x -> x./median(x), A, 1)

0.181818	0.709677	0.823529
0.363636	0.774194	0.862745
0.545455	0.83871	0.901961
0.727273	0.903226	0.941176
0.909091	0.967742	0.980392
1.09091	1.03226	1.01961
1.27273	1.09677	1.05882
1.45455	1.16129	1.09804
1.63636	1.22581	1.13725
1.81818	1.29032	1.17647

5. Conclusion

The discussed functions will be improved on a continuous basis to (1) minimize repetitive iterative computations and (2) emphasize code efficiency and brevity. New functions to be added based on feasibility and future needs as appropriate.

6. References

1. Julia - map()
2. Julia - mapreduce()
3. Julia - mapslices()
4. R programming language
5. R - afp, the original library
6. R - afp - mop()
7. R - sweep()
8. Donat Studios (2017). CSV To Markdown Table Generator.

7. See also

Advanced R by Hadley Wickham - Functionals chapter

End of Document