--- title: "Logic in R" author: "Colin Rundel" date: "2018-08-29" output: xaringan::moon_reader: css: "slides.css" lib_dir: libs nature: highlightStyle: github highlightLines: true countIncrementalSlides: false --- exclude: true ```{r, message=FALSE, warning=FALSE, include=FALSE} options( htmltools.dir.version = FALSE, # for blogdown width=80 ) htmltools::tagList(rmarkdown::html_dependency_font_awesome()) ``` --- class: middle count: false # (Almost)
Everything is a Vector --- ## Types of vectors The fundamental building block of data in R are vectors (collections of related values, objects, other data structures, etc). --
R has two fundamental vector classes: * Vectors (**atomic** vectors) - collections of values that are all of the *same* type (e.g. all logical values, all numbers, or all character strings). * Lists (**generic** vectors) - collections of *any* type of R object, even other lists (meaning they can have a hierarchical/tree-like structure). --- ## Atomic Vectors R has six atomic vector types:
.center[ `logical`, `double`, `integer`, `character`, `complex`, `raw` ]
For today we'll mostly worry about the first type, we'll discuss the subsequent three next week (the last two almost never come up). --- count: false class: middle # Conditionals --- ## Logical (boolean) operations | Operator | Operation | Vectorized? |:-----------------------------:|:-------------:|:------------: | x | y | or | Yes | `x & y` | and | Yes | `!x` | not | Yes | x || y | or | No | `x && y` | and | No |`xor(x,y)` | exclusive or | Yes --- ## Vectorized? ```{r} x = c(TRUE,FALSE,TRUE) y = c(FALSE,TRUE,TRUE) ``` .pull-left[ ```{r} x | y x || y ``` ] .pull-right[ ```{r} x & y x && y ``` ] --- ## Length coercion ```{r} x = c(TRUE,FALSE,TRUE) y = c(TRUE) z = c(FALSE,TRUE) ``` -- .pull-left[ ```{r} x | y x & y ``` ] -- .pull-right[ ```{r} y | z y & z ``` ] -- ```{r} x | z ``` --- ## Comparisons Operator | Comparison | Vectorized? :----------:|:--------------------------:|:----------------: `x < y` | less than | Yes `x > y` | greater than | Yes `x <= y` | less than or equal to | Yes `x >= y` | greater than or equal to | Yes `x != y` | not equal to | Yes `x == y` | equal to | Yes `x %in% y` | contains | Yes (for `x`) --- ## Comparisons ```{r} x = c("A","B","C") z = c("A") ``` .pull-left[ ```{r} x == z x != z x > z ``` ] -- .pull-right[ ```{r} x %in% z z %in% x ``` ] --- ## Conditional Control Flow Conditional execution of code blocks is achieved via `if` statements. ```{r} x = c(1,3) ``` -- ```{r} if (3 %in% x) print("This!") ``` -- ```{r} if (1 %in% x) print("That!") ``` -- ```{r} if (5 %in% x) print("Other!") ``` --- ## `if` is not vectorized ```{r} x = c(1,3) ``` -- ```{r} if (x %in% 3) print("Now Here!") ``` -- ```{r} if (x %in% 1) print("Now Here!") ``` --- ## Collapsing logical vectors There are a couple of helper functions for collapsing a logical vector down to a single value: `any`, `all` ```{r} x = c(3,4,1) ``` .pull-left[ ```{r} x >= 2 any(x >= 2) all(x >= 2) ``` ] .pull-right[ ```{r} x <= 4 any(x <= 4) all(x <= 4) ``` ] --- ## Nesting Conditionals .pull-left[ ```{r} x = 3 if (x < 0) { "Negative" } else if (x > 0) { "Positive" } else { "Zero" } ``` ] .pull-right[ ```{r} x = 0 if (x < 0) { "Negative" } else if (x > 0) { "Positive" } else { "Zero" } ``` ] --- class: middle count: false # Error Checking --- ## `stop` and `stopifnot` Often we want to validate user input or function arguments - if our assumptions are not met then we often want to report the error and stop execution. ```{r error=TRUE} ok = FALSE if (!ok) stop("Things are not ok.") stopifnot(ok) ``` *Note* - an error (like the one generated by `stop`) will prevent an RMarkdown document from compiling unless `error=TRUE` is set for that code chunk --- ## Style choices .pull-left[ Do stuff: ```{r eval=FALSE} if (condition_one) { ## ## Do stuff ## } else if (condition_two) { ## ## Do other stuff ## } else if (condition_error) { stop("Condition error occured") } ``` ] .pull-right[ Do stuff (better): ```{r eval=FALSE} # Do stuff better if (condition_error) { stop("Condition error occured") } if (condition_one) { ## ## Do stuff ## } else if (condition_two) { ## ## Do other stuff ## } ``` ] --- ## Exercise 1 Write a set of conditional(s) that satisfies the following requirements, * If `x` is greater than 3 and `y` is less than or equal to 3 then print "Hello world!" * Otherwise if `x` is greater than 3 print "!dlrow olleH" * If `x` is less than or equal to 3 then print "Something else ..." * Stop execution if x is odd and y is even and report an error, don't print any of the text strings above. Test out your code by trying various values of `x` and `y`. --- class: middle count: false # Loops --- ## `for` loops Simplest, and most common type of loop in R - given a vector iterate through the elements and evaluate the code block for each. ```{r} res = c() for(x in 1:10) { res = c(res, x^2) } res ``` -- ```{r} res = c() for(y in list(1:3, LETTERS[1:7], c(TRUE,FALSE))) { res = c(res, length(y)) } res ```
*Note* - the code above is terrible for several reasons, you should never write anything that looks like this --- ## `while` loops Repeat until the given condition is **not** met (i.e. evaluates to `FALSE`) ```{r} i = 1 res = rep(NA,10) while (i <= 10) { res[i] = i^2 i = i+1 } res ``` --- ## `repeat` loops Repeat until `break` ```{r} i = 1 res = rep(NA,10) repeat { res[i] = i^2 i = i+1 if (i > 10) break } res ``` --- class: split-50 ## Special keywords - `break` and `next` These are special actions that only work *inside* of a loop * `break` - ends the current *loop* (inner-most) * `next` - ends the current *iteration* .pull-left[ ```{r} res = c() for(i in 1:10) { if (i %% 2 == 0) break res = c(res, i) print(res) } ``` ] .pull-right[ ```{r} res = c() for(i in 1:10) { if (i %% 2 == 0) next res = c(res,i) print(res) } ``` ] --- ## Some helper functions Often we want to use a loop across the indexes of an object and not the elements themselves. There are several useful functions to help you do this: `:`, `length`, `seq`, `seq_along`, `seq_len`, etc. .pull-left[ ```{r} 4:7 length(4:7) seq(4,7) ``` ] .pull-right[ ```{r} seq_along(4:7) seq_len(length(4:7)) seq(4,7,by=2) ``` ] --- ## Exercise 2 Below is a vector containing all prime numbers between 2 and 100: .center[ ```r primes = c( 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97) ``` ] If you were given the vector `x = c(3,4,12,19,23,51,61,63,78)`, write the R code necessary to print only the values of `x` that are *not* prime (without using subsetting or the `%in%` operator). Your code should use *nested* loops to iterate through the vector of primes and `x`. --- count: false # Acknowledgments Above materials are derived in part from the following sources: * Hadley Wickham - [Advanced R](http://adv-r.had.co.nz/) * [R Language Definition](http://stat.ethz.ch/R-manual/R-devel/doc/manual/R-lang.html)