{"id":908,"date":"2023-10-24T07:27:23","date_gmt":"2023-10-24T11:27:23","guid":{"rendered":"https:\/\/www.clayford.net\/statistics\/?p=908"},"modified":"2023-11-11T08:31:22","modified_gmt":"2023-11-11T13:31:22","slug":"some-r-fundamentals","status":"publish","type":"post","link":"https:\/\/www.clayford.net\/statistics\/some-r-fundamentals\/","title":{"rendered":"Some R Fundamentals"},"content":{"rendered":"

I recently came across the book R Programming for Bioinformatics<\/a> at my local library and decided to check it out. I don\u2019t do bioinformatics and the book is a little old (published in 2009), but I figured I would browse through it anyway. Chapter 2 is titled R Language Fundamentals. As I was flipping through it I found several little nuggets of information that I had either forgotten about over the years or never knew in the first place. I decided to document them here.<\/p>\n

Variable names<\/strong><\/p>\n

Variable names cannot begin with a digit or underscore, and if they begin with a period they cannot be followed by a number. But we can bend these rules by quoting the names with backticks.<\/p>\n

`_evil` <- "probably not wise"\r\n`_evil`<\/code><\/pre>\n
## [1] "probably not wise"<\/code><\/pre>\n
`.666_number of the beast` <- sqrt(666^2)\r\n`.666_number of the beast`<\/code><\/pre>\n
## [1] 666<\/code><\/pre>\n
rm(`_evil`, `.666_number of the beast`)<\/code><\/pre>\n
Attributes<\/strong><\/p>\n
Attributes can be attached to any R object except NULL. They can be useful for storing metadata among many other things. For example, add a source for a dataset.<\/p>\n
d <- VADeaths\r\nattr(d, "source") <- "Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525\u2013535."<\/code><\/pre>\n
To see the source:<\/p>\n
attr(d, "source")<\/code><\/pre>\n
## [1] "Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525\u2013535."<\/code><\/pre>\n
To see all attributes of an object:<\/p>\n
attributes(d)<\/code><\/pre>\n
## $dim\r\n## [1] 5 4\r\n## \r\n## $dimnames\r\n## $dimnames[[1]]\r\n## [1] "50-54" "55-59" "60-64" "65-69" "70-74"\r\n## \r\n## $dimnames[[2]]\r\n## [1] "Rural Male"   "Rural Female" "Urban Male"   "Urban Female"\r\n## \r\n## \r\n## $source\r\n## [1] "Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525\u2013535."<\/code><\/pre>\n
To remove an attribute:<\/p>\n
attr(d, "source") <- NULL<\/code><\/pre>\n
Not all attributes are displayed when called on an object. For example, after fitting a linear model, it appears there are only two attributes.<\/p>\n
m <- lm(dist ~ speed, data = cars)\r\nattributes(m)<\/code><\/pre>\n
## $names\r\n##  [1] "coefficients"  "residuals"     "effects"       "rank"         \r\n##  [5] "fitted.values" "assign"        "qr"            "df.residual"  \r\n##  [9] "xlevels"       "call"          "terms"         "model"        \r\n## \r\n## $class\r\n## [1] "lm"<\/code><\/pre>\n
However, elements of the model object also have attributes. For example, the terms element has 10 attributes.<\/p>\n
out <- attributes(m$terms)\r\nlength(out)<\/code><\/pre>\n
## [1] 10<\/code><\/pre>\n
names(out)<\/code><\/pre>\n
##  [1] "variables"    "factors"      "term.labels"  "order"        "intercept"   \r\n##  [6] "response"     "class"        ".Environment" "predvars"     "dataClasses"<\/code><\/pre>\n
attr(m$terms, "factors")<\/code><\/pre>\n
##       speed\r\n## dist      0\r\n## speed     1<\/code><\/pre>\n
The colon operator<\/strong><\/p>\n
I often forget the colon operator can work with decimal values.<\/p>\n
2.5:10.5<\/code><\/pre>\n
## [1]  2.5  3.5  4.5  5.5  6.5  7.5  8.5  9.5 10.5<\/code><\/pre>\n
And can go backwards:<\/p>\n
10.2:1.2<\/code><\/pre>\n
##  [1] 10.2  9.2  8.2  7.2  6.2  5.2  4.2  3.2  2.2  1.2<\/code><\/pre>\n
zero length vectors<\/strong><\/p>\n
The sum of zero length vector is 0, but the product of a zero length vector is 1.<\/p>\n
x <- numeric()\r\nlength(x)<\/code><\/pre>\n
## [1] 0<\/code><\/pre>\n
sum(x)<\/code><\/pre>\n
## [1] 0<\/code><\/pre>\n
prod(x)<\/code><\/pre>\n
## [1] 1<\/code><\/pre>\n
This is ensures expected behavior when working with sums and products:<\/p>\n
# 12 + 0\r\nsum(12, x)<\/code><\/pre>\n
## [1] 12<\/code><\/pre>\n
# 12 * 1\r\nprod(12, x)<\/code><\/pre>\n
## [1] 12<\/code><\/pre>\n
.Machine<\/strong><\/p>\n
The .Machine<\/code> variable holds information about the numerical characteristics of your machine. For example, the largest integer my machine can represent:<\/p>\n
.Machine$integer.max<\/code><\/pre>\n
## [1] 2147483647<\/code><\/pre>\n
If I add 1 to that, the result is numeric, not an integer.<\/p>\n
x <- .Machine$integer.max\r\nx2 <- x + 1\r\nis.integer(x2)<\/code><\/pre>\n
## [1] FALSE<\/code><\/pre>\n
If I add 1L (an explicit integer) to that, the result is a warning and a NA. My machine cannot represent that integer.<\/p>\n
x2 <- x + 1L<\/code><\/pre>\n
## Warning in x + 1L: NAs produced by integer overflow<\/code><\/pre>\n
x2<\/code><\/pre>\n
## [1] NA<\/code><\/pre>\n
Recoding factors<\/strong><\/p>\n
There are several convenience functions in other packages for recoding variables such as recode<\/code> in the {car} package, case_when<\/code> in {dplyr}, and a bunch of functions in the {forcats} package. But it\u2019s good to remember how to use base R to recode factors. Create a list with the recoding definitions and assign to the levels of the factor.<\/p>\n
g <- sample(letters[1:5], 30, replace = TRUE)\r\ng <- factor(g)\r\ng<\/code><\/pre>\n
##  [1] e c d c d c b a e c a d e e d b e c b c b c b d d c b a d e\r\n## Levels: a b c d e<\/code><\/pre>\n
Put \u201ca\u201d and \u201cb\u201d into one group, \u201cc\u201d and \u201cd\u201d into another group, and keep \u201ce\u201d in it\u2019s own group.<\/p>\n
lst <- list("A" = c("a", "b"),\r\n            "B" = c("c", "d"),\r\n            "C" = "e")\r\nlevels(g) <- lst\r\ng<\/code><\/pre>\n
##  [1] C B B B B B A A C B A B C C B A C B A B A B A B B B A A B C\r\n## Levels: A B C<\/code><\/pre>\n
If we like we can add an attribute to store the definition.<\/p>\n
attr(g, "recoding") <- c("A = {ab}, B = {cd}, C = {e}")\r\ng<\/code><\/pre>\n
##  [1] C B B B B B A A C B A B C C B A C B A B A B A B B B A A B C\r\n## attr(,"recoding")\r\n## [1] A = {ab}, B = {cd}, C = {e}\r\n## Levels: A B C<\/code><\/pre>\n
lists can have dimensions<\/strong><\/p>\n
Something more interesting than applicable is that lists can have dimensions.<\/p>\n
M <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))\r\nXsq <- chisq.test(M) # produces 9 element list\r\nXsq <- unclass(Xsq) # remove htest class\r\ndim(Xsq) <- c(3,3)\r\nXsq<\/code><\/pre>\n
##      [,1]         [,2]                         [,3]     \r\n## [1,] 30.07015     "Pearson's Chi-squared test" numeric,6\r\n## [2,] 2            "M"                          table,6  \r\n## [3,] 2.953589e-07 table,6                      table,6<\/code><\/pre>\n
Xsq[1,3]<\/code><\/pre>\n
## [[1]]\r\n##          A        B        C\r\n## A 703.6714 319.6453 533.6834\r\n## B 542.3286 246.3547 411.3166<\/code><\/pre>\n
Environments<\/strong><\/p>\n
We are not restricted to creating objects in the Global Environment. We can create our own environments using the new.env()<\/code> function and then create objects in that environment. We can use the dollar sign operator or the assign()<\/code> function.<\/p>\n
e1 <- new.env()\r\ne1$mod <- lm(dist ~ speed, data = cars)\r\ne1$cumTotal <- function(x)tail(cumsum(x), n = 1)\r\nassign("vals", c(20, 23, 34, 19), envir = e1)\r\nls(e1)<\/code><\/pre>\n
## [1] "cumTotal" "mod"      "vals"<\/code><\/pre>\n
ls() # list objects in Global Environment<\/code><\/pre>\n
##  [1] "d"   "e1"  "g"   "lst" "m"   "M"   "out" "x"   "x2"  "Xsq"<\/code><\/pre>\n
We can access objects in our environment using the dollar sign operator or the get()<\/code> and mget()<\/code> functions.<\/p>\n
e1$cumTotal(c(2,4,6))<\/code><\/pre>\n
## [1] 12<\/code><\/pre>\n
get("vals", envir = e1)<\/code><\/pre>\n
## [1] 20 23 34 19<\/code><\/pre>\n
mget(c("mod", "vals"), envir = e1) # get more than one object<\/code><\/pre>\n
## $mod\r\n## \r\n## Call:\r\n## lm(formula = dist ~ speed, data = cars)\r\n## \r\n## Coefficients:\r\n## (Intercept)        speed  \r\n##     -17.579        3.932  \r\n## \r\n## \r\n## $vals\r\n## [1] 20 23 34 19<\/code><\/pre>\n
We can save the environment and reload it in a future session.<\/p>\n
save(e1, file = "e1.Rdata")\r\nrm(e1)\r\nload(file = "e1.Rdata")<\/code><\/pre>\n
We can also change the environment associated with an object that was created in the Global Environment.<\/p>\n
f <- function(x)(vals + 1000) # vals object defined in e1 environment\r\nenvironment(f) <- e1\r\nf<\/code><\/pre>\n
## function(x)(vals + 1000)\r\n## <environment: 0x000001b5ec98d4e0><\/code><\/pre>\n
f()<\/code><\/pre>\n
## [1] 1020 1023 1034 1019<\/code><\/pre>\n
Notice if we remove the environment using rm()<\/code>, the function still remains in that environment and we have access to its objects<\/p>\n
rm(e1)\r\nf<\/code><\/pre>\n
## function(x)(vals + 1000)\r\n## <environment: 0x000001b5ec98d4e0><\/code><\/pre>\n
f()<\/code><\/pre>\n
## [1] 1020 1023 1034 1019<\/code><\/pre>\n
rm(e1)<\/code> simply removes the binding<\/em> between the symbol \u201ce1\u201d and structure that contains the objects. Since the environment can be reached as the environment of f()<\/code>, it remains available.<\/p>\n
Brackets and Dollar Signs<\/strong><\/p>\n
I found this sentence enlightening: \u201cOne way of describing the behavior of the single bracket operator is that the type of the return value matches the type of the value it is applied to.\u201d (p.\u00a028) I like this in favor of metaphors involving trains<\/a>.<\/p>\n
lst <- list(a1 = 1:5, b = c("d", "g"), c = 99)\r\nlst["a1"] # returns a list<\/code><\/pre>\n
## $a1\r\n## [1] 1 2 3 4 5<\/code><\/pre>\n
[[<\/code> and $<\/code> extract single values.<\/p>\n
lst[["a1"]]<\/code><\/pre>\n
## [1] 1 2 3 4 5<\/code><\/pre>\n
lst$a1<\/code><\/pre>\n
## [1] 1 2 3 4 5<\/code><\/pre>\n
The $<\/code> operator supports partial matching.<\/p>\n
lst$a<\/code><\/pre>\n
## [1] 1 2 3 4 5<\/code><\/pre>\n
The [<\/code> and [[<\/code> operators support expressions, but not partial matching.<\/p>\n
ans <- "c"\r\nlst[ans]<\/code><\/pre>\n
## $c\r\n## [1] 99<\/code><\/pre>\n
lst[[ans]]<\/code><\/pre>\n
## [1] 99<\/code><\/pre>\n
If names are duplicated in named vectors, then only the value corresponding to the first one is returned when subsetting with brackets.<\/p>\n
x <- c("a" = 1, "a" = 2)\r\nx["a"]<\/code><\/pre>\n
## a \r\n## 1<\/code><\/pre>\n
The %in%<\/code> operator can be useful to get all elements with the same name.<\/p>\n
x[names(x) %in% "a"]<\/code><\/pre>\n
## a a \r\n## 1 2<\/code><\/pre>\n
Matrix indexing<\/strong><\/p>\n
I don\u2019t work with arrays that often, but when I do I often forget that I can index them with a matrix. Below I extract the value in row 1, column 4, from each of the 3 layers of the iris3 array.<\/p>\n
m <- matrix(c(1,4,1,\r\n              1,4,2,\r\n              1,4,3), \r\n            ncol = 3, byrow = TRUE)\r\niris3[m]<\/code><\/pre>\n
## [1] 0.2 1.4 2.5<\/code><\/pre>\n
Of course we can get the same result (in this case) using subsetting indices.<\/p>\n
iris3[1,4,]<\/code><\/pre>\n
##     Setosa Versicolor  Virginica \r\n##        0.2        1.4        2.5<\/code><\/pre>\n
Negative subscripts<\/strong><\/p>\n
Negative subscripts can appear on the left side<\/em> of assignment.<\/p>\n
x <- 1:10\r\nx[-(2:4)] <- 99\r\nx<\/code><\/pre>\n
##  [1] 99  2  3  4 99 99 99 99 99 99<\/code><\/pre>\n
Subsetting without dimensions<\/strong><\/p>\n
Use empty double brackets to select all elements and not change any attributes.<\/p>\n
x <- matrix(10:1, ncol = 2)\r\nx<\/code><\/pre>\n
##      [,1] [,2]\r\n## [1,]   10    5\r\n## [2,]    9    4\r\n## [3,]    8    3\r\n## [4,]    7    2\r\n## [5,]    6    1<\/code><\/pre>\n
x[] <- sort(x)\r\nx<\/code><\/pre>\n
##      [,1] [,2]\r\n## [1,]    1    6\r\n## [2,]    2    7\r\n## [3,]    3    8\r\n## [4,]    4    9\r\n## [5,]    5   10<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
I recently came across the book R Programming for Bioinformatics at my local library and decided to check it out…. Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-908","post","type-post","status-publish","format-standard","hentry","category-using-r"],"_links":{"self":[{"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/posts\/908","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/comments?post=908"}],"version-history":[{"count":3,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/posts\/908\/revisions"}],"predecessor-version":[{"id":912,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/posts\/908\/revisions\/912"}],"wp:attachment":[{"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/media?parent=908"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/categories?post=908"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.clayford.net\/statistics\/wp-json\/wp\/v2\/tags?post=908"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}