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Miscellaneous, Analytic R Kernels
An R package with a set of general use functions for data analytics. This is developed mostly for personal use and has no real goal other than to limit the time I spend searching where I did that thing that I think I could use again because it worked well but this problem might be slightly different and I know I had to change it before.
Some parts happily ripped from and (hopefully) credited to others.
You can download the current CRAN version with:
install.packages("mark")You can the development version from GitHub with:
remotes::install_github("jmbarbone/mark")This package contains a many variety of functions, some useful, some not so much. Below are a selection of a few functions that could potential be useful for others:
library(mark)
#>
#> Attaching package: 'mark'
#> The following object is masked from 'package:usethis':
#>
#> use_author
#> The following objects are masked from 'package:base':
#>
#> sort_by, withinGet dates from sloppy entries:
bad_dates <- c("2020 Dec 8th", "1970 May", "??", "1984 UNK UN")
date_from_partial(bad_dates)
#> [1] "2020-12-08" "1970-05-01" NA "1984-01-01"
date_from_partial(bad_dates, method = "max")
#> [1] "2020-12-08" "1970-05-31" NA "1984-12-31"
date_from_partial(c("May 2000", "08Dec2020"), format = "dmy")
#> [1] "2000-05-01" "2020-12-08"Slice strings:
x <- stringi::stri_rand_lipsum(1)
str_slice(x, n = 50L)
#> [1] "Lorem ipsum dolor sit amet, nisl eleifend sed proi"
#> [2] "n sed at. Class maximus, ante mi sed ridiculus eni"
#> [3] "m mus, sollicitudin. Maecenas penatibus luctus don"
#> [4] "ec turpis erat pretium in vulputate accumsan. Amet"
#> [5] " quis arcu phasellus facilisi facilisis odio integ"
#> [6] "er sit. Nunc venenatis duis vitae in non mauris ri"
#> [7] "sus. Vel consectetur sed sapien arcu sed massa nec"
#> [8] " egestas, malesuada condimentum felis a? Et ut pel"
#> [9] "lentesque consequat sed at torquent, sociosqu. Sod"
#> [10] "ales donec arcu laoreet luctus auctor mauris mauri"
#> [11] "s nisl primis nascetur feugiat scelerisque libero."
#> [12] " Sed maximus vehicula dictum lacus libero pharetra"
#> [13] " sed. Egestas maximus venenatis egestas leo orci, "
#> [14] "tellus consectetur velit litora nascetur, a. Ferme"
#> [15] "ntum aptent lobortis elementum netus integer variu"
#> [16] "s euismod ac ornare porttitor non ut quam, mollis."
#> [17] " Scelerisque cursus amet primis. Vestibulum non co"
#> [18] "nsectetur aliquam mollis velit accumsan. Condiment"
#> [19] "um sit sed eu dapibus habitant faucibus interdum. "
#> [20] "Vel libero, amet lacus aliquam ac sit porta, leo l"
#> [21] "eo."
str_slice_by_word(x)
#> [1] "Lorem ipsum dolor sit amet, nisl eleifend sed proin sed at. Class maximus, ante"
#> [2] "mi sed ridiculus enim mus, sollicitudin. Maecenas penatibus luctus donec turpis"
#> [3] "erat pretium in vulputate accumsan. Amet quis arcu phasellus facilisi facilisis"
#> [4] "odio integer sit. Nunc venenatis duis vitae in non mauris risus. Vel consectetur"
#> [5] "sed sapien arcu sed massa nec egestas, malesuada condimentum felis a? Et ut"
#> [6] "pellentesque consequat sed at torquent, sociosqu. Sodales donec arcu laoreet"
#> [7] "luctus auctor mauris mauris nisl primis nascetur feugiat scelerisque libero. Sed"
#> [8] "maximus vehicula dictum lacus libero pharetra sed. Egestas maximus venenatis"
#> [9] "egestas leo orci, tellus consectetur velit litora nascetur, a. Fermentum aptent"
#> [10] "lobortis elementum netus integer varius euismod ac ornare porttitor non ut quam,"
#> [11] "mollis. Scelerisque cursus amet primis. Vestibulum non consectetur aliquam"
#> [12] "mollis velit accumsan. Condimentum sit sed eu dapibus habitant faucibus"
#> [13] "interdum. Vel libero, amet lacus aliquam ac sit porta, leo leo."Read in bibliographies:
file <- system.file("extdata", "example-bib.txt", package = "mark")
bib <- read_bib(file)
tibble::as_tibble(bib)
#> # A tibble: 13 × 23
#> key field author title journal year number pages month note volume
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 article arti… Peter… The … The na… 1993 2 201-… 7 An o… 4
#> 2 book book Peter… The … <NA> 1993 <NA> <NA> 7 An o… 4
#> 3 booklet book… Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> 4 conference conf… Peter… The … <NA> 1993 <NA> 213 7 An o… 4
#> 5 inbook inbo… Peter… The … <NA> 1993 <NA> 201-… 7 An o… 4
#> 6 incollection inco… Peter… The … <NA> 1993 <NA> 201-… 7 An o… 4
#> 7 manual manu… Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> 8 mastersthes… mast… Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> 9 misc misc Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> 10 phdthesis phdt… Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> 11 proceedings proc… <NA> The … <NA> 1993 <NA> <NA> 7 An o… 4
#> 12 techreport tech… Peter… The … <NA> 1993 2 <NA> 7 An o… <NA>
#> 13 unpublished unpu… Peter… The … <NA> 1993 <NA> <NA> 7 An o… <NA>
#> # ℹ 12 more variables: publisher <chr>, series <chr>, address <chr>,
#> # edition <chr>, isbn <chr>, howpublished <chr>, booktitle <chr>,
#> # editor <chr>, organization <chr>, chapter <chr>, school <chr>,
#> # institution <chr>More matching:
1:10 %out% c(1, 3, 5, 9) # opposite of %in%
#> [1] FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
letters[1:5] %wo% letters[3:7]
#> [1] "a" "b"
letters[1:5] %wi% letters[3:7]
#> [1] "c" "d" "e"Small functions for working with data.frames:
x <- list(a = 1:5, b = letters[1:5])
quick_df(x)
#> a b
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d
#> 5 5 e
vector2df(x[["b"]], name = NULL)
#> value
#> 1 a
#> 2 b
#> 3 c
#> 4 d
#> 5 e
quick_dfl(a = 1:3, b = list(1:5, 6:10, 11:15))
#> a b
#> 1 1 1, 2, 3, 4, 5
#> 2 2 6, 7, 8, 9, 10
#> 3 3 11, 12, 13, 14, 15Counts and proportions:
set.seed(42)
x <- sample(1:5, 20, TRUE, 5:1/2)
counts(x)
#> 4 5 1 3 2
#> 2 4 4 5 5
props(x)
#> 4 5 1 3 2
#> 0.10 0.20 0.20 0.25 0.25
df <- as.data.frame(matrix(sample(1:2, 60, TRUE), byrow = TRUE, ncol = 3))
counts(df, c("V1", "V2"))
#> V1 V2 freq
#> 1 1 1 5
#> 2 1 2 4
#> 3 2 2 8
#> 4 2 1 3
props(df, 1:3)
#> V1 V2 V3 prop
#> 1 1 1 1 0.4285714
#> 2 1 1 2 0.2857143
#> 3 1 2 2 0.4285714
#> 4 2 2 1 0.7142857
#> 5 2 1 2 0.4285714
#> 6 2 2 2 0.4285714
#> 7 1 2 1 0.1428571Date time differences:
x <- as.POSIXlt("2021-02-13 05:02:30", tz = "America/New_York") + c(0, -1, 2) * 3600 * 24
y <- as.POSIXlt("2020-02-13 05:02:30", tz = "America/New_York") + c(0, -2, 4) * 3600 * 24
# comparison with base::difftime() (note the order of x and y)
difftime(y, x, units = "days")
#> Time differences in days
#> [1] -366 -367 -364
diff_time_days(x, y)
#> Time differences in days
#> [1] -366 -367 -364
difftime(y, x, units = "secs")
#> Time differences in secs
#> [1] -31622400 -31708800 -31449600
diff_time_secs(x, y)
#> Time differences in seconds
#> [1] -31622400 -31708800 -31449600
# Year (by days, months, etc)
diff_time_years(x, y)
#> Time differences in years (365 days)
#> [1] -1.0027397 -1.0054795 -0.9972603
diff_time_myears(x, y)
#> Time differences in years (30-day months)
#> [1] -1.016667 -1.019444 -1.011111
# Set time zones
diff_time_hours(x, y, "GMT", "America/New_York")
#> Time differences in hours
#> [1] -8789 -8813 -8741
diff_time_hours(x, x, "GMT", c("America/Los_Angeles", "America/New_York", "Europe/London")) # note x, x
#> Time differences in hours
#> [1] -8 -5 0
diff_time_days(x, y, NULL, 31536000)
#> Time differences in days
#> [1] -0.994213 -1.994213 1.005787Simple factors:
fact(c("a", "c", NA, "a", "b", NA, "a", "c")) # no sorting
#> [1] a c <NA> a b <NA> a c
#> Levels: a c b <NA>
fact(c(-1, 5, 2, NA, 3)) # sorting
#> [1] -1 5 2 <NA> 3
#> Levels: -1 2 3 5 <NA>
fact(c(NA, FALSE, TRUE, FALSE, TRUE, NA)) # fixed
#> [1] <NA> FALSE TRUE FALSE TRUE <NA>
#> Levels: TRUE FALSE <NA>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.