tjmisc

The goal of tjmisc is to gather miscellaneous helper functions, mostly for use in my dissertation.

Apologies in advance. I think "misc" packages are kind of bad because packages should be focused on specific problems: for example, my helper packages for working on polynomials, printing numbers or tidying MCMC samples. Having modular code snapping together like Lego blocks is better than a grab-bag of functions, it's true, but using library(helpers) is much, much better than using source("helpers.R"). So here we are... in the grab-bag.

Examples

sample_n_of() is like dplyr's sample_n() but it samples groups.

library(dplyr, warn.conflicts = FALSE)
#> Warning: package 'dplyr' was built under R version 3.4.2
library(tjmisc)
set.seed(11022017)

data <- tibble::tibble(
  day = 1:10 %>% rep(10) %>% sort(),
  id  = 1:10 %>% rep(10),
  block = letters[1:5] %>% rep(10) %>% sort() %>% rep(2),
  value = rnorm(100) %>% round(2))

# data from 2 days
sample_n_of(data, 2, day)
#> # A tibble: 20 x 4
#>      day    id block value
#>    <int> <int> <chr> <dbl>
#>  1     8     1     c  0.87
#>  2     8     2     c  0.31
#>  3     8     3     c -1.73
#>  4     8     4     c -1.49
#>  5     8     5     c  0.38
#>  6     8     6     c  0.20
#>  7     8     7     c -1.87
#>  8     8     8     c  2.02
#>  9     8     9     c  1.36
#> 10     8    10     c  0.94
#> 11    10     1     e  0.64
#> 12    10     2     e -0.76
#> 13    10     3     e -1.68
#> 14    10     4     e -1.86
#> 15    10     5     e  1.02
#> 16    10     6     e  0.12
#> 17    10     7     e  0.35
#> 18    10     8     e  0.43
#> 19    10     9     e -0.43
#> 20    10    10     e -1.71

# data from 1 id
sample_n_of(data, 1, id)
#> # A tibble: 10 x 4
#>      day    id block value
#>    <int> <int> <chr> <dbl>
#>  1     1     2     a -0.01
#>  2     2     2     b  1.28
#>  3     3     2     c -0.29
#>  4     4     2     d  0.49
#>  5     5     2     e -0.39
#>  6     6     2     a  0.22
#>  7     7     2     b  0.30
#>  8     8     2     c  0.31
#>  9     9     2     d -1.11
#> 10    10     2     e -0.76

# data from 2 block-id pairs
sample_n_of(data, 2, block, id)
#> # A tibble: 4 x 4
#>     day    id block value
#>   <int> <int> <chr> <dbl>
#> 1     1     5     a  0.72
#> 2     4     9     d -0.31
#> 3     6     5     a -0.21
#> 4     9     9     d  0.92

ggpreview() is like ggplot2's ggsave() but it saves an image to a temporary file and then opens it in the system viewer. If you've ever found yourself in a loop of saving a plot, leaving RStudio to doubleclick the file, sighing, going back to RStudio, tweaking the height or width or plot theme, ever so slowly spiraling in on your desired plot, then ggpreview() is for you.