README.md
In tjmahr/tjmisc: TJ's Miscellany
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.
Installation
You can install the tjmisc from github with:
# install.packages("devtools")
devtools::install_github("tjmahr/tjmisc")
Examples
Sample groups of data
sample_n_of() is like dplyr’s sample_n() but it samples groups.
library(dplyr, warn.conflicts = FALSE)
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 3 days
sample_n_of(data, 3, day)
#> # A tibble: 30 x 4
#> day id block value
#> <int> <int> <chr> <dbl>
#> 1 1 1 a -0.51
#> 2 1 2 a -0.01
#> 3 1 3 a -0.79
#> 4 1 4 a -0.74
#> 5 1 5 a 0.72
#> 6 1 6 a -1.17
#> 7 1 7 a 1.11
#> 8 1 8 a 1.38
#> 9 1 9 a -0.89
#> 10 1 10 a -0.06
#> # ... with 20 more rows
# 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 4 a -0.74
#> 2 2 4 b -0.46
#> 3 3 4 c 1.99
#> 4 4 4 d -0.7
#> 5 5 4 e -0.41
#> 6 6 4 a 0.84
#> 7 7 4 b -0.01
#> 8 8 4 c -1.49
#> 9 9 4 d -0.59
#> 10 10 4 e -1.86
# 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 3 8 c 0.06
#> 2 4 5 d -0.69
#> 3 8 8 c 2.02
#> 4 9 5 d -0.51
Tidy quantiles
tidy_quantile() returns a dataframe with quantiles for a given
variable. I like to use it to select values for plotting model
predictions.
penguins <- palmerpenguins::penguins
penguins %>%
tidy_quantile(bill_length_mm)
#> # A tibble: 5 x 2
#> quantile bill_length_mm
#> <chr> <dbl>
#> 1 10% 36.6
#> 2 30% 40.2
#> 3 50% 44.4
#> 4 70% 47.4
#> 5 90% 50.8
penguins %>%
group_by(species) %>%
tidy_quantile(bill_length_mm)
#> # A tibble: 15 x 3
#> species quantile bill_length_mm
#> <fct> <chr> <dbl>
#> 1 Adelie 10% 35.5
#> 2 Adelie 30% 37.3
#> 3 Adelie 50% 38.8
#> 4 Adelie 70% 40.3
#> 5 Adelie 90% 42.1
#> 6 Chinstrap 10% 45.2
#> 7 Chinstrap 30% 46.6
#> 8 Chinstrap 50% 49.6
#> 9 Chinstrap 70% 50.8
#> 10 Chinstrap 90% 52.1
#> 11 Gentoo 10% 43.5
#> 12 Gentoo 30% 45.6
#> 13 Gentoo 50% 47.3
#> 14 Gentoo 70% 49.1
#> 15 Gentoo 90% 50.8
Tidy correlations
tidy_correlation() calculates correlations between pairs of selected
dataframe columns. It accepts dplyr::select() selection semantics, and
it respects grouped dataframes.
penguins %>%
tidy_correlation(bill_length_mm, bill_depth_mm, flipper_length_mm)
#> # A tibble: 3 x 5
#> column1 column2 estimate n p.value
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 bill_depth_mm bill_length_mm -0.235 342 0
#> 2 flipper_length_mm bill_length_mm 0.656 342 0
#> 3 flipper_length_mm bill_depth_mm -0.584 342 0
penguins %>%
dplyr::group_by(species) %>%
tidy_correlation(dplyr::ends_with("mm"))
#> # A tibble: 9 x 6
#> species column1 column2 estimate n p.value
#> <fct> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Adelie bill_depth_mm bill_length_mm 0.392 151 0
#> 2 Adelie flipper_length_mm bill_length_mm 0.326 151 0
#> 3 Adelie flipper_length_mm bill_depth_mm 0.308 151 0.0001
#> 4 Chinstrap bill_depth_mm bill_length_mm 0.654 68 0
#> 5 Chinstrap flipper_length_mm bill_length_mm 0.472 68 0
#> 6 Chinstrap flipper_length_mm bill_depth_mm 0.580 68 0
#> 7 Gentoo bill_depth_mm bill_length_mm 0.643 123 0
#> 8 Gentoo flipper_length_mm bill_length_mm 0.661 123 0
#> 9 Gentoo flipper_length_mm bill_depth_mm 0.707 123 0
Pairwise comparisons
compare_pairs() compares all pairs of values among levels of a
categorical variable. Hmmm, that sounds confusing. Here’s an example. We
compute the difference in average score between each pair of workers.
to_compare <- nlme::Machines %>%
group_by(Worker) %>%
summarise(avg_score = mean(score)) %>%
print()
#> # A tibble: 6 x 2
#> Worker avg_score
#> <ord> <dbl>
#> 1 6 50.6
#> 2 2 58.0
#> 3 4 59.6
#> 4 1 60.9
#> 5 3 66.1
#> 6 5 62.7
to_compare %>%
compare_pairs(Worker, avg_score) %>%
rename(difference = value) %>%
mutate(
across(where(is.numeric), round, 1)
)
#> # A tibble: 15 x 2
#> pair difference
#> <fct> <dbl>
#> 1 1-6 10.3
#> 2 1-4 1.3
#> 3 1-2 2.9
#> 4 2-6 7.4
#> 5 3-6 15.5
#> 6 3-4 6.5
#> 7 3-2 8.1
#> 8 3-1 5.2
#> 9 4-6 9
#> 10 4-2 1.6
#> 11 5-6 12.1
#> 12 5-4 3.1
#> 13 5-3 -3.4
#> 14 5-2 4.7
#> 15 5-1 1.8
Plotting a matrix
ggmatplot() plots the columns of a matrix as individual lines, much
like matplot() in base R.
Here we plot a spline basis matrix for penguin bill length. By default
it plots the columns with unique row number as the x-axis.
# Create a 10-column natural spline bases
sorted_lengths <- sort(penguins$bill_length_mm)
length_ns <- splines::ns(sorted_lengths, df = 10)
ggmatplot(length_ns)
Alternatively, you can supply a column number and make it the x axis.
In this example, we bind on the original data and use it as the x-axis
column. This makes the lines much smoother because the spline basis was
built on the bill lengths, not on row numbers.
ggmatplot(cbind(sorted_lengths, length_ns), x_axis_column = 1)
By default, duplicated rows are removed. We can choose to keep them. The
little flat steps along the curve are the repeated rows. We can also
change the number of colors to use. The package also provides
annotate_label_grey() for making labels on ggplot2’s default grey
background.
ggmatplot(length_ns, unique_rows = FALSE, n_colors = 1) +
annotate_label_grey("splines!", 20, .65, size = 5)
Et cetera
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.
seq_along_rows() saves a few keystrokes in for-loops that iterate over
dataframe rows.
cars %>% head(5) %>% seq_along_rows()
#> [1] 1 2 3 4 5
cars %>% head(0) %>% seq_along_rows()
#> integer(0)
is_same_as_last and replace_if_same_as_last() are helpers for
formatting tables. I use them to replace repeating values in a text
column with blanks.
mtcars %>%
tibble::rownames_to_column("name") %>%
slice(1:10) %>%
select(cyl, name, mpg) %>%
arrange(cyl, mpg) %>%
mutate_at(c("cyl"), replace_if_same_as_last, "") %>%
knitr::kable()
| cyl | name | mpg |
|:----|:------------------|-----:|
| 4 | Datsun 710 | 22.8 |
| | Merc 230 | 22.8 |
| | Merc 240D | 24.4 |
| 6 | Valiant | 18.1 |
| | Merc 280 | 19.2 |
| | Mazda RX4 | 21.0 |
| | Mazda RX4 Wag | 21.0 |
| | Hornet 4 Drive | 21.4 |
| 8 | Duster 360 | 14.3 |
| | Hornet Sportabout | 18.7 |
fct_add_counts() adds counts to a factor’s labels.
# Create a factor with some random counts
set.seed(20190124)
random_penguins <- penguins %>%
dplyr::sample_n(250, replace = TRUE)
table(random_penguins$species)
#>
#> Adelie Chinstrap Gentoo
#> 108 41 101
# Updated factors
random_penguins$species %>% levels()
#> [1] "Adelie" "Chinstrap" "Gentoo"
random_penguins$species %>% fct_add_counts() %>% levels()
#> [1] "Adelie (108)" "Chinstrap (41)" "Gentoo (101)"
You can tweak the format for the first label. I like to use this for
plotting by stating the unit next to the first count.
random_penguins$species %>%
fct_add_counts(first_fmt = "{levels} ({counts} penguins)") %>%
levels()
#> [1] "Adelie (108 penguins)" "Chinstrap (41)" "Gentoo (101)"
Behind the scenes, fct_add_counts() uses the function
fct_glue_labels() to construct labels using a
glue-templating
string. Therefore, fct_glue_labels() would be a more appropriate
function for generic relabeling using glue:
random_penguins$species %>%
fct_glue_labels(
fmt = "{tolower(levels)}",
first_fmt = "Species: {tolower(levels)}"
) %>%
levels()
#> [1] "Species: adelie" "chinstrap" "gentoo"
Comparing two sets
When I need to merge two datasets together, I have to go through a
little dance to figure out which elements are in your_data and which
are in my_data. compare_sets() performs all of R’s set operations so
I can skim over the differences.
your_data <- c(1, 2, 3, 3, 4, 5)
my_data <- c(4, 4, 4, 5, 6, 7, 8)
str(compare_sets(your_data, my_data))
#> List of 10
#> $ lengths : Named int [1:9] 6 7 5 5 1 3 3 2 8
#> ..- attr(*, "names")= chr [1:9] "your_data" "my_data" "unique(your_data)" "unique(my_data)" ...
#> $ your_data : num [1:6] 1 2 3 3 4 5
#> $ my_data : num [1:7] 4 4 4 5 6 7 8
#> $ unique(your_data) : num [1:5] 1 2 3 4 5
#> $ unique(my_data) : num [1:5] 4 5 6 7 8
#> $ setequal(your_data, my_data) : logi FALSE
#> $ setdiff(your_data, my_data) : num [1:3] 1 2 3
#> $ setdiff(my_data, your_data) : num [1:3] 6 7 8
#> $ intersect(your_data, my_data): num [1:2] 4 5
#> $ union(your_data, my_data) : num [1:8] 1 2 3 4 5 6 7 8
Jekyll helpers
I also include functions I use to create and maintain my website.
jekyll_create_rmd_draft() creates a post in the _R/_drafts folder.
withr::with_dir(tempdir(), {
dir.create("_R")
dir.create("_R/_drafts")
# Basic use
jekyll_create_rmd_draft(slug = "today-i-learned")
# Accepts a date
jekyll_create_rmd_draft(
slug = "yesterday-i-learned",
date = Sys.Date() - 1
)
# Filler text used if slug is not provided
jekyll_create_rmd_draft()
})
#> Creating file: ./_R/_drafts/2021-04-28-today-i-learned.Rmd
#> Creating file: ./_R/_drafts/2021-04-27-yesterday-i-learned.Rmd
#> Creating file: ./_R/_drafts/2021-04-28-sceptical-joey.Rmd
More involved demos
These are things that I would have used in the demo above but cut and
moved down here to keep that overview succinct.
Comparing pairs of values over a posterior distribution
I wrote compare_pairs() to compute posterior differences in Bayesian
models. For the sake of example, let’s fit a Bayesian model of average
bill length for each species in penguins. We could get these estimates
more directly using the default dummy-coding of factors, but let’s
ignore that for now.
library(rstanarm)
#> Loading required package: Rcpp
#> This is rstanarm version 2.21.1
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
m <- stan_glm(
bill_length_mm ~ species - 1,
penguins,
family = gaussian
)
Now, we have a posterior distribution of species means.
newdata <- data.frame(species = unique(penguins$species))
p_means <- posterior_linpred(m, newdata = newdata) %>%
as.data.frame() %>%
tibble::as_tibble() %>%
setNames(newdata$species) %>%
tibble::rowid_to_column("draw") %>%
tidyr::gather(species, mean, -draw) %>%
print()
#> # A tibble: 12,000 x 3
#> draw species mean
#> <int> <chr> <dbl>
#> 1 1 Adelie 38.8
#> 2 2 Adelie 39.0
#> 3 3 Adelie 38.8
#> 4 4 Adelie 38.8
#> 5 5 Adelie 38.8
#> 6 6 Adelie 38.7
#> 7 7 Adelie 38.5
#> 8 8 Adelie 38.8
#> 9 9 Adelie 38.8
#> 10 10 Adelie 38.8
#> # ... with 11,990 more rows
For each posterior sample, we can compute pairwise differences of means
with compare_means().
pair_diffs <- compare_pairs(p_means, species, mean) %>%
print()
#> # A tibble: 12,000 x 3
#> draw pair value
#> <int> <fct> <dbl>
#> 1 1 Chinstrap-Adelie 10.2
#> 2 2 Chinstrap-Adelie 10.2
#> 3 3 Chinstrap-Adelie 10.0
#> 4 4 Chinstrap-Adelie 10.7
#> 5 5 Chinstrap-Adelie 9.22
#> 6 6 Chinstrap-Adelie 10.5
#> 7 7 Chinstrap-Adelie 10.3
#> 8 8 Chinstrap-Adelie 10.2
#> 9 9 Chinstrap-Adelie 9.83
#> 10 10 Chinstrap-Adelie 9.89
#> # ... with 11,990 more rows
library(ggplot2)
ggplot(pair_diffs) +
aes(x = pair, y = value) +
stat_summary(fun.data = median_hilow, geom = "linerange") +
stat_summary(fun.data = median_hilow, fun.args = list(conf.int = .8),
size = 2, geom = "linerange") +
stat_summary(fun.y = median, size = 5, shape = 3, geom = "point") +
labs(x = NULL, y = "Difference in posterior means") +
coord_flip()
#> Warning: `fun.y` is deprecated. Use `fun` instead.
…which should look like the effect ranges in the dummy-coded models.
m2 <- update(m, bill_length_mm ~ species)
m3 <- update(
m,
bill_length_mm ~ species,
data = penguins %>% mutate(species = forcats::fct_rev(species))
)
Give or take a few decimals of precision and give or take changes in
signs because of changes in who was subtracted from whom.
# Adelie versus others
m2 %>%
posterior_interval(regex_pars = "species") %>%
round(2)
#> 5% 95%
#> speciesChinstrap 9.32 10.76
#> speciesGentoo 8.12 9.30
# Gentoo versus others
m3 %>%
rstanarm::posterior_interval(regex_pars = "species") %>%
round(2)
#> 5% 95%
#> speciesChinstrap 0.59 2.07
#> speciesAdelie -9.31 -8.12
# differences from compare_pairs()
pair_diffs %>%
tidyr::spread(pair, value) %>%
select(-draw) %>%
as.matrix() %>%
posterior_interval() %>%
round(2)
#> 5% 95%
#> Chinstrap-Adelie 9.31 10.76
#> Gentoo-Chinstrap -2.09 -0.58
#> Gentoo-Adelie 8.09 9.32
tjmahr/tjmisc documentation built on Feb. 8, 2023, 12:21 p.m.
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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.
Installation
You can install the tjmisc from github with:
# install.packages("devtools")
devtools::install_github("tjmahr/tjmisc")
Examples
Sample groups of data
sample_n_of() is like dplyr’s sample_n() but it samples groups.
library(dplyr, warn.conflicts = FALSE)
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 3 days
sample_n_of(data, 3, day)
#> # A tibble: 30 x 4
#> day id block value
#> <int> <int> <chr> <dbl>
#> 1 1 1 a -0.51
#> 2 1 2 a -0.01
#> 3 1 3 a -0.79
#> 4 1 4 a -0.74
#> 5 1 5 a 0.72
#> 6 1 6 a -1.17
#> 7 1 7 a 1.11
#> 8 1 8 a 1.38
#> 9 1 9 a -0.89
#> 10 1 10 a -0.06
#> # ... with 20 more rows
# 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 4 a -0.74
#> 2 2 4 b -0.46
#> 3 3 4 c 1.99
#> 4 4 4 d -0.7
#> 5 5 4 e -0.41
#> 6 6 4 a 0.84
#> 7 7 4 b -0.01
#> 8 8 4 c -1.49
#> 9 9 4 d -0.59
#> 10 10 4 e -1.86
# 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 3 8 c 0.06
#> 2 4 5 d -0.69
#> 3 8 8 c 2.02
#> 4 9 5 d -0.51
Tidy quantiles
tidy_quantile() returns a dataframe with quantiles for a given
variable. I like to use it to select values for plotting model
predictions.
penguins <- palmerpenguins::penguins
penguins %>%
tidy_quantile(bill_length_mm)
#> # A tibble: 5 x 2
#> quantile bill_length_mm
#> <chr> <dbl>
#> 1 10% 36.6
#> 2 30% 40.2
#> 3 50% 44.4
#> 4 70% 47.4
#> 5 90% 50.8
penguins %>%
group_by(species) %>%
tidy_quantile(bill_length_mm)
#> # A tibble: 15 x 3
#> species quantile bill_length_mm
#> <fct> <chr> <dbl>
#> 1 Adelie 10% 35.5
#> 2 Adelie 30% 37.3
#> 3 Adelie 50% 38.8
#> 4 Adelie 70% 40.3
#> 5 Adelie 90% 42.1
#> 6 Chinstrap 10% 45.2
#> 7 Chinstrap 30% 46.6
#> 8 Chinstrap 50% 49.6
#> 9 Chinstrap 70% 50.8
#> 10 Chinstrap 90% 52.1
#> 11 Gentoo 10% 43.5
#> 12 Gentoo 30% 45.6
#> 13 Gentoo 50% 47.3
#> 14 Gentoo 70% 49.1
#> 15 Gentoo 90% 50.8
Tidy correlations
tidy_correlation() calculates correlations between pairs of selected
dataframe columns. It accepts dplyr::select() selection semantics, and
it respects grouped dataframes.
penguins %>%
tidy_correlation(bill_length_mm, bill_depth_mm, flipper_length_mm)
#> # A tibble: 3 x 5
#> column1 column2 estimate n p.value
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 bill_depth_mm bill_length_mm -0.235 342 0
#> 2 flipper_length_mm bill_length_mm 0.656 342 0
#> 3 flipper_length_mm bill_depth_mm -0.584 342 0
penguins %>%
dplyr::group_by(species) %>%
tidy_correlation(dplyr::ends_with("mm"))
#> # A tibble: 9 x 6
#> species column1 column2 estimate n p.value
#> <fct> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Adelie bill_depth_mm bill_length_mm 0.392 151 0
#> 2 Adelie flipper_length_mm bill_length_mm 0.326 151 0
#> 3 Adelie flipper_length_mm bill_depth_mm 0.308 151 0.0001
#> 4 Chinstrap bill_depth_mm bill_length_mm 0.654 68 0
#> 5 Chinstrap flipper_length_mm bill_length_mm 0.472 68 0
#> 6 Chinstrap flipper_length_mm bill_depth_mm 0.580 68 0
#> 7 Gentoo bill_depth_mm bill_length_mm 0.643 123 0
#> 8 Gentoo flipper_length_mm bill_length_mm 0.661 123 0
#> 9 Gentoo flipper_length_mm bill_depth_mm 0.707 123 0
Pairwise comparisons
compare_pairs() compares all pairs of values among levels of a
categorical variable. Hmmm, that sounds confusing. Here’s an example. We
compute the difference in average score between each pair of workers.
to_compare <- nlme::Machines %>%
group_by(Worker) %>%
summarise(avg_score = mean(score)) %>%
print()
#> # A tibble: 6 x 2
#> Worker avg_score
#> <ord> <dbl>
#> 1 6 50.6
#> 2 2 58.0
#> 3 4 59.6
#> 4 1 60.9
#> 5 3 66.1
#> 6 5 62.7
to_compare %>%
compare_pairs(Worker, avg_score) %>%
rename(difference = value) %>%
mutate(
across(where(is.numeric), round, 1)
)
#> # A tibble: 15 x 2
#> pair difference
#> <fct> <dbl>
#> 1 1-6 10.3
#> 2 1-4 1.3
#> 3 1-2 2.9
#> 4 2-6 7.4
#> 5 3-6 15.5
#> 6 3-4 6.5
#> 7 3-2 8.1
#> 8 3-1 5.2
#> 9 4-6 9
#> 10 4-2 1.6
#> 11 5-6 12.1
#> 12 5-4 3.1
#> 13 5-3 -3.4
#> 14 5-2 4.7
#> 15 5-1 1.8
Plotting a matrix
ggmatplot() plots the columns of a matrix as individual lines, much
like matplot() in base R.
Here we plot a spline basis matrix for penguin bill length. By default it plots the columns with unique row number as the x-axis.
# Create a 10-column natural spline bases
sorted_lengths <- sort(penguins$bill_length_mm)
length_ns <- splines::ns(sorted_lengths, df = 10)
ggmatplot(length_ns)
Alternatively, you can supply a column number and make it the x axis. In this example, we bind on the original data and use it as the x-axis column. This makes the lines much smoother because the spline basis was built on the bill lengths, not on row numbers.
ggmatplot(cbind(sorted_lengths, length_ns), x_axis_column = 1)
By default, duplicated rows are removed. We can choose to keep them. The
little flat steps along the curve are the repeated rows. We can also
change the number of colors to use. The package also provides
annotate_label_grey() for making labels on ggplot2’s default grey
background.
ggmatplot(length_ns, unique_rows = FALSE, n_colors = 1) +
annotate_label_grey("splines!", 20, .65, size = 5)
Et cetera
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.
seq_along_rows() saves a few keystrokes in for-loops that iterate over
dataframe rows.
cars %>% head(5) %>% seq_along_rows()
#> [1] 1 2 3 4 5
cars %>% head(0) %>% seq_along_rows()
#> integer(0)
is_same_as_last and replace_if_same_as_last() are helpers for
formatting tables. I use them to replace repeating values in a text
column with blanks.
mtcars %>%
tibble::rownames_to_column("name") %>%
slice(1:10) %>%
select(cyl, name, mpg) %>%
arrange(cyl, mpg) %>%
mutate_at(c("cyl"), replace_if_same_as_last, "") %>%
knitr::kable()
| cyl | name | mpg | |:----|:------------------|-----:| | 4 | Datsun 710 | 22.8 | | | Merc 230 | 22.8 | | | Merc 240D | 24.4 | | 6 | Valiant | 18.1 | | | Merc 280 | 19.2 | | | Mazda RX4 | 21.0 | | | Mazda RX4 Wag | 21.0 | | | Hornet 4 Drive | 21.4 | | 8 | Duster 360 | 14.3 | | | Hornet Sportabout | 18.7 |
fct_add_counts() adds counts to a factor’s labels.
# Create a factor with some random counts
set.seed(20190124)
random_penguins <- penguins %>%
dplyr::sample_n(250, replace = TRUE)
table(random_penguins$species)
#>
#> Adelie Chinstrap Gentoo
#> 108 41 101
# Updated factors
random_penguins$species %>% levels()
#> [1] "Adelie" "Chinstrap" "Gentoo"
random_penguins$species %>% fct_add_counts() %>% levels()
#> [1] "Adelie (108)" "Chinstrap (41)" "Gentoo (101)"
You can tweak the format for the first label. I like to use this for plotting by stating the unit next to the first count.
random_penguins$species %>%
fct_add_counts(first_fmt = "{levels} ({counts} penguins)") %>%
levels()
#> [1] "Adelie (108 penguins)" "Chinstrap (41)" "Gentoo (101)"
Behind the scenes, fct_add_counts() uses the function
fct_glue_labels() to construct labels using a
glue-templating
string. Therefore, fct_glue_labels() would be a more appropriate
function for generic relabeling using glue:
random_penguins$species %>%
fct_glue_labels(
fmt = "{tolower(levels)}",
first_fmt = "Species: {tolower(levels)}"
) %>%
levels()
#> [1] "Species: adelie" "chinstrap" "gentoo"
Comparing two sets
When I need to merge two datasets together, I have to go through a
little dance to figure out which elements are in your_data and which
are in my_data. compare_sets() performs all of R’s set operations so
I can skim over the differences.
your_data <- c(1, 2, 3, 3, 4, 5)
my_data <- c(4, 4, 4, 5, 6, 7, 8)
str(compare_sets(your_data, my_data))
#> List of 10
#> $ lengths : Named int [1:9] 6 7 5 5 1 3 3 2 8
#> ..- attr(*, "names")= chr [1:9] "your_data" "my_data" "unique(your_data)" "unique(my_data)" ...
#> $ your_data : num [1:6] 1 2 3 3 4 5
#> $ my_data : num [1:7] 4 4 4 5 6 7 8
#> $ unique(your_data) : num [1:5] 1 2 3 4 5
#> $ unique(my_data) : num [1:5] 4 5 6 7 8
#> $ setequal(your_data, my_data) : logi FALSE
#> $ setdiff(your_data, my_data) : num [1:3] 1 2 3
#> $ setdiff(my_data, your_data) : num [1:3] 6 7 8
#> $ intersect(your_data, my_data): num [1:2] 4 5
#> $ union(your_data, my_data) : num [1:8] 1 2 3 4 5 6 7 8
Jekyll helpers
I also include functions I use to create and maintain my website.
jekyll_create_rmd_draft() creates a post in the _R/_drafts folder.
withr::with_dir(tempdir(), {
dir.create("_R")
dir.create("_R/_drafts")
# Basic use
jekyll_create_rmd_draft(slug = "today-i-learned")
# Accepts a date
jekyll_create_rmd_draft(
slug = "yesterday-i-learned",
date = Sys.Date() - 1
)
# Filler text used if slug is not provided
jekyll_create_rmd_draft()
})
#> Creating file: ./_R/_drafts/2021-04-28-today-i-learned.Rmd
#> Creating file: ./_R/_drafts/2021-04-27-yesterday-i-learned.Rmd
#> Creating file: ./_R/_drafts/2021-04-28-sceptical-joey.Rmd
More involved demos
These are things that I would have used in the demo above but cut and moved down here to keep that overview succinct.
Comparing pairs of values over a posterior distribution
I wrote compare_pairs() to compute posterior differences in Bayesian
models. For the sake of example, let’s fit a Bayesian model of average
bill length for each species in penguins. We could get these estimates
more directly using the default dummy-coding of factors, but let’s
ignore that for now.
library(rstanarm)
#> Loading required package: Rcpp
#> This is rstanarm version 2.21.1
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
m <- stan_glm(
bill_length_mm ~ species - 1,
penguins,
family = gaussian
)
Now, we have a posterior distribution of species means.
newdata <- data.frame(species = unique(penguins$species))
p_means <- posterior_linpred(m, newdata = newdata) %>%
as.data.frame() %>%
tibble::as_tibble() %>%
setNames(newdata$species) %>%
tibble::rowid_to_column("draw") %>%
tidyr::gather(species, mean, -draw) %>%
print()
#> # A tibble: 12,000 x 3
#> draw species mean
#> <int> <chr> <dbl>
#> 1 1 Adelie 38.8
#> 2 2 Adelie 39.0
#> 3 3 Adelie 38.8
#> 4 4 Adelie 38.8
#> 5 5 Adelie 38.8
#> 6 6 Adelie 38.7
#> 7 7 Adelie 38.5
#> 8 8 Adelie 38.8
#> 9 9 Adelie 38.8
#> 10 10 Adelie 38.8
#> # ... with 11,990 more rows
For each posterior sample, we can compute pairwise differences of means
with compare_means().
pair_diffs <- compare_pairs(p_means, species, mean) %>%
print()
#> # A tibble: 12,000 x 3
#> draw pair value
#> <int> <fct> <dbl>
#> 1 1 Chinstrap-Adelie 10.2
#> 2 2 Chinstrap-Adelie 10.2
#> 3 3 Chinstrap-Adelie 10.0
#> 4 4 Chinstrap-Adelie 10.7
#> 5 5 Chinstrap-Adelie 9.22
#> 6 6 Chinstrap-Adelie 10.5
#> 7 7 Chinstrap-Adelie 10.3
#> 8 8 Chinstrap-Adelie 10.2
#> 9 9 Chinstrap-Adelie 9.83
#> 10 10 Chinstrap-Adelie 9.89
#> # ... with 11,990 more rows
library(ggplot2)
ggplot(pair_diffs) +
aes(x = pair, y = value) +
stat_summary(fun.data = median_hilow, geom = "linerange") +
stat_summary(fun.data = median_hilow, fun.args = list(conf.int = .8),
size = 2, geom = "linerange") +
stat_summary(fun.y = median, size = 5, shape = 3, geom = "point") +
labs(x = NULL, y = "Difference in posterior means") +
coord_flip()
#> Warning: `fun.y` is deprecated. Use `fun` instead.
…which should look like the effect ranges in the dummy-coded models.
m2 <- update(m, bill_length_mm ~ species)
m3 <- update(
m,
bill_length_mm ~ species,
data = penguins %>% mutate(species = forcats::fct_rev(species))
)
Give or take a few decimals of precision and give or take changes in signs because of changes in who was subtracted from whom.
# Adelie versus others
m2 %>%
posterior_interval(regex_pars = "species") %>%
round(2)
#> 5% 95%
#> speciesChinstrap 9.32 10.76
#> speciesGentoo 8.12 9.30
# Gentoo versus others
m3 %>%
rstanarm::posterior_interval(regex_pars = "species") %>%
round(2)
#> 5% 95%
#> speciesChinstrap 0.59 2.07
#> speciesAdelie -9.31 -8.12
# differences from compare_pairs()
pair_diffs %>%
tidyr::spread(pair, value) %>%
select(-draw) %>%
as.matrix() %>%
posterior_interval() %>%
round(2)
#> 5% 95%
#> Chinstrap-Adelie 9.31 10.76
#> Gentoo-Chinstrap -2.09 -0.58
#> Gentoo-Adelie 8.09 9.32
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