My library of R functions I sometimes find useful

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List of functions

Hypothesis testing
Data Transformation
Working with test statistics
Plotting-related

Installation

To install the development version from GitHub use the remotes package:

remotes::install_github("lcpilling/lukesRlib")

I periodically “release” a version number once it works as expected. The development version may contain things that do not yet “work”

# To install the latest release, use:
remotes::install_github("lcpilling/lukesRlib@*release")

# To install a specific version (see tags), use:
remotes::install_github("lcepilling/lukesRlib@v0.2.0")

Hypothesis testing

tidy_ci()

tidy_ci() (“tidy with CIs”) runs broom::tidy() and returns the tidy estimates with CIs calculated as EST +/- 1.96*SE (i.e., the Wald method)

Motivation: by default the {broom} package uses confint() to estimate CIs. For GLMs this calculates CIs via the profile likelihood method. When using large datasets this takes a long time and does not meaningfully alter the CIs compared to calculating using 1.96*SE

tidy_ci() does a few other nice things: hides the intercept by default, automatically detects logistic/CoxPH/CRR models and exponentiates the estimates, and if p==0 returns the ‘extreme p’ as a string. Other options include -log10 p-values. See the tidy_ci() Wiki page for more details

Examples

fit_linear = glm(bmi ~ age + sex, data = d)
tidy_ci(fit_linear)
#> Linear model (estimate=coefficient) :: N=449811 :: R2=0.0099
#> # A tibble: 2 x 8
#>   term  estimate std.error statistic   p.value conf.low conf.high p.extreme
#>   <chr>    <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> <chr>    
#> 1 age     0.0196  0.000847     23.1  4.72e-118   0.0179    0.0212 NA       
#> 2 sex     0.703   0.0137       51.4  0           0.676     0.729  9.39e-576

Provided N and R^2 estimate. Calculated “extreme p” where p rounded to 0

library(survival)
fit_coxph = coxph(Surv(time, status) ~ age + sex + as.factor(smoking_status), data = d)
tidy_ci(fit_coxph)
#> CoxPH model (estimate=Hazard Ratio) :: N=449811, Nevents=31025
#> # A tibble: 4 x 8
#>   term             estimate std.error statistic  p.value conf.low conf.high
#>   <chr>               <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 age                 0.995  0.000837     -6.56 5.28e-11    0.993     0.996
#> 2 sex                 1.04   0.0109        3.66 2.52e- 4    1.02      1.06 
#> 3 smoking_status-1    1.04   0.0120        3.26 1.13e- 3    1.02      1.06 
#> 4 smoking_status-2    1.03   0.0149        2.16 3.08e- 2    1.00      1.06

Automatically identified the input as from a coxph model and exponentiated estimate/CIs. Also provided N and Nevents. Also, tidied “as.factor()” variable names.

get_assoc()

get_assoc() (phonetically: “get-a-sock”) makes PheWAS in R easy and fast. It gets the tidy model output for categorical or continuous exposures, from linear, logistic, or CoxPH models. Output includes N and N cases, outcome, and model info. User can provide multiples exposures and outcomes.

Example: Categorical exposure in logistic regression

get_assoc(x="smoking_status", y="chd", z="+age", d=ukb, model="logistic", af=TRUE)
#> A tibble: 3 x 12
#>   outcome exposure         estimate std.error statistic p.value conf.low conf.high     n n_cases model   
#>   <chr>   <chr>               <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl> <dbl>   <dbl> <chr>   
#> 1 chd     smoking_status-0    NA       NA         NA    NA        NA         NA     1073     146 logistic
#> 2 chd     smoking_status-1     1.24     0.126      1.72  0.0852    0.970      1.59   918     180 logistic
#> 3 chd     smoking_status-2     1.48     0.181      2.16  0.0311    1.04       2.11   285      52 logistic

The estimate is the Odds Ratio from a logistic regression model, and n and n_cases show the numbers for each category of the exposure (here af=TRUE i.e., treat as factor), including the reference group.

Example: Multiple exposures on single outcome (i.e., a “PheWAS”)

x_vars = c("bmi","ldl","sbp")
get_assoc(x=x_vars, y="chd", z="+age+sex", d=ukb, model="logistic")
#> # A tibble: 3 x 11
#>   outcome exposure estimate std.error statistic p.value conf.low conf.high     n n_cases model   
#>   <chr>   <chr>       <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl> <int>   <int> <chr>   
#> 1 chd     bmi         1.08    0.0113      6.97  3.1e-12    1.06       1.11  4692     324 logistic
#> 2 chd     ldl         0.980   0.0689     -0.300 7.6e- 1    0.856      1.12  4498     311 logistic
#> 3 chd     sbp         1.01    0.00333     3.53  4.2e- 4    1.01       1.02  4561     316 logistic

Multiple exposures and outcomes can be provided simultaneously. Here, the exposures are continuous.

Example: stratified analyses

res_all     = ukb |> 
                get_assoc(x=x_vars, y="chd", z="+age+sex", model="logistic", note="All")
res_males   = ukb |> 
                filter(sex=="Male") |> 
                get_assoc(x=x_vars, y="chd", z="+age", model="logistic", note="Males")
res_females = ukb |> 
                filter(sex=="Female") |> 
                get_assoc(x=x_vars, y="chd", z="+age", model="logistic", note="Females")

res = list_rbind(list(res_all, res_males, res_females))

Data is first argument. get_assoc() can be on the right-side of other dplyr functions. Stratified models can be therefore easily performed. The note argument means output is labelled and can be combined into a single data frame easily.

Data Transformation

carrec()

From Steve Miller’s package: carrec() (phonetically: “car-wreck”) is a port of car::recode() to avoid clashes in the {car} package. This offers STATA-like recoding features for R.

For example, if a variable of interest is on a 1-10 scale and you want to code values 6 and above to be 1, and code values of 1-5 to be 0, you would do:

x = seq(1, 10)
x
#> [1]  1  2  3  4  5  6  7  8  9 10

carrec(x, "1:5=0;6:10=1")
#> [1] 0 0 0 0 0 1 1 1 1 1

inv_norm()

Inverse (quantile) normalise a quantitative trait (vector) i.e., transform to a normal distribution with mean=0 and sd=1

x_in = inv_norm(x)

df = df |> mutate(x_in = inv_norm(x))

z_trans()

Z-transform a quantitative trait (vector) i.e., convert to mean=0 and sd=1, maintaining original distribution

x_z = z_trans(x)

df = df |> mutate(x_z = z_trans(x))

Working with test statistics

get_se()

Calculate Standard Error from Confidence Intervals.

lci = 0.1
uci = 0.3

# Default denominator is (1.96*2) equivalent to 95% confidence (p<0.05)
get_se(lci, uci)
#>  [1] 0.05102041

# custom denominator e.g., if CIs correspond to a p-value 5*10-8
get_se(lci, uci, denominator=lukesRlib::get_z(5e-8)*2)   
#>  [1] 0.01834421

get_z()

Return a Z-statistic from a given p-value

p = 1e-10
get_z(p)
#>  [1] 6.466951

get_p()

Return a p-value from a z (or t) statistic

z = 10
get_p(z)
#>  [1] 1.523971e-23

get_p_extreme()

Normally R will round numbers < 1*10-324 to zero. This function returns the “extreme p-value” as a string. Provide a z (or t) statistic

z = 50
get_p_extreme(z)
#>  [1] "2.16e-545"

get_p_neglog10()

Returns the -log10 p-value. Provide a z (or t) statistic

z = 50
get_p_neglog10(z)
#>  [1] 544.6653

get_p_neglog10_n()

Returns the -log10 p-value. Provide a z (or t) statistic and n (sample size)

z = 50
n = 100000
get_p_neglog10_n(z, n)
#>  [1] 537.9851

annotate_textp()

Annotate a ggplot2 plot with text. Allows one to specify the relative position of the figure easily. Configurable margin, text and box justification. The added bonus in the following code is that you can specify which facet to annotate with something like facets=data.frame(cat1='blue', cat2='tall').

Function originally by Rosen Matev (from https://stackoverflow.com/questions/22488563/ggplot2-annotate-layer-position-in-r)

To get it aligned nicely in the middle, it may also require adjusting box_just depending on plot width etc. Other options include size and alpha

qplot(1:10,1:10) + annotate_textp('Text annotation\nx=1, y=0, hjust=1', x=1, y=0, hjust=1)
qplot(1:10,1:10) + annotate_textp('Text annotation\nx=0.1, y=0.9, hjust=0', x=0, y=1, hjust=0)
qplot(1:10,1:10) + annotate_textp('Text annotation\nx = 0.5, y=0.5, hjust=0.5\nbox_just=c(0.5,0.5)', x=0.5, y=0.5, hjust=0.5, box_just=c(0.5,0.5))
qplot(1:10,1:10) + annotate_textp('Text annotation\nx = 0.5, y=0.5, hjust=0.5\nbox_just=c(0.5,0.5)\nsize=14, alpha=0.5', x=0.5, y=0.5, hjust=0.5, box_just=c(0.5,0.5), size=13, alpha=0.5)

theme_minimal_modified()

Nice modification to ggplot’s theme_minimal by Albert Rapp (https://alberts-newsletter.beehiiv.com/p/ggplot-theme)

library(tidyverse)
penguins <- palmerpenguins::penguins |> 
  filter(!is.na(sex))

basic_plot <- penguins |> 
  ggplot(aes(x = bill_length_mm, y = body_mass_g, fill = species)) +
  geom_point(shape = 21, size = 5, alpha = 0.85, color = 'grey10') +
  labs(
    x = 'Bill length (in mm)',
    y = element_blank(),
    fill = 'Species',
    title = 'Penguins from the Palmer Archipelago',
    subtitle = 'Penguin weights (in g)',
    caption = 'Data: {palmerpenguins} R package'
  )

basic_plot +
  theme_minimal(base_size = 16, base_family = 'Source Sans Pro') +
  scale_fill_manual(values = c('Adelie'= '#da2c38', 'Chinstrap'= '#FED18C', 'Gentoo'= '#30C5FF')) +  # scale_fill_manual(values = thematic::okabe_ito(6)) +
  theme_minimal_modified()

lukesRlib