Update to 0.2.7

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lukesRlib
 Title: My library of R functions I sometimes find useful
-Version: 0.2.6.9000
+Version: 0.2.7
 Authors@R: c(person("Luke", "Pilling", 
                     email = "L.Pilling@exeter.ac.uk", 
                     role = c("aut", "cre"),

NEWS.md

@@ -1,9 +1,10 @@
-# lukesRlib 0.2.6.9000 (13th Dec 2023)
+# lukesRlib 0.2.7 (29th Jan 2024)
 
 * `get_assoc()` - add `return_all_terms` argument. Default is false. If TRUE, adds a new col `terms` and returns the estimates for all independent variables in the models.
 * `get_assoc()` - remove `subset_d` argument
 * `get_assoc()` - fixed if exposure or outcome needs to be protected by backticks (previously failed if variable was something like "bmi-1")
 * `get_assoc()` - fixed problem where covariate estimates were also included in certain situations (e.g., if x="age" do not get covariate "percentage")
+* Update examples for many functions
 
 # lukesRlib 0.2.6 (13 Nov 2023)
 

R/annotate_textp.R

@@ -18,31 +18,32 @@
 #' @param alpha Numeric. Transparency (0:1). Default=NA
 #'
 #' @examples
-#' 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=14, alpha=0.5)
+#' p1 = ggplot2::ggplot(data.frame(x=1:10,y=1:10), ggplot2::aes(x,y)) + ggplot2::geom_point()
+#' p1 + annotate_textp('Text annotation\nx=1, y=0, hjust=1', x=1, y=0, hjust=1)
+#' p1 + annotate_textp('Text annotation\nx=0.1, y=0.9, hjust=0', x=0, y=1, hjust=0)
+#' p1 + 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))
+#' p1 + 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=14, alpha=0.5)
 #'
 #' @export
 #'
 
 annotate_textp = function(label, x, y, facets=NULL, hjust=0, vjust=0, color='black', alpha=NA,
                           family=thm$text$family, size=thm$text$size, fontface=1, lineheight=1.0,
-                          box_just=ifelse(c(x,y)<0.5,0,1), margin=unit(size/2, 'pt'), thm=theme_get()) {
+                          box_just=ifelse(c(x,y)<0.5,0,1), margin=unit(size/2, 'pt'), thm=ggplot2::theme_get()) {
   x = scales::squish_infinite(x)
   y = scales::squish_infinite(y)
   data = if (is.null(facets)) data.frame(x=NA) else data.frame(x=NA, facets)
 
   tg = grid::textGrob(
     label, x=0, y=0, hjust=hjust, vjust=vjust,
-    gp=grid::gpar(col=alpha(color, alpha), fontsize=size, fontfamily=family, fontface=fontface, lineheight=lineheight)
+    gp=grid::gpar(col=scales::alpha(color, alpha), fontsize=size, fontfamily=family, fontface=fontface, lineheight=lineheight)
   )
   ts = grid::unit.c(grid::grobWidth(tg), grid::grobHeight(tg))
   vp = grid::viewport(x=x, y=y, width=ts[1], height=ts[2], just=box_just)
   tg = grid::editGrob(tg, x=ts[1]*hjust, y=ts[2]*vjust, vp=vp)
   inner = grid::grobTree(tg, vp=grid::viewport(width=unit(1, 'npc')-margin*2, height=unit(1, 'npc')-margin*2))
 
-  layer(
+  ggplot2::layer(
     data = NULL,
     stat = StatIdentity,
     position = PositionIdentity,

R/gg_themes.R

@@ -12,23 +12,23 @@
 #'
 #' @examples
 #' penguins = palmerpenguins::penguins |> 
-#'   filter(!is.na(sex))
+#'   dplyr::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(
+#'   ggplot2::ggplot(ggplot2::aes(x = bill_length_mm, y = body_mass_g, fill = species)) +
+#'   ggplot2::geom_point(shape = 21, size = 5, alpha = 0.85, color = 'grey10') +
+#'   ggplot2::labs(
 #'     x = 'Bill length (in mm)',
-#'     y = element_blank(),
+#'     y = ggplot2::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')) +
+#'   ggplot2::theme_minimal(base_size = 16, base_family = 'Source Sans Pro') +
+#'   ggplot2::scale_fill_manual(values = c('Adelie'= '#da2c38', 'Chinstrap'= '#FED18C', 'Gentoo'= '#30C5FF')) +
 #'   theme_minimal_modified()
 #'
 #' @export

R/p_values.R

@@ -13,7 +13,6 @@
 #' @examples
 #' z = 10
 #' get_p(z)
-#' #>  [1] 1.523971e-23
 #'
 #' @export
 
@@ -40,7 +39,6 @@ get_p = function(z) {
 #' @examples
 #' p = 1e-10
 #' get_z(p)
-#' #>  [1] 6.466951
 #'
 #' @export
 
@@ -68,7 +66,6 @@ get_z = function(p,
 #' @examples
 #' z = 50
 #' get_p_extreme(z)
-#' #>  [1] "2.16e-545"
 #'
 #' @export
 #'
@@ -100,7 +97,6 @@ get_p_extreme = function(z)  {
 #' @examples
 #' z = 50
 #' get_p_neglog10(z)
-#' #>  [1] 544.0977
 #'
 #' @export
 #'
@@ -132,7 +128,6 @@ get_p_neglog10 = function(z,
 #' z = 50
 #' n = 100000
 #' get_p_neglog10_n(z, n)
-#' #>  [1] 537.9851
 #'
 #' @export
 #'
@@ -167,7 +162,6 @@ get_p_neglog10_n = function(z,
 #' lci = 0.1
 #' uci = 0.3
 #' get_se(lci, uci)
-#' #>  [1] 0.05102041
 #'
 #' @export
 

R/transform.R

@@ -11,7 +11,9 @@
 #' @param x a numeric vector to be inverse normalized
 #'
 #' @examples
-#' inv_norm(x)
+#' summary(example_data$sbp)
+#' sbp_in = inv_norm(example_data$sbp)
+#' summary(sbp_in)
 #'
 #' @export
 #'
@@ -36,7 +38,9 @@ inv_norm = function(x) {
 #' @param x a numeric vector to be z-transformed
 #'
 #' @examples
-#' z_trans(x)
+#' summary(example_data$sbp)
+#' sbp_in = z_trans(example_data$sbp)
+#' summary(sbp_in)
 #'
 #' @export
 #'

README.md

@@ -4,7 +4,7 @@
 My library of R functions I sometimes find useful
 
 <!-- badges: start -->
-[![](https://img.shields.io/badge/version-0.2.6.9000-informational.svg)](https://github.com/lukepilling/lukesRlib)
+[![](https://img.shields.io/badge/version-0.2.7-informational.svg)](https://github.com/lukepilling/lukesRlib)
 [![](https://img.shields.io/github/last-commit/lukepilling/lukesRlib.svg)](https://github.com/lukepilling/lukesRlib/commits/master)
 [![](https://img.shields.io/badge/lifecycle-experimental-orange)](https://www.tidyverse.org/lifecycle/#experimental)
 [![DOI](https://zenodo.org/badge/590063045.svg)](https://zenodo.org/badge/latestdoi/590063045)
@@ -14,18 +14,9 @@ My library of R functions I sometimes find useful
 
 ## List of functions
   - [Hypothesis testing](#hypothesis-testing)
-    - [tidy_ci()](#tidy_ci)
-    - [get_assoc()](#get_assoc)
   - [Data Transformation](#data-transformation)
-    - [carrec()](#carrec)
-    - [inv_norm()](#inv_norm)
-    - [z_trans()](#z_trans)
   - [Working with test statistics](#working-with-test-statistics)
-    - [get_se()](#get_se), [get_z()](#get_z), [get_p()](#get_p)
-    - [get_p_extreme()](#get_p_extreme), [get_p_neglog10()](#get_p_neglog10), [get_p_neglog10_n()](#get_p_neglog10_n)
   - [Plotting-related](#plotting-related)
-    - [annotate_textp()](#annotate_textp)
-    - [theme_minimal_modified()](#theme_minimal_modified)
 
 ## Installation
 To install the development version from GitHub use the `remotes` package:
@@ -88,37 +79,23 @@ Automatically identified the input as from a coxph model and exponentiated estim
 
 ### get_assoc()
 
-`get_assoc()` (phonetically: "get-a-sock") makes PheWAS in R easy and fast, utilizing the {purrr} `map2()` function. It gets the tidy model output for categorical or continuous exposures, from linear, logistic, or CoxPH models: includes N and N cases, outcome, and model info. User can provide multiples exposures and outcomes. 
+`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. 
 
-See the [`get_assoc()` Wiki](https://github.com/lukepilling/lukesRlib/wiki/get_assoc()) details 
+#### Example: Categorical exposure in logistic regression
 
-#### Examples
-
-##### Get tidy model output for an exposure/outcome combination
-```r
-get_assoc(x="height", y="weight", z="+age+sex", d=ukb)
-#> A tibble: 1 x 10
-#>   outcome exposure estimate std.error statistic   p.value conf.low conf.high     n model
-#>   <chr>   <chr>       <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl> <int> <chr>
-#> 1 weight  height      0.762    0.0296      25.7 3.36e-137    0.704     0.820  4981 lm
-```
-
-The above example is equivalent to `tidy_ci(glm(weight~height+age+sex, data=ukb))` with added `n`
-
-##### Categorical exposure in logistic regression - stratified analysis
 ```r
-get_assoc(x="smoking_status", y="chd", z="+age", d=ukb |> filter(sex==1), model="logistic", af=TRUE, note="Males")
+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    note 
-#>   <chr>   <chr>               <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl> <dbl>   <dbl> <chr>    <chr>
-#> 1 chd     smoking_status-0    NA       NA         NA    NA        NA         NA     1073     146 logistic Males
-#> 2 chd     smoking_status-1     1.24     0.126      1.72  0.0852    0.970      1.59   918     180 logistic Males
-#> 3 chd     smoking_status-2     1.48     0.181      2.16  0.0311    1.04       2.11   285      52 logistic Males
+#>   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
 ```
 
-In the above example, the `estimate` is the Odds Ratio from a logistic regression model. The `n` and `n_cases` are directly from the model object and reflect those included in the model after excluding missing participants. The exposure is categorical, and a reference category line has been added to include the N and Ncases for that group. 
+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. 
 
-##### Multiple exposures on single outcome (i.e., a "PheWAS")
+#### Example: Multiple exposures on single outcome (i.e., a "PheWAS")
 ```r
 x_vars = c("bmi","ldl","sbp")
 get_assoc(x=x_vars, y="chd", z="+age+sex", d=ukb, model="logistic")
@@ -130,9 +107,9 @@ get_assoc(x=x_vars, y="chd", z="+age+sex", d=ukb, model="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.
+Multiple exposures and outcomes can be provided simultaneously. Here, the exposures are continuous.
 
-##### Tidyverse formatted. Data is first argument. `get_assoc()` can be on the right-side of other `dplyr` functions
+#### Example: stratified analyses
 ```r
 res_all     = ukb |> 
                 get_assoc(x=x_vars, y="chd", z="+age+sex", model="logistic", note="All")
@@ -146,6 +123,7 @@ res_females = ukb |>
 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