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README for package sbwtools

Author: S. Bryan West

sbwtools

Development Branch

sbwtools is a package containing my most frequently used custom functions I have either written myself or borrowed from other, more skilled programmers.

System Information

Updated on 2021-05-27 15:22:41

sessioninfo::platform_info()
#>  setting  value                       
#>  version  R version 4.0.3 (2020-10-10)
#>  os       macOS Catalina 10.15.7      
#>  system   x86_64, darwin17.0          
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_US.UTF-8                 
#>  ctype    en_US.UTF-8                 
#>  tz       America/New_York            
#>  date     2021-05-27

Installation

sbwtools is only available from GitHub. Install with:

# install.packages("devtools")
devtools::install_github("sbw78/sbwtools")

Credits

Credit to Kevin Ushey for the functions hh, ht, last, se. He created these functions for his .Rprofile dotfile, which you can find at his github repo.

Credit to Jacob Long for the theme_apa used in make_forest_plot.

Function examples

ci_df

Build a dataframe with the mean and confidence intervals of a continuous variable, with optional grouping.

library(sbwtools)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
ci_data <- iris %>% 
  as_tibble() %>% 
  ci_boot_df(., Petal.Length, Species)

ci_boot_df

Same as ci_df, but with bootstrapped sampling

ci_boot_data <- iris %>% 
  as_tibble() %>% 
  ci_boot_df(., Petal.Length, Species)

custom_kable

Customized knitr::kable output.

custom_kable(ci_data, caption = "Summarized data from iris",
             col.names = c("Species", "n", "Petal Length", "Lower CI", "Upper CI", "sd"))

custom_kable(ci_data, caption = "Summarized data from iris",
             col.names = c("Species", "n", "Petal Length", "Lower CI", "Upper CI", "sd")) %>% 
  kableExtra::footnote(general = "1000 bootstrapped samples")

format_pval

Format p-values (i.e. “< .001” for values less than .001).

fit <- lm(Sepal.Length ~ Species, data = iris)
summary_df <- broom::tidy(fit)

# Without formatting p-value
summary_df
#> # A tibble: 3 x 5
#>   term              estimate std.error statistic   p.value
#>   <chr>                <dbl>     <dbl>     <dbl>     <dbl>
#> 1 (Intercept)          5.01     0.0728     68.8  1.13e-113
#> 2 Speciesversicolor    0.930    0.103       9.03 8.77e- 16
#> 3 Speciesvirginica     1.58     0.103      15.4  2.21e- 32

# Formatted p-value
mutate(summary_df, p.value = format_pval(p.value))
#> # A tibble: 3 x 5
#>   term              estimate std.error statistic p.value
#>   <chr>                <dbl>     <dbl>     <dbl> <chr>  
#> 1 (Intercept)          5.01     0.0728     68.8  <.001  
#> 2 Speciesversicolor    0.930    0.103       9.03 <.001  
#> 3 Speciesvirginica     1.58     0.103      15.4  <.001

hh and ht

hh: Get first 5 rows and first 5 columns of a data frame or matrix

ht: Get the first 10 rows and last 10 rows of a data frame or matrix.

Credit: Kevin Ushey

hh(iris)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa

ht(iris)
#>     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
#> 1            5.1         3.5          1.4         0.2    setosa
#> 2            4.9         3.0          1.4         0.2    setosa
#> 3            4.7         3.2          1.3         0.2    setosa
#> 4            4.6         3.1          1.5         0.2    setosa
#> 5            5.0         3.6          1.4         0.2    setosa
#> 6            5.4         3.9          1.7         0.4    setosa
#> 7            4.6         3.4          1.4         0.3    setosa
#> 8            5.0         3.4          1.5         0.2    setosa
#> 9            4.4         2.9          1.4         0.2    setosa
#> 10           4.9         3.1          1.5         0.1    setosa
#> 141          6.7         3.1          5.6         2.4 virginica
#> 142          6.9         3.1          5.1         2.3 virginica
#> 143          5.8         2.7          5.1         1.9 virginica
#> 144          6.8         3.2          5.9         2.3 virginica
#> 145          6.7         3.3          5.7         2.5 virginica
#> 146          6.7         3.0          5.2         2.3 virginica
#> 147          6.3         2.5          5.0         1.9 virginica
#> 148          6.5         3.0          5.2         2.0 virginica
#> 149          6.2         3.4          5.4         2.3 virginica
#> 150          5.9         3.0          5.1         1.8 virginica

last

Get the last element of a vector or list.

Credit: Kevin Ushey

last(letters)
#> [1] "z"

make_forest_plot

Build a forest plot.

Credit: Jacob Long, author of the jtools package (github) from which this function borrows the ggplot2 theme theme_apa().

p <- make_forest_plot(ci_data, mean_Petal.Length, Species, xlab = "Mean petal length",
                 ylab = "Species", ci_low, ci_high)

p

regex_build

Combine a character vector into a single regex list.

regex_build(c("apples", "oranges", "bananas"))
#> [1] "\\bapples\\b|\\boranges\\b|\\bbananas\\b"
regex_build(c("apples", "passion fruit", "oranges"), modifier = "multi")
#> [1] "\\s*?\\bapples\\b\\s*?|\\s*?\\bpassion fruit\\b\\s*?|\\s*?\\boranges\\b\\s*?"

se

Calculate standard error.

Credit: Kevin Ushey

se(iris$Sepal.Length)
#> [1] 0.06761132