In RemPsyc/rempsyc: Convenience Functions for Psychology

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

rempsyc: Convenience functions for psychology

flextable::set_flextable_defaults(background.color = "white")

options(scipen = 999)
library(rempsyc)

R package of convenience functions to make your workflow faster and easier. Easily customizable plots (via ggplot2), nice APA tables exportable to Word (via flextable), easily run statistical tests or check assumptions, and automatize various other tasks. Mostly geared at researchers in the psychological sciences. The package is still under active development. Feel free to open an issue to ask for help, report a bug, or request a feature.

Top 40 new CRAN packages (2022)!

This is one of the most helpful R packages I've used in years! It saves hours of time and patience and is super easy to implement! - Mark (more testimonials)

Installation

You can install the rempsyc package directly from CRAN:

install.packages("rempsyc")

Or the development version from the r-universe (note that there is a 24-hour delay with GitHub):

install.packages("rempsyc", repos = c(
  rempsyc = "https://rempsyc.r-universe.dev",
  CRAN = "https://cloud.r-project.org"))

Or from GitHub, for the very latest version:

# If package `remotes` isn't already installed, install it with `install.packages("remotes")`
remotes::install_github("rempsyc/rempsyc")

You can load the package and open the help file, and click "Index" at the bottom. You will see all the available functions listed.

library(rempsyc)
?rempsyc

Dependencies: Because rempsyc is a package of convenience functions relying on several external packages, it uses (inspired by the easystats packages) a minimalist philosophy of only installing packages that you need when you need them through rlang::check_installed(). Should you wish to specifically install all suggested dependencies at once (you can view the full list by clicking on the CRAN badge on this page), you can run the following (be warned that this may take a long time, as some of the suggested packages are only used in the vignettes or examples):

install.packages("rempsyc", dependencies = TRUE)

Overview

section.1 <- "Nice APA tables"
section.2 <- "T-tests, planned contrasts, regressions, moderations, simple slopes"
section.3 <- "Visualization"
section.4 <- "Utility functions"
section.5 <- "Testing assumptions"
section.6 <- "lavaanExtra"

cute_cat <- function(x, header.level = 1) {
  cat(rep("#", header.level), " ", x, sep = "")
}

cute_TOC <- function(section) {
  cat("[", section, "]",
    "(#", tolower(gsub(" ", "-", gsub(",", "", section))), ")",
    "<a name = '", section, "'/>",
    "\n \n",
    sep = ""
  )
}

invisible(lapply(
  list(
    section.1, section.2, section.3, section.4, section.5, section.6
  ),
  cute_TOC
))

Nice APA tables

`nice_table`

Make nice APA tables easily through a wrapper around the flextable package with sensical defaults and automatic formatting features.

The tables can be opened in Word with print(table, preview ="docx"), or saved to Word with the flextable::save_as_docx function, and are flextable objects, and can be modified as such. The function also integrates with objects from the broom and report packages. Full tutorial: https://rempsyc.remi-theriault.com/articles/table

Note: For a smoother and more integrated presentation flow, this function is now featured along the other functions.

T-tests, planned contrasts, regressions, moderations, simple slopes

`nice_t_test`

Easily compute t-test analyses, with effect sizes, and format in publication-ready format. Supports multiple dependent variables at once. The 95% confidence interval is for the effect size (Cohen's d).

library(rempsyc)

t.tests <- nice_t_test(
  data = mtcars,
  response = c("mpg", "disp", "drat", "wt"),
  group = "am"
)
t.tests

# Format t-test results
t_table <- nice_table(t.tests)
t_table

table_temp <- flextable::autofit(t_table)
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_t_test-1.png",
  expand = 0, res = 300
)

# Open in Word for quick copy-pasting
print(my_table, preview = "docx")

# Or save to Word
flextable::save_as_docx(t_table, path = "D:/R treasures/t_tests.docx")

Full tutorial: https://rempsyc.remi-theriault.com/articles/t-test

`nice_contrasts`

Easily compute regression with planned contrast analyses (pairwise comparisons similar to t-tests but more powerful when more than 2 groups), and format in publication-ready format. Supports multiple dependent variables at once (but supports only three groups for the moment). In this particular case, the confidence intervals are bootstraped around the Cohen's d.

set.seed(100)

contrasts <- nice_contrasts(
  data = mtcars,
  response = c("mpg", "disp"),
  group = "cyl",
  covariates = "hp"
)
contrasts

# Format contrasts results
nice_table(contrasts, highlight = .001)

table_temp <- contrasts |>
  nice_table(highlight = .001) |>
  flextable::autofit()
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_contrasts-1.png",
  expand = 0, res = 300
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/contrasts

`nice_mod`

Easily compute moderation analyses, with effect sizes, and format in publication-ready format. Supports multiple dependent variables and covariates at once.

moderations <- nice_mod(
  data = mtcars,
  response = c("mpg", "disp"),
  predictor = "gear",
  moderator = "wt"
)
moderations

# Format moderation results
nice_table(moderations, highlight = TRUE)

table_temp <- moderations |>
  nice_table(highlight = TRUE) |>
  flextable::autofit()
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_mod-1.png",
  expand = 0, res = 300
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/moderation

`nice_lm`

For more complicated models not supported by nice_mod, one can define the model in the traditional way and feed it to nice_lm instead. Supports multiple lm models as well.

model1 <- lm(mpg ~ cyl + wt * hp, mtcars)
model2 <- lm(qsec ~ disp + drat * carb, mtcars)
mods <- nice_lm(list(model1, model2), standardize = TRUE)
mods

# Format moderation results
nice_table(mods, highlight = TRUE)

table_temp <- mods |>
  nice_table(highlight = TRUE) |>
  flextable::autofit()
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_lm-1.png",
  expand = 0, res = 300
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/moderation

`nice_slopes`

Easily compute simple slopes in moderation analysis, with effect sizes, and format in publication-ready format. Supports multiple dependent variables and covariates at once.

simple.slopes <- nice_slopes(
  data = mtcars,
  response = c("mpg", "disp"),
  predictor = "gear",
  moderator = "wt"
)
simple.slopes

# Format simple slopes results
nice_table(simple.slopes)

table_temp <- simple.slopes |>
  nice_table() |>
  flextable::autofit()
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_slopes-1.png",
  expand = 0, res = 300
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/moderation

`nice_lm_slopes`

For more complicated models not supported by nice_slopes, one can define the model in the traditional way and feed it to nice_lm_slopes instead. Supports multiple lm models as well, but the predictor and moderator need to be the same for these models (the dependent variable can change).

model1 <- lm(mpg ~ gear * wt, mtcars)
model2 <- lm(disp ~ gear * wt, mtcars)
my.models <- list(model1, model2)
simple.slopes <- nice_lm_slopes(my.models, predictor = "gear", moderator = "wt", standardize = TRUE)
simple.slopes

# Format simple slopes results
nice_table(simple.slopes)

table_temp <- simple.slopes |>
  nice_table() |>
  flextable::autofit()
flextable::save_as_image(table_temp,
  path = "man/figures/README-nice_lm_slopes-1.png",
  expand = 0, res = 300
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/moderation

Visualization

All plots can be saved with the ggplot2::ggsave() function. They are ggplot2 objects so can be modified as such.

`nice_violin`

Make nice violin plots easily with 95% bootstrapped confidence intervals.

nice_violin(
  data = ToothGrowth,
  group = "dose",
  response = "len",
  xlabels = c("Low", "Medium", "High"),
  comp1 = 1,
  comp2 = 3,
  has.d = TRUE,
  d.y = 30
)

# Save plot
ggplot2::ggsave("niceplot.pdf",
  width = 7, height = 7, unit = "in",
  dpi = 300, path = "D:/R treasures/"
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/violin

`nice_scatter`

Make nice scatter plots easily.

nice_scatter(
  data = mtcars,
  predictor = "wt",
  response = "mpg",
  has.confband = TRUE,
  has.r = TRUE,
  has.p = TRUE
)

nice_scatter(
  data = mtcars,
  predictor = "wt",
  response = "mpg",
  group = "cyl",
  has.confband = TRUE
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/scatter

`plot_means_over_time`

Make nice plots of means over time, usually for randomized controlled trials with several groups over several time measurements. Error bars represent 95% confidence intervals adjusted for within-subject variance as by the method of Morey (2008).

data <- mtcars
names(data)[6:3] <- paste0("T", 1:4, "_some-time-variable")

plot_means_over_time(
  data = data,
  response = names(data)[6:3],
  group = "cyl",
  groups.order = "decreasing",
  significance_bars_x = c(3.15, 4.15),
  significance_stars = c("*", "***"),
  significance_stars_x = c(3.25, 4.35),
  # significance_stars_y: List with structure: list(c("group1", "group2", time))
  significance_stars_y = list(c("4", "8", time = 3),
                              c("4", "8", time = 4)))

`grouped_bar_chart`

iris2 <- iris
iris2$plant <- c(
  rep("yes", 45),
  rep("no", 45),
  rep("maybe", 30),
  rep("NA", 30)
)
grouped_bar_chart(
  data = iris2,
  response = "plant",
  group = "Species"
)

`overlap_circle`

Interpolating the Inclusion of the Other in the Self Scale (self-other merging) easily.

# Score of 3.5 (25% overlap)
overlap_circle(3.5)

# Score of 6.84 (81.8% overlap)
overlap_circle(6.84)

Full tutorial: https://rempsyc.remi-theriault.com/articles/circles

`cormatrix_excel`

Easily output a correlation matrix and export it to Microsoft Excel, with the first row and column frozen, and correlation coefficients colour-coded based on their effect size (0.0-0.2: small (pink/light blue); 0.2-0.4: medium (orange/blue); 0.4-1.0: large (red/dark blue)).

cormatrix_excel(
  data = infert,
  filename = "cormatrix1",
  select = c(
    "age", "parity", "induced", "case", "spontaneous",
    "stratum", "pooled.stratum"
  )
)

unlink("cormatrix1.xlsx")

Utility functions

`nice_na`

Nicely reports NA values according to existing guidelines (i.e, reporting absolute or percentage of item-based missing values, plus each scale's maximum amount of missing values for a given participant). Accordingly, allows specifying a list of columns representing questionnaire items to produce a questionnaire-based report of missing values.

# Create synthetic data frame for the demonstration
set.seed(50)
df <- data.frame(
  scale1_Q1 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale1_Q2 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale1_Q3 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale2_Q1 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale2_Q2 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale2_Q3 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale3_Q1 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale3_Q2 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA),
  scale3_Q3 = c(sample(c(NA, 1:6), replace = TRUE), NA, NA)
)

# Then select your scales by name
nice_na(df, scales = c("scale1", "scale2", "scale3"))

# Or whole dataframe
nice_na(df)

`extract_duplicates`

Extracts ALL duplicates (including the first one, contrary to duplicated or dplyr::distinct) to a data frame for visual inspection.

df1 <- data.frame(
  id = c(1, 2, 3, 1, 3),
  item1 = c(NA, 1, 1, 2, 3),
  item2 = c(NA, 1, 1, 2, 3),
  item3 = c(NA, 1, 1, 2, 3)
)
df1

extract_duplicates(df1, id = "id")

`best_duplicate`

Extracts the "best" duplicate: the one with the fewer number of missing values (in case of ties, picks the first one).

best_duplicate(df1, id = "id")

`scale_mad`

Scale and center ("standardize") data based on the median and median absolute deviation (MAD).

scale_mad(mtcars$mpg)

`find_mad`

Identify outliers based on (e.g.,) 3 median absolute deviations (MAD) from the median.

find_mad(data = mtcars, col.list = names(mtcars)[c(1:7, 10:11)], criteria = 3)

`winsorize_mad`

Winsorize outliers based on (e.g.,) 3 median absolute deviations (MAD).

winsorize_mad(mtcars$qsec, criteria = 3)

`nice_reverse`

Easily recode scores (reverse-score), typically for questionnaire answers.

# Reverse score of 5 with a maximum score of 5
nice_reverse(5, 5)

# Reverse scores with maximum = 4 and minimum = 0
nice_reverse(1:4, 4, min = 0)

# Reverse scores with maximum = 3 and minimum = -3
nice_reverse(-3:3, 3, min = -3)

`format_value`

Easily format p or r values. Note: converts to character class for use in figures or manuscripts to accommodate e.g., "< .001".

format_p(0.0041231)
format_p(t.tests$p)
format_r(moderations$sr2)
format_d(t.tests$d)

`nice_randomize`

Randomize easily with different designs.

# Specify design, number of conditions, number of participants, and names of conditions:
nice_randomize(
  design = "between", Ncondition = 4, n = 8,
  condition.names = c("BP", "CX", "PZ", "ZL")
)

# Within-Group Design
nice_randomize(
  design = "within", Ncondition = 3, n = 3,
  condition.names = c("SV", "AV", "ST")
)

Full tutorial: https://rempsyc.remi-theriault.com/articles/randomize

Testing assumptions