README

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

rempsyc: Convenience functions for psychology

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.

Installation

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"))

# 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

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
#>   Dependent Variable         t       df              p         d   CI_lower
#> 1                mpg -3.767123 18.33225 0.001373638333 -1.477947 -2.2659732
#> 2               disp  4.197727 29.25845 0.000230041299  1.445221  0.6417836
#> 3               drat -5.646088 27.19780 0.000005266742 -2.003084 -2.8592770
#> 4                 wt  5.493905 29.23352 0.000006272020  1.892406  1.0300224
#>     CI_upper
#> 1 -0.6705684
#> 2  2.2295594
#> 3 -1.1245499
#> 4  2.7329219

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

# 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")

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.

contrasts <- nice_contrasts(
  data = mtcars,
  response = c("mpg", "disp"),
  group = "cyl",
  covariates = "hp"
)
contrasts
#>   Dependent Variable  Comparison df         t              p         d
#> 1                mpg cyl4 - cyl6 28  3.640418 0.001092088865  2.147244
#> 2                mpg cyl4 - cyl8 28  3.663188 0.001028617005  3.587739
#> 3                mpg cyl6 - cyl8 28  1.290359 0.207480642577  1.440495
#> 4               disp cyl4 - cyl6 28 -2.703423 0.011534398020 -1.514296
#> 5               disp cyl4 - cyl8 28 -6.040561 0.000001640986 -4.803022
#> 6               disp cyl6 - cyl8 28 -4.861413 0.000040511099 -3.288726
#>     CI_lower   CI_upper
#> 1  1.3531871  3.1223071
#> 2  2.7156109  4.4756393
#> 3  0.8435009  1.9939088
#> 4 -2.2636521 -0.8826532
#> 5 -5.8560355 -3.7464170
#> 6 -4.2833778 -2.2040887

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

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
#>   Model Number Dependent Variable Predictor df           B          t
#> 1            1                mpg      gear 28 -0.08718042 -0.7982999
#> 2            1                mpg        wt 28 -0.94959988 -8.6037724
#> 3            1                mpg   gear:wt 28 -0.23559962 -2.1551077
#> 4            2               disp      gear 28 -0.07488985 -0.6967831
#> 5            2               disp        wt 28  0.83273987  7.6662883
#> 6            2               disp   gear:wt 28 -0.08758665 -0.8140664
#>                   p         sr2     CI_lower   CI_upper
#> 1 0.431415645312884 0.004805465 0.0000000000 0.02702141
#> 2 0.000000002383144 0.558188818 0.3142326391 0.80214500
#> 3 0.039899695159515 0.035022025 0.0003502202 0.09723370
#> 4 0.491683361920264 0.003546038 0.0000000000 0.02230154
#> 5 0.000000023731710 0.429258143 0.1916386492 0.66687764
#> 6 0.422476456495512 0.004840251 0.0000000000 0.02679265

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

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
#>   Model Number Dependent Variable Predictor df          B          t
#> 1            1                mpg       cyl 27 -0.1082286 -0.7180977
#> 2            1                mpg        wt 27 -0.6230206 -5.7013627
#> 3            1                mpg        hp 27 -0.2874898 -2.4045781
#> 4            1                mpg     wt:hp 27  0.2875867  3.2329593
#> 5            2               qsec      disp 27 -0.4315891 -1.9746464
#> 6            2               qsec      drat 27 -0.3337401 -1.5296603
#> 7            2               qsec      carb 27 -0.5092480 -3.3234897
#> 8            2               qsec drat:carb 27 -0.2307906 -1.0825727
#>                p         sr2     CI_lower   CI_upper
#> 1 0.478865160370 0.002159615 0.0000000000 0.01306786
#> 2 0.000004663587 0.136134000 0.0218243033 0.25044370
#> 3 0.023318649649 0.024215142 0.0002421514 0.06327779
#> 4 0.003221753406 0.043773344 0.0004377334 0.09898662
#> 5 0.058616844828 0.070256689 0.0000000000 0.19796621
#> 6 0.137733654712 0.042159840 0.0000000000 0.14133523
#> 7 0.002563609014 0.199020425 0.0019902043 0.40691582
#> 8 0.288572032972 0.021116556 0.0000000000 0.09136014

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

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
#>   Model Number Dependent Variable Predictor (+/-1 SD) df           B          t
#> 1            1                mpg       gear (LOW-wt) 28  0.14841920  1.0767040
#> 2            1                mpg      gear (MEAN-wt) 28 -0.08718042 -0.7982999
#> 3            1                mpg      gear (HIGH-wt) 28 -0.32278004 -1.9035367
#> 4            2               disp       gear (LOW-wt) 28  0.01269680  0.0935897
#> 5            2               disp      gear (MEAN-wt) 28 -0.07488985 -0.6967831
#> 6            2               disp      gear (HIGH-wt) 28 -0.16247650 -0.9735823
#>            p           sr2 CI_lower    CI_upper
#> 1 0.29080233 0.00874170174        0 0.038860523
#> 2 0.43141565 0.00480546484        0 0.027021406
#> 3 0.06729622 0.02732283901        0 0.081796622
#> 4 0.92610159 0.00006397412        0 0.002570652
#> 5 0.49168336 0.00354603816        0 0.022301536
#> 6 0.33860037 0.00692298820        0 0.033253212

# Format simple slopes results
nice_table(simple.slopes)

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
#>   Model Number Dependent Variable Predictor (+/-1 SD) df           B          t
#> 1            1                mpg       gear (LOW-wt) 28  0.14841920  1.0767040
#> 2            1                mpg      gear (MEAN-wt) 28 -0.08718042 -0.7982999
#> 3            1                mpg      gear (HIGH-wt) 28 -0.32278004 -1.9035367
#> 4            2               disp       gear (LOW-wt) 28  0.01269680  0.0935897
#> 5            2               disp      gear (MEAN-wt) 28 -0.07488985 -0.6967831
#> 6            2               disp      gear (HIGH-wt) 28 -0.16247650 -0.9735823
#>            p           sr2 CI_lower    CI_upper
#> 1 0.29080233 0.00874170174        0 0.038860523
#> 2 0.43141565 0.00480546484        0 0.027021406
#> 3 0.06729622 0.02732283901        0 0.081796622
#> 4 0.92610159 0.00006397412        0 0.002570652
#> 5 0.49168336 0.00354603816        0 0.022301536
#> 6 0.33860037 0.00692298820        0 0.033253212

# Format simple slopes results
nice_table(simple.slopes)

Visualization

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

nice_violin

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/"
)

nice_scatter

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
)

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)

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"
  )
)
#> # Correlation Matrix (pearson-method)
#> 
#> Parameter      |      age |   parity |  induced |     case | spontaneous |  stratum | pooled.stratum
#> ----------------------------------------------------------------------------------------------------
#> age            |          |     0.08 |    -0.10 | 3.53e-03 |       -0.08 | -0.21*** |        -0.17**
#> parity         |     0.08 |          |  0.45*** | 8.91e-03 |     0.31*** | -0.31*** |           0.12
#> induced        |    -0.10 |  0.45*** |          |     0.02 |    -0.27*** |    -0.10 |          0.16*
#> case           | 3.53e-03 | 8.91e-03 |     0.02 |          |     0.36*** | 3.83e-03 |       4.86e-03
#> spontaneous    |    -0.08 |  0.31*** | -0.27*** |  0.36*** |             |     0.06 |        0.21***
#> stratum        | -0.21*** | -0.31*** |    -0.10 | 3.83e-03 |        0.06 |          |        0.75***
#> pooled.stratum |  -0.17** |     0.12 |    0.16* | 4.86e-03 |     0.21*** |  0.75*** |               
#> 
#> p-value adjustment method: none
#> 
#> 
#>  [Correlation matrix 'cormatrix1.xlsx' has been saved to working directory (or where specified).]
#> NULL

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"))
#>                   var items na cells na_percent na_max na_max_percent all_na
#> 1 scale1_Q1:scale1_Q3     3  6    27      22.22      3            100      2
#> 2 scale2_Q1:scale2_Q3     3  9    27      33.33      3            100      2
#> 3 scale3_Q1:scale3_Q3     3  8    27      29.63      3            100      2
#> 4               Total     9 23    81      28.40      9            100      2

# Or whole dataframe
nice_na(df)
#>                   var items na cells na_percent na_max na_max_percent all_na
#> 1 scale1_Q1:scale3_Q3     9 23    81       28.4      9            100      2

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
#>   id item1 item2 item3
#> 1  1    NA    NA    NA
#> 2  2     1     1     1
#> 3  3     1     1     1
#> 4  1     2     2     2
#> 5  3     3     3     3

extract_duplicates(df1, id = "id")
#>   Row id item1 item2 item3 count_na
#> 1   1  1    NA    NA    NA        3
#> 2   4  1     2     2     2        0
#> 3   3  3     1     1     1        0
#> 4   5  3     3     3     3        0

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")
#> (2 duplicates removed)
#>   id item1 item2 item3
#> 1  1     2     2     2
#> 2  2     1     1     1
#> 3  3     1     1     1

scale_mad

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

scale_mad(mtcars$mpg)
#>  [1]  0.33262558  0.33262558  0.66525116  0.40654238 -0.09239599 -0.20327119
#>  [7] -0.90548075  0.96091834  0.66525116  0.00000000 -0.25870878 -0.51741757
#> [13] -0.35110478 -0.73916796 -1.62616950 -1.62616950 -0.83156395  2.43925425
#> [19]  2.06967028  2.71644224  0.42502157 -0.68373036 -0.73916796 -1.09027273
#> [25]  0.00000000  1.49681511  1.25658552  2.06967028 -0.62829276  0.09239599
#> [31] -0.77612635  0.40654238

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)
#> 2 outlier(s) based on 3 median absolute deviations for variable(s): 
#>  mpg, cyl, disp, hp, drat, wt, qsec, gear, carb 
#> 
#> Outliers per variable: 
#> 
#> $qsec
#>   Row qsec_mad
#> 1   9 3.665557
#> 
#> $carb
#>   Row carb_mad
#> 1  31 4.046945

winsorize_mad

winsorize_mad(mtcars$qsec, criteria = 3)
#>  [1] 16.46000 17.02000 18.61000 19.44000 17.02000 20.22000 15.84000 20.00000
#>  [9] 21.95765 18.30000 18.90000 17.40000 17.60000 18.00000 17.98000 17.82000
#> [17] 17.42000 19.47000 18.52000 19.90000 20.01000 16.87000 17.30000 15.41000
#> [25] 17.05000 18.90000 16.70000 16.90000 14.50000 15.50000 14.60000 18.60000

nice_reverse

# Reverse score of 5 with a maximum score of 5
nice_reverse(5, 5)
#> [1] 1

# Reverse scores with maximum = 4 and minimum = 0
nice_reverse(1:4, 4, min = 0)
#> [1] 3 2 1 0

# Reverse scores with maximum = 3 and minimum = -3
nice_reverse(-3:3, 3, min = -3)
#> [1]  3  2  1  0 -1 -2 -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)
#> [1] ".004"
format_p(t.tests$p)
#> [1] ".001"   "< .001" "< .001" "< .001"
format_r(moderations$sr2)
#> [1] ".00" ".56" ".04" ".00" ".43" ".00"
format_d(t.tests$d)
#> [1] "-1.48" "1.45"  "-2.00" "1.89"

nice_randomize

# 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")
)
#>   id Condition
#> 1  1        ZL
#> 2  2        BP
#> 3  3        PZ
#> 4  4        CX
#> 5  5        CX
#> 6  6        PZ
#> 7  7        BP
#> 8  8        ZL

# Within-Group Design
nice_randomize(
  design = "within", Ncondition = 3, n = 3,
  condition.names = c("SV", "AV", "ST")
)
#>   id    Condition
#> 1  1 SV - AV - ST
#> 2  2 AV - ST - SV
#> 3  3 AV - SV - ST

Testing assumptions

nice_assumptions

# Create regression model
model <- lm(mpg ~ wt * cyl + gear, data = mtcars)
# View results
View(nice_assumptions(model))

nice_normality

nice_normality(
  data = iris,
  variable = "Sepal.Length",
  group = "Species",
  grid = FALSE,
  shapiro = TRUE,
  histogram = TRUE
)

plot_outliers

Visually check outliers based on (e.g.) +/- 3 MAD (median absolute deviations) or SD (standard deviations).

plot_outliers(airquality,
  group = "Month",
  response = "Ozone"
)


plot_outliers(airquality,
  response = "Ozone",
  method = "sd"
)

nice_var

Obtain variance per group as well as check for the rule of thumb of one group having variance four times bigger than any of the other groups.

nice_var(
  data = iris,
  variable = "Sepal.Length",
  group = "Species"
)
#>        Species Setosa Versicolor Virginica Variance.ratio Criteria
#> 1 Sepal.Length  0.124      0.266     0.404            3.3        4
#>   Heteroscedastic
#> 1           FALSE

nice_varplot

nice_varplot(
  data = iris,
  variable = "Sepal.Length",
  group = "Species"
)

lavaanExtra

For an alternative, vector-based syntax to lavaan (a latent variable analysis/structural equation modeling package), as well as other convenience functions such as naming paths and defining indirect links automatically, see my other package, lavaanExtra.

Support me and this package

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

rempsyc: Convenience functions for psychology

Installation

Overview

Nice APA tables

`nice_table`

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

`nice_t_test`

`nice_contrasts`

`nice_mod`

`nice_lm`

`nice_slopes`

`nice_lm_slopes`

Visualization

`nice_violin`

`nice_scatter`

`plot_means_over_time`

`grouped_bar_chart`

`overlap_circle`

`cormatrix_excel`

Utility functions

`nice_na`

`extract_duplicates`

`best_duplicate`

`scale_mad`

`find_mad`

`winsorize_mad`

`nice_reverse`

`format_value`

`nice_randomize`

Testing assumptions

`nice_assumptions`

`nice_normality`

`plot_outliers`

`nice_var`

`nice_varplot`

`lavaanExtra`

Support me and this package