Package 'apaTables' reference manual

Title:	Create American Psychological Association (APA) Style Tables
Description:	A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) and latex code containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user.
Authors:	David Stanley [aut, cre]
Maintainer:	David Stanley <[email protected]>
License:	MIT License + file LICENSE
Version:	3.0.0
Built:	2025-01-26 04:57:06 UTC
Source:	https://github.com/dstanley4/apatables

album data from textbook

Description

A data set from Field et al (2012)

Usage

data(album)
data(album)

Format

A data frame with 200 rows and 4 variables:

adverts: Amount spent of adverts, thousands of pounds
sales: Album sales in thousands
airplay: Number of times songs from album played on radio week prior to release
attract: Attractiveness rating of band members

Source

https://studysites.sagepub.com/dsur/study/

References

Field, A., Miles, J., & Field, Z. (2012) Discovering Statistics Using R. Sage: Chicago.

Creates a table of means and standard deviations for a 1-way ANOVA design in APA style

Description

Creates a table of means and standard deviations for a 1-way ANOVA design in APA style

Usage

apa.1way.table(
  iv,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = FALSE,
  landscape = FALSE
)
apa.1way.table(
  iv,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = FALSE,
  landscape = FALSE
)

Arguments

`iv`	Name of independent variable column in data frame
`dv`	Name of dependent variable column in data frame
`data`	Project data frame name
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`show.conf.interval`	(TRUE/FALSE) Display confidence intervals in table.
`landscape`	(TRUE/FALSE) Make RTF file landscape

Value

APA table object

Examples

# Example 1: 1-way from Field et al. (2012) Discovery Statistics Using R

table1 <- apa.1way.table(iv = dose, dv = libido,
               data = viagra, table.number = 1)

apa.save(filename = "table1.doc", table1)

# Create a table for your PDF
# Include the line below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}
# Example 1: 1-way from Field et al. (2012) Discovery Statistics Using R

table1 <- apa.1way.table(iv = dose, dv = libido,
               data = viagra, table.number = 1)

apa.save(filename = "table1.doc", table1)

# Create a table for your PDF
# Include the line below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}

Creates a table of means and standard deviations for a 2-way ANOVA design in APA style

Description

Creates a table of means and standard deviations for a 2-way ANOVA design in APA style

Usage

apa.2way.table(
  iv1,
  iv2,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = FALSE,
  show.marginal.means = FALSE,
  landscape = TRUE
)
apa.2way.table(
  iv1,
  iv2,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = FALSE,
  show.marginal.means = FALSE,
  landscape = TRUE
)

Arguments

`iv1`	Name of independent variable 1 column in data frame
`iv2`	Name of independent variable 2 column in data frame
`dv`	Name of dependent variable column in data frame
`data`	Project data frame name
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`show.conf.interval`	(TRUE/FALSE) Display confidence intervals in table. Negates show.marginal.means = TRUE.
`show.marginal.means`	(TRUE/FALSE) Show marginal means in output. Only used if show.conf.interval = FALSE.
`landscape`	(TRUE/FALSE) Make RTF file landscape

Value

APA table object

Examples

# Example 2: 2-way from Fidler & Thompson (2001)

table2 <- apa.2way.table(iv1 = a, iv2 = b, dv = dv,
                          data = fidler_thompson,
                          landscape = TRUE,
                          table.number = 2)


# Example 3: 2-way from Field et al. (2012) Discovery Statistics Using R

table3 <- apa.2way.table(iv1 = gender, iv2 = alcohol, dv = attractiveness,
                         data = goggles, table.number = 3)


# Save both Table 2 and Table 3 in a single .doc document
apa.save(filename = "my_tables.doc", table2, table3)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)

# delete demo file
if (file.exists("my_tables.doc")) {
     file.remove("my_tables.doc")
}
# Example 2: 2-way from Fidler & Thompson (2001)

table2 <- apa.2way.table(iv1 = a, iv2 = b, dv = dv,
                          data = fidler_thompson,
                          landscape = TRUE,
                          table.number = 2)


# Example 3: 2-way from Field et al. (2012) Discovery Statistics Using R

table3 <- apa.2way.table(iv1 = gender, iv2 = alcohol, dv = attractiveness,
                         data = goggles, table.number = 3)


# Save both Table 2 and Table 3 in a single .doc document
apa.save(filename = "my_tables.doc", table2, table3)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)

# delete demo file
if (file.exists("my_tables.doc")) {
     file.remove("my_tables.doc")
}

Creates a fixed-effects ANOVA table in APA style

Description

Creates a fixed-effects ANOVA table in APA style

Usage

apa.aov.table(
  lm_output,
  filename,
  table.number = 0,
  conf.level = 0.9,
  type = 3
)
apa.aov.table(
  lm_output,
  filename,
  table.number = 0,
  conf.level = 0.9,
  type = 3
)

Arguments

`lm_output`	Regression (i.e., lm) result objects. Typically, one for each block in the regression.
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`conf.level`	Level of confidence for interval around partial eta-squared (.90 or .95). A value of .90 is the default, this helps to create consistency between the CI overlapping with zero and conclusions based on the p-value.
`type`	Sum of Squares Type. Type II or Type III; specify, 2 or 3, respectively. Default value is 3.

Value

APA table object

References

Smithson, M. (2001). Correct confidence intervals for various regression effect sizes and parameters: The importance of noncentral distributions in computing intervals. Educational and Psychological Measurement, 61(4), 605-632.

Fidler, F., & Thompson, B. (2001). Computing correct confidence intervals for ANOVA fixed-and random-effects effect sizes. Educational and Psychological Measurement, 61(4), 575-604.

Examples


#Example 1: 1-way from Field et al. (2012) Discovery Statistics Using R
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(libido ~ dose, data = viagra)
table1 <- apa.aov.table(lm_output, table.number = 4)


# Example 2: 2-way from Fidler & Thompson (2001)
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(dv ~ a*b, data = fidler_thompson)
table2 <- apa.aov.table(lm_output, table.number = 5)


#Example 3: 2-way from Field et al. (2012) Discovery Statistics Using R
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(attractiveness ~ gender*alcohol, data = goggles)
table3 <- apa.aov.table(lm_output, table.number = 6)


# Save all three table in the same .doc document
apa.save(filename = "my_tables.doc", table1, table2, table3)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)


# delete demo file
if (file.exists("my_tables.doc")) {
     file.remove("my_tables.doc")
}

#Example 1: 1-way from Field et al. (2012) Discovery Statistics Using R
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(libido ~ dose, data = viagra)
table1 <- apa.aov.table(lm_output, table.number = 4)


# Example 2: 2-way from Fidler & Thompson (2001)
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(dv ~ a*b, data = fidler_thompson)
table2 <- apa.aov.table(lm_output, table.number = 5)


#Example 3: 2-way from Field et al. (2012) Discovery Statistics Using R
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(attractiveness ~ gender*alcohol, data = goggles)
table3 <- apa.aov.table(lm_output, table.number = 6)


# Save all three table in the same .doc document
apa.save(filename = "my_tables.doc", table1, table2, table3)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)


# delete demo file
if (file.exists("my_tables.doc")) {
     file.remove("my_tables.doc")
}

Creates a correlation table in APA style with means and standard deviations

Description

Creates a correlation table in APA style with means and standard deviations

Usage

apa.cor.table(
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = TRUE,
  show.sig.stars = TRUE,
  show.pvalue = TRUE,
  landscape = TRUE
)
apa.cor.table(
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = TRUE,
  show.sig.stars = TRUE,
  show.pvalue = TRUE,
  landscape = TRUE
)

Arguments

`data`	Project data frame
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`show.conf.interval`	(TRUE/FALSE) Display confidence intervals in table. This argument is deprecated and will be removed from later versions.
`show.sig.stars`	(TRUE/FALSE) Display stars for significance in table.
`show.pvalue`	(TRUE/FALSE) Display p-value in table.
`landscape`	(TRUE/FALSE) Make RTF file landscape

Value

APA table object

Examples

# View top few rows of attitude data set
head(attitude)

# Use apa.cor.table function
table1 <- apa.cor.table(attitude)


# Save Table 1 in a .doc document
apa.save(filename = "table1.doc", table1)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}
# View top few rows of attitude data set
head(attitude)

# Use apa.cor.table function
table1 <- apa.cor.table(attitude)


# Save Table 1 in a .doc document
apa.save(filename = "table1.doc", table1)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}

Creates a d-values for all paired comparisons in APA style

Description

Creates a d-values for all paired comparisons in APA style

Usage

apa.d.table(
  iv,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = TRUE,
  landscape = TRUE
)
apa.d.table(
  iv,
  dv,
  data,
  filename = NA,
  table.number = 0,
  show.conf.interval = TRUE,
  landscape = TRUE
)

Arguments

`iv`	Name of independent variable column in data frame for all paired comparisons
`dv`	Name of dependent variable column in data frame for all paired comparisons
`data`	Project data frame name
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`show.conf.interval`	(TRUE/FALSE) Display confidence intervals in table. This argument is deprecated and will be removed from later versions.
`landscape`	(TRUE/FALSE) Make RTF file landscape

Value

APA table object

Examples

# View top few rows of viagra data set from Discovering Statistics Using R
head(viagra)

# Use apa.d.table function
table1 <- apa.d.table(iv = dose, dv = libido, data = viagra)


# Save Table 1 in a .doc document
apa.save(filename = "table1.doc", table1)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}
# View top few rows of viagra data set from Discovering Statistics Using R
head(viagra)

# Use apa.d.table function
table1 <- apa.d.table(iv = dose, dv = libido, data = viagra)


# Save Table 1 in a .doc document
apa.save(filename = "table1.doc", table1)


# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)

# delete demo file
if (file.exists("table1.doc")) {
     file.remove("table1.doc")
}

Creates an ANOVA table in APA style based output of ezANOVA command from ez package

Description

Creates an ANOVA table in APA style based output of ezANOVA command from ez package

Usage

apa.ezANOVA.table(
  ez.output,
  correction = "GG",
  table.title = "",
  filename,
  table.number = 0
)
apa.ezANOVA.table(
  ez.output,
  correction = "GG",
  table.title = "",
  filename,
  table.number = 0
)

Arguments

`ez.output`	Output object from ezANOVA command from ez package
`correction`	Type of sphercity correction: "none", "GG", or "HF" corresponding to none, Greenhouse-Geisser and Huynh-Feldt, respectively.
`table.title`	String containing text for table title
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line

Value

APA table object

Examples

if  (requireNamespace("ez", quietly = TRUE)){
if  (requireNamespace("apaTables", quietly = TRUE)){
if  (requireNamespace("tidyr", quietly = TRUE)){


#
# ** Example 1: Between Participant Predictors
#

goggles <- apaTables::goggles

# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
goggles_results <- ez::ezANOVA(data = goggles,
                          dv = attractiveness,
                          between = .(gender, alcohol),
                          participant ,
                          detailed = TRUE)


# Make APA table - save after all 3 examples
goggles_table <- apa.ezANOVA.table(goggles_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(goggles_table)


#
# ** Example 2: Within Participant Predictors
#

drink_attitude_wide <- apaTables::drink_attitude_wide

# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.

drink_attitude_long <- tidyr::pivot_longer(drink_attitude_wide,
                              cols = beer_positive:water_neutral,
                             names_to = c("drink", "imagery"),
                             names_sep = "_",
                              values_to = "attitude")


drink_attitude_long$drink <- as.factor(drink_attitude_long$drink)
drink_attitude_long$imagery <- as.factor(drink_attitude_long$imagery)


# Set contrasts to match Field et al. (2012) textbook output

alcohol_vs_water <- c(1, 1, -2)
beer_vs_wine <- c(-1, 1, 0)
negative_vs_other <- c(1, -2, 1)
positive_vs_neutral <- c(-1, 0, 1)
contrasts(drink_attitude_long$drink) <- cbind(alcohol_vs_water, beer_vs_wine)
contrasts(drink_attitude_long$imagery) <- cbind(negative_vs_other, positive_vs_neutral)


# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
drink_attitude_results <- ez::ezANOVA(data = drink_attitude_long,
                   dv = .(attitude), wid = .(participant),
                   within = .(drink, imagery),
                   type = 3, detailed = TRUE)


# Make APA table - save after all 3 examples
drink_table <- apa.ezANOVA.table(drink_attitude_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(drink_table)


#
# ** Example 3: Between and Within Participant Predictors
#

dating_wide <- apaTables::dating_wide

# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.


dating_long <- tidyr::pivot_longer(dating_wide,
                              cols = attractive_high:ugly_none,
                             names_to = c("looks", "personality"),
                             names_sep = "_",
                              values_to = "date_rating")
#'
dating_long$looks <- as.factor(dating_long$looks)
dating_long$personality <- as.factor(dating_long$personality)


# Set contrasts to match Field et al. (2012) textbook output

some_vs_none <- c(1, 1, -2)
hi_vs_av <- c(1, -1, 0)
attractive_vs_ugly <- c(1, 1, -2)
attractive_vs_average <- c(1, -1, 0)
contrasts(dating_long$personality) <- cbind(some_vs_none, hi_vs_av)
contrasts(dating_long$looks) <- cbind(attractive_vs_ugly, attractive_vs_average)


# Use ezANOVA

options(digits = 10)
dating_results <-ez::ezANOVA(data = dating_long, dv = .(date_rating),
                         wid = .(participant),
                         between = .(gender),
                         within = .(looks, personality),
                         type = 3, detailed = TRUE)


# Make APA table - save after all 3 examples
dating_table <- apa.ezANOVA.table(dating_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(dating_table)


#
# Saving all three tables
#
apa.save("tables_ezANOVA.doc",
                goggles_table,
                 drink_table,
                dating_table)

# delete demo file
if (file.exists("tables_ezANOVA.doc")) {
     file.remove("tables_ezANOVA.doc")
}
}}}
if  (requireNamespace("ez", quietly = TRUE)){
if  (requireNamespace("apaTables", quietly = TRUE)){
if  (requireNamespace("tidyr", quietly = TRUE)){


#
# ** Example 1: Between Participant Predictors
#

goggles <- apaTables::goggles

# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
goggles_results <- ez::ezANOVA(data = goggles,
                          dv = attractiveness,
                          between = .(gender, alcohol),
                          participant ,
                          detailed = TRUE)


# Make APA table - save after all 3 examples
goggles_table <- apa.ezANOVA.table(goggles_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(goggles_table)


#
# ** Example 2: Within Participant Predictors
#

drink_attitude_wide <- apaTables::drink_attitude_wide

# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.

drink_attitude_long <- tidyr::pivot_longer(drink_attitude_wide,
                              cols = beer_positive:water_neutral,
                             names_to = c("drink", "imagery"),
                             names_sep = "_",
                              values_to = "attitude")


drink_attitude_long$drink <- as.factor(drink_attitude_long$drink)
drink_attitude_long$imagery <- as.factor(drink_attitude_long$imagery)


# Set contrasts to match Field et al. (2012) textbook output

alcohol_vs_water <- c(1, 1, -2)
beer_vs_wine <- c(-1, 1, 0)
negative_vs_other <- c(1, -2, 1)
positive_vs_neutral <- c(-1, 0, 1)
contrasts(drink_attitude_long$drink) <- cbind(alcohol_vs_water, beer_vs_wine)
contrasts(drink_attitude_long$imagery) <- cbind(negative_vs_other, positive_vs_neutral)


# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
drink_attitude_results <- ez::ezANOVA(data = drink_attitude_long,
                   dv = .(attitude), wid = .(participant),
                   within = .(drink, imagery),
                   type = 3, detailed = TRUE)


# Make APA table - save after all 3 examples
drink_table <- apa.ezANOVA.table(drink_attitude_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(drink_table)


#
# ** Example 3: Between and Within Participant Predictors
#

dating_wide <- apaTables::dating_wide

# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.


dating_long <- tidyr::pivot_longer(dating_wide,
                              cols = attractive_high:ugly_none,
                             names_to = c("looks", "personality"),
                             names_sep = "_",
                              values_to = "date_rating")
#'
dating_long$looks <- as.factor(dating_long$looks)
dating_long$personality <- as.factor(dating_long$personality)


# Set contrasts to match Field et al. (2012) textbook output

some_vs_none <- c(1, 1, -2)
hi_vs_av <- c(1, -1, 0)
attractive_vs_ugly <- c(1, 1, -2)
attractive_vs_average <- c(1, -1, 0)
contrasts(dating_long$personality) <- cbind(some_vs_none, hi_vs_av)
contrasts(dating_long$looks) <- cbind(attractive_vs_ugly, attractive_vs_average)


# Use ezANOVA

options(digits = 10)
dating_results <-ez::ezANOVA(data = dating_long, dv = .(date_rating),
                         wid = .(participant),
                         between = .(gender),
                         within = .(looks, personality),
                         type = 3, detailed = TRUE)


# Make APA table - save after all 3 examples
dating_table <- apa.ezANOVA.table(dating_results)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(dating_table)


#
# Saving all three tables
#
apa.save("tables_ezANOVA.doc",
                goggles_table,
                 drink_table,
                dating_table)

# delete demo file
if (file.exists("tables_ezANOVA.doc")) {
     file.remove("tables_ezANOVA.doc")
}
}}}

Create output for papaja

Description

Create output for papaja

Usage

apa.knit.table.for.papaja(table_object, latex_font_size = "footnotesize")
apa.knit.table.for.papaja(table_object, latex_font_size = "footnotesize")

Arguments

`table_object`	Previously constructed apaTable object
`latex_font_size`	A strijg that indicates normalsize, small, footnotesize, scriptsize

Value

Save status

Create latex output for an apaTables object

Description

Create latex output for an apaTables object

Usage

apa.knit.table.for.pdf(
  table_object,
  table_note = NULL,
  table_title = NULL,
  line_spacing = 1
)
apa.knit.table.for.pdf(
  table_object,
  table_note = NULL,
  table_title = NULL,
  line_spacing = 1
)

Arguments

`table_object`	Previously constructed apaTable object
`table_note`	Replace default table note with this text
`table_title`	Replace default table title with this text
`line_spacing`	Line spacing multiplier for table

Value

Save status

Creates a regresion table in APA style

Description

Creates a regresion table in APA style

Usage

apa.reg.table(..., filename = NA, table.number = 0, prop.var.conf.level = 0.95)
apa.reg.table(..., filename = NA, table.number = 0, prop.var.conf.level = 0.95)

Arguments

`...`	Regression (i.e., lm) result objects. Typically, one for each block in the regression.
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`prop.var.conf.level`	Level of confidence (.90 or .95, default .95) for interval around sr2, R2, and Delta R2. Use of .90 confidence level helps to create consistency between the CI overlapping with zero and conclusions based on the p-value for that block (or block difference).

Value

APA table object

References

sr2 and delta R2 confidence intervals calculated via:

Alf Jr, E. F., & Graf, R. G. (1999). Asymptotic confidence limits for the difference between two squared multiple correlations: A simplified approach. Psychological Methods, 4(1), 70.

Note that Algina, Keselman, & Penfield (2008) found this approach can under some circumstances lead to inaccurate CIs on proportion of variance values. You might consider using the Algina, Keselman, & Penfield (2008) approach via the apa.reg.boot.table function

Examples

# View top few rows of goggles data set
# from Discovering Statistics Using R
head(album)

# Single block example
blk1 <- lm(sales ~ adverts + airplay, data=album)
apa.reg.table(blk1)
table1 <- apa.reg.table(blk1,table.number = 1)

# Two block example, more than two blocks can be used
blk1 <- lm(sales ~ adverts, data=album)
blk2 <- lm(sales ~ adverts + airplay + attract, data=album)
table2 <- apa.reg.table(blk1, blk2, table.number = 2)

# Interaction product-term test with blocks
blk1 <- lm(sales ~ adverts + airplay, data=album)
blk2 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
table3 <- apa.reg.table(blk1, blk2, table.number = 3)

# Interaction product-term test with blocks and additional product terms
blk1<-lm(sales ~ adverts + airplay, data=album)
blk2<-lm(sales ~ adverts + airplay + I(adverts*adverts) + I(airplay*airplay), data=album)
blk3<-lm(sales~adverts+airplay+I(adverts*adverts)+I(airplay*airplay)+I(adverts*airplay),data=album)
table4 <- apa.reg.table(blk1,blk2,blk3, table.number = 4)

#Interaction product-term test with single regression (i.e., semi-partial correlation focus)
blk1 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
table5 <- apa.reg.table(blk1, table.number = 5)

# Save Table 1 in a .doc document
apa.save(filename = "regression_tables.doc",
         table1,
         table2,
         table3,
         table4,
         table5)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)
apa.knit.table.for.pdf(table4)
apa.knit.table.for.pdf(table5)

# delete demo file
if (file.exists("regression_tables.doc")) {
     file.remove("regression_tables.doc")
}
# View top few rows of goggles data set
# from Discovering Statistics Using R
head(album)

# Single block example
blk1 <- lm(sales ~ adverts + airplay, data=album)
apa.reg.table(blk1)
table1 <- apa.reg.table(blk1,table.number = 1)

# Two block example, more than two blocks can be used
blk1 <- lm(sales ~ adverts, data=album)
blk2 <- lm(sales ~ adverts + airplay + attract, data=album)
table2 <- apa.reg.table(blk1, blk2, table.number = 2)

# Interaction product-term test with blocks
blk1 <- lm(sales ~ adverts + airplay, data=album)
blk2 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
table3 <- apa.reg.table(blk1, blk2, table.number = 3)

# Interaction product-term test with blocks and additional product terms
blk1<-lm(sales ~ adverts + airplay, data=album)
blk2<-lm(sales ~ adverts + airplay + I(adverts*adverts) + I(airplay*airplay), data=album)
blk3<-lm(sales~adverts+airplay+I(adverts*adverts)+I(airplay*airplay)+I(adverts*airplay),data=album)
table4 <- apa.reg.table(blk1,blk2,blk3, table.number = 4)

#Interaction product-term test with single regression (i.e., semi-partial correlation focus)
blk1 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
table5 <- apa.reg.table(blk1, table.number = 5)

# Save Table 1 in a .doc document
apa.save(filename = "regression_tables.doc",
         table1,
         table2,
         table3,
         table4,
         table5)

# Create a table for your PDF
# Include the lines below in your rmarkdown or Quarto document
apa.knit.table.for.pdf(table1)
apa.knit.table.for.pdf(table2)
apa.knit.table.for.pdf(table3)
apa.knit.table.for.pdf(table4)
apa.knit.table.for.pdf(table5)

# delete demo file
if (file.exists("regression_tables.doc")) {
     file.remove("regression_tables.doc")
}

Save previously constructed APA table objects in a single .doc file

Description

Save previously constructed APA table objects in a single .doc file

Usage

apa.save(filename, ..., paper = "us")
apa.save(filename, ..., paper = "us")

Arguments

`filename`	Filename (e.g., my.tables.doc)
`...`	apaTable objects to be saved
`paper`	Use "us" or "a4". Default is "us".

Value

Save status

Examples

library(apaTables)

table1 <- apa.1way.table(iv = dose, dv = libido,
               data = viagra, table.number = 1)

table2 <- apa.2way.table(iv1 = gender, iv2 = alcohol,
                         dv = attractiveness,
                         data = goggles, table.number = 1)

apa.save(filename = "my.tables.doc", table1, table2)

# delete demo file
if (file.exists("my.tables.doc")) {
     file.remove("my.tables.doc")
}
library(apaTables)

table1 <- apa.1way.table(iv = dose, dv = libido,
               data = viagra, table.number = 1)

table2 <- apa.2way.table(iv1 = gender, iv2 = alcohol,
                         dv = attractiveness,
                         data = goggles, table.number = 1)

apa.save(filename = "my.tables.doc", table1, table2)

# delete demo file
if (file.exists("my.tables.doc")) {
     file.remove("my.tables.doc")
}

Create American Psychological Association (APA) Style Tables

Description

A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) and latex code containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user. Examples are provided in this documentation and at http://www.StatsCanBeFun.com.

Details

Bugs and feature requests can be reported at: https://github.com/dstanley4/apaTables/issues

Tutorial at: https://dstanley4.github.io/apaTables/articles/apaTables.html

Currently, the following tables can be created:

Correlation tables - Correlation tables (with confidence intervals and descriptive statistics) are created from data frames using apa.cor.table.
Single "block" regression tables - Single "block" regression tables are created from a regression object using apa.reg.table.
Multiple "block" regression tables - Multiple "block" regression tables are created from regression objects using apa.reg.table.
ANOVA tables - An ANOVA F-table can be created via apa.aov.table from a regression object (i.e. lm output or aov output). Cell mean/standard deviation tables for 1- and 2-way designs are created from data frames using apa.1way.table and apa.2way.table.
ezANOVA tables from ez package - An ANOVA F-table from ezANOVA output can be created via apa.ezANOVA.table.
Standardized mean difference (i.e., d-value) tables (with confidence intervals and descriptive statistics) illustrating all possible paired comparisons using a single independent variable are created from data frames using apa.d.table.

Package:	apaTables
Type:	Package
Version:	3.0.0
Date:	2023-06-29
License:	MIT

Author(s)

Author:	David J. Stanley [email protected]
Maintainer:	David J. Stanley [email protected]

dating data from textbook

Description

A data set from Field et al (2012)

Usage

data(dating_wide)
data(dating_wide)

Format

A data frame with 20 rows and 11 columns. Gender is a between subjects variable. Looks and Personality are within subject variables. Both gender and particpant are factors.

participant: Factor: Participant ID number
gender: Factor: Gender: Male/Female
attractive_high: Date rating where looks=attractive and personality=high
average_high: Date rating where looks=average and personality=high
ugly_high: Date rating where looks=ugly and personality=high
attractive_some: Date rating where looks=attractive and personality=some
average_some: Date rating where looks=average and personality=some
ugly_some: Date rating where looks=ugly and personality=some
attractive_none: Date rating where looks=attractive and personality=none
average_none: Date rating where looks=average and personality=none
ugly_none: Date rating where looks=ugly and personality=none

Source

https://studysites.sagepub.com/dsur/study/

References

Field, A., Miles, J., & Field, Z. (2012) Discovering Statistics Using R. Sage: Chicago.

drink attitude data from textbook

Description

A data set from Field et al (2012)

Usage

data(drink_attitude_wide)
data(drink_attitude_wide)

Format

A data frame with 20 rows and 10 columns. Drink and Imagery are within subject variables. Particpant is a factor.

participant: Factor: Participant ID number
beer_positive: Attitude where drink=beer and imagery=positive
beer_negative: Attitude where drink=beer and imagery=negative
beer_neutral: Attitude where drink=beer and imagery=neutral
wine_positive: Attitude where drink=wine and imagery=positive
wine_negative: Attitude where drink=wine and imagery=negative
wine_neutral: Attitude where drink=wine and imagery=neutral
water_positive: Attitude where drink=water and imagery=positive
water_negative: Attitude where drink=water and imagery=negative
water_neutral: Attitude where drink=water and imagery=neutral

Source

https://studysites.sagepub.com/dsur/study/

References

Field, A., Miles, J., & Field, Z. (2012) Discovering Statistics Using R. Sage: Chicago.

Eysenck data

Description

A data set from Howell (2012)

Usage

data(Eysenck)
data(Eysenck)

Format

A data frame with 100 rows and 3 variables:

Age: Young or Old
Condition: Experimental learning condition
Recall: Level of word recall

Source

https://www.uvm.edu/~statdhtx/methods8/DataFiles/Tab13-2.dat

References

Howell, D. (2012). Statistical methods for psychology. Cengage Learning.

Fidler & Thompson (2001) Fixed-Effects ANOVA data

Description

A data set from Fidler & Thompson (2001)

Usage

data(fidler_thompson)
data(fidler_thompson)

Format

A data frame with 24 rows and 3 variables:

a: Independent variable: a
b: Independent variable: b
dv: Dependent variable: dv

References

Fidler, F. & Thompson, B. (2001). Computing correct confidence intervals for ANOVA fixed- and random-effects effect sizes. Educational and Psychological Measurement, 61, 575-604.

Calculates confidence interval for partial eta-squared in a fixed-effects ANOVA

Description

Calculates confidence interval for partial eta-squared in a fixed-effects ANOVA

Usage

get.ci.partial.eta.squared(F.value, df1, df2, conf.level = 0.9)
get.ci.partial.eta.squared(F.value, df1, df2, conf.level = 0.9)

Arguments

`F.value`	The F-value for the fixed-effect
`df1`	Degrees of freedom for the fixed-effect
`df2`	Degrees of freedom error
`conf.level`	Confidence level (0 to 1). For partial eta-squared a confidence level of .90 is traditionally used rather than .95.

Value

List with confidence interval values (LL and UL)

Examples

# Smithson (2001) p. 619
get.ci.partial.eta.squared(F.value=6.00, df1=1, df2=42, conf.level=.90)
get.ci.partial.eta.squared(F.value=2.65, df1=6, df2=42, conf.level=.90)
get.ci.partial.eta.squared(F.value=2.60, df1=6, df2=42, conf.level=.90)

# Fidler & Thompson (2001) Fixed Effects 2x4 p. 594 (Table 6) / p. 596 (Table 8)
get.ci.partial.eta.squared(F.value=1.50, df1=1, df2=16, conf.level=.90)
get.ci.partial.eta.squared(F.value=4.00, df1=3, df2=16, conf.level=.90)
get.ci.partial.eta.squared(F.value=1.50, df1=3, df2=16, conf.level=.90)
# Smithson (2001) p. 619
get.ci.partial.eta.squared(F.value=6.00, df1=1, df2=42, conf.level=.90)
get.ci.partial.eta.squared(F.value=2.65, df1=6, df2=42, conf.level=.90)
get.ci.partial.eta.squared(F.value=2.60, df1=6, df2=42, conf.level=.90)

# Fidler & Thompson (2001) Fixed Effects 2x4 p. 594 (Table 6) / p. 596 (Table 8)
get.ci.partial.eta.squared(F.value=1.50, df1=1, df2=16, conf.level=.90)
get.ci.partial.eta.squared(F.value=4.00, df1=3, df2=16, conf.level=.90)
get.ci.partial.eta.squared(F.value=1.50, df1=3, df2=16, conf.level=.90)

goggles data from textbook

Description

A data set from Field et al (2012)

Usage

data(goggles)
data(goggles)

Format

A data frame with 48 rows and 3 variables:

participant: Participant identification number
gender: Gender of participant
alcohol: Amount alcohol consumed
attractiveness: Perceived attractiveness

Source

https://studysites.sagepub.com/dsur/study/

References

Field, A., Miles, J., & Field, Z. (2012) Discovering Statistics Using R. Sage: Chicago.

viagra data from textbook

Description

A data set from Field et al (2012)

Usage

data(viagra)
data(viagra)

Format

A data frame with 15 rows and 2 variables:

dose: Level of viagra dose
libido: Libido after taking viagra

Source

https://studysites.sagepub.com/dsur/study/

References

Field, A., Miles, J., & Field, Z. (2012) Discovering Statistics Using R. Sage: Chicago.

Package 'apaTables'

Help Index

album data from textbook

Description

Usage

Format

Source

References

Creates a table of means and standard deviations for a 1-way ANOVA design in APA style

Description

Usage

Arguments

Value

Examples

Creates a table of means and standard deviations for a 2-way ANOVA design in APA style

Description

Usage

Arguments

Value

Examples

Creates a fixed-effects ANOVA table in APA style

Description

Usage

Arguments

Value

References

Examples

Creates a correlation table in APA style with means and standard deviations

Description

Usage

Arguments

Value

Examples

Creates a d-values for all paired comparisons in APA style

Description

Usage

Arguments

Value

Examples

Creates an ANOVA table in APA style based output of ezANOVA command from ez package

Description

Usage

Arguments

Value

Examples

Create output for papaja

Description

Usage

Arguments

Value

Create latex output for an apaTables object

Description

Usage

Arguments

Value

Creates a regresion table in APA style

Description

Usage

Arguments

Value

References

Examples

Save previously constructed APA table objects in a single .doc file

Description

Usage

Arguments

Value

Examples

Create American Psychological Association (APA) Style Tables

Description

Details

Author(s)

dating data from textbook

Description

Usage

Format

Source

References

drink attitude data from textbook

Description