Title: Create Publication Quality Tables and Plots
Version: 1.0.0
Description: Create publication quality plots and tables for Item Response Theory and Classical Test theory based item analysis, exploratory and confirmatory factor analysis.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
BugReports: https://github.com/masiraji/tabledown/issues
Language: en-US
Depends: R (≥ 2.10)
Imports: psych, MOTE, stats, dplyr, tibble, magrittr, data.table, tidyselect, lavaan, mirt, ggplot2, plotly, kutils, tidyr
Suggests: rmarkdown, knitr, markdown, spelling, testthat (≥ 3.0.0)
URL: https://masiraji.github.io/tabledown/
NeedsCompilation: no
Packaged: 2024-05-02 13:27:14 UTC; mushfiqulanwarsiraji
Author: Mushfiqul Anwar Siraji ORCID iD [aut, cre]
Maintainer: Mushfiqul Anwar Siraji <siraji1993@gmail.com>
Repository: CRAN
Date/Publication: 2024-05-02 13:40:03 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Structural Validity data of FFMQ

Description

This is the structural validation data of Bangla Five Facet Mindfulness Questionnaire

Usage

FFMQ.CFA

Format

A data frame with 277 rows and 47 variables:

ID

double COLUMN_DESCRIPTION

Gender

character COLUMN_DESCRIPTION

Education

character COLUMN_DESCRIPTION

Education Years

double COLUMN_DESCRIPTION

Income

double COLUMN_DESCRIPTION

Profession

character COLUMN_DESCRIPTION

Marital Status

character COLUMN_DESCRIPTION

Social_status

double COLUMN_DESCRIPTION

item1

double COLUMN_DESCRIPTION

item2

double COLUMN_DESCRIPTION

Ritem3

double COLUMN_DESCRIPTION

item4

double COLUMN_DESCRIPTION

Ritem5

double COLUMN_DESCRIPTION

item6

double COLUMN_DESCRIPTION

item7

double COLUMN_DESCRIPTION

Ritem8

double COLUMN_DESCRIPTION

item9

double COLUMN_DESCRIPTION

Ritem10

double COLUMN_DESCRIPTION

item11

double COLUMN_DESCRIPTION

Ritem12

double COLUMN_DESCRIPTION

Ritem13

double COLUMN_DESCRIPTION

Ritem14

double COLUMN_DESCRIPTION

item15

double COLUMN_DESCRIPTION

Ritem16

double COLUMN_DESCRIPTION

Ritem17

double COLUMN_DESCRIPTION

Ritem18

double COLUMN_DESCRIPTION

item19

double COLUMN_DESCRIPTION

item20

double COLUMN_DESCRIPTION

item21

double COLUMN_DESCRIPTION

Ritem22

double COLUMN_DESCRIPTION

Ritem23

double COLUMN_DESCRIPTION

item24

double COLUMN_DESCRIPTION

Ritem25

double COLUMN_DESCRIPTION

item26

double COLUMN_DESCRIPTION

item27

double COLUMN_DESCRIPTION

Ritem28

double COLUMN_DESCRIPTION

item29

double COLUMN_DESCRIPTION

Ritem30

double COLUMN_DESCRIPTION

item31

double COLUMN_DESCRIPTION

item32

double COLUMN_DESCRIPTION

item33

double COLUMN_DESCRIPTION

Ritem34

double COLUMN_DESCRIPTION

Ritem35

double COLUMN_DESCRIPTION

item36

double COLUMN_DESCRIPTION

item37

double COLUMN_DESCRIPTION

Ritem38

double COLUMN_DESCRIPTION

Ritem39

double COLUMN_DESCRIPTION

Source

https://github.com/masiraji/tabledown/tree/main/data-raw

Correlational based Valididity evidence of FFMQ

Description

Correlational based Valididity evidence of Bangla FFMQ

Usage

FFMQ.Val

Format

A data frame with 255 rows and 106 variables:

id

double COLUMN_DESCRIPTION

Age

double COLUMN_DESCRIPTION

Gender

double COLUMN_DESCRIPTION

Education Years

double COLUMN_DESCRIPTION

Profession

character COLUMN_DESCRIPTION

Marital Status

character COLUMN_DESCRIPTION

Social_Status

double COLUMN_DESCRIPTION

item1

double COLUMN_DESCRIPTION

item2

double COLUMN_DESCRIPTION

Ritem3

double COLUMN_DESCRIPTION

item4

double COLUMN_DESCRIPTION

Ritem5

double COLUMN_DESCRIPTION

item6

double COLUMN_DESCRIPTION

item7

double COLUMN_DESCRIPTION

Ritem8

double COLUMN_DESCRIPTION

item9

double COLUMN_DESCRIPTION

Ritem10

double COLUMN_DESCRIPTION

item11

double COLUMN_DESCRIPTION

Ritem12

double COLUMN_DESCRIPTION

Ritem13

double COLUMN_DESCRIPTION

Ritem14

double COLUMN_DESCRIPTION

item15

double COLUMN_DESCRIPTION

Ritem16

double COLUMN_DESCRIPTION

Ritem17

double COLUMN_DESCRIPTION

Ritem18

double COLUMN_DESCRIPTION

item19

double COLUMN_DESCRIPTION

item20

double COLUMN_DESCRIPTION

item21

double COLUMN_DESCRIPTION

Ritem22

double COLUMN_DESCRIPTION

Ritem23

double COLUMN_DESCRIPTION

item24

double COLUMN_DESCRIPTION

Ritem25

double COLUMN_DESCRIPTION

item26

double COLUMN_DESCRIPTION

item27

double COLUMN_DESCRIPTION

Ritem28

double COLUMN_DESCRIPTION

item29

double COLUMN_DESCRIPTION

Ritem30

double COLUMN_DESCRIPTION

item31

double COLUMN_DESCRIPTION

item32

double COLUMN_DESCRIPTION

item33

double COLUMN_DESCRIPTION

Ritem34

double COLUMN_DESCRIPTION

Ritem35

double COLUMN_DESCRIPTION

item36

double COLUMN_DESCRIPTION

item37

double COLUMN_DESCRIPTION

Ritem38

double COLUMN_DESCRIPTION

Ritem39

double COLUMN_DESCRIPTION

EI1

character COLUMN_DESCRIPTION

EI2

character COLUMN_DESCRIPTION

EI3

character COLUMN_DESCRIPTION

EI4

character COLUMN_DESCRIPTION

EI5

character COLUMN_DESCRIPTION

EI6

character COLUMN_DESCRIPTION

EI7

character COLUMN_DESCRIPTION

EI8

character COLUMN_DESCRIPTION

EI9

character COLUMN_DESCRIPTION

EI10

character COLUMN_DESCRIPTION

EI11

character COLUMN_DESCRIPTION

EI12

character COLUMN_DESCRIPTION

EI13

character COLUMN_DESCRIPTION

EI14

character COLUMN_DESCRIPTION

EI15

character COLUMN_DESCRIPTION

EI16

character COLUMN_DESCRIPTION

EI17

character COLUMN_DESCRIPTION

EI18

character COLUMN_DESCRIPTION

EI19

character COLUMN_DESCRIPTION

EI20

character COLUMN_DESCRIPTION

EI21

character COLUMN_DESCRIPTION

EI22

character COLUMN_DESCRIPTION

EI23

character COLUMN_DESCRIPTION

EI24

character COLUMN_DESCRIPTION

EI25

character COLUMN_DESCRIPTION

EI26

character COLUMN_DESCRIPTION

EI27

character COLUMN_DESCRIPTION

EI28

character COLUMN_DESCRIPTION

EI29

character COLUMN_DESCRIPTION

EI30

character COLUMN_DESCRIPTION

EI31

character COLUMN_DESCRIPTION

EI32

character COLUMN_DESCRIPTION

EI33

character COLUMN_DESCRIPTION

EI34

character COLUMN_DESCRIPTION

O1

character COLUMN_DESCRIPTION

O2

character COLUMN_DESCRIPTION

O3

character COLUMN_DESCRIPTION

O4

character COLUMN_DESCRIPTION

O5

character COLUMN_DESCRIPTION

O6

character COLUMN_DESCRIPTION

O7

character COLUMN_DESCRIPTION

O8

character COLUMN_DESCRIPTION

O9

character COLUMN_DESCRIPTION

O10

character COLUMN_DESCRIPTION

E1

character COLUMN_DESCRIPTION

E2

character COLUMN_DESCRIPTION

E3

character COLUMN_DESCRIPTION

E4

character COLUMN_DESCRIPTION

E5

character COLUMN_DESCRIPTION

E6

character COLUMN_DESCRIPTION

E7

character COLUMN_DESCRIPTION

E8

character COLUMN_DESCRIPTION

N1

character COLUMN_DESCRIPTION

N2

character COLUMN_DESCRIPTION

N3

character COLUMN_DESCRIPTION

N4

character COLUMN_DESCRIPTION

N5

character COLUMN_DESCRIPTION

N6

character COLUMN_DESCRIPTION

N7

character COLUMN_DESCRIPTION

N8

character COLUMN_DESCRIPTION

Source

https://github.com/masiraji/tabledown/tree/main/data-raw

Gantt Data

Description

Demo project breakdown to create Gantt

Usage

Gantt

Format

A data frame with 25 rows and 4 variables:

wp

character Main Component

activity

character Activities

start_date

character Start Date

end_date

character End Date

Source

https://github.com/masiraji/tabledown/tree/main/data-raw

Validation Data of Bangla Rotter I-E Scale

Description

This is the validation data of Bangla Rotter's Internal and External Scale.

Usage

Rotter

Format

A data frame with 478 rows and 91 variables:

id

double Id

sample

character EFA or CEA

Age

double Age

Gender

character Gender

Educational Status

character Educational Status

Education Years

double COLUMN_DESCRIPTION

Income

double COLUMN_DESCRIPTION

Religion

double COLUMN_DESCRIPTION

Marital Status

double COLUMN_DESCRIPTION

Social Stance

double COLUMN_DESCRIPTION

item2

double COLUMN_DESCRIPTION

item3

double COLUMN_DESCRIPTION

item4

double COLUMN_DESCRIPTION

item5

double COLUMN_DESCRIPTION

item6

double COLUMN_DESCRIPTION

item7

double COLUMN_DESCRIPTION

item9

double COLUMN_DESCRIPTION

item10

double COLUMN_DESCRIPTION

item11

double COLUMN_DESCRIPTION

item12

double COLUMN_DESCRIPTION

item13

double COLUMN_DESCRIPTION

item15

double COLUMN_DESCRIPTION

item16

double COLUMN_DESCRIPTION

item17

double COLUMN_DESCRIPTION

item18

double COLUMN_DESCRIPTION

item20

double COLUMN_DESCRIPTION

item21

double COLUMN_DESCRIPTION

item22

double COLUMN_DESCRIPTION

item23

double COLUMN_DESCRIPTION

item25

double COLUMN_DESCRIPTION

item26

double COLUMN_DESCRIPTION

item28

double COLUMN_DESCRIPTION

item29

double COLUMN_DESCRIPTION

O1

double COLUMN_DESCRIPTION

O2

double COLUMN_DESCRIPTION

O3

double COLUMN_DESCRIPTION

O4

double COLUMN_DESCRIPTION

O5

double COLUMN_DESCRIPTION

O6

double COLUMN_DESCRIPTION

O7

double COLUMN_DESCRIPTION

O8

double COLUMN_DESCRIPTION

O9

double COLUMN_DESCRIPTION

O10

double COLUMN_DESCRIPTION

Total_Opennes

double COLUMN_DESCRIPTION

E1

double COLUMN_DESCRIPTION

E2

double COLUMN_DESCRIPTION

E3

double COLUMN_DESCRIPTION

E4

double COLUMN_DESCRIPTION

E5

double COLUMN_DESCRIPTION

E6

double COLUMN_DESCRIPTION

E7

double COLUMN_DESCRIPTION

E8

double COLUMN_DESCRIPTION

Total_Extro

double COLUMN_DESCRIPTION

N1

double COLUMN_DESCRIPTION

N2

double COLUMN_DESCRIPTION

N3

double COLUMN_DESCRIPTION

N4

double COLUMN_DESCRIPTION

N5

double COLUMN_DESCRIPTION

N6

double COLUMN_DESCRIPTION

N7

double COLUMN_DESCRIPTION

N8

double COLUMN_DESCRIPTION

Total_Neuro

double COLUMN_DESCRIPTION

DIR1

double COLUMN_DESCRIPTION

DIR2

double COLUMN_DESCRIPTION

DI3

double COLUMN_DESCRIPTION

DIR4

double COLUMN_DESCRIPTION

DI5

double COLUMN_DESCRIPTION

DIR6

double COLUMN_DESCRIPTION

DI7

double COLUMN_DESCRIPTION

DIR8

double COLUMN_DESCRIPTION

DI9

double COLUMN_DESCRIPTION

DI10

double COLUMN_DESCRIPTION

DIR11

double COLUMN_DESCRIPTION

DI12

double COLUMN_DESCRIPTION

DI13

double COLUMN_DESCRIPTION

DIR14

double COLUMN_DESCRIPTION

DI15

double COLUMN_DESCRIPTION

DI16

double COLUMN_DESCRIPTION

DIR17

double COLUMN_DESCRIPTION

DI18

double COLUMN_DESCRIPTION

DIR19

double COLUMN_DESCRIPTION

DI20

double COLUMN_DESCRIPTION

DI21

double COLUMN_DESCRIPTION

DIR22

double COLUMN_DESCRIPTION

DIR23

double COLUMN_DESCRIPTION

DIR24

double COLUMN_DESCRIPTION

DI25

double COLUMN_DESCRIPTION

DIR26

double COLUMN_DESCRIPTION

DIR27

double COLUMN_DESCRIPTION

DI28

double COLUMN_DESCRIPTION

DI_Total

double COLUMN_DESCRIPTION

Source

https://github.com/masiraji/tabledown/tree/main/data-raw

Spot Data

Description

Additional demo data for GanTT

Usage

Spot

Format

A data frame with 29 rows and 3 variables:

activity

character Activity

spot_type

character Progress Status

spot_date

character Date of Reporting Progress

Source

https://github.com/masiraji/tabledown/tree/main/data-raw

A Function for calculating time spent in bed.

Description

This function will help you to calculate the time a person spent in bed based on their sleep log. This type of calculation is very common in sleep research. However, as one can guess, working with dates in R is a bit tricky. This function will ease the task. More importantly you do not require to entry the dates to calculate bed time. Just wake up time and time to go to bed is enough (24 hour format).

Usage

bedTime(x, y)

Arguments

x

A vector containing time to do to bed.

y

A vector containing time of wake.

Value

Calculates time spent in bed in hours. Output class is numeric.

Examples

#Please use 24 hour format.
#Easiest way is to enter the data as character.
bed <-c("20:00", "21:00", "23:00")
wake <-c("6:00", "7:00", "8:00")
bedtime <- bedTime(bed, wake)

A Function for Creating Publication Quality Tables with CFA fit indices.

Description

This function will create publication worthy tables with CFA fit indices from lavaan class object.

Usage

cfa.tab(x, robust = FALSE)

Arguments

x

A lavaan class object.

robust

If TRUE, will provide robust fit indices when applicable instead of the default indices.

A Function for Creating Publication Quality Tables with CFA fit indices from several lavaan objects.

Description

Often researchers are required to show fit indices from several CFA models. This function will create publication worthy tables with CFA fit indices from several lavaan class objects. #' To run this function successfully one need to provide at least two lavaan objects. This command supports up-to five lavaan models.

Usage

cfa.tab.multi(x, y, z = NULL, a = NULL, b = NULL, robust = FALSE)

Arguments

x

first object of class lavaan (Mandatory).

y

second object of class lavaan (Mandatory).

z

third object of class lavaan (Optional).

a

fourth object of class lavaan (Optional).

b

fifth object of class lavaan (Optional).

robust

If TRUE, will provide robust fit indices when applicable instead of the default indices.

A Function for Descriptive data for item analysis.

Description

This function will create a publication ready essential descriptive table for item analysis. Normality is tested using shapiro.test from base stats with Bonferroni Correction.

Usage

des.tab(df, reverse = FALSE)

Arguments

df

A data frame.

reverse

If TRUE, will provide indicate which items had a negative correlation and reverse them

Value

Returns a summary table of descriptives in a data frame structure.

Examples

data <- tabledown::Rotter[, 11:31]
table <- des.tab(data)

A Function for Creating Publication Quality Factor Tables.

Description

This function will create publication worthy factor tables from objects created from psych pack. I have came across this beautiful piece of codes at https://www.anthonyschmidt.co/post/2020-09-27-efa-tables-in-r/ and modified it a bit.

Usage

fac.tab(x, cut, complexity = TRUE)

Arguments

x

A psych package object.

cut

The value under which all factor loading will be suppressed.

complexity

To add complexity parameters.

Value

A publication ready summary table for the Factor analysis conducted by psych Package. Output structure is data frame.

Examples

data <- tabledown::Rotter[, 11:31]
correlations <- psych::polychoric(data, correct = 0)
fa.5F.1 <- psych::fa(r=correlations$rho, nfactors = 5, fm= "pa",rotate ="varimax",
residuals = TRUE, SMC = TRUE, n.obs =428)
table <- fac.tab(fa.5F.1, .3)
#always save the output into an object

A Function for Creating Publication Quality Item Response Theory based item characteristic plot.

Description

This function will create publication worthy Item Response Theory based item characteristic plot using ggplot2 from objects created from mirt pack. Using ggplot2 will enable the user to modify the item characteristic plot.

Usage

ggicc(model, item, theta)

Arguments

model

A mirt package fitted object.

item

Item number (i.e. 1,2,3,4).

theta

Theta range. Put only one number. Theta =3 will be considered as theta range (-3 to 3).

Value

A publication quality item characteristic plot. Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL', SE = TRUE, Se.type = 'MHRM')

plot <- tabledown::ggicc(model, 1, 3)

A Function for Creating Publication Quality Item Response Theory based item information plot.

Description

This function will create publication worthy Item Response Theory based item information plot. using ggplot2 from objects created from mirt pack.

Usage

ggiteminfo(model, item, theta)

Arguments

model

A mirt package fitted object.

item

Item number (i.e. 1,2,3,4).

theta

Theta range. Put only one number. Theta =3 will be considered as theta range (-3 to 3).

Value

A publication quality item information plot.Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')

plot <- ggiteminfo(model, 1, 3)

A Function for Creating Publication Quality Item Response Theory based reliability plot.

Description

This function will create publication worthy Item Response Theory based based reliability plot with standard error using ggplot2 from objects created from mirt pack. Using ggplot2 will enable the user to modify the Item plot.

Usage

ggreliability(dataframe, model)

Arguments

dataframe

your data.

model

A mirt package fitted object.

Value

A publication quality reliability plot (dashed line). Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')

plot <- ggreliability(data, model)

A Function for Creating Item Response Theory based reliability plot based on plotly.

Description

This function will create Item Response Theory based based reliability plot with standard error using ggplot2 and plotly from objects created from mirt pack. Using ggplot2 will enable the user to modify the Item plot.

Usage

ggreliability_plotly(dataframe, model)

Arguments

dataframe

your data.

model

A mirt package fitted object.

Value

A publication quality reliability plot (dashed line). Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')

plot <- ggreliability_plotly(data, model)

A Function for Creating Publication Quality Item Response Theory based test information plot.

Description

This function will create publication worthy Item Response Theory based Test information plot using ggplot2 from objects created from mirt pack. Using ggplot2 will enable the user to modify the Item plot.

Usage

ggtestinfo(dataframe, model)

Arguments

dataframe

your data.

model

A mirt package fitted object.

Value

A publication quality Test information plot. Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')

plot <- ggtestinfo(data, model)

A Function for Creating Publication Quality Item Response Theory based test information plot with standard error.

Description

This function will create publication worthy Item Response Theory based Test information plot with standard error using ggplot2 from objects created from mirt pack. Using ggplot2 will enable the user to modify the Item plot.

Usage

ggtestinfo_se(dataframe, model)

Arguments

dataframe

your data.

model

A mirt package fitted object.

Value

A publication quality Test information plot with standard error (dashed line). Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')

plot <- ggtestinfo(data, model)

A Function for Creating Item Response Theory based test information plot with standard error with plotly.

Description

This function will create Item Response Theory based Test information plot with standard error using ggplot2 and plotly from objects created from mirt pack. Using ggplot2 will enable the user to modify the Item plot.

Usage

ggtestinfo_se_ploty(dataframe, model)

Arguments

dataframe

your data.

model

A mirt package fitted object.

Value

A publication quality Test information plot with standard error (dashed line). Output object is a ggplot object.

Examples

data <- tabledown::Rotter[, 11:31]
model <- mirt::mirt(data, model = 1, itemtype = '2PL')
plot <- ggtestinfo_se_ploty(data, model)

A Function for gtExtra package friendly data summary.

Description

This function will gtExtra package friendly data summary using the datafrmae provided psych pack.

Usage

gt_tab(dataframe, recode_code)

Arguments

dataframe

Dataframe with all items.

recode_code

Recode key

Value

A publication ready descriptive summary table in png format.

Examples

data <- tabledown::FFMQ.CFA[, c(9,10,12,14)]
recode_code <- c( "1" = "Never or very rarely true", "2" = "Rarely true",
"3"= "Sometimes true","4" = "Often true","5" = "Very often or always true")
sample_tab <- gt_tab(data,recode_code)

A Function for computing univariate normality test on data frame.

Description

This function will compute normality on entire data set. Sometime in dlookr package p values turns out to be null thus failing to test normality of the data set. This is a good alternative of dlookr function. Here normality is tested using shapiro.test from base stats.

Usage

normality.loop(df, bonf = TRUE, alpha = 0.05)

Arguments

df

A data frame.

bonf

If TRUE a bonferonni correction will be conducted.

alpha

Desired alpha.

Value

Provides normality tests results for all columns in a wide data frame in a list format.

Examples

data <- tabledown::Rotter[, 11:31]
normality.loop(data)

Produce Publication Quality Tables and Plots

Description

The tabledown package provides necessary data frames used throughout the book and some neat functions.

tabledown Data-frames

  1. Rotter: Psychometric validation data of Bangla Rotter's Internal- External Scale.

  2. Gantt and Spot: Two sample data-frames for creating project management Gantt chart.

  3. FFMQ.CFA: Structural Validation data of Bangla Five Factor Mindfulness Questionnaire.

  4. FFMQ.Val:Correlational Validity evidences of Bangla Five Factor Mindfulness Questionnaire.

tabledown functions

This packages includes some neat and useful functions to create tables and figures suitable for journal submission:

  1. fac.tab(): Creates a publication ready table from the output of "psych" package based factor analysis.

  2. des.tab(): Creates a publication ready descriptive table of Item analysis with the reporting of normality assumptions.

  3. normality.loop(): Compute normality test on the whole data frame. No grouping variable required.

  4. bedTime(): Calculate total time spent in bed from the sleep log entry.

  5. cfa.tab():Creates a table with necessary fit indices from a "lavaan" class objects.

  6. cfa.tab/multi():creates a table with necessary fit indices from several lavaan class objects.

  7. ggicc: Creates a ggplot2 based publication ready Item Characteristics Curve from the "mirt" package based item response theory estimations.

  8. ggiteminfo: Creates a ggplot2 based publication ready Item Information Curve from the "mirt" package based item response theory estimations.

  9. ggtestinfo: Creates a ggplot2 based publication ready Test Information Curve from the "mirt" package based item response theory estimations.

  10. ggtestinfo_se: Creates a ggplot2 based publication ready Test Information Curve with standard error from the "mirt" package based item response theory estimations. It is advisable that you load tidyverse along with tabledown

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