Codebook table esmtools

Packages: dplyr, esmtools, readxl

A codebook serves as a reference document that can help data analysts and researchers understand the structure and characteristics of the dataset quickly.

The ‘codebook_table()’ function is a custom function that creates a codebook table, which contains descriptions, statistics, and visualizations for each variable in a given dataframe. Additionally, it can integrate a codebook produced in spreadsheet software, such as Excel, with the codebook that’s generated. This capability streamlines the process by leveraging existing codebooks (for instance made for the data collection) or by specifying part of the codebook using spreadsheet software. By merging the codebooks, the final codebook table includes both the pre-existing metadata and the newly generated statistics and visualizations, offering a comprehensive overview of the dataset.

To merge the original codebook, users need to import it and ensure that the variable names in the dataframe match those in the original codebook. Users also need to inform the ‘origin_vars’, in which column in the original codebook to find the variable names.

Below, we created an example for the simulated dataset:

Note

To enhance readibility and sharing, an option is to generate a distinct HTML output for the codebook by utilizing the ‘html_output’ parameter (e.g., html_output=“report_examples/Codebook_table_example.html”). To see an example of an output, click here.

More specifications are available (see the documentation website).

library(esmtools)

# Import original codebook
library(readxl)
origin_cbook = read_excel("data/cbook_original.xlsx")

# Create codebook table
codebook_table(data, 
               origin_cbook=origin_cbook, 
               origin_vars="Variable")

Variable	Description	Standard	Item_wording	Item_origin	Dim	Rating_source	Coding	Comment	R_type	missing	stats	Values	Freq	Date_stats	Hist	Boxplot
dyad	Dyad identification number	Number: 1-30				pre-study		A grouping variable	numeric	0 (0%)	n_uni = 30.00 min = 1.00 q25 = 8.00 median = 15.50 q75 = 23.00 max = 30.00 mean = 15.50 sd = 8.66
role	Role of a participant within a dyad	number: 1, 2	What is your gender			participant	dummy variable	A grouping variable	integer	0 (0%)		1 2	2100 (50%) 2100 (50%)
obsno	ESM questionnaire (beep) number of the observatio	number: 0 - 50				app	starting from 1		integer	0 (0%)	n_uni = 70.00 min = 1.00 q25 = 18.00 median = 35.50 q75 = 53.00 max = 70.00 mean = 35.50 sd = 20.21
id	Participant identification number	Number: 1-60				pre-study	starting from 1		numeric	0 (0%)	n_uni = 60.00 min = 1.00 q25 = 15.75 median = 30.50 q75 = 45.25 max = 60.00 mean = 30.50 sd = 17.32
age	Age of the participant	number: 20 - 50	What is your age?			participant			integer	0 (0%)	n_uni = 19.00 min = 25.00 q25 = 25.00 median = 35.50 q75 = 42.25 max = 65.00 mean = 35.10 sd = 9.79
cond_dyad	Treatment condition to which the dyad was assigned	0=control group, 1=experimental group				experimental condition			character	0 (0%)		condA condB	2100 (50%) 2100 (50%)
scheduled	Timestamps (e.g., “2023/04/14 10:23:47”) of when the ESM questionnaire was scheduled	Timestamp				app			POSIXct	2 (0%)				min=2018-02-02 08:59:47 max=2019-06-10 11:59:46
sent	Timestamps (e.g., “2023/04/14 10:23:47”) of when the ESM questionnaire was sent	Timestamp				app			POSIXct	0 (0%)				min=2018-02-02 08:59:51 max=2019-06-10 11:59:54
start	Timestamps (e.g., “2023/04/14 10:23:47”) of when the ESM questionnaire was opened by the participant	Timestamp				app			POSIXct	1254 (30%)				min=2018-02-02 09:00:31 max=2019-06-10 12:00:15
end	Timestamps (e.g., “2023/04/14 10:23:47”) of when the ESM questionnaire was ended by the participant	Timestamp				app			POSIXct	1254 (30%)				min=2018-02-02 09:03:07 max=2019-06-10 12:02:30
contact	Partner's in contact since the last beep	Number: 0 - 1	Have you been in contact with your partner since the last beep?		Partner's contact	participant	Recode with numbers		integer	1254 (30%)		0 1	2584 (61.524%) 362 (8.619%)
PA1	A positive affect (PA) item	Sliders: 1-100	… relaxed	Emotion scale	PA	participant			integer	1254 (30%)	n_uni = 95.00 min = 1.00 q25 = 4.00 median = 18.00 q75 = 32.00 max = 100.00 mean = 23.09 sd = 23.54
PA2	A positive affect (PA) item	Sliders: 1-100	… happy	Emotion scale	PA	participant			integer	1254 (30%)	n_uni = 94.00 min = 1.00 q25 = 3.00 median = 19.00 q75 = 33.00 max = 100.00 mean = 21.77 sd = 21.50
PA3	A positive affect (PA) item	Sliders: 1-100	… angry	Emotion scale	NA.	participant			integer	1254 (30%)	n_uni = 96.00 min = 1.00 q25 = 3.00 median = 16.00 q75 = 31.00 max = 100.00 mean = 23.32 sd = 25.91
NA1	A negative affect (NA) item	Sliders: 1-100	… sad	Emotion scale	NA.	participant			integer	1254 (30%)	n_uni = 99.00 min = 1.00 q25 = 1.00 median = 11.00 q75 = 31.00 max = 100.00 mean = 21.36 sd = 26.53
NA2	A negative affect (NA) item	Sliders: 1-100	… sressed	Emotion scale	NA.	participant	revesed item		integer	1254 (30%)	n_uni = 67.00 min = 1.00 q25 = 1.00 median = 7.00 q75 = 15.00 max = 83.00 mean = 10.50 sd = 11.53
NA3	A negative affect (NA) item	Sliders: 1-100	… joyful	Emotion scale	PA	participant			integer	1254 (30%)	n_uni = 101.00 min = 1.00 q25 = 40.00 median = 72.00 q75 = 89.00 max = 100.00 mean = 63.72 sd = 28.66
location	Participant's location since the last beep	Number: 0 -4	What have you done since the last beep?	Activity scale	Activity	participant	Recode with numbers		character	1254 (30%)
valid	Valid (1) or invalid (0) ovservation	Number: 0 - 1				preprocessing	followed compliance definition		numeric	0 (0%)		0 1	1254 (29.857%) 2946 (70.143%)