First, load the package, set the plot theme and get some data.
# Load the package
library(volker)
# Set the basic plot theme
theme_set(theme_vlkr())
# Load an example dataset ds from the package
ds <- volker::chatgpt
Decide whether your data is categorical or metric and choose the appropriate function:
tab_counts()
shows frequency tables.plot_counts()
generates simple and stacked bar
charts.effect_counts()
calculates test statistics for
categorical data.tab_metrics()
creates tables with distribution
parameters.plot_metrics()
visualises distributions in density
plots, box plots or scatter plots.effect_metrics()
calculates test statistics for metric
data.The column selection determines whether to analyse single variables, item lists or to compare and correlate multiple variables. Try it out!
# A single variable
tab_counts(ds, use_private)
Usage: in private context | n | p |
---|---|---|
never | 12 | 12% |
rarely | 40 | 40% |
several times a month | 30 | 30% |
several times a week | 15 | 15% |
almost daily | 4 | 4% |
total | 101 | 100% |
# A list of variables
tab_counts(ds, c(use_private, use_work))
Usage | never | rarely | several times a month | several times a week | almost daily | total |
---|---|---|---|---|---|---|
in private context | 12% (12) | 40% (40) | 30% (30) | 15% (15) | 4% (4) | 100% (101) |
in professional context | 38% (38) | 21% (21) | 15% (15) | 17% (17) | 10% (10) | 100% (101) |
# Variables matched by a pattern
tab_counts(ds, starts_with("use_"))
Usage | never | rarely | several times a month | several times a week | almost daily | total |
---|---|---|---|---|---|---|
in private context | 12% (12) | 40% (40) | 30% (30) | 15% (15) | 4% (4) | 100% (101) |
in professional context | 38% (38) | 21% (21) | 15% (15) | 17% (17) | 10% (10) | 100% (101) |
# One metric variable
tab_metrics(ds, sd_age)
Age | value |
---|---|
min | 18 |
q1 | 27 |
median | 38 |
q3 | 52 |
max | 68 |
mean | 39.7 |
sd | 13.8 |
n | 101 |
# Multiple metric items
tab_metrics(ds, starts_with("cg_adoption_"))
Expectations | min | q1 | median | q3 | max | mean | sd | n |
---|---|---|---|---|---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. | 1 | 3 | 4 | 4 | 5 | 3.4 | 1.0 | 97 |
Using ChatGPT brings financial benefits. | 1 | 2 | 3 | 4 | 5 | 2.7 | 1.2 | 97 |
Using ChatGPT is advantageous in many tasks. | 1 | 3 | 4 | 4 | 5 | 3.6 | 1.1 | 97 |
Compared to other systems, using ChatGPT is more fun. | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 97 |
Much can go wrong when using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.1 | 97 |
There are legal issues with using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.2 | 97 |
The security of user data is not guaranteed with ChatGPT. | 1 | 3 | 3 | 4 | 5 | 3.2 | 1.0 | 97 |
Using ChatGPT could bring personal disadvantages. | 1 | 2 | 3 | 3 | 5 | 2.7 | 1.1 | 97 |
In my environment, using ChatGPT is standard. | 1 | 2 | 2 | 3 | 5 | 2.5 | 1.1 | 97 |
Almost everyone in my environment uses ChatGPT. | 1 | 1 | 2 | 3 | 5 | 2.4 | 1.2 | 97 |
Not using ChatGPT is considered being an outsider. | 1 | 1 | 2 | 3 | 5 | 2.0 | 1.2 | 97 |
Using ChatGPT brings me recognition from my environment. | 1 | 1 | 2 | 3 | 5 | 2.3 | 1.2 | 97 |
4 missing case(s) omitted.
Provide a grouping column in the third parameter to compare different groups.
tab_counts(ds, adopter, sd_gender)
Innovator type | total | female | male | diverse |
---|---|---|---|---|
I try new offers immediately |
15% (15) |
2% (2) |
12% (12) |
1% (1) |
I try new offers rather quickly |
62% (63) |
25% (25) |
38% (38) |
0% (0) |
I wait until offers establish themselves |
22% (22) |
13% (13) |
9% (9) |
0% (0) |
I only use new offers when I have no other choice |
1% (1) |
0% (0) |
1% (1) |
0% (0) |
total |
100% (101) |
40% (40) |
59% (60) |
1% (1) |
For metric variables, you can compare the mean values.
# Compare the means of one grouping variable (including the confidence interval)
tab_metrics(ds, sd_age, sd_gender, ci = TRUE)
Gender | min | q1 | median | q3 | max | mean | sd | ci low | ci high | n |
---|---|---|---|---|---|---|---|---|---|---|
female | 18 | 25.8 | 38.0 | 44.2 | 63 | 37.5 | 13.4 | 33.2 | 41.8 | 40 |
male | 19 | 32.5 | 38.5 | 52.0 | 68 | 41.2 | 14.0 | 37.6 | 44.8 | 60 |
diverse | 33 | 33.0 | 33.0 | 33.0 | 33 | 33.0 | 1 | |||
total | 18 | 27.0 | 38.0 | 52.0 | 68 | 39.7 | 13.8 | 37.0 | 42.4 | 101 |
By default, the crossing variable is treated as categorical. You can change this behaviour using the metric-parameter to calculate correlations:
# Correlate two metric variables
tab_metrics(ds, sd_age, use_work, metric = TRUE, ci = TRUE)
Item 1 | Item 2 | n | Pearson’s r | ci low | ci high |
---|---|---|---|---|---|
Age | Usage: in professional context | 101 | -0.2 | -0.38 | 0 |
Each table function has a corresponding plot function with parameters
to pimp the result. See the function help (F1 key) to learn the options.
For example, you can use the prop
parameter to grow bars to
100%. The numbers
parameter prints frequencies and
percentages onto the bars.
ds |>
filter(sd_gender != "diverse") |>
plot_counts(adopter, sd_gender, prop="rows", numbers=c("p","n"))
Further, the effect-functions conduct statistical tests:
ds |>
filter(sd_gender != "diverse") |>
effect_counts(adopter, sd_gender)
Statistic | Value |
---|---|
Cramer’s V | 0.28 |
Number of cases | 100 |
Degrees of freedom | |
Chi-squared | 7.87 |
p value | 0.030 |
stars | * |
Reports combine plots, tables and effect calculations. Optionally, for item batteries, an index, clusters or factors are calculated and reported.
To see an example or develop own reports, use the volker report template in RStudio:
Have fun with developing own reports!
To generate a volker-report from any R-Markdown document, add
volker::html_report
to the output options of your Markdown
document:
---
title: "How to create reports?"
output:
volker::html_report
---
Then, you can generate combined outputs using the report-functions. One advantage of the report-functions is that plots are automatically scaled to fit the page. See the function help for further options (F1 key).
ds %>%
filter(sd_gender != "diverse") %>%
report_metrics(starts_with("cg_adoption_"), sd_gender, box=TRUE, ci=TRUE)
4 missing case(s) omitted.
Expectations | total | female | male |
---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. |
3.4 (1.0) |
3.6 (1.0) |
3.3 (1.0) |
Using ChatGPT brings financial benefits. |
2.7 (1.2) |
2.6 (1.2) |
2.7 (1.2) |
Using ChatGPT is advantageous in many tasks. |
3.6 (1.1) |
3.7 (1.0) |
3.5 (1.1) |
Compared to other systems, using ChatGPT is more fun. |
3.5 (1.0) |
3.6 (1.0) |
3.5 (1.0) |
Much can go wrong when using ChatGPT. |
3.1 (1.1) |
3.1 (1.0) |
3.1 (1.2) |
There are legal issues with using ChatGPT. |
3.1 (1.2) |
3.0 (1.0) |
3.1 (1.3) |
The security of user data is not guaranteed with ChatGPT. |
3.2 (1.0) |
3.0 (1.0) |
3.3 (1.1) |
Using ChatGPT could bring personal disadvantages. |
2.7 (1.1) |
2.5 (0.9) |
2.8 (1.2) |
In my environment, using ChatGPT is standard. |
2.5 (1.1) |
2.5 (0.9) |
2.5 (1.3) |
Almost everyone in my environment uses ChatGPT. |
2.4 (1.2) |
2.4 (1.0) |
2.3 (1.3) |
Not using ChatGPT is considered being an outsider. |
2.0 (1.2) |
1.8 (1.0) |
2.1 (1.3) |
Using ChatGPT brings me recognition from my environment. |
2.3 (1.2) |
2.4 (1.2) |
2.3 (1.3) |
4 missing case(s) omitted.
By default, a header and tabsheets are automatically created. You can mix in custom content.
FALSE
and add your own title.FALSE
and adding a new header on the
fifth level (5 x # followed by the tab name). Close your custom new
tabsheet with #### {-}
(4 x #).All together, the following report output is generated by the pattern:
#> ### Adoption types
#>
#> ```{r echo=FALSE}
#> ds %>%
#> filter(sd_gender != "diverse") %>%
#> report_counts(adopter, sd_gender, prop="rows", title=FALSE, close=FALSE, box=TRUE, ci=TRUE)
#> ```
#>
#> ##### Method
#> Basis: Only male and female respondents.
#>
#> #### {-}
Innovator type | total | female | male |
---|---|---|---|
I try new offers immediately |
100% (14) |
14% (2) |
86% (12) |
I try new offers rather quickly |
100% (63) |
40% (25) |
60% (38) |
I wait until offers establish themselves |
100% (22) |
59% (13) |
41% (9) |
I only use new offers when I have no other choice |
100% (1) |
0% (0) |
100% (1) |
total |
100% (100) |
40% (40) |
60% (60) |
Basis: Only male and female respondents.
Plot and table functions share a number of parameters that can be used to customize the outputs. Lookup the available parameters in the help of the specific function.
The theme_vlkr()-function lets you customise colors:
theme_set(theme_vlkr(
base_fill = c("#F0983A","#3ABEF0","#95EF39","#E35FF5","#7A9B59"),
base_gradient = c("#FAE2C4","#F0983A")
))
Labels used in plots and tables are stored in the comment attribute
of the variable. You can inspect all labels using the
codebook()
-function:
codebook(ds)
#> # A tibble: 94 × 6
#> item_name item_group item_class item_label value_name value_label
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 case case numeric case <NA> <NA>
#> 2 sd_age sd numeric Age <NA> <NA>
#> 3 cg_activities cg character Activities with C… <NA> <NA>
#> 4 adopter adopter factor Innovator type I try new… I try new …
#> 5 adopter adopter factor Innovator type I try new… I try new …
#> 6 adopter adopter factor Innovator type I wait un… I wait unt…
#> 7 adopter adopter factor Innovator type I only us… I only use…
#> 8 adopter adopter factor Innovator type [no answe… [no answer]
#> 9 sd_gender sd factor Gender female female
#> 10 sd_gender sd factor Gender male male
#> # ℹ 84 more rows
You can set specific column labels by providing a named list to the
items-parameter of labs_apply()
:
ds %>%
labs_apply(
items = list(
"cg_adoption_advantage_01" = "Allgemeine Vorteile",
"cg_adoption_advantage_02" = "Finanzielle Vorteile",
"cg_adoption_advantage_03" = "Vorteile bei der Arbeit",
"cg_adoption_advantage_04" = "Macht mehr Spaß"
)
) %>%
tab_metrics(starts_with("cg_adoption_advantage_"))
Item | min | q1 | median | q3 | max | mean | sd | n |
---|---|---|---|---|---|---|---|---|
Allgemeine Vorteile | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 99 |
Finanzielle Vorteile | 1 | 2 | 3 | 4 | 5 | 2.7 | 1.2 | 99 |
Vorteile bei der Arbeit | 1 | 3 | 4 | 4 | 5 | 3.6 | 1.1 | 99 |
Macht mehr Spaß | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 99 |
2 missing case(s) omitted.
Labels for values inside a column can be adjusted by providing a
named list to the values-parameter of labs_apply()
. In
addition, select the columns where value labels should be changed:
ds %>%
labs_apply(
cols=starts_with("cg_adoption"),
values = list(
"1" = "Stimme überhaupt nicht zu",
"2" = "Stimme nicht zu",
"3" = "Unentschieden",
"4" = "Stimme zu",
"5" = "Stimme voll und ganz zu"
)
) %>%
plot_metrics(starts_with("cg_adoption"))
To conveniently manage all labels of a dataset, save the result of
codebook()
to an Excel file, change the labels manually in
a copy of the Excel file, and finally call labs_apply()
with your revised codebook.
library(readxl)
library(writexl)
# Save codebook to a file
codes <- codebook(ds)
write_xlsx(codes,"codebook.xlsx")
# Load and apply a codebook from a file
codes <- read_xlsx("codebook_revised.xlsx")
ds <- labs_apply(ds, codebook)
Be aware that some data operations such as mutate()
from
the tidyverse loose labels on their way. In this case, store the labels
(in the codebook attribute of the data frame) before the operation and
restore them afterwards:
ds %>%
labs_store() %>%
mutate(sd_age = 2024 - sd_age) %>%
labs_restore() %>%
tab_metrics(sd_age)
Age | value |
---|---|
min | 1956 |
q1 | 1972 |
median | 1986 |
q3 | 1997 |
max | 2006 |
mean | 1984.3 |
sd | 13.8 |
n | 101 |
You can calculate mean indexes from a bunch of items using
add_index()
. A new column is created with the average value
of all selected columns for each case.
Reliability and number of items are calculated with
psych::alpha()
and stored as column attribute named
“psych.alpha”. The reliability values are printed by
tab_metrics()
.
Add a single index
ds %>%
add_index(starts_with("cg_adoption_")) %>%
tab_metrics(idx_cg_adoption)
Index: cg_adoption | value |
---|---|
min | 1 |
q1 | 2.4 |
median | 2.8 |
q3 | 3.2 |
max | 5 |
mean | 2.9 |
sd | 0.6 |
n | 97 |
items | 12 |
alpha | 0.81 |
4 missing case(s) omitted.
Compare the index values by group
ds %>%
add_index(starts_with("cg_adoption_")) %>%
tab_metrics(idx_cg_adoption, adopter)
Innovator type | min | q1 | median | q3 | max | mean | sd | n | items | alpha |
---|---|---|---|---|---|---|---|---|---|---|
I try new offers immediately | 1.5 | 3.2 | 3.3 | 4.1 | 5.0 | 3.5 | 0.9 | 15 | 12 | 0.81 |
I try new offers rather quickly | 1.8 | 2.5 | 2.8 | 3.1 | 3.8 | 2.8 | 0.5 | 61 | 12 | 0.81 |
I wait until offers establish themselves | 1.0 | 2.4 | 2.7 | 3.0 | 3.8 | 2.7 | 0.6 | 20 | 12 | 0.81 |
I only use new offers when I have no other choice | 2.4 | 2.4 | 2.4 | 2.4 | 2.4 | 2.4 | 1 | 12 | 0.81 | |
total | 1.0 | 2.4 | 2.8 | 3.2 | 5.0 | 2.9 | 0.6 | 97 | 12 | 0.81 |
4 missing case(s) omitted.
Add multiple indizes and summarize them
ds %>%
add_index(starts_with("cg_adoption_")) %>%
add_index(starts_with("cg_adoption_advantage")) %>%
add_index(starts_with("cg_adoption_fearofuse")) %>%
add_index(starts_with("cg_adoption_social")) %>%
tab_metrics(starts_with("idx_cg_adoption"))
Item | min | q1 | median | q3 | max | mean | sd | n | items | alpha |
---|---|---|---|---|---|---|---|---|---|---|
Index: cg_adoption | 1 | 2.4 | 2.8 | 3.2 | 5 | 2.9 | 0.6 | 97 | 12 | 0.81 |
Index: cg_adoption_advantage_0 | 1 | 3.0 | 3.5 | 3.8 | 5 | 3.3 | 0.9 | 97 | 4 | 0.8 |
Index: cg_adoption_fearofuse_0 | 1 | 2.5 | 3.0 | 3.5 | 5 | 3.0 | 0.8 | 97 | 4 | 0.7 |
Index: cg_adoption_social_0 | 1 | 1.5 | 2.0 | 3.0 | 5 | 2.3 | 1.0 | 97 | 4 | 0.84 |
4 missing case(s) omitted.
The easiest way to conduct factor analysis or cluster analyses is to
use the respective parameters in the report_metrics()
function.
ds |>
report_metrics(starts_with("cg_adoption"), factors = TRUE, clusters = TRUE)
4 missing case(s) omitted.
Expectations | min | q1 | median | q3 | max | mean | sd | n |
---|---|---|---|---|---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. | 1 | 3 | 4 | 4 | 5 | 3.4 | 1.0 | 97 |
Using ChatGPT brings financial benefits. | 1 | 2 | 3 | 4 | 5 | 2.7 | 1.2 | 97 |
Using ChatGPT is advantageous in many tasks. | 1 | 3 | 4 | 4 | 5 | 3.6 | 1.1 | 97 |
Compared to other systems, using ChatGPT is more fun. | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 97 |
Much can go wrong when using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.1 | 97 |
There are legal issues with using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.2 | 97 |
The security of user data is not guaranteed with ChatGPT. | 1 | 3 | 3 | 4 | 5 | 3.2 | 1.0 | 97 |
Using ChatGPT could bring personal disadvantages. | 1 | 2 | 3 | 3 | 5 | 2.7 | 1.1 | 97 |
In my environment, using ChatGPT is standard. | 1 | 2 | 2 | 3 | 5 | 2.5 | 1.1 | 97 |
Almost everyone in my environment uses ChatGPT. | 1 | 1 | 2 | 3 | 5 | 2.4 | 1.2 | 97 |
Not using ChatGPT is considered being an outsider. | 1 | 1 | 2 | 3 | 5 | 2.0 | 1.2 | 97 |
Using ChatGPT brings me recognition from my environment. | 1 | 1 | 2 | 3 | 5 | 2.3 | 1.2 | 97 |
4 missing case(s) omitted.
4 missing case(s) omitted.
Expectations | Component 1 | Component 2 | Component 3 | communality |
---|---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. | 0.1 | 0.9 | 0.0 | 0.8 |
Using ChatGPT brings financial benefits. | 0.5 | 0.5 | 0.3 | 0.6 |
Using ChatGPT is advantageous in many tasks. | 0.2 | 0.8 | 0.0 | 0.7 |
Compared to other systems, using ChatGPT is more fun. | 0.2 | 0.8 | 0.0 | 0.7 |
Much can go wrong when using ChatGPT. | -0.1 | -0.2 | 0.8 | 0.7 |
There are legal issues with using ChatGPT. | 0.2 | 0.2 | 0.6 | 0.5 |
The security of user data is not guaranteed with ChatGPT. | 0.1 | 0.1 | 0.7 | 0.6 |
Using ChatGPT could bring personal disadvantages. | 0.2 | -0.1 | 0.7 | 0.6 |
In my environment, using ChatGPT is standard. | 0.9 | 0.2 | 0.0 | 0.8 |
Almost everyone in my environment uses ChatGPT. | 0.8 | 0.2 | 0.1 | 0.7 |
Not using ChatGPT is considered being an outsider. | 0.7 | 0.0 | 0.3 | 0.6 |
Using ChatGPT brings me recognition from my environment. | 0.8 | 0.2 | 0.0 | 0.6 |
4 missing case(s) omitted.
Component | Eigenvalue | Proportion of variance | Cumulative proportion of variance |
---|---|---|---|
Component 1 | 3.0 | 0.3 | 0.3 |
Component 2 | 2.5 | 0.2 | 0.5 |
Component 3 | 2.2 | 0.2 | 0.6 |
Test | Statistic | value |
---|---|---|
KMO Test | Cases | 97 |
KMO Test | Variables | 12 |
KMO Test | Cases-to-Variables Ratio | 8.08 |
KMO Test | Overall MSA | 0.74 |
Bartlett Test | Chi-squared | 463.54 |
Bartlett Test | df | 66 |
Bartlett Test | p | 0.000 |
Bartlett Test | stars | *** |
Component | Eigenvalue |
---|---|
1 | 4.2 |
2 | 2.1 |
3 | 1.4 |
4 | 0.8 |
5 | 0.7 |
6 | 0.6 |
7 | 0.5 |
8 | 0.5 |
9 | 0.4 |
10 | 0.4 |
11 | 0.3 |
12 | 0.2 |
Automatically selected k=3 by comparing eigenvalues with random data.
4 missing case(s) omitted.
Expectations | total | Cluster 1 | Cluster 2 |
---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. |
3.4 (1.0) |
3.1 (1.0) |
3.8 (0.9) |
Using ChatGPT brings financial benefits. |
2.7 (1.2) |
2.0 (0.9) |
3.4 (1.0) |
Using ChatGPT is advantageous in many tasks. |
3.6 (1.1) |
3.1 (1.2) |
4.0 (0.7) |
Compared to other systems, using ChatGPT is more fun. |
3.5 (1.0) |
3.2 (1.0) |
3.9 (0.8) |
Much can go wrong when using ChatGPT. |
3.1 (1.1) |
3.2 (1.1) |
3.0 (1.1) |
There are legal issues with using ChatGPT. |
3.1 (1.2) |
3.0 (1.2) |
3.2 (1.1) |
The security of user data is not guaranteed with ChatGPT. |
3.2 (1.0) |
3.0 (1.1) |
3.4 (1.0) |
Using ChatGPT could bring personal disadvantages. |
2.7 (1.1) |
2.6 (1.0) |
2.9 (1.2) |
In my environment, using ChatGPT is standard. |
2.5 (1.1) |
1.7 (0.6) |
3.4 (0.9) |
Almost everyone in my environment uses ChatGPT. |
2.4 (1.2) |
1.6 (0.6) |
3.3 (0.9) |
Not using ChatGPT is considered being an outsider. |
2.0 (1.2) |
1.4 (0.6) |
2.6 (1.3) |
Using ChatGPT brings me recognition from my environment. |
2.3 (1.2) |
1.7 (0.8) |
3.0 (1.3) |
4 missing case(s) omitted.
Cluster | n | p |
---|---|---|
Cluster 1 | 50 | 52% |
Cluster 2 | 47 | 48% |
total | 97 | 100% |
Statistic | Value |
---|---|
Within-Cluster Sum of Squares | 910.04 |
Between-Cluster Sum of Squares | 241.96 |
Clusters k | WSS |
---|---|
1 | 1152.0 |
2 | 910.0 |
3 | 803.0 |
4 | 724.4 |
5 | 666.7 |
6 | 632.6 |
7 | 605.7 |
8 | 579.7 |
9 | 524.8 |
10 | 513.8 |
Automatically selected k=2 by the elbow criterion.
Currently, cluster analysis is performed using kmeans and factor analysis is a principal component analysis. Setting the parameters to true, automatically generates scree plots and selects the number of factors or clusters. Alternatively, you can explicitly specify the numbers.
If you want to work with the results, use add_factors()
and add_clusters()
respectively. For factor analysis, new
columns prefixed with “fct_” are created to store the factor loadings
based on the specified number of factors. For clustering, an additional
column prefixed with “cls_” is added that assigns each observation to a
cluster number. In the next step, you can use the new columns as shown
below.
To automatically determine the optimal number of factors or clusters based on diagnostics, set k = NULL.
Add factor analysis results
ds |>
add_factors(starts_with("cg_adoption"), k = 3) |>
report_metrics(fct_cg_adoption_1, fct_cg_adoption_2, metric = TRUE)
4 missing case(s) omitted.
Item 1 | Item 2 | n | Pearson’s r |
---|---|---|---|
1 | 2 | 97 | 0 |
4 missing case(s) omitted.
Automatically determine the number of factors
ds |>
add_factors(starts_with("cg_adoption"), k = NULL) |>
factor_tab(starts_with("fct_cg_adoption"))
Expectations | Component 1 | Component 2 | Component 3 | communality |
---|---|---|---|---|
ChatGPT has clear advantages compared to similar offerings. | 0.1 | 0.9 | 0.0 | 0.8 |
Using ChatGPT brings financial benefits. | 0.5 | 0.5 | 0.3 | 0.6 |
Using ChatGPT is advantageous in many tasks. | 0.2 | 0.8 | 0.0 | 0.7 |
Compared to other systems, using ChatGPT is more fun. | 0.2 | 0.8 | 0.0 | 0.7 |
Much can go wrong when using ChatGPT. | -0.1 | -0.2 | 0.8 | 0.7 |
There are legal issues with using ChatGPT. | 0.2 | 0.2 | 0.6 | 0.5 |
The security of user data is not guaranteed with ChatGPT. | 0.1 | 0.1 | 0.7 | 0.6 |
Using ChatGPT could bring personal disadvantages. | 0.2 | -0.1 | 0.7 | 0.6 |
In my environment, using ChatGPT is standard. | 0.9 | 0.2 | 0.0 | 0.8 |
Almost everyone in my environment uses ChatGPT. | 0.8 | 0.2 | 0.1 | 0.7 |
Not using ChatGPT is considered being an outsider. | 0.7 | 0.0 | 0.3 | 0.6 |
Using ChatGPT brings me recognition from my environment. | 0.8 | 0.2 | 0.0 | 0.6 |
Component | Eigenvalue | Proportion of variance | Cumulative proportion of variance |
---|---|---|---|
Component 1 | 3.0 | 0.3 | 0.3 |
Component 2 | 2.5 | 0.2 | 0.5 |
Component 3 | 2.2 | 0.2 | 0.6 |
Test | Statistic | value |
---|---|---|
KMO Test | Cases | 97 |
KMO Test | Variables | 12 |
KMO Test | Cases-to-Variables Ratio | 8.08 |
KMO Test | Overall MSA | 0.74 |
Bartlett Test | Chi-squared | 463.54 |
Bartlett Test | df | 66 |
Bartlett Test | p | 0.000 |
Bartlett Test | stars | *** |
Component | Eigenvalue |
---|---|
1 | 4.2 |
2 | 2.1 |
3 | 1.4 |
4 | 0.8 |
5 | 0.7 |
6 | 0.6 |
7 | 0.5 |
8 | 0.5 |
9 | 0.4 |
10 | 0.4 |
11 | 0.3 |
12 | 0.2 |
Compare values by cluster
ds |>
add_clusters(starts_with("cg_adoption"), k = 3) |>
report_counts(sd_gender, cls_cg_adoption, prop = "cols")
4 missing case(s) omitted.
Gender | total | Cluster 1 | Cluster 2 | Cluster 3 |
---|---|---|---|---|
female |
38% (37) |
32% (9) |
45% (18) |
34% (10) |
male |
61% (59) |
68% (19) |
55% (22) |
62% (18) |
diverse |
1% (1) |
0% (0) |
0% (0) |
3% (1) |
total |
100% (97) |
100% (28) |
100% (40) |
100% (29) |
4 missing case(s) omitted.
The volker-package is based on standard methods for data handling and visualisation. You can produce all outputs with a handful of functions. The package just makes your code dry - don’t repeat yourself - and wraps often used snippets into a simple interface.
The package provides print- and knit-functions that pimp console and
markdown output. To make this work, the cleaned data, produced plots,
tables and markdown snippets gain new classes (vlkr_df
,
vlkr_plt
, vlkr_tbl
, vlkr_list
,
vlkr_rprt
).
Basically, all table values are calculated two tidyverse functions:
count()
is used to produce countsskim()
is used to produce metricsTo shape the data frames, two essential functions come into play:
group_by()
is used to calculate grouped outputspivot_longer()
brings multiple items into a format
where the item name becomes a grouping variable.Plots are generated by ggplot()
.
Statistical tests, clustering and factor analysis are largely based on the stats, psych, car and effectsize packages.
Thanks to all the maintainers, authors and contributors of the packages that make the world of data a magical place.