Introduction
How to generate tables and plots?
The plot and table type is determined by your column selection. Try it out!
Categorical variables
# A single variable
tab_counts(data, 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% |
| Missing | 0 |
# A list of variables
tab_counts(data, c(use_private, use_work))| Usage: in pr | never | rarely | several times a month | several times a week | almost daily | Total |
|---|---|---|---|---|---|---|
| ivate context | 12% (12) | 40% (40) | 30% (30) | 15% (15) | 4% (4) | 100% (101) |
| ofessional context | 38% (38) | 21% (21) | 15% (15) | 17% (17) | 10% (10) | 100% (101) |
# Variables matched by a pattern
tab_counts(data, starts_with("use_"))| Usage: in pr | never | rarely | several times a month | several times a week | almost daily | Total |
|---|---|---|---|---|---|---|
| ivate context | 12% (12) | 40% (40) | 30% (30) | 15% (15) | 4% (4) | 100% (101) |
| ofessional context | 38% (38) | 21% (21) | 15% (15) | 17% (17) | 10% (10) | 100% (101) |
Metric variables
To select the approriate function, you need to decide whether your data is categorical or metric.
# One metric variable
tab_metrics(data, sd_age)| Age | value |
|---|---|
| min | 18 |
| q1 | 27 |
| median | 38 |
| q3 | 52 |
| max | 68 |
| m | 39.7 |
| sd | 13.8 |
| missing | 0 |
| n | 101 |
# Multiple metric items
tab_metrics(data, starts_with("cg_adoption_"))| Expectations | min | q1 | median | q3 | max | m | sd | missing | n |
|---|---|---|---|---|---|---|---|---|---|
| ChatGPT has clear advantages compared to similar offerings. | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 2 | 101 |
| Using ChatGPT brings financial benefits. | 1 | 2 | 3 | 4 | 5 | 2.7 | 1.2 | 0 | 101 |
| Using ChatGPT is advantageous in many tasks. | 1 | 3 | 4 | 4 | 5 | 3.6 | 1.1 | 0 | 101 |
| Compared to other systems, using ChatGPT is more fun. | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 0 | 101 |
| Much can go wrong when using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.1 | 0 | 101 |
| There are legal issues with using ChatGPT. | 1 | 2 | 3 | 4 | 5 | 3.1 | 1.2 | 0 | 101 |
| The security of user data is not guaranteed with ChatGPT. | 1 | 3 | 3 | 4 | 5 | 3.2 | 1.0 | 1 | 101 |
| Using ChatGPT could bring personal disadvantages. | 1 | 2 | 3 | 3 | 5 | 2.7 | 1.1 | 0 | 101 |
| In my environment, using ChatGPT is standard. | 1 | 2 | 2 | 3 | 5 | 2.5 | 1.1 | 1 | 101 |
| Almost everyone in my environment uses ChatGPT. | 1 | 1 | 2 | 3 | 5 | 2.4 | 1.2 | 0 | 101 |
| Not using ChatGPT is considered being an outsider. | 1 | 1 | 2 | 3 | 5 | 2.0 | 1.2 | 1 | 101 |
| Using ChatGPT brings me recognition from my environment. | 1 | 1 | 2 | 3 | 5 | 2.3 | 1.2 | 0 | 101 |
plot_metrics(data, starts_with("cg_adoption_"))
Cross tabulation and group comparison
Provide a grouping column in the third parameter to compare different groups.
tab_counts(data, adopter, sd_gender)| Gender | Total | I try new offers immediately | I try new offers rather quickly | I wait until offers establish themselves | I only use new offers when I have no other choice |
|---|---|---|---|---|---|
| female |
40% (40) |
2% (2) |
25% (25) |
13% (13) |
0% (0) |
| male |
59% (60) |
12% (12) |
38% (38) |
9% (9) |
1% (1) |
| diverse |
1% (1) |
1% (1) |
0% (0) |
0% (0) |
0% (0) |
| Total |
100% (101) |
15% (15) |
62% (63) |
22% (22) |
1% (1) |
In the corresponding plot function, you can use the prop parameter to grow bars to 100%. The numbers parameter prints the percentages onto the bars.
data |>
filter(sd_gender != "diverse") |>
plot_counts(adopter, sd_gender, prop="rows", numbers="p")
For metric variables, you can compare the mean values.
# Compare the means of one variable
tab_metrics(data, sd_age, sd_gender)| Gender | min | q1 | median | q3 | max | m | sd | missing | n |
|---|---|---|---|---|---|---|---|---|---|
| female | 18 | 25.8 | 38.0 | 44.2 | 63 | 37.5 | 13.4 | 0 | 40 |
| male | 19 | 32.5 | 38.5 | 52.0 | 68 | 41.2 | 14.0 | 0 | 60 |
| diverse | 33 | 33.0 | 33.0 | 33.0 | 33 | 33.0 | NA | 0 | 1 |
| Total | 18 | 27.0 | 38.0 | 52.0 | 68 | 39.7 | 13.8 | 0 | 101 |
# Compare the means of multiple items
tab_metrics(data, starts_with("cg_adoption_"), sd_gender)| Expectations | Total | female | male | diverse |
|---|---|---|---|---|
| ChatGPT has clear advantages compared to similar offerings. |
3.4 (1.0) |
3.6 (1.0) |
3.3 (1.0) |
4.0 (NA) |
| Using ChatGPT brings financial benefits. |
2.7 (1.2) |
2.6 (1.2) |
2.7 (1.2) |
3.0 (NA) |
| Using ChatGPT is advantageous in many tasks. |
3.6 (1.1) |
3.7 (1.0) |
3.5 (1.1) |
4.0 (NA) |
| Compared to other systems, using ChatGPT is more fun. |
3.5 (1.0) |
3.6 (1.0) |
3.5 (1.0) |
3.0 (NA) |
| Much can go wrong when using ChatGPT. |
3.1 (1.1) |
3.1 (1.0) |
3.1 (1.2) |
3.0 (NA) |
| There are legal issues with using ChatGPT. |
3.1 (1.2) |
3.0 (1.0) |
3.1 (1.3) |
3.0 (NA) |
| The security of user data is not guaranteed with ChatGPT. |
3.2 (1.0) |
3.0 (1.0) |
3.3 (1.1) |
3.0 (NA) |
| Using ChatGPT could bring personal disadvantages. |
2.7 (1.1) |
2.5 (0.9) |
2.8 (1.2) |
4.0 (NA) |
| In my environment, using ChatGPT is standard. |
2.5 (1.1) |
2.5 (0.9) |
2.5 (1.3) |
4.0 (NA) |
| Almost everyone in my environment uses ChatGPT. |
2.4 (1.2) |
2.4 (1.0) |
2.3 (1.3) |
4.0 (NA) |
| Not using ChatGPT is considered being an outsider. |
2.0 (1.2) |
1.8 (1.0) |
2.1 (1.3) |
4.0 (NA) |
| Using ChatGPT brings me recognition from my environment. |
2.3 (1.2) |
2.4 (1.2) |
2.3 (1.3) |
3.0 (NA) |
Automatically generate reports
Reports combine plots and tables. Optionally, for item batteries, an index is calculated and reported.
To see an example or develop own reports, use the volker report template in RStudio:
- Create a new R Markdown document from the main menu
- In the popup select the “From Template” option
- Select the volker template.
- The template contains a working example. Just click knit to see the result.
Have fun with developing own reports!
Alternatively, manually 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.
The main entry point for reports are the report-functions. See the function help or the report vignette for further options.
data %>%
filter(sd_gender != "diverse") %>%
report_metrics(starts_with("cg_adoption_"), sd_gender)Expectations
Plot
Table
| 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) |
Index: Plot
Index: Table
| Gender | min | q1 | median | q3 | max | m | sd | missing | n | items | alpha |
|---|---|---|---|---|---|---|---|---|---|---|---|
| female | 2 | 2.5 | 2.9 | 3.1 | 3.8 | 2.9 | 0.4 | 0 | 40 | 12 | 0.81 |
| male | 1 | 2.5 | 2.8 | 3.2 | 5.0 | 2.9 | 0.7 | 0 | 60 | 12 | 0.81 |
| Total | 1 | 2.5 | 2.8 | 3.2 | 5.0 | 2.9 | 0.6 | 0 | 100 | 12 | 0.81 |
If you want to add content before the report outputs, set the title
parameter to FALSE and add your own title.
A good place for methodological details is a tabsheet next to the
“Plot” and the “Table” buttons. You can add a tab by setting the
close-parameter to FALSE and adding a new header on the
fifth level (5 x # followed by the tab name). Close your new tabsheet
with #### {-} (4 x #). See the example Markdown behind this
vignette.
Adoption types
data %>%
filter(sd_gender != "diverse") %>%
report_counts(adopter, sd_gender, prop="rows", title= FALSE, close= FALSE)Plot
Table
| Gender | Total | I try new offers immediately | I try new offers rather quickly | I wait until offers establish themselves | I only use new offers when I have no other choice |
|---|---|---|---|---|---|
| female |
100% (40) |
5% (2) |
62% (25) |
32% (13) |
0% (0) |
| male |
100% (60) |
20% (12) |
63% (38) |
15% (9) |
2% (1) |
| Total |
100% (100) |
14% (14) |
63% (63) |
22% (22) |
1% (1) |
Method
Basis: Only male and female respondents.
Customizing outputs
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:
- labels: Labels are extracted from the column attributes, if present.
Set to FALSE to output bare column names and values.
- title: All plots usually get a title derived from the column
attributes or column names. Set to FALSE to suppress the title or
provide a title of your choice as a character value.
- percent: Frequency tables show percentages by default. Set to FALSE to get raw proportions - easier to postprocess in further calculations.
- digits: Tables containing means and standard deviations by default round values to one digit. Increase the number to show more digits.
- numbers: Bar plots give quick impressions, tables provide exact numbers. In bar charts you can combine both and print the frequencies onto the bars. Set the numbers parameter to “n”, “p” or c(“n”,“p”). To prevent cluttering and overlaps, numbers are only plotted on bars larger than 5%.
- values: The more variables you desire, the denser the output must be. Some tables try to serve you insights at the maximum and show two values in one cell, for example the absolute counts (n) and the percentages (p), or the mean (m) and the standard deviation (sd). Control your desire with the values-parameter.
- prop: Calculating percentages in a cross tab requires careful selection of the base. You can choose between total, row or column percentages. For stacked bar charts, displaying row percentages instead of total percentages gives a direct visual comparison of groups.
- negative: In surveys, negative values such as -9 or -2 are often used to mark missing values or residual answers (“I don’t know”). Therefore, all metric tables and plots remove negative values before calculation distribution parameters such as the mean. Set negative to TRUE for including those values. By the way: The handy prepare()-function can be used to recode all -9 to NA in a dataset.
- missings: The number of missing values is an important indicator for data quality. In reports, the missings usually are ommited and so do the package functions by default. For data set statics - for example when you monitor an ongoing survey or prepare a data set - you should set the missings-parameter to TRUE if the function supports it.
- ordered: Sometimes categories have an order, from low to high or from few to many. It helps visual inspections to plot ordered values with shaded colors instead of arbitrary colors. For frequency plots, you can inform the method about the desired order. By default the functions try to automatically detect a sensitive order.
- limits: Do you know how to create misleading graphs? It happens when you truncate the minimum or maximum value in a scale. The scale limits are automatically guessed by the package functions (work in progress). Use the limits-parameter to manually fix any misleading graphs.
Custom labels: Where do they come from?
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(data)# 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 <NA> case <NA> <NA>
2 sd_age sd <NA> Age <NA> <NA>
3 cg_activities cg <NA> Activities with C… <NA> <NA>
4 use_private use <NA> Usage: in private… 1 never
5 use_private use <NA> Usage: in private… 2 rarely
6 use_private use <NA> Usage: in private… 3 several ti…
7 use_private use <NA> Usage: in private… 4 several ti…
8 use_private use <NA> Usage: in private… 5 almost dai…
9 use_work use <NA> Usage: in profess… 1 never
10 use_work use <NA> Usage: in profess… 2 rarely
# ℹ 84 more rowsYou can set custom or new labels with labs_apply() by
providing a tibble with item names in the first column and item labels
in the second column.
newlabels <- tribble(
~item_name, ~item_label,
"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"
)
data %>%
labs_apply(newlabels) %>%
tab_metrics_items(starts_with("cg_adoption_advantage_"))| Item | min | q1 | median | q3 | max | m | sd | missing | n |
|---|---|---|---|---|---|---|---|---|---|
| Allgemeine Vorteile | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 2 | 101 |
| Finanzielle Vorteile | 1 | 2 | 3 | 4 | 5 | 2.7 | 1.2 | 0 | 101 |
| Vorteile bei der Arbeit | 1 | 3 | 4 | 4 | 5 | 3.6 | 1.1 | 0 | 101 |
| Macht mehr Spaß | 1 | 3 | 4 | 4 | 5 | 3.5 | 1.0 | 0 | 101 |
You can remove all labels with labs_clear() to get a
plain dataset.
data %>%
labs_clear(everything()) %>%
tab_counts(starts_with("cg_adoption_advantage_"))| cg_adoption_advantage_0 | 1 | 2 | 3 | 4 | 5 | Total |
|---|---|---|---|---|---|---|
| 1 | 6% (6) | 8% (8) | 34% (34) | 37% (37) | 14% (14) | 100% (99) |
| 2 | 22% (22) | 21% (21) | 29% (29) | 21% (21) | 6% (6) | 100% (99) |
| 3 | 6% (6) | 10% (10) | 21% (21) | 45% (45) | 17% (17) | 100% (99) |
| 4 | 6% (6) | 4% (4) | 35% (35) | 39% (39) | 15% (15) | 100% (99) |
With the labels parameter, you achieve a similar result.
data %>%
tab_counts(starts_with("cg_adoption_advantage_"), labels= FALSE)| cg_adoption_advantage_0 | 1 | 2 | 3 | 4 | 5 | Total |
|---|---|---|---|---|---|---|
| 1 | 6% (6) | 8% (8) | 34% (34) | 37% (37) | 14% (14) | 100% (99) |
| 2 | 22% (22) | 21% (21) | 29% (29) | 21% (21) | 6% (6) | 100% (99) |
| 3 | 6% (6) | 10% (10) | 21% (21) | 45% (45) | 17% (17) | 100% (99) |
| 4 | 6% (6) | 4% (4) | 35% (35) | 39% (39) | 15% (15) | 100% (99) |
Scales
You can calculate mean indexes from a bunch of items using
idx_add(). 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
data %>%
idx_add(starts_with("cg_adoption_")) %>%
tab_metrics(idx_cg_adoption)| Index: cg_adoption | value |
|---|---|
| min | 1 |
| q1 | 2.5 |
| median | 2.8 |
| q3 | 3.2 |
| max | 5 |
| m | 2.9 |
| sd | 0.6 |
| missing | 0 |
| n | 101 |
| items | 12 |
| alpha | 0.81 |
Compare the index values by group
data %>%
idx_add(starts_with("cg_adoption_")) %>%
tab_metrics(idx_cg_adoption, adopter)| Innovator type | min | q1 | median | q3 | max | m | sd | missing | n | items | alpha |
|---|---|---|---|---|---|---|---|---|---|---|---|
| I try new offers immediately | 1.5 | 3.2 | 3.3 | 4.1 | 5.0 | 3.5 | 0.9 | 0 | 15 | 12 | 0.81 |
| I try new offers rather quickly | 1.8 | 2.5 | 2.8 | 3.1 | 3.8 | 2.8 | 0.5 | 0 | 63 | 12 | 0.81 |
| I wait until offers establish themselves | 1.0 | 2.4 | 2.8 | 3.1 | 3.8 | 2.7 | 0.6 | 0 | 22 | 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 | NA | 0 | 1 | 12 | 0.81 |
| Total | 1.0 | 2.5 | 2.8 | 3.2 | 5.0 | 2.9 | 0.6 | 0 | 101 | 12 | 0.81 |
Add multiple indizes and summarize them
data %>%
idx_add(starts_with("cg_adoption_")) %>%
idx_add(starts_with("cg_adoption_advantage")) %>%
idx_add(starts_with("cg_adoption_fearofuse")) %>%
idx_add(starts_with("cg_adoption_social")) %>%
tab_metrics(starts_with("idx_cg_adoption"))| Index: cg_adoption | min | q1 | median | q3 | max | m | sd | missing | n |
|---|---|---|---|---|---|---|---|---|---|
| Index: cg_adoption | 1 | 2.5 | 2.8 | 3.2 | 5 | 2.9 | 0.6 | 0 | 101 |
| _advantage | 1 | 3.0 | 3.5 | 3.8 | 5 | 3.3 | 0.8 | 0 | 101 |
| _fearofuse | 1 | 2.5 | 3.0 | 3.5 | 5 | 3.0 | 0.8 | 0 | 101 |
| _social | 1 | 1.5 | 2.0 | 2.8 | 5 | 2.3 | 1.0 | 0 | 101 |
What’s behind the scenes?
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.
Basically, all table values are calculated two tidyverse functions:
- count() is used to produce counts
- skim() is used to produce metrics
To shape the data frames, two essential functions come into play:
- group_by() is used to calculate grouped outputs
- pivot_longer() brings multiple items into a format where the item name becomes a grouping variable.
Plots are generated by ggplot().
The package provides print- and knit-functions that pimp console and
markdown output. To make this work, the cleanded data, produced plots,
tables and markdown snippets gain new classes (vlkr_df,
vlkr_plt, vlkr_tbl,
vlkr_rprt).