README.md
In LukasWallrich/timesaveR: Functions to Accelerate (Academic) Data Analysis and Reporting
timesaveR 
This package aims to speed up research projects in social psychology
(and related fields). For that, it primarily includes some functions
that help lay the groundwork and others that facilitate the reporting of
results.
Among others, the package can help with the following:
- Creating scales, including reverse-coding and reporting their
internal consistency,
- Creating correlation tables in APA style, including descriptive
statistics and confidence intervals, and an option to use survey
weights, multiple imputation or full-information maximum-likelihood
estimation
- Creating regression tables comparing standardised and
non-standardised regression coefficients, and comparing the F-change
between two nested models
- Formatting numbers for statistical reporting, including rounding
with trailing zeros, or displaying numbers as p-values or as
confidence intervals
- Data entry and transfer, for quick interactive use. This includes
splitting clipboard content into vectors, converting tibbles to
tribble code, or obtaining nicely formatted code and results to paste
into another application
Why use this package?
There are many packages that support data analysis and reporting. For
instance, the psych package offers functions to create scales, while
the modelsummary package offers options to create customisable tables
in a wide variety of output format. They power many of the functions
offered here ‘under the hood.’
apa and papaja are two packages that directly support the reporting
of results in APA style - they can complement this package well.
However, none of the existing offered quite what we needed. This package
- takes an end-to-end view of the data analysis process, streamlining
time-consuming steps at various stages
- offers analysis templates that make it easy to get started,
particularly for R novices,
- prioritises publication-readiness and good reporting practices over
customisability in creating tables and charts
- integrates with the
tidyverse by supporting tidy evaluation and
returning tibbles where possible
Installation
You can install timesaveR from GitHub with the command below. If you do
not have the remotes-package installed, run
install.packages("remote") first.
remotes::install_github('lukaswallrich/timesaveR')
Get started
There are many functions in the package, and we will create vignettes
detailing various use cases. However, the following can give you an
initial flavor. The examples use data from the European Social Survey
Wave 7 (2014). Here, I ignore survey weights. However, the package
offers similar functions for analysing weighted survey data, which are
explained in the survey data vignette.
Load the package
(I also load dplyr since that is the recommended usage - of course,
there are base R alternatives for all of the steps.)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.
Create scales
Let’s create scales for health behaviours and depressive symptoms, each
including some reverse coding.
scales <- list(
depression = c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl",
"enjlf", "fltsd", "cldgng"),
healthy_eating = c("etfruit", "eatveg")
)
scales_reverse <- list(
depression = c("wrhpp", "enjlf"),
healthy_eating = c("etfruit", "eatveg")
)
scales <- make_scales(ess_health, items = scales, reversed = scales_reverse)
#> The following scales will be calculated with specified reverse coding: depression, healthy_eating
#Check descriptives, including reliability
scales$descriptives
#> # A tibble: 2 × 10
#> Scale n_items reliability reliability_method mean SD reversed rev_min
#> <chr> <int> <dbl> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 depression 8 0.802 cronbachs_alpha 1.67 0.484 wrhpp e… 1
#> 2 healthy_e… 2 0.658 spearman_brown 4.97 1.11 etfruit… 1
#> # ℹ 2 more variables: rev_max <dbl>, text <chr>
#Add scale scores to dataset
ess_health <- bind_cols(ess_health, scales$scores)
Report correlations and descriptive statistics
Next, we are often interested in descriptive statistics, variable
distributions and correlations.
ess_health %>% select(agea, health, depression, healthy_eating) %>%
cor_matrix() %>% report_cor_table()
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Variable
M (SD)
1
2
3
- agea
50.61 (18.51)
- health
2.26 (0.92)
.28 ***[0.25, 0.30]
- depression
1.67 (0.48)
.03 *[0.00, 0.05]
.42 ***[0.40, 0.44]
- healthy_eating
4.97 (1.11)
.17 ***[0.15, 0.19]
-.09 ***[-0.11, -0.07]
-.13 ***[-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, ** p < .01, *** p < .001
It is often helpful to rename variables in this step
Use get_rename_tribbles(ess_health) to get most of this code
var_renames <- tibble::tribble(
~old, ~new,
"agea", "Age",
"health", "Poor health",
"depression", "Depression",
"healthy_eating", "Healthy eating",
)
A rename tibble or vector automatically only selects the variables included into it
ess_health %>% cor_matrix(var_names = var_renames) %>% report_cor_table()
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<p>text-indent: 20px;
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<h1>qvuoajfxcb .gt_indent_5 {</h1>
<p>text-indent: 25px;
}
<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="<em>M (SD)</em>"><em>M (SD)</em></th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>
- Age
50.61 (18.51)
- Poor health
2.26 (0.92)
.28 [0.25, 0.30]
- Depression
1.67 (0.48)
.03 [0.00, 0.05]
.42 [0.40, 0.44]
- Healthy eating
4.97 (1.11)
.17 [0.15, 0.19]
-.09 [-0.11, -0.07]
-.13 [-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, * p < .01, p < .001
Often, it is also interesting to include variable distributions
ess_health %>% cor_matrix(var_names = var_renames) %>%
report_cor_table(add_distributions = TRUE, data = ess_health)
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<h1>actthsbpbh .gt_stub {</h1>
<p>color: #333333;
background-color: #FFFFFF;
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<p>color: #333333;
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background-color: #FFFFFF;
text-transform: inherit;
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<h1>actthsbpbh .gt_indent_5 {</h1>
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<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="<em>M (SD)</em>"><em>M (SD)</em></th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Distributions">Distributions</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>
- Age
50.61 (18.51)
![]()
- Poor health
2.26 (0.92)
![]()
.28 [0.25, 0.30]
- Depression
1.67 (0.48)
![]()
.03 [0.00, 0.05]
.42 [0.40, 0.44]
- Healthy eating
4.97 (1.11)
![]()
.17 [0.15, 0.19]
-.09 [-0.11, -0.07]
-.13 [-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, * p < .01, p < .001
Describe categorical variables and their relation with an outcome
Often, we are also interested in how the means of an outcome variable
differ between different groups. It can be fiddly to get these tables
and the pairwise significance tests done, but this function does it in a
breeze.
# Start with this in the console - that gets you 80% of the tribbles below.
# get_rename_tribbles(ess_health, gndr, cntry)
var_renames <- tribble(
~old, ~new,
"gndr", "Gender",
"cntry", "Country"
)
level_renames <- tribble(
~var, ~level_old, ~level_new,
"gndr", "1", "male",
"gndr", "2", "female",
"cntry", "DE", "Germany",
"cntry", "FR", "France",
"cntry", "GB", "UK"
)
report_cat_vars(ess_health, health, gndr, cntry, var_names = var_renames,
level_names = level_renames)
body{background-color:white;}
#ypnxlklxpy table {
font-family: times, system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
#ypnxlklxpy thead, #ypnxlklxpy tbody, #ypnxlklxpy tfoot, #ypnxlklxpy tr, #ypnxlklxpy td, #ypnxlklxpy th {
border-style: none;
}
#ypnxlklxpy p {
margin: 0;
padding: 0;
}
#ypnxlklxpy .gt_table {
display: table;
border-collapse: collapse;
line-height: normal;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: 12px;
font-weight: normal;
font-style: none;
background-color: #FFFFFF;
width: auto;
border-top-style: none;
border-top-width: 2px;
border-top-color: #FFFFFF;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
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N
Share
M (SD)
Gender
male
3482
48.2%
2.23 (0.90) a
female
3744
51.8%
2.30 (0.93) b
Country
Germany
3045
42.1%
2.34 (0.88) a
France
1917
26.5%
2.29 (0.89) a
UK
2264
31.3%
2.14 (0.97) b
M and SD are used to represent mean and standard
deviation for health for that group, respectively.
Within each variable, the means of groups with different superscripts
differ with p \< .05 (p-values were adjusted
using the Holm-method.)
Report regression models with standardized coefficients
In psychology, it is often expected that regression models are reported
with both unstandardised (B) and standardized (beta) coefficients. This
can be fiddly as separate tables will contain too much redundant
information. The functions below easily run a model with standardised
variables and create a publication-ready table.
ess_health$gndr <- factor(ess_health$gndr)
#Standard lm model
mod1 <- lm(depression ~ agea + gndr + health + cntry, ess_health)
#Model with standardised coefficients
mod2 <- lm_std(depression ~ agea + gndr + health + cntry, ess_health)
report_lm_with_std(mod1, mod2)
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B (SE)
β \[95% CI\]
(Intercept)
1.20 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
agea
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
gndr2
0.12 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.47\]
cntryFR
-0.01 (0.01)
-0.02 \[-0.08, 0.03\]
cntryGB
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
N
7171
R2
.20
F-tests
F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized
(Fox, 2015), β for dummy variables are semi-standardized, indicating the
impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \*\* p \< .01, \*\*\*
p \< .001
#Often the coefficients should be renamed - get_coef_rename_tribble(mod1)
#is the starting point. In that, markdown formatting can be used.
coef_names <- tribble(
~old, ~new,
"(Intercept)", "*(Intercept)*",
"agea", "Age",
"gndr2", "Gender *(female)*",
"health", "Poor health",
"cntryFR", "France *(vs DE)*",
"cntryGB", "UK *(vs DE)*"
)
report_lm_with_std(mod1, mod2, coef_renames = coef_names)
body{background-color:white;}
#qnkfogpjwm table {
font-family: times, system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
#qnkfogpjwm thead, #qnkfogpjwm tbody, #qnkfogpjwm tfoot, #qnkfogpjwm tr, #qnkfogpjwm td, #qnkfogpjwm th {
border-style: none;
}
#qnkfogpjwm p {
margin: 0;
padding: 0;
}
#qnkfogpjwm .gt_table {
display: table;
border-collapse: collapse;
line-height: normal;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: 12px;
font-weight: normal;
font-style: none;
background-color: #FFFFFF;
width: auto;
border-top-style: none;
border-top-width: 2px;
border-top-color: #FFFFFF;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}
#qnkfogpjwm .gt_caption {
padding-top: 4px;
padding-bottom: 4px;
}
#qnkfogpjwm .gt_title {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
border-bottom-color: #FFFFFF;
border-bottom-width: 0;
}
#qnkfogpjwm .gt_subtitle {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 3px;
padding-bottom: 5px;
padding-left: 5px;
padding-right: 5px;
border-top-color: #FFFFFF;
border-top-width: 0;
}
#qnkfogpjwm .gt_heading {
background-color: #FFFFFF;
text-align: center;
border-bottom-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_bottom_border {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #000000;
}
#qnkfogpjwm .gt_col_headings {
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_col_heading {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
padding-left: 5px;
padding-right: 5px;
overflow-x: hidden;
}
#qnkfogpjwm .gt_column_spanner_outer {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
padding-top: 0;
padding-bottom: 0;
padding-left: 4px;
padding-right: 4px;
}
#qnkfogpjwm .gt_column_spanner_outer:first-child {
padding-left: 0;
}
#qnkfogpjwm .gt_column_spanner_outer:last-child {
padding-right: 0;
}
#qnkfogpjwm .gt_column_spanner {
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 5px;
overflow-x: hidden;
display: inline-block;
width: 100%;
}
#qnkfogpjwm .gt_spanner_row {
border-bottom-style: hidden;
}
#qnkfogpjwm .gt_group_heading {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
text-align: left;
}
#qnkfogpjwm .gt_empty_group_heading {
padding: 0.5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: middle;
}
#qnkfogpjwm .gt_from_md > :first-child {
margin-top: 0;
}
#qnkfogpjwm .gt_from_md > :last-child {
margin-bottom: 0;
}
#qnkfogpjwm .gt_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
margin: 10px;
border-top-style: none;
border-top-width: 1px;
border-top-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
overflow-x: hidden;
}
#qnkfogpjwm .gt_stub {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_stub_row_group {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
vertical-align: top;
}
#qnkfogpjwm .gt_row_group_first td {
border-top-width: 2px;
}
#qnkfogpjwm .gt_row_group_first th {
border-top-width: 2px;
}
#qnkfogpjwm .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_first_summary_row {
border-top-style: none;
border-top-color: #D3D3D3;
}
#qnkfogpjwm .gt_first_summary_row.thick {
border-top-width: 2px;
}
#qnkfogpjwm .gt_last_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#qnkfogpjwm .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: none;
border-top-width: 6px;
border-top-color: #D3D3D3;
}
#qnkfogpjwm .gt_last_grand_summary_row_top {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: none;
border-bottom-width: 6px;
border-bottom-color: #D3D3D3;
}
#qnkfogpjwm .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}
#qnkfogpjwm .gt_table_body {
border-top-style: solid;
border-top-width: 1px;
border-top-color: #000000;
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
}
#qnkfogpjwm .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_left {
text-align: left;
}
#qnkfogpjwm .gt_center {
text-align: center;
}
#qnkfogpjwm .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}
#qnkfogpjwm .gt_font_normal {
font-weight: normal;
}
#qnkfogpjwm .gt_font_bold {
font-weight: bold;
}
#qnkfogpjwm .gt_font_italic {
font-style: italic;
}
#qnkfogpjwm .gt_super {
font-size: 65%;
}
#qnkfogpjwm .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#qnkfogpjwm .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#qnkfogpjwm .gt_indent_1 {
text-indent: 5px;
}
#qnkfogpjwm .gt_indent_2 {
text-indent: 10px;
}
#qnkfogpjwm .gt_indent_3 {
text-indent: 15px;
}
#qnkfogpjwm .gt_indent_4 {
text-indent: 20px;
}
#qnkfogpjwm .gt_indent_5 {
text-indent: 25px;
}
B (SE)
β \[95% CI\]
(Intercept)
1.20 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
Age
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
Gender (female)
0.12 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
Poor health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.47\]
France (vs DE)
-0.01 (0.01)
-0.02 \[-0.08, 0.03\]
UK (vs DE)
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
N
7171
R2
.20
F-tests
F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized
(Fox, 2015), β for dummy variables are semi-standardized, indicating the
impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \*\* p \< .01, \*\*\*
p \< .001
#You can also easily display multiple nested models side-by-side and get the
#F-change significance test. For that, all models need to be fit on the same
#dataset, so that I will drop all missing data.
mod1 <- lm(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))
mod2 <- lm_std(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))
mod3 <- lm(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))
mod4 <- lm_std(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))
coef_names <- tribble(
~old, ~new,
"(Intercept)", "*(Intercept)*",
"agea", "Age",
"gndr2", "Gender *(female)*",
"health", "Poor health",
"cntryFR", "France *(vs DE)*",
"cntryGB", "UK *(vs DE)*",
"eisced", "Education",
"agea:gndr2", "Age x Female",
)
report_lm_with_std(mod = list(mod1, mod3), mod_std = list(mod2, mod4),
coef_renames = coef_names, R2_change = TRUE)
#> Warning: Automatic coercion from double to character was deprecated in purrr 1.0.0.
#> ℹ Please use an explicit call to `as.character()` within `map_chr()` instead.
#> ℹ The deprecated feature was likely used in the timesaveR package.
#> Please report the issue at
#> <https://github.com/LukasWallrich/timesaveR/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
body{background-color:white;}
#qfvwbyqenp table {
font-family: times, system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
#qfvwbyqenp thead, #qfvwbyqenp tbody, #qfvwbyqenp tfoot, #qfvwbyqenp tr, #qfvwbyqenp td, #qfvwbyqenp th {
border-style: none;
}
#qfvwbyqenp p {
margin: 0;
padding: 0;
}
#qfvwbyqenp .gt_table {
display: table;
border-collapse: collapse;
line-height: normal;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: 12px;
font-weight: normal;
font-style: none;
background-color: #FFFFFF;
width: auto;
border-top-style: none;
border-top-width: 2px;
border-top-color: #FFFFFF;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}
#qfvwbyqenp .gt_caption {
padding-top: 4px;
padding-bottom: 4px;
}
#qfvwbyqenp .gt_title {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
border-bottom-color: #FFFFFF;
border-bottom-width: 0;
}
#qfvwbyqenp .gt_subtitle {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 3px;
padding-bottom: 5px;
padding-left: 5px;
padding-right: 5px;
border-top-color: #FFFFFF;
border-top-width: 0;
}
#qfvwbyqenp .gt_heading {
background-color: #FFFFFF;
text-align: center;
border-bottom-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qfvwbyqenp .gt_bottom_border {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #000000;
}
#qfvwbyqenp .gt_col_headings {
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qfvwbyqenp .gt_col_heading {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
padding-left: 5px;
padding-right: 5px;
overflow-x: hidden;
}
#qfvwbyqenp .gt_column_spanner_outer {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
padding-top: 0;
padding-bottom: 0;
padding-left: 4px;
padding-right: 4px;
}
#qfvwbyqenp .gt_column_spanner_outer:first-child {
padding-left: 0;
}
#qfvwbyqenp .gt_column_spanner_outer:last-child {
padding-right: 0;
}
#qfvwbyqenp .gt_column_spanner {
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 5px;
overflow-x: hidden;
display: inline-block;
width: 100%;
}
#qfvwbyqenp .gt_spanner_row {
border-bottom-style: hidden;
}
#qfvwbyqenp .gt_group_heading {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
text-align: left;
}
#qfvwbyqenp .gt_empty_group_heading {
padding: 0.5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: middle;
}
#qfvwbyqenp .gt_from_md > :first-child {
margin-top: 0;
}
#qfvwbyqenp .gt_from_md > :last-child {
margin-bottom: 0;
}
#qfvwbyqenp .gt_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
margin: 10px;
border-top-style: none;
border-top-width: 1px;
border-top-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
overflow-x: hidden;
}
#qfvwbyqenp .gt_stub {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
}
#qfvwbyqenp .gt_stub_row_group {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
vertical-align: top;
}
#qfvwbyqenp .gt_row_group_first td {
border-top-width: 2px;
}
#qfvwbyqenp .gt_row_group_first th {
border-top-width: 2px;
}
#qfvwbyqenp .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qfvwbyqenp .gt_first_summary_row {
border-top-style: none;
border-top-color: #D3D3D3;
}
#qfvwbyqenp .gt_first_summary_row.thick {
border-top-width: 2px;
}
#qfvwbyqenp .gt_last_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#qfvwbyqenp .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qfvwbyqenp .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: none;
border-top-width: 6px;
border-top-color: #D3D3D3;
}
#qfvwbyqenp .gt_last_grand_summary_row_top {
padding-top: 8px;
padding-bottom: 8px;
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Model 1
Model 2
B (SE)
β \[95% CI\]
B (SE)
β \[95% CI\]
(Intercept)
1.21 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
1.27 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
Age
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
-0.00 (0.00)\*\*\*
-0.14 \[-0.17, -0.10\]
Gender (female)
0.11 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
0.03 (0.03)
0.24 \[0.20, 0.28\]
Poor health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.46\]
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.46\]
France (vs DE)
-0.01 (0.01)
-0.03 \[-0.08, 0.02\]
-0.01 (0.01)
-0.03 \[-0.08, 0.02\]
UK (vs DE)
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
Education
-0.00 (0.00)†
-0.02 \[-0.04, 0.00\]
Age x Female
0.00 (0.00)\*\*
0.07 \[0.02, 0.11\]
N
6852
6852
R2
.20
.20
F-tests
F(5, 6846) = 337.06, p \< .001
F(7, 6844) = 243.10, p \< .001
Change
ΔR2 = .00, F(2, 6844) = 6.78, p =
.001
Given that dummy variables lose their interpretability when standardized
(Fox, 2015), β for dummy variables are semi-standardized, indicating the
impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \*\* p \< .01, \*\*\*
p \< .001
Related/alternative packages
modelsummary
allows you to create highly customisable tables with data summaries or
the output of statistical models that can be saved in a wide range of
formats.apa mostly
offers functions that turn the output of statistical tests (e.g.,
t-tests) into text, in line with APA guidelines.papaja offers the opportunity to
create full APA-style journal manuscripts in R. It’s apa_table
function is a generic alternative to the table functions in this
package, which support many more types of models, but includes fewer
details.
LukasWallrich/timesaveR documentation built on Nov. 29, 2024, 4:47 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
timesaveR
This package aims to speed up research projects in social psychology (and related fields). For that, it primarily includes some functions that help lay the groundwork and others that facilitate the reporting of results.
Among others, the package can help with the following:
- Creating scales, including reverse-coding and reporting their internal consistency,
- Creating correlation tables in APA style, including descriptive statistics and confidence intervals, and an option to use survey weights, multiple imputation or full-information maximum-likelihood estimation
- Creating regression tables comparing standardised and non-standardised regression coefficients, and comparing the F-change between two nested models
- Formatting numbers for statistical reporting, including rounding with trailing zeros, or displaying numbers as p-values or as confidence intervals
- Data entry and transfer, for quick interactive use. This includes splitting clipboard content into vectors, converting tibbles to tribble code, or obtaining nicely formatted code and results to paste into another application
Why use this package?
There are many packages that support data analysis and reporting. For
instance, the psych package offers functions to create scales, while
the modelsummary package offers options to create customisable tables
in a wide variety of output format. They power many of the functions
offered here ‘under the hood.’
apa and papaja are two packages that directly support the reporting
of results in APA style - they can complement this package well.
However, none of the existing offered quite what we needed. This package
- takes an end-to-end view of the data analysis process, streamlining time-consuming steps at various stages
- offers analysis templates that make it easy to get started, particularly for R novices,
- prioritises publication-readiness and good reporting practices over customisability in creating tables and charts
- integrates with the
tidyverseby supporting tidy evaluation and returning tibbles where possible
Installation
You can install timesaveR from GitHub with the command below. If you do
not have the remotes-package installed, run
install.packages("remote") first.
remotes::install_github('lukaswallrich/timesaveR')
Get started
There are many functions in the package, and we will create vignettes detailing various use cases. However, the following can give you an initial flavor. The examples use data from the European Social Survey Wave 7 (2014). Here, I ignore survey weights. However, the package offers similar functions for analysing weighted survey data, which are explained in the survey data vignette.
Load the package
(I also load dplyr since that is the recommended usage - of course,
there are base R alternatives for all of the steps.)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.
Create scales
Let’s create scales for health behaviours and depressive symptoms, each including some reverse coding.
scales <- list(
depression = c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl",
"enjlf", "fltsd", "cldgng"),
healthy_eating = c("etfruit", "eatveg")
)
scales_reverse <- list(
depression = c("wrhpp", "enjlf"),
healthy_eating = c("etfruit", "eatveg")
)
scales <- make_scales(ess_health, items = scales, reversed = scales_reverse)
#> The following scales will be calculated with specified reverse coding: depression, healthy_eating
#Check descriptives, including reliability
scales$descriptives
#> # A tibble: 2 × 10
#> Scale n_items reliability reliability_method mean SD reversed rev_min
#> <chr> <int> <dbl> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 depression 8 0.802 cronbachs_alpha 1.67 0.484 wrhpp e… 1
#> 2 healthy_e… 2 0.658 spearman_brown 4.97 1.11 etfruit… 1
#> # ℹ 2 more variables: rev_max <dbl>, text <chr>
#Add scale scores to dataset
ess_health <- bind_cols(ess_health, scales$scores)
Report correlations and descriptive statistics
Next, we are often interested in descriptive statistics, variable distributions and correlations.
ess_health %>% select(agea, health, depression, healthy_eating) %>%
cor_matrix() %>% report_cor_table()
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Variable
M (SD)
1
2
3
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, ** p < .01, *** p < .001
- agea
50.61 (18.51)
- health
2.26 (0.92)
.28 ***[0.25, 0.30]
- depression
1.67 (0.48)
.03 *[0.00, 0.05]
.42 ***[0.40, 0.44]
- healthy_eating
4.97 (1.11)
.17 ***[0.15, 0.19]
-.09 ***[-0.11, -0.07]
-.13 ***[-0.15, -0.10]
It is often helpful to rename variables in this step
Use get_rename_tribbles(ess_health) to get most of this code
var_renames <- tibble::tribble( ~old, ~new, "agea", "Age", "health", "Poor health", "depression", "Depression", "healthy_eating", "Healthy eating", )
A rename tibble or vector automatically only selects the variables included into it
ess_health %>% cor_matrix(var_names = var_renames) %>% report_cor_table()
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M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, * p < .01, p < .001
<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="<em>M (SD)</em>"><em>M (SD)</em></th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>
- Age
50.61 (18.51)
- Poor health
2.26 (0.92)
.28 [0.25, 0.30]
- Depression
1.67 (0.48)
.03 [0.00, 0.05]
.42 [0.40, 0.44]
- Healthy eating
4.97 (1.11)
.17 [0.15, 0.19]
-.09 [-0.11, -0.07]
-.13 [-0.15, -0.10]
Often, it is also interesting to include variable distributions
ess_health %>% cor_matrix(var_names = var_renames) %>% report_cor_table(add_distributions = TRUE, data = ess_health)
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M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, * p < .01, p < .001
<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="<em>M (SD)</em>"><em>M (SD)</em></th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Distributions">Distributions</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>
- Age
50.61 (18.51)
- Poor health
2.26 (0.92)
.28 [0.25, 0.30]
- Depression
1.67 (0.48)
.03 [0.00, 0.05]
.42 [0.40, 0.44]
- Healthy eating
4.97 (1.11)
.17 [0.15, 0.19]
-.09 [-0.11, -0.07]
-.13 [-0.15, -0.10]
Describe categorical variables and their relation with an outcome
Often, we are also interested in how the means of an outcome variable differ between different groups. It can be fiddly to get these tables and the pairwise significance tests done, but this function does it in a breeze.
# Start with this in the console - that gets you 80% of the tribbles below.
# get_rename_tribbles(ess_health, gndr, cntry)
var_renames <- tribble(
~old, ~new,
"gndr", "Gender",
"cntry", "Country"
)
level_renames <- tribble(
~var, ~level_old, ~level_new,
"gndr", "1", "male",
"gndr", "2", "female",
"cntry", "DE", "Germany",
"cntry", "FR", "France",
"cntry", "GB", "UK"
)
report_cat_vars(ess_health, health, gndr, cntry, var_names = var_renames,
level_names = level_renames)
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N
Share
M (SD)
Gender
48.2%
51.8%
Country
42.1%
26.5%
31.3%
M and SD are used to represent mean and standard
deviation for health for that group, respectively.
Within each variable, the means of groups with different superscripts
differ with p \< .05 (p-values were adjusted
using the Holm-method.)
male
3482
2.23 (0.90) a
female
3744
2.30 (0.93) b
Germany
3045
2.34 (0.88) a
France
1917
2.29 (0.89) a
UK
2264
2.14 (0.97) b
Report regression models with standardized coefficients
In psychology, it is often expected that regression models are reported with both unstandardised (B) and standardized (beta) coefficients. This can be fiddly as separate tables will contain too much redundant information. The functions below easily run a model with standardised variables and create a publication-ready table.
ess_health$gndr <- factor(ess_health$gndr)
#Standard lm model
mod1 <- lm(depression ~ agea + gndr + health + cntry, ess_health)
#Model with standardised coefficients
mod2 <- lm_std(depression ~ agea + gndr + health + cntry, ess_health)
report_lm_with_std(mod1, mod2)
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}
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border-top-width: 2px;
}
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color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
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padding-right: 5px;
}
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border-top-style: none;
border-top-color: #D3D3D3;
}
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padding-right: 5px;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#wetzcqixyw .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#wetzcqixyw .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
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border-top-color: #D3D3D3;
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padding-bottom: 8px;
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border-bottom-style: none;
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border-bottom-color: #D3D3D3;
}
#wetzcqixyw .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
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#wetzcqixyw .gt_table_body {
border-top-style: solid;
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border-top-color: #000000;
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border-bottom-width: 1px;
border-bottom-color: #000000;
}
#wetzcqixyw .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
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border-left-width: 2px;
border-left-color: #D3D3D3;
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border-right-width: 2px;
border-right-color: #D3D3D3;
}
#wetzcqixyw .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#wetzcqixyw .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
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border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#wetzcqixyw .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#wetzcqixyw .gt_left {
text-align: left;
}
#wetzcqixyw .gt_center {
text-align: center;
}
#wetzcqixyw .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}
#wetzcqixyw .gt_font_normal {
font-weight: normal;
}
#wetzcqixyw .gt_font_bold {
font-weight: bold;
}
#wetzcqixyw .gt_font_italic {
font-style: italic;
}
#wetzcqixyw .gt_super {
font-size: 65%;
}
#wetzcqixyw .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#wetzcqixyw .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#wetzcqixyw .gt_indent_1 {
text-indent: 5px;
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text-indent: 10px;
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text-indent: 15px;
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#wetzcqixyw .gt_indent_4 {
text-indent: 20px;
}
#wetzcqixyw .gt_indent_5 {
text-indent: 25px;
}
B (SE)
β \[95% CI\]
N
7171
R2
.20
F-tests
(Intercept)
1.20 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
agea
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
gndr2
0.12 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.47\]
cntryFR
-0.01 (0.01)
-0.02 \[-0.08, 0.03\]
cntryGB
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable. † p \< .1, \* p \< .05, \*\* p \< .01, \*\*\* p \< .001
#Often the coefficients should be renamed - get_coef_rename_tribble(mod1)
#is the starting point. In that, markdown formatting can be used.
coef_names <- tribble(
~old, ~new,
"(Intercept)", "*(Intercept)*",
"agea", "Age",
"gndr2", "Gender *(female)*",
"health", "Poor health",
"cntryFR", "France *(vs DE)*",
"cntryGB", "UK *(vs DE)*"
)
report_lm_with_std(mod1, mod2, coef_renames = coef_names)
body{background-color:white;}
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font-family: times, system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
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border-style: none;
}
#qnkfogpjwm p {
margin: 0;
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}
#qnkfogpjwm .gt_table {
display: table;
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border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}
#qnkfogpjwm .gt_caption {
padding-top: 4px;
padding-bottom: 4px;
}
#qnkfogpjwm .gt_title {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
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border-bottom-color: #FFFFFF;
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}
#qnkfogpjwm .gt_subtitle {
color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 3px;
padding-bottom: 5px;
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padding-right: 5px;
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}
#qnkfogpjwm .gt_heading {
background-color: #FFFFFF;
text-align: center;
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border-left-style: none;
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border-left-color: #D3D3D3;
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border-bottom-style: solid;
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border-bottom-color: #000000;
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border-top-style: none;
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border-top-color: #D3D3D3;
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border-bottom-color: #000000;
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border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_col_heading {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
padding-left: 5px;
padding-right: 5px;
overflow-x: hidden;
}
#qnkfogpjwm .gt_column_spanner_outer {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
padding-top: 0;
padding-bottom: 0;
padding-left: 4px;
padding-right: 4px;
}
#qnkfogpjwm .gt_column_spanner_outer:first-child {
padding-left: 0;
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padding-right: 0;
}
#qnkfogpjwm .gt_column_spanner {
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 5px;
overflow-x: hidden;
display: inline-block;
width: 100%;
}
#qnkfogpjwm .gt_spanner_row {
border-bottom-style: hidden;
}
#qnkfogpjwm .gt_group_heading {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
text-align: left;
}
#qnkfogpjwm .gt_empty_group_heading {
padding: 0.5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
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border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: middle;
}
#qnkfogpjwm .gt_from_md > :first-child {
margin-top: 0;
}
#qnkfogpjwm .gt_from_md > :last-child {
margin-bottom: 0;
}
#qnkfogpjwm .gt_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
margin: 10px;
border-top-style: none;
border-top-width: 1px;
border-top-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
overflow-x: hidden;
}
#qnkfogpjwm .gt_stub {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_stub_row_group {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
vertical-align: top;
}
#qnkfogpjwm .gt_row_group_first td {
border-top-width: 2px;
}
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border-top-width: 2px;
}
#qnkfogpjwm .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_first_summary_row {
border-top-style: none;
border-top-color: #D3D3D3;
}
#qnkfogpjwm .gt_first_summary_row.thick {
border-top-width: 2px;
}
#qnkfogpjwm .gt_last_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#qnkfogpjwm .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#qnkfogpjwm .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: none;
border-top-width: 6px;
border-top-color: #D3D3D3;
}
#qnkfogpjwm .gt_last_grand_summary_row_top {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
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border-bottom-style: none;
border-bottom-width: 6px;
border-bottom-color: #D3D3D3;
}
#qnkfogpjwm .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}
#qnkfogpjwm .gt_table_body {
border-top-style: solid;
border-top-width: 1px;
border-top-color: #000000;
border-bottom-style: solid;
border-bottom-width: 1px;
border-bottom-color: #000000;
}
#qnkfogpjwm .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
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border-right-width: 2px;
border-right-color: #D3D3D3;
}
#qnkfogpjwm .gt_footnote {
margin: 0px;
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padding-top: 4px;
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}
#qnkfogpjwm .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
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border-right-width: 2px;
border-right-color: #D3D3D3;
}
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text-align: center;
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text-align: right;
font-variant-numeric: tabular-nums;
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}
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}
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font-size: 65%;
}
#qnkfogpjwm .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#qnkfogpjwm .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#qnkfogpjwm .gt_indent_1 {
text-indent: 5px;
}
#qnkfogpjwm .gt_indent_2 {
text-indent: 10px;
}
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text-indent: 15px;
}
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text-indent: 20px;
}
#qnkfogpjwm .gt_indent_5 {
text-indent: 25px;
}
B (SE)
β \[95% CI\]
N
7171
R2
.20
F-tests
(Intercept)
1.20 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
Age
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
Gender (female)
0.12 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
Poor health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.47\]
France (vs DE)
-0.01 (0.01)
-0.02 \[-0.08, 0.03\]
UK (vs DE)
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable. † p \< .1, \* p \< .05, \*\* p \< .01, \*\*\* p \< .001
#You can also easily display multiple nested models side-by-side and get the
#F-change significance test. For that, all models need to be fit on the same
#dataset, so that I will drop all missing data.
mod1 <- lm(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))
mod2 <- lm_std(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))
mod3 <- lm(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))
mod4 <- lm_std(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))
coef_names <- tribble(
~old, ~new,
"(Intercept)", "*(Intercept)*",
"agea", "Age",
"gndr2", "Gender *(female)*",
"health", "Poor health",
"cntryFR", "France *(vs DE)*",
"cntryGB", "UK *(vs DE)*",
"eisced", "Education",
"agea:gndr2", "Age x Female",
)
report_lm_with_std(mod = list(mod1, mod3), mod_std = list(mod2, mod4),
coef_renames = coef_names, R2_change = TRUE)
#> Warning: Automatic coercion from double to character was deprecated in purrr 1.0.0.
#> ℹ Please use an explicit call to `as.character()` within `map_chr()` instead.
#> ℹ The deprecated feature was likely used in the timesaveR package.
#> Please report the issue at
#> <https://github.com/LukasWallrich/timesaveR/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
body{background-color:white;}
#qfvwbyqenp table {
font-family: times, system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
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}
#qfvwbyqenp p {
margin: 0;
padding: 0;
}
#qfvwbyqenp .gt_table {
display: table;
border-collapse: collapse;
line-height: normal;
margin-left: auto;
margin-right: auto;
color: #333333;
font-size: 12px;
font-weight: normal;
font-style: none;
background-color: #FFFFFF;
width: auto;
border-top-style: none;
border-top-width: 2px;
border-top-color: #FFFFFF;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #A8A8A8;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
}
#qfvwbyqenp .gt_caption {
padding-top: 4px;
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}
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color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
border-bottom-color: #FFFFFF;
border-bottom-width: 0;
}
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color: #333333;
font-size: 12px;
font-weight: initial;
padding-top: 3px;
padding-bottom: 5px;
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padding-right: 5px;
border-top-color: #FFFFFF;
border-top-width: 0;
}
#qfvwbyqenp .gt_heading {
background-color: #FFFFFF;
text-align: center;
border-bottom-color: #FFFFFF;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qfvwbyqenp .gt_bottom_border {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #000000;
}
#qfvwbyqenp .gt_col_headings {
border-top-style: none;
border-top-width: 2px;
border-top-color: #D3D3D3;
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border-bottom-width: 1px;
border-bottom-color: #000000;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
}
#qfvwbyqenp .gt_col_heading {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: normal;
text-transform: inherit;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 6px;
padding-left: 5px;
padding-right: 5px;
overflow-x: hidden;
}
#qfvwbyqenp .gt_column_spanner_outer {
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Model 1
Model 2
B (SE)
β \[95% CI\]
B (SE)
β \[95% CI\]
N
6852
6852
R2
.20
.20
F-tests
(Intercept)
1.21 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
1.27 (0.02)\*\*\*
-0.14 \[-0.18, -0.10\]
Age
-0.00 (0.00)\*\*\*
-0.10 \[-0.12, -0.08\]
-0.00 (0.00)\*\*\*
-0.14 \[-0.17, -0.10\]
Gender (female)
0.11 (0.01)\*\*\*
0.24 \[0.20, 0.28\]
0.03 (0.03)
0.24 \[0.20, 0.28\]
Poor health
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.46\]
0.23 (0.01)\*\*\*
0.44 \[0.42, 0.46\]
France (vs DE)
-0.01 (0.01)
-0.03 \[-0.08, 0.02\]
-0.01 (0.01)
-0.03 \[-0.08, 0.02\]
UK (vs DE)
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
0.04 (0.01)\*\*
0.08 \[0.03, 0.13\]
Education
-0.00 (0.00)†
-0.02 \[-0.04, 0.00\]
Age x Female
0.00 (0.00)\*\*
0.07 \[0.02, 0.11\]
F(5, 6846) = 337.06, p \< .001
F(7, 6844) = 243.10, p \< .001
Change ΔR2 = .00, F(2, 6844) = 6.78, p = .001 Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable. † p \< .1, \* p \< .05, \*\* p \< .01, \*\*\* p \< .001Related/alternative packages
modelsummaryallows you to create highly customisable tables with data summaries or the output of statistical models that can be saved in a wide range of formats.apamostly offers functions that turn the output of statistical tests (e.g., t-tests) into text, in line with APA guidelines.papajaoffers the opportunity to create full APA-style journal manuscripts in R. It’sapa_tablefunction is a generic alternative to the table functions in this package, which support many more types of models, but includes fewer details.
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