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Hi @crsh,
results tables from repeated-measures ANOVA may contain df and df.residual columns where sometimes the values are whole numbers, sometimes they have two digits after the decimal point, e.g.:
A data.frame with 7 labelled columns:
term estimate conf.int statistic df df.residual p.value
1 Treatment .312 [.000, .581] 4.63 2 10 .038
2 Gender .111 [.000, .437] 2.56 1 10 .141
3 Phase .264 [.003, .479] 20.87 1.60 15.99 < .001
4 Hour .185 [.000, .319] 17.01 1.84 18.41 < .001
5 Treatment $\\times$ Gender .218 [.000, .501] 2.86 2 10 .104
6 Treatment $\\times$ Phase .144 [.000, .292] 4.90 3.20 15.99 .012
7 Gender $\\times$ Phase .004 [.000, .000] 0.21 1.60 15.99 .766
8 Treatment $\\times$ Hour .002 [.000, .000] 0.09 3.68 18.41 .979
9 Gender $\\times$ Hour .005 [.000, .000] 0.41 1.84 18.41 .653
10 Phase $\\times$ Hour .023 [.000, .000] 1.15 3.60 35.96 .346
11 Treatment $\\times$ Gender $\\times$ Phase .021 [.000, .000] 0.64 3.20 15.99 .612
12 Treatment $\\times$ Gender $\\times$ Hour .016 [.000, .000] 0.62 3.68 18.41 .641
13 Treatment $\\times$ Phase $\\times$ Hour .013 [.000, .000] 0.33 7.19 35.96 .940
14 Gender $\\times$ Phase $\\times$ Hour .014 [.000, .000] 0.69 3.60 35.96 .589
15 Treatment $\\times$ Gender $\\times$ Phase $\\times$ Hour .029 [.000, .000] 0.74 7.19 35.96 .646
term : Effect
estimate : $\\hat{\\eta}^2_G$
conf.int : 90\\% CI
statistic: $F$
df : $\\mathit{df}^{\\mathrm{GG}}$
... (2 more labels)
attr(,"class")
[1] "apa_results" "list"
For better legibility, I wondered whether we should pad the whole numbers with whitespace in print.apa_results_table(), such as:
$table
A data.frame with 7 labelled columns:
term estimate conf.int statistic df df.residual p.value
1 Treatment .312 [.000, .581] 4.63 2 10 .038
2 Gender .111 [.000, .437] 2.56 1 10 .141
3 Phase .264 [.003, .479] 20.87 1.60 15.99 < .001
4 Hour .185 [.000, .319] 17.01 1.84 18.41 < .001
5 Treatment $\\times$ Gender .218 [.000, .501] 2.86 2 10 .104
6 Treatment $\\times$ Phase .144 [.000, .292] 4.90 3.20 15.99 .012
7 Gender $\\times$ Phase .004 [.000, .000] 0.21 1.60 15.99 .766
8 Treatment $\\times$ Hour .002 [.000, .000] 0.09 3.68 18.41 .979
9 Gender $\\times$ Hour .005 [.000, .000] 0.41 1.84 18.41 .653
10 Phase $\\times$ Hour .023 [.000, .000] 1.15 3.60 35.96 .346
11 Treatment $\\times$ Gender $\\times$ Phase .021 [.000, .000] 0.64 3.20 15.99 .612
12 Treatment $\\times$ Gender $\\times$ Hour .016 [.000, .000] 0.62 3.68 18.41 .641
13 Treatment $\\times$ Phase $\\times$ Hour .013 [.000, .000] 0.33 7.19 35.96 .940
14 Gender $\\times$ Phase $\\times$ Hour .014 [.000, .000] 0.69 3.60 35.96 .589
15 Treatment $\\times$ Gender $\\times$ Phase $\\times$ Hour .029 [.000, .000] 0.74 7.19 35.96 .646
term : Effect
estimate : $\\hat{\\eta}^2_G$
conf.int : 90\\% CI
statistic: $F$
df : $\\mathit{df}^{\\mathrm{GG}}$
... (2 more labels)
attr(,"class")
[1] "apa_results" "list"
What do you think? I would be happy to provide a PR for this one.
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