orrelation-in-r/, https://www.onlinespss.com/pearson-correlation-in-r/ #correlation_SS_vs_SH_T2 cor.t
possibly add more flow in calculating correlations section by breaking each cor.test up by variable
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danicalyktey.quarto.pub danicalyktey.quarto.pub
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# Print and save to the plots folder print(SH_19_T1_vs_SS) `geom_smooth()` using formula = 'y ~ x'
what is happening with the cluster of data around 0 in Figure 5 below - may be important to change the scale or discuss this common response in the results
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This plot shows the distribution of SH_11, SH_14, and SH_19,
spot where having the variable be more specific to the statement may be easier for the reader to follow
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Reliability Statistics for Activating, Nonsleep, And Environment SH Items
shorter section titles here may help bring more flow to the report: ex reliability stats or reliability testing as a title for this section: then if you wanted to add more specificity you could have the sub headings be nonsleep, envi, etc
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pping = aes( x = SH_T1, y = SLEEPSCORE)) + geom_point(position = "jitter")
any best fit line or residuals that could be plotted along with these two variables to make the correlation or lack of correlation more apparent may be helpful along with a fig caption and clear title
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.7 Visualize 1.7.1 SH T1
muiltiple visualize sections with various length titles: consider making subheadings and keeping the larger headings shorter: also possible renaming of the SH variables to be more recognizable with a specific measure -- ex from variable in a later section SH_11 consider changing to SH_WORRY (referencing statement about worry before sleep--- association is easier for the viewer than the SH_#)
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umber of categories should be increased in order to
is this a title for the next section or a note--- breaks up the flow of the page a bit: consider making into a comment in the code chunk
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source: https://fripublichealth.quarto.pub/zerosum/report-preview.html#introduction, r manual combined <- combined %>% filter(!is.na(SH_10_T2)) %>% mutate(
looks like numerical values are being assigned to the variables here- for clarity add explanation of chunks and maybe a label on each that differentiates each SH variable: helps organize for reader
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carty.github.io/FRIplaybook/composite.html scoreItems(keys = SH_T2_keys, items = combined) Call: scoreItems(keys = SH_T2_keys, items = combined) (Unstandardized) Alpha: SLEEPHYGIENE_T2 alpha 0.7 Standard errors of unstandardized Alpha: SLEEPHYGIENE_T2
is this section the same composite scoring or something else-- add explanation or maybe sub heading for clarity-- the alpha and standard error values make me think this is some type of modeling but source labeled as more composite scoring?
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wide_17data %>% left_join( wide_dailydata_clean %>% select("PASSWORD", "SLEEPSCORE_T1",
purpose of joining the datasets-- what is the purpose/explanation for this section of code
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<- day17data %>% pivot_wider( id_cols = PASSWORD, names_from = SURVEYDAY, values_from =
potentially add explanation note to indicate the purpose of converting long to wide format for the survey data
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indicate areas for improvement in future studies that may lead to different results.
possible area to add specificity: ie could future research focus on alternative measures of sleep hygiene or alternatives to self reporting. may also be a perfect spot to connect back to purpose-- promoting wellbeing through sleep or preventing harm of mental illbeing using sleep
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