Compares the population against an intervention in a summary table
Source:R/compare_population.R
compare_population.RdThe population comparison is a summary of the co-variates in a data set with no reference to outcome, but comparing intervention groups. It will report summary statistics for continuous and counts for categorical data, for each of the intervention groups, and reports on the significance of the association in relation to the intervention groups. It gives a clear summary of whether data is correlated to intervention.
Usage
compare_population(
df,
...,
label_fn = label_extractor(df),
units = extract_units(df),
override_type = list(),
override_method = list(),
layout = "compact",
override_percent_dp = list(),
override_real_dp = list(),
p_format = names(.pvalue.defaults),
font_size = getOption("tableone.font_size", 8),
font = getOption("tableone.font", "Arial"),
footer_text = NULL,
show_binary_value = NULL,
raw_output = FALSE
)Arguments
- df
a dataframe of individual observations. If using the
tidyselectsyntax data grouping defines the intervention group and should be present. if the formula interface is used the first variable in the right hand side of the formula is used as the intervention, in which case grouping is ignored.- ...
the columns of variables we wish to summarise. This can be given as a
tidyselectspecification (seeutils::vignette("syntax", package = "tidyselect")), identifying the columns. Alternatively it can be given as a formula of the natureoutcome ~ intervention + covariate_1 + covariate_2 + ....which may be more convenient if you are going on to do a model fit later. If the latter format the left hand side is ignored (outcomes are not usual in this kind of table).
- label_fn
(optional) a function for mapping a co-variate column name to printable label. This is by default a no-operation and the output table will contain the dataframe column names as labels. A simple alternative would be some form of dplyr::case_when lookup, or a string function such as stringr::str_to_sentence. (N.b. this function must be vectorised). Any value provided here will be overridden by the
options("tableone.labeller" = my_label_fn)which allows global setting of the labeller.- units
(optional) a named list of units, following a
c(<colname_1> = "<unit_1>", <colname_2> = "<unit_2>", ...)format. columns not present in this list are assumed to have no units. Units may be involved in the formatting of the summary output.- override_type
(optional) a named list of data summary types. The default type for a column in a data set are calculated using heurisitics depending on the nature of the data (categorical or continuous), and result of normality tests. if you want to override this the options are "subtype_count","median_iqr","mean_sd","skipped" and you specify this on a column by column bases with a named list (e.g
c("Petal.Width"="mean_sd")). Overriding the default does not check the type of data is correct for the summary type and will potentially cause errors if this is not done correctly.- override_method
if you want to override the comparison method for a particular variable the options are "chi-sq trend","fisher","t-test","2-sided wilcoxon","2-sided ks","anova","kruskal-wallis","no comparison" and you specify this on a column by column bases with a named list (e.g
c("Petal.Width"="t-test"))- layout
(optional) various layouts are defined as default. As of this version of
tableonethey are "relaxed","compact","micro","simple","single","missing". The layouts can be customised using the optionsoptions("tableone.format_list"=list(...)"), and this is described in more detail in the vignettes.- override_percent_dp
(optional) a named list of overrides for the default precision of formatting percentages, following a
c(<colname_1> = 2, <colname_2> = 4, ...)format. columns not present in this list will use the defaults defined in the layout. See the vignette on customisation.- override_real_dp
(optional) a named list of overrides for the default precision of formatting real values, following a
c(<colname_1> = 2, <colname_2> = 4, ...)format. columns not present in this list will use the defaults defined in the layout. See theutils::vignette("customisation", package="tableone").- p_format
the format of the p-values: one of "sampl", "nejm", "jama", "lancet", "aim" but any value here is overridden by the
option("tableone.pvalue_formatter"=function(...))- font_size
(optional) the font size for the table in points
- font
(optional) the font family for the table (which will be matched to closest on your system)
- footer_text
any text that needs to be added at the end of the table, setting this to FALSE dsables the whole footer (as does
options("tableone.hide_footer"=TRUE)).- show_binary_value
if set this will filter the display of covariates where the number of possibilities is exactly 2 to this value.
- raw_output
return comparison as
t1_signifdataframe rather than formatted table
Examples
# the heuristics detect that Petals in the iris data set are not normally
# distributed and hence report median and IQR:
iris %>% dplyr::group_by(Species) %>% compare_population(tidyselect::everything())
#> mean_sd summary for Sepal.Length
#> mean_sd summary for Sepal.Width
#> median_iqr summary for Petal.Length
#> median_iqr summary for Petal.Width
#> anova test on Sepal.Length
#> anova test on Sepal.Width
#> kruskal-wallis test on Petal.Length
#> kruskal-wallis test on Petal.Width
#> ───────────────────────────────────────────────────────────────────────────────
#> setosa versicolor virginica
#> Variable Characteri Value Value Value P value
#> stic (N=50) (N=50) (N=50)
#> ───────────────────────────────────────────────────────────────────────────────
#> Sepal.Leng Mean ± SD 5.01 ± 5.94 ± 6.59 ± <0.001 †
#> th 0.352 0.516 0.636
#> Sepal.Widt Mean ± SD 3.43 ± 2.77 ± 2.97 ± <0.001 †
#> h 0.379 0.314 0.322
#> Petal.Leng Median 1.5 4.35 5.55 <0.001 ††
#> th [IQR] [1.4—1.58] [4—4.6] [5.1—5.88]
#> Petal.Widt Median 0.2 1.3 2 <0.001 ††
#> h [IQR] [0.2—0.3] [1.2—1.5] [1.8—2.3]
#> ───────────────────────────────────────────────────────────────────────────────
#> †, Analysis of variance (linear model) (continuous); ††,
#> Kruskal-Wallis rank sum test (continuous)
#> Normal distributions determined by the Anderson-Darling test
#> (P>0.005)
#> An adjusted P value of 0.0125 may be considered significant.
#>
#> Column names: Variable, Characteristic, 1, 2, 3, P value
# Missing data
old = options("tableone.show_pvalue_method"=FALSE)
missing_diamonds %>%
dplyr::group_by(is_colored) %>%
compare_population(-color, layout="relaxed")
#> median_iqr summary for carat
#> subtype_count summary for cut
#> subtype_count summary for clarity
#> median_iqr summary for depth
#> median_iqr summary for table
#> median_iqr summary for price
#> median_iqr summary for x
#> median_iqr summary for y
#> median_iqr summary for z
#> 2-sided wilcoxon test on carat
#> Significance testing skipped due to missing values: carat
#> fisher test on cut
#> Significance testing skipped due to missing values: cut
#> fisher test on clarity
#> Significance testing skipped due to missing values: clarity
#> 2-sided wilcoxon test on depth
#> Significance testing skipped due to missing values: depth
#> 2-sided wilcoxon test on table
#> Significance testing skipped due to missing values: table
#> 2-sided wilcoxon test on price
#> Significance testing skipped due to missing values: price
#> 2-sided wilcoxon test on x
#> Significance testing skipped due to missing values: x
#> 2-sided wilcoxon test on y
#> Significance testing skipped due to missing values: y
#> 2-sided wilcoxon test on z
#> Significance testing skipped due to missing values: z
#> ──────────────────────────────────────────────────────────────────────────────
#> clear colored
#> Variable Characte Value Count Value Count P value
#> ristic (N=16572 (N=37368
#> ) )
#> ──────────────────────────────────────────────────────────────────────────────
#> carat Median 0.53 14921 0.74 33761 —
#> [IQR] [0.36—0. [0.41—1.
#> 9] 13]
#> ──────────────────────────────────────────────────────────────────────────────
#> cut Fair % 2.3% 349/1498 3.3% 1105/335 —
#> [95% CI] [2.1%—2. 0 [3.1%—3. 73
#> 6%] 5%]
#> Good % 9.7% 1449/149 9.0% 3013/335
#> [95% CI] [9.2%—10 80 [8.7%—9. 73
#> .2%] 3%]
#> Very 23.7% 3550/149 21.6% 7266/335
#> Good % [23.0%—2 80 [21.2%—2 73
#> [95% CI] 4.4%] 2.1%]
#> Premium 23.7% 3556/149 26.5% 8904/335
#> % [95% [23.1%—2 80 [26.1%—2 73
#> CI] 4.4%] 7.0%]
#> Ideal % 40.6% 6076/149 39.6% 13285/33
#> [95% CI] [39.8%—4 80 [39.0%—4 573
#> 1.3%] 0.1%]
#> ──────────────────────────────────────────────────────────────────────────────
#> clarity I1 % 0.9% 129/1488 1.6% 535/3364 —
#> [95% CI] [0.7%—1. 7 [1.5%—1. 0
#> 0%] 7%]
#> SI2 % 18.5% 2755/148 16.4% 5510/336
#> [95% CI] [17.9%—1 87 [16.0%—1 40
#> 9.1%] 6.8%]
#> SI1 % 27.3% 4064/148 22.9% 7692/336
#> [95% CI] [26.6%—2 87 [22.4%—2 40
#> 8.0%] 3.3%]
#> VS2 % 25.1% 3733/148 21.7% 7287/336
#> [95% CI] [24.4%—2 87 [21.2%—2 40
#> 5.8%] 2.1%]
#> VS1 % 12.1% 1798/148 16.5% 5557/336
#> [95% CI] [11.6%—1 87 [16.1%—1 40
#> 2.6%] 6.9%]
#> VVS2 % 9.3% 1390/148 9.5% 3180/336
#> [95% CI] [8.9%—9. 87 [9.1%—9. 40
#> 8%] 8%]
#> VVS1 % 5.4% 809/1488 7.4% 2489/336
#> [95% CI] [5.1%—5. 7 [7.1%—7. 40
#> 8%] 7%]
#> IF % 1.4% 209/1488 4.1% 1390/336
#> [95% CI] [1.2%—1. 7 [3.9%—4. 40
#> 6%] 3%]
#> ──────────────────────────────────────────────────────────────────────────────
#> depth Median 61.8 14920 61.9 33664 —
#> [IQR] [61—62.5 [61.1—62
#> ] .5]
#> ──────────────────────────────────────────────────────────────────────────────
#> table Median 57 14925 57 33782 —
#> [IQR] [56—59] [56—59]
#> ──────────────────────────────────────────────────────────────────────────────
#> price Median 1.78e+03 14985 2.79e+03 33690 —
#> [IQR] [893—4.1 [992—6.0
#> 2e+03] 1e+03]
#> ──────────────────────────────────────────────────────────────────────────────
#> x Median 5.24 14939 5.83 33638 —
#> [IQR] [4.58—6. [4.77—6.
#> 18] 7]
#> ──────────────────────────────────────────────────────────────────────────────
#> y Median 5.24 14897 5.85 33681 —
#> [IQR] [4.58—6. [4.78—6.
#> 18] 69]
#> ──────────────────────────────────────────────────────────────────────────────
#> z Median 3.22 14985 3.6 33574 —
#> [IQR] [2.81—3. [2.95—4.
#> 83] 13]
#> ──────────────────────────────────────────────────────────────────────────────
#> Significance determined using Not calculated due to
#> missing values (continuous variables) or Not calculated
#> due to missing values (ordered variables)
#> Normal distributions determined by the Anderson-Darling
#> test (P>0.005)
#> An adjusted P value of 0.00556 may be considered
#> significant.
#>
#> Column names: Variable, Characteristic, 1, 2, 3, 4, P value
tmp = missing_diamonds %>% explicit_na() %>% dplyr::group_by(is_colored)
tmp %>% compare_population(-color,
footer_text = c(
"IQR: Interquartile range; CI: Confidence interval",
"Line two")
)
#> median_iqr summary for carat
#> subtype_count summary for cut
#> subtype_count summary for clarity
#> median_iqr summary for depth
#> median_iqr summary for table
#> median_iqr summary for price
#> median_iqr summary for x
#> median_iqr summary for y
#> median_iqr summary for z
#> 2-sided wilcoxon test on carat
#> Significance testing skipped due to missing values: carat
#> fisher test on cut
#> fisher test on clarity
#> 2-sided wilcoxon test on depth
#> Significance testing skipped due to missing values: depth
#> 2-sided wilcoxon test on table
#> Significance testing skipped due to missing values: table
#> 2-sided wilcoxon test on price
#> Significance testing skipped due to missing values: price
#> 2-sided wilcoxon test on x
#> Significance testing skipped due to missing values: x
#> 2-sided wilcoxon test on y
#> Significance testing skipped due to missing values: y
#> 2-sided wilcoxon test on z
#> Significance testing skipped due to missing values: z
#> ────────────────────────────────────────────────────────────────────────────
#> clear colored
#> Variable Characterist Value Value P value
#> ic (N=16572) (N=37368)
#> ────────────────────────────────────────────────────────────────────────────
#> carat Median [IQR] 0.53 0.74 —
#> [0.36—0.9] [0.41—1.13]
#> ────────────────────────────────────────────────────────────────────────────
#> cut Fair % [95% 2.1% 3.0% <0.001
#> CI] (n) [1.9%—2.3%] [2.8%—3.1%]
#> (349) (1105)
#> Good % [95% 8.7% 8.1%
#> CI] (n) [8.3%—9.2%] [7.8%—8.3%]
#> (1449) (3013)
#> Very Good % 21.4% 19.4%
#> [95% CI] (n) [20.8%—22.1% [19.0%—19.8%
#> ] (3550) ] (7266)
#> Premium % 21.5% 23.8%
#> [95% CI] (n) [20.8%—22.1% [23.4%—24.3%
#> ] (3556) ] (8904)
#> Ideal % [95% 36.7% 35.6%
#> CI] (n) [35.9%—37.4% [35.1%—36.0%
#> ] (6076) ] (13285)
#> <missing> % 9.6% 10.2%
#> [95% CI] (n) [9.2%—10.1%] [9.9%—10.5%]
#> (1592) (3795)
#> ────────────────────────────────────────────────────────────────────────────
#> clarity I1 % [95% 0.8% 1.4% <0.001
#> CI] (n) [0.7%—0.9%] [1.3%—1.6%]
#> (129) (535)
#> SI2 % [95% 16.6% 14.7%
#> CI] (n) [16.1%—17.2% [14.4%—15.1%
#> ] (2755) ] (5510)
#> SI1 % [95% 24.5% 20.6%
#> CI] (n) [23.9%—25.2% [20.2%—21.0%
#> ] (4064) ] (7692)
#> VS2 % [95% 22.5% 19.5%
#> CI] (n) [21.9%—23.2% [19.1%—19.9%
#> ] (3733) ] (7287)
#> VS1 % [95% 10.8% 14.9%
#> CI] (n) [10.4%—11.3% [14.5%—15.2%
#> ] (1798) ] (5557)
#> VVS2 % [95% 8.4% 8.5%
#> CI] (n) [8.0%—8.8%] [8.2%—8.8%]
#> (1390) (3180)
#> VVS1 % [95% 4.9% 6.7%
#> CI] (n) [4.6%—5.2%] [6.4%—6.9%]
#> (809) (2489)
#> IF % [95% 1.3% 3.7%
#> CI] (n) [1.1%—1.4%] [3.5%—3.9%]
#> (209) (1390)
#> <missing> % 10.2% 10.0%
#> [95% CI] (n) [9.7%—10.6%] [9.7%—10.3%]
#> (1685) (3728)
#> ────────────────────────────────────────────────────────────────────────────
#> depth Median [IQR] 61.8 61.9 —
#> [61—62.5] [61.1—62.5]
#> ────────────────────────────────────────────────────────────────────────────
#> table Median [IQR] 57 [56—59] 57 [56—59] —
#> ────────────────────────────────────────────────────────────────────────────
#> price Median [IQR] 1.78e+03 2.79e+03 —
#> [893—4.12e+0 [992—6.01e+0
#> 3] 3]
#> ────────────────────────────────────────────────────────────────────────────
#> x Median [IQR] 5.24 5.83 —
#> [4.58—6.18] [4.77—6.7]
#> ────────────────────────────────────────────────────────────────────────────
#> y Median [IQR] 5.24 5.85 —
#> [4.58—6.18] [4.78—6.69]
#> ────────────────────────────────────────────────────────────────────────────
#> z Median [IQR] 3.22 3.6 —
#> [2.81—3.83] [2.95—4.13]
#> ────────────────────────────────────────────────────────────────────────────
#> Significance determined using Fisher's exact test
#> (categorical variables) or Not calculated due to missing
#> values (continuous variables)
#> Normal distributions determined by the Anderson-Darling test
#> (P>0.005)
#> IQR: Interquartile range; CI: Confidence interval
#> Line two
#> An adjusted P value of 0.00556 may be considered
#> significant.
#>
#> Column names: Variable, Characteristic, 1, 2, P value
options(old)