# README.md In tldr: T Loux Doing R: Functions to Simplify Data Analysis and Reporting

This package gives a number of functions to aid common data analysis processes and reporting statistical results in an RMarkdown file. Data analysis functions combine multiple base R functions used to describe simple bivariate relationships into a single, easy to use function. Reporting functions will return character strings to report p-values, confidence intervals, and hypothesis test and regression results. Strings will be LaTeX-formatted as necessary and will knit pretty in an RMarkdown document. The package also provides a wrapper for the CreateTableOne function in the tableone package to make the results knitable.

## Data analysis functions

Suppose we have the following data:

pred1 = sample(letters[1:3], size=50, replace=TRUE)
out1 = sample(letters[4:6], size=50, replace=TRUE)
out2 = rnorm(50)


We can investigate the relationship between pred1 and out1 using cat_compare():

cat_compare(x=pred1, y=out1)

## Warning in chisq.test(tab_no_miss): Chi-squared approximation may be incorrect

## $counts ## y ## x d e f Sum ## a 8 7 3 18 ## b 6 2 9 17 ## c 7 4 4 15 ## Sum 21 13 16 50 ## ##$chisq
##
##  Pearson's Chi-squared test
##
## data:  tab_no_miss
## X-squared = 6.5486, df = 4, p-value = 0.1618
##
##
## $CramersV ## [1] 0.2559017 ## ##$plot


We can investigate the distribution of out2 across levels of pred1 using num_compare():

num_compare(y=out2, grp=pred1)

## $summary_stats ## n obs mis mean stdev med q1 q3 ## a 18 18 0 0.006755781 1.0851542 0.04793630 -0.4201223 0.8188215 ## b 17 17 0 0.001604250 0.8911016 0.07865256 -0.2591153 0.5775767 ## c 15 15 0 0.198539217 1.0332958 -0.07142822 -0.2773362 0.6744763 ## ##$decomp
## Call:
##    aov(formula = y ~ grp, data = mydat)
##
## Terms:
##                      grp Residuals
## Sum of Squares   0.39657  47.67131
## Deg. of Freedom        2        47
##
## Residual standard error: 1.007116
## Estimated effects may be unbalanced
##
## $eta_sq ## [1] 0.008250299 ## ##$plot


## inline and write functions

• inline_test()
• inline_reg()
• inline_coef()
• inline_anova()
• write_int()
• write_p()
• as_perc()

Using the data above, we can obtain some inferential results:

x = rnorm(50)
y = rnorm(50)
a = sample(letters[1:3], size=50, replace=TRUE)
b = sample(letters[1:3], size=50, replace=TRUE)

test1 = t.test(x)
test2 = chisq.test(table(a,b))
model1 = lm(y ~ x)
model2 = lm(y ~ a)


We can then report the results of the hypothesis test inline using inline_test(test1) and get the following: (t(49) = -0.7), (p = 0.49). Simiarly, inline_test(test2) will report the results of the chi-squared test: (\chi^2(4) = 4.85), (p = 0.3). So far inline_test only works for (t) and chi-squared tests, but the goal is to add more functionality - requests gladly accepted.

The regression results can be reported with inline_reg(model1) and inline_coef(model1, 'x') to get (R^2 = 0.02), (F(1,48) = 0.81), (p = 0.37) and (b = -0.14), (t(48) = -0.9), (p = 0.37), respectively. In addition, inline_anova(model2) will report the ANOVA F statistic and relevant results: (F(2,47) = 2.81), (p = 0.07). So far inline_reg and inline_coef currently work for lm and glm objects; inline_anova only works for lm objects.

We can also report the confidence intervals using write_int() with a length-2 vector of interval endpoints. For example, write_int(c(3.04, 4.7)) and write_int(test1\$conf.int) yield (3.04, 4.70) and (-0.37, 0.18), respectively. If a 2-column matrix is provided to write_int(), the entries in each row will be formatted into an interval and a character vector will be returned.

P-values can be reported using write_p(). This function will take either a numeric value or a list-like object with an element named p.value. For example, write_p(0.00002) gives (p < 0.01) and write_p(test1) gives (p = 0.49).

Many R functions produce proportions, though analysts may want to report the output as a percentage. as_perc() will do this. For example, as_perc(0.01) will produce 1%.

See the help files of all functions described above for more details and options. For example, all test and regression reporting functions have wrappers ending in _p which report only the p-value of the input.

## KreateTableOne

The package also provides the function KreateTableOne, a wrapper for CreateTableOne from the tableone package which makes the results knitable. First use KreateTableOne in an R chunk with results='hide' (or ouside the RMarkdown document), then recall the saved data frame in a new chunk. For example:

table1 = KreateTableOne(x=mtcars, strata='am',
factorVars='vs')
colnames(table1)[1:2] = c('am = 0', 'am = 1')


Then

knitr::kable(table1[, 1:3], align='r')


| | am = 0 | am = 1 | p | | :--------------- | --------------: | -------------: | ------: | | n | 19 | 13 | | | mpg (mean (SD)) | 17.15 (3.83) | 24.39 (6.17) | \<0.001 | | cyl (mean (SD)) | 6.95 (1.54) | 5.08 (1.55) | 0.002 | | disp (mean (SD)) | 290.38 (110.17) | 143.53 (87.20) | \<0.001 | | hp (mean (SD)) | 160.26 (53.91) | 126.85 (84.06) | 0.180 | | drat (mean (SD)) | 3.29 (0.39) | 4.05 (0.36) | \<0.001 | | wt (mean (SD)) | 3.77 (0.78) | 2.41 (0.62) | \<0.001 | | qsec (mean (SD)) | 18.18 (1.75) | 17.36 (1.79) | 0.206 | | vs = 1 (%) | 7 (36.8) | 7 (53.8) | 0.556 | | am (mean (SD)) | 0.00 (0.00) | 1.00 (0.00) | \<0.001 | | gear (mean (SD)) | 3.21 (0.42) | 4.38 (0.51) | \<0.001 | | carb (mean (SD)) | 2.74 (1.15) | 2.92 (2.18) | 0.754 |

## Try the tldr package in your browser

Any scripts or data that you put into this service are public.

tldr documentation built on May 29, 2024, 9:13 a.m.