In melindahiggins2000/N741pkg: Package of R Functions and Rmarkdown Templates for N741

Functions Included

To date, this N741pkg package includes 2 functions tbl.continuous and tbl.cat for creating summary statistics tables for numeric (continuous) data and categorical (or ordinal) variables.

Creating summary statistics tables for numeric (continuous) variables

The tbl.continuous function will work for any numeric variable. Ideally, you will run these statistics on continuous measures but can compute these summary statistics for ordinal data as well. The resulting data.frame output from this function can be used easily with knitr::kable() to create a table of summary statistics. This summary includes both informative descriptives (sample size, missing data, minimum, maximum), parametric statistics (mean, standard deviation), and non-parametric statistics (median, 25th and 75th percentile for the interquartile range).

• tbl.continuous(x,x$a,"label for var a")
  • This function creates a table of summary statistics for a numeric variable in a data frame.
  • There are 3 arguments you need to provide:
    • a data.frame, df
    • a column variable from the data.frame df$var
    • a character vector to serve as a label for the variable "var label"
  • The returned table is a data.frame object with 1 row and 10 variables/columns. These 10 columns include:
    • item: the variable label provided
    • n: the number of non-missing values
    • missing: the number of missing values
    • min: the minimum value
    • avg: the average/mean value
    • SD: the standard deviation of the values
    • median: the median value
    • Q1: the 25th percentile (1st quartile) of the values
    • Q3: the 75th percentile (3rd quartile) of the values
    • max: the maximum value

Example using the mtcars dataset with tbl.continuous

Using the mtcars built-in dataset, you can create separate data.frame object "tables" (t1, t2, ...) and then combine these using rbind() (bind data.frame objects together by rows) to make a bigger data.frame containing the summary stats for multiple variables at once which can be printed using knitr::kable(). Each row of the resulting table is a different variable (column) containing the summary statistics for that variable.

m <- mtcars
t1 <- N741pkg::tbl.continuous(m,m$mpg,"Miles per Gallon")
t2 <- N741pkg::tbl.continuous(m,m$disp,"Engine Displacement")
t3 <- N741pkg::tbl.continuous(m,m$wt,"Car Weight")
t4 <- N741pkg::tbl.continuous(m,m$hp,"Horsepower")
t5 <- N741pkg::tbl.continuous(m,m$qsec,"1/4 mile time")
knitr::kable(rbind(t1,t2,t3,t4,t5),
             caption = "Table of Summary Stats for Numeric Variables in mtcars")

Creating summary statistics tables for categorical (or ordinal) variables

• tbl.cat(gx,gx$b)
  • This function creates a table of summary statistics (frequencies and percents) for a categorical (or ordinal) variable in a data frame.
  • There are 2 arguments you need to provide:
    • a "grouped" data.frame, gdf SEE NOTE
    • a column variable from the "grouped" data.frame gdf$var
  • NOTE A "grouped" data.frame can be created using the dplyr::group_by() function. See the example provided with help(tbl.cat). You need to "group_by" the categorical (or ordinal) variable you want to make the table for.
  • The returned table is a data.frame object with 1 row and 3 variables/columns. These 3 columns include:
    • var: the categorical variable categories or ordinal levels
    • freq: the frequencies (or counts) for each category or level
    • pct: the percentage of the total number of rows - these percents are NOT adjusted for missing. However, the frequency and percentage of NAs are provided.

Example using the mtcars dataset with tbl.cat

In the mtcars dataset, there are several ordinal variables for which running frequency summaries is useful. Let's run frequency summaries for:

• cyl "Number of Cylinders"
• am "Transmission (0=automatic, 1=manual)"
• gear "Number of forward gears"

# create grouped data.frame of m by cylinders
gm <- dplyr::group_by(m,cyl)
t1 <- N741pkg::tbl.cat(gm,gm$cyl)
knitr::kable(t1,
             caption = "Frequency Table for Number of Cylinders")

# create grouped data.frame of m by am
gm <- dplyr::group_by(m,am)
t1 <- N741pkg::tbl.cat(gm,gm$am)
knitr::kable(t1,
             caption = "Frequency Table for Transmission")

# create grouped data.frame of m by gear
gm <- dplyr::group_by(m,gear)
t1 <- N741pkg::tbl.cat(gm,gm$gear)
knitr::kable(t1,
             caption = "Frequency Table for Number of Forward Gears")

Generic Vignette information - included for further examples

Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The html_vignette output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The html_vignette format:

• Never uses retina figures
• Has a smaller default figure size
• Uses a custom CSS stylesheet instead of the default Twitter Bootstrap style

Vignette Info

Note the various macros within the vignette section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the title field and the \VignetteIndexEntry to match the title of your vignette.

Styles

The html_vignette template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:

output: 
  rmarkdown::html_vignette:
    css: mystyles.css

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

melindahiggins2000/N741pkg documentation built on May 22, 2019, 6:50 p.m.