10 pairbind() and pairbind_df()

The function pairbind() appends two datasets’ rows in a pairwise fashion. In other words, it appends the 1st row of the first dataset with the 1st row of the second dataset, the 2nd row of the first with the 2nd row of the second, and so on. This function can be useful for kable-friendly frequency distribution tables.

The two required inputs are x and y, each being a 2D object (e.g. dataframe, matrix, etc). The output is a matrix.

The function pairbind_df() accomplishes the same as pairbind() except that the output is a data frame.

10.0.1 Creating a kable-friendly Frequency Distribution Table

library(tidyverse) # For Data management

## Warning: package 'tidyverse' was built under R version 3.6.2

## Warning: package 'ggplot2' was built under R version 3.6.3

## Warning: package 'tibble' was built under R version 3.6.3

## Warning: package 'dplyr' was built under R version 3.6.3

## Warning: package 'forcats' was built under R version 3.6.3

library(magrittr)  # For the update assignment pipe operator %<>%
library(knitr)     # For kable().

## Warning: package 'knitr' was built under R version 3.6.3

library(kableExtra)# For other kable functions.

## Warning: package 'kableExtra' was built under R version 3.6.3

# Frequencies
freq_df <- with(diamonds, table(color, clarity)) %>%
  as.data.frame() %>%
  spread(NCOL(.) - 1, NCOL(.)) %>%
  mutate(Total = apply(.[, 2:NCOL(.)], 1, sum))

# Percentages
 prop_df <- freq_df 
 prop_df[, 2:NCOL(prop_df)] <- prop_df[, 2:NCOL(prop_df)]/prop_df$Total
 prop_df[, 2:NCOL(prop_df)] %<>% 
   map_df(~ paste0(round(.x*100), '%'))
  freq_df %<>% 
  format(., big.mark = ',', scientific = FALSE) %>%
  map_df(as.character)

# Output
prop_df %<>% map_df(as.character)

pb <- pairbind_df(freq_df, prop_df)

pb %>%
  kable(booktabs = TRUE) %>%
  kable_styling(full_width = TRUE) %>%
  collapse_rows(1, valign = 'top', latex_hline = 'major')

color	I1	SI2	SI1	VS2	VS1	VVS2	VVS1	IF	Total
D	42	1,370	2,083	1,697	705	553	252	73	6,775
D	1%	20%	31%	25%	10%	8%	4%	1%	100%
E	102	1,713	2,426	2,470	1,281	991	656	158	9,797
E	1%	17%	25%	25%	13%	10%	7%	2%	100%
F	143	1,609	2,131	2,201	1,364	975	734	385	9,542
F	1%	17%	22%	23%	14%	10%	8%	4%	100%
G	150	1,548	1,976	2,347	2,148	1,443	999	681	11,292
G	1%	14%	17%	21%	19%	13%	9%	6%	100%
H	162	1,563	2,275	1,643	1,169	608	585	299	8,304
H	2%	19%	27%	20%	14%	7%	7%	4%	100%
I	92	912	1,424	1,169	962	365	355	143	5,422
I	2%	17%	26%	22%	18%	7%	7%	3%	100%
J	50	479	750	731	542	131	74	51	2,808
J	2%	17%	27%	26%	19%	5%	3%	2%	100%