Overview

Methods

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Header 2

tibble(
  a = Sys.time() + runif(1e3) * 86400,
  b = Sys.Date() + runif(1e3) * 30,
  c = 1:1e3,
  d = runif(1e3),
  e = sample(letters, 1e3, replace = TRUE)
)
#> # A tibble: 1,000 x 5
#>    a                   b              c      d e    
#>    <dttm>              <date>     <int>  <dbl> <chr>
#>  1 2020-03-13 02:21:14 2020-03-25     1 0.386  i    
#>  2 2020-03-13 06:44:38 2020-03-19     2 0.952  h    
#>  3 2020-03-13 12:25:15 2020-03-26     3 0.726  j    
#>  4 2020-03-13 12:29:13 2020-04-09     4 0.147  k    
#>  5 2020-03-13 09:32:24 2020-04-08     5 0.606  d    
#>  6 2020-03-13 13:51:40 2020-03-31     6 0.144  p    
#>  7 2020-03-13 12:28:18 2020-04-10     7 0.681  g    
#>  8 2020-03-13 06:44:31 2020-04-05     8 0.365  y    
#>  9 2020-03-13 04:18:51 2020-03-14     9 0.0783 p    
#> 10 2020-03-13 20:49:29 2020-03-14    10 0.594  z    
#> # … with 990 more rows

Data

The diamonds data set will be used as an example.

diamonds %>% select(cut, carat, color, clarity) %>% head() %>%  knitr::kable()

cut	carat	color	clarity
Ideal	0.23	E	SI2
Premium	0.21	E	SI1
Good	0.23	E	VS1
Premium	0.29	I	VS2
Good	0.31	J	SI2
Very Good	0.24	J	VVS2

Here’s a new section

Plot

data <- diamonds %>% 
  group_by(cut) %>% 
  count()

ggplot(data, aes(cut, n, fill = cut)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Blues")  +
  theme_minimal()

Sample R Markdown Document

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