tidyplots (version 0.2.1)

all_rows: Subset data rows

Description

Subset data rows

Usage

all_rows()
filter_rows(..., .by = NULL)
max_rows(order_by, n, by = NULL, with_ties = TRUE, na_rm = FALSE)
min_rows(order_by, n, by = NULL, with_ties = TRUE, na_rm = FALSE)
first_rows(n, by = NULL)
last_rows(n, by = NULL)
sample_rows(n, by = NULL)

Value

A function to achieve the desired data subsetting.

Arguments

...

<data-masking> Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

.by, by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

order_by

<data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.

n

The number of rows to select. If not are supplied, n = 1 will be used. If n is greater than the number of rows in the group, the result will be silently truncated to the group size.

A negative value of n will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in dplyr::arrange()), so they will only be included if there are insufficient non-missing values to reach n.

Examples

# Highlight all animals
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = all_rows(),
  color = "red", shape = 1, size = 3)

# Highlight 3 animals with the highest weight
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = max_rows(weight, n = 3),
  color = "red", shape = 1, size = 3)

# Highlight 3 animals with the lowest weight
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = min_rows(weight, n = 3),
  color = "red", shape = 1, size = 3)

# Highlight the first 3 animals in the dataset
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = first_rows(n = 3),
  color = "red", shape = 1, size = 3)

# Highlight the last 3 animals in the dataset
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = last_rows(n = 3),
  color = "red", shape = 1, size = 3)

# Highlight 3 random animals
animals %>%
 tidyplot(x = weight, y = size) %>%
 add_data_points() %>%
 add_data_points(data = sample_rows(n = 3),
  color = "red", shape = 1, size = 3)