In pvrqualitasag/PedigreeFromTvdData: Import pedigree data from TVD-Input files

Examples taken from http://dplyr.tidyverse.org/articles/programming.html

require(dplyr)

Programming recipes

set.seed(123)
df1 <- tibble::tibble(x = 1:3, y = 3:1)
df2 <- tibble::tibble(x = runif(3), y = rnorm(3))

Working with different datasets

a <- 12
mutate(df1, y = a + x)
mutate(df2, y = a + x)

Avoiding duplication using a function

mutate_y <- function(df){
  mutate(df, y = a + x)
}

mutate_y(df1)

Using .data inside the function

mutate_y <- function(df){
  mutate(df, y = .data$a + .data$x)
}

# mutate_y(df1) # produces an error

Different expressions

Writing functions is difficult, if arguments should take up variable names. Taking indices values is not recommended as this might lead to confusions and might produce bugs which are very difficult to track and to correct.

(df <- tibble(
  g1 = c(1, 1, 2, 2, 2),
  g2 = c(1, 2, 1, 2, 1),
  a = sample(5), 
  b = sample(5)
))

Computing summary statistics according to both grouping variables.

df %>%
  group_by(g1) %>%
  summarise(a = mean(a))

df %>%
  group_by(g2) %>%
  summarise(a = mean(a))

This leads to a lot of code duplication. The only thing that changes is the name of the grouping variable. This should be re-factored into a function where the name of the grouping variable should be given as a function argument.

my_summarise <- function(df, group_var) {
  df %>%
    group_by(!!group_var) %>%
    summarise(a = mean(a))
}

my_summarise(df, quo(g1))

my_summarise(df, quo(g2))

Run the conversion to quosure in the function, to make the calls look nicer

my_summarise <- function(df, group_by) {
  group_by <- enquo(group_by)
  print(group_by)

  df %>%
    group_by(!!group_by) %>%
    summarise(a = mean(a))
}
my_summarise(df, g1)

my_summarise(df, g2)

Different input variable

Computing three summaries with varying input variables

summarise(df, mean = mean(a), sum = sum(a), n = n())

Same thing but with different input

summarise(df, mean = mean(a*b), sum = sum(a*b), n = n())

The following statements show like a first idea on how the above two chunks might be re-factored into a funcion.

my_var <- quo(a)
summarise(df, mean = mean(!!my_var), sum = sum(!!my_var), n = n())

The quosure can also be done arount the summarise() call

quo(summarise(df, 
              mean = mean(!!my_var), 
              sum = sum(!!my_var), 
              n = n()))

Putting this result into a function and remembering to replace quo() by enquo()

my_summarise2 <- function(df, expr){
  expr <- enquo(expr)
  summarise(df,
            mean = mean(!!expr),
            sum  = sum(!!expr),
            n = n())
}
my_summarise2(df, a)

my_summarise2(df, a*b)

Different input and output variables

Changing the input and the output variables according to the following

mutate(df, mean_a = mean(a), sum_a = sum(a))

mutate(df, mean_b = mean(b), sum_b = sum(b))

Turning this into a function, we need to paste together strings as names and using quo_name() to convert the input expressions into strings. Assignment is done by the := helper from rlang

my_mutate <- function(df, expr){
  expr <- enquo(expr)
  cat("expr\n");print(expr)
  cat("quo_name(expr)\n");print(quo_name(expr))
  mean_name <- paste0("mean_", quo_name(expr))
  sum_name <- paste0("sum_", quo_name(expr))
  cat("mean_name\n");print(mean_name)
  cat("sum_name\n");print(sum_name)

  mutate(df,
         !!mean_name := mean(!!expr),
         !!sum_name := sum(!!expr))
}
my_mutate(df, a)

my_mutate(df, b)

Trying to get some help for debugging

my_var <- quo(a)
mutate(df, mean_a = mean(!!my_var))

For checking what happens from the perspective of dplyr, we can do the following

quo(mutate(df, mean_a = mean(!!my_var)))

Capturing different variables

Finally, if my_summarise() should accept a varying number of grouping variables, we may have to make the following changes

use the three dots to capture the varying number of arguments
use quos() to capture the three dots as a list of formulas
Use !!! instead of !! to splice the arguments into group_by()

my_summarise3 <- function(df, ...){
  group_var <- quos(...)

  df %>%
    group_by(!!!group_var) %>%
    summarise(a = mean(a))
}
my_summarise3(df, g1, g2)