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Other functions

other_functions.Rmd

count2

The goal of count2 is to count the number of observations, giving a initial count.

count2(mtcars,cyl)
#> Storing counts in `nn`, as `n` already present in input
#> ℹ Use `name = "new_name"` to pick a new name.
#>    n nn
#> 1  7  1
#> 2 11  1
#> 3 14  1

count_na

The goal of count_na is to count the number of missing observations.

count_na(c(2,2,NA))
#> [1] 1

count_na(c(2,2,2))
#> [1] 0

cut_by_quantile

The goal of cut_by_quantile is to divide a numeric variable by a set of quantiles.


set.seed(123);x <- rnorm(100)

quartiles <- seq(0,1,by = .25)

table(cut_by_quantile(x,q = quartiles))
#> 
#>  [-2.31,-0.494] (-0.494,0.0618]  (0.0618,0.692]    (0.692,2.19] 
#>              25              25              25              25

expand_grid_unique

The goal of expand_grid_unique is to create a grid of all combination from two variables, with no repetition of pairs, not matter the position.

expand_grid_unique(x = 1:3,y = 1:3)
#> # A tibble: 3 × 2
#>      V1    V2
#>   <int> <int>
#> 1     1     2
#> 2     1     3
#> 3     2     3

You can also set the argument include_equals to TRUE, so equal pairs are kept.

expand_grid_unique(x = 1:3,y = 1:3, include_equals = TRUE)
#> # A tibble: 6 × 2
#>      V1    V2
#>   <int> <int>
#> 1     1     1
#> 2     1     2
#> 3     1     3
#> 4     2     2
#> 5     2     3
#> 6     3     3

obj_to_string

The goal of obj_to_string is to return the name of an R object as a string.

x <- c(1,2,3,5,7,8,12,100)

obj_to_string(x)
#> [1] "x"

replace_boolean

The goal of replace_boolean is to replace the values of a boolean variable to other values.


replace_boolean(c(T,T,T,F,F),1,2)
#> [1] 1 1 1 2 2

replace_na

The goal of replace_na is to replace the NA value to another.


replace_na(c(NA,NA,NA),1)
#> [1] 1 1 1

row_number_unique

The goal of row_number_unique is to get the row number, but considering the unique values of a variable.

tibble(x = c(1,1,1,2,3,4,5,5)) %>% 
  mutate(
    row_number = row_number(),
    row_number_unique = row_number_unique(x)
  )
#> # A tibble: 8 × 3
#>       x row_number row_number_unique
#>   <dbl>      <int>             <int>
#> 1     1          1                 1
#> 2     1          2                 1
#> 3     1          3                 1
#> 4     2          4                 2
#> 5     3          5                 3
#> 6     4          6                 4
#> 7     5          7                 5
#> 8     5          8                 5

rpearson

The goal of rpearson is to simulate data, where two variables will be linear correlated with a normal distribution, using pearson correlation coefficient as an argument.

set.seed(123);df <- rpearson(n = 100, pearson = .85, mean = 3)

df %>% 
  ggplot(aes(x,y))+
  geom_point()+
  geom_smooth(method = "lm", se = FALSE)+
  plt_theme_xy()
#> `geom_smooth()` using formula = 'y ~ x'