Functions – exercise solutions

Day 2, B

Author

Michael C Sachs

Iterating over data with functions

  1. Load the Palmer penguins dataset.
  2. Write a function to compute the mean and standard deviation of a numeric vector. We will apply this function to the numeric variables in penguins, and also by different subgroups
library(palmerpenguins)

Attaching package: 'palmerpenguins'
The following objects are masked from 'package:datasets':

    penguins, penguins_raw
mean_sd1 <- function(x, na.rm = TRUE) {
  
  res <- c(mean = mean(x, na.rm = na.rm), 
    sd = sd(x, na.rm = na.rm))

  res
  
}

mean_sd1(penguins$bill_depth_mm)
     mean        sd 
17.151170  1.974793

Using the function

  1. Use your function to compute the mean and sd of all the numeric variables in penguins.
numeric_cols <- sapply(penguins, is.numeric) ## determine which cols are numeric
apply(penguins[numeric_cols], MARGIN = 2, FUN = mean_sd1)
     bill_length_mm bill_depth_mm flipper_length_mm body_mass_g         year
mean      43.921930     17.151170         200.91520   4201.7544 2008.0290698
sd         5.459584      1.974793          14.06171    801.9545    0.8183559
  1. Use your function to compute the mean and sd of body mass by species and sex
split(penguins$body_mass_g, list(penguins$species, penguins$sex)) |>
  lapply(FUN = mean_sd1)
$Adelie.female
     mean        sd 
3368.8356  269.3801 

$Chinstrap.female
     mean        sd 
3527.2059  285.3339 

$Gentoo.female
     mean        sd 
4679.7414  281.5783 

$Adelie.male
     mean        sd 
4043.4932  346.8116 

$Chinstrap.male
     mean        sd 
3938.9706  362.1376 

$Gentoo.male
     mean        sd 
5484.8361  313.1586 
# sapply converts the result to a matrix
split(penguins$body_mass_g, list(penguins$species, penguins$sex)) |>
  sapply(FUN = mean_sd1)
     Adelie.female Chinstrap.female Gentoo.female Adelie.male Chinstrap.male
mean     3368.8356        3527.2059     4679.7414   4043.4932      3938.9706
sd        269.3801         285.3339      281.5783    346.8116       362.1376
     Gentoo.male
mean   5484.8361
sd      313.1586
# tapply has a different syntax and each element of the matrix is a list
tapres <- tapply(penguins$body_mass_g, list(penguins$species, penguins$sex), 
       mean_sd1)
tapres
          female    male     
Adelie    numeric,2 numeric,2
Chinstrap numeric,2 numeric,2
Gentoo    numeric,2 numeric,2
tapres[1,1]
[[1]]
     mean        sd 
3368.8356  269.3801 
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
penguins |> group_by(species, sex) |>
  summarize(mean_sd1(body_mass_g))
Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
  always returns an ungrouped data frame and adjust accordingly.
`summarise()` has grouped output by 'species', 'sex'. You can override using
the `.groups` argument.
# A tibble: 16 × 3
# Groups:   species, sex [8]
   species   sex    `mean_sd1(body_mass_g)`
   <fct>     <fct>                    <dbl>
 1 Adelie    female                   3369.
 2 Adelie    female                    269.
 3 Adelie    male                     4043.
 4 Adelie    male                      347.
 5 Adelie    <NA>                     3540 
 6 Adelie    <NA>                      477.
 7 Chinstrap female                   3527.
 8 Chinstrap female                    285.
 9 Chinstrap male                     3939.
10 Chinstrap male                      362.
11 Gentoo    female                   4680.
12 Gentoo    female                    282.
13 Gentoo    male                     5485.
14 Gentoo    male                      313.
15 Gentoo    <NA>                     4588.
16 Gentoo    <NA>                      338.
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
pengdt <- data.table(penguins)

pengdt[, mean_sd1(body_mass_g), by = list(species, sex)]
      species    sex        V1
       <fctr> <fctr>     <num>
 1:    Adelie   male 4043.4932
 2:    Adelie   male  346.8116
 3:    Adelie female 3368.8356
 4:    Adelie female  269.3801
 5:    Adelie   <NA> 3540.0000
 6:    Adelie   <NA>  477.1661
 7:    Gentoo female 4679.7414
 8:    Gentoo female  281.5783
 9:    Gentoo   male 5484.8361
10:    Gentoo   male  313.1586
11:    Gentoo   <NA> 4587.5000
12:    Gentoo   <NA>  338.1937
13: Chinstrap female 3527.2059
14: Chinstrap female  285.3339
15: Chinstrap   male 3938.9706
16: Chinstrap   male  362.1376
## . can be used as shorthand for list in data table
pengdt[, mean_sd1(body_mass_g), by = .(species, sex)]
      species    sex        V1
       <fctr> <fctr>     <num>
 1:    Adelie   male 4043.4932
 2:    Adelie   male  346.8116
 3:    Adelie female 3368.8356
 4:    Adelie female  269.3801
 5:    Adelie   <NA> 3540.0000
 6:    Adelie   <NA>  477.1661
 7:    Gentoo female 4679.7414
 8:    Gentoo female  281.5783
 9:    Gentoo   male 5484.8361
10:    Gentoo   male  313.1586
11:    Gentoo   <NA> 4587.5000
12:    Gentoo   <NA>  338.1937
13: Chinstrap female 3527.2059
14: Chinstrap female  285.3339
15: Chinstrap   male 3938.9706
16: Chinstrap   male  362.1376

Classes and custom generics

  1. Modify your mean and sd function so that the data structure that is returned has class “meansd”. There are two ways to do this:
    1. Say the object you currently return is called res, instead of res, return structure(res, class = "meansd")
    2. Add the line class(res) <- "meansd" before returning `res``
    3. Use the attr function to create and assign additional information, for example the name of the variable, You can get the name of the object passed to x using deparse1(substitute(x)).
mean_sd2 <- function(x, na.rm = TRUE) {
  res <- c(mean = mean(x, na.rm = na.rm), 
           sd = sd(x, na.rm = na.rm))
  
  attr(res, "variable") <- deparse1(substitute(x))
  attr(res, "sampsize") <- sum(!is.na(x))
  class(res) <- "meansd"
  res
}
  1. Write a custom print function print.meansd that nicely prints the mean and standard deviation. Use the functions round and paste functions to create a string, then print it out using the cat function.
print.meansd <- function(x, digits = 2) {
  
  msd <- paste0(round(x["mean"], digits = digits), " (", 
                round(x["sd"], digits = digits), ")")
  
  cat("mean (sd) of ", 
      attr(x, "variable"), ":", 
      msd, "\n")
  
}

mean_sd2(penguins$body_mass_g)
mean (sd) of  penguins$body_mass_g : 4201.75 (801.95)

More functions

  1. Write another function that constructs a one-sample t-statistic from an estimated mean and standard deviation. Recall that the t-statistic to test the null hypothesis that \(\mu = \mu_0\) is \[ T = \frac{\overline{X} - \mu_0}{\hat{\sigma}/\sqrt{n}} \] where \(\overline{X}\) is the sample mean and \(\hat{\sigma}\) is the sample standard deviation and \(n\) is the sample size.
  2. Write another function that takes the t-statistic and calculates a p-value
  3. Compose your custom functions in order to test the null hypothesis that the mean body mass of penguins is 4000g. Try using the pipe operator |>.
t_stat <- function(x, mu_0 = 0) {
  ## x should be of class "meansd"
  stopifnot(inherits(x, "meansd"))
  
  Tstat <- unname((x["mean"] - mu_0) / 
                    (x["sd"] / sqrt(attr(x, "sampsize"))))
  attr(Tstat, "df") <- attr(x, "sampsize") - 1
  Tstat
  
}


p_value <- function(x) {

  2 * pt(-abs(x), df = attr(x, "df"))
  
}

mean_sd2(penguins$body_mass_g) |> t_stat(mu_0 = 4000) |> p_value()
[1] 4.69977e-06
attr(,"df")
[1] 341