![]() ![]() “We are significantly investing in generative AI across all of our businesses,” Amazon said in a statement to CNN on Monday, referring to the technology that underpins services such as ChatGPT. The summary features the disclaimer: “AI-generated from the text of customer reviews.” On the Amazon product page for Apple’s third-generation AirPods, for example, the AI feature now sums up the more than 4,000 user ratings to note that the wireless headphones “have received positive feedback from customers regarding their sound quality and battery life.” But, it adds, “mixed opinions were also expressed about the performance, durability, fit, comfort, and value of the headphones.” ![]() When there are multiple functions, they create new # variables instead of modifying the variables in place: by_species %>% summarise_all ( list ( min, max ) ) #> # A tibble: 3 × 9 #> Species Sepal.Length_fn1 Sepal.Width_fn1 Petal.Length_fn1 #> #> 1 setosa 4.3 2.3 1 #> 2 versicolor 4.9 2 3 #> 3 virginica 4.9 2.2 4.5 #> # ℹ 5 more variables: Petal.Width_fn1, Sepal.Length_fn2, #> # Sepal.Width_fn2, Petal.Length_fn2, Petal.Width_fn2 # -> by_species %>% summarise ( across ( everything ( ), list (min = min, max = max ) ) ) #> # A tibble: 3 × 9 #> Species Sepal.Length_min Sepal.Length_max Sepal.Width_min #> #> 1 setosa 4.3 5.8 2.3 #> 2 versicolor 4.9 7 2 #> 3 virginica 4.9 7.9 2.2 #> # ℹ 5 more variables: Sepal.Width_max, Petal.Length_min, #> # Petal.Length_max, Petal.Width_min, Petal.Amazon is experimenting with using artificial intelligence to sum up customer feedback about products on the site, with the potential to cut down on the time shoppers spend sifting through reviews before making a purchase. 97.3 87.6 by_species % group_by ( Species ) # If you want to apply multiple transformations, pass a list of # functions. x, na.rm = TRUE ) ) ) #> # A tibble: 1 × 3 #> height mass birth_year #> #> 1 174. 97.3 87.6 starwars %>% summarise ( across ( where ( is.numeric ), ~ mean (. Here we apply mean() to the numeric columns: starwars %>% summarise_if ( is.numeric, mean, na.rm = TRUE ) #> # A tibble: 1 × 3 #> height mass birth_year #> #> 1 174. 97.3 # The _if() variants apply a predicate function (a function that # returns TRUE or FALSE) to determine the relevant subset of # columns. 97.3 # -> starwars %>% summarise ( across ( height : mass, ~ mean (. 97.3 # You can also supply selection helpers to _at() functions but you have # to quote them with vars(): starwars %>% summarise_at ( vars ( height : mass ), mean, na.rm = TRUE ) #> # A tibble: 1 × 2 #> height mass #> #> 1 174. 97.3 # -> starwars %>% summarise ( across ( c ( "height", "mass" ), ~ mean (. # The _at() variants directly support strings: starwars %>% summarise_at ( c ( "height", "mass" ), mean, na.rm = TRUE ) #> # A tibble: 1 × 2 #> height mass #> #> 1 174. Name collisions in the new columns are disambiguated using a unique suffix. vars is named, a new column by that name will be created. Similarly, vars() accepts named and unnamed arguments. If a function is unnamed and the name cannot be derived automatically, ![]() ![]() funs argument can be a named or unnamed list. The names of the functions are used to name the new columns Ĭoncatenating the names of the input variables and the names of theįunctions, separated with an underscore "_". vars is of the form vars(a_single_column)) and. The names of the input variables are used to name the new columns įor _at functions, if there is only one unnamed variable (i.e., If there is only one unnamed function (i.e. Input variables and the names of the functions. The names of the new columns are derived from the names of the ![]()
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