# top_frac, top_n, transmute, transmute_all, transmute_at, # summarize, summarize_all, summarize_at, summarize_if, tally, # slice, summarise, summarise_all, summarise_at, summarise_if, # sample_n, select, select_all, select_at, select_if, semi_join, # rename, rename_all, rename_at, rename_if, right_join, sample_frac, # inner_join, left_join, mutate, mutate_all, mutate_at, mutate_if, # full_join, group_by, group_by_all, group_by_at, group_by_if, # distinct_at, distinct_if, filter, filter_all, filter_at, filter_if, # add_count, add_tally, anti_join, count, distinct, distinct_all, # Attaching package: 'tidylog' # The following objects are masked from 'package:dplyr': The database connections essentially remove that limitation in that you can have a database of many 100s GB, conduct queries on it directly and pull back just what you need for analysis in R.ĭplyr is loaded with the tidyverse metapackage. This addresses a common problem with R in that all operations are conducted in memory and thus the amount of data you can work with is limited by available memory. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query returned. An additional feature is the ability to work with data stored directly in an external database. dplyr addresses this by porting much of the computation to C++.
The thinking behind it was largely inspired by the package plyr which has been in use for some time but suffered from being slow in some cases. It is built to work directly with tibbles. 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.The package dplyr is a fairly new (2014) package that tries to provide easy tools for the most common data manipulation tasks. 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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