Naming output variables with a different notation: i.e. You can find the complete documentation for this function here. The names of the output variables is given by the notation: variable_function: i.e. Note: In each example, we utilized the dplyr across() function. Summarise_each(funs(min, max), mpg, disp) Summarise(min_mpg = min(mpg), min_disp = min(disp), max_mpg = max(mpg), max_disp = max(disp)) Summarise_each(funs(mean), mean_mpg = mpg, mean_disp = disp)Ĭase 4: apply many functions to many variablesĪs in the previous cases both functions: summarise() and summarise_each() provide a valid alternative. In order to achieve this result we shall appropriately rename the variables we pass to. Possibly we would prefer something like: mean_mpg and mean_disp. I want to add a column called percent to compare how much of the total is part of a given segment (industry). I can't seem to get the syntax correct to do what I'm trying. In this case we loose track of the name of the function applied to the variables: mean(). The scoped variants of summarise() make it easy to apply the same transformation to multiple variables. technobrat September 23, 2021, 7:45pm 1 Hi, I'm trying to get a summary going with a condition. The names of the output variables is given by the name of the variables: mpg and disp. Summarise(mean_mpg = mean(mpg), mean_disp = mean(disp)) Both functions summarise() and summarise_each() can be usedįunction summarise() has again a more intuitive syntax and the names of output variables can be specified in the usual simple form: max_mpg = max(mpg) # without group Summarise_each (funs(min_mpg = min, max_mpg = max), mpg)Ĭase 3: apply one function to many variables If we prefer something like: min_mpg and max_mpg we shall rename the functions we call within funs(): # without group In this case we loose the name of the variable the function is applied to. The names of the output variables is given by the name of the functions: min and max. When we apply many functions to one variable, the use of summarise_each() provides a more compact and tidy notation: # without group The names of the output variables can be specified in simple forms like: max_mpg = max(mpg) Summarise (min_mpg = min(mpg), max_mpg = max(mpg)) In this case we can use both functions summarise() and summarise_each().įunction summarise() has a more intuitive syntax: # without group So to fill the gap, we’re introducing two new functions ifall() and ifany(). Case 2: apply many functions to one variable across() is very useful within summarise() and mutate(), but it’s hard to use it with filter() because it is not clear how the results would be combined into one logical vector. The following are some sample codes: select by column name dplyr::select(sim.dat. drop = FALSE ) #> # A tibble: 4 × 2 #> type n #> #> 1 a 3 #> 2 b 0 #> 3 c 1 #> 4 NA 1 # Or, using `group_by()`: df2 %>% group_by ( type. If you want to select columns instead of rows, you can use select(). This is useful # when the data has already been aggregated once df % count ( gender ) #> # A tibble: 2 × 2 #> gender n #> #> 1 female 2 #> 2 male 1 # counts runs: df %>% count ( gender, wt = runs ) #> # A tibble: 2 × 2 #> gender n #> #> 1 female 5 #> 2 male 10 # When factors are involved, `.drop = FALSE` can be used to retain factor # levels that don't appear in the data df2 % count ( type ) #> # A tibble: 3 × 2 #> type n #> #> 1 a 3 #> 2 c 1 #> 3 NA 1 df2 %>% count ( type. # count() is a convenient way to get a sense of the distribution of # values in a dataset starwars %>% count ( species ) #> # A tibble: 38 × 2 #> species n #> #> 1 Aleena 1 #> 2 Besalisk 1 #> 3 Cerean 1 #> 4 Chagrian 1 #> 5 Clawdite 1 #> 6 Droid 6 #> 7 Dug 1 #> 8 Ewok 1 #> 9 Geonosian 1 #> 10 Gungan 3 #> # ℹ 28 more rows starwars %>% count ( species, sort = TRUE ) #> # A tibble: 38 × 2 #> species n #> #> 1 Human 35 #> 2 Droid 6 #> 3 NA 4 #> 4 Gungan 3 #> 5 Kaminoan 2 #> 6 Mirialan 2 #> 7 Twi'lek 2 #> 8 Wookiee 2 #> 9 Zabrak 2 #> 10 Aleena 1 #> # ℹ 28 more rows starwars %>% count ( sex, gender, sort = TRUE ) #> # A tibble: 6 × 3 #> sex gender n #> #> 1 male masculine 60 #> 2 female feminine 16 #> 3 none masculine 5 #> 4 NA NA 4 #> 5 hermaphroditic masculine 1 #> 6 none feminine 1 starwars %>% count (birth_decade = round ( birth_year, - 1 ) ) #> # A tibble: 15 × 2 #> birth_decade n #> #> 1 10 1 #> 2 20 6 #> 3 30 4 #> 4 40 6 #> 5 50 8 #> 6 60 4 #> 7 70 4 #> 8 80 2 #> 9 90 3 #> 10 100 1 #> 11 110 1 #> 12 200 1 #> 13 600 1 #> 14 900 1 #> 15 NA 44 # use the `wt` argument to perform a weighted count.
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