![]() ![]() Shed the societal and cultural narratives holding you back and let. quantile() was hard to use previously because it returns multiple values. ndistinct() counts the number of unique/distinct combinations in a set of one or more vectors. RStudio, and the tidyverse, a collection of R packages designed to work. Visit the Getting Started guide or, for more detailed examples, go straight to the Learn page. These packages provide a comprehensive foundation for creating and using models of all types. The fact that it has a package means that all packages. ![]() To demonstrate this new flexibility in a more useful situation, let’s take a look at quantile(). Modeling with the tidyverse uses the collection of tidymodels packages, which largely replace the modelr package used in R4DS. Tidyverse is a package that installs a series of other packages. This is a big change to summarise() but it should have minimal impact on existing code because it broadens the interface: all existing code will continue to work, and a number of inputs that would have previously errored now work. To put this another way, before dplyr 1.0.0, each summary had to be a single value (one row, one column), but now we’ve lifted that restriction so each summary can generate a rectangle of arbitrary size. (This isn’t very useful when used directly, but as you’ll see shortly, it’s really useful inside of functions.) ![]() Df %>% group_by ( grp ) %>% summarise ( tibble ( min = min ( x ), mean = mean ( x ))) #> `summarise()` ungrouping output (override with `.groups` argument) #> # A tibble: 2 x 3 #> grp min mean #> * #> 1 1 -2.69 -0.843 #> 2 2 -2.73 -0.434 Function writing now includes details on how to wrap tidyverse functions (dealing with the challenges of. 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. You will learn about the Tidyverse, what tidy data really is, and how to practically achieve it with packages such as dplyr, tidyr, lubridate, and forcats. A brief summary of the biggest changes follows. 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. ![]() This vignette will walk a reader through the tblsummary () function, and the various. # 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. The tblsummary () function calculates descriptive statistics for continuous, categorical, and dichotomous variables in R, and presents the results in a beautiful, customizable summary table ready for publication (for example, Table 1 or demographic tables). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |