Août 6 2020
r case_when multiple columnstamoxifen und alkohol
case_when() from multiple columns. !! #> [37] "37" "38" "39" "fizz" "41" "buzz"
```r If you want# to explicitly handle NA values you can use the `is.na` function:# All RHS values need to be of the same type. Conditional recode using `case_when` with `grepl` and value matching `%in%` #> Luke Skywalker 172 77 masculine Human other #> [64] "other" "other" "other" "other" "other" "large" "large" "other" "other" #> [31] NA NA NA NA "fizz" NA NA NA NA "fizz" TRUE ~ "short"
# 2 medium 5.292593 Goal: recode if it is a Mercedes and it has 4 or 6 to 8 cylinders using mtcars data. mutate(Merc68 = case_when( (cyl %in% c(6, 8) & grepl("Merc ", rownames(mtcars)) == TRUE) ~ "Yes", This function allows you to vectorise multiple if_else() statements. mtcars %>% In some cases, especially for a data analyst, people might not have the title but they still identify themselves as data scientists. ``` # 2 medium 5.241176
In this tutorial, you will learn how to select or subset data frame columns by names and position using the R function select() and pull() [in dplyr package].
#> [49] "buzz" "fizz" #> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
case_when.Rd.
iris2 <- dplyr::mutate(iris, group = dplyr::case_when(!!! I'm learning R and I have the following matrix with categorical variables. ) If so, here's a bit of a wild way to do it (basically lifted from I've added a few comments in the reprex where I'm stuck...I posted a simplified (and probably much clearer) version of my second question on This topic was automatically closed 7 days after the last reply. rownames_to_column() %>% #> [37] "other" "other" "other" "other" "other" "other" "other" "other" "other" Easy. #> [82] "other" "other" "other" "other" "other" "other"# Like an if statement, the arguments are evaluated in order, so you must# proceed from the most specific to the most general. #> [25] "fizz" "26" "27" "buzz" "29" "fizz" Dplyr package in R is provided with select() function which re orders the columns. #> [28] "other" "other" "other" "other" "other" "other" "other" "large" "large"
dplyr::summarize( )
get(my_column) > 5 ~ "medium", #> [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" cases))
If no … #> [25] "fizz" "26" "27" "buzz" "29" "fizz" WHEN (column3 = awe and column4 = kls) THEN 2. TRUE ~ "Other")) # # A tibble: 3 x 2 mean = mean(!! #> [43] "43" "44" "fizz" "46" "47" "48" This technique conveniently works when you decide to keep 1-4 columns (just because of typing it out). # 3 short 3.007407 ## Conditional recode #> # `case_when()` is not a tidy eval function. Hi. #> Darth Vader 202 136 masculine Human large We’ll also show how to remove columns from a data frame. # 3 short 4.787500 The RHS does not need to be #> [46] "other" "other" "other" "other" "other" "other" "other" "other" "large" dplyr::summarize( summary_function <- function(my_column) { Dplyr package in R is provided with select() function which select the columns based on conditions. #> [19] "other" "other" "other" "other" "other" "other" "other" "other" "other" For above example, for some row: if column1 equals xyz and column2 equals asd then the expression will return 1. #> [8] 1.2247449 1.4142136#> [1] "other" "robot" "robot" "large" "other" "other" "other" "robot" "other" If you'd like to reuse# the same patterns, extract the `case_when()` call in a normal# Such functions can be used inside `mutate()` as well:# `case_when()` ignores `NULL` inputs.
#> [21] "buzz" NA NA NA "fizz" NA NA "buzz" NA "fizz" #> [41] NA "buzz" NA NA "fizz" NA NA NA "buzz" "fizz"#> [1] "1" "nope" "nope" "nope" "fizz" "6"
In SQL, If Else statement is defined in CASE WHEN. dplyr, purrr. dplyr::group_by(iris2, sepal_category), A CASE expression returns *one* value. # 1 long 6.378022 # # A tibble: 3 x 2 This is useful when you'd# like to use a pattern only under certain conditions. With data science being a new and rapidly growing field, there can be confusion about what defines a "data scientist."
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