Web< tidy-select > Columns to transform. You can't select grouping columns because they are already automatically handled by the verb (i.e. summarise () or mutate () ). .fns Functions to apply to each of the selected columns. Possible values are: A function, e.g. mean. A purrr-style lambda, e.g. ~ mean (.x, na.rm = TRUE) WebFeb 26, 2024 · Replacing NA's in a dataframe/tibble tidyverse That's a good solution. I've been using: df [is.na (df)] <- 0 but it does not fit in a pipe chain very smoothly. I really like mutate_if (is.numeric, funs (replace_na (., 0))) thanks for sharing what you figured out! 2 Likes sbl_bah February 26, 2024, 7:06pm #3 I'm sorry for the late answer.
Pivoting • tidyr - Tidyverse
WebA data frame. key, value < tidy-select > Columns to use for key and value. fill If set, missing values will be replaced with this value. Note that there are two types of missingness in the input: explicit missing values (i.e. NA ), and implicit missings, rows that simply aren't present. Both types of missing value will be replaced by fill. convert WebDec 13, 2024 · Working with dates in R requires more attention than working with other object classes. Below, we offer some tools and example to make this process less painful. Luckily, dates can be wrangled easily with practice, and … can parasitic stds be cured
Spread a key-value pair across multiple columns - Tidyverse
WebJul 26, 2024 · To analyse JSON data in R, ideally what we want is a way of first consistently converting it into tidy data (e.g. a tidy tibble). Fortunately, the fromJSON()function from the excellent jsonlitepackage makes converting JSON objects into R objects a pretty trivial task. WebAs you noticed above, I have used the following methods to replace NA values with 0 in R. Using is.na () Using replace () Using replace () from imputeTS package Using coalesce () from dplyr package Using mutate (), … WebJul 4, 2024 · and I want to replace the NA value in column b by 2. First approach: tiny %>% mutate (b = case_when (is.na (b) ~ 2, TRUE ~ b)) #> Error: must be a double vector, not a `factor` object. Second approach: tiny %>% mutate ( b = case_when ( is.na (b) ~ factor (2, levels = levels (b)), TRUE ~ b ) ) #> # A tibble: 2 x 2 #> a b #> #> 1 1 1 ... can parasites cause infection