R - Data Frames - A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values f Method 2: Remove or Drop rows with NA using complete.cases() function. This r function will examine a dataframe and return a vector of the rows which contain missing values. In the previous example with complete.cases() function, we considered the rows without any missing values. We can examine the dropped records and purge them if we wish. Given the following vector: x <- c(1, 1, 4, 5, 4, 6) To find the position of duplicate elements in x, use this: Create new variable using case when statement in R: Case when with multiple condition. Return a logical vector indicating which cases are complete, i.e., have no missing values. df1[complete.cases(df1),] so after removing NA and NaN the resultant dataframe will be To remove rows of a dataframe that has all NAs, use dataframe subsetting as shown below Using complete.cases() to remove (missing) NA and NaN values. You can achieve the same outcome by using the second template (don’t forget to place a closing bracket at the end of your DataFrame – as captured in the third line of the code below): We will be creating additional variable Price_band using mutate function and case when statement.Price_band consist of “Medium”,”High” and “Low” based on price value. Alternatively, use complete.cases() and sum it (complete.cases() returns a logical vector [TRUE or FALSE] indicating if any observations are NA for any rows. Remove rows of R Dataframe with all NAs. Find and drop duplicate elements. But in this example, we will consider rows with NAs but not all NAs. To find all unique combinations of x, y and z, including those not present in the data, supply each variable as a separate argument: expand(df, x, y, z).. To find only the combinations that occur in the data, use nesting: expand(df, nesting(x, y, z)).. You can combine the two forms. Columns can be atomic vectors or lists. The R function duplicated() returns a logical vector where TRUE specifies which elements of a vector or data frame are duplicates.. so the new variables are created using multiple conditions in the case_when() function of R. The uppercase versions will work with vectors, which are treated as if they were a 1 column matrix, and are robust if you end up subsetting your data such that R drops an empty dimension. Find Complete Cases. data: A data frame.... Specification of columns to expand. The values in R match with those in our dataset. # na in R - complete.cases example fullrecords <- collecteddata[!complete.cases(collecteddata)] droprecords <- collecteddata[complete.cases(collecteddata)]