The question
Below is the code:
library(tidyverse) df <- tibble( ~col1, ~col2, ~col3, 1, 2, 3, 1, NA, 3, NA, 2, 3 )
I can remove all NA abservations with the help of drop_na():
df %>% drop_na()
Or remove all NA in a single column (col1 for example):
df %>% drop_na(col1)
Why I cannot just use a regular != filter pipe ?
df %>% filter(col1 != NA)
Why do we use a special function from tidyr to remove NAs?
5 answers
This is not directly related to dplyr::filter. However, any comparison involving NA, such as NA==NA, will yield NA as a result.
R is unaware of the context of your analysis.
Essentially, it does not permit comparison operators to treat NA as a valid value.
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R is unaware of the context of your analysis.
Essentially, it does not permit comparison operators to treat NA as a valid value.
Are you considering a career in data analysis? Our Data Analyst Certification Course will provide you with the essential tools and techniques for success.
Try this:
df %>% filter(!is.na(col1))
This was simple, direct and perfect...thank you!
In R, null values do not possess a concept of equality. Consequently, the expression NA == NA yields NA. In reality, comparing NA with any object in R will also result in NA. The filter function in dplyr necessitates a boolean argument. Therefore, when it processes col1 and checks for inequality using filter(col1 != NA), the command 'col1 != NA' consistently produces NA values for every row in col1. Since this does not constitute a boolean expression, the filter function fails to evaluate correctly.
Is it possible to generate a list similar to the one below to identify the rows that contain no null values, and subsequently use this list with the dplyr function to filter rows based on their position values?
In this case, na.omit(airquality$Ozone) will yield the values that are not null.
Afterward, can we supply the list of positions to the filtering function?
In this case, na.omit(airquality$Ozone) will yield the values that are not null.
Afterward, can we supply the list of positions to the filtering function?
The dplyr package includes the 'filter()' function for filtering data, but its capabilities extend beyond that. With dplyr, you can execute filtering operations that may be challenging or complex to achieve using SQL or conventional business intelligence tools, all in a straightforward and intuitive manner. For instance, consider a flight dataset where we can eliminate NA values using the filter keyword.
flight %>% select(FL_DATE, CARRIER, ORIGIN, ORIGIN_CITY_NAME, ORIGIN_STATE_ABR, DEP_DELAY, DEP_TIME, ARR_DELAY, ARR_TIME) %>% filter(!is.na(ARR_DELAY))
Thanks, that worked