removing blanks/NA's

[FIC]

How would you:

1. Remove NA rows from the dataset
2. Remove blank rows from the dataset

[andresrcs]

Could you elaborate more on what do you mean by "blank rows"?, ideally, could you ask this with a minimal REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[FIC]

remove:

1. rows that explicitly say "N/A" or "Null"
2. rows that are missing a value

[andresrcs]

You are still not being clear but maybe this could give you a clue

library(dplyr)
df <- data.frame(stringsAsFactors = FALSE,
                 x = c(NA, "N/A", "null", "", "1", "2", "3"),
                 y = 1:7) 
df
#>      x y
#> 1 <NA> 1
#> 2  N/A 2
#> 3 null 3
#> 4      4
#> 5    1 5
#> 6    2 6
#> 7    3 7
df %>% 
    mutate_all(~ifelse(. %in% c("N/A", "null", ""), NA, .)) %>% 
    na.omit()
#>   x y
#> 5 1 5
#> 6 2 6
#> 7 3 7

^{Created on 2019-04-06 by the reprex package (v0.2.1.9000)}

[FIC]

yes, this is helpful.

Can you explain what the bolded means

df %>%
mutate_all(**~**ifelse(. %in% c("N/A", "null", ""), NA, .)) %>%
na.omit()

[andresrcs]

df is the name of the sample dataset I have created to exemplify the solution

df <- data.frame(stringsAsFactors = FALSE,
                 x = c(NA, "N/A", "null", "", "1", "2", "3"),
                 y = 1:7) 
df
#>      x y
#> 1 <NA> 1
#> 2  N/A 2
#> 3 null 3
#> 4      4
#> 5    1 5
#> 6    2 6
#> 7    3 7

NA stand for Not Available, and is the way of R to represent missing values, any other form is treated as a character string i.e. c("N/A", "null", "")

%>% this is called the pipe operator and concatenates commands together to make code more readable, the previous code would be equivalent to

na.omit(mutate_all(df, ~ifelse(. %in% c("N/A", "null", ""),  NA, .)))

[FIC]

Thank you!! Got it, you are assigning N/A, nulls and blanks as "NA", which R recognizes as a missing value. Then you are omitting these values.

Final questions about the syntax:

• What does the ~ mean before the ifelse
• What do the periods (.) before the %in% and after the NA, mean?

[andresrcs]

@FIC

What does the ~ mean before the ifelse

This means I'm passing a quosure style lambda ~ function.

@FIC

What do the periods (.) before the %in% and after the NA, mean?

The dot represents any variable in the dataframe

Try reading the documentation for the function to get a better understanding.

?mutate_all