Dealing with not available (NA) values

In R, NA stands for not available, which means that the data point is missing. If a variable you wish to analyse contains missing values, there are usually two main options:

Remove the observations with missing values
Replace the missing values with actual values using an imputation technique.

We will use the first option, which is the simplest solution.

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Helsinki Open Data Science

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Instrucciones del ejercicio

Create a smaller version of the human data by selecting the variables defined in keep
Use complete.cases() on human to print out a logical "completeness indicator" vector
Adjust the code: Define comp as the completeness indicator and print out the resulting data frame. When is the indicator FALSE and when is it TRUE? (hint: ?complete.cases()).
filter() out all the rows with any NA values. Right now, TRUE is recycled so that nothing is filtered out.

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# human with modified GNI and dplyr are available

# columns to keep
keep <- c("Country", "Edu2.FM", "Labo.FM", "Life.Exp", "Edu.Exp", "GNI", "Mat.Mor", "Ado.Birth", "Parli.F")

# select the 'keep' columns
human <- select(human, one_of(keep))

# print out a completeness indicator of the 'human' data
complete.cases(human)

# print out the data along with a completeness indicator as the last column
data.frame(human[-1], comp = "change me!")

# filter out all rows with NA values
human_ <- filter(human, TRUE)

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