**R – Statistical Odds & Ends**, and kindly contributed to R-bloggers)

In R, we can subset a data frame `df`

easily by putting the conditional in square brackets after `df`

. For example, if I want all the rows in `df`

which have value equal to 1 in the column `colA`

, all I have to do is

df[df$colA == 1, ]

Recently, I realized that this approach can be problematic when there are NAs present in the data! For example, let `df`

be the following data frame:

df <- data.frame(colA = c(1, 2, 3, NA, NA), colB = c("a", "b", "c", "d", NA)) df # co1A colB # 1 1 a # 2 2 b # 3 3 c # 4 NA d # 5 NA

If I want to pull out all rows such that the value in `colA`

is equal to 2, I would use the following code expecting just one row to be returned:

df[df$colA == 2, ] # colA colB # 2 2 b # NA NA# NA.1 NA

All the rows with NA in `colA`

also got included, and all the values in those rows got converted into NAs! To avoid these rows from being added, we have to explicitly check that the values are not NAs:

df[!is.na(df$colA) & df$colA == 2, ] # colA colB # 2 2 b

`dplyr`

acts a bit differently in that it removes NAs by default and so you do not have to check for them:

library(dplyr) df %>% filter(colA == 2) # colA colB # 2 2 b

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