Deal with missing data

Identify missing data with is.na()

Find out which and how many observations have missing data

In R, missing values are coded as NA (not available)

vec <- c(2, 5, 7)
vec[5]

[1] NA

(vec1 <- c(10, 20, NA, 40, 50, NA))

[1] 10 20 NA 40 50 NA

length(vec1)

[1] 6

Identify missing values with is.na()

is.na(vec1)

[1] FALSE FALSE  TRUE FALSE FALSE  TRUE

any(is.na(vec1))

[1] TRUE

which(is.na(vec1))

[1] 3 6

sum(is.na(vec1))

[1] 2

Identify missing data in matrices

vec2   <- c(NA, 7, 9, 10, 1, 8)
(matNA <- rbind(vec1, vec2))

     [,1] [,2] [,3] [,4] [,5] [,6]
vec1   10   20   NA   40   50   NA
vec2   NA    7    9   10    1    8

is.na(matNA)

      [,1]  [,2]  [,3]  [,4]  [,5]  [,6]
vec1 FALSE FALSE  TRUE FALSE FALSE  TRUE
vec2  TRUE FALSE FALSE FALSE FALSE FALSE

Behavior of NA in different situations

Missing data in index vectors

LETTERS[c(1, NA, 3)]

[1] "A" NA  "C"

Missing data in factors

factor(LETTERS[c(1, NA, 3)])

[1] A    <NA> C   
Levels: A C

factor(LETTERS[c(1, NA, 3)], exclude=NULL)

[1] A    <NA> C   
Levels: A C <NA>

Missing data in logical expressions

NA & TRUE

[1] NA

TRUE | NA

[1] TRUE

vecNA   <- c(-3, 2, 0, NA, -7, 5)
(logIdx <- vecNA > 0)

[1] FALSE  TRUE FALSE    NA FALSE  TRUE

vecNA[logIdx]

[1]  2 NA  5

vecNA[which(logIdx)]

[1] 2 5

Code missing values as NA

When data is entered in other applications (spreadsheets, SPSS, etc.), missing values are often coded as a reserved numeric value, e.g., 99 or 9999. These values need to be replaced with NA.

In vectors

vec <- c(30, 25, 23, 21, -999, 999)
is.na(vec) <- vec %in% c(-999, 999)
vec

[1] 30 25 23 21 NA NA

In matrices

(mat <- matrix(c(30, 25, 23, 21, -999, 999), nrow=2, ncol=3))

     [,1] [,2] [,3]
[1,]   30   23 -999
[2,]   25   21  999

is.na(mat) <- mat %in% c(-999, 999)
mat

     [,1] [,2] [,3]
[1,]   30   23   NA
[2,]   25   21   NA

Statistical analysis with missing data

In vectors

vecNA <- c(-3, 2, 0, NA, -7, 5)
mean(vecNA)

[1] NA

goodIdx <- !is.na(vecNA)
mean(vecNA[goodIdx])

[1] -0.6

sd(na.omit(vecNA))

[1] 4.615192

sum(vecNA, na.rm=TRUE)

[1] -3

In matrices

ageNA  <- c(18, NA, 27, 22)
DV1    <- c(NA, 1, 5, -3)
DV2    <- c(9, 4, 2, 7)
(matNA <- cbind(ageNA, DV1, DV2))

     ageNA DV1 DV2
[1,]    18  NA   9
[2,]    NA   1   4
[3,]    27   5   2
[4,]    22  -3   7

apply(matNA, 1, FUN=mean)

[1]        NA        NA 11.333333  8.666667

apply(matNA, 1, FUN=mean, na.rm=TRUE)

[1] 13.500000  2.500000 11.333333  8.666667

Casewise deletion of missing data

(rowNAidx <- apply(is.na(matNA), 1, any))

[1]  TRUE  TRUE FALSE FALSE

matNA[!rowNAidx, ]

     ageNA DV1 DV2
[1,]    27   5   2
[2,]    22  -3   7

na.omit(matNA)

     ageNA DV1 DV2
[1,]    27   5   2
[2,]    22  -3   7
attr(,"na.action")
[1] 2 1
attr(,"class")
[1] "omit"

colMeans(na.omit(matNA))

ageNA   DV1   DV2 
 24.5   1.0   4.5 

cov(matNA, use="complete.obs")

      ageNA DV1   DV2
ageNA  12.5  20 -12.5
DV1    20.0  32 -20.0
DV2   -12.5 -20  12.5

all(cov(matNA, use="complete.obs") == cov(na.omit(matNA)))

[1] TRUE

Set casewise deletion as a permanent option for statistical functions (another choice is "na.fail")

options(na.action="na.omit")

Pairwise deletion of missing data

rowMeans(matNA)

[1]        NA        NA 11.333333  8.666667

rowMeans(mat, na.rm=TRUE)

[1] 26.5 23.0

cov(matNA, use="pairwise.complete.obs")

          ageNA DV1        DV2
ageNA  20.33333  20 -16.000000
DV1    20.00000  16 -10.000000
DV2   -16.00000 -10   9.666667

Further resources

• CRAN Task View ‘Missing Data’

Useful packages

Multiple imputation is supported by functions in packages mice and Amelia.

Get the article source from GitHub

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