# -Dealing with NA's

## #Test for missing values

```r
# vector with missing data
x <- c(1:4, NA, 6:7, NA)
x
## [1]  1  2  3  4 NA  6  7 NA

is.na(x)
## [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE

# data frame with missing data
df <- data.frame(col1 = c(1:3, NA),
                 col2 = c("this", NA,"is", "text"), 
                 col3 = c(TRUE, FALSE, TRUE, TRUE), 
                 col4 = c(2.5, 4.2, 3.2, NA),
                 stringsAsFactors = FALSE)

# identify NAs in full data frame
is.na(df)
##       col1  col2  col3  col4
## [1,] FALSE FALSE FALSE FALSE
## [2,] FALSE  TRUE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE
## [4,]  TRUE FALSE FALSE  TRUE

# identify NAs in specific data frame column
is.na(df$col4)
## [1] FALSE FALSE FALSE  TRUE
```

&#x20;To identify the location or the number of NAs we can leverage the `which()` and `sum()` functions:

```r
# identify location of NAs in vector
which(is.na(x))
## [1] 5 8

# identify count of NAs in data frame
sum(is.na(df))
## [1] 3
```

For data frames, a convenient shortcut to compute the total missing values in each column is to use `colSums()`:

```r
colSums(is.na(df))
## col1 col2 col3 col4
##    1    1    0    1
```

## #Recode missing values

&#x20;we can recode missing values in vector `x` with the mean values in `x` by first subsetting the vector to identify `NA`s and then assign these elements a value.&#x20;

```r
# recode missing values with the mean
# vector with missing data
x <- c(1:4, NA, 6:7, NA)
x
## [1]  1  2  3  4 NA  6  7 NA

x[is.na(x)] <- mean(x, na.rm = TRUE)

round(x, 2)
## [1] 1.00 2.00 3.00 4.00 3.83 6.00 7.00 3.83

# data frame that codes missing values as 99
df <- data.frame(col1 = c(1:3, 99), col2 = c(2.5, 4.2, 99, 3.2))

# change 99s to NAs
df[df == 99] <- NA
df
##   col1 col2
## 1    1  2.5
## 2    2  4.2
## 3    3   NA
## 4   NA  3.2
```

Recode value in data frame:

```r
# data frame with missing data
df <- data.frame(col1 = c(1:3, NA),
                 col2 = c("this", NA,"is", "text"), 
                 col3 = c(TRUE, FALSE, TRUE, TRUE), 
                 col4 = c(2.5, 4.2, 3.2, NA),
                 stringsAsFactors = FALSE)
                 
df$col4[is.na(df$col4)] <- mean(df$col4, na.rm = TRUE)
df
##   col1 col2  col3 col4
## 1    1 this  TRUE  2.5
## 2    2 <NA> FALSE  4.2
## 3    3   is  TRUE  3.2
## 4   NA text  TRUE  3.3
```

## #Exclude missing values

There are many ways excluding value.

\*1.&#x20;

```r
# A vector with missing values
x <- c(1:4, NA, 6:7, NA)

# including NA values will produce an NA output
mean(x)
## [1] NA

# excluding NA values will calculate the mathematical operation 
# for all non-missing values
mean(x, na.rm = TRUE)
## [1] 3.833333
```

\*2.&#x20;

```r
# data frame with missing values
df <- data.frame(col1 = c(1:3, NA),
                 col2 = c("this", NA,"is", "text"), 
                 col3 = c(TRUE, FALSE, TRUE, TRUE), 
                 col4 = c(2.5, 4.2, 3.2, NA),
                 stringsAsFactors = FALSE)

df
##   col1 col2  col3 col4
## 1    1 this  TRUE  2.5
## 2    2 <NA> FALSE  4.2
## 3    3   is  TRUE  3.2
## 4   NA text  TRUE   NA
```

\*3.&#x20;

```r
complete.cases(df)
## [1]  TRUE FALSE  TRUE FALSE

# subset with complete.cases to get complete cases
df[complete.cases(df), ]
##   col1 col2 col3 col4
## 1    1 this TRUE  2.5
## 3    3   is TRUE  3.2

# or subset with `!` operator to get incomplete cases
df[!complete.cases(df), ]
##   col1 col2  col3 col4
## 2    2 <NA> FALSE  4.2
## 4   NA text  TRUE   NA
```

\*4.

```
# or use na.omit() to get same as above
na.omit(df)
##   col1 col2 col3 col4
## 1    1 this TRUE  2.5
## 3    3   is TRUE  3.2
```

## #Exercises

1. How many missing values are in the built-in data set `airquality`?
2. Which variables are the missing values concentrated in?
3. How would you impute the mean or median for these values?
4. How would you omit all rows containing missing values?


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