Visualizing Surveys in R by Valtonen Teppo;

Author:Valtonen, Teppo;
Language: eng
Format: epub
Publisher: CRC Press LLC
Published: 2023-11-03T00:00:00+00:00

8.2.1 Parse NA values in numeric variables

Since I have read the data as text, I can also use the parse_integer() and parse_numeric() functions described in Section 6.1 and 6.2, respectively:

df %>% select( LSATIS11R_A) %>% mutate( lifesat11 = parse_integer( LSATIS11R_A, na = c( '97', '98', '99' ) ) ) %>% head()

## # A tibble: 6 x 2 ## LSATIS11R_A lifesat11 ## <chr> <int> ## 1 8 8 ## 2 8 8 ## 3 9 9 ## 4 8 8 ## 5 8 8 ## 6 10 10

8.2.2 NA values in categorical variables

If I have categorical variables, probably the easiest way is to use the function factor() and define non-NA values with the levels argument:

df %>% select( PAIFRQ3M_A) %>% mutate( pain = factor( PAIFRQ3M_A, levels = c( 1, 2, 3, 4 ) ) ) %>% head()

## # A tibble: 6 x 2 ## PAIFRQ3M_A pain ## <chr> <fct> ## 1 2 2 ## 2 4 4 ## 3 2 2 ## 4 4 4 ## 5 8 <NA> ## 6 8 <NA>

Like I described in Section 6.3, I can also use the function parse_factor() but if I don't want to get warnings, I have to define both NA and non-NA values explicitly:

df %>% select( PAIFRQ3M_A) %>% mutate( pain = parse_factor( PAIFRQ3M_A, levels = c( '1', '2', '3', '4' ), na = c( '7', '8', '9' ) ) ) %>% head()

## # A tibble: 6 x 2 ## PAIFRQ3M_A pain ## <chr> <fct> ## 1 2 2 ## 2 4 4 ## 3 2 2 ## 4 4 4 ## 5 8 <NA> ## 6 8 <NA>

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