To demonstrate the basic functionality of fauxnaif, let’s first load the package and an example dataset.
library(fauxnaif)
library(magrittr)
fauxnaif::faux_census
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 2.80e4 Christian
#> 2 NY Woman 89 Latino 1.49e5 Spiritual not religio~
#> 3 CA Female 48 White 4.79e5 Catholic
#> 4 TX Male 63 latinx 8.50e4 christian
#> 5 PA Male 47 asian 4.19e4 Baptist
#> 6 TX Gender is a socia~ 57 Race is a soci~ 1.00e7 Religion is the opiat~
#> 7 Canada Male 49 white 1.49e5 methodist
#> 8 TX Female 50 White 9.88e4 Lutheran
#> 9 NY f 557 white 9.07e4 Agnostic
#> 10 WA F 33 White 4.50e4 Jewish
#> 11 TX Male 30 White 1.27e5 none
#> 12 OH Non-binary 42 Caucasian 2.16e4 Roman Catholic
#> 13 NC Female 22 African Americ~ 7.42e4 atheist
#> 14 LA Male 2 White 6.10e4 Christian
#> 15 LA Female 28 Black 2.00e4 Not religious
#> 16 CA male 34 Asian American 7.74e4 Christian
#> 17 TN M 64 white 1.00e7 Nothing
#> 18 FL Female 68 white 4.71e4 None
#> 19 OH Male 39 black 2.38e4 baptist
#> 20 NH male 73 Hispanic 3.32e4 ChristianWe can see the example dataset in full above. The data is a small section of census-like information. This dataset needs a lot of cleaning. Other tools like dplyr and tidyr would likely be needed to really analyze this data, but we’ll focus on the aspects that can be handled by fauxnaif.
First, let’s look at the simplest issue in this dataset: income.
faux_census$income
#> [1] 28000 148800 479000 85000 41900 9999999 149000 98800 90750
#> [10] 45010 127000 21600 74200 61000 20000 77400 9999999 47100
#> [19] 23800 33200Printing the vector of incomes, one value stands out: while most respondents’ have values in the tens to hundreds of thousands, two respondents have incomes of 9999999. It’s common for datasets you receive from other sources to use an unrealistically high value (often a string of 9s) to indicate NA. We can clean this using na_if_in().
na_if_in(faux_census$income, 9999999)
#> [1] 28000 148800 479000 85000 41900 NA 149000 98800 90750 45010
#> [11] 127000 21600 74200 61000 20000 77400 NA 47100 23800 33200The new variable has NAs in the place of those strings of 9s.
As an alternative, we can use the magrittr pipe (%>%) to pass an input into na_if_in():
faux_census$income %>% na_if_in(9999999)
#> [1] 28000 148800 479000 85000 41900 NA 149000 98800 90750 45010
#> [11] 127000 21600 74200 61000 20000 77400 NA 47100 23800 33200This produces the same result.
This task could have been completed using the version of na_if_in() included in the dplyr package. However, moving forward we will use more advanced functionality of fauxnaif.
Let’s now examine the age variable:
In this case, we see two improbable values: 557 and 2 (assuming this is a survey of adults). Using dplyr, this would have to be addressed using two steps:
faux_census$age %>% dplyr::na_if(557) %>% dplyr::na_if(2)
#> [1] 80 89 48 63 47 57 49 50 NA 33 30 42 22 NA 28 34 64 68 39 73But using fauxnaif we can simplify this to a single step:
In the above example, we were able to examine our dataset and select the values that were unrealistic. In real-life analyses, we often can’t look at each observation one by one to find unrealistic values, but we often do know the range of realistic values. Using na_if_not(), we can specify which values are realistic and discard those that are not.
Returning to the age variable, let’s replace values with NA if they are not between 18 (the minimum age we expect to enter the survey) and 122 (the world record for the oldest person).
faux_census$age %>% na_if_not(18:122)
#> [1] 80 89 48 63 47 57 49 50 NA 33 30 42 22 NA 28 34 64 68 39 73This has the same effect as specifying the unrealistic values directly, but no longer requires you to directly examine each observation.
Another way to approach this problem is to use a formula to specify the range of acceptable values. This is particularly useful when dealing with non-integer values, where the colon operator (:) will not work:
but
Formulas in fauxnaif are based on the formula syntax used in rlang and purrr. They are introduced with a tilde (~) and indicate each observation with a dot (.).
To clean the age variable, we will need two formulas. One will replace values less than 18 and another will replace values greater than 122:
faux_census$age %>% na_if_in(~ . < 18, ~ . > 122)
#> [1] 80 89 48 63 47 57 49 50 NA 33 30 42 22 NA 28 34 64 68 39 73If you really want to get this down to a single argument, you can use more advanced relational operators provided by packages like intrval, inops, or invctr.
For example, intrval’s closed interval operator (%[]%) allows you to check if a value is between two values, even if it is not an integer:
With this, we can clean the age variable using only one formula argument:
faux_census$age %>% na_if_not(~ . %[]% c(18, 122))
#> [1] 80 89 48 63 47 57 49 50 NA 33 30 42 22 NA 28 34 64 68 39 73or
Formulas are not only useful when dealing with numeric variables. While it’s straightforward to use relational operators to specify replacements in numeric variables, we can also use more complex formulas to handle other data types.
Let’s take a look at the religion variable:
faux_census$religion
#> [1] "Christian"
#> [2] "Spiritual not religious"
#> [3] "Catholic"
#> [4] "christian"
#> [5] "Baptist"
#> [6] "Religion is the opiate of the people"
#> [7] "methodist"
#> [8] "Lutheran"
#> [9] "Agnostic"
#> [10] "Jewish"
#> [11] "none"
#> [12] "Roman Catholic"
#> [13] "atheist"
#> [14] "Christian"
#> [15] "Not religious"
#> [16] "Christian"
#> [17] "Nothing"
#> [18] "None"
#> [19] "baptist"
#> [20] "Christian"While there are a few things we might want to clean in this variable, one clear issue is the respondent who did not answer the question but instead used the space to give an opinion: “Religion is the opiate of the people”.
We could use the most basic form of na_if_in() to simply remove this answer:
faux_census$religion %>% na_if_in("Religion is the opiate of the people")
#> [1] "Christian" "Spiritual not religious"
#> [3] "Catholic" "christian"
#> [5] "Baptist" NA
#> [7] "methodist" "Lutheran"
#> [9] "Agnostic" "Jewish"
#> [11] "none" "Roman Catholic"
#> [13] "atheist" "Christian"
#> [15] "Not religious" "Christian"
#> [17] "Nothing" "None"
#> [19] "baptist" "Christian"But in a larger analysis, we may prefer to have a simple rule for excluding answers. Perhaps we decide that answers longer than 25 characters are unlikely to be genuine. In that case, we can use a formula operating on the number of characters (nchar(.)) in a response:
faux_census$religion %>% na_if_in(~ nchar(.) > 25)
#> [1] "Christian" "Spiritual not religious"
#> [3] "Catholic" "christian"
#> [5] "Baptist" NA
#> [7] "methodist" "Lutheran"
#> [9] "Agnostic" "Jewish"
#> [11] "none" "Roman Catholic"
#> [13] "atheist" "Christian"
#> [15] "Not religious" "Christian"
#> [17] "Nothing" "None"
#> [19] "baptist" "Christian"Finally, there are cases when we can use a simple function to replace values.
Returning to the income variable, we know that NA is indicated using the largest value. Rather than specifying it directly, we can simply tell fauxnaif to replace the variable’s maximum value:
faux_census$income %>% na_if_in(max)
#> [1] 28000 148800 479000 85000 41900 NA 149000 98800 90750 45010
#> [11] 127000 21600 74200 61000 20000 77400 NA 47100 23800 33200We can do the same with the age variable, where both the lowest and highest values are unrealistic:
faux_census$age %>% na_if_in(min, max)
#> [1] 80 89 48 63 47 57 49 50 NA 33 30 42 22 NA 28 34 64 68 39 73But beware! If no respondent had given an unrealistic answer, replacing the minimum or maximum values could result in the loss of real data! It is often better to use more complicated formulas than simpler functions for your replacements.
Often in data analysis, we prefer to work within a single data frame than operating on individual vectors. fauxnaif is built to handle this use case.
A simple solution is to use na_if_in() or na_if_not() within dplyr’s mutate() function.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:fauxnaif':
#>
#> na_if
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
faux_census %>% mutate(income = na_if_in(income, 9999999))
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 28000 Christian
#> 2 NY Woman 89 Latino 148800 Spiritual not religio~
#> 3 CA Female 48 White 479000 Catholic
#> 4 TX Male 63 latinx 85000 christian
#> 5 PA Male 47 asian 41900 Baptist
#> 6 TX Gender is a socia~ 57 Race is a soci~ NA Religion is the opiat~
#> 7 Canada Male 49 white 149000 methodist
#> 8 TX Female 50 White 98800 Lutheran
#> 9 NY f 557 white 90750 Agnostic
#> 10 WA F 33 White 45010 Jewish
#> 11 TX Male 30 White 127000 none
#> 12 OH Non-binary 42 Caucasian 21600 Roman Catholic
#> 13 NC Female 22 African Americ~ 74200 atheist
#> 14 LA Male 2 White 61000 Christian
#> 15 LA Female 28 Black 20000 Not religious
#> 16 CA male 34 Asian American 77400 Christian
#> 17 TN M 64 white NA Nothing
#> 18 FL Female 68 white 47100 None
#> 19 OH Male 39 black 23800 baptist
#> 20 NH male 73 Hispanic 33200 ChristianSometimes, the same replacement function can be used in multiple columns. Here, the respondent who didn’t give a real answer to the religion question seemed to do the same with the gender and race questions. You can specify multiple columns using dplyr’s across() is you would like to make replacements based on the same criteria:
faux_census %>%
mutate(across(c(religion, gender, race), na_if_in, ~ nchar(.) > 25))
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 28000 Christian
#> 2 NY Woman 89 Latino 148800 Spiritual not religious
#> 3 CA Female 48 White 479000 Catholic
#> 4 TX Male 63 latinx 85000 christian
#> 5 PA Male 47 asian 41900 Baptist
#> 6 TX <NA> 57 <NA> 9999999 <NA>
#> 7 Canada Male 49 white 149000 methodist
#> 8 TX Female 50 White 98800 Lutheran
#> 9 NY f 557 white 90750 Agnostic
#> 10 WA F 33 White 45010 Jewish
#> 11 TX Male 30 White 127000 none
#> 12 OH Non-binary 42 Caucasian 21600 Roman Catholic
#> 13 NC Female 22 African American 74200 atheist
#> 14 LA Male 2 White 61000 Christian
#> 15 LA Female 28 Black 20000 Not religious
#> 16 CA male 34 Asian American 77400 Christian
#> 17 TN M 64 white 9999999 Nothing
#> 18 FL Female 68 white 47100 None
#> 19 OH Male 39 black 23800 baptist
#> 20 NH male 73 Hispanic 33200 ChristianRather than specifying columns manually, we can also select columns using a predicate function with dplyr’s where().
For example, we may want to remove strings of 9s in any numeric column:
faux_census %>% mutate(across(where(is.numeric), na_if_in, ~ grepl("999", .)))
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 28000 Christian
#> 2 NY Woman 89 Latino 148800 Spiritual not religio~
#> 3 CA Female 48 White 479000 Catholic
#> 4 TX Male 63 latinx 85000 christian
#> 5 PA Male 47 asian 41900 Baptist
#> 6 TX Gender is a socia~ 57 Race is a soci~ NA Religion is the opiat~
#> 7 Canada Male 49 white 149000 methodist
#> 8 TX Female 50 White 98800 Lutheran
#> 9 NY f 557 white 90750 Agnostic
#> 10 WA F 33 White 45010 Jewish
#> 11 TX Male 30 White 127000 none
#> 12 OH Non-binary 42 Caucasian 21600 Roman Catholic
#> 13 NC Female 22 African Americ~ 74200 atheist
#> 14 LA Male 2 White 61000 Christian
#> 15 LA Female 28 Black 20000 Not religious
#> 16 CA male 34 Asian American 77400 Christian
#> 17 TN M 64 white NA Nothing
#> 18 FL Female 68 white 47100 None
#> 19 OH Male 39 black 23800 baptist
#> 20 NH male 73 Hispanic 33200 ChristianWhile this replacement was intended for three specific columns, no variable contains a legitimate answer longer than 25 characters. In this case, rather than specifying the variable of interest, we can simply use dplyr’s everything() to make the replacement in all columns:
faux_census %>% mutate(across(everything(), na_if_in, ~ nchar(.) > 25))
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 28000 Christian
#> 2 NY Woman 89 Latino 148800 Spiritual not religious
#> 3 CA Female 48 White 479000 Catholic
#> 4 TX Male 63 latinx 85000 christian
#> 5 PA Male 47 asian 41900 Baptist
#> 6 TX <NA> 57 <NA> 9999999 <NA>
#> 7 Canada Male 49 white 149000 methodist
#> 8 TX Female 50 White 98800 Lutheran
#> 9 NY f 557 white 90750 Agnostic
#> 10 WA F 33 White 45010 Jewish
#> 11 TX Male 30 White 127000 none
#> 12 OH Non-binary 42 Caucasian 21600 Roman Catholic
#> 13 NC Female 22 African American 74200 atheist
#> 14 LA Male 2 White 61000 Christian
#> 15 LA Female 28 Black 20000 Not religious
#> 16 CA male 34 Asian American 77400 Christian
#> 17 TN M 64 white 9999999 Nothing
#> 18 FL Female 68 white 47100 None
#> 19 OH Male 39 black 23800 baptist
#> 20 NH male 73 Hispanic 33200 ChristianIn a data analysis pipeline, we can combine several steps to produce a usable dataset. Combining our interval check for age, our check for strings of 9s in numeric variables, and our check for long responses in character variables, we can yield much cleaner data:
faux_census %>%
mutate(
age = na_if_not(age, 18:122),
across(where(is.numeric), na_if_in, ~ grepl("999", .)),
across(everything(), na_if_in, ~ nchar(.) > 25)
)
#> # A tibble: 20 x 6
#> state gender age race income religion
#> <chr> <chr> <dbl> <chr> <dbl> <chr>
#> 1 CA female 80 Native American 28000 Christian
#> 2 NY Woman 89 Latino 148800 Spiritual not religious
#> 3 CA Female 48 White 479000 Catholic
#> 4 TX Male 63 latinx 85000 christian
#> 5 PA Male 47 asian 41900 Baptist
#> 6 TX <NA> 57 <NA> NA <NA>
#> 7 Canada Male 49 white 149000 methodist
#> 8 TX Female 50 White 98800 Lutheran
#> 9 NY f NA white 90750 Agnostic
#> 10 WA F 33 White 45010 Jewish
#> 11 TX Male 30 White 127000 none
#> 12 OH Non-binary 42 Caucasian 21600 Roman Catholic
#> 13 NC Female 22 African American 74200 atheist
#> 14 LA Male NA White 61000 Christian
#> 15 LA Female 28 Black 20000 Not religious
#> 16 CA male 34 Asian American 77400 Christian
#> 17 TN M 64 white NA Nothing
#> 18 FL Female 68 white 47100 None
#> 19 OH Male 39 black 23800 baptist
#> 20 NH male 73 Hispanic 33200 Christian