ref_lvl: Change reference level of (numeric) factors
In sjmisc: Data and Variable Transformation Functions
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
Changes the reference level of (numeric) factor.
Usage
1
Arguments
x
A vector or data frame.
...
Optional, unquoted names of variables that should be selected for
further processing. Required, if x is a data frame (and no
vector) and only selected variables from x should be processed.
You may also use functions like : or tidyselect's
select-helpers.
See 'Examples' or package-vignette.
lvl
Either numeric, indicating the new reference level, or a string,
indicating the value label from the new reference level. If x is a
factor with non-numeric factor levels, relevel(x, ref = lvl) is
returned. See 'Examples'.
Details
Unlike relevel, this function behaves differently
for factor with numeric factor levels or for labelled data, i.e. factors
with value labels for the values. ref_lvl() changes the reference
level by recoding the factor's values using the rec function.
Hence, all values from lowest up to the reference level indicated by
lvl are recoded, with lvl starting as lowest factor value.
For factors with non-numeric factor levels, the function simply returns
relevel(x, ref = lvl). See 'Examples'.
Value
x with new reference level. If x
is a data frame, the complete data frame x will be returned,
where variables specified in ... will be re-leveled;
if ... is not specified, applies to all variables in the
data frame.
See Also
to_factor to convert numeric vectors into factors;
rec to recode variables.
Examples
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data(efc)
x <- to_factor(efc$e42dep)
str(x)
frq(x)
# see column "val" in frq()-output, which indicates
# how values/labels were recoded after using ref_lvl()
x <- ref_lvl(x, lvl = 3)
str(x)
frq(x)
library(dplyr)
dat <- efc %>%
select(c82cop1, c83cop2, c84cop3) %>%
to_factor()
frq(dat)
ref_lvl(dat, c82cop1, c83cop2, lvl = 2) %>% frq()
# compare numeric and string value for "lvl"-argument
x <- to_factor(efc$e42dep)
frq(x)
ref_lvl(x, lvl = 2) %>% frq()
ref_lvl(x, lvl = "slightly dependent") %>% frq()
# factors with non-numeric factor levels
data(iris)
levels(iris$Species)
levels(ref_lvl(iris$Species, lvl = 3))
levels(ref_lvl(iris$Species, lvl = "versicolor"))
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: wc: Permission denied
Could not detect number of cores, defaulting to 1.
Factor w/ 4 levels "1","2","3","4": 3 3 3 4 4 4 4 4 4 4 ...
- attr(*, "labels")= Named num [1:4] 1 2 3 4
..- attr(*, "names")= chr [1:4] "independent" "slightly dependent" "moderately dependent" "severely dependent"
- attr(*, "label")= chr "elder's dependency"
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Factor w/ 4 levels "1","2","3","4": 1 1 1 4 4 4 4 4 4 4 ...
- attr(*, "label")= chr "elder's dependency"
- attr(*, "labels")= Named num [1:4] 1 2 3 4
..- attr(*, "names")= chr [1:4] "moderately dependent" "independent" "slightly dependent" "severely dependent"
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 moderately dependent 306 33.70 33.96 33.96
2 independent 66 7.27 7.33 41.29
3 slightly dependent 225 24.78 24.97 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# do you feel you cope well as caregiver? <categorical>
val label frq raw.prc valid.prc cum.prc
1 never 3 0.33 0.33 0.33
2 sometimes 97 10.68 10.77 11.10
3 often 591 65.09 65.59 76.69
4 always 210 23.13 23.31 100.00
NA NA 7 0.77 NA NA
# do you find caregiving too demanding? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 186 20.48 20.62 20.62
2 Sometimes 547 60.24 60.64 81.26
3 Often 130 14.32 14.41 95.68
4 Always 39 4.30 4.32 100.00
NA NA 6 0.66 NA NA
# does caregiving cause difficulties in your relationship with your friends? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 516 56.83 57.21 57.21
2 Sometimes 252 27.75 27.94 85.14
3 Often 82 9.03 9.09 94.24
4 Always 52 5.73 5.76 100.00
NA NA 6 0.66 NA NA
# do you feel you cope well as caregiver? <categorical>
val label frq raw.prc valid.prc cum.prc
1 sometimes 97 10.68 10.77 10.77
2 never 3 0.33 0.33 11.10
3 often 591 65.09 65.59 76.69
4 always 210 23.13 23.31 100.00
NA NA 7 0.77 NA NA
# do you find caregiving too demanding? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Sometimes 547 60.24 60.64 60.64
2 Never 186 20.48 20.62 81.26
3 Often 130 14.32 14.41 95.68
4 Always 39 4.30 4.32 100.00
NA NA 6 0.66 NA NA
# does caregiving cause difficulties in your relationship with your friends? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 516 56.83 57.21 57.21
2 Sometimes 252 27.75 27.94 85.14
3 Often 82 9.03 9.09 94.24
4 Always 52 5.73 5.76 100.00
NA NA 6 0.66 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 slightly dependent 225 24.78 24.97 24.97
2 independent 66 7.27 7.33 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Warning message:
`x` has no factor level indicated by the reference level `value`.
[1] "setosa" "versicolor" "virginica"
[1] "setosa" "versicolor" "virginica"
Warning message:
`x` needs to be a factor with numeric factor levels.
[1] "setosa" "versicolor" "virginica"
Warning message:
`x` needs to be a factor with numeric factor levels.
sjmisc documentation built on Dec. 11, 2021, 9:34 a.m.
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Description Usage Arguments Details Value See Also Examples
Description
Changes the reference level of (numeric) factor.
Usage
1 |
Arguments
x |
A vector or data frame. |
... |
Optional, unquoted names of variables that should be selected for
further processing. Required, if |
lvl |
Either numeric, indicating the new reference level, or a string,
indicating the value label from the new reference level. If |
Details
Unlike relevel, this function behaves differently
for factor with numeric factor levels or for labelled data, i.e. factors
with value labels for the values. ref_lvl() changes the reference
level by recoding the factor's values using the rec function.
Hence, all values from lowest up to the reference level indicated by
lvl are recoded, with lvl starting as lowest factor value.
For factors with non-numeric factor levels, the function simply returns
relevel(x, ref = lvl). See 'Examples'.
Value
x with new reference level. If x
is a data frame, the complete data frame x will be returned,
where variables specified in ... will be re-leveled;
if ... is not specified, applies to all variables in the
data frame.
See Also
to_factor to convert numeric vectors into factors;
rec to recode variables.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | data(efc)
x <- to_factor(efc$e42dep)
str(x)
frq(x)
# see column "val" in frq()-output, which indicates
# how values/labels were recoded after using ref_lvl()
x <- ref_lvl(x, lvl = 3)
str(x)
frq(x)
library(dplyr)
dat <- efc %>%
select(c82cop1, c83cop2, c84cop3) %>%
to_factor()
frq(dat)
ref_lvl(dat, c82cop1, c83cop2, lvl = 2) %>% frq()
# compare numeric and string value for "lvl"-argument
x <- to_factor(efc$e42dep)
frq(x)
ref_lvl(x, lvl = 2) %>% frq()
ref_lvl(x, lvl = "slightly dependent") %>% frq()
# factors with non-numeric factor levels
data(iris)
levels(iris$Species)
levels(ref_lvl(iris$Species, lvl = 3))
levels(ref_lvl(iris$Species, lvl = "versicolor"))
|
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: wc: Permission denied
Could not detect number of cores, defaulting to 1.
Factor w/ 4 levels "1","2","3","4": 3 3 3 4 4 4 4 4 4 4 ...
- attr(*, "labels")= Named num [1:4] 1 2 3 4
..- attr(*, "names")= chr [1:4] "independent" "slightly dependent" "moderately dependent" "severely dependent"
- attr(*, "label")= chr "elder's dependency"
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Factor w/ 4 levels "1","2","3","4": 1 1 1 4 4 4 4 4 4 4 ...
- attr(*, "label")= chr "elder's dependency"
- attr(*, "labels")= Named num [1:4] 1 2 3 4
..- attr(*, "names")= chr [1:4] "moderately dependent" "independent" "slightly dependent" "severely dependent"
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 moderately dependent 306 33.70 33.96 33.96
2 independent 66 7.27 7.33 41.29
3 slightly dependent 225 24.78 24.97 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# do you feel you cope well as caregiver? <categorical>
val label frq raw.prc valid.prc cum.prc
1 never 3 0.33 0.33 0.33
2 sometimes 97 10.68 10.77 11.10
3 often 591 65.09 65.59 76.69
4 always 210 23.13 23.31 100.00
NA NA 7 0.77 NA NA
# do you find caregiving too demanding? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 186 20.48 20.62 20.62
2 Sometimes 547 60.24 60.64 81.26
3 Often 130 14.32 14.41 95.68
4 Always 39 4.30 4.32 100.00
NA NA 6 0.66 NA NA
# does caregiving cause difficulties in your relationship with your friends? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 516 56.83 57.21 57.21
2 Sometimes 252 27.75 27.94 85.14
3 Often 82 9.03 9.09 94.24
4 Always 52 5.73 5.76 100.00
NA NA 6 0.66 NA NA
# do you feel you cope well as caregiver? <categorical>
val label frq raw.prc valid.prc cum.prc
1 sometimes 97 10.68 10.77 10.77
2 never 3 0.33 0.33 11.10
3 often 591 65.09 65.59 76.69
4 always 210 23.13 23.31 100.00
NA NA 7 0.77 NA NA
# do you find caregiving too demanding? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Sometimes 547 60.24 60.64 60.64
2 Never 186 20.48 20.62 81.26
3 Often 130 14.32 14.41 95.68
4 Always 39 4.30 4.32 100.00
NA NA 6 0.66 NA NA
# does caregiving cause difficulties in your relationship with your friends? <categorical>
val label frq raw.prc valid.prc cum.prc
1 Never 516 56.83 57.21 57.21
2 Sometimes 252 27.75 27.94 85.14
3 Often 82 9.03 9.09 94.24
4 Always 52 5.73 5.76 100.00
NA NA 6 0.66 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 slightly dependent 225 24.78 24.97 24.97
2 independent 66 7.27 7.33 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
# elder's dependency <categorical>
val label frq raw.prc valid.prc cum.prc
1 independent 66 7.27 7.33 7.33
2 slightly dependent 225 24.78 24.97 32.30
3 moderately dependent 306 33.70 33.96 66.26
4 severely dependent 304 33.48 33.74 100.00
NA NA 7 0.77 NA NA
Warning message:
`x` has no factor level indicated by the reference level `value`.
[1] "setosa" "versicolor" "virginica"
[1] "setosa" "versicolor" "virginica"
Warning message:
`x` needs to be a factor with numeric factor levels.
[1] "setosa" "versicolor" "virginica"
Warning message:
`x` needs to be a factor with numeric factor levels.
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