README.md
In urswilke/tablab: Miscellaneous functions for labelled data
tablab
The goal of tablab is to tabulate and compare variables’ values and
their labels of labelled dataframes.
Installation
Install the package via:
devtools::install_github("urswilke/tablab")
Introduction
The package tablab mainly consists of 2 families of functions:
tab_* tabulating count and label information of dataframes
(denoted df), andcmp_* comparing this data for all dataframes in a list l.
The following attributes are tabulated / compared:
var The name of the variables in the dataframe(s).type The type of the variables in the dataframe(s).varlab The variable label of the variable in the dataframe.val The value of the variable in the dataframe.vallab The value label of the variable in the dataframe.
Examples
Simple example
Assume you have a dataframe of labelled variables:
library(tablab)
library(dplyr)
library(purrr)
set.seed(1)
x <- haven::labelled(
sample(1:10),
label = "variable label",
labels = c("1lab" =1 , "2lab" = 2, "3lab" = 3)
)
y <- haven::labelled(
LETTERS[sample(2:6, size = 10, replace = TRUE)],
label = "char_var_lab",
labels = c("Alab" ="A" , "Blab" = "B", "Clab" = "C")
)
df <- data.frame(id = 1:10, x, y)
df
#> id x y
#> 1 1 9 D
#> 2 2 4 D
#> 3 3 7 B
#> 4 4 1 F
#> 5 5 2 F
#> 6 6 5 C
#> 7 7 3 C
#> 8 8 10 B
#> 9 9 6 F
#> 10 10 8 F
tablab consists of functions to tabulate summaries of these labelled
dataframes. See for instance the function:
tab_all(df)
#> # A tibble: 15 × 6
#> var nv cv n vallab varlab
#> <chr> <int> <chr> <int> <chr> <chr>
#> 1 x 1 <NA> 1 1lab variable label
#> 2 x 2 <NA> 1 2lab variable label
#> 3 x 3 <NA> 1 3lab variable label
#> 4 x 4 <NA> 1 <NA> variable label
#> 5 x 5 <NA> 1 <NA> variable label
#> 6 x 6 <NA> 1 <NA> variable label
#> 7 x 7 <NA> 1 <NA> variable label
#> 8 x 8 <NA> 1 <NA> variable label
#> 9 x 9 <NA> 1 <NA> variable label
#> 10 x 10 <NA> 1 <NA> variable label
#> 11 y NA B 2 Blab char_var_lab
#> 12 y NA C 2 Clab char_var_lab
#> 13 y NA D 2 <NA> char_var_lab
#> 14 y NA F 4 <NA> char_var_lab
#> 15 y NA A NA Alab char_var_lab
The result shows for every variable var in the data, its
numeric/character values nv/cv, their counts n, as well as the
variable and value labels varlab and vallab.
More examples
path <- system.file("examples", "iris.sav", package = "haven")
df <- haven::read_sav(path)
df <- tibble(id = 1:10,
sex = haven::labelled(c(2, 1, 2, 1, 1, 2, 2, 1, 2, 1),
label = "sex",
labels = c(MALES = 1, FEMALES = 2)),
age = c(24, 23, 23, 41, 23, 39, 30, 18, 31, 48),
marital = haven::labelled(c(1, 7, 2, 6, 4, 5, 3, 8, 4, 2),
label = "marital status",
labels = c("single" = 1,
"steady relationship" = 2,
"living with partner" = 3,
"married first time" = 4,
"remarried" = 5,
"separated" = 6,
"divorced" = 7,
"widowed" = 8))
)
df
#> # A tibble: 10 × 4
#> id sex age marital
#> <int> <dbl+lbl> <dbl> <dbl+lbl>
#> 1 1 2 [FEMALES] 24 1 [single]
#> 2 2 1 [MALES] 23 7 [divorced]
#> 3 3 2 [FEMALES] 23 2 [steady relationship]
#> 4 4 1 [MALES] 41 6 [separated]
#> 5 5 1 [MALES] 23 4 [married first time]
#> 6 6 2 [FEMALES] 39 5 [remarried]
#> 7 7 2 [FEMALES] 30 3 [living with partner]
#> 8 8 1 [MALES] 18 8 [widowed]
#> 9 9 2 [FEMALES] 31 4 [married first time]
#> 10 10 1 [MALES] 48 2 [steady relationship]
Create modified copy:
df2 <-
df %>%
# Remove rows 9 & 10:
slice(-c(9:10)) %>%
# Create Variable new_var equal to 1:
mutate(new_var = 1)
# Change variable label:
attr(df2$new_var, "label") <- "new something"
Tabulate the variable labels for the dataframes in the list:
list(df, df2) %>% map(tab_varlabs)
#> [[1]]
#> # A tibble: 2 × 2
#> var varlab
#> <chr> <chr>
#> 1 sex sex
#> 2 marital marital status
#>
#> [[2]]
#> # A tibble: 3 × 2
#> var varlab
#> <chr> <chr>
#> 1 sex sex
#> 2 marital marital status
#> 3 new_var new something
Change value:
df2[1:2,"sex"] <- 3
Tabulate the count comparison:
list(df, df2) %>% cmp_cts()
#> # A tibble: 25 × 6
#> var nv1 cv1 nv2 cv2 n
#> <chr> <int> <chr> <int> <chr> <int>
#> 1 sex 1 <NA> 1 <NA> 3
#> 2 sex 1 <NA> 3 <NA> 1
#> 3 sex 1 <NA> NA <NA> 1
#> 4 sex 2 <NA> 2 <NA> 3
#> 5 sex 2 <NA> 3 <NA> 1
#> 6 sex 2 <NA> NA <NA> 1
#> 7 age 18 <NA> 18 <NA> 1
#> 8 age 23 <NA> 23 <NA> 3
#> 9 age 24 <NA> 24 <NA> 1
#> 10 age 30 <NA> 30 <NA> 1
#> # … with 15 more rows
More label and value modifications:
attr(df2$sex, "labels") <- c(male = 1, female = 2, `Third Gender` = 3)
# Remove variable label:
attr(df2$sex, "label") <- NULL
# Change variable "marital" to 9 in rows 3 & 4:
df2[3:4,"marital"] <- 9
# Add another value label for the code 9:
attr(df2$marital, "labels") <- c(attr(df2$marital, "labels"), c("other" = 9))
df2
#> # A tibble: 8 × 5
#> id sex age marital new_var
#> <int> <dbl+lbl> <dbl> <dbl+lbl> <dbl>
#> 1 1 3 [Third Gender] 24 1 [single] 1
#> 2 2 3 [Third Gender] 23 7 [divorced] 1
#> 3 3 2 [female] 23 9 [other] 1
#> 4 4 1 [male] 41 9 [other] 1
#> 5 5 1 [male] 23 4 [married first time] 1
#> 6 6 2 [female] 39 5 [remarried] 1
#> 7 7 2 [female] 30 3 [living with partner] 1
#> 8 8 1 [male] 18 8 [widowed] 1
Tabulate the variable labels for the dataframes in the list:
list(df, df2) %>% map(tab_vallabs)
#> [[1]]
#> # A tibble: 10 × 4
#> var nv vallab cv
#> <chr> <dbl> <chr> <chr>
#> 1 sex 1 MALES <NA>
#> 2 sex 2 FEMALES <NA>
#> 3 marital 1 single <NA>
#> 4 marital 2 steady relationship <NA>
#> 5 marital 3 living with partner <NA>
#> 6 marital 4 married first time <NA>
#> 7 marital 5 remarried <NA>
#> 8 marital 6 separated <NA>
#> 9 marital 7 divorced <NA>
#> 10 marital 8 widowed <NA>
#>
#> [[2]]
#> # A tibble: 12 × 4
#> var nv vallab cv
#> <chr> <dbl> <chr> <chr>
#> 1 sex 1 male <NA>
#> 2 sex 2 female <NA>
#> 3 sex 3 Third Gender <NA>
#> 4 marital 1 single <NA>
#> 5 marital 2 steady relationship <NA>
#> 6 marital 3 living with partner <NA>
#> 7 marital 4 married first time <NA>
#> 8 marital 5 remarried <NA>
#> 9 marital 6 separated <NA>
#> 10 marital 7 divorced <NA>
#> 11 marital 8 widowed <NA>
#> 12 marital 9 other <NA>
Tabulate the comparison of count and label data and only show rows with
differences:
list(df, df2) %>%
cmp_all() %>%
filter(any_diff)
#> # A tibble: 15 × 15
#> var n nv1 nv2 nv_diff cv1 cv2 cv_diff vallab1 vallab2
#> <chr> <int> <dbl> <dbl> <lgl> <chr> <chr> <lgl> <chr> <chr>
#> 1 sex 3 1 1 FALSE <NA> <NA> FALSE MALES male
#> 2 sex 1 1 3 TRUE <NA> <NA> FALSE MALES Third Gend…
#> 3 sex 1 1 NA TRUE <NA> <NA> FALSE MALES <NA>
#> 4 sex 3 2 2 FALSE <NA> <NA> FALSE FEMALES female
#> 5 sex 1 2 3 TRUE <NA> <NA> FALSE FEMALES Third Gend…
#> 6 sex 1 2 NA TRUE <NA> <NA> FALSE FEMALES <NA>
#> 7 age 1 31 NA TRUE <NA> <NA> FALSE <NA> <NA>
#> 8 age 1 48 NA TRUE <NA> <NA> FALSE <NA> <NA>
#> 9 marital 1 2 9 TRUE <NA> <NA> FALSE steady rel… other
#> 10 marital 1 2 NA TRUE <NA> <NA> FALSE steady rel… <NA>
#> 11 marital 1 4 NA TRUE <NA> <NA> FALSE married fi… <NA>
#> 12 marital 1 6 9 TRUE <NA> <NA> FALSE separated other
#> 13 marital 0 NA 2 TRUE <NA> <NA> FALSE <NA> steady rel…
#> 14 marital 0 NA 6 TRUE <NA> <NA> FALSE <NA> separated
#> 15 new_var 8 NA 1 TRUE <NA> <NA> FALSE <NA> <NA>
#> # … with 5 more variables: vallab_diff <lgl>, varlab1 <chr>, varlab2 <chr>,
#> # varlab_diff <lgl>, any_diff <lgl>
urswilke/tablab documentation built on Oct. 17, 2022, 8:19 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
tablab
The goal of tablab is to tabulate and compare variables’ values and their labels of labelled dataframes.
Installation
Install the package via:
devtools::install_github("urswilke/tablab")
Introduction
The package tablab mainly consists of 2 families of functions:
tab_*tabulating count and label information of dataframes (denoteddf), andcmp_*comparing this data for all dataframes in a listl.
The following attributes are tabulated / compared:
varThe name of the variables in the dataframe(s).typeThe type of the variables in the dataframe(s).varlabThe variable label of the variable in the dataframe.valThe value of the variable in the dataframe.vallabThe value label of the variable in the dataframe.
Examples
Simple example
Assume you have a dataframe of labelled variables:
library(tablab)
library(dplyr)
library(purrr)
set.seed(1)
x <- haven::labelled(
sample(1:10),
label = "variable label",
labels = c("1lab" =1 , "2lab" = 2, "3lab" = 3)
)
y <- haven::labelled(
LETTERS[sample(2:6, size = 10, replace = TRUE)],
label = "char_var_lab",
labels = c("Alab" ="A" , "Blab" = "B", "Clab" = "C")
)
df <- data.frame(id = 1:10, x, y)
df
#> id x y
#> 1 1 9 D
#> 2 2 4 D
#> 3 3 7 B
#> 4 4 1 F
#> 5 5 2 F
#> 6 6 5 C
#> 7 7 3 C
#> 8 8 10 B
#> 9 9 6 F
#> 10 10 8 F
tablab consists of functions to tabulate summaries of these labelled dataframes. See for instance the function:
tab_all(df)
#> # A tibble: 15 × 6
#> var nv cv n vallab varlab
#> <chr> <int> <chr> <int> <chr> <chr>
#> 1 x 1 <NA> 1 1lab variable label
#> 2 x 2 <NA> 1 2lab variable label
#> 3 x 3 <NA> 1 3lab variable label
#> 4 x 4 <NA> 1 <NA> variable label
#> 5 x 5 <NA> 1 <NA> variable label
#> 6 x 6 <NA> 1 <NA> variable label
#> 7 x 7 <NA> 1 <NA> variable label
#> 8 x 8 <NA> 1 <NA> variable label
#> 9 x 9 <NA> 1 <NA> variable label
#> 10 x 10 <NA> 1 <NA> variable label
#> 11 y NA B 2 Blab char_var_lab
#> 12 y NA C 2 Clab char_var_lab
#> 13 y NA D 2 <NA> char_var_lab
#> 14 y NA F 4 <NA> char_var_lab
#> 15 y NA A NA Alab char_var_lab
The result shows for every variable var in the data, its
numeric/character values nv/cv, their counts n, as well as the
variable and value labels varlab and vallab.
More examples
path <- system.file("examples", "iris.sav", package = "haven")
df <- haven::read_sav(path)
df <- tibble(id = 1:10,
sex = haven::labelled(c(2, 1, 2, 1, 1, 2, 2, 1, 2, 1),
label = "sex",
labels = c(MALES = 1, FEMALES = 2)),
age = c(24, 23, 23, 41, 23, 39, 30, 18, 31, 48),
marital = haven::labelled(c(1, 7, 2, 6, 4, 5, 3, 8, 4, 2),
label = "marital status",
labels = c("single" = 1,
"steady relationship" = 2,
"living with partner" = 3,
"married first time" = 4,
"remarried" = 5,
"separated" = 6,
"divorced" = 7,
"widowed" = 8))
)
df
#> # A tibble: 10 × 4
#> id sex age marital
#> <int> <dbl+lbl> <dbl> <dbl+lbl>
#> 1 1 2 [FEMALES] 24 1 [single]
#> 2 2 1 [MALES] 23 7 [divorced]
#> 3 3 2 [FEMALES] 23 2 [steady relationship]
#> 4 4 1 [MALES] 41 6 [separated]
#> 5 5 1 [MALES] 23 4 [married first time]
#> 6 6 2 [FEMALES] 39 5 [remarried]
#> 7 7 2 [FEMALES] 30 3 [living with partner]
#> 8 8 1 [MALES] 18 8 [widowed]
#> 9 9 2 [FEMALES] 31 4 [married first time]
#> 10 10 1 [MALES] 48 2 [steady relationship]
Create modified copy:
df2 <-
df %>%
# Remove rows 9 & 10:
slice(-c(9:10)) %>%
# Create Variable new_var equal to 1:
mutate(new_var = 1)
# Change variable label:
attr(df2$new_var, "label") <- "new something"
Tabulate the variable labels for the dataframes in the list:
list(df, df2) %>% map(tab_varlabs)
#> [[1]]
#> # A tibble: 2 × 2
#> var varlab
#> <chr> <chr>
#> 1 sex sex
#> 2 marital marital status
#>
#> [[2]]
#> # A tibble: 3 × 2
#> var varlab
#> <chr> <chr>
#> 1 sex sex
#> 2 marital marital status
#> 3 new_var new something
Change value:
df2[1:2,"sex"] <- 3
Tabulate the count comparison:
list(df, df2) %>% cmp_cts()
#> # A tibble: 25 × 6
#> var nv1 cv1 nv2 cv2 n
#> <chr> <int> <chr> <int> <chr> <int>
#> 1 sex 1 <NA> 1 <NA> 3
#> 2 sex 1 <NA> 3 <NA> 1
#> 3 sex 1 <NA> NA <NA> 1
#> 4 sex 2 <NA> 2 <NA> 3
#> 5 sex 2 <NA> 3 <NA> 1
#> 6 sex 2 <NA> NA <NA> 1
#> 7 age 18 <NA> 18 <NA> 1
#> 8 age 23 <NA> 23 <NA> 3
#> 9 age 24 <NA> 24 <NA> 1
#> 10 age 30 <NA> 30 <NA> 1
#> # … with 15 more rows
More label and value modifications:
attr(df2$sex, "labels") <- c(male = 1, female = 2, `Third Gender` = 3)
# Remove variable label:
attr(df2$sex, "label") <- NULL
# Change variable "marital" to 9 in rows 3 & 4:
df2[3:4,"marital"] <- 9
# Add another value label for the code 9:
attr(df2$marital, "labels") <- c(attr(df2$marital, "labels"), c("other" = 9))
df2
#> # A tibble: 8 × 5
#> id sex age marital new_var
#> <int> <dbl+lbl> <dbl> <dbl+lbl> <dbl>
#> 1 1 3 [Third Gender] 24 1 [single] 1
#> 2 2 3 [Third Gender] 23 7 [divorced] 1
#> 3 3 2 [female] 23 9 [other] 1
#> 4 4 1 [male] 41 9 [other] 1
#> 5 5 1 [male] 23 4 [married first time] 1
#> 6 6 2 [female] 39 5 [remarried] 1
#> 7 7 2 [female] 30 3 [living with partner] 1
#> 8 8 1 [male] 18 8 [widowed] 1
Tabulate the variable labels for the dataframes in the list:
list(df, df2) %>% map(tab_vallabs)
#> [[1]]
#> # A tibble: 10 × 4
#> var nv vallab cv
#> <chr> <dbl> <chr> <chr>
#> 1 sex 1 MALES <NA>
#> 2 sex 2 FEMALES <NA>
#> 3 marital 1 single <NA>
#> 4 marital 2 steady relationship <NA>
#> 5 marital 3 living with partner <NA>
#> 6 marital 4 married first time <NA>
#> 7 marital 5 remarried <NA>
#> 8 marital 6 separated <NA>
#> 9 marital 7 divorced <NA>
#> 10 marital 8 widowed <NA>
#>
#> [[2]]
#> # A tibble: 12 × 4
#> var nv vallab cv
#> <chr> <dbl> <chr> <chr>
#> 1 sex 1 male <NA>
#> 2 sex 2 female <NA>
#> 3 sex 3 Third Gender <NA>
#> 4 marital 1 single <NA>
#> 5 marital 2 steady relationship <NA>
#> 6 marital 3 living with partner <NA>
#> 7 marital 4 married first time <NA>
#> 8 marital 5 remarried <NA>
#> 9 marital 6 separated <NA>
#> 10 marital 7 divorced <NA>
#> 11 marital 8 widowed <NA>
#> 12 marital 9 other <NA>
Tabulate the comparison of count and label data and only show rows with differences:
list(df, df2) %>%
cmp_all() %>%
filter(any_diff)
#> # A tibble: 15 × 15
#> var n nv1 nv2 nv_diff cv1 cv2 cv_diff vallab1 vallab2
#> <chr> <int> <dbl> <dbl> <lgl> <chr> <chr> <lgl> <chr> <chr>
#> 1 sex 3 1 1 FALSE <NA> <NA> FALSE MALES male
#> 2 sex 1 1 3 TRUE <NA> <NA> FALSE MALES Third Gend…
#> 3 sex 1 1 NA TRUE <NA> <NA> FALSE MALES <NA>
#> 4 sex 3 2 2 FALSE <NA> <NA> FALSE FEMALES female
#> 5 sex 1 2 3 TRUE <NA> <NA> FALSE FEMALES Third Gend…
#> 6 sex 1 2 NA TRUE <NA> <NA> FALSE FEMALES <NA>
#> 7 age 1 31 NA TRUE <NA> <NA> FALSE <NA> <NA>
#> 8 age 1 48 NA TRUE <NA> <NA> FALSE <NA> <NA>
#> 9 marital 1 2 9 TRUE <NA> <NA> FALSE steady rel… other
#> 10 marital 1 2 NA TRUE <NA> <NA> FALSE steady rel… <NA>
#> 11 marital 1 4 NA TRUE <NA> <NA> FALSE married fi… <NA>
#> 12 marital 1 6 9 TRUE <NA> <NA> FALSE separated other
#> 13 marital 0 NA 2 TRUE <NA> <NA> FALSE <NA> steady rel…
#> 14 marital 0 NA 6 TRUE <NA> <NA> FALSE <NA> separated
#> 15 new_var 8 NA 1 TRUE <NA> <NA> FALSE <NA> <NA>
#> # … with 5 more variables: vallab_diff <lgl>, varlab1 <chr>, varlab2 <chr>,
#> # varlab_diff <lgl>, any_diff <lgl>
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