Look for keywords variable names and descriptions / Create a data dictionary
Source:R/lookfor.R
look_for.Rdlook_for emulates the lookfor Stata command in R. It supports
searching into the variable names of regular R data frames as well as into
variable labels descriptions, factor levels and value labels.
The command is meant to help users finding variables in large datasets.
Usage
look_for(
data,
...,
labels = TRUE,
values = TRUE,
ignore.case = TRUE,
details = c("basic", "none", "full")
)
lookfor(
data,
...,
labels = TRUE,
values = TRUE,
ignore.case = TRUE,
details = c("basic", "none", "full")
)
generate_dictionary(
data,
...,
labels = TRUE,
values = TRUE,
ignore.case = TRUE,
details = c("basic", "none", "full")
)
# S3 method for class 'look_for'
print(x, ...)
look_for_and_select(
data,
...,
labels = TRUE,
values = TRUE,
ignore.case = TRUE
)
convert_list_columns_to_character(x)
lookfor_to_long_format(x)Arguments
- data
a data frame or a survey object
- ...
optional list of keywords, a character string (or several character strings), which can be formatted as a regular expression suitable for a
base::grep()pattern, or a vector of keywords; displays all variables if not specified- labels
whether or not to search variable labels (descriptions);
TRUEby default- values
whether or not to search within values (factor levels or value labels);
TRUEby default- ignore.case
whether or not to make the keywords case sensitive;
TRUEby default (case is ignored during matching)- details
add details about each variable (full details could be time consuming for big data frames,
FALSEis equivalent to"none"andTRUEto"full")- x
a tibble returned by
look_for()
Value
a tibble data frame featuring the variable position, name and description (if it exists) in the original data frame
Details
When no keyword is provided, it will produce a data dictionary of the overall data frame.
The function looks into the variable names for matches to the
keywords. If available, variable labels are included in the search scope.
Variable labels of data.frame imported with foreign or
memisc packages will also be taken into account (see to_labelled()).
If no keyword is provided, it will return all variables of data.
look_for(), lookfor() and generate_dictionary() are equivalent.
By default, results will be summarized when printing. To deactivate default
printing, use dplyr::as_tibble().
lookfor_to_long_format() could be used to transform results with one row
per factor level and per value label.
Use convert_list_columns_to_character() to convert named list columns into
character vectors (see examples).
look_for_and_select() is a shortcut for selecting some variables and
applying dplyr::select() to return a data frame with only the selected
variables.
Author
François Briatte f.briatte@gmail.com, Joseph Larmarange joseph@larmarange.net
Examples
look_for(iris)
#> pos variable label col_type missing values
#> 1 Sepal.Length — dbl 0
#> 2 Sepal.Width — dbl 0
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
# Look for a single keyword.
look_for(iris, "petal")
#> pos variable label col_type missing values
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
look_for(iris, "s")
#> pos variable label col_type missing values
#> 1 Sepal.Length — dbl 0
#> 2 Sepal.Width — dbl 0
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
iris %>%
look_for_and_select("s") %>%
head()
#> Sepal.Length Sepal.Width Species
#> 1 5.1 3.5 setosa
#> 2 4.9 3.0 setosa
#> 3 4.7 3.2 setosa
#> 4 4.6 3.1 setosa
#> 5 5.0 3.6 setosa
#> 6 5.4 3.9 setosa
# Look for with a regular expression
look_for(iris, "petal|species")
#> pos variable label col_type missing values
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
look_for(iris, "s$")
#> pos variable label col_type missing values
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
# Look for with several keywords
look_for(iris, "pet", "sp")
#> pos variable label col_type missing values
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
look_for(iris, "pet", "sp", "width")
#> pos variable label col_type missing values
#> 2 Sepal.Width — dbl 0
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
look_for(iris, "Pet", "sp", "width", ignore.case = FALSE)
#> pos variable label col_type missing values
#> 3 Petal.Length — dbl 0
#> 4 Petal.Width — dbl 0
# Look_for can search within factor levels or value labels
look_for(iris, "vers")
#> pos variable label col_type missing values
#> 5 Species — fct 0 setosa
#> versicolor
#> virginica
# Quicker search without variable details
look_for(iris, details = "none")
#> pos variable label
#> 1 Sepal.Length —
#> 2 Sepal.Width —
#> 3 Petal.Length —
#> 4 Petal.Width —
#> 5 Species —
# To obtain more details about each variable
look_for(iris, details = "full")
#> pos variable label col_type missing unique_values values
#> 1 Sepal.Length — dbl 0 35 range: 4.3 - 7.9
#> 2 Sepal.Width — dbl 0 23 range: 2 - 4.4
#> 3 Petal.Length — dbl 0 43 range: 1 - 6.9
#> 4 Petal.Width — dbl 0 22 range: 0.1 - 2.5
#> 5 Species — fct 0 3 setosa
#> versicolor
#> virginica
#> na_values na_range
#>
#>
#>
#>
#>
#>
#>
# To deactivate default printing, convert to tibble
look_for(iris, details = "full") %>%
dplyr::as_tibble()
#> # A tibble: 5 × 14
#> pos variable label col_type missing levels value_labels class type
#> <int> <chr> <chr> <chr> <int> <named lis> <named list> <nam> <chr>
#> 1 1 Sepal.Length NA dbl 0 <NULL> <NULL> <chr> doub…
#> 2 2 Sepal.Width NA dbl 0 <NULL> <NULL> <chr> doub…
#> 3 3 Petal.Length NA dbl 0 <NULL> <NULL> <chr> doub…
#> 4 4 Petal.Width NA dbl 0 <NULL> <NULL> <chr> doub…
#> 5 5 Species NA fct 0 <chr [3]> <NULL> <chr> inte…
#> # ℹ 5 more variables: na_values <named list>, na_range <named list>,
#> # n_na <int>, unique_values <int>, range <named list>
# To convert named lists into character vectors
look_for(iris) %>% convert_list_columns_to_character()
#> # A tibble: 5 × 7
#> pos variable label col_type missing levels value_labels
#> <int> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 1 Sepal.Length NA dbl 0 "" ""
#> 2 2 Sepal.Width NA dbl 0 "" ""
#> 3 3 Petal.Length NA dbl 0 "" ""
#> 4 4 Petal.Width NA dbl 0 "" ""
#> 5 5 Species NA fct 0 "setosa; versicolor; v… ""
# Long format with one row per factor and per value label
look_for(iris) %>% lookfor_to_long_format()
#> # A tibble: 7 × 7
#> pos variable label col_type missing levels value_labels
#> <int> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 1 Sepal.Length NA dbl 0 NA NA
#> 2 2 Sepal.Width NA dbl 0 NA NA
#> 3 3 Petal.Length NA dbl 0 NA NA
#> 4 4 Petal.Width NA dbl 0 NA NA
#> 5 5 Species NA fct 0 setosa NA
#> 6 5 Species NA fct 0 versicolor NA
#> 7 5 Species NA fct 0 virginica NA
# Both functions can be combined
look_for(iris) %>%
lookfor_to_long_format() %>%
convert_list_columns_to_character()
#> # A tibble: 7 × 7
#> pos variable label col_type missing levels value_labels
#> <int> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 1 Sepal.Length NA dbl 0 NA NA
#> 2 2 Sepal.Width NA dbl 0 NA NA
#> 3 3 Petal.Length NA dbl 0 NA NA
#> 4 4 Petal.Width NA dbl 0 NA NA
#> 5 5 Species NA fct 0 setosa NA
#> 6 5 Species NA fct 0 versicolor NA
#> 7 5 Species NA fct 0 virginica NA
# Labelled data
d <- dplyr::tibble(
region = labelled_spss(
c(1, 2, 1, 9, 2, 3),
c(north = 1, south = 2, center = 3, missing = 9),
na_values = 9,
label = "Region of the respondent"
),
sex = labelled(
c("f", "f", "m", "m", "m", "f"),
c(female = "f", male = "m"),
label = "Sex of the respondent"
)
)
look_for(d)
#> pos variable label col_type missing values
#> 1 region Region of the respondent dbl+lbl 1 [1] north
#> [2] south
#> [3] center
#> [9] missing
#> 2 sex Sex of the respondent chr+lbl 0 [f] female
#> [m] male
d %>%
look_for() %>%
lookfor_to_long_format() %>%
convert_list_columns_to_character()
#> # A tibble: 6 × 7
#> pos variable label col_type missing levels value_labels
#> <int> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 1 region Region of the respondent dbl+lbl 1 NA [1] north
#> 2 1 region Region of the respondent dbl+lbl 1 NA [2] south
#> 3 1 region Region of the respondent dbl+lbl 1 NA [3] center
#> 4 1 region Region of the respondent dbl+lbl 1 NA [9] missing
#> 5 2 sex Sex of the respondent chr+lbl 0 NA [f] female
#> 6 2 sex Sex of the respondent chr+lbl 0 NA [m] male