look_for()
vignettes/look_for.Rmd
look_for.RmdIt is a common need to easily get a description of all variables in a data frame.
When a data frame is converted into a tibble (e.g. with
dplyr::as_tibble()), it as a nice printing showing the
first rows of the data frame as well as the type of column.
## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ℹ 140 more rowsHowever, when you have too many variables, all of them cannot be printed and their are just listed.
data(fertility, package = "questionr")
women## # A tibble: 2,000 × 17
## id_woman id_household weight interview_date date_of_birth age residency
## <dbl> <dbl> <dbl> <date> <date> <dbl> <hvn_lbll>
## 1 391 381 1.80 2012-05-05 1997-03-07 15 2
## 2 1643 1515 1.80 2012-01-23 1982-01-06 30 2
## 3 85 85 1.80 2012-01-21 1979-01-01 33 2
## 4 881 844 1.80 2012-01-06 1968-03-29 43 2
## 5 1981 1797 1.80 2012-05-11 1986-05-25 25 2
## 6 1072 1015 0.998 2012-02-20 1993-07-03 18 2
## 7 1978 1794 0.998 2012-02-23 1967-01-28 45 2
## 8 1607 1486 0.998 2012-02-20 1989-01-21 23 2
## 9 738 711 0.192 2012-03-09 1962-07-24 49 2
## 10 1656 1525 0.192 2012-03-15 1980-12-25 31 2
## # ℹ 1,990 more rows
## # ℹ 10 more variables: region <hvn_lbll>, instruction <hvn_lbll>,
## # employed <hvn_lbl_>, matri <hvn_lbll>, religion <hvn_lbll>,
## # newspaper <hvn_lbll>, radio <hvn_lbll>, tv <hvn_lbll>,
## # ideal_nb_children <hvn_lbl_>, test <hvn_lbl_>Note: in R console, value labels (if defined) are usually printed but they do not appear in a R markdown document like this vignette.
dplyr::glimpse()
The function dplyr::glimpse() allows you to have a quick
look at all the variables in a data frame.
glimpse(iris)## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…
glimpse(women)## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <hvn_lbll> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ region <hvn_lbll> 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2,…
## $ instruction <hvn_lbll> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 0,…
## $ employed <hvn_lbl_> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ matri <hvn_lbll> 0, 2, 2, 2, 1, 0, 1, 1, 2, 5, 2, 3, 0, 2, 1, 2,…
## $ religion <hvn_lbll> 1, 3, 2, 3, 2, 2, 3, 1, 3, 3, 2, 3, 2, 2, 2, 2,…
## $ newspaper <hvn_lbll> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ radio <hvn_lbll> 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,…
## $ tv <hvn_lbll> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,…
## $ ideal_nb_children <hvn_lbl_> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6, …
## $ test <hvn_lbl_> 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,…It will show you the first values of each variable as well as the type of each variable. However, some important informations are not displayed:
labelled::look_for()
look_for() provided by the labelled package
will print in the console a data dictionary of all variables, showing
variable labels when available, the type of variable and a list of
values corresponding to:
details = "full").## 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(women)## pos variable label col_type missing values
## 1 id_woman Woman Id dbl 0
## 2 id_household Household Id dbl 0
## 3 weight Sample weight dbl 0
## 4 interview_date Interview date date 0
## 5 date_of_birth Date of birth date 0
## 6 age Age at last anniv~ dbl 0
## 7 residency Urban / rural res~ dbl+lbl 0 [1] urban
## [2] rural
## 8 region Region dbl+lbl 0 [1] North
## [2] East
## [3] South
## [4] West
## 9 instruction Level of instruct~ dbl+lbl 0 [0] none
## [1] primary
## [2] secondary
## [3] higher
## 10 employed Employed? dbl+lbl 7 [0] no
## [1] yes
## [9] missing
## 11 matri Matrimonial status dbl+lbl 0 [0] single
## [1] married
## [2] living togeth~
## [3] windowed
## [4] divorced
## [5] separated
## 12 religion Religion dbl+lbl 4 [1] Muslim
## [2] Christian
## [3] Protestant
## [4] no religion
## [5] other
## 13 newspaper Read newspaper? dbl+lbl 0 [0] no
## [1] yes
## 14 radio Listen to radio? dbl+lbl 0 [0] no
## [1] yes
## 15 tv Watch TV? dbl+lbl 0 [0] no
## [1] yes
## 16 ideal_nb_children Ideal number of c~ dbl+lbl 0 [96] don't know
## [99] missing
## 17 test Ever tested for H~ dbl+lbl 29 [0] no
## [1] yes
## [9] missingNote that lookfor() and
generate_dictionary() are synonyms of
look_for() and works exactly in the same way.
If there is not enough space to print full labels in the console,
they will be truncated (truncation is indicated by a
~).
When a data frame has dozens or even hundreds of variables, it could become difficult to find a specific variable. In such case, you can provide an optional list of keywords, which can be simple character strings or regular expression, to search for specific variables.
# 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
# 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 will take variable labels into account
look_for(women, "read", "level")## pos variable label col_type missing values
## 9 instruction Level of instruction dbl+lbl 0 [0] none
## [1] primary
## [2] secondary
## [3] higher
## 13 newspaper Read newspaper? dbl+lbl 0 [0] no
## [1] yesBy default, look_for() will look through both variable
names and variables labels. Use labels = FALSE to look only
through variable names.
look_for(women, "read")## pos variable label col_type missing values
## 13 newspaper Read newspaper? dbl+lbl 0 [0] no
## [1] yes
look_for(women, "read", labels = FALSE)## Nothing found. Sorry.Similarly, the search is by default case insensitive. To make the
search case sensitive, use ignore.case = FALSE.
look_for(iris, "sepal")## pos variable label col_type missing values
## 1 Sepal.Length — dbl 0
## 2 Sepal.Width — dbl 0
look_for(iris, "sepal", ignore.case = FALSE)## Nothing found. Sorry.If you just want to use the search feature of look_for()
without computing the details of each variable, simply indicate
details = "none" or details = FALSE.
look_for(women, "id", details = "none")## pos variable label
## 1 id_woman Woman Id
## 2 id_household Household Id
## 7 residency Urban / rural residency
## 16 ideal_nb_children Ideal number of childrenIf you want more details (but can be time consuming for big data
frames), indicate details = "full" or
details = TRUE.
look_for(women, details = "full")## pos variable label col_type missing unique_values
## 1 id_woman Woman Id dbl 0 2000
## 2 id_household Household Id dbl 0 1814
## 3 weight Sample weight dbl 0 351
## 4 interview_date Interview date date 0 165
## 5 date_of_birth Date of birth date 0 1740
## 6 age Age at last anniv~ dbl 0 36
## 7 residency Urban / rural res~ dbl+lbl 0 2
##
## 8 region Region dbl+lbl 0 4
##
##
##
## 9 instruction Level of instruct~ dbl+lbl 0 4
##
##
##
## 10 employed Employed? dbl+lbl 7 3
##
##
## 11 matri Matrimonial status dbl+lbl 0 6
##
##
##
##
##
## 12 religion Religion dbl+lbl 4 6
##
##
##
##
## 13 newspaper Read newspaper? dbl+lbl 0 2
##
## 14 radio Listen to radio? dbl+lbl 0 2
##
## 15 tv Watch TV? dbl+lbl 0 2
##
## 16 ideal_nb_children Ideal number of c~ dbl+lbl 0 18
##
## 17 test Ever tested for H~ dbl+lbl 29 3
##
##
## values na_values na_range
## range: 1 - 2000
## range: 1 - 1814
## range: 0.044629 -~
## range: 2011-12-01~
## range: 1962-02-07~
## range: 14 - 49
## [1] urban
## [2] rural
## [1] North
## [2] East
## [3] South
## [4] West
## [0] none
## [1] primary
## [2] secondary
## [3] higher
## [0] no 9
## [1] yes
## [9] missing
## [0] single
## [1] married
## [2] living togeth~
## [3] windowed
## [4] divorced
## [5] separated
## [1] Muslim
## [2] Christian
## [3] Protestant
## [4] no religion
## [5] other
## [0] no
## [1] yes
## [0] no
## [1] yes
## [0] no
## [1] yes
## [96] don't know
## [99] missing
## [0] no 9
## [1] yes
## [9] missing## Rows: 17
## Columns: 14
## $ pos <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
## $ variable <chr> "id_woman", "id_household", "weight", "interview_date", …
## $ label <chr> "Woman Id", "Household Id", "Sample weight", "Interview …
## $ col_type <chr> "dbl", "dbl", "dbl", "date", "date", "dbl", "dbl+lbl", "…
## $ missing <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4, 0, 0, 0, 0, 29
## $ levels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, …
## $ value_labels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <1, 2>, …
## $ class <named list> "numeric", "numeric", "numeric", "Date", "Date", …
## $ type <chr> "double", "double", "double", "double", "double",…
## $ na_values <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <…
## $ na_range <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, …
## $ n_na <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4, 0, 0, 0, 0, 29
## $ unique_values <int> 2000, 1814, 351, 165, 1740, 36, 2, 4, 4, 3, 6, 6,…
## $ range <named list> <1, 2000>, <1, 1814>, <0.044629, 4.396831>, <2011…look_for()
look_for() returns a detailed tibble which is summarized
before printing. To deactivate default printing and see full results,
simply use dplyr::as_tibble(),
dplyr::glimpse() or even utils::View().
## # A tibble: 17 × 7
## pos variable label col_type missing levels value_labels
## <int> <chr> <chr> <chr> <int> <name> <named list>
## 1 1 id_woman Woman Id dbl 0 <NULL> <NULL>
## 2 2 id_household Household Id dbl 0 <NULL> <NULL>
## 3 3 weight Sample weight dbl 0 <NULL> <NULL>
## 4 4 interview_date Interview date date 0 <NULL> <NULL>
## 5 5 date_of_birth Date of birth date 0 <NULL> <NULL>
## 6 6 age Age at last ann… dbl 0 <NULL> <NULL>
## 7 7 residency Urban / rural r… dbl+lbl 0 <NULL> <dbl [2]>
## 8 8 region Region dbl+lbl 0 <NULL> <dbl [4]>
## 9 9 instruction Level of instru… dbl+lbl 0 <NULL> <dbl [4]>
## 10 10 employed Employed? dbl+lbl 7 <NULL> <dbl [3]>
## 11 11 matri Matrimonial sta… dbl+lbl 0 <NULL> <dbl [6]>
## 12 12 religion Religion dbl+lbl 4 <NULL> <dbl [5]>
## 13 13 newspaper Read newspaper? dbl+lbl 0 <NULL> <dbl [2]>
## 14 14 radio Listen to radio? dbl+lbl 0 <NULL> <dbl [2]>
## 15 15 tv Watch TV? dbl+lbl 0 <NULL> <dbl [2]>
## 16 16 ideal_nb_children Ideal number of… dbl+lbl 0 <NULL> <dbl [2]>
## 17 17 test Ever tested for… dbl+lbl 29 <NULL> <dbl [3]>## Rows: 17
## Columns: 7
## $ pos <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
## $ variable <chr> "id_woman", "id_household", "weight", "interview_date", "…
## $ label <chr> "Woman Id", "Household Id", "Sample weight", "Interview d…
## $ col_type <chr> "dbl", "dbl", "dbl", "date", "date", "dbl", "dbl+lbl", "d…
## $ missing <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4, 0, 0, 0, 0, 29
## $ levels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <…
## $ value_labels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <1, 2>, <…The tibble returned by look_for() could be easily
manipulated for advanced programming.
When a column has several values for one variable
(e.g. levels or value_labels), results as
stored with nested named list. You can convert named lists into simpler
character vectors, you can use
convert_list_columns_to_character().
look_for(women) %>% convert_list_columns_to_character()## # A tibble: 17 × 7
## pos variable label col_type missing levels value_labels
## <int> <chr> <chr> <chr> <int> <chr> <chr>
## 1 1 id_woman Woman Id dbl 0 "" ""
## 2 2 id_household Household Id dbl 0 "" ""
## 3 3 weight Sample weight dbl 0 "" ""
## 4 4 interview_date Interview date date 0 "" ""
## 5 5 date_of_birth Date of birth date 0 "" ""
## 6 6 age Age at last ann… dbl 0 "" ""
## 7 7 residency Urban / rural r… dbl+lbl 0 "" "[1] urban;…
## 8 8 region Region dbl+lbl 0 "" "[1] North;…
## 9 9 instruction Level of instru… dbl+lbl 0 "" "[0] none; …
## 10 10 employed Employed? dbl+lbl 7 "" "[0] no; [1…
## 11 11 matri Matrimonial sta… dbl+lbl 0 "" "[0] single…
## 12 12 religion Religion dbl+lbl 4 "" "[1] Muslim…
## 13 13 newspaper Read newspaper? dbl+lbl 0 "" "[0] no; [1…
## 14 14 radio Listen to radio? dbl+lbl 0 "" "[0] no; [1…
## 15 15 tv Watch TV? dbl+lbl 0 "" "[0] no; [1…
## 16 16 ideal_nb_children Ideal number of… dbl+lbl 0 "" "[96] don't…
## 17 17 test Ever tested for… dbl+lbl 29 "" "[0] no; [1…Alternatively, you can use lookfor_to_long_format() to
transform results into a long format with one row per factor level and
per value label.
look_for(women) %>% lookfor_to_long_format()## # A tibble: 41 × 7
## pos variable label col_type missing levels value_labels
## <int> <chr> <chr> <chr> <int> <chr> <chr>
## 1 1 id_woman Woman Id dbl 0 NA NA
## 2 2 id_household Household Id dbl 0 NA NA
## 3 3 weight Sample weight dbl 0 NA NA
## 4 4 interview_date Interview date date 0 NA NA
## 5 5 date_of_birth Date of birth date 0 NA NA
## 6 6 age Age at last annive… dbl 0 NA NA
## 7 7 residency Urban / rural resi… dbl+lbl 0 NA [1] urban
## 8 7 residency Urban / rural resi… dbl+lbl 0 NA [2] rural
## 9 8 region Region dbl+lbl 0 NA [1] North
## 10 8 region Region dbl+lbl 0 NA [2] East
## # ℹ 31 more rowsBoth can be combined:
## # A tibble: 41 × 7
## pos variable label col_type missing levels value_labels
## <int> <chr> <chr> <chr> <int> <chr> <chr>
## 1 1 id_woman Woman Id dbl 0 NA NA
## 2 2 id_household Household Id dbl 0 NA NA
## 3 3 weight Sample weight dbl 0 NA NA
## 4 4 interview_date Interview date date 0 NA NA
## 5 5 date_of_birth Date of birth date 0 NA NA
## 6 6 age Age at last annive… dbl 0 NA NA
## 7 7 residency Urban / rural resi… dbl+lbl 0 NA [1] urban
## 8 7 residency Urban / rural resi… dbl+lbl 0 NA [2] rural
## 9 8 region Region dbl+lbl 0 NA [1] North
## 10 8 region Region dbl+lbl 0 NA [2] East
## # ℹ 31 more rows