look_for()It 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.
library(dplyr)iris %>% as_tibble()## # A tibble: 150 x 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
## # ... with 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 x 17
## id_woman id_household weight interview_date date_of_birth age residency
## <dbl> <dbl> <dbl> <date> <date> <dbl> <dbl+lbl>
## 1 391 381 1.80 2012-05-05 1997-03-07 15 2 [rural]
## 2 1643 1515 1.80 2012-01-23 1982-01-06 30 2 [rural]
## 3 85 85 1.80 2012-01-21 1979-01-01 33 2 [rural]
## 4 881 844 1.80 2012-01-06 1968-03-29 43 2 [rural]
## 5 1981 1797 1.80 2012-05-11 1986-05-25 25 2 [rural]
## 6 1072 1015 0.998 2012-02-20 1993-07-03 18 2 [rural]
## 7 1978 1794 0.998 2012-02-23 1967-01-28 45 2 [rural]
## 8 1607 1486 0.998 2012-02-20 1989-01-21 23 2 [rural]
## 9 738 711 0.192 2012-03-09 1962-07-24 49 2 [rural]
## 10 1656 1525 0.192 2012-03-15 1980-12-25 31 2 [rural]
## # ... with 1,990 more rows, and 10 more variables: region <dbl+lbl>,
## # instruction <dbl+lbl>, employed <dbl+lbl>, matri <dbl+lbl>,
## # religion <dbl+lbl>, newspaper <dbl+lbl>, radio <dbl+lbl>, tv <dbl+lbl>,
## # ideal_nb_children <dbl+lbl>, test <dbl+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 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ~
## $ region <dbl+lbl> 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, ~
## $ instruction <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 0, ~
## $ employed <dbl+lbl> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ matri <dbl+lbl> 0, 2, 2, 2, 1, 0, 1, 1, 2, 5, 2, 3, 0, 2, 1, 2, ~
## $ religion <dbl+lbl> 1, 3, 2, 3, 2, 2, 3, 1, 3, 3, 2, 3, 2, 2, 2, 2, ~
## $ newspaper <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ~
## $ radio <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, ~
## $ tv <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, ~
## $ ideal_nb_children <dbl+lbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, ~
## $ test <dbl+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").library(labelled)
look_for(iris)## pos variable label col_type values
## 1 Sepal.Length — dbl
## 2 Sepal.Width — dbl
## 3 Petal.Length Length of petal dbl
## 4 Petal.Width Width of Petal dbl
## 5 Species — fct setosa
## versicolor
## virginicalook_for(women)## pos variable label col_type values
## 1 id_woman Woman Id dbl
## 2 id_household Household Id dbl
## 3 weight Sample weight dbl
## 4 interview_date Interview date date
## 5 date_of_birth Date of birth date
## 6 age Age at last anniversary ~ dbl
## 7 residency Urban / rural residency dbl+lbl [1] urban
## [2] rural
## 8 region Region dbl+lbl [1] North
## [2] East
## [3] South
## [4] West
## 9 instruction Level of instruction dbl+lbl [0] none
## [1] primary
## [2] secondary
## [3] higher
## 10 employed Employed? dbl+lbl [0] no
## [1] yes
## [9] missing
## 11 matri Matrimonial status dbl+lbl [0] single
## [1] married
## [2] living together
## [3] windowed
## [4] divorced
## [5] separated
## 12 religion Religion dbl+lbl [1] Muslim
## [2] Christian
## [3] Protestant
## [4] no religion
## [5] other
## 13 newspaper Read newspaper? dbl+lbl [0] no
## [1] yes
## 14 radio Listen to radio? dbl+lbl [0] no
## [1] yes
## 15 tv Watch TV? dbl+lbl [0] no
## [1] yes
## 16 ideal_nb_children Ideal number of children dbl+lbl [96] don't know
## [99] missing
## 17 test Ever tested for HIV? dbl+lbl [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 values
## 3 Petal.Length Length of petal dbl
## 4 Petal.Width Width of Petal dbllook_for(iris, "s")## pos variable label col_type values
## 1 Sepal.Length — dbl
## 2 Sepal.Width — dbl
## 5 Species — fct setosa
## versicolor
## virginica# Look for with a regular expression
look_for(iris, "petal|species")## pos variable label col_type values
## 3 Petal.Length Length of petal dbl
## 4 Petal.Width Width of Petal dbl
## 5 Species — fct setosa
## versicolor
## virginicalook_for(iris, "s$")## pos variable label col_type values
## 5 Species — fct setosa
## versicolor
## virginica# Look for with several keywords
look_for(iris, "pet", "sp")## pos variable label col_type values
## 3 Petal.Length Length of petal dbl
## 4 Petal.Width Width of Petal dbl
## 5 Species — fct setosa
## versicolor
## virginica# Look_for will take variable labels into account
look_for(women, "read", "level")## pos variable label col_type values
## 9 instruction Level of instruction dbl+lbl [0] none
## [1] primary
## [2] secondary
## [3] higher
## 13 newspaper Read newspaper? dbl+lbl [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 values
## 13 newspaper Read newspaper? dbl+lbl [0] no
## [1] yeslook_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 values
## 1 Sepal.Length — dbl
## 2 Sepal.Width — dbllook_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 values
## 1 id_woman Woman Id dbl range: 1 - 2000
## 2 id_household Household Id dbl range: 1 - 1814
## 3 weight Sample weight dbl range: 0.044629 - 4.3~
## 4 interview_date Interview date date range: 2011-12-01 - 2~
## 5 date_of_birth Date of birth date range: 1962-02-07 - 1~
## 6 age Age at last anniversa~ dbl range: 14 - 49
## 7 residency Urban / rural residen~ dbl+lbl [1] urban
## [2] rural
## 8 region Region dbl+lbl [1] North
## [2] East
## [3] South
## [4] West
## 9 instruction Level of instruction dbl+lbl [0] none
## [1] primary
## [2] secondary
## [3] higher
## 10 employed Employed? dbl+lbl [0] no
## [1] yes
## [9] missing
## 11 matri Matrimonial status dbl+lbl [0] single
## [1] married
## [2] living together
## [3] windowed
## [4] divorced
## [5] separated
## 12 religion Religion dbl+lbl [1] Muslim
## [2] Christian
## [3] Protestant
## [4] no religion
## [5] other
## 13 newspaper Read newspaper? dbl+lbl [0] no
## [1] yes
## 14 radio Listen to radio? dbl+lbl [0] no
## [1] yes
## 15 tv Watch TV? dbl+lbl [0] no
## [1] yes
## 16 ideal_nb_children Ideal number of child~ dbl+lbl [96] don't know
## [99] missing
## 17 test Ever tested for HIV? dbl+lbl [0] no
## [1] yes
## [9] missinglook_for(women, details = "full") %>%
dplyr::glimpse()## Rows: 17
## Columns: 13
## $ 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", "~
## $ levels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, ~
## $ value_labels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <~
## $ 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>, <NULL>, ~
## $ na_range <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <~
## $ unique_values <int> 2000, 1814, 351, 165, 1740, 36, 2, 4, 4, 3, 6, 6,~
## $ n_na <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 4, 0, 0, 0, 0, 29
## $ range <named list> <1, 2000>, <1, 1814>, <0.044629, 4.396831>, <2011-12-01,~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().
look_for(women) %>% View()look_for(women) %>% as_tibble()## # A tibble: 17 x 6
## pos variable label col_type levels value_labels
## <int> <chr> <chr> <chr> <named l> <named list>
## 1 1 id_woman Woman Id dbl <NULL> <NULL>
## 2 2 id_household Household Id dbl <NULL> <NULL>
## 3 3 weight Sample weight dbl <NULL> <NULL>
## 4 4 interview_date Interview date date <NULL> <NULL>
## 5 5 date_of_birth Date of birth date <NULL> <NULL>
## 6 6 age Age at last annivers~ dbl <NULL> <NULL>
## 7 7 residency Urban / rural reside~ dbl+lbl <NULL> <dbl [2]>
## 8 8 region Region dbl+lbl <NULL> <dbl [4]>
## 9 9 instruction Level of instruction dbl+lbl <NULL> <dbl [4]>
## 10 10 employed Employed? dbl+lbl <NULL> <dbl [3]>
## 11 11 matri Matrimonial status dbl+lbl <NULL> <dbl [6]>
## 12 12 religion Religion dbl+lbl <NULL> <dbl [5]>
## 13 13 newspaper Read newspaper? dbl+lbl <NULL> <dbl [2]>
## 14 14 radio Listen to radio? dbl+lbl <NULL> <dbl [2]>
## 15 15 tv Watch TV? dbl+lbl <NULL> <dbl [2]>
## 16 16 ideal_nb_children Ideal number of chil~ dbl+lbl <NULL> <dbl [2]>
## 17 17 test Ever tested for HIV? dbl+lbl <NULL> <dbl [3]>glimpse(look_for(women))## Rows: 17
## Columns: 6
## $ 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~
## $ levels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <~
## $ value_labels <named list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, <1~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 x 6
## pos variable label col_type levels value_labels
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 1 id_woman Woman Id dbl "" ""
## 2 2 id_household Household ~ dbl "" ""
## 3 3 weight Sample wei~ dbl "" ""
## 4 4 interview_date Interview ~ date "" ""
## 5 5 date_of_birth Date of bi~ date "" ""
## 6 6 age Age at las~ dbl "" ""
## 7 7 residency Urban / ru~ dbl+lbl "" "[1] urban; [2] rural"
## 8 8 region Region dbl+lbl "" "[1] North; [2] East; [3~
## 9 9 instruction Level of i~ dbl+lbl "" "[0] none; [1] primary; ~
## 10 10 employed Employed? dbl+lbl "" "[0] no; [1] yes; [9] mi~
## 11 11 matri Matrimonia~ dbl+lbl "" "[0] single; [1] married~
## 12 12 religion Religion dbl+lbl "" "[1] Muslim; [2] Christi~
## 13 13 newspaper Read newsp~ dbl+lbl "" "[0] no; [1] yes"
## 14 14 radio Listen to ~ dbl+lbl "" "[0] no; [1] yes"
## 15 15 tv Watch TV? dbl+lbl "" "[0] no; [1] yes"
## 16 16 ideal_nb_children Ideal numb~ dbl+lbl "" "[96] don't know; [99] m~
## 17 17 test Ever teste~ dbl+lbl "" "[0] no; [1] yes; [9] mi~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 x 6
## pos variable label col_type levels value_labels
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 1 id_woman Woman Id dbl <NA> <NA>
## 2 2 id_household Household Id dbl <NA> <NA>
## 3 3 weight Sample weight dbl <NA> <NA>
## 4 4 interview_date Interview date date <NA> <NA>
## 5 5 date_of_birth Date of birth date <NA> <NA>
## 6 6 age Age at last anniversary (i~ dbl <NA> <NA>
## 7 7 residency Urban / rural residency dbl+lbl <NA> [1] urban
## 8 7 residency Urban / rural residency dbl+lbl <NA> [2] rural
## 9 8 region Region dbl+lbl <NA> [1] North
## 10 8 region Region dbl+lbl <NA> [2] East
## # ... with 31 more rowsBoth can be combined:
look_for(women) %>%
lookfor_to_long_format() %>%
convert_list_columns_to_character()## # A tibble: 41 x 6
## pos variable label col_type levels value_labels
## <int> <chr> <chr> <chr> <chr> <chr>
## 1 1 id_woman Woman Id dbl <NA> <NA>
## 2 2 id_household Household Id dbl <NA> <NA>
## 3 3 weight Sample weight dbl <NA> <NA>
## 4 4 interview_date Interview date date <NA> <NA>
## 5 5 date_of_birth Date of birth date <NA> <NA>
## 6 6 age Age at last anniversary (i~ dbl <NA> <NA>
## 7 7 residency Urban / rural residency dbl+lbl <NA> [1] urban
## 8 7 residency Urban / rural residency dbl+lbl <NA> [2] rural
## 9 8 region Region dbl+lbl <NA> [1] North
## 10 8 region Region dbl+lbl <NA> [2] East
## # ... with 31 more rows