The goal of datadictionary is to create a data
dictionary from any dataframe or tibble in your R environment. While
other packages exist I found they were complicated to use and/or the
output wasn’t what I was after. This package attempts to solve those
problems by presenting tabular summaries of the dataset in a format that
fits easily in a pane or screen, using a single line of code.
It includes an overall summary of the dataset and at-a-glance summaries of each variable. All variables have a count of missing included, and different summaries are provided based on the data class.
For factors, labelled data and logicals the summary will include the name of each level with the level number in parentheses where appropriate. A value for the count of units in each level is included.
For dates, integers and other numeric types of data the summary includes statistical summaries such as mean, median, mode, minimum and maximum. A value for each is included in the table.
Character variables include only a count of unique values and missing values. This is the default so if you include a class of data that isn’t yet implemented you should get this output.
You can nominate one or more identifier variables, for example individuals and clusters, so you only get a count of unique and missing values rather than nonsense numeric summaries.
You can also include a vector to add labels if you want descriptions included in the document. Lastly, you can opt for the output to write directly to Excel.
You can install the current version of datadictionary
from CRAN using:
install.packages("datadictionary")You can install the development version of
datadictionary from GitHub with:
# install.packages("devtools")
devtools::install_github("DoctorBJones/datadictionary")You can print a basic data dictionary directly to your console or assign it to an object in your environment:
library(datadictionary)
create_dictionary(esoph)
#> item label class summary value
#> 1 Rows in dataset 88
#> 2 Columns in dataset 5
#> 3 agegp No label ordered factor 25-34 (1) 15
#> 4 35-44 (2) 15
#> 5 45-54 (3) 16
#> 6 55-64 (4) 16
#> 7 65-74 (5) 15
#> 8 75+ (6) 11
#> 9 missing 0
#> 10 alcgp No label ordered factor 0-39g/day (1) 23
#> 11 40-79 (2) 23
#> 12 80-119 (3) 21
#> 13 120+ (4) 21
#> 14 missing 0
#> 15 tobgp No label ordered factor 0-9g/day (1) 24
#> 16 10-19 (2) 24
#> 17 20-29 (3) 20
#> 18 30+ (4) 20
#> 19 missing 0
#> 20 ncases No label numeric mean 2
#> 21 median 1
#> 22 min 0
#> 23 max 17
#> 24 missing 0
#> 25 ncontrols No label numeric mean 9
#> 26 median 4
#> 27 min 0
#> 28 max 60
#> 29 missing 0
esoph_dictionary <- create_dictionary(esoph)You specify one or more identifier variables by passing a quoted
string or vector of quoted strings to id_var. This is
useful if you have hierarchical data, for example and have identifiers
for individuals, clusters or blocks.
# create fake id variables
mtcars$id1 <- 1:nrow(mtcars)
mtcars$id2 <- mtcars$id1*10
create_dictionary(mtcars, id_var = c("id1", "id2"))
#> item label class summary value
#> 1 Rows in dataset 32
#> 2 Columns in dataset 13
#> 3 id1 Unique identifier unique values 32
#> 4 missing 0
#> 5 id2 Unique identifier unique values 32
#> 6 missing 0
#> 7 mpg No label numeric mean 20
#> 8 median 19
#> 9 min 10.4
#> 10 max 33.9
#> 11 missing 0
#> 12 cyl No label numeric mean 6
#> 13 median 6
#> 14 min 4
#> 15 max 8
#> 16 missing 0
#> 17 disp No label numeric mean 231
#> 18 median 196
#> 19 min 71.1
#> 20 max 472
#> 21 missing 0
#> 22 hp No label numeric mean 147
#> 23 median 123
#> 24 min 52
#> 25 max 335
#> 26 missing 0
#> 27 drat No label numeric mean 4
#> 28 median 4
#> 29 min 2.76
#> 30 max 4.93
#> 31 missing 0
#> 32 wt No label numeric mean 3
#> 33 median 3
#> 34 min 1.51
#> 35 max 5.42
#> 36 missing 0
#> 37 qsec No label numeric mean 18
#> 38 median 18
#> 39 min 14.5
#> 40 max 22.9
#> 41 missing 0
#> 42 vs No label numeric mean 0
#> 43 median 0
#> 44 min 0
#> 45 max 1
#> 46 missing 0
#> 47 am No label numeric mean 0
#> 48 median 0
#> 49 min 0
#> 50 max 1
#> 51 missing 0
#> 52 gear No label numeric mean 4
#> 53 median 4
#> 54 min 3
#> 55 max 5
#> 56 missing 0
#> 57 carb No label numeric mean 3
#> 58 median 2
#> 59 min 1
#> 60 max 8
#> 61 missing 0You can also optionally add labels for unlabelled variables. You need
to pass a named vector to var_labels where the names
correspond to columns in your dataset. The vector must be of the same
length as your dataset.
# Create labels as a named vector.
iris.labels <- c(Sepal.Length = "Sepal length in mm",
Sepal.Width = "Sepal width in mm",
Petal.Length = "Petal length in mm",
Petal.Width = "Petal width in mm",
Species = "Species of iris")
create_dictionary(iris, var_labels = iris.labels)
#> item label class summary value
#> 1 Rows in dataset 150
#> 2 Columns in dataset 5
#> 3 Sepal.Length Sepal length in mm numeric mean 6
#> 4 median 6
#> 5 min 4.3
#> 6 max 7.9
#> 7 missing 0
#> 8 Sepal.Width Sepal width in mm numeric mean 3
#> 9 median 3
#> 10 min 2
#> 11 max 4.4
#> 12 missing 0
#> 13 Petal.Length Petal length in mm numeric mean 4
#> 14 median 4
#> 15 min 1
#> 16 max 6.9
#> 17 missing 0
#> 18 Petal.Width Petal width in mm numeric mean 1
#> 19 median 1
#> 20 min 0.1
#> 21 max 2.5
#> 22 missing 0
#> 23 Species Species of iris factor setosa (1) 50
#> 24 versicolor (2) 50
#> 25 virginica (3) 50
#> 26 missing 0You can also write directly to Excel from the
create_dictionary function if you pass a file path and name
as a quoted string to the file parameter. There is no
visible output for this use.
create_dictionary(ChickWeight, file = "chickweight_dictionary.xlsx")The package also includes a function to create a summary of a single variable in your dataset. There are no other arguments to this function.
summarise_variable(iris, "Sepal.Length")
#> item label class summary value
#> 1 Sepal.Length No label numeric mean 6
#> 2 median 6
#> 3 min 4.3
#> 4 max 7.9
#> 5 missing 0
summarise_variable(ChickWeight, "Diet")
#> item label class summary value
#> 1 Diet No label factor 1 (1) 220
#> 2 2 (2) 120
#> 3 3 (3) 120
#> 4 4 (4) 118
#> 5 missing 0