The goal of datadictionary is to create a data
dictionary from any dataset in your R environment. While other packages
exist I found the output wasn’t able to be easily captured in a single
view. They were either very long, such as many pages of output in word
or pdf format, or very wide, such as concatenating all levels of factor
variables in a single string. This package attempts to solve those
problems by presenting summaries of the dataset in a format that fits
easily in a pane or screen, without using word or pdf outputs. It also
presents different summaries for different variable types.
It includes overall summaries of rows and columns and at-a-glance summaries of each variable including mean, median, min and max for numeric variables, counts in each level for factors, and the number of responses for character variables. All variable summaries include a count of missing values.
You can nominate one or more id variables, for example individuals and clusters, so you don’t get nonsense data summaries for these variables. You can also include a vector to add labels if you want descriptions included in the document. You can opt for the output to write directly to Excel.
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 can also specify one or more identifier variables. This is useful if you have hierarchical data, for example and have identifiers for individual, 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 variables that have no labels. You can specify all columns or only a few. You need to pass a named vector where the names correspond to columns in your dataset.
# Create labels as a named vector.
iris.labels <- c(Sepal.Length = "Sepal length in mm", Sepal.Width = "Sepal width in mm")
create_dictionary(iris, var_labels = iris.labels)
#> Input object size: 7256 bytes; 5 variables 150 observations
#> New object size: 8344 bytes; 5 variables 150 observations
#> item label class summary value
#> 1 Rows in dataset 150
#> 2 Columns in dataset 5
#> 3 Sepal.Length Sepal length in mm labelled numeric mean 6
#> 4 median 6
#> 5 min 4.3
#> 6 max 7.9
#> 7 missing 0
#> 8 Sepal.Width Sepal width in mm labelled numeric mean 3
#> 9 median 3
#> 10 min 2
#> 11 max 4.4
#> 12 missing 0
#> 13 Petal.Length No label numeric mean 4
#> 14 median 4
#> 15 min 1
#> 16 max 6.9
#> 17 missing 0
#> 18 Petal.Width No label numeric mean 1
#> 19 median 1
#> 20 min 0.1
#> 21 max 2.5
#> 22 missing 0
#> 23 Species No label 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. 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.
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