wakefield
wakefield is designed to quickly generate random data sets. The user
passes n (number of rows) and predefined vectors to the r_data_frame
function to produce a dplyr::tbl_df object.
Table of Contents
Installation
To download the development version of wakefield:
Download the zip
ball or tar
ball, decompress
and run R CMD INSTALL on it, or use the pacman package to install
the development version:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/wakefield")
pacman::p_load(dplyr, tidyr, ggplot2)
Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/trinker/wakefield/issues
- send a pull request on: https://github.com/trinker/wakefield/
- compose a friendly e-mail to: tyler.rinker@gmail.com
Demonstration
Getting Started
The r_data_frame function (random data frame) takes n (the number of
rows) and any number of variables (columns). These columns are typically
produced from a wakefield variable function. Each of these variable
functions has a pre-set behavior that produces a named vector of n
length, allowing the user to lazily pass unnamed functions (optionally,
without call parenthesis). The column name is hidden as a varname
attribute. For example here we see the race variable function:
race(n=10)
## [1] White White White White White White White White White White
## Levels: White Hispanic Black Asian Bi-Racial Native Other Hawaiian
attributes(race(n=10))
## $levels
## [1] "White" "Hispanic" "Black" "Asian" "Bi-Racial" "Native"
## [7] "Other" "Hawaiian"
##
## $class
## [1] "variable" "factor"
##
## $varname
## [1] "Race"
When this variable is used inside of r_data_frame the varname is
used as a column name. Additionally, the n argument is not set within
variable functions but is set once in r_data_frame:
r_data_frame(
n = 500,
race
)
## # A tibble: 500 x 1
## Race
## <fct>
## 1 Hispanic
## 2 Asian
## 3 White
## 4 White
## 5 White
## 6 Black
## 7 White
## 8 Hispanic
## 9 White
## 10 Hispanic
## # ... with 490 more rows
The power of r_data_frame is apparent when we use many modular
variable functions:
r_data_frame(
n = 500,
id,
race,
age,
sex,
hour,
iq,
height,
died
)
## # A tibble: 500 x 8
## ID Race Age Sex Hour IQ Height Died
## <chr> <fct> <int> <fct> <S3: times> <dbl> <dbl> <lgl>
## 1 001 White 50 Male 00:00:00 88 68 TRUE
## 2 002 White 31 Male 00:00:00 118 72 FALSE
## 3 003 White 60 Male 00:00:00 109 68 TRUE
## 4 004 White 72 Female 00:00:00 112 68 TRUE
## 5 005 White 81 Male 00:00:00 103 71 TRUE
## 6 006 Black 21 Male 00:00:00 87 72 FALSE
## 7 007 White 64 Female 00:00:00 103 71 FALSE
## 8 008 White 26 Female 00:00:00 95 65 TRUE
## 9 009 White 41 Male 00:00:00 109 74 FALSE
## 10 010 White 64 Male 00:00:00 103 70 TRUE
## # ... with 490 more rows
There are 49 wakefield based variable functions to chose from,
spanning R's various data types (see ?variables for details).
| age | dice | hair | military | sex_inclusive |
| animal | dna | height | month | smokes |
| answer | dob | income | name | speed |
| area | dummy | internet_browser | normal | state |
| car | education | iq | political | string |
| children | employment | language | race | upper |
| coin | eye | level | religion | valid |
| color | grade | likert | sat | year |
| date_stamp | grade_level | lorem_ipsum | sentence | zip_code |
| death | group | marital | sex |
Available Variable Functions
However, the user may also pass their own vector producing functions or
vectors to r_data_frame. Those with an n argument can be set by
r_data_frame:
r_data_frame(
n = 500,
id,
Scoring = rnorm,
Smoker = valid,
race,
age,
sex,
hour,
iq,
height,
died
)
## # A tibble: 500 x 10
## ID Scoring Smoker Race Age Sex Hour IQ Height Died
## <chr> <dbl> <lgl> <fct> <int> <fct> <S3: tim> <dbl> <dbl> <lgl>
## 1 001 1.78 TRUE White 70 Female 00:00:00 87 66 FALSE
## 2 002 1.38 TRUE White 41 Female 00:00:00 98 61 TRUE
## 3 003 0.641 FALSE White 81 Female 00:00:00 104 68 FALSE
## 4 004 0.461 FALSE White 84 Female 00:00:00 109 70 TRUE
## 5 005 0.179 FALSE White 23 Female 00:00:00 109 70 FALSE
## 6 006 0.0787 FALSE White 32 Female 00:00:00 105 69 TRUE
## 7 007 -1.52 FALSE White 69 Female 00:00:00 104 71 FALSE
## 8 008 1.04 FALSE White 24 Female 00:00:00 105 74 TRUE
## 9 009 -0.390 FALSE White 66 Female 00:00:00 103 70 FALSE
## 10 010 1.13 FALSE Hispanic 84 Male 00:00:00 93 68 TRUE
## # ... with 490 more rows
r_data_frame(
n = 500,
id,
age, age, age,
grade, grade, grade
)
## # A tibble: 500 x 7
## ID Age_1 Age_2 Age_3 Grade_1 Grade_2 Grade_3
## <chr> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 001 67 47 46 93.7 89.9 83.7
## 2 002 19 26 74 91.8 89 93.1
## 3 003 56 20 27 86.5 84.2 80.8
## 4 004 81 30 71 91.4 90.6 88.8
## 5 005 27 64 81 86.9 94.2 89.8
## 6 006 44 81 28 94.7 87.4 90.9
## 7 007 20 36 70 93.1 93.2 94.1
## 8 008 62 73 22 88.1 85.2 87.4
## 9 009 24 78 53 88.1 84.6 87.5
## 10 010 62 35 26 87.3 83.6 89.8
## # ... with 490 more rows
While passing variable functions to r_data_frame without call
parenthesis is handy, the user may wish to set arguments. This can be
done through call parenthesis as we do with data.frame or
dplyr::data_frame:
r_data_frame(
n = 500,
id,
Scoring = rnorm,
Smoker = valid,
`Reading(mins)` = rpois(lambda=20),
race,
age(x = 8:14),
sex,
hour,
iq,
height(mean=50, sd = 10),
died
)
## # A tibble: 500 x 11
## ID Scoring Smoker `Reading(mins)` Race Age Sex Hour IQ
## <chr> <dbl> <lgl> <int> <fct> <int> <fct> <S3: tim> <dbl>
## 1 001 0.900 TRUE 16 White 10 Female 00:00:00 103
## 2 002 -0.111 FALSE 19 White 9 Male 00:00:00 99
## 3 003 0.531 FALSE 16 White 13 Male 00:00:00 94
## 4 004 -1.87 FALSE 26 White 8 Female 00:00:00 83
## 5 005 -0.539 FALSE 18 White 14 Male 00:00:00 104
## 6 006 -0.646 FALSE 19 White 10 Female 00:00:00 97
## 7 007 0.326 TRUE 21 White 9 Male 00:00:00 106
## 8 008 2.34 FALSE 20 White 9 Female 00:00:00 99
## 9 009 -0.156 TRUE 27 White 8 Male 00:30:00 104
## 10 010 0.763 TRUE 17 Black 13 Female 00:30:00 108
## # ... with 490 more rows, and 2 more variables: Height <dbl>, Died <lgl>
Random Missing Observations
Often data contains missing values. wakefield allows the user to add
a proportion of missing values per column/vector via the r_na (random
NA). This works nicely within a dplyr/magrittr %>% then
pipeline:
r_data_frame(
n = 30,
id,
race,
age,
sex,
hour,
iq,
height,
died,
Scoring = rnorm,
Smoker = valid
) %>%
r_na(prob=.4)
## # A tibble: 30 x 10
## ID Race Age Sex Hour IQ Height Died Scoring Smoker
## <chr> <fct> <int> <fct> <S3: tim> <dbl> <dbl> <lgl> <dbl> <lgl>
## 1 01 White 81 Male <NA> NA NA NA 0.862 TRUE
## 2 02 White 86 <NA> 00:30:00 109 73 TRUE -0.274 FALSE
## 3 03 Hispanic NA Male 03:30:00 111 65 FALSE NA TRUE
## 4 04 White 48 <NA> 03:30:00 NA 72 NA 0.692 TRUE
## 5 05 White 78 <NA> 03:30:00 NA NA TRUE NA FALSE
## 6 06 White 80 Male 05:00:00 NA 66 NA -0.494 TRUE
## 7 07 <NA> NA Male <NA> NA 67 NA 0.861 FALSE
## 8 08 <NA> NA Male <NA> 88 70 TRUE -1.76 TRUE
## 9 09 <NA> NA Female 07:30:00 82 NA FALSE NA NA
## 10 10 <NA> 59 <NA> 10:00:00 90 63 TRUE 1.30 TRUE
## # ... with 20 more rows
Repeated Measures & Time Series
The r_series function allows the user to pass a single wakefield
function and dictate how many columns (j) to produce.
set.seed(10)
r_series(likert, j = 3, n=10)
## # A tibble: 10 x 3
## Likert_1 Likert_2 Likert_3
## * <ord> <ord> <ord>
## 1 Neutral Disagree Strongly Disagree
## 2 Agree Neutral Disagree
## 3 Neutral Strongly Agree Disagree
## 4 Disagree Neutral Agree
## 5 Strongly Agree Agree Neutral
## 6 Agree Neutral Disagree
## 7 Agree Strongly Agree Strongly Disagree
## 8 Agree Agree Agree
## 9 Disagree Agree Disagree
## 10 Neutral Strongly Disagree Agree
Often the user wants a numeric score for Likert type columns and similar
variables. For series with multiple factors the as_integer converts
all columns to integer values. Additionally, we may want to specify
column name prefixes. This can be accomplished via the variable
function's name argument. Both of these features are demonstrated
here.
set.seed(10)
as_integer(r_series(likert, j = 5, n=10, name = "Item"))
## # A tibble: 10 x 5
## Item_1 Item_2 Item_3 Item_4 Item_5
## * <int> <int> <int> <int> <int>
## 1 3 2 1 3 4
## 2 4 3 2 5 4
## 3 3 5 2 5 5
## 4 2 3 4 1 2
## 5 5 4 3 3 4
## 6 4 3 2 2 5
## 7 4 5 1 1 5
## 8 4 4 4 1 3
## 9 2 4 2 2 5
## 10 3 1 4 3 1
r_series can be used within a r_data_frame as well.
set.seed(10)
r_data_frame(n=100,
id,
age,
sex,
r_series(likert, 3, name = "Question")
)
## # A tibble: 100 x 6
## ID Age Sex Question_1 Question_2 Question_3
## * <chr> <int> <fct> <ord> <ord> <ord>
## 1 001 54 Male Agree Agree Strongly Disagr~
## 2 002 40 Male Neutral Strongly Agree Disagree
## 3 003 48 Male Disagree Neutral Disagree
## 4 004 67 Male Strongly Disagree Neutral Disagree
## 5 005 24 Female Strongly Agree Strongly Disagree Strongly Disagr~
## 6 006 34 Female Disagree Disagree Agree
## 7 007 37 Female Disagree Strongly Agree Strongly Disagr~
## 8 008 37 Male Strongly Disagree Agree Agree
## 9 009 62 Female Agree Strongly Agree Strongly Agree
## 10 010 48 Male Strongly Disagree Strongly Disagree Agree
## # ... with 90 more rows
set.seed(10)
r_data_frame(n=100,
id,
age,
sex,
r_series(likert, 5, name = "Item", integer = TRUE)
)
## # A tibble: 100 x 8
## ID Age Sex Item_1 Item_2 Item_3 Item_4 Item_5
## * <chr> <int> <fct> <int> <int> <int> <int> <int>
## 1 001 54 Male 4 4 1 1 1
## 2 002 40 Male 3 5 2 1 2
## 3 003 48 Male 2 3 2 1 2
## 4 004 67 Male 1 3 2 4 3
## 5 005 24 Female 5 1 1 5 4
## 6 006 34 Female 2 2 4 3 4
## 7 007 37 Female 2 5 1 5 2
## 8 008 37 Male 1 4 4 5 5
## 9 009 62 Female 4 5 5 4 3
## 10 010 48 Male 1 1 4 1 2
## # ... with 90 more rows
Related Series
The user can also create related series via the relate argument in
r_series. It allows the user to specify the relationship between
columns. relate may be a named list of or a short hand string of the
form of "fM_sd" where:
-
fis one of (+, -, *, /) -
Mis a mean value -
sdis a standard deviation of the mean value
For example you may use relate = "*4_1". If relate = NULL no
relationship is generated between columns. I will use the short hand
string form here.
Some Examples With Variation
r_series(grade, j = 5, n = 100, relate = "+1_6")
## # A tibble: 100 x 5
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5
## * <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 84.5 92.5 91.6 87.4 76.7
## 2 93.1 85 81.8 87.8 91.3
## 3 81.6 67.5 52.6 48.8 56.8
## 4 92.5 89.3 95.3 102. 94.5
## 5 96.6 95.9 98.7 116. 115.
## 6 89.7 88.1 88.8 89. 86.4
## 7 92.8 91.7 98.3 98.7 102.
## 8 92.1 92.9 92.6 85.5 93.1
## 9 90.6 96.9 104. 108. 106.
## 10 96 94.8 84.3 91.1 107.
## # ... with 90 more rows
r_series(age, 5, 100, relate = "+5_0")
## # A tibble: 100 x 5
## Age_1 Age_2 Age_3 Age_4 Age_5
## * <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 37 42 47 52 57
## 2 37 42 47 52 57
## 3 51 56 61 66 71
## 4 29 34 39 44 49
## 5 72 77 82 87 92
## 6 50 55 60 65 70
## 7 23 28 33 38 43
## 8 59 64 69 74 79
## 9 85 90 95 100 105
## 10 77 82 87 92 97
## # ... with 90 more rows
r_series(likert, 5, 100, name ="Item", relate = "-.5_.1")
## # A tibble: 100 x 5
## Item_1 Item_2 Item_3 Item_4 Item_5
## * <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 1 0 -1 -1
## 2 3 2 1 1 0
## 3 1 1 1 0 0
## 4 4 3 3 2 1
## 5 2 1 1 0 0
## 6 2 1 1 1 0
## 7 1 0 0 -1 -2
## 8 2 2 1 1 0
## 9 2 2 1 0 0
## 10 3 3 3 3 3
## # ... with 90 more rows
r_series(grade, j = 5, n = 100, relate = "*1.05_.1")
## # A tibble: 100 x 5
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5
## * <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 85.7 94.3 113. 113. 113.
## 2 86.4 77.8 77.8 85.5 85.5
## 3 90.6 99.7 89.7 98.7 109.
## 4 89.1 89.1 89.1 71.3 71.3
## 5 87 95.7 115. 103. 114.
## 6 93.9 103. 124. 136. 136.
## 7 80.1 72.1 64.9 84.3 84.3
## 8 91.7 110. 132. 132. 145.
## 9 87.4 96.1 96.1 106. 116.
## 10 92.9 92.9 83.6 92.0 101.
## # ... with 90 more rows
Adjust Correlations
Use the sd command to adjust correlations.
round(cor(r_series(grade, 8, 10, relate = "+1_2")), 2)
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5 Grade_6 Grade_7 Grade_8
## Grade_1 1.00 0.85 0.64 0.39 0.28 0.25 0.28 0.15
## Grade_2 0.85 1.00 0.86 0.68 0.61 0.56 0.56 0.47
## Grade_3 0.64 0.86 1.00 0.77 0.70 0.80 0.86 0.78
## Grade_4 0.39 0.68 0.77 1.00 0.94 0.80 0.65 0.74
## Grade_5 0.28 0.61 0.70 0.94 1.00 0.85 0.69 0.73
## Grade_6 0.25 0.56 0.80 0.80 0.85 1.00 0.92 0.89
## Grade_7 0.28 0.56 0.86 0.65 0.69 0.92 1.00 0.91
## Grade_8 0.15 0.47 0.78 0.74 0.73 0.89 0.91 1.00
round(cor(r_series(grade, 8, 10, relate = "+1_0")), 2)
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5 Grade_6 Grade_7 Grade_8
## Grade_1 1 1 1 1 1 1 1 1
## Grade_2 1 1 1 1 1 1 1 1
## Grade_3 1 1 1 1 1 1 1 1
## Grade_4 1 1 1 1 1 1 1 1
## Grade_5 1 1 1 1 1 1 1 1
## Grade_6 1 1 1 1 1 1 1 1
## Grade_7 1 1 1 1 1 1 1 1
## Grade_8 1 1 1 1 1 1 1 1
round(cor(r_series(grade, 8, 10, relate = "+1_20")), 2)
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5 Grade_6 Grade_7 Grade_8
## Grade_1 1.00 0.26 0.27 0.40 0.21 -0.21 -0.36 -0.41
## Grade_2 0.26 1.00 0.77 0.60 0.64 0.50 0.53 0.46
## Grade_3 0.27 0.77 1.00 0.78 0.76 0.66 0.62 0.66
## Grade_4 0.40 0.60 0.78 1.00 0.95 0.76 0.59 0.55
## Grade_5 0.21 0.64 0.76 0.95 1.00 0.82 0.65 0.61
## Grade_6 -0.21 0.50 0.66 0.76 0.82 1.00 0.90 0.82
## Grade_7 -0.36 0.53 0.62 0.59 0.65 0.90 1.00 0.94
## Grade_8 -0.41 0.46 0.66 0.55 0.61 0.82 0.94 1.00
round(cor(r_series(grade, 8, 10, relate = "+15_20")), 2)
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5 Grade_6 Grade_7 Grade_8
## Grade_1 1.00 -0.10 -0.50 -0.39 -0.25 -0.52 -0.26 -0.31
## Grade_2 -0.10 1.00 0.74 0.50 0.13 0.03 0.36 0.46
## Grade_3 -0.50 0.74 1.00 0.81 0.48 0.41 0.71 0.78
## Grade_4 -0.39 0.50 0.81 1.00 0.75 0.66 0.58 0.75
## Grade_5 -0.25 0.13 0.48 0.75 1.00 0.91 0.70 0.74
## Grade_6 -0.52 0.03 0.41 0.66 0.91 1.00 0.58 0.57
## Grade_7 -0.26 0.36 0.71 0.58 0.70 0.58 1.00 0.78
## Grade_8 -0.31 0.46 0.78 0.75 0.74 0.57 0.78 1.00
Visualize the Relationship
dat <- r_data_frame(12,
name,
r_series(grade, 100, relate = "+1_6")
)
dat %>%
gather(Time, Grade, -c(Name)) %>%
mutate(Time = as.numeric(gsub("\\D", "", Time))) %>%
ggplot(aes(x = Time, y = Grade, color = Name, group = Name)) +
geom_line(size=.8) +
theme_bw()
Expanded Dummy Coding
The user may wish to expand a factor into j dummy coded columns. The
r_dummy function expands a factor into j columns and works similar
to the r_series function. The user may wish to use the original factor
name as the prefix to the j columns. Setting prefix = TRUE within
r_dummy accomplishes this.
set.seed(10)
r_data_frame(n=100,
id,
age,
r_dummy(sex, prefix = TRUE),
r_dummy(political)
)
## # A tibble: 100 x 9
## ID Age Sex_Male Sex_Female Democrat Republican Constitution
## * <chr> <int> <int> <int> <int> <int> <int>
## 1 001 54 1 0 1 0 0
## 2 002 40 1 0 1 0 0
## 3 003 48 1 0 0 1 0
## 4 004 67 1 0 0 1 0
## 5 005 24 0 1 1 0 0
## 6 006 34 0 1 0 1 0
## 7 007 37 0 1 0 1 0
## 8 008 37 1 0 0 0 0
## 9 009 62 0 1 1 0 0
## 10 010 48 1 0 0 1 0
## # ... with 90 more rows, and 2 more variables: Libertarian <int>,
## # Green <int>
Visualizing Column Types
It is helpful to see the column types and NAs as a visualization. The
table_heat (also the plot method assigned to tbl_df as well) can
provide visual glimpse of data types and missing cells.
set.seed(10)
r_data_frame(n=100,
id,
dob,
animal,
grade, grade,
death,
dummy,
grade_letter,
gender,
paragraph,
sentence
) %>%
r_na() %>%
plot(palette = "Set1")
