<img src="inst/wakefield_logo/r_wakefield.png" width="60%", alt="">
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)
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
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 Hispanic Hispanic Hispanic
## [8] White Hispanic Hispanic
## 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
)
## Source: local data frame [500 x 1]
##
## Race
## 1 Asian
## 2 White
## 3 White
## 4 White
## 5 White
## 6 White
## 7 White
## 8 White
## 9 White
## 10 Black
## .. ...
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
)
## Source: local data frame [500 x 8]
##
## ID Race Age Sex Hour IQ Height Died
## 1 001 White 31 Male 00:00:00 96 69 TRUE
## 2 002 White 30 Female 00:00:00 106 63 FALSE
## 3 003 White 25 Female 00:00:00 101 73 FALSE
## 4 004 Asian 28 Male 00:00:00 115 71 TRUE
## 5 005 White 23 Female 00:00:00 116 64 TRUE
## 6 006 Black 21 Female 00:00:00 104 67 FALSE
## 7 007 White 31 Male 00:00:00 88 67 FALSE
## 8 008 White 20 Female 00:00:00 95 64 TRUE
## 9 009 White 30 Female 00:00:00 99 69 FALSE
## 10 010 White 32 Female 00:30:00 101 71 TRUE
## .. ... ... ... ... ... ... ... ...
There are 70 wakefield based variable functions to chose from,
spanning R's various data types (see ?variables for details).
| age | dob | height | marital | sentence |
| animal | dummy | height_cm | math | sex |
| answer | education | height_in | military | sex_inclusive |
| area | ela | income | month | smokes |
| birth | employment | internet_browser | name | speed |
| car | eye | iq | normal | speed_kph |
| children | gender | language | normal_round | speed_mph |
| coin | gender_inclusive | level | paragraph | state |
| color | gpa | likert | pet | string |
| date_stamp | grade | likert_5 | political | upper |
| death | grade_letter | likert_7 | primary | upper_factor |
| dice | grade_level | lorem_ipsum | race | valid |
| died | group | lower | religion | year |
| dna | hair | lower_factor | sat | zip_code |
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
)
## Source: local data frame [500 x 10]
##
## ID Scoring Smoker Race Age Sex Hour IQ Height Died
## 1 001 -0.03018767 FALSE White 27 Male 00:00:00 97 74 TRUE
## 2 002 0.03050577 TRUE Hispanic 27 Male 00:00:00 93 65 TRUE
## 3 003 0.60863520 TRUE Black 24 Male 00:00:00 99 68 FALSE
## 4 004 -0.97741261 TRUE Hispanic 23 Female 00:00:00 97 70 TRUE
## 5 005 1.15887015 TRUE White 34 Male 00:00:00 112 69 TRUE
## 6 006 0.57096513 TRUE White 21 Female 00:00:00 97 67 TRUE
## 7 007 0.45796772 FALSE White 20 Male 00:00:00 105 75 TRUE
## 8 008 0.59157830 FALSE White 29 Female 00:00:00 110 69 FALSE
## 9 009 0.23460367 TRUE White 30 Female 00:00:00 106 64 TRUE
## 10 010 -1.57987123 FALSE Hispanic 20 Male 00:00:00 92 73 FALSE
## .. ... ... ... ... ... ... ... ... ... ...
r_data_frame(
n = 500,
id,
age, age, age,
grade, grade, grade
)
## Source: local data frame [500 x 7]
##
## ID Age_1 Age_2 Age_3 Grade_1 Grade_2 Grade_3
## 1 001 28 24 31 88.9 90.3 84.8
## 2 002 29 33 32 97.9 85.1 94.2
## 3 003 27 28 29 89.8 88.4 92.5
## 4 004 25 24 27 89.7 87.4 88.2
## 5 005 23 22 35 94.5 88.4 86.0
## 6 006 21 30 31 94.3 87.8 87.2
## 7 007 28 35 22 92.0 83.2 93.7
## 8 008 34 26 35 85.3 79.0 86.5
## 9 009 24 27 30 88.6 87.3 87.6
## 10 010 26 31 29 84.0 84.0 95.4
## .. ... ... ... ... ... ... ...
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
)
## Source: local data frame [500 x 11]
##
## ID Scoring Smoker Reading(mins) Race Age Sex Hour IQ
## 1 001 0.4169310 TRUE 28 White 12 Male 00:00:00 102
## 2 002 -0.8618017 TRUE 22 White 9 Female 00:00:00 115
## 3 003 1.3912870 TRUE 25 White 13 Male 00:00:00 111
## 4 004 0.8545399 FALSE 15 White 12 Male 00:00:00 90
## 5 005 0.4676475 FALSE 16 Black 10 Male 00:00:00 98
## 6 006 -0.2059796 TRUE 20 Asian 8 Male 00:00:00 105
## 7 007 -1.0069360 TRUE 16 White 10 Male 00:00:00 104
## 8 008 -0.4932512 FALSE 24 White 9 Male 00:00:00 91
## 9 009 0.1333219 TRUE 23 Black 8 Male 00:00:00 81
## 10 010 0.3700422 TRUE 28 Hispanic 11 Male 00:00:00 97
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: Height (dbl), Died (lgl)
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)
## Source: local data frame [30 x 10]
##
## ID Race Age Sex Hour IQ Height Died Scoring Smoker
## 1 01 NA NA Female 00:30:00 101 69 FALSE NA FALSE
## 2 02 NA 23 Female <NA> NA 67 TRUE 1.1572230 NA
## 3 03 NA NA NA 01:30:00 112 77 FALSE NA NA
## 4 04 White NA Female <NA> NA 63 NA NA NA
## 5 05 Black NA Female 04:30:00 NA NA TRUE NA FALSE
## 6 06 White 35 NA 05:00:00 112 NA FALSE 0.2570224 NA
## 7 07 White 26 Male 07:00:00 99 70 NA -0.0395981 NA
## 8 08 White 23 NA 07:00:00 117 NA FALSE NA NA
## 9 09 White NA NA 08:00:00 NA NA NA -0.7170792 TRUE
## 10 10 NA 35 Male 08:00:00 NA NA NA NA TRUE
## .. .. ... ... ... ... ... ... ... ... ...
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)
## Source: local data frame [10 x 3]
##
## Likert_1 Likert_2 Likert_3
## 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"))
## Source: local data frame [10 x 5]
##
## Item_1 Item_2 Item_3 Item_4 Item_5
## 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")
)
## Source: local data frame [100 x 6]
##
## ID Age Sex Question_1 Question_2 Question_3
## 1 001 28 Male Agree Agree Strongly Disagree
## 2 002 24 Male Neutral Strongly Agree Disagree
## 3 003 26 Male Disagree Neutral Disagree
## 4 004 31 Male Strongly Disagree Neutral Disagree
## 5 005 21 Female Strongly Agree Strongly Disagree Strongly Disagree
## 6 006 23 Female Disagree Disagree Agree
## 7 007 24 Female Disagree Strongly Agree Strongly Disagree
## 8 008 24 Male Strongly Disagree Agree Agree
## 9 009 29 Female Agree Strongly Agree Strongly Agree
## 10 010 26 Male Strongly Disagree Strongly Disagree Agree
## .. ... ... ... ... ... ...
set.seed(10)
r_data_frame(n=100,
id,
age,
sex,
r_series(likert, 5, name = "Item", integer = TRUE)
)
## Source: local data frame [100 x 8]
##
## ID Age Sex Item_1 Item_2 Item_3 Item_4 Item_5
## 1 001 28 Male 4 4 1 1 1
## 2 002 24 Male 3 5 2 1 2
## 3 003 26 Male 2 3 2 1 2
## 4 004 31 Male 1 3 2 4 3
## 5 005 21 Female 5 1 1 5 4
## 6 006 23 Female 2 2 4 3 4
## 7 007 24 Female 2 5 1 5 2
## 8 008 24 Male 1 4 4 5 5
## 9 009 29 Female 4 5 5 4 3
## 10 010 26 Male 1 1 4 1 2
## .. ... ... ... ... ... ... ... ...
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 valuesdis 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.
r_series(grade, j = 5, n = 100, relate = "+1_6")
## Source: local data frame [100 x 5]
##
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5
## 1 84.5 92.5 91.6 87.4 76.7
## 2 93.1 85.0 81.8 87.8 91.3
## 3 81.6 67.5 52.6 48.8 56.8
## 4 92.5 89.3 95.3 102.2 94.5
## 5 96.6 95.9 98.7 115.9 114.7
## 6 89.7 88.1 88.8 89.0 86.4
## 7 92.8 91.7 98.3 98.7 101.6
## 8 92.1 92.9 92.6 85.5 93.1
## 9 90.6 96.9 103.9 107.6 106.2
## 10 96.0 94.8 84.3 91.1 106.6
## .. ... ... ... ... ...
r_series(age, 5, 100, relate = "+5_0")
## Source: local data frame [100 x 5]
##
## Age_1 Age_2 Age_3 Age_4 Age_5
## 1 24 29 34 39 44
## 2 24 29 34 39 44
## 3 27 32 37 42 47
## 4 22 27 32 37 42
## 5 32 37 42 47 52
## 6 27 32 37 42 47
## 7 21 26 31 36 41
## 8 29 34 39 44 49
## 9 35 40 45 50 55
## 10 33 38 43 48 53
## .. ... ... ... ... ...
r_series(likert, 5, 100, name ="Item", relate = "-.5_.1")
## Source: local data frame [100 x 5]
##
## Item_1 Item_2 Item_3 Item_4 Item_5
## 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
## .. ... ... ... ... ...
r_series(grade, j = 5, n = 100, relate = "*1.05_.1")
## Source: local data frame [100 x 5]
##
## Grade_1 Grade_2 Grade_3 Grade_4 Grade_5
## 1 85.7 94.27 113.124 113.1240 113.1240
## 2 86.4 77.76 77.760 85.5360 85.5360
## 3 90.6 99.66 89.694 98.6634 108.5297
## 4 89.1 89.10 89.100 71.2800 71.2800
## 5 87.0 95.70 114.840 103.3560 113.6916
## 6 93.9 103.29 123.948 136.3428 136.3428
## 7 80.1 72.09 64.881 84.3453 84.3453
## 8 91.7 110.04 132.048 132.0480 145.2528
## 9 87.4 96.14 96.140 105.7540 116.3294
## 10 92.9 92.90 83.610 91.9710 101.1681
## .. ... ... ... ... ...
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
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()
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)
)
## Source: local data frame [100 x 9]
##
## ID Age Sex_Male Sex_Female Constitution Democrat Green Libertarian
## 1 001 28 1 0 1 0 0 0
## 2 002 24 1 0 1 0 0 0
## 3 003 26 1 0 0 1 0 0
## 4 004 31 1 0 0 1 0 0
## 5 005 21 0 1 1 0 0 0
## 6 006 23 0 1 0 1 0 0
## 7 007 24 0 1 0 1 0 0
## 8 008 24 1 0 0 0 0 0
## 9 009 29 0 1 1 0 0 0
## 10 010 26 1 0 0 1 0 0
## .. ... ... ... ... ... ... ... ...
## Variables not shown: Republican (int)
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")
