r_data_frame: Data Frame Production (From Variable Functions)
In wakefield: Generate Random Data Sets
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
Produce a tbl_df data frame that allows the user to
lazily pass unnamed wakefield variable functions (optionally, without
call parenthesis).
Usage
1
r_data_frame(n, ..., rep.sep = "_")
Arguments
n
The length to pass to the randomly generated vectors.
rep.sep
A separator to use for repeated variable names. For example
if the age is used three times
(r_data_frame(age, age, age)), the name "Age" will be assigned to all
three columns. The results in column names c("Age_1", "Age_2", "Age_3").
To turn of this behavior use rep.sep = NULL. This results in
c("Age", "Age.1", "Age.2") column names in the
data.frame.
...
A set of optionally named arguments. Using wakefield
variable functions require no name or call parenthesis.
Value
Returns a tbl_df.
Author(s)
Josh O'Brien and Tyler Rinker <tyler.rinker@gmail.com>.
References
https://stackoverflow.com/a/29617983/1000343
See Also
Examples
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r_data_frame(n = 30,
id,
race,
age,
sex,
hour,
iq,
height,
died,
Scoring = rnorm,
Smoker = valid
)
r_data_frame(n = 30,
id,
race,
age(x = 8:14),
Gender = sex,
Time = hour,
iq,
grade, grade, grade, #repeated measures
height(mean=50, sd = 10),
died,
Scoring = rnorm,
Smoker = valid
)
r_data_frame(n = 500,
id,
age, age, age,
grade, grade, grade
)
## Repeated Measures/Time Series
r_data_frame(n=100,
id,
age,
sex,
r_series(likert, 3),
r_series(likert, 4, name = "Item", integer = TRUE)
)
## Expanded Dummy Coded Variables
r_data_frame(n=100,
id,
age,
r_dummy(sex, prefix=TRUE),
r_dummy(political)
)
## `peek` to view al columns
## `plot` (`table_heat`) for a graphic representation
library(dplyr)
r_data_frame(n=100,
id,
dob,
animal,
grade, grade,
death,
dummy,
grade_letter,
gender,
paragraph,
sentence
) %>%
r_na() %>%
peek %>%
plot(palette = "Set1")
Example output
# A tibble: 30 x 10
ID Race Age Sex Hour IQ Height Died Scoring Smoker
<chr> <fct> <int> <fct> <times> <dbl> <dbl> <lgl> <dbl> <lgl>
1 01 Hispanic 24 Male 00:00:00 99 65 FALSE 1.43 TRUE
2 02 White 40 Male 00:00:00 82 67 TRUE -0.338 FALSE
3 03 White 44 Male 00:00:00 103 68 TRUE -0.630 FALSE
4 04 White 18 Female 01:00:00 96 69 TRUE 1.14 TRUE
5 05 White 49 Female 02:00:00 86 65 TRUE 1.26 TRUE
6 06 White 84 Female 02:30:00 85 68 FALSE 0.962 TRUE
7 07 White 52 Male 03:00:00 95 75 FALSE 0.324 FALSE
8 08 White 59 Female 04:30:00 79 69 TRUE 0.996 FALSE
9 09 White 25 Male 04:30:00 106 69 TRUE 1.32 FALSE
10 10 Native 45 Female 05:00:00 116 69 TRUE 1.32 TRUE
# … with 20 more rows
Warning message:
`tbl_df()` is deprecated as of dplyr 1.0.0.
Please use `tibble::as_tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
# A tibble: 30 x 13
ID Race Age Gender Time IQ Grade_1 Grade_2 Grade_3 Height Died
<chr> <fct> <int> <fct> <tim> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
1 01 White 13 Male 01:0… 91 85.6 91.8 86.1 79 TRUE
2 02 White 8 Female 03:3… 86 88 89.1 92.1 56 FALSE
3 03 White 8 Male 04:3… 92 84.6 74.3 88 39 TRUE
4 04 White 12 Male 05:0… 89 90.1 88.5 85.9 41 TRUE
5 05 White 14 Male 05:3… 105 97 90.2 87.7 49 TRUE
6 06 White 8 Male 06:0… 113 89.7 87.9 96 63 TRUE
7 07 White 14 Male 07:0… 109 89 85.4 83.8 57 TRUE
8 08 Hisp… 11 Male 08:0… 92 90.3 88.1 91.4 65 TRUE
9 09 White 14 Male 09:3… 110 80.8 89.5 91.6 54 FALSE
10 10 Hisp… 9 Male 10:3… 121 80.7 87.6 93.4 40 FALSE
# … with 20 more rows, and 2 more variables: Scoring <dbl>, Smoker <lgl>
# 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 41 82 25 94.1 87.4 85.5
2 002 69 85 49 86.7 84.2 97.9
3 003 18 63 39 86.4 90.6 84
4 004 53 47 49 84.4 87.4 91.8
5 005 70 84 82 90.1 82.6 93.7
6 006 39 43 45 83.5 85 85.2
7 007 60 32 45 92 91.4 88.9
8 008 49 19 81 92.7 91.6 79.1
9 009 82 74 89 87.6 95.5 82.8
10 010 23 47 43 90.1 88.2 86.2
# … with 490 more rows
# A tibble: 100 x 10
ID Age Sex Likert_1 Likert_2 Likert_3 Item_1 Item_2 Item_3 Item_4
<chr> <int> <fct> <ord> <ord> <ord> <int> <int> <int> <int>
1 001 36 Female Disagree Agree Strongly… 5 5 3 3
2 002 45 Female Disagree Agree Strongly… 3 1 4 5
3 003 66 Female Strongly … Strongly… Agree 3 1 5 4
4 004 66 Male Strongly … Strongly… Neutral 4 3 5 5
5 005 27 Female Agree Strongly… Agree 3 1 2 3
6 006 89 Male Strongly … Disagree Agree 5 1 3 3
7 007 28 Female Strongly … Agree Neutral 2 4 3 1
8 008 72 Male Strongly … Strongly… Strongly… 5 3 3 2
9 009 52 Female Strongly … Agree Disagree 3 4 1 4
10 010 20 Male Agree Neutral Strongly… 3 4 4 4
# … with 90 more rows
# A tibble: 100 x 6
ID Age Sex_Male Sex_Female Democrat Republican
<chr> <int> <int> <int> <int> <int>
1 001 41 0 1 1 0
2 002 86 0 1 1 0
3 003 25 1 0 1 0
4 004 21 1 0 1 0
5 005 58 1 0 1 0
6 006 87 1 0 0 1
7 007 36 1 0 1 0
8 008 34 0 1 1 0
9 009 52 0 1 1 0
10 010 62 0 1 0 1
# … with 90 more rows
Attaching package: ‘dplyr’
The following object is masked from ‘package:wakefield’:
id
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Source: local data frame [100 x 11]
ID DOB Animal Grade_1 Grade_2 Death Dummy Grade_Lett Gender Paragraph Sentence
1 001 2006-12-01 Roseate Sp 81.7 89 TRUE 0 B+ Female Lorem ipsu It makes a
2 002 2007-05-03 Ant 85.9 88.4 TRUE 1 B+ Female Varius odi But when w
3 003 2007-11-12 Aye Aye 83.2 90.1 TRUE 1 A- Male Ex a, sagi The price
4 004 2007-03-04 Ant 92.3 84.7 TRUE 1 C+ Female In ut. Mau The top fi
5 005 2006-08-23 Radiated T 82.8 86.9 TRUE 0 B+ Female Justo lobo You said t
6 006 2006-07-14 Dhole 89.9 78.7 TRUE 1 A- Female Leo himena Through ou
7 007 2006-10-11 Giant Afri 82.1 88.6 TRUE 0 B Female Vestibulum Candy, wha
8 008 2006-07-30 Roseate Sp 90.5 88.8 TRUE 1 A Female Sociis mae That's the
9 009 2006-11-22 Indian Sta 91.7 <NA> FALSE 0 <NA> Female Sed bibend Let me men
10 010 2006-08-26 Flying Squ 87.3 87.4 <NA> 1 A Female Nisi preti Well, of c
.. ... ... ... ... ... ... ... ... ... ... ...
wakefield documentation built on Sept. 14, 2020, 1:07 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) References See Also Examples
Description
Produce a tbl_df data frame that allows the user to
lazily pass unnamed wakefield variable functions (optionally, without
call parenthesis).
Usage
1 | r_data_frame(n, ..., rep.sep = "_")
|
Arguments
n |
The length to pass to the randomly generated vectors. |
rep.sep |
A separator to use for repeated variable names. For example
if the |
... |
A set of optionally named arguments. Using wakefield variable functions require no name or call parenthesis. |
Value
Returns a tbl_df.
Author(s)
Josh O'Brien and Tyler Rinker <tyler.rinker@gmail.com>.
References
https://stackoverflow.com/a/29617983/1000343
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | r_data_frame(n = 30,
id,
race,
age,
sex,
hour,
iq,
height,
died,
Scoring = rnorm,
Smoker = valid
)
r_data_frame(n = 30,
id,
race,
age(x = 8:14),
Gender = sex,
Time = hour,
iq,
grade, grade, grade, #repeated measures
height(mean=50, sd = 10),
died,
Scoring = rnorm,
Smoker = valid
)
r_data_frame(n = 500,
id,
age, age, age,
grade, grade, grade
)
## Repeated Measures/Time Series
r_data_frame(n=100,
id,
age,
sex,
r_series(likert, 3),
r_series(likert, 4, name = "Item", integer = TRUE)
)
## Expanded Dummy Coded Variables
r_data_frame(n=100,
id,
age,
r_dummy(sex, prefix=TRUE),
r_dummy(political)
)
## `peek` to view al columns
## `plot` (`table_heat`) for a graphic representation
library(dplyr)
r_data_frame(n=100,
id,
dob,
animal,
grade, grade,
death,
dummy,
grade_letter,
gender,
paragraph,
sentence
) %>%
r_na() %>%
peek %>%
plot(palette = "Set1")
|
Example output
# A tibble: 30 x 10
ID Race Age Sex Hour IQ Height Died Scoring Smoker
<chr> <fct> <int> <fct> <times> <dbl> <dbl> <lgl> <dbl> <lgl>
1 01 Hispanic 24 Male 00:00:00 99 65 FALSE 1.43 TRUE
2 02 White 40 Male 00:00:00 82 67 TRUE -0.338 FALSE
3 03 White 44 Male 00:00:00 103 68 TRUE -0.630 FALSE
4 04 White 18 Female 01:00:00 96 69 TRUE 1.14 TRUE
5 05 White 49 Female 02:00:00 86 65 TRUE 1.26 TRUE
6 06 White 84 Female 02:30:00 85 68 FALSE 0.962 TRUE
7 07 White 52 Male 03:00:00 95 75 FALSE 0.324 FALSE
8 08 White 59 Female 04:30:00 79 69 TRUE 0.996 FALSE
9 09 White 25 Male 04:30:00 106 69 TRUE 1.32 FALSE
10 10 Native 45 Female 05:00:00 116 69 TRUE 1.32 TRUE
# … with 20 more rows
Warning message:
`tbl_df()` is deprecated as of dplyr 1.0.0.
Please use `tibble::as_tibble()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
# A tibble: 30 x 13
ID Race Age Gender Time IQ Grade_1 Grade_2 Grade_3 Height Died
<chr> <fct> <int> <fct> <tim> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
1 01 White 13 Male 01:0… 91 85.6 91.8 86.1 79 TRUE
2 02 White 8 Female 03:3… 86 88 89.1 92.1 56 FALSE
3 03 White 8 Male 04:3… 92 84.6 74.3 88 39 TRUE
4 04 White 12 Male 05:0… 89 90.1 88.5 85.9 41 TRUE
5 05 White 14 Male 05:3… 105 97 90.2 87.7 49 TRUE
6 06 White 8 Male 06:0… 113 89.7 87.9 96 63 TRUE
7 07 White 14 Male 07:0… 109 89 85.4 83.8 57 TRUE
8 08 Hisp… 11 Male 08:0… 92 90.3 88.1 91.4 65 TRUE
9 09 White 14 Male 09:3… 110 80.8 89.5 91.6 54 FALSE
10 10 Hisp… 9 Male 10:3… 121 80.7 87.6 93.4 40 FALSE
# … with 20 more rows, and 2 more variables: Scoring <dbl>, Smoker <lgl>
# 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 41 82 25 94.1 87.4 85.5
2 002 69 85 49 86.7 84.2 97.9
3 003 18 63 39 86.4 90.6 84
4 004 53 47 49 84.4 87.4 91.8
5 005 70 84 82 90.1 82.6 93.7
6 006 39 43 45 83.5 85 85.2
7 007 60 32 45 92 91.4 88.9
8 008 49 19 81 92.7 91.6 79.1
9 009 82 74 89 87.6 95.5 82.8
10 010 23 47 43 90.1 88.2 86.2
# … with 490 more rows
# A tibble: 100 x 10
ID Age Sex Likert_1 Likert_2 Likert_3 Item_1 Item_2 Item_3 Item_4
<chr> <int> <fct> <ord> <ord> <ord> <int> <int> <int> <int>
1 001 36 Female Disagree Agree Strongly… 5 5 3 3
2 002 45 Female Disagree Agree Strongly… 3 1 4 5
3 003 66 Female Strongly … Strongly… Agree 3 1 5 4
4 004 66 Male Strongly … Strongly… Neutral 4 3 5 5
5 005 27 Female Agree Strongly… Agree 3 1 2 3
6 006 89 Male Strongly … Disagree Agree 5 1 3 3
7 007 28 Female Strongly … Agree Neutral 2 4 3 1
8 008 72 Male Strongly … Strongly… Strongly… 5 3 3 2
9 009 52 Female Strongly … Agree Disagree 3 4 1 4
10 010 20 Male Agree Neutral Strongly… 3 4 4 4
# … with 90 more rows
# A tibble: 100 x 6
ID Age Sex_Male Sex_Female Democrat Republican
<chr> <int> <int> <int> <int> <int>
1 001 41 0 1 1 0
2 002 86 0 1 1 0
3 003 25 1 0 1 0
4 004 21 1 0 1 0
5 005 58 1 0 1 0
6 006 87 1 0 0 1
7 007 36 1 0 1 0
8 008 34 0 1 1 0
9 009 52 0 1 1 0
10 010 62 0 1 0 1
# … with 90 more rows
Attaching package: ‘dplyr’
The following object is masked from ‘package:wakefield’:
id
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Source: local data frame [100 x 11]
ID DOB Animal Grade_1 Grade_2 Death Dummy Grade_Lett Gender Paragraph Sentence
1 001 2006-12-01 Roseate Sp 81.7 89 TRUE 0 B+ Female Lorem ipsu It makes a
2 002 2007-05-03 Ant 85.9 88.4 TRUE 1 B+ Female Varius odi But when w
3 003 2007-11-12 Aye Aye 83.2 90.1 TRUE 1 A- Male Ex a, sagi The price
4 004 2007-03-04 Ant 92.3 84.7 TRUE 1 C+ Female In ut. Mau The top fi
5 005 2006-08-23 Radiated T 82.8 86.9 TRUE 0 B+ Female Justo lobo You said t
6 006 2006-07-14 Dhole 89.9 78.7 TRUE 1 A- Female Leo himena Through ou
7 007 2006-10-11 Giant Afri 82.1 88.6 TRUE 0 B Female Vestibulum Candy, wha
8 008 2006-07-30 Roseate Sp 90.5 88.8 TRUE 1 A Female Sociis mae That's the
9 009 2006-11-22 Indian Sta 91.7 <NA> FALSE 0 <NA> Female Sed bibend Let me men
10 010 2006-08-26 Flying Squ 87.3 87.4 <NA> 1 A Female Nisi preti Well, of c
.. ... ... ... ... ... ... ... ... ... ... ...
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