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added readme
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@trinker
trinker committed Apr 26, 2015
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2 changes: 1 addition & 1 deletion README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -240,7 +240,7 @@ r_data_frame(n=100,

## Visualizing Column Types

It is helpful to see the column types and `nA`s as a visualization. The `table_heat` (the `plot` method assigned to `tbl_df` as well).
It is helpful to see the column types and `NA`s as a visualization. The `table_heat` (the `plot` method assigned to `tbl_df` as well).

```{r}
set.seed(10)
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124 changes: 62 additions & 62 deletions README.md
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Expand Up @@ -46,8 +46,8 @@ race(n=10)
```

```
## [1] White Hispanic White White White White White
## [8] Hispanic Black White
## [1] Hispanic White White Asian White White Asian
## [8] White White White
## Levels: White Hispanic Black Asian Bi-Racial Native Other Hawaiian
```

Expand Down Expand Up @@ -85,12 +85,12 @@ r_data_frame(
## 2 White
## 3 White
## 4 White
## 5 Black
## 6 White
## 5 White
## 6 Hispanic
## 7 White
## 8 White
## 8 Hispanic
## 9 White
## 10 Black
## 10 White
## .. ...
```

Expand All @@ -115,24 +115,24 @@ r_data_frame(
## Source: local data frame [500 x 8]
##
## ID Race Age Sex Hour IQ Height Died
## 1 001 White 27 Female 00:00:00 111 73 FALSE
## 2 002 Black 33 Female 00:00:00 106 62 TRUE
## 3 003 White 22 Male 00:00:00 113 68 TRUE
## 4 004 Black 34 Male 00:00:00 89 73 FALSE
## 5 005 White 32 Female 00:00:00 86 71 FALSE
## 6 006 White 34 Female 00:00:00 94 74 FALSE
## 7 007 White 21 Female 00:00:00 114 66 FALSE
## 8 008 Hispanic 20 Female 00:00:00 103 66 TRUE
## 9 009 White 23 Female 00:00:00 106 76 FALSE
## 10 010 White 34 Male 00:30:00 117 69 FALSE
## 1 001 White 30 Male 00:00:00 98 65 TRUE
## 2 002 White 20 Male 00:00:00 95 68 FALSE
## 3 003 Hispanic 26 Male 00:00:00 75 71 TRUE
## 4 004 White 26 Female 00:00:00 99 76 TRUE
## 5 005 White 26 Male 00:00:00 111 74 TRUE
## 6 006 White 30 Male 00:00:00 97 72 TRUE
## 7 007 White 26 Female 00:00:00 118 66 FALSE
## 8 008 White 24 Female 00:00:00 96 76 TRUE
## 9 009 White 28 Male 00:00:00 90 64 FALSE
## 10 010 Hispanic 23 Female 00:00:00 96 64 TRUE
## .. ... ... ... ... ... ... ... ...
```


There are a plethora of **wakefield** based variable functions to chose from, spanning **R**'s various data types.

<!-- html table generated in R 3.2.0 by xtable 1.7-4 package -->
<!-- Sun Apr 26 16:57:51 2015 -->
<!-- Sun Apr 26 16:59:36 2015 -->
<table >
<tr> <td> age </td> <td> education </td> <td> likert </td> <td> sat </td> </tr>
<tr> <td> animal </td> <td> employment </td> <td> likert_5 </td> <td> sentence </td> </tr>
Expand Down Expand Up @@ -175,18 +175,18 @@ r_data_frame(
```
## Source: local data frame [500 x 10]
##
## ID Scoring Smoker Race Age Sex Hour IQ Height Died
## 1 001 0.51444754 TRUE White 25 Male 00:00:00 90 69 TRUE
## 2 002 0.04740834 TRUE Hispanic 25 Male 00:00:00 90 65 FALSE
## 3 003 0.05344613 FALSE White 24 Female 00:00:00 92 69 TRUE
## 4 004 0.19811951 TRUE Black 24 Male 00:00:00 113 67 TRUE
## 5 005 -1.82541628 TRUE White 33 Female 00:00:00 85 64 TRUE
## 6 006 -1.65618331 FALSE White 33 Male 00:00:00 89 71 FALSE
## 7 007 0.58282608 TRUE White 23 Female 00:00:00 116 63 TRUE
## 8 008 0.15763932 FALSE Black 34 Female 00:00:00 102 69 FALSE
## 9 009 1.47957323 TRUE Hispanic 30 Male 00:00:00 84 72 TRUE
## 10 010 -0.14410453 FALSE White 27 Male 00:00:00 93 66 TRUE
## .. ... ... ... ... ... ... ... ... ... ...
## ID Scoring Smoker Race Age Sex Hour IQ Height Died
## 1 001 0.1891545 FALSE Asian 31 Female 00:00:00 97 70 FALSE
## 2 002 0.3851468 FALSE Black 33 Male 00:00:00 80 72 TRUE
## 3 003 -0.6534012 FALSE White 25 Male 00:00:00 99 72 TRUE
## 4 004 -0.4856798 FALSE White 27 Male 00:00:00 102 67 FALSE
## 5 005 -1.6418871 TRUE White 23 Female 00:00:00 91 72 TRUE
## 6 006 0.9612601 FALSE White 25 Male 00:00:00 91 66 FALSE
## 7 007 0.6603719 FALSE White 22 Male 00:00:00 102 67 TRUE
## 8 008 -0.6095321 FALSE Black 25 Male 00:00:00 101 63 TRUE
## 9 009 0.7733652 FALSE White 31 Female 00:00:00 92 72 FALSE
## 10 010 -1.2910246 TRUE White 30 Male 00:00:00 101 69 TRUE
## .. ... ... ... ... ... ... ... ... ... ...
```


Expand All @@ -204,16 +204,16 @@ r_data_frame(
## Source: local data frame [500 x 7]
##
## ID Age_1 Age_2 Age_3 Grade_1 Grade_2 Grade_3
## 1 001 26 25 30 91.4 90.6 86.3
## 2 002 33 31 25 93.4 90.0 92.3
## 3 003 23 24 29 95.2 81.5 88.4
## 4 004 31 27 20 83.0 86.9 87.9
## 5 005 20 31 27 94.4 86.5 89.0
## 6 006 32 26 24 86.9 83.6 89.0
## 7 007 35 24 24 88.5 90.1 87.7
## 8 008 28 21 25 91.7 93.0 87.0
## 9 009 34 25 23 78.5 86.6 88.6
## 10 010 29 34 32 90.8 90.9 93.5
## 1 001 23 22 28 82.7 81.8 88.0
## 2 002 33 35 22 93.9 80.9 92.7
## 3 003 30 35 31 90.0 88.1 83.2
## 4 004 24 28 35 87.5 85.4 87.7
## 5 005 25 21 28 91.5 89.5 86.7
## 6 006 33 21 32 84.5 87.6 87.5
## 7 007 27 26 22 89.8 90.9 91.2
## 8 008 31 25 23 89.2 83.5 89.5
## 9 009 27 26 20 87.9 82.9 88.3
## 10 010 34 30 26 97.2 82.6 89.9
## .. ... ... ... ... ... ... ...
```

Expand Down Expand Up @@ -241,18 +241,18 @@ r_data_frame(
```
## Source: local data frame [500 x 11]
##
## ID Scoring Smoker Reading(mins) Race Age Sex Hour IQ
## 1 001 -0.48032839 TRUE 29 White 9 Female 00:00:00 101
## 2 002 -0.96918812 TRUE 20 White 8 Male 00:00:00 112
## 3 003 0.48441654 FALSE 11 White 11 Male 00:00:00 98
## 4 004 0.09563767 TRUE 15 White 11 Female 00:00:00 97
## 5 005 0.49872628 FALSE 17 Hispanic 8 Female 00:00:00 97
## 6 006 0.96743253 TRUE 18 Asian 11 Male 00:00:00 92
## 7 007 0.86340588 FALSE 19 White 14 Male 00:00:00 90
## 8 008 -0.35117793 TRUE 12 White 12 Female 00:00:00 78
## 9 009 -1.15607703 FALSE 19 White 13 Male 00:30:00 78
## 10 010 0.74221850 FALSE 15 Hispanic 14 Female 00:30:00 106
## .. ... ... ... ... ... ... ... ... ...
## ID Scoring Smoker Reading(mins) Race Age Sex Hour IQ
## 1 001 0.5685846 FALSE 21 Black 10 Male 00:00:00 76
## 2 002 0.3586643 FALSE 12 White 14 Male 00:00:00 96
## 3 003 -0.5459710 TRUE 19 Black 10 Female 00:00:00 108
## 4 004 1.1077876 FALSE 17 White 12 Male 00:00:00 103
## 5 005 0.1276507 FALSE 16 Hispanic 11 Male 00:00:00 91
## 6 006 -0.8870031 TRUE 20 White 11 Female 00:00:00 95
## 7 007 1.5667903 FALSE 12 Hispanic 8 Female 00:30:00 98
## 8 008 -0.3795139 FALSE 17 White 14 Male 00:30:00 99
## 9 009 -0.6366337 FALSE 23 Hispanic 13 Female 00:30:00 108
## 10 010 0.3814091 FALSE 19 White 10 Male 00:30:00 90
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: Height (dbl), Died (lgl)
```

Expand Down Expand Up @@ -282,16 +282,16 @@ r_data_frame(
## Source: local data frame [30 x 10]
##
## ID Race Age Sex Hour IQ Height Died Scoring Smoker
## 1 01 NA NA NA 00:00:00 113 66 FALSE NA TRUE
## 2 02 NA 23 Male 01:00:00 89 71 TRUE NA NA
## 3 03 NA 21 NA 02:30:00 106 NA NA -0.1354292 FALSE
## 4 04 NA 24 NA <NA> NA 74 NA 0.9137696 FALSE
## 5 05 White NA Male 05:00:00 101 64 TRUE 0.9870374 NA
## 6 06 NA 22 Male 05:00:00 NA NA NA 0.2531825 TRUE
## 7 07 Black NA Male 06:30:00 104 63 TRUE NA FALSE
## 8 08 White 35 Female <NA> 103 60 TRUE -1.1764235 FALSE
## 9 09 White NA NA 07:00:00 102 NA NA NA FALSE
## 10 10 Hispanic 34 Male 07:00:00 NA 66 NA NA FALSE
## 1 01 White 23 Female 00:00:00 NA 72 FALSE 0.2718548 FALSE
## 2 02 Black 23 Female 00:30:00 116 66 FALSE 1.5218870 NA
## 3 03 White NA NA <NA> NA NA FALSE -0.3200526 NA
## 4 04 Hispanic NA Female 02:30:00 105 68 NA -1.0484394 NA
## 5 05 NA 32 Male <NA> 108 69 FALSE 0.3112662 TRUE
## 6 06 White 35 Male 04:00:00 99 75 NA 0.2034210 NA
## 7 07 Black NA NA <NA> 99 69 FALSE NA TRUE
## 8 08 White NA NA <NA> 111 67 TRUE NA NA
## 9 09 NA 34 Male <NA> 89 NA FALSE NA FALSE
## 10 10 White 23 Male 09:00:00 98 NA FALSE 0.3988629 TRUE
## .. .. ... ... ... ... ... ... ... ... ...
```

Expand Down Expand Up @@ -445,7 +445,7 @@ r_data_frame(n=100,

## Visualizing Column Types

It is helpful to see the column types and `nA`s as a visualization. The `table_heat` (the `plot` method assigned to `tbl_df` as well).
It is helpful to see the column types and `NA`s as a visualization. The `table_heat` (the `plot` method assigned to `tbl_df` as well).


```r
Expand Down

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