README.md
In mikejacktzen/datzen:
The 'datzen' package for R
The datzen package provides three categories of commonly used miscellaneous functions to achieve Data Zen
٩(^ᴗ^)۶
- Themed Branding
- Special Computations
- Data In/Out
The goal of this package is to provide data analysts a few tools to rapidly prototype analysis scripts.
# devtools::install_github('mikejacktzen/datzen',force=TRUE)
library(datzen)
library(dplyr)
Themed Branding
I forgot about the weird base R name of that link function used in logistic regression.
Uhhh, it was what's his face! The one with the logis! Oh, you mean logit?
# ?logit
identical(logit(0.5),stats::qlogis(0.5))
#> [1] TRUE
xx=seq(from=0.01,to=0.99,by=0.01);txtplot::txtplot(x=xx,y=logit(xx))
#> +-+---------+----------+---------+---------+----------+-+
#> 4 + * +
#> | ** |
#> | ** |
#> 2 + ***** +
#> | ******** |
#> | ********** |
#> 0 + ********** +
#> | ******** |
#> -2 + ***** +
#> | ** |
#> | * |
#> -4 + * +
#> +-+---------+----------+---------+---------+----------+-+
#> 0 0.2 0.4 0.6 0.8 1
I forgot about the weird name of its inverse function too! Oh yeah... his ugly cousin, plogis. I know him as expit.
# ?expit
identical(expit(10),stats::plogis(10))
#> [1] TRUE
xxx=seq(from=-10,to=10,by=1);txtplot::txtplot(x=xxx,y=expit(xxx))
#> +-+------------+-----------+------------+-----------+--+
#> 1 + * * * * * * * * +
#> | * |
#> 0.8 + +
#> | * |
#> 0.6 + +
#> | |
#> | * |
#> 0.4 + +
#> | * |
#> 0.2 + +
#> | * |
#> 0 + * * * * * * * * +
#> +-+------------+-----------+------------+-----------+--+
#> -10 -5 0 5 10
Ever wanted a random string of garbage?
I'm too much of a homo-sapien, my phrases are never garbled enough (I blame my parents). I wish I could be more like a computer, or a homo-erectus.
dats_wat_she_said = 10
junk = garble(size_out=dats_wat_she_said)
junk
#> [1] "a%w!k13u4%"
Is your workspace environment too clutered?
It was like that when I got here.
jimmy = 'johns'
nuts = 'planters'
ls()
#> [1] "dats_wat_she_said" "jimmy" "junk"
#> [4] "nuts" "xx" "xxx"
I want to remove All But Deeze ...
rmabd('nuts')
#> kept: nuts
ls()
#> [1] "nuts"
Special Computations
Here you go, have this lm object I made. Bye!
Oh yeah, call me when you reach peak MSE!
model = lm(data = iris,
Sepal.Length ~ Species + as.numeric(Species) +
Species:Sepal.Width + as.factor(Sepal.Width) +
as.factor(Sepal.Width)*as.factor(Petal.Length) +
as.numeric(Species)*as.factor(Petal.Length) +
poly(Sepal.Width,degree = 2))
What's in it? What'd you do to it? Does it have cooties?
class_df_from_term(model,class_post_formula = FALSE)
#> $Species
#> Species
#> "factor"
#>
#> $`as.numeric(Species)`
#> Species
#> "factor"
#>
#> $`as.factor(Sepal.Width)`
#> Sepal.Width
#> "numeric"
#>
#> $`as.factor(Petal.Length)`
#> Petal.Length
#> "numeric"
#>
#> $`poly(Sepal.Width, degree = 2)`
#> Sepal.Width
#> "numeric"
#>
#> $`Species:Sepal.Width`
#> Species Sepal.Width
#> "factor" "numeric"
#>
#> $`as.factor(Sepal.Width):as.factor(Petal.Length)`
#> Sepal.Width Petal.Length
#> "numeric" "numeric"
#>
#> $`as.numeric(Species):as.factor(Petal.Length)`
#> Species Petal.Length
#> "factor" "numeric"
Did you hear? That data.frame standing over there started hanging around with the wrong crowd. Boy I tell you hwat, lm totally changed him.
class_df_from_term(model,class_post_formula = TRUE)
#> $Species
#> Species
#> "factor"
#>
#> $`as.numeric(Species)`
#> as.numeric(Species)
#> "numeric"
#>
#> $`as.factor(Sepal.Width)`
#> as.factor(Sepal.Width)
#> "factor"
#>
#> $`as.factor(Petal.Length)`
#> as.factor(Petal.Length)
#> "factor"
#>
#> $`poly(Sepal.Width, degree = 2)`
#> poly(Sepal.Width, degree = 2)
#> "nmatrix.2"
#>
#> $`Species:Sepal.Width`
#> Species Sepal.Width
#> "factor" "numeric"
#>
#> $`as.factor(Sepal.Width):as.factor(Petal.Length)`
#> as.factor(Sepal.Width) as.factor(Petal.Length)
#> "factor" "factor"
#>
#> $`as.numeric(Species):as.factor(Petal.Length)`
#> as.numeric(Species) as.factor(Petal.Length)
#> "numeric" "factor"
What happened Petal.Length ? You used to be cool.
Since you met up with lm and started interacting with Species you think you're too much of a factor for your old crew, the numeric types, huh?
Get out of my face!
Data In/Out
Ever wanted a generic simulated dataset to prototype modeling commands?
How did you know? I'm tired of using data(iris) because the column names aren't boring enough. Turn up the boring! I just want something fast and generic to swap out later.
simlm(p=7,n=5,output_meta=TRUE)
#> $yx
#> y x1 x2 x3 x4 x5 x6 x7
#> 1 14.56 1 1 0.0233 0.1410 0.7529 0.744 0.442
#> 2 13.01 1 0 0.9697 0.0203 0.9264 0.553 0.208
#> 3 17.25 1 1 0.8865 0.4596 0.8747 0.739 0.357
#> 4 9.43 1 0 0.2764 0.1069 0.0252 0.757 0.051
#> 5 7.26 1 0 0.0873 0.2893 0.9189 0.104 0.401
#>
#> $coef_true
#> [1] 1 2 3 4 5 6 7
#>
#> $noise
#> [1] -0.390 -0.389 -1.556 2.146 -3.185
simlm(p=3,n=100,coef_true = c(69,23,7),output_meta=FALSE) %>% lm(data=., y ~ -1+.)
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = .)
#>
#> Coefficients:
#> x1 x2 x3
#> 68.89 23.10 7.01
Ever wanted to read in 5 random rows of some physical spreadsheet?
Someone handed me this dumb csv with 100 columns. I don't even know what's in it!
set.seed(1); m = matrix(rnorm(100*100),ncol=100,nrow=100)
csv = data.frame(m)
dim(csv)
#> [1] 100 100
tf <- tempfile()
write.csv(csv,tf,row.names=FALSE)
So... of course. I just want a taste of the data. Give me a taste!
I'll use the subset to prototype something that works. Later, I'll run it on the whole shebang.
freadss(input=tf,ss=5) %>% dim
#> [1] 5 100
The Glorious Franken-function itersave()
Ever wanted to just brute force some for loop? Aw poop, it crapped the bed during iteration 69. Now I have to manually restart it. I hope it doesn't do it again. I'm running out of patience, and linen.
shaq = function(meatbag){
if(meatbag %in% 'scrub'){return('dunk on em')}
if(meatbag %in% 'sabonis'){return('elbow his face')}
if(!(meatbag %in% c('scrub','sabonis'))){
stop('shaq is confused')}
}
meatbags = c('scrub','sabonis','scrub','kobe')
names(meatbags) = paste0('arg_',seq_along(meatbags))
testthat::expect_failure(lapply(meatbags,FUN=shaq))
#> Error in FUN(X[[i]], ...): shaq is confused
Uh, some error confused Shaq.
enter, stage trap door
"Meet itersave() "
front row faints
"It's... hideously beautiful"
In a nutshell, itersave works like lapply but when it meets an ugly, unskilled, unqualified, and ungraceful error it will keep trucking along like Shaquille The Diesel O'Neal hitchhiking a ride on Chris Dudley's back
mainDir=paste0(getwd(),'/tests/proto/')
subDir='/temp/'
# list.files(paste0(mainDir,subDir))
unlink(list.files(paste0(mainDir,subDir),full.names = TRUE),recursive=TRUE)
itersave(func_user=shaq,
vec_arg_func=meatbags,
mainDir,subDir)
The meatbags that Shaq successfully put into bodybags.
print('the successes')
#> [1] "the successes"
list.files(paste0(mainDir,subDir))
#> [1] "arg_1.rds" "arg_2.rds" "arg_3.rds" "failed"
It'll also book keep any errors along the way via purrr::safely() and R.utils::withTimeout().
print('the failures')
#> [1] "the failures"
list.files(paste0(mainDir,subDir,'/failed/'))
#> [1] "arg_4.rds"
Along with the out, itersave has an in companion
enter, zipline from balcony
"meet iterload() "
audience faints
iterload(paste0(mainDir,subDir,'/failed'))
#> $arg_4
#> $arg_4$ind_fail
#> [1] 4
#>
#> $arg_4$input_bad
#> [1] "kobe"
#>
#> $arg_4$result_bad
#> <simpleError in (function (meatbag) { if (meatbag %in% "scrub") { return("dunk on em") } if (meatbag %in% "sabonis") { return("elbow his face") } if (!(meatbag %in% c("scrub", "sabonis"))) { stop("shaq is confused") }})("kobe"): shaq is confused>
Ah, it was the 4th argument, Kobe, that boggled Shaq's mind.
Hmm, Shaq wisened up in Miami. He also fattened up in Phoenix, Cleveland, Boston, Hawaii, Catalina, etc.
shaq_wiser = function(meatbag){
if(meatbag %in% 'scrub'){return('dunk on em')}
if(meatbag %in% 'sabonis'){return('elbow his face')}
if(meatbag %in% 'kobe'){return('breakup & makeup')}
if(!(meatbag %in% c('scrub','sabonis','kobe'))){
stop('shaq is confused')}
}
itersave(func_user=shaq_wiser,
vec_arg_func=meatbags,
mainDir,subDir)
So, give me the whole shebang. What was the whole story of Shaq's road trip?
out_il = iterload(paste0(mainDir,subDir))
cbind(meatbags,out_il)
#> meatbags out_il
#> arg_1 "scrub" "dunk on em"
#> arg_2 "sabonis" "elbow his face"
#> arg_3 "scrub" "dunk on em"
#> arg_4 "kobe" "breakup & makeup"
By itself purrr::map with purrr::safely is great, but by design, it'll do everything in one shot (eg batch results). This is not ideal when working with stuff online.
For web data in the wild, expect the unexpected. You have non-homogeneous edge cases aplenty. These Chris Dudley looking edge cases are just waiting in the bushes for you.
Dunk thru them.
mikejacktzen/datzen documentation built on June 14, 2019, 5:23 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
The 'datzen' package for R
The datzen package provides three categories of commonly used miscellaneous functions to achieve Data Zen
٩(^ᴗ^)۶
- Themed Branding
- Special Computations
- Data In/Out
The goal of this package is to provide data analysts a few tools to rapidly prototype analysis scripts.
# devtools::install_github('mikejacktzen/datzen',force=TRUE)
library(datzen)
library(dplyr)
Themed Branding
I forgot about the weird base R name of that link function used in logistic regression.
Uhhh, it was what's his face! The one with the logis! Oh, you mean logit?
# ?logit
identical(logit(0.5),stats::qlogis(0.5))
#> [1] TRUE
xx=seq(from=0.01,to=0.99,by=0.01);txtplot::txtplot(x=xx,y=logit(xx))
#> +-+---------+----------+---------+---------+----------+-+
#> 4 + * +
#> | ** |
#> | ** |
#> 2 + ***** +
#> | ******** |
#> | ********** |
#> 0 + ********** +
#> | ******** |
#> -2 + ***** +
#> | ** |
#> | * |
#> -4 + * +
#> +-+---------+----------+---------+---------+----------+-+
#> 0 0.2 0.4 0.6 0.8 1
I forgot about the weird name of its inverse function too! Oh yeah... his ugly cousin, plogis. I know him as expit.
# ?expit
identical(expit(10),stats::plogis(10))
#> [1] TRUE
xxx=seq(from=-10,to=10,by=1);txtplot::txtplot(x=xxx,y=expit(xxx))
#> +-+------------+-----------+------------+-----------+--+
#> 1 + * * * * * * * * +
#> | * |
#> 0.8 + +
#> | * |
#> 0.6 + +
#> | |
#> | * |
#> 0.4 + +
#> | * |
#> 0.2 + +
#> | * |
#> 0 + * * * * * * * * +
#> +-+------------+-----------+------------+-----------+--+
#> -10 -5 0 5 10
Ever wanted a random string of garbage?
I'm too much of a homo-sapien, my phrases are never garbled enough (I blame my parents). I wish I could be more like a computer, or a homo-erectus.
dats_wat_she_said = 10
junk = garble(size_out=dats_wat_she_said)
junk
#> [1] "a%w!k13u4%"
Is your workspace environment too clutered?
It was like that when I got here.
jimmy = 'johns'
nuts = 'planters'
ls()
#> [1] "dats_wat_she_said" "jimmy" "junk"
#> [4] "nuts" "xx" "xxx"
I want to remove All But Deeze ...
rmabd('nuts')
#> kept: nuts
ls()
#> [1] "nuts"
Special Computations
Here you go, have this lm object I made. Bye!
Oh yeah, call me when you reach peak MSE!
model = lm(data = iris,
Sepal.Length ~ Species + as.numeric(Species) +
Species:Sepal.Width + as.factor(Sepal.Width) +
as.factor(Sepal.Width)*as.factor(Petal.Length) +
as.numeric(Species)*as.factor(Petal.Length) +
poly(Sepal.Width,degree = 2))
What's in it? What'd you do to it? Does it have cooties?
class_df_from_term(model,class_post_formula = FALSE)
#> $Species
#> Species
#> "factor"
#>
#> $`as.numeric(Species)`
#> Species
#> "factor"
#>
#> $`as.factor(Sepal.Width)`
#> Sepal.Width
#> "numeric"
#>
#> $`as.factor(Petal.Length)`
#> Petal.Length
#> "numeric"
#>
#> $`poly(Sepal.Width, degree = 2)`
#> Sepal.Width
#> "numeric"
#>
#> $`Species:Sepal.Width`
#> Species Sepal.Width
#> "factor" "numeric"
#>
#> $`as.factor(Sepal.Width):as.factor(Petal.Length)`
#> Sepal.Width Petal.Length
#> "numeric" "numeric"
#>
#> $`as.numeric(Species):as.factor(Petal.Length)`
#> Species Petal.Length
#> "factor" "numeric"
Did you hear? That data.frame standing over there started hanging around with the wrong crowd. Boy I tell you hwat, lm totally changed him.
class_df_from_term(model,class_post_formula = TRUE)
#> $Species
#> Species
#> "factor"
#>
#> $`as.numeric(Species)`
#> as.numeric(Species)
#> "numeric"
#>
#> $`as.factor(Sepal.Width)`
#> as.factor(Sepal.Width)
#> "factor"
#>
#> $`as.factor(Petal.Length)`
#> as.factor(Petal.Length)
#> "factor"
#>
#> $`poly(Sepal.Width, degree = 2)`
#> poly(Sepal.Width, degree = 2)
#> "nmatrix.2"
#>
#> $`Species:Sepal.Width`
#> Species Sepal.Width
#> "factor" "numeric"
#>
#> $`as.factor(Sepal.Width):as.factor(Petal.Length)`
#> as.factor(Sepal.Width) as.factor(Petal.Length)
#> "factor" "factor"
#>
#> $`as.numeric(Species):as.factor(Petal.Length)`
#> as.numeric(Species) as.factor(Petal.Length)
#> "numeric" "factor"
What happened Petal.Length ? You used to be cool.
Since you met up with lm and started interacting with Species you think you're too much of a factor for your old crew, the numeric types, huh?
Get out of my face!
Data In/Out
Ever wanted a generic simulated dataset to prototype modeling commands?
How did you know? I'm tired of using data(iris) because the column names aren't boring enough. Turn up the boring! I just want something fast and generic to swap out later.
simlm(p=7,n=5,output_meta=TRUE)
#> $yx
#> y x1 x2 x3 x4 x5 x6 x7
#> 1 14.56 1 1 0.0233 0.1410 0.7529 0.744 0.442
#> 2 13.01 1 0 0.9697 0.0203 0.9264 0.553 0.208
#> 3 17.25 1 1 0.8865 0.4596 0.8747 0.739 0.357
#> 4 9.43 1 0 0.2764 0.1069 0.0252 0.757 0.051
#> 5 7.26 1 0 0.0873 0.2893 0.9189 0.104 0.401
#>
#> $coef_true
#> [1] 1 2 3 4 5 6 7
#>
#> $noise
#> [1] -0.390 -0.389 -1.556 2.146 -3.185
simlm(p=3,n=100,coef_true = c(69,23,7),output_meta=FALSE) %>% lm(data=., y ~ -1+.)
#>
#> Call:
#> lm(formula = y ~ -1 + ., data = .)
#>
#> Coefficients:
#> x1 x2 x3
#> 68.89 23.10 7.01
Ever wanted to read in 5 random rows of some physical spreadsheet?
Someone handed me this dumb csv with 100 columns. I don't even know what's in it!
set.seed(1); m = matrix(rnorm(100*100),ncol=100,nrow=100)
csv = data.frame(m)
dim(csv)
#> [1] 100 100
tf <- tempfile()
write.csv(csv,tf,row.names=FALSE)
So... of course. I just want a taste of the data. Give me a taste!
I'll use the subset to prototype something that works. Later, I'll run it on the whole shebang.
freadss(input=tf,ss=5) %>% dim
#> [1] 5 100
The Glorious Franken-function itersave()
Ever wanted to just brute force some for loop? Aw poop, it crapped the bed during iteration 69. Now I have to manually restart it. I hope it doesn't do it again. I'm running out of patience, and linen.
shaq = function(meatbag){
if(meatbag %in% 'scrub'){return('dunk on em')}
if(meatbag %in% 'sabonis'){return('elbow his face')}
if(!(meatbag %in% c('scrub','sabonis'))){
stop('shaq is confused')}
}
meatbags = c('scrub','sabonis','scrub','kobe')
names(meatbags) = paste0('arg_',seq_along(meatbags))
testthat::expect_failure(lapply(meatbags,FUN=shaq))
#> Error in FUN(X[[i]], ...): shaq is confused
Uh, some error confused Shaq.
enter, stage trap door
"Meet itersave() "
front row faints
"It's... hideously beautiful"
In a nutshell, itersave works like lapply but when it meets an ugly, unskilled, unqualified, and ungraceful error it will keep trucking along like Shaquille The Diesel O'Neal hitchhiking a ride on Chris Dudley's back
mainDir=paste0(getwd(),'/tests/proto/')
subDir='/temp/'
# list.files(paste0(mainDir,subDir))
unlink(list.files(paste0(mainDir,subDir),full.names = TRUE),recursive=TRUE)
itersave(func_user=shaq,
vec_arg_func=meatbags,
mainDir,subDir)
The meatbags that Shaq successfully put into bodybags.
print('the successes')
#> [1] "the successes"
list.files(paste0(mainDir,subDir))
#> [1] "arg_1.rds" "arg_2.rds" "arg_3.rds" "failed"
It'll also book keep any errors along the way via purrr::safely() and R.utils::withTimeout().
print('the failures')
#> [1] "the failures"
list.files(paste0(mainDir,subDir,'/failed/'))
#> [1] "arg_4.rds"
Along with the out, itersave has an in companion
enter, zipline from balcony
"meet iterload() "
audience faints
iterload(paste0(mainDir,subDir,'/failed'))
#> $arg_4
#> $arg_4$ind_fail
#> [1] 4
#>
#> $arg_4$input_bad
#> [1] "kobe"
#>
#> $arg_4$result_bad
#> <simpleError in (function (meatbag) { if (meatbag %in% "scrub") { return("dunk on em") } if (meatbag %in% "sabonis") { return("elbow his face") } if (!(meatbag %in% c("scrub", "sabonis"))) { stop("shaq is confused") }})("kobe"): shaq is confused>
Ah, it was the 4th argument, Kobe, that boggled Shaq's mind.
Hmm, Shaq wisened up in Miami. He also fattened up in Phoenix, Cleveland, Boston, Hawaii, Catalina, etc.
shaq_wiser = function(meatbag){
if(meatbag %in% 'scrub'){return('dunk on em')}
if(meatbag %in% 'sabonis'){return('elbow his face')}
if(meatbag %in% 'kobe'){return('breakup & makeup')}
if(!(meatbag %in% c('scrub','sabonis','kobe'))){
stop('shaq is confused')}
}
itersave(func_user=shaq_wiser,
vec_arg_func=meatbags,
mainDir,subDir)
So, give me the whole shebang. What was the whole story of Shaq's road trip?
out_il = iterload(paste0(mainDir,subDir))
cbind(meatbags,out_il)
#> meatbags out_il
#> arg_1 "scrub" "dunk on em"
#> arg_2 "sabonis" "elbow his face"
#> arg_3 "scrub" "dunk on em"
#> arg_4 "kobe" "breakup & makeup"
By itself purrr::map with purrr::safely is great, but by design, it'll do everything in one shot (eg batch results). This is not ideal when working with stuff online.
For web data in the wild, expect the unexpected. You have non-homogeneous edge cases aplenty. These Chris Dudley looking edge cases are just waiting in the bushes for you. Dunk thru them.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.