version 0.0.2

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: getDTeval
 Title: Translating Coding Statements using get() and eval() for
         Improved Run-Time Coding Efficiency
-Version: 0.0.1
+Version: 0.0.2
 Authors@R: c(
     person("David", "Shilane", , "david.shilane@columbia.edu", c("aut")),
     person("Mayur","Bansal", , "mb4511@columbia.edu", c("ctb","cre")),
@@ -14,19 +14,17 @@ Description: The getDTeval() function facilitates the translation of the origina
     The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
 License: GPL-3
 Encoding: UTF-8
-LazyData: true
 RoxygenNote: 7.1.1
-Suggests: knitr, rmarkdown, dplyr, testthat (>= 2.1.0), covr,
-        formulaic, devtools
-Imports: stats, data.table, microbenchmark, utils
+Suggests: knitr, rmarkdown, dplyr, testthat (>= 2.1.0), covr, devtools
+Imports: stats, data.table, formulaic, microbenchmark, utils
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2020-11-12 02:37:05 UTC; mayurbansal
+Packaged: 2021-06-21 06:00:51 UTC; mayurbansal
 Author: David Shilane [aut],
   Mayur Bansal [ctb, cre],
   Anderson Nelson [ctb],
   Caffrey Lee [ctb],
   Zichen Huang [ctb]
 Maintainer: Mayur Bansal <mb4511@columbia.edu>
 Repository: CRAN
-Date/Publication: 2020-11-18 09:00:06 UTC
+Date/Publication: 2021-06-21 06:10:04 UTC

MD5

@@ -1,16 +1,16 @@
-2d70dc68bd5c90ad2cff636b12336e51 *DESCRIPTION
-1e0f73093a442f00cab87711453dd0ee *NAMESPACE
+73bc7d88f248cb1da3a1e580f75d44b0 *DESCRIPTION
+b193a5fbb3212b4f335cf04223af77f6 *NAMESPACE
 b7371fc0a4c0c1d00d271dc95ce397dc *R/benchmark.getDTeval.R
-74ae1944a31083690242505f73e3e578 *R/getDTeval.R
+8b727e5f4d0c2c55e62bcafdc3a49b22 *R/getDTeval.R
 587c9b41c253aef66b8e768a34a36e48 *README.md
 050f8528a7bce23c58bc7bd1705fa932 *build/vignette.rds
 90949be0ccba305a6d68dff94a81dcee *inst/doc/Introduction_to_getDTeval.R
-ba5cb1b68199ad4b6e6325d2a7910f2c *inst/doc/Introduction_to_getDTeval.Rmd
-9bc923cebae80f56727d8a9058cfca46 *inst/doc/Introduction_to_getDTeval.html
+8a6c09a78d3c65b6cb0bd2270c98f479 *inst/doc/Introduction_to_getDTeval.Rmd
+4dc0a4ed6dbef0d25c7b2d87e4a6f3c5 *inst/doc/Introduction_to_getDTeval.html
 5b9e8d16fd83b858ad3d8e37a120a09a *man/benchmark.getDTeval.Rd
 fd8e25f21811f2de60b89441c8c3a341 *man/function.ending.point.Rd
 4c13eb30a97b0e63e90ceb29d719bd30 *man/getDTeval.Rd
 b4764f1aa6e5afbdf05a6f62f66d77d7 *man/translate.fn.calls.Rd
 fb5792231f530f9175ee012c30c8d85a *tests/testthat.R
 2a5b7740d821c45cb03c1e550fad341d *tests/testthat/test-getDteval.R
-ba5cb1b68199ad4b6e6325d2a7910f2c *vignettes/Introduction_to_getDTeval.Rmd
+8a6c09a78d3c65b6cb0bd2270c98f479 *vignettes/Introduction_to_getDTeval.Rmd

NAMESPACE

@@ -5,6 +5,7 @@ export(function.ending.point)
 export(getDTeval)
 export(translate.fn.calls)
 import(data.table)
+import(formulaic)
 import(microbenchmark)
 import(stats)
 import(utils)

R/getDTeval.R

@@ -26,28 +26,32 @@ function.ending.point <- function(all.chars, beginning.index, ...) {
 #' @param function.name Name of the function to be translated to an optimized form. Parameter values should be either 'get(' or 'eval('. 'get(' is set as default
 #' @param envir Specify the environment for the required function. .GlobalEnv is set as default
 #' @param ... provision for additional arguments
+#' @import formulaic
 #' @export
 
 
-translate.fn.calls <- function(the.statement, function.name = "get(", envir = .GlobalEnv, ...){
-
+translate.fn.calls <- function (the.statement, function.name = "get(", envir = .GlobalEnv,
+                                   ...)
+{
   fn.chars <- strsplit(x = function.name, split = "")[[1]]
   all.chars <- strsplit(x = the.statement, split = "")[[1]]
-
   first.chars <- which(all.chars == fn.chars[1])
-
-  the.begin <- first.chars[as.logical(lapply(X = first.chars, FUN = function(x){return(paste(all.chars[x:(x+length(fn.chars)-1)], collapse = "") == function.name)}))]
-
+  the.begin <- first.chars[as.logical(lapply(X = first.chars,
+                                             FUN = function(x) {
+                                               return(paste(all.chars[x:(x + length(fn.chars) -
+                                                                           1)], collapse = "") == function.name)
+                                             }))]
   len <- length(the.begin)
-  if(len > 0){
-    for(i in 1:length(the.begin)){
-      the.end <- function.ending.point(all.chars = all.chars, beginning.index = the.begin[i], ...)
-      the.call <- paste(all.chars[the.begin[i]:the.end], collapse = "")
-      arg <- trimws(x = gsub(pattern = function.name, replacement = "eval(", x = the.call, fixed = TRUE), which = "both")
-
-      evaluated.arg <- eval(expr = parse(text = arg), envir = envir)
-
-      if(function.name == "eval("){
+  if (len > 0) {
+    for (i in 1:length(the.begin)) {
+      the.end <- function.ending.point(all.chars = all.chars,
+                                       beginning.index = the.begin[i], ...)
+      the.call <- paste(all.chars[the.begin[i]:the.end],
+                        collapse = "")
+      arg <- trimws(x = gsub(pattern = function.name, replacement = "eval(",
+                             x = the.call, fixed = TRUE), which = "both")
+      evaluated.arg <- add.backtick(x = eval(expr = parse(text = arg), envir = envir))
+      if (function.name == "eval(") {
         evaluated.arg <- sprintf("'%s'", evaluated.arg)
       }
       all.chars[the.begin[i]:the.end] <- ""

inst/doc/Introduction_to_getDTeval.Rmd

@@ -70,7 +70,7 @@ In the second approach, the **get()** and **eval()** functions are used to conve
 
 Programmatic designs with the get() and eval() functions facilitate calculations in functions and in dynamic applications.  Even for basic analyses, this design creates greater flexibility for possible changes.  If the name of the Age variable were later changed to another value (e.g. Age_Years), the program could be adapted with a simple modification to the age.name variable.  Any downstream use of the Age variable through calls of get() or eval() to age.name would automatically adapt to the change.
 
-However, programmatic designs can lead to reduced runtime efficiency.  In the case of simple calls to the get() function, the performance can greatly decrease.  The following example demonstrates the associated reductions in efficiency in the case of calculating a grouped average.
+However, programmatic designs can lead to reduced runtime efficiency.  In the case of simple calls to the get() function, the performance can greatly decrease.  The following example demonstrates the associated reductions in efficiency in the case of calculating a grouped average. 
 
 ```{r runtime_comparison}
 age.name <- "Age"

inst/doc/Introduction_to_getDTeval.html

@@ -10,7 +10,7 @@
 
 
 
-<meta name="date" content="2020-11-11" />
+<meta name="date" content="2021-06-20" />
 
 <title>Introduction to GetDTeval</title>
 
@@ -178,7 +178,7 @@
 
 
 <h1 class="title toc-ignore">Introduction to GetDTeval</h1>
-<h4 class="date">2020-11-11</h4>
+<h4 class="date">2021-06-20</h4>
 
 </div>
 
@@ -224,9 +224,9 @@ <h2>Introduction</h2>
 results[, Effect_Median := Programmatic_Median/Classic_Median]
 round(x = results, digits = 4)
 #&gt;    Classic_Mean Classic_Median Programmatic_Mean Programmatic_Median
-#&gt; 1:       0.0245         0.0228            0.0747              0.0714
+#&gt; 1:       0.0257         0.0241            0.0629              0.0605
 #&gt;    Effect_Median
-#&gt; 1:        3.1311</code></pre>
+#&gt; 1:        2.5119</code></pre>
 <p>For larger sample sizes and more complex calculations, the effect of programmatic designs can significantly increase the running time complexity.</p>
 <p>The <strong>getDTeval</strong> package was designed to overcome the trade-offs between programmatic designs and running time efficiency. The package creates a means of translating calls to get() and eval() into more optimized coding statements. In doing so, the package not only resolves many observed trade-offs between speed and efficiency; it also creates opportunities to expand the areas in which programmatic designs may be used in common coding applications.</p>
 </div>
@@ -416,9 +416,9 @@ <h3>Example 4</h3>
                     times = 50,
                     seed = 282)
 #&gt;                      category     Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-#&gt; 1:        getDTeval statement 0.009714 0.01114 0.01137 0.01233 0.01222 0.03188
-#&gt; 2: optimized coding statement 0.009148 0.01070 0.01105 0.01266 0.01195 0.05292
-#&gt; 3:  original coding statement 0.056749 0.05780 0.05873 0.06339 0.06508 0.11132</code></pre>
+#&gt; 1:        getDTeval statement 0.008915 0.01036 0.01041 0.01105 0.01052 0.02977
+#&gt; 2: optimized coding statement 0.008581 0.01010 0.01036 0.01169 0.01096 0.05176
+#&gt; 3:  original coding statement 0.045183 0.04851 0.04922 0.05345 0.05330 0.10045</code></pre>
 <p>The original coding statement uses calls to get() to generate results with a programmatic design. In the median and average cases, the running time is substantially worse than using the more optimized statement. There is a real trade-off between the flexibility of the programmatic design and the running time performance of directly naming the variables. However, translating the programmatic design's coding statement using getDTeval() produces running times that are quite similar to what is produced while directly using the optimized statements. These results suggest that getDTeval() can effectively eliminate these tradeoffs.</p>
 </div>
 </div>
@@ -456,7 +456,7 @@ <h3>Example 5: Using eval() in Calculated Quantities with data.table</h3>
 #&gt; 4:      West       50.66667
 #&gt; 
 #&gt; $code
-#&gt; [1] &quot;dat[, .('Mean Awareness' = mean(Awareness) * 100), keyby = Region]&quot;
+#&gt; [1] &quot;dat[, .('`Mean Awareness`' = mean(Awareness) * 100), keyby = Region]&quot;
 #&gt; 
 #&gt; $original.statement
 #&gt; [1] &quot;dat[, .(eval(mean.awareness.name) = mean(get(awareness.name)) * 100), keyby = get(region.name)]&quot;</code></pre>
@@ -484,7 +484,7 @@ <h3>Example 6: Using eval() in Calculated Quantities with dplyr</h3>
 #&gt; 4 West                 0.507
 #&gt; 
 #&gt; $code
-#&gt; [1] &quot;dat %&gt;% group_by(Region) %&gt;% summarize('Mean Awareness'=mean(Awareness,na.rm=T))&quot;
+#&gt; [1] &quot;dat %&gt;% group_by(Region) %&gt;% summarize('`Mean Awareness`'=mean(Awareness,na.rm=T))&quot;
 #&gt; 
 #&gt; $original.statement
 #&gt; [1] &quot;dat %&gt;% group_by(get(region.name)) %&gt;% summarize(eval(mean.awareness.name)=mean(get(awareness.name),na.rm=T))&quot;</code></pre>
@@ -541,7 +541,7 @@ <h4>Example 8: Improving Readability of Grouped Calculations in dplyr</h4>
 #&gt; 4 West                     4.80
 #&gt; 
 #&gt; $code
-#&gt; [1] &quot;dat %&gt;% group_by(Region) %&gt;% summarize('Mean Satisfaction' = mean(Satisfaction, na.rm=T))&quot;
+#&gt; [1] &quot;dat %&gt;% group_by(Region) %&gt;% summarize('`Mean Satisfaction`' = mean(Satisfaction, na.rm=T))&quot;
 #&gt; 
 #&gt; $original.statement
 #&gt; [1] &quot;dat %&gt;% group_by(get(region.name)) %&gt;% summarize(eval(sprintf(\&quot;Mean %s\&quot;, satisfaction.name)) = mean(get(satisfaction.name), na.rm=T))&quot;</code></pre>

vignettes/Introduction_to_getDTeval.Rmd

@@ -70,7 +70,7 @@ In the second approach, the **get()** and **eval()** functions are used to conve
 
 Programmatic designs with the get() and eval() functions facilitate calculations in functions and in dynamic applications.  Even for basic analyses, this design creates greater flexibility for possible changes.  If the name of the Age variable were later changed to another value (e.g. Age_Years), the program could be adapted with a simple modification to the age.name variable.  Any downstream use of the Age variable through calls of get() or eval() to age.name would automatically adapt to the change.
 
-However, programmatic designs can lead to reduced runtime efficiency.  In the case of simple calls to the get() function, the performance can greatly decrease.  The following example demonstrates the associated reductions in efficiency in the case of calculating a grouped average.
+However, programmatic designs can lead to reduced runtime efficiency.  In the case of simple calls to the get() function, the performance can greatly decrease.  The following example demonstrates the associated reductions in efficiency in the case of calculating a grouped average. 
 
 ```{r runtime_comparison}
 age.name <- "Age"