From e095a89da16b4b3b27a3db5b2666c2297f0fce9e Mon Sep 17 00:00:00 2001 From: Gerhard Nachtmann Date: Mon, 3 Aug 2020 17:41:33 +0200 Subject: [PATCH] Fixed some typos and some slightly code improvements --- README.md | 47 +++++++++++++++++++++++++---------------------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 522628d..7ce9ba2 100644 --- a/README.md +++ b/README.md @@ -117,13 +117,13 @@ i.e. drawing a simple histogram of R's built-in Nile River dataset. This is in the very first lesson in my tutorial. Easy! By contrast, the Tidy crowd forbids use of base-R plots, insisting on using **ggplot2** (which again is not Tidy, but is considered as such by the -Tidy advocates). To be Tidy the instructor would have to do something +Tidy advocates). To be Tidy the instructor would have to do something like ``` r > library(ggplot2) > dn <- data.frame(Nile) -> ggplot(dn) + geom_histogram(aes(Nile),dn) +> ggplot(dn) + geom_histogram(aes(Nile)) ``` Here the instructor would have a ton of things to explain -- packages, @@ -153,12 +153,12 @@ In 231 pages, vectors are mentioned just briefly, with no coverage of subscipts. A researcher tweeted in December 2019 that an introductory statistics book by Peter Dalgaard is "now obsolete," because it uses base-R rather -than Tidy. Think of what an update to Tidy would involve, how much extra -complexity it would impose on the students. Here is an example from the +than Tidy. Think of what an update to Tidy would involve, how much extra +complexity it would impose on the students. Here is an example from the book: ``` r -> thue2 <- subset(thuesen,blood.glucose < 7) +> thue2 <- subset(thuesen, blood.glucose < 7) ``` This could easily be in the base-R instructor's second lesson, if not @@ -166,7 +166,7 @@ the first. For Tidy, though, this would have to be changed to ``` r > library(dplyr) -> thue2 <- thue2 %>% filter(blood.glucose < 7) +> thue2 <- thuesen %>% filter(blood.glucose < 7) ``` Here the instructor would first have to teach the pipe operator '%>%', @@ -298,7 +298,7 @@ mtcars %>% Here is the base-R version: ``` r -tapply(mtcars$mpg,mtcars$cyl,mean) +tapply(mtcars$mpg, mtcars$cyl, mean) ``` Both are simple. Both are easily grasped by beginners. After being @@ -312,7 +312,7 @@ It's instructive to look at what happens when one groups by two aspects: ``` r > mtcars %>% -+ group_by(cyl,gear) %>% ++ group_by(cyl, gear) %>% + summarize(mean(mpg)) # A tibble: 8 x 3 # Groups: cyl [3] @@ -326,7 +326,7 @@ It's instructive to look at what happens when one groups by two aspects: 6 6 5 19.7 7 8 3 15.0 8 8 5 15.4 -> tapply(mtcars$mpg,list(mtcars$cyl,mtcars$gear),mean) +> tapply(mtcars$mpg, list(mtcars$cyl, mtcars$gear), mean) 3 4 5 4 21.50 26.925 28.2 6 19.75 19.750 19.7 @@ -360,7 +360,7 @@ group: > mtcars$cyl <- as.factor(mtcars$cyl) > mtcars$gear <- as.factor(mtcars$gear) > mtcars %>% -+ group_by(cyl,gear,.drop=FALSE) %>% ++ group_by(cyl, gear, .drop=FALSE) %>% + summarize(mean(mpg)) # A tibble: 9 x 3 # Groups: cyl [3] @@ -430,7 +430,7 @@ Even Hadley, in *R for Data Science*, says: Actually, most non-FP languages allow passing one function to another, but yes it is a powerful tool, worth the investment of time -- *for the -experienced R programmer*. But again, it's wrong to foce nonprogrammer +experienced R programmer*. But again, it's wrong to force nonprogrammer learners of R to "wrap their heads around" **purrr**. ### purrr vs. base-R example @@ -486,11 +486,12 @@ far easier: ``` r lmr2 <- function(mtcSubset) { - lmout <- lm(mpg ~ wt,data=mtcSubset) + lmout <- lm(mpg ~ wt, data = mtcSubset) summary(lmout)$r.squared } -u <- split(mtcars,mtcars$cyl) -sapply(u,lmr2) +u <- split(mtcars, mtcars$cyl) +# sapply(u, lmr2) # or better ... +vapply(u, lmr2, numeric(1)) # ... explicit type like map_dbl ``` Here **lmr2()** is defined explicitly, as opposed to the Tidy version, with @@ -531,7 +532,7 @@ with **dplyr**. Behold: By contrast, in the base-R version, we indeed stuck to base-R! There are only four main functions to learn in the 'apply' family: **apply()**, -**lapply()**, **sapply()** and **tapply()**. +**lapply()**, **vapply**, **sapply()** and **tapply()**. ### Tibbles @@ -553,12 +554,14 @@ Tidyverse is more teachable because of its "English-like" syntax. Below is a comparison of the "English" **dplyr** to the "non-English" **data.table** (adapted from -[here](https://atrebas.github.io/post/2019-03-03-datatable-dplyr/)): +[here](https://atrebas.github.io/post/2019-03-03-datatable-dplyr/)) +and **base-R** versions: We'll again use R's built-in **mtcars** dataset. ``` r +mtcars[mtcars$cyl == 6, ] # base R syntax mtdt <- as.data.table(mtcars); mtdt[cyl == 6] # data.table syntax -mttb <- as_tibble(mtcars); filter(mttb,cyl == 6) # dplyr syntax +mttb <- as_tibble(mtcars); filter(mttb,cyl == 6) # dplyr syntax ``` Is there really any difference? Can't beginners, even without @@ -598,10 +601,10 @@ The Tidyverse also makes heavy use of **magrittr** *pipes*, e.g. writing the function composition **h(g(f(x)))** as ``` r -f(x) %>% g() %>% h() +f(x) %>% g() %>% h() ``` -Again, the pitch made is that this is "English," in this case in the +Again, the pitch made is that this is "English", in this case in the sense of reading left-to-right. But again, one might question just how valuable that is, and in any event, I personally tend to write such code left-to-right anyway, *without* using pipes: @@ -622,7 +625,7 @@ in the pipe, that argument would be hidden, making it appear that there is only one argument: ``` r -> w <- function(u,v) u+2*v +> w <- function(u, v) u+2*v > 3 %>% w(5) [1] 13 ``` @@ -633,7 +636,7 @@ And what if we want that 3 to play the role of **v**, not **u**? Yes, **magrittr** has a way to do that, the "dot" notation: ``` r -> 3 %>% w(5,.) +> 3 %>% w(5, .) [1] 11 ``` @@ -723,7 +726,7 @@ and to which R beginners should definitely be exposed. But the Tidyverse should be considered advanced R, not for beginners, just as is the case for most complex CRAN packages, and should be -presented, as noted, as an *option*, not as *they* way. +presented, as noted, as an *option*, not as *the* way. *The role of RStudio:*