magrittr
Arwan
8/13/2020
Pada bagian ini saya belajar tentang pipes with magrittr.
note : sedang malas nyusun kata-kata dan ngetik jadi copas
library(tidyverse)
library(magrittr)The point of the pipe (%>%) is to help you write code in a way that is easier to read and understand.
Let’s take a look at an actual data manipulation pipeline where we add a new column to ggplot2::diamonds:
diamonds <- ggplot2::diamonds # mengambil dataset diamond pada package ggplot2
diamonds## # A tibble: 53,940 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
## # ... with 53,930 more rowsdiamonds2 <- diamonds %>%
dplyr::mutate(price_per_carat = price / carat) # melakukan mutate pada package dplyr
diamonds2## # A tibble: 53,940 x 11
## carat cut color clarity depth table price x y z price_per_carat
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 1417.
## 2 0.21 Prem~ E SI1 59.8 61 326 3.89 3.84 2.31 1552.
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 1422.
## 4 0.290 Prem~ I VS2 62.4 58 334 4.2 4.23 2.63 1152.
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 1081.
## 6 0.24 Very~ J VVS2 62.8 57 336 3.94 3.96 2.48 1400
## 7 0.24 Very~ I VVS1 62.3 57 336 3.95 3.98 2.47 1400
## 8 0.26 Very~ H SI1 61.9 55 337 4.07 4.11 2.53 1296.
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 1532.
## 10 0.23 Very~ H VS1 59.4 61 338 4 4.05 2.39 1470.
## # ... with 53,930 more rowspryr::object_size() gives the memory occupied by all of its arguments.
Dapat dilihat pada hasil dibawah diamonds mengkonsumsi 3.46 MB dan diamonds2 mengkonsumsi 3.89 MB.
Karena diamonds adalah bagian diamonds2 yaitu diamonds2 tanpa kolom price_per_carat, maka gabungan dari konsumsi memory dari diamonds dan diamonds2 adalah sebesar diamonds2.
pryr::object_size(diamonds)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp## 3.46 MBpryr::object_size(diamonds2)## 3.89 MBpryr::object_size(diamonds, diamonds2)## 3.89 MBBagaimana bila salah satu value pada kolom carat diubah. Hal tersebut mengakibatkan kolom carat pada diamonds dan diamonds2 berbeda, yang mengakibatkan konsumsi memory gabungan diamonds dan diamonds2 berubah (antara diamonds dan diamonds2, kolom price_per_carat berbeda, jelas karena pada diamonds tidak ada. Dan pada kolom carat berbeda).
diamonds$carat[1] <- NA
pryr::object_size(diamonds)## 3.46 MBpryr::object_size(diamonds2)## 3.89 MBpryr::object_size(diamonds, diamonds2)## 4.32 MBpipes (%>%), setahu saya hanya bisa dilakukan pada dataset berbentuk dataframe.
Other Tools from magrittr
When working with more complex pipes, it’s sometimes useful to call a function for its side effects. Maybe you want to print out the current object, or plot it, or save it to disk.
Hasil dibawah hanya menghasilkan grafik saja, padahal merintahkan juga untuk menampilkan data.
rnorm(100) %>%
matrix(ncol = 2) %>%
plot() %>%
str()
## NULLTo work around this problem, you can use the “tee” pipe. %T>%
rnorm(100) %>%
matrix(ncol = 2) %T>%
plot() %>%
str()
## num [1:50, 1:2] 1.38 0.33 -0.968 -0.304 0.663 ...If you’re working with functions that don’t have a data frame– based API (i.e., you pass them individual vectors, not a dataframe and expressions to be evaluated in the context of that dataframe), you might find %$% useful.
mtcars %>% cor(disp, mpg)
code diatas error. karena pipes tidak bisa bekerja pada vector(karena kolom pada data frame bila diambil menjadi vector).
Karena error gak bisa dimasukkin ke chunk, pas di knit error. Antara Kayanya kalau dalam bentuk markdown gak boleh error chunksnya. Atau sayanya aja gak tau inimah.
mtcars %$%
cor(disp, mpg)## [1] -0.8475514For assignment magrittr provides the %<>% operator, which allows you to replace code like:
mtcars <- mtcars %>%
transform(cyl = cyl * 2)
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 12 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 12 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 8 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 12 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 16 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 12 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 16 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 8 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 8 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 12 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 12 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 16 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 16 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 16 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 16 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 16 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 16 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 8 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 8 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 8 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 8 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 16 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 16 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 16 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 16 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 8 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 8 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 8 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 16 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 12 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 16 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 8 121.0 109 4.11 2.780 18.60 1 1 4 2mtcars %<>% transform(cyl = cyl * 2)
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 24 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 24 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 16 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 24 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 32 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 24 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 32 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 16 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 16 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 24 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 24 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 32 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 32 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 32 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 32 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 32 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 32 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 16 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 16 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 16 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 16 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 32 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 32 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 32 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 32 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 16 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 16 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 16 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 32 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 24 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 32 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 16 121.0 109 4.11 2.780 18.60 1 1 4 2The pipe won’t work for two classes of functions: (saya belum pernah juga make ini jadi belum terlalu paham)
- Functions that use the current environment. For example, assign() will create a new variable with the given name in the current environment:, Other functions with this problem include get() and load().
assign("x", 10)
x## [1] 10"x" %>% assign(100)
x # harusnya 100## [1] 10If you do want to use assign with the pipe, you must be explicit about the environment:
env <- environment()
"x" %>% assign(100, envir = env)
x## [1] 100- Functions that use lazy evaluation. In R, function arguments are only computed when the function uses them, not prior to calling the function. There are a relatively wide class of functions with this behavior, including try(), suppressMessages(), and suppressWarnings() in base R.
tryCatch(stop("!"), error = function(e) "An error")## [1] "An error"stop(“!”) %>% tryCatch(error = function(e) “An error”)
Ini juga error.