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chendaniely/chained_indexing-base_tidyverse.R

Last active October 15, 2021 16:27
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chained_indexing-base_tidyverse.R
# base R -----
mycars <- mtcars
mycars
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
mycars[mycars$mpg > 20, ]$mpg <- 100 # similar to mycars[mycars.mpg > 20]["mpg"] = 100 in pandas
mycars
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 100.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 100.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 100.0 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 100.0 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# Merc 240D 100.0 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 100.0 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# Fiat 128 100.0 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 100.0 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 100.0 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 100.0 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Fiat X1-9 100.0 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 100.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 100.0 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Volvo 142E 100.0 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# reset
mycars <- mtcars
mycars[mycars$mpg > 20, "mpg"] <- 100 # similar to mycars.loc[mtcars.mpg > 20, "mpg"] = 100 in pandas
mycars
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 100.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 100.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 100.0 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 100.0 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# Merc 240D 100.0 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 100.0 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# Fiat 128 100.0 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 100.0 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 100.0 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 100.0 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Fiat X1-9 100.0 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 100.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 100.0 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Volvo 142E 100.0 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# tidyverse -----
library(dplyr)
# reset
mycars <- mtcars
mycars <- mtcars %>%
filter(mpg > 20) %>%
mutate(mpg = 100)
mycars
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 100 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 100 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 100 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 100 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Merc 240D 100 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 100 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Fiat 128 100 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 100 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 100 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 100 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Fiat X1-9 100 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 100 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 100 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Volvo 142E 100 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# reset
mycars <- mtcars
mycars <- mtcars %>%
mutate(mpg = case_when(
mpg > 20 ~ 100,
TRUE ~ mpg
))
mycars
# mpg cyl disp hp drat wt qsec vs am gear carb
# Mazda RX4 100.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 100.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 100.0 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# Hornet 4 Drive 100.0 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# Merc 240D 100.0 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Merc 230 100.0 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
# Fiat 128 100.0 4 78.7 66 4.08 2.200 19.47 1 1 4 1
# Honda Civic 100.0 4 75.7 52 4.93 1.615 18.52 1 1 4 2
# Toyota Corolla 100.0 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Toyota Corona 100.0 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Fiat X1-9 100.0 4 79.0 66 4.08 1.935 18.90 1 1 4 1
# Porsche 914-2 100.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# Lotus Europa 100.0 4 95.1 113 3.77 1.513 16.90 1 1 5 2
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
# Volvo 142E 100.0 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Raw
copy_on_write.R
library(pryr)
pryr::address(mtcars) # "0x1ab08d26910"
dat = mtcars
pryr::address(dat) # "0x1ab0f1e0f88"
# identical is essentially R's way of using == but more safe and reliable
identical(mtcars, dat) # TRUE
dat2 = dat
identical(mtcars, dat2) # TRUE
identical(dat, dat2) # TRUE
pryr::address(mtcars) # "0x1ab08d26910"
pryr::address(dat) # "0x1ab0f1e0f88"
pryr::address(dat2) # "0x1ab0f1e0f88" (same as dat)
dat2$asdf <- "hello" # add a column "asdf" that's just "hello" values
pryr::address(mtcars) # "0x1ab08d26910"
pryr::address(dat) # "0x1ab0f1e0f88"
pryr::address(dat2) # "0x1ab11a68590" (new memory address)
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