这是我的代码。我需要使用purrr::map_df函数对 Dataframe 中的整数列应用一个简单的函数，* 但我需要维护character列 *：
purrr::map_df
character
fun1 <- function(x){(x - mean(x))/sd(x)} df <- mtcars %>% rownames_to_column() df %>% map_df(~ fun1(.x))
cpjpxq1n1#
你的预期产出是什么？像这样吗？
library(tidyverse) fun1 <- function(x) { (x - mean(x)) / sd(x) } df <- mtcars %>% rownames_to_column() %>% as_tibble()
df %>% mutate(across(where(is.integer), fun1)) # A tibble: 32 × 12 rowname mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 0.151 -0.105 -0.571 -0.535 0.568 -0.610 -0.777 -0.868 1.19 0.424 0.735 2 Mazda RX4 Wag 0.151 -0.105 -0.571 -0.535 0.568 -0.350 -0.464 -0.868 1.19 0.424 0.735 3 Datsun 710 0.450 -1.22 -0.990 -0.783 0.474 -0.917 0.426 1.12 1.19 0.424 -1.12 4 Hornet 4 Drive 0.217 -0.105 0.220 -0.535 -0.966 -0.00230 0.890 1.12 -0.814 -0.932 -1.12 5 Hornet Sportabout -0.231 1.01 1.04 0.413 -0.835 0.228 -0.464 -0.868 -0.814 -0.932 -0.503 6 Valiant -0.330 -0.105 -0.0462 -0.608 -1.56 0.248 1.33 1.12 -0.814 -0.932 -1.12 7 Duster 360 -0.961 1.01 1.04 1.43 -0.723 0.361 -1.12 -0.868 -0.814 -0.932 0.735 8 Merc 240D 0.715 -1.22 -0.678 -1.24 0.175 -0.0278 1.20 1.12 -0.814 0.424 -0.503 9 Merc 230 0.450 -1.22 -0.726 -0.754 0.605 -0.0687 2.83 1.12 -0.814 0.424 -0.503 10 Merc 280 -0.148 -0.105 -0.509 -0.345 0.605 0.228 0.253 1.12 -0.814 0.424 0.735 # … with 22 more rows # ℹ Use `print(n = ...)` to see more rows
amrnrhlw2#
更新（删除之前的答案）：
library(dplyr) library(purrr) mtcars %>% select_if(is.numeric) %>% map_df(~ fun1(.)) %>% bind_cols(mtcars %>% rownames_to_column() %>% select(rowname))
使用此输出：
mpg cyl disp hp drat wt qsec vs am gear carb rowname <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> 1 0.151 -0.105 -0.571 -0.535 0.568 -0.610 -0.777 -0.868 1.19 0.424 0.735 Mazda RX4 2 0.151 -0.105 -0.571 -0.535 0.568 -0.350 -0.464 -0.868 1.19 0.424 0.735 Mazda RX4 Wag 3 0.450 -1.22 -0.990 -0.783 0.474 -0.917 0.426 1.12 1.19 0.424 -1.12 Datsun 710 4 0.217 -0.105 0.220 -0.535 -0.966 -0.00230 0.890 1.12 -0.814 -0.932 -1.12 Hornet 4 Drive 5 -0.231 1.01 1.04 0.413 -0.835 0.228 -0.464 -0.868 -0.814 -0.932 -0.503 Hornet Sportabout 6 -0.330 -0.105 -0.0462 -0.608 -1.56 0.248 1.33 1.12 -0.814 -0.932 -1.12 Valiant 7 -0.961 1.01 1.04 1.43 -0.723 0.361 -1.12 -0.868 -0.814 -0.932 0.735 Duster 360 8 0.715 -1.22 -0.678 -1.24 0.175 -0.0278 1.20 1.12 -0.814 0.424 -0.503 Merc 240D 9 0.450 -1.22 -0.726 -0.754 0.605 -0.0687 2.83 1.12 -0.814 0.424 -0.503 Merc 230 10 -0.148 -0.105 -0.509 -0.345 0.605 0.228 0.253 1.12 -0.814 0.424 0.735 Merc 280 # … with 22 more rows # ℹ Use `print(n = ...)` to see more rows
oalqel3c3#
使用map_if
map_if
library(purrr) library(dplyr) mtcars %>% type.convert(as.is = TRUE) %>% rownames_to_column() %>% map_if(.p = ~ inherits(.x, "integer"), .f = fun1) %>% bind_cols()
k4emjkb14#
你可以选择使用mutate_if如下
mutate_if
df %>% mutate_if(is.numeric, ~fun1(.x))
创建于2023年2月19日，使用reprex v2.0.2
rowname mpg cyl disp hp drat wt qsec 1 Mazda RX4 0.15088482 -0.1049878 -0.57061982 -0.53509284 0.56751369 -0.610399567 -0.77716515 2 Mazda RX4 Wag 0.15088482 -0.1049878 -0.57061982 -0.53509284 0.56751369 -0.349785269 -0.46378082 3 Datsun 710 0.44954345 -1.2248578 -0.99018209 -0.78304046 0.47399959 -0.917004624 0.42600682 4 Hornet 4 Drive 0.21725341 -0.1049878 0.22009369 -0.53509284 -0.96611753 -0.002299538 0.89048716 5 Hornet Sportabout -0.23073453 1.0148821 1.04308123 0.41294217 -0.83519779 0.227654255 -0.46378082 6 Valiant -0.33028740 -0.1049878 -0.04616698 -0.60801861 -1.56460776 0.248094592 1.32698675 7 Duster 360 -0.96078893 1.0148821 1.04308123 1.43390296 -0.72298087 0.360516446 -1.12412636 8 Merc 240D 0.71501778 -1.2248578 -0.67793094 -1.23518023 0.17475447 -0.027849959 1.20387148 9 Merc 230 0.44954345 -1.2248578 -0.72553512 -0.75387015 0.60491932 -0.068730634 2.82675459 10 Merc 280 -0.14777380 -0.1049878 -0.50929918 -0.34548584 0.60491932 0.227654255 0.25252621 11 Merc 280C -0.38006384 -0.1049878 -0.50929918 -0.34548584 0.60491932 0.227654255 0.58829513 12 Merc 450SE -0.61235388 1.0148821 0.36371309 0.48586794 -0.98482035 0.871524874 -0.25112717 13 Merc 450SL -0.46302456 1.0148821 0.36371309 0.48586794 -0.98482035 0.524039143 -0.13920420 14 Merc 450SLC -0.81145962 1.0148821 0.36371309 0.48586794 -0.98482035 0.575139986 0.08464175 15 Cadillac Fleetwood -1.60788262 1.0148821 1.94675381 0.85049680 -1.24665983 2.077504765 0.07344945 16 Lincoln Continental -1.60788262 1.0148821 1.84993175 0.99634834 -1.11574009 2.255335698 -0.01608893 17 Chrysler Imperial -0.89442035 1.0148821 1.68856165 1.21512565 -0.68557523 2.174596366 -0.23993487 18 Fiat 128 2.04238943 -1.2248578 -1.22658929 -1.17683962 0.90416444 -1.039646647 0.90727560 19 Honda Civic 1.71054652 -1.2248578 -1.25079481 -1.38103178 2.49390411 -1.637526508 0.37564148 20 Toyota Corolla 2.29127162 -1.2248578 -1.28790993 -1.19142477 1.16600392 -1.412682800 1.14790999 21 Toyota Corona 0.23384555 -1.2248578 -0.89255318 -0.72469984 0.19345729 -0.768812180 1.20946763 22 Dodge Challenger -0.76168319 1.0148821 0.70420401 0.04831332 -1.56460776 0.309415603 -0.54772305 23 AMC Javelin -0.81145962 1.0148821 0.59124494 0.04831332 -0.83519779 0.222544170 -0.30708866 24 Camaro Z28 -1.12671039 1.0148821 0.96239618 1.43390296 0.24956575 0.636460997 -1.36476075 25 Pontiac Firebird -0.14777380 1.0148821 1.36582144 0.41294217 -0.96611753 0.641571082 -0.44699237 26 Fiat X1-9 1.19619000 -1.2248578 -1.22416874 -1.17683962 0.90416444 -1.310481114 0.58829513 27 Porsche 914-2 0.98049211 -1.2248578 -0.89093948 -0.81221077 1.55876313 -1.100967659 -0.64285758 28 Lotus Europa 1.71054652 -1.2248578 -1.09426581 -0.49133738 0.32437703 -1.741772228 -0.53093460 29 Ford Pantera L -0.71190675 1.0148821 0.97046468 1.71102089 1.16600392 -0.048290296 -1.87401028 30 Ferrari Dino -0.06481307 -0.1049878 -0.69164740 0.41294217 0.04383473 -0.457097039 -1.31439542 31 Maserati Bora -0.84464392 1.0148821 0.56703942 2.74656682 -0.10578782 0.360516446 -1.81804880 32 Volvo 142E 0.21725341 -1.2248578 -0.88529152 -0.54967799 0.96027290 -0.446876870 0.42041067 vs am gear carb 1 -0.8680278 1.1899014 0.4235542 0.7352031 2 -0.8680278 1.1899014 0.4235542 0.7352031 3 1.1160357 1.1899014 0.4235542 -1.1221521 4 1.1160357 -0.8141431 -0.9318192 -1.1221521 5 -0.8680278 -0.8141431 -0.9318192 -0.5030337 6 1.1160357 -0.8141431 -0.9318192 -1.1221521 7 -0.8680278 -0.8141431 -0.9318192 0.7352031 8 1.1160357 -0.8141431 0.4235542 -0.5030337 9 1.1160357 -0.8141431 0.4235542 -0.5030337 10 1.1160357 -0.8141431 0.4235542 0.7352031 11 1.1160357 -0.8141431 0.4235542 0.7352031 12 -0.8680278 -0.8141431 -0.9318192 0.1160847 13 -0.8680278 -0.8141431 -0.9318192 0.1160847 14 -0.8680278 -0.8141431 -0.9318192 0.1160847 15 -0.8680278 -0.8141431 -0.9318192 0.7352031 16 -0.8680278 -0.8141431 -0.9318192 0.7352031 17 -0.8680278 -0.8141431 -0.9318192 0.7352031 18 1.1160357 1.1899014 0.4235542 -1.1221521 19 1.1160357 1.1899014 0.4235542 -0.5030337 20 1.1160357 1.1899014 0.4235542 -1.1221521 21 1.1160357 -0.8141431 -0.9318192 -1.1221521 22 -0.8680278 -0.8141431 -0.9318192 -0.5030337 23 -0.8680278 -0.8141431 -0.9318192 -0.5030337 24 -0.8680278 -0.8141431 -0.9318192 0.7352031 25 -0.8680278 -0.8141431 -0.9318192 -0.5030337 26 1.1160357 1.1899014 0.4235542 -1.1221521 27 -0.8680278 1.1899014 1.7789276 -0.5030337 28 1.1160357 1.1899014 1.7789276 -0.5030337 29 -0.8680278 1.1899014 1.7789276 0.7352031 30 -0.8680278 1.1899014 1.7789276 1.9734398 31 -0.8680278 1.1899014 1.7789276 3.2116766 32 1.1160357 1.1899014 0.4235542 -0.5030337
4条答案
按热度按时间cpjpxq1n1#
你的预期产出是什么？像这样吗？
amrnrhlw2#
更新（删除之前的答案）：
使用此输出：
oalqel3c3#
使用
map_ifk4emjkb14#
你可以选择使用
mutate_if如下创建于2023年2月19日，使用reprex v2.0.2