给出一个数据框df：
structure(list(train_user_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), train_idx = c(38L, 38L, 91L, 91L, 78L, 85L, 38L,
38L, 100L, 38L), test_user_id = c(2L, 7L, 1L, 1L, 3L, 4L, 2L,
2L, 4L, 2L), test_idx = c(7L, 1487L, 267L, 205L, 416L, 1228L,
5L, 3L, 1221L, 26L), isSame = c(FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE), D_manhattan_scaled_rank = c(1,
1, 1, 1, 1, 1, 2, 2, 1, 2), D_cosinus_rank = c(2, 2, 2, 2, 2,
2, 2, 2, 2, 2), D_manhattan_scaled = c(0.0657286768232851, 0.0826741765405462,
0.0940875224861761, 0.097775476943595, 0.10376335813558, 0.145577989872983,
0.146052068454132, 0.148619857868654, 0.168290231579623, 0.176975987030052
), D_manhattan_scaled_norm_median = c(-1.00671374438998, -0.997385542790249,
-0.991102695209199, -0.989072540699541, -0.985776316696353, -0.962758092319792,
-0.962497120341812, -0.961083597121844, -0.950255399985173, -0.945474043327689
), D_manhattan_scaled_norm_standard = c(-1.48819317015296, -1.4760112732926,
-1.46780636901353, -1.46515514706224, -1.46085053803271, -1.43079054927003,
-1.43044974041366, -1.42860379037803, -1.41446301744423, -1.4082189420958
), D_manhattan_scaled_adj_std = c(0.0430204530981368, 0.0460635656366407,
0.0324184592356798, 0.0970173017224344, 0.0753537776638339, 0.109856813487073,
0.0574451159458192, 0.0579062455845995, 0.0704125398622902, 0.0629985056910741
), D_manhattan_scaled_adj_std_norm_median = c(-0.503188246554804,
-0.501396401264777, -0.509430911762538, -0.47139382631581, -0.484149740879808,
-0.463833665989625, -0.494694717690257, -0.494423195371945, -0.487059240140704,
-0.491424771169117), D_manhattan_scaled_adj_std_norm_standard = c(-1.48257160625723,
-1.47883448831024, -1.49559146702019, -1.41626035765178, -1.44286441633519,
-1.40049269568096, -1.46485728720877, -1.4642909933806, -1.44893254172815,
-1.45803740387515), D_manhattan = c(0.672887300807173, 0.675930413345677,
0.610974280121391, 2.50949211962699, 1.1051786119143, 1.58216223895939,
0.687311963654855, 0.687773093293636, 0.974947980462479, 0.69286535340011
), D_manhattan_norm_median = c(-0.954621945729433, -0.954491537063699,
-0.957275148434567, -0.875916613319857, -0.936096658748905, -0.915656140170372,
-0.954003795394266, -0.953984034277921, -0.941677524612532, -0.953765812034198
), D_manhattan_norm_standard = c(-1.4490608751289, -1.44889327448836,
-1.45247075951332, -1.3479091299438, -1.42525225821863, -1.39898219412758,
-1.44826643105155, -1.4482410341522, -1.43242476234849, -1.44796057588953
), D_euclidean = c(0.518771643675271, 0.5188097254424, 0.53063275623408,
1.92924997316828, 0.966420157224431, 1.44718873459025, 0.51911028560706,
0.519127719117536, 0.686153917573155, 0.519347425186172), D_euclidean_norm_median = c(-0.90357471715071,
-0.903572834076855, -0.902988206790553, -0.833829138991541, -0.881439315466942,
-0.857666187097651, -0.903557971925472, -0.903557109870209, -0.89529796931099,
-0.903546245806251), D_euclidean_norm_standard = c(-1.44257769296322,
-1.44257514625199, -1.44178448322374, -1.34825221188084, -1.41264128024888,
-1.38048996930628, -1.44255504634578, -1.44255388048295, -1.43138403391866,
-1.44253918767967), D_cosinus = c(0.721004237529186, 0.721004237529186,
1.2063547475627e-05, 3.04804894533106e-05, 0.00015538105717583,
0.000465547437121572, 0.721004237529186, 0.721004237529186, 0.000173984509277303,
0.721004237529186), D_cosinus_norm_median = c(24.0734598908976,
24.0734598908976, -0.0269862437287224, -0.0263706246774829, -0.022195600990392,
-0.0118277379227909, 24.0734598908976, 24.0734598908976, -0.0215737475064442,
24.0734598908976), D_cosinus_norm_standard = c(6.67802092714532,
6.67802092714532, -0.248881727244683, -0.248704787232191, -0.24750481016543,
-0.244524899428419, 6.67802092714532, 6.67802092714532, -0.247326078264596,
6.67802092714532), D_manhattan_scaled_median = c(1.89451285226621,
1.89451285226621, 1.89451285226621, 1.89451285226621, 1.89451285226621,
1.89451285226621, 1.89451285226621, 1.89451285226621, 1.89451285226621,
1.89451285226621), D_manhattan_scaled_mean = c(2.13586416008507,
2.13586416008507, 2.13586416008507, 2.13586416008507, 2.13586416008507,
2.13586416008507, 2.13586416008507, 2.13586416008507, 2.13586416008507,
2.13586416008507), D_manhattan_scaled_std = c(1.3910394999656,
1.3910394999656, 1.3910394999656, 1.3910394999656, 1.3910394999656,
1.3910394999656, 1.3910394999656, 1.3910394999656, 1.3910394999656,
1.3910394999656), D_manhattan_scaled_percentile_90 = c(3.71110091976209,
3.71110091976209, 3.71110091976209, 3.71110091976209, 3.71110091976209,
3.71110091976209, 3.71110091976209, 3.71110091976209, 3.71110091976209,
3.71110091976209), D_manhattan_scaled_adj_std_median = c(0.897591141115348,
0.897591141115348, 0.897591141115348, 0.897591141115348, 0.897591141115348,
0.897591141115348, 0.897591141115348, 0.897591141115348, 0.897591141115348,
0.897591141115348), D_manhattan_scaled_adj_std_mean = c(1.25026954403403,
1.25026954403403, 1.25026954403403, 1.25026954403403, 1.25026954403403,
1.25026954403403, 1.25026954403403, 1.25026954403403, 1.25026954403403,
1.25026954403403), D_manhattan_scaled_adj_std_std = c(0.814293951024465,
0.814293951024465, 0.814293951024465, 0.814293951024465, 0.814293951024465,
0.814293951024465, 0.814293951024465, 0.814293951024465, 0.814293951024465,
0.814293951024465), D_manhattan_scaled_adj_std_percentile_90 = c(2.59590324174216,
2.59590324174216, 2.59590324174216, 2.59590324174216, 2.59590324174216,
2.59590324174216, 2.59590324174216, 2.59590324174216, 2.59590324174216,
2.59590324174216), D_manhattan_median = c(22.9491830993431, 22.9491830993431,
22.9491830993431, 22.9491830993431, 22.9491830993431, 22.9491830993431,
22.9491830993431, 22.9491830993431, 22.9491830993431, 22.9491830993431
), D_manhattan_mean = c(26.9833793358661, 26.9833793358661, 26.9833793358661,
26.9833793358661, 26.9833793358661, 26.9833793358661, 26.9833793358661,
26.9833793358661, 26.9833793358661, 26.9833793358661), D_manhattan_std = c(18.1569266596329,
18.1569266596329, 18.1569266596329, 18.1569266596329, 18.1569266596329,
18.1569266596329, 18.1569266596329, 18.1569266596329, 18.1569266596329,
18.1569266596329), D_manhattan_percentile_90 = c(46.2843849540569,
46.2843849540569, 46.2843849540569, 46.2843849540569, 46.2843849540569,
46.2843849540569, 46.2843849540569, 46.2843849540569, 46.2843849540569,
46.2843849540569), D_euclidean_median = c(18.7919380759661, 18.7919380759661,
18.7919380759661, 18.7919380759661, 18.7919380759661, 18.7919380759661,
18.7919380759661, 18.7919380759661, 18.7919380759661, 18.7919380759661
), D_euclidean_mean = c(22.0900856850802, 22.0900856850802, 22.0900856850802,
22.0900856850802, 22.0900856850802, 22.0900856850802, 22.0900856850802,
22.0900856850802, 22.0900856850802, 22.0900856850802), D_euclidean_std = c(14.9533118019418,
14.9533118019418, 14.9533118019418, 14.9533118019418, 14.9533118019418,
14.9533118019418, 14.9533118019418, 14.9533118019418, 14.9533118019418,
14.9533118019418), D_euclidean_percentile_90 = c(39.0151316707499,
39.0151316707499, 39.0151316707499, 39.0151316707499, 39.0151316707499,
39.0151316707499, 39.0151316707499, 39.0151316707499, 39.0151316707499,
39.0151316707499), D_cosinus_median = c(0.000819387628734736,
0.000819387628734736, 0.000819387628734736, 0.000819387628734736,
0.000819387628734736, 0.000819387628734736, 0.000819387628734736,
0.000819387628734736, 0.000819387628734736, 0.000819387628734736
), D_cosinus_mean = c(0.0259171161436376, 0.0259171161436376,
0.0259171161436376, 0.0259171161436376, 0.0259171161436376, 0.0259171161436376,
0.0259171161436376, 0.0259171161436376, 0.0259171161436376, 0.0259171161436376
), D_cosinus_std = c(0.104085795622488, 0.104085795622488, 0.104085795622488,
0.104085795622488, 0.104085795622488, 0.104085795622488, 0.104085795622488,
0.104085795622488, 0.104085795622488, 0.104085795622488), D_cosinus_percentile_90 = c(0.0307355215440245,
0.0307355215440245, 0.0307355215440245, 0.0307355215440245, 0.0307355215440245,
0.0307355215440245, 0.0307355215440245, 0.0307355215440245, 0.0307355215440245,
0.0307355215440245), experiment = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1)), .Names = c("train_user_id", "train_idx", "test_user_id",
"test_idx", "isSame", "D_manhattan_scaled_rank", "D_cosinus_rank",
"D_manhattan_scaled", "D_manhattan_scaled_norm_median", "D_manhattan_scaled_norm_standard",
"D_manhattan_scaled_adj_std", "D_manhattan_scaled_adj_std_norm_median",
"D_manhattan_scaled_adj_std_norm_standard", "D_manhattan", "D_manhattan_norm_median",
"D_manhattan_norm_standard", "D_euclidean", "D_euclidean_norm_median",
"D_euclidean_norm_standard", "D_cosinus", "D_cosinus_norm_median",
"D_cosinus_norm_standard", "D_manhattan_scaled_median", "D_manhattan_scaled_mean",
"D_manhattan_scaled_std", "D_manhattan_scaled_percentile_90",
"D_manhattan_scaled_adj_std_median", "D_manhattan_scaled_adj_std_mean",
"D_manhattan_scaled_adj_std_std", "D_manhattan_scaled_adj_std_percentile_90",
"D_manhattan_median", "D_manhattan_mean", "D_manhattan_std",
"D_manhattan_percentile_90", "D_euclidean_median", "D_euclidean_mean",
"D_euclidean_std", "D_euclidean_percentile_90", "D_cosinus_median",
"D_cosinus_mean", "D_cosinus_std", "D_cosinus_percentile_90",
"experiment"), row.names = c(NA, -10L), class = c("tbl_df", "tbl",
"data.frame"))
我正在构建一个基于我选择的D_type进行总结的函数。例如，我在选择
D_type <- "D_manhatten_scaled"
我要执行以下操作：
df %>%
summarize(paste0(D_type, "_mean_standard") =- mean(D_type))
换句话说，如何声明根据D_type改变的summarize变量名，而D_type始终是不同的，而无需使用rename_at函数。
例如，此处的摘要变量将为“ D_manhatten_scaled_mean_standard”
在其他情况下，它可以是“ D_cosinus_mean_standard”。
请告知。
答案 0 :(得分：1)
说实话，我不是100％确定我得到了你想要的东西。
但这是我想你想要的：
custom_summary <- function(df, col, ext, fun){
col_name <- paste0(col, ext)
value <- fun(df[[col]])
df_out <- data_frame(value)
names(df_out) <- eval(col_name)
return(df_out)}
custom_summary(df = my_df, col = "D_manhattan_scaled", ext = "_mean_standard", fun = mean)
# # A tibble: 1 x 1
# D_manhattan_scaled_mean_standard
# <dbl>
# 1 0.123
答案 1 :(得分：1)
如果要使用dplyr或tidyverse做这种事情，则需要一些整洁的评估。我建议阅读this。
library(tidyverse)
df <-
structure(
list(
train_user_id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L),
train_idx = c(38L, 38L, 91L, 91L, 78L, 85L, 38L,
38L, 100L, 38L),
test_user_id = c(2L, 7L, 1L, 1L, 3L, 4L, 2L,
2L, 4L, 2L),
test_idx = c(7L, 1487L, 267L, 205L, 416L, 1228L,
5L, 3L, 1221L, 26L),
isSame = c(FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE),
D_manhattan_scaled_rank = c(1,
1, 1, 1, 1, 1, 2, 2, 1, 2),
D_cosinus_rank = c(2, 2, 2, 2, 2,
2, 2, 2, 2, 2),
D_manhattan_scaled = c(
0.0657286768232851,
0.0826741765405462,
0.0940875224861761,
0.097775476943595,
0.10376335813558,
0.145577989872983,
0.146052068454132,
0.148619857868654,
0.168290231579623,
0.176975987030052
),
D_manhattan_scaled_norm_median = c(
-1.00671374438998,
-0.997385542790249,-0.991102695209199,
-0.989072540699541,
-0.985776316696353,
-0.962758092319792,-0.962497120341812,
-0.961083597121844,
-0.950255399985173,
-0.945474043327689
),
D_manhattan_scaled_norm_standard = c(
-1.48819317015296,
-1.4760112732926,-1.46780636901353,
-1.46515514706224,
-1.46085053803271,
-1.43079054927003,-1.43044974041366,
-1.42860379037803,
-1.41446301744423,
-1.4082189420958
),
D_manhattan_scaled_adj_std = c(
0.0430204530981368,
0.0460635656366407,
0.0324184592356798,
0.0970173017224344,
0.0753537776638339,
0.109856813487073,
0.0574451159458192,
0.0579062455845995,
0.0704125398622902,
0.0629985056910741
),
D_manhattan_scaled_adj_std_norm_median = c(
-0.503188246554804,-0.501396401264777,
-0.509430911762538,
-0.47139382631581,
-0.484149740879808,-0.463833665989625,
-0.494694717690257,
-0.494423195371945,
-0.487059240140704,-0.491424771169117
),
D_manhattan_scaled_adj_std_norm_standard = c(
-1.48257160625723,-1.47883448831024,
-1.49559146702019,
-1.41626035765178,
-1.44286441633519,-1.40049269568096,
-1.46485728720877,
-1.4642909933806,
-1.44893254172815,-1.45803740387515
),
D_manhattan = c(
0.672887300807173,
0.675930413345677,
0.610974280121391,
2.50949211962699,
1.1051786119143,
1.58216223895939,
0.687311963654855,
0.687773093293636,
0.974947980462479,
0.69286535340011
),
D_manhattan_norm_median = c(
-0.954621945729433,
-0.954491537063699,-0.957275148434567,
-0.875916613319857,
-0.936096658748905,
-0.915656140170372,-0.954003795394266,
-0.953984034277921,
-0.941677524612532,
-0.953765812034198
),
D_manhattan_norm_standard = c(
-1.4490608751289,
-1.44889327448836,-1.45247075951332,
-1.3479091299438,
-1.42525225821863,
-1.39898219412758,-1.44826643105155,
-1.4482410341522,
-1.43242476234849,
-1.44796057588953
),
D_euclidean = c(
0.518771643675271,
0.5188097254424,
0.53063275623408,
1.92924997316828,
0.966420157224431,
1.44718873459025,
0.51911028560706,
0.519127719117536,
0.686153917573155,
0.519347425186172
),
D_euclidean_norm_median = c(
-0.90357471715071,-0.903572834076855,
-0.902988206790553,
-0.833829138991541,
-0.881439315466942,-0.857666187097651,
-0.903557971925472,
-0.903557109870209,
-0.89529796931099,-0.903546245806251
),
D_euclidean_norm_standard = c(
-1.44257769296322,-1.44257514625199,
-1.44178448322374,
-1.34825221188084,
-1.41264128024888,-1.38048996930628,
-1.44255504634578,
-1.44255388048295,
-1.43138403391866,-1.44253918767967
),
D_cosinus = c(
0.721004237529186,
0.721004237529186,
1.2063547475627e-05,
3.04804894533106e-05,
0.00015538105717583,
0.000465547437121572,
0.721004237529186,
0.721004237529186,
0.000173984509277303,
0.721004237529186
),
D_cosinus_norm_median = c(
24.0734598908976,
24.0734598908976,
-0.0269862437287224,
-0.0263706246774829,
-0.022195600990392,-0.0118277379227909,
24.0734598908976,
24.0734598908976,
-0.0215737475064442,
24.0734598908976
),
D_cosinus_norm_standard = c(
6.67802092714532,
6.67802092714532,
-0.248881727244683,
-0.248704787232191,
-0.24750481016543,-0.244524899428419,
6.67802092714532,
6.67802092714532,
-0.247326078264596,
6.67802092714532
),
D_manhattan_scaled_median = c(
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621,
1.89451285226621
),
D_manhattan_scaled_mean = c(
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507,
2.13586416008507
),
D_manhattan_scaled_std = c(
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656,
1.3910394999656
),
D_manhattan_scaled_percentile_90 = c(
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209,
3.71110091976209
),
D_manhattan_scaled_adj_std_median = c(
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348,
0.897591141115348
),
D_manhattan_scaled_adj_std_mean = c(
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403,
1.25026954403403
),
D_manhattan_scaled_adj_std_std = c(
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465,
0.814293951024465
),
D_manhattan_scaled_adj_std_percentile_90 = c(
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216,
2.59590324174216
),
D_manhattan_median = c(
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431,
22.9491830993431
),
D_manhattan_mean = c(
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661,
26.9833793358661
),
D_manhattan_std = c(
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329,
18.1569266596329
),
D_manhattan_percentile_90 = c(
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569,
46.2843849540569
),
D_euclidean_median = c(
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661,
18.7919380759661
),
D_euclidean_mean = c(
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802,
22.0900856850802
),
D_euclidean_std = c(
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418,
14.9533118019418
),
D_euclidean_percentile_90 = c(
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499,
39.0151316707499
),
D_cosinus_median = c(
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736,
0.000819387628734736
),
D_cosinus_mean = c(
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376,
0.0259171161436376
),
D_cosinus_std = c(
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488,
0.104085795622488
),
D_cosinus_percentile_90 = c(
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245,
0.0307355215440245
),
experiment = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1)
),
.Names = c(
"train_user_id",
"train_idx",
"test_user_id",
"test_idx",
"isSame",
"D_manhattan_scaled_rank",
"D_cosinus_rank",
"D_manhattan_scaled",
"D_manhattan_scaled_norm_median",
"D_manhattan_scaled_norm_standard",
"D_manhattan_scaled_adj_std",
"D_manhattan_scaled_adj_std_norm_median",
"D_manhattan_scaled_adj_std_norm_standard",
"D_manhattan",
"D_manhattan_norm_median",
"D_manhattan_norm_standard",
"D_euclidean",
"D_euclidean_norm_median",
"D_euclidean_norm_standard",
"D_cosinus",
"D_cosinus_norm_median",
"D_cosinus_norm_standard",
"D_manhattan_scaled_median",
"D_manhattan_scaled_mean",
"D_manhattan_scaled_std",
"D_manhattan_scaled_percentile_90",
"D_manhattan_scaled_adj_std_median",
"D_manhattan_scaled_adj_std_mean",
"D_manhattan_scaled_adj_std_std",
"D_manhattan_scaled_adj_std_percentile_90",
"D_manhattan_median",
"D_manhattan_mean",
"D_manhattan_std",
"D_manhattan_percentile_90",
"D_euclidean_median",
"D_euclidean_mean",
"D_euclidean_std",
"D_euclidean_percentile_90",
"D_cosinus_median",
"D_cosinus_mean",
"D_cosinus_std",
"D_cosinus_percentile_90",
"experiment"
),
row.names = c(NA,-10L),
class = c("tbl_df", "tbl",
"data.frame")
)
summarize_var <- function(df, D_type) {
var <- ensym(D_type)
suffix <- "_mean_standard"
df %>%
summarize(!!paste0(var, suffix) := mean(!! var))
}
summarize_var(df, D_manhattan_scaled)
#> # A tibble: 1 x 1
#> D_manhattan_scaled_mean_standard
#> <dbl>
#> 1 0.123
由reprex package（v0.2.1）于2018-11-28创建