我有以下数据：
# A tibble: 49 x 9
date Y X1 X2 X3 X4 X5 X6 ID
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 2016-10-21 1 4.14 18 0 1 0.0770 0.000429 CAT1
2 2016-10-24 1 4.14 17 0 1 0.0793 0.000424 CAT1
3 2016-10-25 0 4.16 16 0 1 0.0804 0.000404 CAT1
4 2016-10-26 0 4.16 15 0 1 0.0626 0.000426 CAT1
5 2016-10-27 0 4.16 14 0 1 0.0587 0.000442 CAT1
6 2016-10-28 0 4.11 13 0 1 0.0610 0.000440 CAT1
7 2016-10-31 0 4.14 12 0 1 0.0704 0.000425 CAT1
8 2016-11-01 1 3.98 11 0 1 0.0867 0.000417 CAT1
9 2016-11-02 0 6.00 10 0 1 0.0673 0.000453 CAT1
10 2016-11-03 0 8.42 10 0 1 0.0877 0.000429 CAT1
# ... with 39 more rows
我想删除所有包含整数的列-除了Y -。
# A tibble: 49 x 6
date Y X1 X5 X6 ID
<date> <dbl> <dbl> <dbl> <dbl> <chr>
1 2016-10-21 1 4.14 0.0770 0.000429 CAT1
2 2016-10-24 1 4.14 0.0793 0.000424 CAT1
3 2016-10-25 0 4.16 0.0804 0.000404 CAT1
4 2016-10-26 0 4.16 0.0626 0.000426 CAT1
5 2016-10-27 0 4.16 0.0587 0.000442 CAT1
6 2016-10-28 0 4.11 0.0610 0.000440 CAT1
7 2016-10-31 0 4.14 0.0704 0.000425 CAT1
8 2016-11-01 1 3.98 0.0867 0.000417 CAT1
9 2016-11-02 0 6.00 0.0673 0.000453 CAT1
10 2016-11-03 0 8.42 0.0877 0.000429 CAT1
# ... with 39 more rows
数据：
df <- structure(list(date = structure(c(17095, 17098, 17099, 17100,
17101, 17102, 17105, 17106, 17107, 17108, 17109, 17112, 17113,
17114, 17115, 17116, 17119, 17120, 17121, 17122, 17123, 17126,
17127, 17128, 17130, 17133, 17134, 17135, 17136, 17137, 17140,
17141, 17142, 17143, 17144, 17147, 17148, 17149, 17150, 17151,
17154, 17155, 17156, 17157, 17158, 17162, 17163, 17164, 17165
), class = "Date"), Y = c(1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,
1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1,
1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0), X1 = c(4.13858526513854,
4.13858526513855, 4.16341131085939, 4.16341131085937, 4.16341131085937,
4.11423119297315, 4.13858526513857, 3.97599968560627, 5.99758130881283,
8.41953801047614, 7.95231443679086, 7.88558780320248, 7.6408950559188,
4.51370117323327, 4.52868963859669, 4.39998987943623, 4.18852747359839,
4.27042958796773, 2.30720560360487, 2.3083029424251, 2.3083029424251,
2.30720560360488, 2.30720560360486, 2.33467572807035, 2.33467572807036,
2.33467572807036, 2.30720560360486, 2.33467572807035, 2.31545097851707,
2.3399413414153, 2.40685890963718, 2.40309440701756, 2.33633188340289,
2.3363318834029, 2.23996107961566, 2.23996107961567, 2.23996107961566,
3.14644299189703, 3.1910343925295, 3.4393907031427, 3.30413087760388,
3.33080017630688, 2.63827508869038, 2.99443088216722, 2.99443088216723,
2.99443088216722, 2.99443088216722, 3.14542139469794, 3.14542139469794
), X2 = c(18, 17, 16, 15, 14, 13, 12, 11, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19,
18, 17, 16, 15, 14, 13, 12, 11, 10, 10, 10, 10, 10, 87, 86, 85,
84, 83, 82, 81), X3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X4 = c(1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1), X5 = c(0.0769944316144198, 0.0793311568823971, 0.0803743155230278,
0.0625789438826206, 0.0586506192715035, 0.0610303101083243, 0.0703939970975855,
0.0867098273608016, 0.0673120522212106, 0.0877296725069155, 0.0923742675361241,
0.0821050363880187, 0.0360531976099817, 0.063410467337928, 0.0289807505667197,
0.0403890038946993, 0.0587200889534704, 0.124855015077667, 0.0636602113103218,
0.0748772236055617, 0.0828248414842617, 0.0588561607897347, 0.0437146614571738,
0.0399432627968126, 0.0535895503558405, 0.0538598239712004, 0.0600971764378981,
0.0543532803438423, 0.0412119504402689, 0.0445593481900316, 0.0471324573693227,
0.0366910541674913, 0.0412784111781792, 0.0338162772274317, 0.0391189676384125,
0.051151724195942, 0.0442197922283997, 0.0458769828703159, 0.0392536462039503,
0.0397989336000519, 0.0293505218180493, 0.0229058449521028, 0.0154019371887762,
0.0102366640366435, 0.00783792657548366, 0.014037818210456, 0.00900392496961011,
0.0148108452415051, 0.0159871581537364), X6 = c(0.000428660536007568,
0.000424348382531349, 0.000403672086504106, 0.000425772306880377,
0.000441567036819891, 0.000440420473928468, 0.000424616565866307,
0.000417156794102717, 0.000453102696396517, 0.000429420158272163,
0.000426339236438714, 0.000424204011080916, 0.000450812884669126,
0.00048728803860348, 0.000526461561504051, 0.000494106517096305,
0.000493488610269819, 0.00041740609044358, 0.000329604373072286,
0.000321981688032803, 0.000313820182149535, 0.000324018084037671,
0.000321928021838835, 0.000325886279909115, 0.000324905583026473,
0.000323263064904554, 0.000315582726878559, 0.00033115144688,
0.000326176783596685, 0.000320421043513733, 0.000317459171547033,
0.000306378296724892, 0.000304230982248009, 0.00031578316067723,
0.000315783239223671, 0.000308443856342272, 0.000302387474982801,
0.000302702247056619, 0.000300580888361005, 0.000298662388842681,
0.000295031270763261, 0.000300955138678924, 0.000308009865186193,
0.00031004060452567, 0.000308580481883199, 0.000309081734643359,
0.000309347430761987, 0.00031610525741575, 0.000316030523318374
), ID = c("CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1", "CAT1",
"CAT1", "CAT1")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-49L))
答案 0 :(得分：2)
一个dplyr选项可能是：
df %>%
select(starts_with("X")) %>%
select_if(~ sum(. %% 1) != 0) %>%
bind_cols(df %>%
select(-starts_with("X")))
X1 X5 X6 date Y ID
<dbl> <dbl> <dbl> <date> <dbl> <chr>
1 4.14 0.0770 0.000429 2016-10-21 1 CAT1
2 4.14 0.0793 0.000424 2016-10-24 1 CAT1
3 4.16 0.0804 0.000404 2016-10-25 0 CAT1
4 4.16 0.0626 0.000426 2016-10-26 0 CAT1
5 4.16 0.0587 0.000442 2016-10-27 0 CAT1
6 4.11 0.0610 0.000440 2016-10-28 0 CAT1
7 4.14 0.0704 0.000425 2016-10-31 0 CAT1
8 3.98 0.0867 0.000417 2016-11-01 1 CAT1
9 6.00 0.0673 0.000453 2016-11-02 0 CAT1
10 8.42 0.0877 0.000429 2016-11-03 0 CAT1
答案 1 :(得分：1)
这里是使用purrr的imap作为子集向量的选项（保留列的原始顺序）。
library(purrr)
not_whole <- function(x,y) {
if (all(is.numeric(x)) & y != "Y") {
!all(as.integer(x) == x)
} else TRUE
}
df[imap_lgl(df, ~ not_whole(.x, .y))]