这可能是该网站上最愚蠢的问题之一（而且我知道还有另一个问题可以解决相同的问题，但是对我没有帮助）。 因此，我有274行矩阵，并创建了一个循环，在每个数字旁边分配一个分数。
scorec2.2=function(x)
{
x$score=NA
for(i in 1:274)
{
if (x[i,"time to sleep"]<= 15){
x[i,"score"]=0
}
if (between(x[i,"time to sleep"], 16, 30 )){ #PS : I tried with the simple x<15 & x=>30 and it didn't work either
x[i,"score"]=1
}
if (between(x[i,"time to sleep"], 31, 60 )){
x[i,"score"]=2
}
if (x[i,"time to sleep"]> 60){
x[i,"score"]=3
}
}
print(x[order(x$score),])
}
scorec2.2(c2.2)
，我收到此错误消息：
Error in x[i, "time to sleep"] : incorrect number of dimensions
In addition: Warning message:
In x$score = NA : Coercing LHS to a list
并且如果我尝试将矩阵转换为data.frame，则会收到其他错误消息：
Error in if (x[i, "time to sleep"] <= 15) { :
missing value where TRUE/FALSE needed
我已经重新阅读了30分钟以上的简单代码，但是我没有纠正应该纠正的内容。 最糟糕的是，我对具有不同评分系统的另一个矩阵使用了相同的代码（因此我只是更改了数字），并且一切正常。 有任何想法吗 ？ 谢谢！
这是我的矩阵：
structure(c(30, 35, 10, 60, 10, 10, 30, 10, 30, 20, 5, 10, 30,
20, 10, 20, 10, 2, 15, 10, 30, 10, 10, 0, 5, 10, 10, 10, 60,
NA, 0, 1, 15, 15, 2, NA, 45, 60, 15, 5, 1, 45, NA, 30, 10, 2,
15, 15, 3, 20, 15, 2, 15, 15, 15, 10, 30, 15, 15, 30, 5, 20,
10, 20, 1, 25, 20, 30, 15, 20, 40, 120, 5, 30, 5, 5, 20, 10,
30, 10, 40, 45, 10, 0, 30, 60, 5, 30, 60, 20, 60, 30, 15, NA,
5, 15, 5, 10, 15, 60, 5, 25, 10, 5, 30, 15, 20, 20, 20, 5, 5,
10, 10, 5, 30, 30, 20, 20, 20, 10, 45, 60, 10, 30, 10, 15, 30,
10, 30, 5, 30, 2, 60, 10, 60, NA, 5, 10, 40, 45, 5, 5, 10, 5,
10, 30, 5, 30, 10, 10, 15, 40, 10, 30, 20, 5, 60, 5, 60, 10,
NA, 10, 10, 30, 2, 20, 10, 10, 30, 60, 20, 15, 5, 10, 10, 35,
10, 10, 10, 30, 1, 30, 10, NA, 5, 5, 10, 10, 8, 10, 3, 5, 45,
10, 30, 60, 15, 75, 40, 15, 20, 30, 90, 5, 10, 5, 7, 0.5, 40,
60, 15, 30, 15, 60, 5, 15, 5, 30, 20, 2, 20, 15, 30, 10, 60,
5, 0, 10, 10, 10, 30, 20, 30, 5, 30, 10, 60, 7, 5, 1, 60, 30,
35, NA, 10, 5, 20, 10, 30, 30, 30, 15, 25, 15, 30, 10, 25, 5,
10, 10, 30, 1, 10, 60, 23, 35, 60, 3, NA, 20, 30, 2, 30, 45), .Dim = c(274L,
1L), .Dimnames = list(NULL, "time to sleep"))
应该看起来像这样
time to sleep
[1,] 30.0
[2,] 35.0
[3,] 10.0
[4,] 60.0
[5,] 10.0
[6,] 10.0
[7,] 30.0
[8,] 10.0
[9,] 30.0
[10,] 20.0
[11,] 5.0
[12,] 10.0
[13,] 30.0
[14,] 20.0
[15,] 10.0
[16,] 20.0
[17,] 10.0
[18,] 2.0
[19,] 15.0
[20,] 10.0
[21,] 30.0
[22,] 10.0
[23,] 10.0
[24,] 0.0
[25,] 5.0
[26,] 10.0
[27,] 10.0
[28,] 10.0
[29,] 60.0
[30,] NA
[31,] 0.0
[32,] 1.0
[33,] 15.0
[34,] 15.0
[35,] 2.0
[36,] NA
[37,] 45.0
[38,] 60.0
[39,] 15.0
[40,] 5.0
[41,] 1.0
[42,] 45.0
[43,] NA
[44,] 30.0
[45,] 10.0
[46,] 2.0
[47,] 15.0
[48,] 15.0
[49,] 3.0
[50,] 20.0
[51,] 15.0
[52,] 2.0
[53,] 15.0
[54,] 15.0
[55,] 15.0
[56,] 10.0
[57,] 30.0
[58,] 15.0
[59,] 15.0
[60,] 30.0
[61,] 5.0
[62,] 20.0
[63,] 10.0
[64,] 20.0
[65,] 1.0
[66,] 25.0
[67,] 20.0
[68,] 30.0
[69,] 15.0
[70,] 20.0
[71,] 40.0
[72,] 120.0
[73,] 5.0
[74,] 30.0
[75,] 5.0
[76,] 5.0
[77,] 20.0
[78,] 10.0
[79,] 30.0
[80,] 10.0
[81,] 40.0
[82,] 45.0
[83,] 10.0
[84,] 0.0
[85,] 30.0
[86,] 60.0
[87,] 5.0
[88,] 30.0
[89,] 60.0
[90,] 20.0
[91,] 60.0
[92,] 30.0
[93,] 15.0
[94,] NA
[95,] 5.0
[96,] 15.0
[97,] 5.0
[98,] 10.0
[99,] 15.0
[100,] 60.0
[101,] 5.0
[102,] 25.0
[103,] 10.0
[104,] 5.0
[105,] 30.0
[106,] 15.0
[107,] 20.0
[108,] 20.0
[109,] 20.0
[110,] 5.0
[111,] 5.0
[112,] 10.0
[113,] 10.0
[114,] 5.0
[115,] 30.0
[116,] 30.0
[117,] 20.0
[118,] 20.0
[119,] 20.0
[120,] 10.0
[121,] 45.0
[122,] 60.0
[123,] 10.0
[124,] 30.0
[125,] 10.0
[126,] 15.0
[127,] 30.0
[128,] 10.0
[129,] 30.0
[130,] 5.0
[131,] 30.0
[132,] 2.0
[133,] 60.0
[134,] 10.0
[135,] 60.0
[136,] NA
[137,] 5.0
[138,] 10.0
[139,] 40.0
[140,] 45.0
[141,] 5.0
[142,] 5.0
[143,] 10.0
[144,] 5.0
[145,] 10.0
[146,] 30.0
[147,] 5.0
[148,] 30.0
[149,] 10.0
[150,] 10.0
[151,] 15.0
[152,] 40.0
[153,] 10.0
[154,] 30.0
[155,] 20.0
[156,] 5.0
[157,] 60.0
[158,] 5.0
[159,] 60.0
[160,] 10.0
[161,] NA
[162,] 10.0
[163,] 10.0
[164,] 30.0
[165,] 2.0
[166,] 20.0
[167,] 10.0
[168,] 10.0
[169,] 30.0
[170,] 60.0
[171,] 20.0
[172,] 15.0
[173,] 5.0
[174,] 10.0
[175,] 10.0
[176,] 35.0
[177,] 10.0
[178,] 10.0
[179,] 10.0
[180,] 30.0
[181,] 1.0
[182,] 30.0
[183,] 10.0
[184,] NA
[185,] 5.0
[186,] 5.0
[187,] 10.0
[188,] 10.0
[189,] 8.0
[190,] 10.0
[191,] 3.0
[192,] 5.0
[193,] 45.0
[194,] 10.0
[195,] 30.0
[196,] 60.0
[197,] 15.0
[198,] 75.0
[199,] 40.0
[200,] 15.0
[201,] 20.0
[202,] 30.0
[203,] 90.0
[204,] 5.0
[205,] 10.0
[206,] 5.0
[207,] 7.0
[208,] 0.5
[209,] 40.0
[210,] 60.0
[211,] 15.0
[212,] 30.0
[213,] 15.0
[214,] 60.0
[215,] 5.0
[216,] 15.0
[217,] 5.0
[218,] 30.0
[219,] 20.0
[220,] 2.0
[221,] 20.0
[222,] 15.0
[223,] 30.0
[224,] 10.0
[225,] 60.0
[226,] 5.0
[227,] 0.0
[228,] 10.0
[229,] 10.0
[230,] 10.0
[231,] 30.0
[232,] 20.0
[233,] 30.0
[234,] 5.0
[235,] 30.0
[236,] 10.0
[237,] 60.0
[238,] 7.0
[239,] 5.0
[240,] 1.0
[241,] 60.0
[242,] 30.0
[243,] 35.0
[244,] NA
[245,] 10.0
[246,] 5.0
[247,] 20.0
[248,] 10.0
[249,] 30.0
[250,] 30.0
[251,] 30.0
[252,] 15.0
[253,] 25.0
[254,] 15.0
[255,] 30.0
[256,] 10.0
[257,] 25.0
[258,] 5.0
[259,] 10.0
[260,] 10.0
[261,] 30.0
[262,] 1.0
[263,] 10.0
[264,] 60.0
[265,] 23.0
[266,] 35.0
[267,] 60.0
[268,] 3.0
[269,] NA
[270,] 20.0
[271,] 30.0
[272,] 2.0
[273,] 30.0
[274,] 45.0
答案 0 :(得分：0)
您的代码中存在一些问题，但这应该可以解决
scorec2.2 = function(x) {
# use cbind and `dimnames<-`
x <- cbind(x, NA)
dimnames(x) <- list(NULL, c("time to sleep", "score"))
for (i in 1:274)
if (is.na(x[i, "time to sleep"])) { # this handles NAs
x[i, "score"] <- NA
} else {
if (x[i, "time to sleep"] <= 15) {
x[i, "score"] <- 0
}
if (between(x[i, "time to sleep"], 16, 30)) {
x[i, "score"] <- 1
}
if (between(x[i, "time to sleep"], 31, 60)) {
x[i, "score"] <- 2
}
if (x[i, "time to sleep"] > 60) {
x[i, "score"] <- 3
}
}
print(x[order(x[, "score"]), ]) # `$` doesn't work with matrices
}
scorec2.2(c2.2)
# time to sleep score
# [1,] 10.0 0
# [2,] 10.0 0
# [3,] 10.0 0
# [4,] 10.0 0
# [5,] 5.0 0
# [6,] 10.0 0
# [7,] 10.0 0
# [8,] 10.0 0
# [9,] 2.0 0
# [10,] 15.0 0
# ...
答案 1 :(得分：0)
整洁的版本，因为您已经使用dplyr::between。
df %>%
as_tibble() %>%
select(time.to.sleep = `time to sleep`) %>%
mutate(
score = case_when(
time.to.sleep <= 15 ~ NA_real_,
between(time.to.sleep , 16, 30) ~ 1,
between(time.to.sleep, 31, 60) ~ 2,
TRUE ~ 3
)
)