How to make a smooth line graph in R using ggplot2
问题描述 投票：0回答：0
我有一个箱线图，显示处理后 cl_conc 的变化，它按天着色并按 soil_type 分面（参见照片 #1）和这行代码：
ggplot(leach.conc, aes(x=factor(treatment, level=c('Liquid', 'Solid', 'KCl', 'Control')), y= cl_conc, fill= as.factor(days))) +
labs(x="Treatment", y="Chloride concentration (mg/L)", fill = "Days") +
scale_fill_brewer() +
geom_boxplot() + facet_wrap(.~soil_type)
我认为这可以用折线图更好地表示，其中天数是 x 轴，处理是彩色的，但仍然按土壤类型分类（参见示例照片 #2）。
但是，我无法让 ggplot 创建此图，我想知道我做错了什么。
我用过这段代码：
ggplot(leach.conc, aes(x = days, y = cl_conc, fill = treatment)) +
geom_line() + facet_wrap(.~soil_type)
但是我收到了这个输出（见照片#3）。它没有按照应有的方式为数据着色，也不是我期望的平滑线。有没有我应该用来平滑数据的功能？
照片 #1
照片 #2
照片 #3
这是我的再现性输出
leach.conc <- dput(leach.conc)
structure(
list(
core = c(
"MS",
"MS",
"MS",
"MS",
"ML",
"ML",
"ML",
"ML",
"MK",
"MK",
"MK",
"MK",
"MC",
"MC",
"MC",
"MC",
"FS",
"FS",
"FS",
"FS",
"FL",
"FL",
"FL",
"FL",
"FK",
"FK",
"FK",
"FK",
"FC",
"FC",
"FC",
"FC",
"MS",
"MS",
"MS",
"MS",
"ML",
"ML",
"ML",
"ML",
"MK",
"MK",
"MK",
"MK",
"MC",
"MC",
"MC",
"MC",
"FS",
"FS",
"FS",
"FS",
"FL",
"FL",
"FL",
"FL",
"FK",
"FK",
"FK",
"FK",
"FC",
"FC",
"FC",
"FC",
"MS",
"MS",
"MS",
"MS",
"ML",
"ML",
"ML",
"ML",
"MK",
"MK",
"MK",
"MK",
"MC",
"MC",
"MC",
"MC",
"FS",
"FS",
"FS",
"FS",
"FL",
"FL",
"FL",
"FL",
"FK",
"FK",
"FK",
"FK",
"FC",
"FC",
"FC",
"FC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CK",
"CL",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC",
"CS",
"CL",
"CK",
"CC",
"PS",
"PL",
"PK",
"PC"
),
core_id = c(
"MS1",
"MS1",
"MS1",
"MS1",
"ML1",
"ML1",
"ML1",
"ML1",
"MK1",
"MK1",
"MK1",
"MK1",
"MC1",
"MC1",
"MC1",
"MC1",
"FS1",
"FS1",
"FS1",
"FS1",
"FL1",
"FL1",
"FL1",
"FL1",
"FK1",
"FK1",
"FK1",
"FK1",
"FC1",
"FC1",
"FC1",
"FC1",
"MS2",
"MS2",
"MS2",
"MS2",
"ML2",
"ML2",
"ML2",
"ML2",
"MK2",
"MK2",
"MK2",
"MK2",
"MC2",
"MC2",
"MC2",
"MC2",
"FS2",
"FS2",
"FS2",
"FS2",
"FL2",
"FL2",
"FL2",
"FL2",
"FK2",
"FK2",
"FK2",
"FK2",
"FC2",
"FC2",
"FC2",
"FC2",
"MS3",
"MS3",
"MS3",
"MS3",
"ML3",
"ML3",
"ML3",
"ML3",
"MK3",
"MK3",
"MK3",
"MK3",
"MC3",
"MC3",
"MC3",
"MC3",
"FS3",
"FS3",
"FS3",
"FS3",
"FL3",
"FL3",
"FL3",
"FL3",
"FK3",
"FK3",
"FK3",
"FK3",
"FC3",
"FC3",
"FC3",
"FC3",
"CS1",
"CL1",
"CK1",
"CC1",
"PS1",
"PL1",
"PK1",
"PC1",
"CS2",
"CL2",
"CK2",
"CC2",
"PS2",
"PL2",
"PK2",
"PC2",
"CS3",
"CL3",
"CK3",
"CC3",
"PS3",
"PL3",
"PK3",
"PC3",
"CS1",
"CL1",
"CK1",
"CC1",
"PS1",
"PL1",
"PK1",
"PC1",
"CS2",
"CL2",
"CK2",
"CC2",
"PS2",
"PL2",
"PK2",
"PC2",
"CS3",
"CL3",
"CK3",
"CC3",
"PS3",
"PL3",
"PK3",
"PC3",
"CS1",
"CL1",
"CK1",
"CC1",
"PS1",
"PL1",
"PK1",
"PC1",
"CS2",
"CL2",
"CK2",
"CC2",
"PS2",
"PL2",
"PK2",
"PC2",
"CS3",
"CL3",
"CK3",
"CC3",
"PS3",
"PL3",
"PK3",
"PC3",
"CS1",
"CK1",
"CL1",
"CC1",
"PS1",
"PL1",
"PK1",
"PC1",
"CS2",
"CL2",
"CK2",
"CC2",
"PS2",
"PL2",
"PK2",
"PC2",
"CS3",
"CL3",
"CK3",
"CC3",
"PS3",
"PL3",
"PK3",
"PC3"
),
soil_type = c(
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"WSL",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"NCL ",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH",
"ESL",
"ESL",
"ESL",
"ESL",
"FAH",
"FAH",
"FAH",
"FAH"
),
treatment = c(
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"Tl",
"Tl",
"Tl",
"SM",
"SM",
"SM",
"SM",
"KCl",
"KCl",
"KCl",
"KCl",
"Control",
"Control",
"Control",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"KCl",
"SM",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control",
"Tl",
"SM",
"KCl",
"Control"
),
days = c(
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
4L,
11L,
18L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
0L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
4L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
11L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L,
18L
),
cl_conc = c(
3.6,
18.1,
18.1,
17.4,
3.8,
77.1,
81.4,
66.8,
4.6,
19.4,
22.3,
36.9,
4.1,
1.9,
1.2,
0.6,
18.2,
27.8,
28.3,
28.3,
15,
107.8,
150.3,
94.6,
12.3,
84.8,
53.4,
51.9,
17.3,
9.1,
4.25,
1.9,
3.4,
19.8,
20.7,
20.5,
2.3,
102,
56.7,
47.4,
2.6,
33.4,
15.3,
19.9,
2.9,
2,
1.2,
0.8,
8.1,
37.1,
39.8,
34.8,
4.9,
81.9,
67.5,
56,
5.9,
41.1,
38.3,
30.9,
17.5,
12.4,
6,
3.1,
2.4,
27.8,
27.8,
24.9,
2.6,
79.7,
65.5,
55.2,
2.1,
13.5,
20.4,
24.7,
2,
1.6,
1.2,
0.7,
8.4,
42.7,
40.5,
30.1,
10.3,
121.2,
73.6,
38,
9.4,
53,
38.5,
22.3,
6.6,
4.7,
1.9,
0.85,
4.3,
5.5,
5.6,
7.3,
3.7,
0.9,
2.8,
7.633503186,
5.245095388,
4.9,
6.3,
5.6,
1.4,
5,
5.6,
1.4,
5.7,
15.80504568,
7.6,
6.5,
2.5,
2,
2.6,
1.6,
46.5,
52.6,
32.9,
2.8,
45.1,
1.3,
51.2,
2.6,
47.59251129,
68.3,
38.8,
5.4,
34.1,
66.7,
23.51266468,
0.6,
34.2,
55.7,
23.8,
5,
42.1,
47.9,
44.3,
0.8,
56.23151874,
81.2,
36.1,
1.6,
36.3,
48.2,
35.6,
1.5,
44.8,
80.9,
34.66600908,
3.1,
33.3,
81.5,
20.2,
0.4,
40.1,
66.8,
24.5,
3.6,
39,
68.2,
36,
0.303367677,
31.1,
23.2,
75.7,
0.6,
26.2,
45.3,
21.3,
0.6,
33.76030379,
47.5,
20.5,
1.1,
28.6,
65.9,
18.9,
0.2,
30.2,
65.5,
23.3,
2.7,
23.9,
64,
24.7,
0.1
),
cl_load = c(
0.0072,
0.058825,
0.0543,
0.0609,
0.0076,
0.26985,
0.26455,
0.2171,
0.0092,
0.0582,
0.0669,
0.119925,
0.0082,
0.0057,
0.0036,
0.00195,
0.0364,
0.09035,
0.0849,
0.0849,
0.03,
0.3773,
0.4509,
0.3311,
0.0246,
0.2756,
0.1602,
0.1557,
0.0346,
0.03185,
0.010625,
0.006175,
0.0034,
0.06435,
0.07245,
0.07175,
0.00345,
0.3315,
0.19845,
0.1659,
0.0039,
0.10855,
0.05355,
0.064675,
0.003625,
0.0065,
0.0039,
0.0026,
0.01215,
0.1113,
0.1393,
0.1044,
0.00735,
0.266175,
0.185625,
0.168,
0.00885,
0.113025,
0.105325,
0.0927,
0.02625,
0.0372,
0.018,
0.010075,
0.006,
0.09035,
0.09035,
0.0747,
0.0065,
0.2391,
0.22925,
0.1656,
0.004725,
0.0405,
0.0663,
0.06175,
0.0055,
0.0048,
0.0036,
0.0021,
0.0252,
0.1281,
0.131625,
0.097825,
0.0309,
0.3939,
0.2392,
0.114,
0.0235,
0.17225,
0.125125,
0.0669,
0.0165,
0.015275,
0.006175,
0.0023375,
0.0043,
0.00825,
0.0042,
0.0073,
0.0037,
0.001575,
0.0014,
0.011450255,
0.005245095,
0.0049,
0.007875,
0.0042,
0.00175,
0.005,
0.0112,
0.0014,
0.00285,
0.015805046,
0.0114,
0.008125,
0.00375,
0.0025,
0.00325,
0.0032,
0.11625,
0.14465,
0.0987,
0.0084,
0.0902,
0.0039,
0.1152,
0.00715,
0.118981278,
0.2049,
0.1164,
0.0135,
0.093775,
0.2001,
0.064659828,
0.0018,
0.0855,
0.1671,
0.06545,
0.0125,
0.094725,
0.131725,
0.099675,
0.0024,
0.177129284,
0.2436,
0.1083,
0.0048,
0.1089,
0.15665,
0.1068,
0.0045,
0.1344,
0.2427,
0.11266453,
0.010075,
0.0999,
0.2445,
0.0606,
0.0012,
0.1203,
0.2004,
0.0735,
0.0126,
0.117,
0.2046,
0.117,
0.000985945,
0.0933,
0.0696,
0.2268,
0.0018,
0.0917,
0.15855,
0.0639,
0.00195,
0.101280911,
0.1425,
0.0615,
0.003025,
0.0858,
0.214175,
0.0567,
0.00065,
0.1057,
0.212875,
0.075725,
0.0081,
0.0717,
0.192,
0.0741,
3e-04
)
),
class = "data.frame",
row.names = c(NA,-192L)
)
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