R 带条件子集的ggplot2循环图

数据描述：

    example=structure(list(Year = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L), .Label = c("2001", "2002", "2003", "2004", "2005", 
"2013", "2014", "2015"), class = "factor"), StandID = structure(c(10L, 
2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 
18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L
), .Label = c("1NB", "1NC", "1NT", "1NTB", "1RB", "1RC", "1RT", 
"1RTB", "1SB", "1SC", "1ST", "1STB", "2NB", "2NC", "2NT", "2NTB", 
"2RB", "2RC", "2RT", "2RTB", "2SB", "2SC", "2ST", "2STB", "3NB", 
"3NC", "3NT", "3NTB", "3RB", "3RC", "3RT", "3RTB", "3SB", "3SC", 
"3ST", "3STB"), class = "factor"), Block = structure(c(1L, 1L, 
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"
), class = "factor"), Aspect = structure(c(3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L), .Label = c("N", "R", "S"), class = "factor"), 
Treat = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("B", "C", "T", "TB"), class = "factor"), 
Variant = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("CS", "OBS", "SN"), class = "factor"), 
Fuels = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("F", "NF", "OBS"), class = "factor"), 
Weather = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("NW", "OBS", "W"), class = "factor"), 
X1HR = c(0.321666667, 0.177777778, 0.216111111, 0.280555556, 
0.255555556, 0.251666667, 0.296666667, 0.231111111, 0.22, 
0.27556628, 0.298042506, 0.440185249, 0.36150676, 0.398630172, 
0.367523015, 0.345717251, 0.349305987, 0.412227929, 0.242860824, 
0.258737177, 0.394024998, 0.287317872, 0.321927488, 0.281322986, 
0.313588411, 0.303123146, 0.383658946), X10HR = c(0.440555556, 
0.32, 0.266666667, 0.292222222, 0.496666667, 0.334444444, 
0.564444444, 0.424444444, 0.432777778, 0.775042951, 0.832148314, 
1.08174026, 1.023838878, 0.976997674, 0.844206274, 0.929837704, 
1.0527215, 1.089246511, 0.88642776, 0.920596302, 1.209707737, 
1.083737493, 1.077612877, 0.92481339, 1.041637182, 1.149550319, 
1.229776621), X100HR = c(0.953888889, 1.379444444, 0.881666667, 
1.640555556, 2.321666667, 1.122222222, 1.907777778, 1.633888889, 
1.208333333, 1.832724094, 2.149356842, 2.364475727, 2.493232965, 
2.262988567, 1.903909683, 2.135747433, 2.256677628, 2.288722038, 
1.997704744, 2.087135553, 2.524872541, 2.34671092, 2.338253498, 
2.06796217, 2.176314831, 2.580271006, 2.857197046), X1000HR = c(4.766666667, 
8.342222222, 3.803333333, 8.057777778, 10.11444444, 6.931111111, 
6.980555556, 13.20611111, 1.853333333, 3.389177084, 4.915714741, 
2.795267582, 2.48227787, 2.218413353, 1.64684248, 2.716156483, 
2.913746119, 2.238629341, 3.449863434, 3.432626724, 3.617531776, 
3.641639471, 3.453454971, 3.176793337, 3.459602833, 3.871166945, 
2.683447838), LITTER = c(2.4, 2.219444444, 2.772222222, 2.596666667, 
2.693888889, 2.226111111, 2.552222222, 3.109444444, 2.963333333, 
2.882233381, 3.025934696, 3.174396992, 3.291081667, 2.897673607, 
2.737119675, 2.987895727, 3.679605484, 2.769756079, 2.882241249, 
3.02594161, 3.174404144, 3.291091681, 2.897681713, 2.737129688, 
2.987901449, 3.679611444, 2.769766569), DUFF = c(1.483333333, 
1.723888889, 0.901666667, 1.520555556, 1.49, 1.366111111, 
0.551666667, 1.056111111, 0.786111111, 2.034614563, 2.349547148, 
1.685223818, 2.301301956, 2.609308243, 2.21895647, 2.043699026, 
2.142618418, 0.953421116, 4.968493462, 4.990526676, 5.012362003, 
5.023665905, 4.974074364, 4.947199821, 4.976779461, 5.082509995, 
3.55211544), simID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CS_F_NW", "CS_F_W", 
"CS_NF_NW", "CS_NF_W", "OBS_OBS_OBS", "SN_F_NW", "SN_F_W", 
"SN_NF_NW", "SN_NF_W"), class = "factor")), .Names = c("Year", 
"StandID", "Block", "Aspect", "Treat", "Variant", "Fuels", "Weather", 
"X1HR", "X10HR", "X100HR", "X1000HR", "LITTER", "DUFF", "simID"
), row.names = c(37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L), class = "data.frame")

我有一个长格式的数据集，包含多个不同的分组变量（在数据示例中：StandID和simID）

我想做什么：

    example=structure(list(Year = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L), .Label = c("2001", "2002", "2003", "2004", "2005", 
"2013", "2014", "2015"), class = "factor"), StandID = structure(c(10L, 
2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 
18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L
), .Label = c("1NB", "1NC", "1NT", "1NTB", "1RB", "1RC", "1RT", 
"1RTB", "1SB", "1SC", "1ST", "1STB", "2NB", "2NC", "2NT", "2NTB", 
"2RB", "2RC", "2RT", "2RTB", "2SB", "2SC", "2ST", "2STB", "3NB", 
"3NC", "3NT", "3NTB", "3RB", "3RC", "3RT", "3RTB", "3SB", "3SC", 
"3ST", "3STB"), class = "factor"), Block = structure(c(1L, 1L, 
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"
), class = "factor"), Aspect = structure(c(3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L), .Label = c("N", "R", "S"), class = "factor"), 
Treat = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("B", "C", "T", "TB"), class = "factor"), 
Variant = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("CS", "OBS", "SN"), class = "factor"), 
Fuels = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("F", "NF", "OBS"), class = "factor"), 
Weather = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("NW", "OBS", "W"), class = "factor"), 
X1HR = c(0.321666667, 0.177777778, 0.216111111, 0.280555556, 
0.255555556, 0.251666667, 0.296666667, 0.231111111, 0.22, 
0.27556628, 0.298042506, 0.440185249, 0.36150676, 0.398630172, 
0.367523015, 0.345717251, 0.349305987, 0.412227929, 0.242860824, 
0.258737177, 0.394024998, 0.287317872, 0.321927488, 0.281322986, 
0.313588411, 0.303123146, 0.383658946), X10HR = c(0.440555556, 
0.32, 0.266666667, 0.292222222, 0.496666667, 0.334444444, 
0.564444444, 0.424444444, 0.432777778, 0.775042951, 0.832148314, 
1.08174026, 1.023838878, 0.976997674, 0.844206274, 0.929837704, 
1.0527215, 1.089246511, 0.88642776, 0.920596302, 1.209707737, 
1.083737493, 1.077612877, 0.92481339, 1.041637182, 1.149550319, 
1.229776621), X100HR = c(0.953888889, 1.379444444, 0.881666667, 
1.640555556, 2.321666667, 1.122222222, 1.907777778, 1.633888889, 
1.208333333, 1.832724094, 2.149356842, 2.364475727, 2.493232965, 
2.262988567, 1.903909683, 2.135747433, 2.256677628, 2.288722038, 
1.997704744, 2.087135553, 2.524872541, 2.34671092, 2.338253498, 
2.06796217, 2.176314831, 2.580271006, 2.857197046), X1000HR = c(4.766666667, 
8.342222222, 3.803333333, 8.057777778, 10.11444444, 6.931111111, 
6.980555556, 13.20611111, 1.853333333, 3.389177084, 4.915714741, 
2.795267582, 2.48227787, 2.218413353, 1.64684248, 2.716156483, 
2.913746119, 2.238629341, 3.449863434, 3.432626724, 3.617531776, 
3.641639471, 3.453454971, 3.176793337, 3.459602833, 3.871166945, 
2.683447838), LITTER = c(2.4, 2.219444444, 2.772222222, 2.596666667, 
2.693888889, 2.226111111, 2.552222222, 3.109444444, 2.963333333, 
2.882233381, 3.025934696, 3.174396992, 3.291081667, 2.897673607, 
2.737119675, 2.987895727, 3.679605484, 2.769756079, 2.882241249, 
3.02594161, 3.174404144, 3.291091681, 2.897681713, 2.737129688, 
2.987901449, 3.679611444, 2.769766569), DUFF = c(1.483333333, 
1.723888889, 0.901666667, 1.520555556, 1.49, 1.366111111, 
0.551666667, 1.056111111, 0.786111111, 2.034614563, 2.349547148, 
1.685223818, 2.301301956, 2.609308243, 2.21895647, 2.043699026, 
2.142618418, 0.953421116, 4.968493462, 4.990526676, 5.012362003, 
5.023665905, 4.974074364, 4.947199821, 4.976779461, 5.082509995, 
3.55211544), simID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CS_F_NW", "CS_F_W", 
"CS_NF_NW", "CS_NF_W", "OBS_OBS_OBS", "SN_F_NW", "SN_F_W", 
"SN_NF_NW", "SN_NF_W"), class = "factor")), .Names = c("Year", 
"StandID", "Block", "Aspect", "Treat", "Variant", "Fuels", "Weather", 
"X1HR", "X10HR", "X100HR", "X1000HR", "LITTER", "DUFF", "simID"
), row.names = c(37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L), class = "data.frame")

我需要根据一个唯一的分组变量，从这个数据集中为多个列创建简单的散点图（x=预测，y=观察）

我尝试使用标准绘图的一个例子是

obs=subset(example,simID=="OBS_OBS_OBS")
csfnw=example[example$simID== "CS_F_NW",]

plot(obs$X1HR,csfnw$X1HR)

我需要对所有simID和第9-14列执行此操作。（数据示例共12张图表）

我尝试过的：

    example=structure(list(Year = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L), .Label = c("2001", "2002", "2003", "2004", "2005", 
"2013", "2014", "2015"), class = "factor"), StandID = structure(c(10L, 
2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 
18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L
), .Label = c("1NB", "1NC", "1NT", "1NTB", "1RB", "1RC", "1RT", 
"1RTB", "1SB", "1SC", "1ST", "1STB", "2NB", "2NC", "2NT", "2NTB", 
"2RB", "2RC", "2RT", "2RTB", "2SB", "2SC", "2ST", "2STB", "3NB", 
"3NC", "3NT", "3NTB", "3RB", "3RC", "3RT", "3RTB", "3SB", "3SC", 
"3ST", "3STB"), class = "factor"), Block = structure(c(1L, 1L, 
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"
), class = "factor"), Aspect = structure(c(3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L), .Label = c("N", "R", "S"), class = "factor"), 
Treat = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("B", "C", "T", "TB"), class = "factor"), 
Variant = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("CS", "OBS", "SN"), class = "factor"), 
Fuels = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("F", "NF", "OBS"), class = "factor"), 
Weather = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("NW", "OBS", "W"), class = "factor"), 
X1HR = c(0.321666667, 0.177777778, 0.216111111, 0.280555556, 
0.255555556, 0.251666667, 0.296666667, 0.231111111, 0.22, 
0.27556628, 0.298042506, 0.440185249, 0.36150676, 0.398630172, 
0.367523015, 0.345717251, 0.349305987, 0.412227929, 0.242860824, 
0.258737177, 0.394024998, 0.287317872, 0.321927488, 0.281322986, 
0.313588411, 0.303123146, 0.383658946), X10HR = c(0.440555556, 
0.32, 0.266666667, 0.292222222, 0.496666667, 0.334444444, 
0.564444444, 0.424444444, 0.432777778, 0.775042951, 0.832148314, 
1.08174026, 1.023838878, 0.976997674, 0.844206274, 0.929837704, 
1.0527215, 1.089246511, 0.88642776, 0.920596302, 1.209707737, 
1.083737493, 1.077612877, 0.92481339, 1.041637182, 1.149550319, 
1.229776621), X100HR = c(0.953888889, 1.379444444, 0.881666667, 
1.640555556, 2.321666667, 1.122222222, 1.907777778, 1.633888889, 
1.208333333, 1.832724094, 2.149356842, 2.364475727, 2.493232965, 
2.262988567, 1.903909683, 2.135747433, 2.256677628, 2.288722038, 
1.997704744, 2.087135553, 2.524872541, 2.34671092, 2.338253498, 
2.06796217, 2.176314831, 2.580271006, 2.857197046), X1000HR = c(4.766666667, 
8.342222222, 3.803333333, 8.057777778, 10.11444444, 6.931111111, 
6.980555556, 13.20611111, 1.853333333, 3.389177084, 4.915714741, 
2.795267582, 2.48227787, 2.218413353, 1.64684248, 2.716156483, 
2.913746119, 2.238629341, 3.449863434, 3.432626724, 3.617531776, 
3.641639471, 3.453454971, 3.176793337, 3.459602833, 3.871166945, 
2.683447838), LITTER = c(2.4, 2.219444444, 2.772222222, 2.596666667, 
2.693888889, 2.226111111, 2.552222222, 3.109444444, 2.963333333, 
2.882233381, 3.025934696, 3.174396992, 3.291081667, 2.897673607, 
2.737119675, 2.987895727, 3.679605484, 2.769756079, 2.882241249, 
3.02594161, 3.174404144, 3.291091681, 2.897681713, 2.737129688, 
2.987901449, 3.679611444, 2.769766569), DUFF = c(1.483333333, 
1.723888889, 0.901666667, 1.520555556, 1.49, 1.366111111, 
0.551666667, 1.056111111, 0.786111111, 2.034614563, 2.349547148, 
1.685223818, 2.301301956, 2.609308243, 2.21895647, 2.043699026, 
2.142618418, 0.953421116, 4.968493462, 4.990526676, 5.012362003, 
5.023665905, 4.974074364, 4.947199821, 4.976779461, 5.082509995, 
3.55211544), simID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CS_F_NW", "CS_F_W", 
"CS_NF_NW", "CS_NF_W", "OBS_OBS_OBS", "SN_F_NW", "SN_F_W", 
"SN_NF_NW", "SN_NF_W"), class = "factor")), .Names = c("Year", 
"StandID", "Block", "Aspect", "Treat", "Variant", "Fuels", "Weather", 
"X1HR", "X10HR", "X100HR", "X1000HR", "LITTER", "DUFF", "simID"
), row.names = c(37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L), class = "data.frame")

我遇到的问题是y轴需要保持不变，同时在x轴的不同子集中循环

我承认，我不知道最好的方法是什么。。。我认为这在一瞬间很容易，因为数据已经是长格式的，我只需要指向数据的一个子集

1） 我最初的方法是尝试拼接数据，这样每个simID都有自己的数据帧，并将其与观测数据帧进行比较，但我不知道如何将其传递给ggplot

2） 我的第二个想法是制作一个makeGraph函数，它包含了我想要的所有美学元素，并使用某种应用程序将所有东西都传递给函数，但我两个都无法实现

 makePlot=function(dat,x,y) {
 ggplot(data=dat,aes(x=x,y=y))+geom_point(shape=Treat)+theme_bw()
}

我能做的就是将数据帧分解成变量向量，然后传递给某种循环/应用

sims=levels(example$simID)
sims2=sims[sims != "OBS_OBS_OBS"]
fuel_classes=colnames(example)[9:14]

谢谢

数据示例：

    example=structure(list(Year = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L), .Label = c("2001", "2002", "2003", "2004", "2005", 
"2013", "2014", "2015"), class = "factor"), StandID = structure(c(10L, 
2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 
18L, 34L, 26L, 30L, 10L, 2L, 6L, 22L, 14L, 18L, 34L, 26L, 30L
), .Label = c("1NB", "1NC", "1NT", "1NTB", "1RB", "1RC", "1RT", 
"1RTB", "1SB", "1SC", "1ST", "1STB", "2NB", "2NC", "2NT", "2NTB", 
"2RB", "2RC", "2RT", "2RTB", "2SB", "2SC", "2ST", "2STB", "3NB", 
"3NC", "3NT", "3NTB", "3RB", "3RC", "3RT", "3RTB", "3SB", "3SC", 
"3ST", "3STB"), class = "factor"), Block = structure(c(1L, 1L, 
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"
), class = "factor"), Aspect = structure(c(3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L), .Label = c("N", "R", "S"), class = "factor"), 
Treat = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("B", "C", "T", "TB"), class = "factor"), 
Variant = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("CS", "OBS", "SN"), class = "factor"), 
Fuels = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("F", "NF", "OBS"), class = "factor"), 
Weather = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("NW", "OBS", "W"), class = "factor"), 
X1HR = c(0.321666667, 0.177777778, 0.216111111, 0.280555556, 
0.255555556, 0.251666667, 0.296666667, 0.231111111, 0.22, 
0.27556628, 0.298042506, 0.440185249, 0.36150676, 0.398630172, 
0.367523015, 0.345717251, 0.349305987, 0.412227929, 0.242860824, 
0.258737177, 0.394024998, 0.287317872, 0.321927488, 0.281322986, 
0.313588411, 0.303123146, 0.383658946), X10HR = c(0.440555556, 
0.32, 0.266666667, 0.292222222, 0.496666667, 0.334444444, 
0.564444444, 0.424444444, 0.432777778, 0.775042951, 0.832148314, 
1.08174026, 1.023838878, 0.976997674, 0.844206274, 0.929837704, 
1.0527215, 1.089246511, 0.88642776, 0.920596302, 1.209707737, 
1.083737493, 1.077612877, 0.92481339, 1.041637182, 1.149550319, 
1.229776621), X100HR = c(0.953888889, 1.379444444, 0.881666667, 
1.640555556, 2.321666667, 1.122222222, 1.907777778, 1.633888889, 
1.208333333, 1.832724094, 2.149356842, 2.364475727, 2.493232965, 
2.262988567, 1.903909683, 2.135747433, 2.256677628, 2.288722038, 
1.997704744, 2.087135553, 2.524872541, 2.34671092, 2.338253498, 
2.06796217, 2.176314831, 2.580271006, 2.857197046), X1000HR = c(4.766666667, 
8.342222222, 3.803333333, 8.057777778, 10.11444444, 6.931111111, 
6.980555556, 13.20611111, 1.853333333, 3.389177084, 4.915714741, 
2.795267582, 2.48227787, 2.218413353, 1.64684248, 2.716156483, 
2.913746119, 2.238629341, 3.449863434, 3.432626724, 3.617531776, 
3.641639471, 3.453454971, 3.176793337, 3.459602833, 3.871166945, 
2.683447838), LITTER = c(2.4, 2.219444444, 2.772222222, 2.596666667, 
2.693888889, 2.226111111, 2.552222222, 3.109444444, 2.963333333, 
2.882233381, 3.025934696, 3.174396992, 3.291081667, 2.897673607, 
2.737119675, 2.987895727, 3.679605484, 2.769756079, 2.882241249, 
3.02594161, 3.174404144, 3.291091681, 2.897681713, 2.737129688, 
2.987901449, 3.679611444, 2.769766569), DUFF = c(1.483333333, 
1.723888889, 0.901666667, 1.520555556, 1.49, 1.366111111, 
0.551666667, 1.056111111, 0.786111111, 2.034614563, 2.349547148, 
1.685223818, 2.301301956, 2.609308243, 2.21895647, 2.043699026, 
2.142618418, 0.953421116, 4.968493462, 4.990526676, 5.012362003, 
5.023665905, 4.974074364, 4.947199821, 4.976779461, 5.082509995, 
3.55211544), simID = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CS_F_NW", "CS_F_W", 
"CS_NF_NW", "CS_NF_W", "OBS_OBS_OBS", "SN_F_NW", "SN_F_W", 
"SN_NF_NW", "SN_NF_W"), class = "factor")), .Names = c("Year", 
"StandID", "Block", "Aspect", "Treat", "Variant", "Fuels", "Weather", 
"X1HR", "X10HR", "X100HR", "X1000HR", "LITTER", "DUFF", "simID"
), row.names = c(37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 
82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 127L, 128L, 129L, 
130L, 131L, 132L, 133L, 134L, 135L), class = "data.frame")

---

你实际上走对了方向。如果所有图都相同，只需生成一个函数，然后使用循环在子集上循环。对于您的示例，可以这样做：

library(ggplot2)
# the plot function
plotFun = function(dat, title) {
  ggplot(data=dat) + 
    geom_point(aes(x = x, y = y), shape=18) + 
    ggtitle(title) +
    theme_bw()
}
# columns of interest
colIdx = 9:14
# split on all values of simID
dfList = split(example, example$simID)
# simID has never appearing factors. These are removed
dfList = dfList[lapply(dfList, nrow) != 0]
# make empty array for saving plots
plotList = array(list(), dim = c(length(dfList), length(dfList), length(colIdx)),
                 dimnames = list(names(dfList), names(dfList), names(example)[colIdx]))
# the first two loops loop over all unique combinations of dfList
for (i in 2:length(dfList)) { 
  for (j in 1:(i-1)) {
    # loop over target variables
    for (k in seq_along(colIdx)) {
      # store variables to plot in a temporary dataframe
      tempDf = data.frame(x = dfList[[i]][, colIdx[k]],
                          y = dfList[[j]][, colIdx[k]])
      # add a title so we can see in the plot what is plotted vs what
      title = paste0(names(dfList)[i], ":", names(dfList[[i]])[colIdx[k]], " VS ",
                     names(dfList)[j], ":", names(dfList[[j]])[colIdx[k]])
      # make and save plot
      plotList[[i, j, k]] = plotFun(tempDf, title)
    }
  }
}
# call the plots like this
plotList[[2, 1, 4]]
# Note that we only filled the lower triangle of combinations
# therefore indexing with [[1, 1, 1]] just returns NULL
plotList[, , 1]

---

这个过程可能会更加优化，但在创建图形时，我会追求速度以上的清晰度，因为速度通常不是问题。

我认为在某个地方会有一个嵌套循环，但无法确定它是如何工作的。还有一个问题：如何保持Y轴恒定？我基本上是想绘制第5列中的所有图。如果我把观察到的变量拉到一个新的变量（dfList$OBS_OBS_OBS_OBS）中，我觉得我需要以某种方式将y从TempDF拉到前面的循环电路中？或者我只是将tempDF中的引用更改为y=obs$something？您可以在plot函数中使用函数

scale\u y\u continuous（breaks=…）

，为每个绘图指定相同的y轴。我不明白你的第二个问题，你的意思是你只想制作

plotList[，5，]

？是的，我正在尝试制作plotList[，5，]，总共8*6个绘图