各ファセットの最高から最低までのgeom_linesを注文してください

Question

私には、comp_idという4つのレベル（comp1〜comp4）を持つ因子があります。私はgeom_lineプロットで最高から最低まで各レベルを注文したいと思います。

私はあなたが見ることができるように、それはすべてのレベルのためにうまく働いx軸のラベル

+scale_x_discrete(labels = dat[, setNames(as.character(predictor), ord)]) 

を置き換えるために、このスクリプトを

library(data.table) 
library(ggplot2) 
dat <- as.data.table(df) 
dat[, ord := sprintf("%02i", frank(dat, comp_id, -value, ties.method = "first"))] 


ggplot(dat, aes(x = ord, y = value , group = comp_id , colour = comp_id))+ 
    geom_line()+ 
    facet_wrap(~comp_id, ncol = 1, scales = "free_x", labeller = label_parsed, drop = TRUE)+ 
    theme(axis.text.x=element_text(angle=35, vjust=1, hjust=1, 
           )) 

を使用して、このプロット

を得ましたcomp3を除き、注文された変数（100〜105）は彼らは最後にプロットされるはずだったファセットの始めにプロットされています。何がうまくいかなかったのだろうか。どんな提案も感謝します。ここで

DATA

> dput(df) 
structure(list(predictor = c("c_C2", "c_C3", "c_C4", "d_D2", 
"d_D3", "d_D4", "d_D5", "h_BF", "h_BFI", "h_ER", "h_f", "h_PET", 
"h_QuFl", "h_Ra", "l_Da", "l_NaCo", "l_ShBe", "m_a", "m_DrDe", 
"m_ElRa", "m_MeElm", "m_MeSlPe", "Mr_Co", "Mr_GRAv", "Mr_GREy", 
"Mr_Mu", "Mr_Sa", "s_SaLo", "s_SiLo", "s_sSiLo", "s_Stl", "Sr_Li", 
"Sr_SaCoCoTe", "Sr_SaLoSi", "Sr_SaMubcl", "c_C2", "c_C3", "c_C4", 
"d_D2", "d_D3", "d_D4", "d_D5", "h_BF", "h_BFI", "h_ER", "h_f", 
"h_PET", "h_QuFl", "h_Ra", "l_Da", "l_NaCo", "l_ShBe", "m_a", 
"m_DrDe", "m_ElRa", "m_MeElm", "m_MeSlPe", "Mr_Co", "Mr_GRAv", 
"Mr_GREy", "Mr_Mu", "Mr_Sa", "s_SaLo", "s_SiLo", "s_sSiLo", "s_Stl", 
"Sr_Li", "Sr_SaCoCoTe", "Sr_SaLoSi", "Sr_SaMubcl", "c_C2", "c_C3", 
"c_C4", "d_D2", "d_D3", "d_D4", "d_D5", "h_BF", "h_BFI", "h_ER", 
"h_f", "h_PET", "h_QuFl", "h_Ra", "l_Da", "l_NaCo", "l_ShBe", 
"m_a", "m_DrDe", "m_ElRa", "m_MeElm", "m_MeSlPe", "Mr_Co", "Mr_GRAv", 
"Mr_GREy", "Mr_Mu", "Mr_Sa", "s_SaLo", "s_SiLo", "s_sSiLo", "s_Stl", 
"Sr_Li", "Sr_SaCoCoTe", "Sr_SaLoSi", "Sr_SaMubcl", "c_C2", "c_C3", 
"c_C4", "d_D2", "d_D3", "d_D4", "d_D5", "h_BF", "h_BFI", "h_ER", 
"h_f", "h_PET", "h_QuFl", "h_Ra", "l_Da", "l_NaCo", "l_ShBe", 
"m_a", "m_DrDe", "m_ElRa", "m_MeElm", "m_MeSlPe", "Mr_Co", "Mr_GRAv", 
"Mr_GREy", "Mr_Mu", "Mr_Sa", "s_SaLo", "s_SiLo", "s_sSiLo", "s_Stl", 
"Sr_Li", "Sr_SaCoCoTe", "Sr_SaLoSi", "Sr_SaMubcl"), comp_id = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 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, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("comp1", 
"comp2", "comp3", "comp4"), class = "factor"), value = c(0.0633325075111356, 
-0.0193713154441617, 0.000785081075580719, 0.287610195287972, 
-0.0913783988809322, -0.122928438782758, 0.305621459875726, 0.0356570047659489, 
0.367574915852176, -0.240835821698893, 0.0035597425358522, 0.295952594554233, 
-0.0439920206129066, -0.235580426938533, 0.191947159509267, -0.132931615006652, 
0.065155805120025, 0.038311284807646, 0.187182963731454, 0.120969596703282, 
-0.118935354491654, -0.173851183397175, 0.125870264508295, 0.158977975187947, 
-0.209351605852615, -0.0231602829054583, 0.078383405846316, 0.0959455355349004, 
0.238306328058919, -0.188667962455942, -0.138302814516594, -0.0586994514783439, 
0.019524606432138, 0.210636138928319, -0.204454169255484, -0.149879080476447, 
0.282741114373524, -0.272911905666994, 0.102508662574812, -0.35056583225677, 
0.257262737814283, 0.202117594283655, 0.191773977367133, 0.298513575892895, 
0.139576016330362, 0.165641757285727, -0.071542760140058, 0.116819894570386, 
0.145104320521166, 0.126636637925691, 0.0810830011112734, -0.0949935353116725, 
0.0785254958291791, 0.0326439188223452, 0.065833153228218, 0.155405435626813, 
0.128737420120173, 0.214943178842044, -0.0210359058420932, 0.0117832135586799, 
0.0762824228178598, -0.29145271973574, -0.17089908579109, -0.0992003952524557, 
0.163749177828358, 0.196561728687348, 0.0951493527111932, 0.17238711709624, 
0.0638301486629609, -0.0351097560634362, 0.0647994534663104, 
-0.154895398844537, 0.186448424833243, 0.240881706707846, -0.241364320964797, 
-0.089459273670017, 0.0491598702691844, -0.200660845431752, -0.0339722426751736, 
0.131396251991635, -0.195471026941394, -0.05919918680627, -0.184160478394361, 
0.129464190293723, 0.193021703469902, 0.178985522376368, -0.245966624042807, 
-0.23478025602535, 0.198620462933836, -0.157573246492692, -0.00808698000885529, 
0.0413693509741982, -0.121020524702316, 0.105148862728949, 0.214386790903084, 
-0.204515275979768, -0.0906160054540168, -0.276985960928353, 
0.0768294557774406, -0.074181085595352, 0.138680723918144, -0.119684214245213, 
-0.0919678069134681, 0.322602153170851, 0.228878715511945, -0.433082572929477, 
0.05754301130056, 0.130719232236558, 0.253999327778221, 0.0469683234741709, 
-0.0258294537417061, -0.258318910865727, -0.00406472629347961, 
-0.165003562015847, -0.0292142578447021, 0.00862320222199929, 
0.0875367120866572, 0.0331716236283754, -0.0418387105725687, 
-0.12523142839593, -0.200857915084298, 0.138378222132672, 0.00992811008724002, 
-0.0201043482518474, -0.148894977354092, -0.323240591170999, 
-0.0556713655820164, 0.379033571103569, -0.264420286734383, 0.127560649906739, 
-0.00546455207923468, -0.203293330594455, -0.122085266718802, 
-0.0970860819632599, -0.173818516285048, -0.0585031143296301, 
0.125084378608705, 0.0655074180474436, 0.254339734692359, 0.00114212078410835 
)), class = "data.frame", .Names = c("predictor", "comp_id", 
"value"), row.names = c(NA, -140L)) 

Answer 1

tidyverseと連続尺度@missuseにより、素敵な答えに加えて

library(tidyverse) 
df %>% 
    arrange(comp_id, desc(value)) %>% #arrange by comp_id and descending value 
    mutate(ord = 1:n()) -> dat #create the x scale 

ggplot(dat, aes(x = ord, y = value , group = comp_id , colour = comp_id))+ 
    geom_line()+ 
    facet_wrap(~comp_id, ncol = 1, scales = "free_x", drop = TRUE)+ 
    theme(axis.text.x=element_text(angle=35, vjust=1, hjust=1)) + 
    scale_x_continuous(labels = dat$predictor, breaks = dat$ord, expand = c(0.02, 0.02)) 

Answer 2

を使用したアプローチである、という別の方法がありました私が望むものを私にくれた。 x軸ラベル

as.numeric(as.character(ord)) 

を交換しながら、最終的なスクリプトは

ggplot(dat, aes(x = as.factor(as.numeric(as.character(ord))), y = value , group = comp_id , colour = comp_id))+ 
    geom_line()+ 
    facet_wrap(~comp_id, ncol = 1, scales = "free_x", labeller = label_parsed, drop = TRUE)+ 
    theme(axis.text.x=element_text(angle=35, vjust=1, hjust=1, 
))+ 
    scale_x_discrete(labels = dat[, setNames(as.character(predictor), as.numeric(as.character(ord)))]) 

であるX軸

aes(x = as.factor(as.numeric(as.character(ord))) 

及び文字として/数字として用い有する因子/数値として/ as.characterとして使用