英文:
Reordering only 2 x-tick labels in a ggplot, while the rest are ordered by being factors
问题
Sure, here is the translated code section:
sum_tab_cond_Loc <- sum_tabnew[!condition == "empty",
list(n_samples=as.double(.N),
sum_dark=as.double(sum(pixel_diff_by_sec)),
mean=as.double(mean(pixel_diff_by_sec)),
median=as.double(median(pixel_diff_by_sec)),
sd=sd(pixel_diff_by_sec),
sem=std.error(pixel_diff_by_sec)),
by=list(condition, loc_coord, loc_id)]
sum_tab_cond_Loc$condition <- factor(sum_tab_cond_Loc$condition,
levels = gtools::mixedsort(unique(sum_tab_cond_Loc$condition)))
p_mean_condition_time <- ggplot((sum_tab_cond_Loc), aes(x=condition, y=mean, color=condition, fill=condition, group=condition)) +
geom_line(lwd=1) +
geom_point(size=5,shape=22, stroke=1.4, color="black") +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=0.1, position=position_dodge(width=0.9))
I've removed the HTML encoding (e.g., < and ") and translated it to plain text.
英文:
Here is my data for this question:
> sum_tab_cond_Loc
condition loc_coord loc_id n_samples sum_dark mean median sd
1: WT_CTL a1 1 89 14816 166.47191011 0 381.5107853
2: MUT10 a2 2 89 5748 64.58426966 0 156.9145547
3: MUT9 a3 3 89 0 0.00000000 0 0.0000000
4: MUT8 a4 4 89 0 0.00000000 0 0.0000000
5: MUT7 a5 5 89 1065 11.96629213 0 112.8897742
6: MUT6 a6 6 89 2 0.02247191 0 0.2119996
7: MUT5 a7 7 89 23891 268.43820225 85 409.9059469
8: MUT4 a8 8 89 4691 52.70786517 0 197.9350331
9: MUT3 a9 9 89 17244 193.75280899 37 330.9582259
10: MUT2 a10 10 89 19653 220.82022472 84 294.4198286
11: MUT1 a11 11 89 32244 362.29213483 187 390.1419090
12: MUT_CTL a12 12 89 20547 230.86516854 144 284.6004053
13: WT_CTL b1 13 89 9 0.10112360 0 0.6224027
14: MUT10 b2 14 89 934 10.49438202 0 59.0065720
15: MUT9 b3 15 89 2591 29.11235955 0 184.8712966
16: MUT8 b4 16 89 5 0.05617978 0 0.5299989
17: MUT7 b5 17 89 3146 35.34831461 0 95.4808526
18: MUT6 b6 18 89 0 0.00000000 0 0.0000000
19: MUT5 b7 19 89 12080 135.73033708 0 379.4003571
20: MUT4 b8 20 89 855 9.60674157 0 78.7162075
21: MUT3 b9 21 89 27055 303.98876404 140 412.2091949
22: MUT2 b10 22 89 29327 329.51685393 225 348.2187176
23: MUT1 b11 23 89 31765 356.91011236 186 416.8646674
24: MUT_CTL b12 24 89 22682 254.85393258 161 318.7144116
25: WT_CTL c1 25 89 16498 185.37078652 117 245.1523822
26: MUT10 c2 26 89 1169 13.13483146 0 91.5546522
27: MUT9 c3 27 89 6932 77.88764045 0 247.3136782
28: MUT8 c4 28 89 228 2.56179775 0 23.6359487
29: MUT7 c5 29 89 624 7.01123596 0 43.9693836
30: MUT6 c6 30 89 5643 63.40449438 0 277.5699224
31: MUT5 c7 31 89 19713 221.49438202 93 320.8856750
32: MUT4 c8 32 89 26 0.29213483 0 1.4632020
33: MUT3 c9 33 89 13328 149.75280899 21 290.6393141
34: MUT2 c10 34 89 16007 179.85393258 73 248.5571797
35: MUT1 c11 35 89 19859 223.13483146 94 315.2131382
36: MUT_CTL c12 36 89 38148 428.62921348 316 417.0589500
37: WT_CTL d1 37 89 692 7.77528090 0 61.8612364
38: MUT10 d2 38 89 5397 60.64044944 0 268.0002649
39: MUT9 d3 39 89 16 0.17977528 0 0.8469437
40: MUT8 d4 40 89 0 0.00000000 0 0.0000000
41: MUT7 d5 41 89 1958 22.00000000 0 129.0508180
42: MUT6 d6 42 89 13699 153.92134831 0 318.4321758
43: MUT5 d7 43 89 3734 41.95505618 0 119.0242441
44: MUT4 d8 44 89 19 0.21348315 0 1.7219961
45: MUT3 d9 45 89 19630 220.56179775 49 322.1956265
46: MUT2 d10 46 89 26129 293.58426966 156 349.9139767
47: MUT1 d11 47 89 46637 524.01123596 412 571.9543945
48: MUT_CTL d12 48 89 36407 409.06741573 235 469.2885524
49: WT_CTL e1 49 89 14722 165.41573034 0 295.4558423
50: MUT10 e2 50 89 4546 51.07865169 0 231.8792899
51: MUT9 e3 51 89 542 6.08988764 0 57.2378690
52: MUT8 e4 52 89 11 0.12359551 0 1.1659977
53: MUT7 e5 53 89 210 2.35955056 0 22.0465660
54: MUT6 e6 54 89 8996 101.07865169 0 307.3379922
55: MUT5 e7 55 89 17278 194.13483146 0 500.1860834
56: MUT4 e8 56 89 1724 19.37078652 0 113.0606617
57: MUT3 e9 57 89 11738 131.88764045 20 223.2198487
58: MUT2 e10 58 89 27963 314.19101124 146 406.5002202
59: MUT1 e11 59 89 32120 360.89887640 209 417.5670345
60: MUT_CTL e12 60 89 23596 265.12359551 127 358.5456678
61: WT_CTL f1 61 89 12156 136.58426966 0 322.6320663
62: MUT10 f2 62 89 10804 121.39325843 0 247.2559227
63: MUT9 f3 63 89 11 0.12359551 0 0.7510633
64: MUT8 f4 64 89 307 3.44943820 0 32.3282213
65: MUT7 f5 65 89 31 0.34831461 0 2.8005727
66: MUT6 f6 66 89 5828 65.48314607 0 117.8844882
67: MUT5 f7 67 89 8880 99.77528090 0 299.0273913
68: MUT4 f8 68 89 227 2.55056180 0 23.6354949
69: MUT3 f9 69 89 19924 223.86516854 128 323.9902563
70: MUT2 f10 70 89 30185 339.15730337 269 391.6490167
71: MUT1 f11 71 89 23295 261.74157303 182 327.0673939
72: MUT_CTL f12 72 89 33740 379.10112360 258 407.9149601
73: WT_CTL g1 73 89 1957 21.98876404 0 150.4140871
74: MUT10 g2 74 89 1883 21.15730337 0 112.9026027
75: MUT9 g3 75 89 9 0.10112360 0 0.7693702
76: MUT8 g4 76 89 0 0.00000000 0 0.0000000
77: MUT7 g5 77 89 0 0.00000000 0 0.0000000
78: MUT6 g6 78 89 14625 164.32584270 0 367.9194198
79: MUT5 g7 79 89 10308 115.82022472 0 282.0390673
80: MUT4 g8 80 89 3891 43.71910112 0 235.0162948
81: MUT3 g9 81 89 14606 164.11235955 3 408.9459132
82: MUT2 g10 82 89 20714 232.74157303 118 317.0294125
83: MUT1 g11 83 89 35864 402.96629213 236 489.6790797
84: MUT_CTL g12 84 89 30346 340.96629213 218 414.3528209
85: WT_CTL h1 85 89 5688 63.91011236 0 286.1771797
86: MUT10 h2 86 89 1 0.01123596 0 0.1059998
87: MUT9 h3 87 89 0 0.00000000 0 0.0000000
88: MUT8 h4 88 89 536 6.02247191 0 53.8480559
89: MUT7 h5 89 89 1071 12.03370787 0 113.5257729
90: MUT6 h6 90 89 9495 106.68539326 0 367.4626015
91: MUT5 h7 91 89 10 0.11235955 0 0.6980200
92: MUT4 h8 92 89 0 0.00000000 0 0.0000000
93: MUT3 h9 93 89 19009 213.58426966 0 395.8312258
94: MUT2 h10 94 89 41792 469.57303371 303 487.6894477
95: MUT1 h11 95 89 38532 432.94382022 387 383.2050353
96: MUT_CTL h12 96 89 23303 261.83146067 135 332.8016825
condition loc_coord loc_id n_samples sum_dark mean median sd
So I have this code in which I am trying to generate a figure. The conditions along the x-axis are declared to be factors. What I am wondering is if it is possible to move two of these condition labels to the left, so that the order is WT_CTL, MUT_CTL, MUT1, MUT2, MUT3, etc. Is it possible to move only two of the x-tick labels and have the rest sorted as factors?
sum_tab_cond_Loc <- sum_tabnew[!condition == "empty",
list(n_samples=as.double(.N),
sum_dark=as.double(sum(pixel_diff_by_sec)),
mean=as.double(mean(pixel_diff_by_sec)),
median=as.double(median(pixel_diff_by_sec)),
sd=sd(pixel_diff_by_sec),
sem=std.error(pixel_diff_by_sec)),
by=list(condition, loc_coord, loc_id)]
sum_tab_cond_Loc$condition <- factor(sum_tab_cond_Loc$condition,
levels = gtools::mixedsort(unique(sum_tab_cond_Loc$condition)))
p_mean_condition_time <- ggplot((sum_tab_cond_Loc), aes(x=condition, y=mean, color=condition, fill=condition, group=condition)) +
geom_line(lwd=1) +
geom_point(size=5,shape=22, stroke=1.4, color="black") +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=0.1, position=position_dodge(width=0.9))
答案1
得分: 1
以下是代码的翻译部分：
这里是一份演示，使用一些更简单的数据。
library(ggplot2)
mtcars <- datasets::mtcars
mtcars$gear2 = factor(paste0("gear", mtcars$gear))
Before（之前）：
ggplot(mtcars, aes(x=gear2, y=mpg, color=cyl, fill=cyl, group=cyl)) +
geom_line(lwd=1, position=position_dodge(width=0.9)) +
geom_point(size=5,shape=22, stroke=1.4, color="black", position=position_dodge(width=0.9)) +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mpg-2, ymax=mpg+2), width=0.1, position=position_dodge(width=0.9))
After（之后），使用相同的绘图代码：
[![enter image description here][2]][2]
请注意，这里的代码是基于你提供的信息进行翻译的。如果需要进一步的帮助，请告诉我。
英文:
Here is a demonstration with some simpler data.
library(ggplot2)
mtcars <- datasets::mtcars
mtcars$gear2 = factor(paste0("gear", mtcars$gear))
Before:
ggplot(mtcars, aes(x=gear2, y=mpg, color=cyl, fill=cyl, group=cyl)) +
geom_line(lwd=1, position=position_dodge(width=0.9)) +
geom_point(size=5,shape=22, stroke=1.4, color="black", position=position_dodge(width=0.9)) +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mpg-2, ymax=mpg+2), width=0.1, position=position_dodge(width=0.9))
Here we can adjust the ordering of the gear2 factor:
mtcars$gear2 = forcats::fct_relevel(mtcars$gear2, "gear4", after = 0)
After, with same plot code:
答案2
得分: 0
以下是翻译好的部分：
"Another potential solution is to 'manually' specify the levels, i.e. put MUT_CTL and WT_CTL at the beginning of the levels and exclude MUT_CTL and WT_CTL from the sorting, e.g.
library(tidyverse)
sum_tab_cond_Loc <- read.table(text = " condition loc_coord loc_id n_samples sum_dark mean median sd
1: WT_CTL a1 1 89 14816 166.47191011 0 381.5107853
2: MUT10 a2 2 89 5748 64.58426966 0 156.9145547
3: MUT9 a3 3 89 0 0.00000000 0 0.0000000
4: MUT8 a4 4 89 0 0.00000000 0 0.0000000
5: MUT7 a5 5 89 1065 11.96629213 0 112.8897742
...
96: MUT_CTL h12 96 89 23303 261.83146067 135 332.8016825",
header = TRUE)
sum_tab_cond_Loc %>%
mutate(sem = sd / sqrt(nrow(sum_tab_cond_Loc)),
condition = factor(
condition,
levels = c("WT_CTL", "MUT_CTL",
gtools::mixedsort(unique(sum_tab_cond_Loc$condition)[!unique(sum_tab_cond_Loc$condition) %in% c("WT_CTL", "MUT_CTL")])))) %>%
ggplot(aes(x=condition, y=mean,
color=condition, fill=condition,
group=condition)) +
geom_line(lwd=1) +
geom_point(size=5,shape=22, stroke=1.4, color="black") +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=0.1,
position=position_dodge(width=0.9))
"
英文:
Another potential solution is to 'manually' specify the levels, i.e. put MUT_CTL and WT_CTL at the beginning of the levels and exclude MUT_CTL and WT_CTL from the sorting, e.g.
library(tidyverse)
sum_tab_cond_Loc <- read.table(text = " condition loc_coord loc_id n_samples sum_dark mean median sd
1: WT_CTL a1 1 89 14816 166.47191011 0 381.5107853
2: MUT10 a2 2 89 5748 64.58426966 0 156.9145547
3: MUT9 a3 3 89 0 0.00000000 0 0.0000000
4: MUT8 a4 4 89 0 0.00000000 0 0.0000000
5: MUT7 a5 5 89 1065 11.96629213 0 112.8897742
6: MUT6 a6 6 89 2 0.02247191 0 0.2119996
7: MUT5 a7 7 89 23891 268.43820225 85 409.9059469
8: MUT4 a8 8 89 4691 52.70786517 0 197.9350331
9: MUT3 a9 9 89 17244 193.75280899 37 330.9582259
10: MUT2 a10 10 89 19653 220.82022472 84 294.4198286
11: MUT1 a11 11 89 32244 362.29213483 187 390.1419090
12: MUT_CTL a12 12 89 20547 230.86516854 144 284.6004053
13: WT_CTL b1 13 89 9 0.10112360 0 0.6224027
14: MUT10 b2 14 89 934 10.49438202 0 59.0065720
15: MUT9 b3 15 89 2591 29.11235955 0 184.8712966
16: MUT8 b4 16 89 5 0.05617978 0 0.5299989
17: MUT7 b5 17 89 3146 35.34831461 0 95.4808526
18: MUT6 b6 18 89 0 0.00000000 0 0.0000000
19: MUT5 b7 19 89 12080 135.73033708 0 379.4003571
20: MUT4 b8 20 89 855 9.60674157 0 78.7162075
21: MUT3 b9 21 89 27055 303.98876404 140 412.2091949
22: MUT2 b10 22 89 29327 329.51685393 225 348.2187176
23: MUT1 b11 23 89 31765 356.91011236 186 416.8646674
24: MUT_CTL b12 24 89 22682 254.85393258 161 318.7144116
25: WT_CTL c1 25 89 16498 185.37078652 117 245.1523822
26: MUT10 c2 26 89 1169 13.13483146 0 91.5546522
27: MUT9 c3 27 89 6932 77.88764045 0 247.3136782
28: MUT8 c4 28 89 228 2.56179775 0 23.6359487
29: MUT7 c5 29 89 624 7.01123596 0 43.9693836
30: MUT6 c6 30 89 5643 63.40449438 0 277.5699224
31: MUT5 c7 31 89 19713 221.49438202 93 320.8856750
32: MUT4 c8 32 89 26 0.29213483 0 1.4632020
33: MUT3 c9 33 89 13328 149.75280899 21 290.6393141
34: MUT2 c10 34 89 16007 179.85393258 73 248.5571797
35: MUT1 c11 35 89 19859 223.13483146 94 315.2131382
36: MUT_CTL c12 36 89 38148 428.62921348 316 417.0589500
37: WT_CTL d1 37 89 692 7.77528090 0 61.8612364
38: MUT10 d2 38 89 5397 60.64044944 0 268.0002649
39: MUT9 d3 39 89 16 0.17977528 0 0.8469437
40: MUT8 d4 40 89 0 0.00000000 0 0.0000000
41: MUT7 d5 41 89 1958 22.00000000 0 129.0508180
42: MUT6 d6 42 89 13699 153.92134831 0 318.4321758
43: MUT5 d7 43 89 3734 41.95505618 0 119.0242441
44: MUT4 d8 44 89 19 0.21348315 0 1.7219961
45: MUT3 d9 45 89 19630 220.56179775 49 322.1956265
46: MUT2 d10 46 89 26129 293.58426966 156 349.9139767
47: MUT1 d11 47 89 46637 524.01123596 412 571.9543945
48: MUT_CTL d12 48 89 36407 409.06741573 235 469.2885524
49: WT_CTL e1 49 89 14722 165.41573034 0 295.4558423
50: MUT10 e2 50 89 4546 51.07865169 0 231.8792899
51: MUT9 e3 51 89 542 6.08988764 0 57.2378690
52: MUT8 e4 52 89 11 0.12359551 0 1.1659977
53: MUT7 e5 53 89 210 2.35955056 0 22.0465660
54: MUT6 e6 54 89 8996 101.07865169 0 307.3379922
55: MUT5 e7 55 89 17278 194.13483146 0 500.1860834
56: MUT4 e8 56 89 1724 19.37078652 0 113.0606617
57: MUT3 e9 57 89 11738 131.88764045 20 223.2198487
58: MUT2 e10 58 89 27963 314.19101124 146 406.5002202
59: MUT1 e11 59 89 32120 360.89887640 209 417.5670345
60: MUT_CTL e12 60 89 23596 265.12359551 127 358.5456678
61: WT_CTL f1 61 89 12156 136.58426966 0 322.6320663
62: MUT10 f2 62 89 10804 121.39325843 0 247.2559227
63: MUT9 f3 63 89 11 0.12359551 0 0.7510633
64: MUT8 f4 64 89 307 3.44943820 0 32.3282213
65: MUT7 f5 65 89 31 0.34831461 0 2.8005727
66: MUT6 f6 66 89 5828 65.48314607 0 117.8844882
67: MUT5 f7 67 89 8880 99.77528090 0 299.0273913
68: MUT4 f8 68 89 227 2.55056180 0 23.6354949
69: MUT3 f9 69 89 19924 223.86516854 128 323.9902563
70: MUT2 f10 70 89 30185 339.15730337 269 391.6490167
71: MUT1 f11 71 89 23295 261.74157303 182 327.0673939
72: MUT_CTL f12 72 89 33740 379.10112360 258 407.9149601
73: WT_CTL g1 73 89 1957 21.98876404 0 150.4140871
74: MUT10 g2 74 89 1883 21.15730337 0 112.9026027
75: MUT9 g3 75 89 9 0.10112360 0 0.7693702
76: MUT8 g4 76 89 0 0.00000000 0 0.0000000
77: MUT7 g5 77 89 0 0.00000000 0 0.0000000
78: MUT6 g6 78 89 14625 164.32584270 0 367.9194198
79: MUT5 g7 79 89 10308 115.82022472 0 282.0390673
80: MUT4 g8 80 89 3891 43.71910112 0 235.0162948
81: MUT3 g9 81 89 14606 164.11235955 3 408.9459132
82: MUT2 g10 82 89 20714 232.74157303 118 317.0294125
83: MUT1 g11 83 89 35864 402.96629213 236 489.6790797
84: MUT_CTL g12 84 89 30346 340.96629213 218 414.3528209
85: WT_CTL h1 85 89 5688 63.91011236 0 286.1771797
86: MUT10 h2 86 89 1 0.01123596 0 0.1059998
87: MUT9 h3 87 89 0 0.00000000 0 0.0000000
88: MUT8 h4 88 89 536 6.02247191 0 53.8480559
89: MUT7 h5 89 89 1071 12.03370787 0 113.5257729
90: MUT6 h6 90 89 9495 106.68539326 0 367.4626015
91: MUT5 h7 91 89 10 0.11235955 0 0.6980200
92: MUT4 h8 92 89 0 0.00000000 0 0.0000000
93: MUT3 h9 93 89 19009 213.58426966 0 395.8312258
94: MUT2 h10 94 89 41792 469.57303371 303 487.6894477
95: MUT1 h11 95 89 38532 432.94382022 387 383.2050353
96: MUT_CTL h12 96 89 23303 261.83146067 135 332.8016825",
header = TRUE)
sum_tab_cond_Loc %>%
mutate(sem = sd / sqrt(nrow(sum_tab_cond_Loc)),
condition = factor(
condition,
levels = c("WT_CTL", "MUT_CTL",
gtools::mixedsort(unique(sum_tab_cond_Loc$condition)[!unique(sum_tab_cond_Loc$condition) %in% c("WT_CTL", "MUT_CTL")])))) %>%
ggplot(aes(x=condition, y=mean,
color=condition, fill=condition,
group=condition)) +
geom_line(lwd=1) +
geom_point(size=5,shape=22, stroke=1.4, color="black") +
labs(title=" ", x='Conditions', y = "Means of Pixel Differences") +
theme_classic(base_size = 20)+
geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=0.1,
position=position_dodge(width=0.9))
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<sup>Created on 2023-04-20 with reprex v2.0.2</sup>
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