I created a graph using geom_line and geom_point via ggplot. I want my axes to meet at (0,0) and I want my lines and data points to be in front of the axes instead of behind as shown:
I've tried:
coord_cartesian(clip = 'off')
putting geom_line and geom_point at the end
creating a base graph then add geom_line and geom_point
playing around with the functions of coord_cartesian
manually setting xlim =c(-0.1, 25) and ylim=c(-0.1, 1500)
data7 is as follows:
Treatment Days N mean sd se
1 1 0 7 204.7000000 41.579963 15.7157488
2 1 2 7 255.0571429 41.116617 15.5406205
3 1 5 7 290.6000000 49.506498 18.7116974
4 1 8 7 330.8142857 49.044144 18.5369442
5 1 12 7 407.5142857 95.584194 36.1274294
6 1 15 7 540.8571429 164.299390 62.0993323
7 1 19 7 737.5285714 308.786359 116.7102736
8 1 21 7 978.4571429 502.506726 189.9296898
9 2 0 7 205.7428571 46.902482 17.7274721
10 2 2 7 227.5571429 47.099889 17.8020846
11 2 5 7 232.4857143 59.642922 22.5429054
12 2 8 7 247.9857143 66.478529 25.1265220
13 2 12 7 272.0428571 79.173162 29.9246423
14 2 15 7 289.1142857 82.847016 31.3132288
15 2 19 7 312.3857143 105.648591 39.9314140
16 2 21 7 334.7142857 121.569341 45.9488920
17 3 0 7 212.2285714 47.549263 17.9719320
18 3 2 7 235.4142857 52.689671 19.9148237
19 3 5 7 177.0714286 54.895225 20.7484447
20 3 8 7 205.2571429 72.611451 27.4445489
21 3 12 7 247.8142857 119.369558 45.1174522
22 3 15 7 280.4285714 140.825847 53.2271669
23 3 19 7 366.9142857 210.573799 79.5894149
24 3 21 7 451.0428571 289.240793 109.3227438
25 4 0 7 211.6857143 24.329161 9.1955587
26 4 2 7 227.8428571 28.762525 10.8712127
27 4 5 7 205.9428571 49.148919 18.5765451
28 4 8 7 153.1142857 25.189246 9.5206399
29 4 12 7 128.2571429 43.145910 16.3076210
30 4 15 7 104.1714286 45.161662 17.0695038
31 4 19 7 85.4714286 51.169708 19.3403318
32 4 21 7 66.9000000 52.724567 19.9280133
33 5 0 7 216.7857143 39.957829 15.1026398
34 5 2 7 212.2000000 27.037135 10.2190765
35 5 5 7 115.5000000 37.094070 14.0202405
36 5 8 7 46.1000000 34.925492 13.2005952
37 5 12 7 29.3142857 24.761222 9.3588621
38 5 15 6 10.0666667 13.441974 5.4876629
39 5 19 6 6.4000000 11.692733 4.7735382
40 5 21 6 5.3666667 12.662017 5.1692467
41 6 0 7 206.6857143 40.359155 15.2543269
42 6 2 7 197.0428571 40.608327 15.3485048
43 6 5 7 106.2142857 58.279654 22.0276388
44 6 8 7 46.0571429 62.373014 23.5747833
45 6 12 7 31.7571429 49.977457 18.8897031
46 6 15 7 28.1142857 45.437995 17.1739480
47 6 19 7 26.2857143 38.414946 14.5194849
48 6 21 7 32.7428571 53.203003 20.1088450
49 7 0 7 193.2000000 37.300447 14.0982437
50 7 2 7 133.2428571 26.462606 10.0019250
51 7 5 7 3.8142857 7.445900 2.8142857
52 7 8 7 0.7142857 1.496026 0.5654449
53 7 12 7 0.0000000 0.000000 0.0000000
54 7 15 7 0.0000000 0.000000 0.0000000
55 7 19 7 0.0000000 0.000000 0.0000000
56 7 21 7 0.0000000 0.000000 0.0000000
My code is as follows:
ggplot(data7, aes(Days, mean, color=Treatment)) +
geom_line() +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.5, size= 0.25) +
geom_point(size=2.5) +
scale_colour_hue(limits = c("1", "2", "3", "4", "5", "6", "7")) +
scale_x_continuous(expand = c(0, 0), limits = c(0, NA), breaks = scales::pretty_breaks(n = 10)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA), breaks = scales::pretty_breaks(n = 8)) +
theme_classic() +
theme(axis.text = element_text(color = "#000000"), plot.title = element_text(hjust = 0.5)) +
coord_cartesian(clip = 'off')
Here's one approach that omits the axis lines/ticks and then explicitly layers them below the rest of the plot layers. Because the new lines/ticks are drawn as literal objects, they will then ignore any other theming you may later apply. With control comes responsibility ...
This method has the side-effect of a "simple" axis tick, just the + symbol, which shows as a cross-line at each point. This is in contrast to the standard way (typically just pointing outwards). I'm guessing that something more robust could be devised, but I thought "simple" up-front could be adapted in other ways.
Taking the literal code of your ggplot(...) + ... and storing as gg, no changes. First we'll extract the tick marks. If you are confident enough (or not OCD-enough) to determine the tick locations yourself, then feel free to hard-code it. This method (of using ggplot_build then extracting the ...$x$breaks) has the advantage of matching the tick and label locations, especially if they might change with different/updated data.
ticks <- with(ggplot_build(gg)$layout$panel_params[[1]],
na.omit(rbind(
data.frame(x = x$breaks, y = 0),
data.frame(x = 0, y = y$breaks)
)))
head(ticks,3); tail(ticks,3)
# x y
# 1 0 0
# 2 2 0
# 3 4 0
# x y
# 16 0 600
# 17 0 800
# 18 0 1000
From here, I'll take a cue from https://stackoverflow.com/a/20250185/3358272 and prepend some layers below all of the others. (This is where I identify the + symbol for axis ticks, using shape=3.)
gg$layers <- c(
geom_hline(aes(yintercept = 0)),
geom_vline(aes(xintercept = 0)),
geom_point(data = ticks, aes(x, y), shape = 3, inherit.aes = FALSE),
gg$layers)
Now we just plot the previously-generated gg, adding a cue to omit the theme axis lines/ticks.
gg + theme(axis.line = element_blank(), axis.ticks = element_blank())
Data, including converting Treatment to character (to avoid continuous/discrete warnings from scale_colour_hue):
data7 <- read.table(header=TRUE, text = "
Treatment Days N mean sd se
1 1 0 7 204.7000000 41.579963 15.7157488
2 1 2 7 255.0571429 41.116617 15.5406205
3 1 5 7 290.6000000 49.506498 18.7116974
4 1 8 7 330.8142857 49.044144 18.5369442
5 1 12 7 407.5142857 95.584194 36.1274294
6 1 15 7 540.8571429 164.299390 62.0993323
7 1 19 7 737.5285714 308.786359 116.7102736
8 1 21 7 978.4571429 502.506726 189.9296898
9 2 0 7 205.7428571 46.902482 17.7274721
10 2 2 7 227.5571429 47.099889 17.8020846
11 2 5 7 232.4857143 59.642922 22.5429054
12 2 8 7 247.9857143 66.478529 25.1265220
13 2 12 7 272.0428571 79.173162 29.9246423
14 2 15 7 289.1142857 82.847016 31.3132288
15 2 19 7 312.3857143 105.648591 39.9314140
16 2 21 7 334.7142857 121.569341 45.9488920
17 3 0 7 212.2285714 47.549263 17.9719320
18 3 2 7 235.4142857 52.689671 19.9148237
19 3 5 7 177.0714286 54.895225 20.7484447
20 3 8 7 205.2571429 72.611451 27.4445489
21 3 12 7 247.8142857 119.369558 45.1174522
22 3 15 7 280.4285714 140.825847 53.2271669
23 3 19 7 366.9142857 210.573799 79.5894149
24 3 21 7 451.0428571 289.240793 109.3227438
25 4 0 7 211.6857143 24.329161 9.1955587
26 4 2 7 227.8428571 28.762525 10.8712127
27 4 5 7 205.9428571 49.148919 18.5765451
28 4 8 7 153.1142857 25.189246 9.5206399
29 4 12 7 128.2571429 43.145910 16.3076210
30 4 15 7 104.1714286 45.161662 17.0695038
31 4 19 7 85.4714286 51.169708 19.3403318
32 4 21 7 66.9000000 52.724567 19.9280133
33 5 0 7 216.7857143 39.957829 15.1026398
34 5 2 7 212.2000000 27.037135 10.2190765
35 5 5 7 115.5000000 37.094070 14.0202405
36 5 8 7 46.1000000 34.925492 13.2005952
37 5 12 7 29.3142857 24.761222 9.3588621
38 5 15 6 10.0666667 13.441974 5.4876629
39 5 19 6 6.4000000 11.692733 4.7735382
40 5 21 6 5.3666667 12.662017 5.1692467
41 6 0 7 206.6857143 40.359155 15.2543269
42 6 2 7 197.0428571 40.608327 15.3485048
43 6 5 7 106.2142857 58.279654 22.0276388
44 6 8 7 46.0571429 62.373014 23.5747833
45 6 12 7 31.7571429 49.977457 18.8897031
46 6 15 7 28.1142857 45.437995 17.1739480
47 6 19 7 26.2857143 38.414946 14.5194849
48 6 21 7 32.7428571 53.203003 20.1088450
49 7 0 7 193.2000000 37.300447 14.0982437
50 7 2 7 133.2428571 26.462606 10.0019250
51 7 5 7 3.8142857 7.445900 2.8142857
52 7 8 7 0.7142857 1.496026 0.5654449
53 7 12 7 0.0000000 0.000000 0.0000000
54 7 15 7 0.0000000 0.000000 0.0000000
55 7 19 7 0.0000000 0.000000 0.0000000
56 7 21 7 0.0000000 0.000000 0.0000000")
data7$Treatment <- as.character(data7$Treatment)
A fairly straightforward way to do this is just to move the panel in front of the axes once the plot elements are created (i.e. as a grobTree). The grobTree contains a layout data frame which allows you to move plot elements forwards or backwards by adjusting their z component.
If you store your plot as p, then the code would be:
ggp <- ggplot_gtable(ggplot_build(p))
ggp$layout$z[which(ggp$layout$name == "panel")] <- max(ggp$layout$z) + 1
grid::grid.draw(ggp)
Plot code:
This is just the original plot except I have added a vline at 0 and an hline at 0 in case bringing the panel forwards clips your axis lines).
p <- ggplot(data7, aes(Days, mean, color=Treatment)) +
geom_hline(aes(yintercept = 0)) +
geom_vline(aes(xintercept = 0)) +
geom_line() +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=0.5, size= 0.25) +
geom_point(size=2.5) +
scale_colour_hue(limits = c("1", "2", "3", "4", "5", "6", "7")) +
scale_x_continuous(expand = c(0, 0), limits = c(0, NA), breaks = scales::pretty_breaks(n = 10)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, NA), breaks = scales::pretty_breaks(n = 8)) +
theme_classic() +
theme(axis.text = element_text(color = "#000000"), plot.title = element_text(hjust = 0.5)) +
coord_cartesian(clip = 'off')
Related
I need to create bins for every completed rotation e.g. 360° and bins will be of varying lengths. I have created a for loop but with 100,000+ rows it is slow. I tried to implement using dplyr and/or other non-loop methods but am unclear where and how to declare the cutoffs. None of the examples I found for either dplyr or cut() seemed to address my problem.
Sample data:
x <- c(seq(90, .5, length.out = 3),
seq(359.5, .2, length.out = 5),
seq(358.9, .8, length.out = 8),
seq(359.2, .3, length.out = 11),
seq(358.3, .1, length.out = 15))
df <- data.frame(x)
df$bin <- NA
df[1,2] <- 1
For loop:
for(i in 2:nrow(df)) {
if(df[i,1] < df[i-1,1]) {
df[i,2] <- df[i-1,2]
} else {
df[i,2] <- df[i-1,2] + 1
}
}
How are the results in df$bin achieved without using a loop?
It looks like you could do:
df$binnew <- cumsum(c(1, diff(df$x) > 0))
Compare:
x bin binnew
1 90.00000 1 1
2 45.25000 1 1
3 0.50000 1 1
4 359.50000 2 2
5 269.67500 2 2
6 179.85000 2 2
7 90.02500 2 2
8 0.20000 2 2
9 358.90000 3 3
10 307.74286 3 3
11 256.58571 3 3
12 205.42857 3 3
13 154.27143 3 3
14 103.11429 3 3
15 51.95714 3 3
16 0.80000 3 3
17 359.20000 4 4
18 323.31000 4 4
19 287.42000 4 4
20 251.53000 4 4
21 215.64000 4 4
22 179.75000 4 4
23 143.86000 4 4
24 107.97000 4 4
25 72.08000 4 4
26 36.19000 4 4
27 0.30000 4 4
28 358.30000 5 5
29 332.71429 5 5
30 307.12857 5 5
31 281.54286 5 5
32 255.95714 5 5
33 230.37143 5 5
34 204.78571 5 5
35 179.20000 5 5
36 153.61429 5 5
37 128.02857 5 5
38 102.44286 5 5
39 76.85714 5 5
40 51.27143 5 5
41 25.68571 5 5
42 0.10000 5 5
I'm trying to plot a ggplot graph and instead of the size of point indicating the count, I need to plot the overlapping count number. Can you help me?
https://imgur.com/a/pm1SsWd
Thank you very much!
My data:
ID CIM DD
1 8 8
2 8 8
3 8 4
4 4 4
5 2 2
6 8 8
7 8 8
8 8 8
9 2 2
10 2 2
11 2 4
12 4 4
13 8 4
14 2 2
15 4 4
16 4 8
17 2 4
18 16 8
19 8 16
20 16 16
21 2 4
22 16 8
23 8 8
24 8 8
25 8 8
26 4 4
27 1 2
28 4 8
29 8 8
30 2 4
31 8 8
32 2 2
33 1 2
34 4 8
35 8 8
36 16 8
37 8 8
38 4 4
39 4 8
40 4 8
41 8 8
42 8 8
43 2 2
I used the code below to make an overlapping count graph as shown in an image link:
https://imgur.com/a/pm1SsWd
breaks = c(1,2,4,8,16)
labels = as.character(breaks)
ggplot(data = Data,aes(CIM,DD)) +
geom_count()+
scale_x_continuous(limits = c(1, 32), breaks = breaks, labels = labels,name = "CIM")+
scale_y_continuous(limits = c(1, 32), breaks = breaks, labels = labels,name = "DD")
Take a look at this example:
Add count as label to points in geom_count
You could do the following with your data:
p <- ggplot(data = Data,aes(CIM,DD)) +
geom_count(show.legend = FALSE)+
scale_x_continuous(limits = c(1, 32), breaks = breaks, labels = labels,name = "CIM") +
scale_y_continuous(limits = c(1, 32), breaks = breaks, labels = labels,name = "DD") +
scale_size_continuous(range = c(10, 10))
p + geom_text(data = ggplot_build(p)$data[[1]], aes(x, y, label = n), color = "#ffffff")
You can adjust the range in scale_size_continuous if you wish to vary the size of points.
I am trying to create an animated plot using gganimate.
When I pass the following factor dat$period to transition_states,
I get 3 static images. I would prefer to have the points "move" from state-to-
state.
Here is my code:
plot <-
ggplot(data = dat, aes(x = age, y = value, color = period)) +
geom_point(size = 3, aes(group = period)) +
facet_wrap(~group)+
transition_states(states=period, transition_length = 2, state_length = 1) +
ease_aes('linear')+
enter_fade()+
exit_fade()
plot
Here is my data:
record period value age group
1 1 start 45 24 a
2 2 start 6 22 c
3 3 start 23 32 b
4 4 start 67 11 a
5 1 middle 42 24 a
6 2 middle 65 22 c
7 3 middle 28 32 b
8 4 middle 11 11 a
9 1 end 23 24 a
10 2 end 14 22 c
11 3 end 34 32 b
12 4 end 21 11 a
13 5 start 5 12 c
14 6 start 9 23 c
15 7 start 53 47 b
16 8 start 17 32 a
17 5 middle 15 12 c
18 6 middle 6 23 c
19 7 middle 23 47 b
20 8 middle 67 32 a
21 5 end 51 12 c
22 6 end 16 23 c
23 7 end 8 47 b
24 8 end 41 32 a
The points appear/disappear - I would like the points to travel on the screen between states - any help appreciated
The group aesthetic is used to determine which rows in each period's data are treated as the same objects. You need group = record here:
ggplot(data = dat, aes(x = age, y = value, color = period)) +
geom_point(size = 3, aes(group = record)) +
facet_wrap(~ group)+
transition_states(states=period, transition_length = 2, state_length = 1) +
ease_aes('linear')+
enter_fade()+
exit_fade()
I have the following generated data frame called Raw_Data:
Time Velocity Type
1 10 1 a
2 20 2 a
3 30 3 a
4 40 4 a
5 50 5 a
6 10 2 b
7 20 4 b
8 30 6 b
9 40 8 b
10 50 9 b
11 10 3 c
12 20 6 c
13 30 9 c
14 40 11 c
15 50 13 c
When plotting each Type, with the following:
ggplot(Raw_Data, aes(x=Time, y=Velocity))+geom_point() + facet_grid(Type ~.)
the y-axis increments as:
1, 11, 13, 2, 3, 4, 5, 6, 7, 8, 9
The y-axis labels should be in order - why has 11 and 12 appeared after 1?
I have created the data frame as follows using your sample data:
mydata <- read.table(text="Time Velocity Type
1 10 1 a
2 20 2 a
3 30 3 a
4 40 4 a
5 50 5 a
6 10 2 b
7 20 4 b
8 30 6 b
9 40 8 b
10 50 9 b
11 10 3 c
12 20 6 c
13 30 9 c
14 40 11 c
15 50 13 c", header=TRUE)
Followed by the command
ggplot(mydata, aes(x=Time, y=Velocity))+geom_point() + facet_grid(Type ~.)
which correctly displays the plot as shown in picture below
Note: changing the call to ggplot as shown below:
ggplot(mydata, aes(x=Time, y=as.character(Velocity))) +
geom_point() +
facet_grid(Type ~.)
reproduces the problem you mentioned. So you need to convert the Velocity variable to appropriate type i.e. integer in your case.
I'm not sure why I am having such a problem with my x-scale labels repeating as opposed to just labeling where there is a measured point. Additionally, my labels for my legend are not working.
FamIncome Ethnicity mean.bmi
1 1 1 28.54250
2 1 2 26.66300
3 1 3 26.62105
4 1 4 29.51396
5 1 5 25.66722
6 2 1 29.62404
7 2 2 28.08393
8 2 3 28.62215
9 2 4 28.97561
10 2 5 25.57714
11 3 1 29.52630
12 3 2 28.27235
13 3 3 29.67060
14 3 4 31.36768
15 3 5 26.13361
16 4 1 30.83368
17 4 2 30.80814
18 4 3 29.29594
19 4 4 29.18521
20 4 5 24.80550
21 5 1 29.76500
22 5 2 29.24404
23 5 3 28.89435
24 5 4 31.48172
25 5 5 28.02522
26 6 1 30.05087
27 6 2 29.88574
28 6 3 29.53793
29 6 4 30.97993
30 6 5 25.57857
31 7 1 30.31787
32 7 2 29.28055
33 7 3 28.50421
34 7 4 30.65427
35 7 5 26.66094
36 8 1 29.15000
37 8 2 29.02789
38 8 3 28.36507
39 8 4 33.51915
40 8 5 28.38263
41 9 1 28.17679
42 9 2 28.74731
43 9 3 28.06196
44 9 4 31.38483
45 9 5 26.96000
46 10 1 28.71633
47 10 2 33.44409
48 10 3 30.63048
49 10 4 30.22587
50 10 5 27.36375
51 14 1 30.78161
52 14 2 27.43575
53 14 3 28.96817
54 14 4 32.22378
55 14 5 25.62778
56 15 1 29.15982
57 15 2 27.42672
58 15 3 27.60567
59 15 4 30.05013
60 15 5 26.80271
code below:
a <- ggplot(nh1, aes(x=FamIncome, y=mean.bmi)) + geom_line(aes(group=Ethnicity, colour = Ethnicity)) + geom_point()
a = a + labs(list(title="Average BMI versus Family Income", x = "Family Income", y = "Average BMI"))
a = a + scale_x_discrete(breaks=c("1","2","3","4","5","6","7","8","9","10","14","15"),
labels = c("0-4,999", "5K-9,999", "10K-14,999", "15K-19,999", "20K-24,999", "25K-34,999", "35K-44,999", "45K-54,999", "55K-64,999", "65K-74,999", "75K-100K", "Over 100K"))
a = a + theme(axis.text.x=element_text(angle=-90))
a = a + scale_colour_continuous(name = "Ethnicity",
breaks=c("5","4","3","2","1"),
labels=c("Other Race/Multi", "Black","White","Other Hispanic", "Mexican-American"))
a
I cannot post a picture of the image that I'm getting until I get 2 more "reputation" points
Try converting your x variable to a factor:
a <- ggplot(nh1, aes(x=factor(FamIncome), y=mean.bmi)) + geom_line(aes(group=Ethnicity, colour = factor(Ethnicity)))
a = a + labs(list(title="Average BMI versus Family Income", x = "Family Income", y = "Average BMI"))
a = a + scale_x_discrete("Family Income", labels = c("0-4,999", "5K-9,999", "10K-14,999", "15K-19,999", "20K-24,999", "25K-34,999", "35K-44,999", "45K-54,999", "55K-64,999", "65K-74,999", "75K-100K", "Over 100K"))
a = a + opts(axis.text.x=theme_text(angle=-90))
a = a + scale_colour_discrete(name = "Ethnicity",
breaks=c("5","4","3","2","1"),
labels=c("Other Race/Multi", "Black","White","Other Hispanic", "Mexican-American"))
With a numeric x variable, ggplot is treating it as a numeric scale, when you really intended it to be categorical. Also note the confusing between fill and colour. fill is for two dimensional filled regions.