I have a df like this.
> te1.m.comb
temp variable value
1 35 Light.180.1.x.MAX1 10.398333
3 35 Dark.180.1.x.MAX1 -4.337142
5 35 Light.288.5.x.MAX3 17.825376
7 35 Dark.288.5.x.MAX3 -4.331998
9 35 Light.D125.x.K1 15.150205
11 35 Dark.D125.x.K1 -4.376553
13 35 Light.SO443WL.x.SO479WL 11.003542
15 35 Dark.SO443WL.x.SO479WL -3.216878
17 35 Light.SO450WL.x.SO465WL 15.970640
19 35 Dark.SO450WL.x.SO465WL -3.109330
21 35 Light.SO459WL.x.SO469WL 11.393617
23 35 Dark.SO459WL.x.SO469WL -3.857454
2 40 Light.180.1.x.MAX1 8.589651
4 40 Dark.180.1.x.MAX1 -5.569157
6 40 Light.288.5.x.MAX3 15.977499
8 40 Dark.288.5.x.MAX3 -5.582502
10 40 Light.D125.x.K1 13.651815
12 40 Dark.D125.x.K1 -5.243391
14 40 Light.SO443WL.x.SO479WL 8.518077
16 40 Dark.SO443WL.x.SO479WL -4.861841
18 40 Light.SO450WL.x.SO465WL 13.691814
20 40 Dark.SO450WL.x.SO465WL -4.514559
22 40 Light.SO459WL.x.SO469WL 9.262019
24 40 Dark.SO459WL.x.SO469WL -5.138836
I would like to plot the relative proportions using ggplot. For example, instead of plotting each of the variable and its value, i would like to plot the ratio value of Light.180.1.x.MAX1 / Dark.180.1.x.MAX1 i.e 10.398333/-4.337142 and so on. How can i do that in ggplot?
Here is my boxplot code which just plots each of the variable and its value..
ggplot(te1.m.comb, aes(variable, value)) + geom_boxplot() + facet_grid(temp ~.)
I renamed your data.frame df so that the reading can be easy and added the ratio column:
df$ratio = with(df, c(value/c(value[-1],NA)))
Here is the plot:
library(ggplot2)
ggplot(df, aes(variable, ratio)) +
geom_bar(stat = "identity") +
facet_grid(temp~.) + ยจ
scale_y_reverse()
Related
I have a dataset which contains the data of pairs playing a game. I have a barplot that shows the total games played by the pairs. But now I want those bars('number') to be filled with the amount of games they successfully completed('sum'). I can't get it to work. The barplot is created like this:
barplot(height = game_count$number, xlab = 'Pairs', ylim = c(0,35), ylab='Games played')
The data looks like this:
participants sum number
1 06104873220647518670 30 32
2 06105747340637377404 23 24
3 06113978630633565020 28 32
4 06121794480617858550 25 27
5 06122613960611857952 23 26
6 06123139380653583516 25 28
7 06123650620648276595 28 32
8 06124453210624910109 32 34
9 06127993700610846968 24 26
10 06128440030639764541 19 24
11 06132461300624244572 26 30
12 06137611390651588167 25 28
13 06145014400637290807 16 19
14 06163181050611257617 30 30
15 06172024240651919112 21 23
One option can be ggplot2:
library(ggplot2)
#Code
game_count$Freq <- game_count$sum/game_count$number
#Plot
ggplot(game_count,aes(x=1:nrow(game_count),y=Freq))+
geom_col(fill='cyan3',color='black')+
xlab('')
Output:
This worked for me:
barplot(t(game_correct[c('number', 'sum')]), beside=TRUE, ylim=c(0,35), col=c('black', 'green'), main='Games played and successive games by the pairs', xlab='Pairs', ylab='Games')
Result in this graph:
I would like to compile some data into a ggplot() line plot of different colors.
It's rainfall in various places over 100 days, and the data is quite different between locations which is giving me fits.
I've tried using different suggestions from this forum and they don't seem to be working well for this data. Sample data:
Time Location1 Location2 Location3
0 48 99.2966479761526 2
1 51 98.7287820735946 4
2 58 98.4803262236528 4.82842712474619
3 43 97.8941490454599 5.46410161513775
4 47 96.6091435402632 6
5 47 95.207282404881 6.47213595499958
6 41 94.8696538619697 6.89897948556636
7 34 94.6514389757067 7.29150262212918
8 40 93.7297335476615 7.65685424949238
9 57 93.2440731907263 8
My code thus far is
ggplot(Rain) +
geom_line(aes(x=Time,y=Location1,col="red")) +
geom_line(aes(x=Time,y=Location2,col="blue")) +
geom_line(aes(x=Time,y=Location3,col="green")) +
scale_color_manual(labels = c("Location 1","Location 2","Location 3"),
values = c("red","blue","green")) +
xlab("Time (Days)") + ylab("Rainfall (Inches)") + labs(color="Locations") +
ggtitle("Rainfall Over 100 Days In Three Locations")
So far it gives me everything that I want but for some reason the colors are wrong when I plot it, i.e. it plots location 1 in green while I told it red in my first geom_line.
library(tidyr)
library(ggplot2)
df_long <- gather(data = df1, Place, Rain, -Time)
ggplot(df_long) +
geom_line(aes(x=Time, y=Rain, color=Place))
Data:
df1 <- read.table(text="Time Location1 Location2 Location3
0 48 99.2966479761526 2
1 51 98.7287820735946 4
2 58 98.4803262236528 4.82842712474619
3 43 97.8941490454599 5.46410161513775
4 47 96.6091435402632 6
5 47 95.207282404881 6.47213595499958
6 41 94.8696538619697 6.89897948556636
7 34 94.6514389757067 7.29150262212918
8 40 93.7297335476615 7.65685424949238
9 57 93.2440731907263 8",
header=T, stringsAsFactors=F)
I'm trying to add error bars to a second curve (using dataset "pmfprofbs01"), but I'm having problems and I couldn't fix this.
There are a few threads on this error, but unfortunately it looks like every other answer is case specific, and I'm not able to overcome this error in my code. I am able to plot a first smoothed curve (stat_smooth) and overlapping errorbars (using geom_errobar). The problem rises when I try to add a second curve to the same graph, for comparison purposes.
With following code, I get the following error: "Error: Aesthetics must be either length 1 or the same as the data (35): ymin, ymax, x, y, colour"
I am looking to add additional errorbars to the second smoothed curve (corresponding to datasets pmfprof01 and pmfprofbs01).
Could someone explain why I keep getting this error? The code works until using the second call of geom_errorbar().
These are my 4 datasets (all used as data frames):
- pmfprof1 and pmfprof01 are the two datasets used for applying the smoothing method.
- pmfprofbs1 and pmfprofbs01 contain additional information based on an error analysis for plotting error bars.
> pmfprof1
Z correctedpmfprof1
1 -1.1023900 -8.025386e-22
2 -1.0570000 6.257110e-02
3 -1.0116000 1.251420e-01
4 -0.9662020 2.143170e-01
5 -0.9208040 3.300960e-01
6 -0.8754060 4.658550e-01
7 -0.8300090 6.113410e-01
8 -0.7846110 4.902430e-01
9 -0.7392140 3.344200e-01
10 -0.6938160 4.002040e-01
11 -0.6484190 1.215460e-01
12 -0.6030210 -1.724360e-01
13 -0.5576240 -6.077170e-01
14 -0.5122260 -1.513420e+00
15 -0.4668290 -2.075330e+00
16 -0.4214310 -2.617160e+00
17 -0.3760340 -3.350500e+00
18 -0.3306360 -4.076220e+00
19 -0.2852380 -4.926540e+00
20 -0.2398410 -5.826390e+00
21 -0.1944430 -6.761300e+00
22 -0.1490460 -7.301530e+00
23 -0.1036480 -7.303880e+00
24 -0.0582507 -7.026800e+00
25 -0.0128532 -6.627960e+00
26 0.0325444 -6.651490e+00
27 0.0779419 -6.919830e+00
28 0.1233390 -6.686490e+00
29 0.1687370 -6.129060e+00
30 0.2141350 -6.120890e+00
31 0.2595320 -6.455160e+00
32 0.3049300 -6.554560e+00
33 0.3503270 -6.983390e+00
34 0.3957250 -7.413500e+00
35 0.4411220 -6.697370e+00
36 0.4865200 -5.477230e+00
37 0.5319170 -4.552890e+00
38 0.5773150 -3.393060e+00
39 0.6227120 -2.449930e+00
40 0.6681100 -2.183190e+00
41 0.7135080 -1.673980e+00
42 0.7589050 -8.003740e-01
43 0.8043030 -2.918780e-01
44 0.8497000 -1.159710e-01
45 0.8950980 9.123767e-22
> pmfprof01
Z correctedpmfprof01
1 -1.25634000 -1.878749e-21
2 -1.20387000 -1.750190e-01
3 -1.15141000 -3.500380e-01
4 -1.09894000 -6.005650e-01
5 -1.04647000 -7.935110e-01
6 -0.99400600 -8.626150e-01
7 -0.94153900 -1.313880e+00
8 -0.88907200 -2.067770e+00
9 -0.83660500 -2.662440e+00
10 -0.78413800 -4.514190e+00
11 -0.73167100 -7.989510e+00
12 -0.67920400 -1.186870e+01
13 -0.62673800 -1.535970e+01
14 -0.57427100 -1.829150e+01
15 -0.52180400 -2.067170e+01
16 -0.46933700 -2.167890e+01
17 -0.41687000 -2.069820e+01
18 -0.36440300 -1.662640e+01
19 -0.31193600 -1.265950e+01
20 -0.25946900 -1.182580e+01
21 -0.20700200 -1.213370e+01
22 -0.15453500 -1.233680e+01
23 -0.10206800 -1.235160e+01
24 -0.04960160 -1.123630e+01
25 0.00286531 -9.086940e+00
26 0.05533220 -6.562710e+00
27 0.10779900 -4.185860e+00
28 0.16026600 -3.087430e+00
29 0.21273300 -2.005150e+00
30 0.26520000 -9.295540e-02
31 0.31766700 1.450360e+00
32 0.37013400 1.123910e+00
33 0.42260100 2.426750e-01
34 0.47506700 1.213370e-01
35 0.52753400 5.265226e-21
> pmfprofbs1
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.1023900 -8.025386e-22 0.00000000 0.0000000 0.00000000 0.0000000
2 -1.0570000 6.257110e-02 1.46519200 0.6691245 0.09974719 0.2010273
3 -1.0116000 1.251420e-01 1.62453300 0.6368053 0.09492933 0.1913175
4 -0.9662020 2.143170e-01 1.62111600 0.7200497 0.10733867 0.2163269
5 -0.9208040 3.300960e-01 1.44754700 0.7236743 0.10787900 0.2174158
6 -0.8754060 4.658550e-01 1.67509800 0.7148755 0.10656735 0.2147724
7 -0.8300090 6.113410e-01 1.78144200 0.7374481 0.10993227 0.2215539
8 -0.7846110 4.902430e-01 1.73058700 0.7701354 0.11480501 0.2313743
9 -0.7392140 3.344200e-01 0.97430090 0.7809477 0.11641681 0.2346227
10 -0.6938160 4.002040e-01 1.26812000 0.8033838 0.11976139 0.2413632
11 -0.6484190 1.215460e-01 0.93601510 0.7927926 0.11818254 0.2381813
12 -0.6030210 -1.724360e-01 0.63201080 0.8210839 0.12239996 0.2466809
13 -0.5576240 -6.077170e-01 0.05952252 0.8653050 0.12899205 0.2599664
14 -0.5122260 -1.513420e+00 0.57893690 0.8858471 0.13205429 0.2661379
15 -0.4668290 -2.075330e+00 -0.08164613 0.8921298 0.13299086 0.2680255
16 -0.4214310 -2.617160e+00 -1.08074600 0.8906925 0.13277660 0.2675937
17 -0.3760340 -3.350500e+00 -1.67279700 0.9081813 0.13538367 0.2728479
18 -0.3306360 -4.076220e+00 -2.50074900 1.0641550 0.15863486 0.3197076
19 -0.2852380 -4.926540e+00 -3.12062200 1.0639080 0.15859804 0.3196333
20 -0.2398410 -5.826390e+00 -4.47060100 1.1320770 0.16876008 0.3401136
21 -0.1944430 -6.761300e+00 -5.40812700 1.1471780 0.17101120 0.3446504
22 -0.1490460 -7.301530e+00 -6.42419100 1.1685490 0.17419700 0.3510710
23 -0.1036480 -7.303880e+00 -5.79613500 1.1935850 0.17792915 0.3585926
24 -0.0582507 -7.026800e+00 -5.85496900 1.2117630 0.18063896 0.3640539
25 -0.0128532 -6.627960e+00 -6.70480400 1.1961400 0.17831002 0.3593602
26 0.0325444 -6.651490e+00 -8.27106200 1.3376870 0.19941060 0.4018857
27 0.0779419 -6.919830e+00 -8.79402900 1.3582760 0.20247983 0.4080713
28 0.1233390 -6.686490e+00 -8.35947700 1.3673080 0.20382624 0.4107848
29 0.1687370 -6.129060e+00 -8.04437600 1.3921620 0.20753126 0.4182518
30 0.2141350 -6.120890e+00 -8.18588300 1.5220550 0.22689456 0.4572759
31 0.2595320 -6.455160e+00 -8.37217600 1.5436800 0.23011823 0.4637728
32 0.3049300 -6.554560e+00 -8.59346400 1.6276880 0.24264140 0.4890116
33 0.3503270 -6.983390e+00 -8.88378700 1.6557140 0.24681927 0.4974316
34 0.3957250 -7.413500e+00 -9.72709800 1.6569390 0.24700188 0.4977996
35 0.4411220 -6.697370e+00 -9.46033400 1.6378470 0.24415582 0.4920637
36 0.4865200 -5.477230e+00 -8.37590600 1.6262700 0.24243002 0.4885856
37 0.5319170 -4.552890e+00 -7.52867000 1.6617010 0.24771176 0.4992302
38 0.5773150 -3.393060e+00 -6.89192300 1.6667330 0.24846189 0.5007420
39 0.6227120 -2.449930e+00 -6.25115300 1.6670390 0.24850750 0.5008340
40 0.6681100 -2.183190e+00 -6.05373800 1.6720180 0.24924973 0.5023298
41 0.7135080 -1.673980e+00 -5.10526700 1.6668400 0.24847784 0.5007742
42 0.7589050 -8.003740e-01 -4.42001600 1.6561830 0.24688918 0.4975725
43 0.8043030 -2.918780e-01 -4.26640200 1.6588970 0.24729376 0.4983878
44 0.8497000 -1.159710e-01 -4.46318500 1.6533830 0.24647179 0.4967312
45 0.8950980 9.123767e-22 -5.17173200 1.6557990 0.24683194 0.4974571
> pmfprofbs01
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.25634000 -1.878749e-21 0.000000 0.0000000 0.00000000 0.0000000
2 -1.20387000 -1.750190e-01 2.316589 0.4646486 0.07853995 0.1596124
3 -1.15141000 -3.500380e-01 2.320647 0.4619668 0.07808664 0.1586911
4 -1.09894000 -6.005650e-01 2.635883 0.6519826 0.11020517 0.2239639
5 -1.04647000 -7.935110e-01 2.814679 0.6789875 0.11476983 0.2332404
6 -0.99400600 -8.626150e-01 2.588038 0.7324196 0.12380151 0.2515949
7 -0.94153900 -1.313880e+00 2.033736 0.7635401 0.12906183 0.2622852
8 -0.88907200 -2.067770e+00 2.394285 0.8120181 0.13725611 0.2789380
9 -0.83660500 -2.662440e+00 2.465425 0.9485307 0.16033095 0.3258317
10 -0.78413800 -4.514190e+00 0.998115 1.0177400 0.17202946 0.3496059
11 -0.73167100 -7.989510e+00 -1.585430 1.0502190 0.17751941 0.3607628
12 -0.67920400 -1.186870e+01 -5.740894 1.2281430 0.20759406 0.4218819
13 -0.62673800 -1.535970e+01 -9.325951 1.3289330 0.22463068 0.4565045
14 -0.57427100 -1.829150e+01 -12.010540 1.3279860 0.22447060 0.4561792
15 -0.52180400 -2.067170e+01 -14.672770 1.3296720 0.22475559 0.4567583
16 -0.46933700 -2.167890e+01 -14.912250 1.3192610 0.22299581 0.4531820
17 -0.41687000 -2.069820e+01 -12.850570 1.3288470 0.22461614 0.4564749
18 -0.36440300 -1.662640e+01 -6.093746 1.3497100 0.22814263 0.4636416
19 -0.31193600 -1.265950e+01 -5.210692 1.3602240 0.22991982 0.4672533
20 -0.25946900 -1.182580e+01 -6.041660 1.3818700 0.23357866 0.4746890
21 -0.20700200 -1.213370e+01 -5.765808 1.3854680 0.23418683 0.4759249
22 -0.15453500 -1.233680e+01 -6.985883 1.4025360 0.23707185 0.4817880
23 -0.10206800 -1.235160e+01 -7.152865 1.4224030 0.24042999 0.4886125
24 -0.04960160 -1.123630e+01 -3.600538 1.4122650 0.23871635 0.4851300
25 0.00286531 -9.086940e+00 -0.751673 1.5764920 0.26647578 0.5415439
26 0.05533220 -6.562710e+00 2.852910 1.5535620 0.26259991 0.5336672
27 0.10779900 -4.185860e+00 5.398850 1.5915640 0.26902342 0.5467214
28 0.16026600 -3.087430e+00 6.262459 1.6137360 0.27277117 0.5543377
29 0.21273300 -2.005150e+00 8.047920 1.6283340 0.27523868 0.5593523
30 0.26520000 -9.295540e-02 11.168640 1.6267620 0.27497297 0.5588123
31 0.31766700 1.450360e+00 12.345900 1.6363310 0.27659042 0.5620994
32 0.37013400 1.123910e+00 12.124650 1.6289230 0.27533824 0.5595546
33 0.42260100 2.426750e-01 11.279890 1.6137100 0.27276677 0.5543288
34 0.47506700 1.213370e-01 11.531670 1.6311490 0.27571450 0.5603193
35 0.52753400 5.265226e-21 11.284980 1.6662890 0.28165425 0.5723903
The code for plotting both curves is:
deltamean01<-pmfprofbs01[,"bsmean"]-
pmfprofbs01[,"correctedpmfprof01"]
correctmean01<-pmfprofbs01[,"bsmean"]-deltamean01
deltamean1<-pmfprofbs1[,"bsmean"]-
pmfprofbs1[,"correctedpmfprof1"]
correctmean1<-pmfprofbs1[,"bsmean"]-deltamean1
pl<- ggplot(pmfprof1, aes(x=pmfprof1[,1], y=pmfprof1[,2],
colour="red")) +
list(
stat_smooth(method = "gam", formula = y ~ s(x), size = 1,
colour="chartreuse3",fill="chartreuse3", alpha = 0.3),
geom_line(data=pmfprof1,linetype=4, size=0.5,colour="chartreuse3"),
geom_errorbar(aes(ymin=correctmean1-pmfprofbs1[,"bsci"],
ymax=correctmean1+pmfprofbs1[,"bsci"]),
data=pmfprofbs1,colour="chartreuse3",
width=0.02,size=0.9),
geom_point(data=pmfprof1,size=1,colour="chartreuse3"),
xlab(expression(xi*(nm))),
ylab("PMF (KJ/mol)"),
## GCD
geom_errorbar(aes(ymin=correctmean01-pmfprofbs01[,"bsci"],
ymax=correctmean01+pmfprofbs01[,"bsci"]),
data=pmfprofbs01,
width=0.02,size=0.9),
geom_line(data=pmfprof01,aes(x=pmfprof01[,1],y=pmfprof01[,2]),
linetype=4, size=0.5,colour="darkgreen"),
stat_smooth(data=pmfprof01,method = "gam",aes(x=pmfprof01[,1],pmfprof01[,2]),
formula = y ~ s(x), size = 1,
colour="darkgreen",fill="darkgreen", alpha = 0.3),
theme(text = element_text(size=20),
axis.text.x = element_text(size=20,colour="black"),
axis.text.y = element_text(size=20,colour="black")),
scale_x_continuous(breaks=number_ticks(8)),
scale_y_continuous(breaks=number_ticks(8)),
theme(panel.background = element_rect(fill ='white',
colour='gray')),
theme(plot.background = element_rect(fill='white',
colour='white')),
theme(legend.position="none"),
theme(legend.key = element_blank()),
theme(legend.title = element_text(colour='gray', size=20)),
NULL
)
pl
This is the result of using pl,
[enter image description here][1]
[1]: https://i.stack.imgur.com/x8FjY.png
Thanks in advance for any suggestion,
I have a problem with drawing stacked barplot with ggplot. My data looks like this:
timeInterval TotalWilling TotalAccepted SimID
1 16 12 Sim1
1 23 23 Sim2
1 63 60 Sim3
1 69 60 Sim4
1 61 60 Sim5
1 60 54 Sim6
2 16 8 Sim1
2 23 21 Sim2
2 63 52 Sim3
2 69 64 Sim4
2 61 45 Sim5
2 60 32 Sim6
3 16 14 Sim1
3 23 11 Sim2
3 63 59 Sim3
3 69 69 Sim4
3 61 28 Sim5
3 60 36 Sim6
I would like to draw a stacked barplot for each simID over a timeInterval, and Willing and Accepted should be stacked. I achieved the barplot with the following simple code:
dat <- read.csv("myDat.csv")
meltedDat <- melt(dat,id.vars = c("SimID", "timeInterval"))
ggplot(meltedDat, aes(timeInterval, value, fill = variable)) + facet_wrap(~ SimID) +
geom_bar(stat="identity", position = "stack")
I get the following graph:
Here my problem is that I would like to put percentages on each stack. Which means, I want to put percentage as for Willing label: (Willing/(Willing+Accepted)) and for Accepted part, ((Accepted/(Accepted+Willing)) so that I can see how many percent is willing how many is accepted such as 45 on red part of stack to 55 on blue part for each stack. I cannot seem to achieve this kind of labeling.
Any hint is appreciated.
applied from Showing data values on stacked bar chart in ggplot2
meltedDat <- melt(dat,id.vars = c("SimID", "timeInterval"))
meltedDat$normvalue <- meltedDat$value
meltedDat$valuestr <- sprintf("%.2f%%", meltedDat$value, meltedDat$normvalue*100)
meltedDat <- ddply(meltedDat, .(timeInterval, SimID), transform, pos = cumsum(normvalue) - (0.5 * normvalue))
ggplot(meltedDat, aes(timeInterval, value, fill = variable)) + facet_wrap(~ SimID) + geom_bar(stat="identity", position = "stack") + geom_text(aes(x=timeInterval, y=pos, label=valuestr), size=2)
also, it looks like you may have some of your variables coded as factors.
I have a data frame imported in excel with the following values:
> dt <- read.csv(file="teste1.csv",head=TRUE,sep=";")
> dt
hour occur time tt
1 1 one 00:00:59 59
2 2 one 08:40:02 31202
3 3 one 07:09:59 25799
4 4 one 01:22:16 4936
5 5 one 01:30:28 5428
6 6 one 01:28:57 5337
7 7 one 19:05:34 68734
8 8 one 01:57:47 7067
9 9 one 00:13:17 797
10 10 one 12:14:48 44088
11 11 one 23:24:43 84283
12 12 one 13:23:14 48194
13 13 one 02:28:51 8931
14 14 one 14:21:24 51684
15 15 one 13:26:14 48374
16 16 one 00:27:24 1644
17 17 one 15:56:51 57411
18 18 one 11:07:50 40070
19 19 one 07:18:18 26298
20 20 one 07:33:13 27193
21 21 one 10:02:03 36123
22 22 one 11:30:32 41432
23 23 one 21:21:27 76887
24 24 one 00:49:18 2958
25 1 two 21:01:11 75671
26 2 two 11:00:40 39640
27 3 two 21:40:09 78009
28 4 two 01:05:37 3937
29 5 two 00:44:17 2657
30 6 two 12:43:21 45801
31 7 two 10:53:49 39229
32 8 two 08:29:09 30549
33 9 two 05:07:46 18466
34 10 two 17:32:37 63157
35 11 two 09:35:16 34516
36 12 two 03:04:19 11059
37 13 two 23:09:13 83353
38 14 two 01:15:49 4549
39 15 two 14:24:33 51873
40 16 two 01:12:53 4373
41 17 two 21:20:11 76811
42 18 two 02:25:21 8721
43 19 two 01:17:37 4657
44 20 two 15:07:50 54470
45 21 two 22:27:32 80852
46 22 two 01:41:07 6067
47 23 two 09:40:23 34823
48 24 two 05:31:17 19877
I want to create a circular time with stacked rose based on the data frame, ie, each stacked rose are grouped by column occur, and the size is defined by column time.
The column hour indicates the x position of each rose.
So I tried in this way but the result doesn't match with what I want:
ggplot(dt, aes(x = hour, fill = occur)) + geom_histogram(breaks = seq(0,
24), width = 2, colour = "grey") + coord_polar(start = 0) + theme_minimal() +
scale_fill_brewer() + scale_x_continuous("", limits = c(0, 24), breaks = seq(0, 24), labels = seq(0,
24))
What I'm doing wrong? I want something like this http://blog.odotech.com/Portals/57087/images/French%20landfill%20wind%20rose.png
I hope I've explained correctly. Thank you!
Not sure, but hope it helps:
Convert your time value to numeric (I used chron package, but there are numerous other ways, so you don't have to call this library, but it's just to make it more straighforward):
library(chron)
x$tt<-hours(times(x$time))*3600+minutes(times(x$time))*60+seconds(times(x$time))
And make a graph:
p<-ggplot(x, aes(x = hour, y=tt,fill = occur)) +
geom_bar(breaks = seq(0,24), width = 2, colour="grey",stat = "identity") +
theme_minimal() +
scale_fill_brewer()+coord_polar(start=0)+
scale_x_continuous("", limits = c(0, 24), breaks = seq(0, 24), labels = seq(0,24))
Is that ok?
Here some cases have only 1 colors, but it's due to the scaling issues, as some have time near 24 hours, while others are in seconds only.
You can try separate graphs using facet (it's better to play with colors afterwards :))
p+facet_grid(~occur)+ theme(axis.title.y = theme_blank(),
axis.text.y = theme_blank())
The circular graph is good if you're comparing data by hours, but if you also want to compare differences in occur variable, think it's better to show in old fashion bar graphs.