I have the following out :
t1 t2 res
103 19 28.66667
222 49 28.66667
140 36 28.66667
102 33 24.66667
88 37 24.66667
38 22 24.66667
34 19 36.00000
102 25 36.00000
506 25 36.00000
73 9 39.00000
55 17 39.00000
34 17 39.00000
20 22 38.33333
50 67 38.33333
30 19 38.33333
27 15 34.00000
40 21 34.00000
35 16 34.00000
34 17 37.00000
22 29 37.00000
12 30 37.00000
25 39 26.33333
20 53 26.33333
22 20 26.33333
I have plotted the boxplot of both of t1 and t2 in Y-axis and res in X-axis, after I reshape the data and melt them. My question is how to to choose the color inside each of results and is it possible to change the filling to grid or shadowing filling so if I print the graph in black and white I will be still able to differentiate between t1 and t2 boxplot.
below is my code, it is auto generating different color but I want be able to choose!!:
ggplot(df_melted, aes(x = factor(res), y =value, fill=variable)) +
geom_boxplot(las=1,varwidth=T,border="black",col="red",medlwd=3,whiskcol="black",staplecol="blue",top=T)+
coord_cartesian(ylim = c(0, 200))
Note: df_melted is the data after applying melt command.
scale_fill_grey and theme_bw could be what you're after.
Try this:
ggplot(df_melted, aes(x = factor(res), y =value, fill=variable)) +
geom_boxplot()+
scale_fill_grey(start = .5, end = .9) +
theme_bw()
Related
This question already has answers here:
How to fill with different colors between two lines? (originally: fill geom_polygon with different colors above and below y = 0 (or any other value)?)
(4 answers)
Closed 5 years ago.
I have this df
x acc
1 1902-01-01 0.782887804
2 1903-01-01 -0.003144199
3 1904-01-01 0.100006276
4 1905-01-01 0.326173392
5 1906-01-01 1.285114692
6 1907-01-01 2.844399973
7 1920-01-01 -0.300232190
8 1921-01-01 1.464389342
9 1922-01-01 0.142638653
10 1923-01-01 -0.020162385
11 1924-01-01 0.361928571
12 1925-01-01 0.616325588
13 1926-01-01 -0.108206003
14 1927-01-01 -0.318441954
15 1928-01-01 -0.267884586
16 1929-01-01 -0.022473777
17 1930-01-01 -0.294452983
18 1931-01-01 -0.654927109
19 1932-01-01 -0.263508341
20 1933-01-01 0.622530992
21 1934-01-01 1.009666043
22 1935-01-01 0.675484421
23 1936-01-01 1.209162008
24 1937-01-01 1.655280986
25 1948-01-01 2.080021785
26 1949-01-01 0.854572563
27 1950-01-01 0.997540963
28 1951-01-01 1.000244163
29 1952-01-01 0.958322941
30 1953-01-01 0.816259474
31 1954-01-01 0.814488644
32 1955-01-01 1.233694537
33 1958-01-01 0.460120970
34 1959-01-01 0.344201474
35 1960-01-01 1.601430139
36 1961-01-01 0.387850967
37 1962-01-01 -0.385954401
38 1963-01-01 0.699355708
39 1964-01-01 0.084519926
40 1965-01-01 0.708964572
41 1966-01-01 1.456280443
42 1967-01-01 1.479412638
43 1968-01-01 1.199000726
44 1969-01-01 0.282942042
45 1970-01-01 -0.181724504
46 1971-01-01 0.012170186
47 1972-01-01 -0.095891043
48 1973-01-01 -0.075384446
49 1974-01-01 -0.156668145
50 1975-01-01 -0.303023258
51 1976-01-01 -0.516027310
52 1977-01-01 -0.826791524
53 1980-01-01 -0.947112221
54 1981-01-01 -1.634878300
55 1982-01-01 -1.955298323
56 1987-01-01 -1.854447550
57 1988-01-01 -1.458955443
58 1989-01-01 -1.256102245
59 1990-01-01 -0.864108585
60 1991-01-01 -1.293373024
61 1992-01-01 -1.049530431
62 1993-01-01 -1.002526230
63 1994-01-01 -0.868783614
64 1995-01-01 -1.081858981
65 1996-01-01 -1.302103374
66 1997-01-01 -1.288048194
67 1998-01-01 -1.455750340
68 1999-01-01 -1.015467069
69 2000-01-01 -0.682789640
70 2001-01-01 -0.811058004
71 2002-01-01 -0.972374057
72 2003-01-01 -0.536505225
73 2004-01-01 -0.518686263
74 2005-01-01 -0.976298621
75 2006-01-01 -0.946429713
I would like plot the data in this kind:
where on x axes there is column x of df, and on y axes column acc.
Is possible plot it with ggplot?
I tried with this code:
ggplot(df,aes(x=x,y=acc))+
geom_linerange(data =df , aes(colour = ifelse(acc <0, "blue", "red")),ymin=min(df),ymax=max(cdf))
but the result is this:
Please, how I can do it?
Is this what you want? I'm not sure.
ggplot(data = df,mapping = aes(x,acc))+geom_segment(data = df , mapping = aes(x=x,y=ystart,xend=x,yend=acc,color=col))
df$x=year(as.Date(df$x))
df$ystart=0
df$col=ifelse(df$acc>=0,"blue","red")
My question is similar to Fill region between two loess-smoothed lines in R with ggplot1
But I have two groups.
g1<-ggplot(NVIQ_predict,aes(cogn.age, predict, color=as.factor(NVIQ_predict$group)))+
geom_smooth(aes(x = cogn.age, y = upper,group=group),se=F)+
geom_line(aes(linetype = group), size = 0.8)+
geom_smooth(aes(x = cogn.age, y = lower,group=group),se=F)
I want to fill red and blue for each group.
I tried:
gg1 <- ggplot_build(g1)
df2 <- data.frame(x = gg1$data[[1]]$x,
ymin = gg1$data[[1]]$y,
ymax = gg1$data[[3]]$y)
g1 + geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax),fill = "grey", alpha = 0.4)
But it gave me the error: Aesthetics must either be length one, or the same length as the dataProblems
I get the same error every time my geom_ribbon() data and ggplot() data differ.
Can somebody help me with it? Thank you so much!
My data looks like:
> NVIQ_predict
cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2
According to Gregor, I tried inherit.aes = FALSE, the error is gone. But my plot looks like:
We've got all the info we need. Now we just need to, ahem, connect the dots ;-)
First the input data:
NVIQ_predict <- read.table(text = "
id cogn.age predict upper lower group
1 7 39.04942 86.68497 18.00000 1
2 8 38.34993 82.29627 18.00000 1
3 10 37.05174 74.31657 18.00000 1
4 11 36.45297 70.72421 18.00000 1
5 12 35.88770 67.39555 18.00000 1
6 13 35.35587 64.32920 18.00000 1
7 14 34.85738 61.52322 18.00000 1
8 16 33.95991 56.68024 18.00000 1
9 17 33.56057 54.63537 18.00000 1
10 18 33.19388 52.83504 18.00000 1
11 19 32.85958 51.27380 18.00000 1
12 20 32.55752 49.94791 18.00000 1
13 21 32.28766 48.85631 18.00000 1
14 24 31.67593 47.09206 18.00000 1
15 25 31.53239 46.91136 18.00000 1
16 28 31.28740 48.01764 18.00000 1
17 32 31.36627 50.55201 18.00000 1
18 35 31.73386 53.19630 18.00000 1
19 36 31.91487 54.22624 18.00000 1
20 37 32.13026 55.25721 18.00000 1
21 38 32.38237 56.26713 18.00000 1
22 40 32.98499 58.36229 18.00000 1
23 44 34.59044 62.80187 18.00000 1
24 45 35.06804 64.01951 18.00000 1
25 46 35.57110 65.31888 18.00000 1
26 47 36.09880 66.64696 17.93800 1
27 48 36.72294 67.60053 17.97550 1
28 49 37.39182 68.49995 18.03062 1
29 50 38.10376 69.35728 18.10675 1
30 51 38.85760 70.17693 18.18661 1
31 52 39.65347 70.95875 18.27524 1
32 53 40.49156 71.70261 18.38020 1
33 54 41.35332 72.44006 17.90682 1
34 59 46.37849 74.91802 18.63206 1
35 60 47.53897 75.66218 19.64432 1
36 61 48.74697 76.43933 20.82346 1
37 63 51.30607 78.02426 23.73535 1
38 71 63.43129 86.05467 40.43482 1
39 72 65.15618 87.44794 42.72704 1
40 73 66.92714 88.95324 45.01966 1
41 84 89.42079 114.27939 68.03834 1
42 85 91.73831 117.44007 69.83676 1
43 7 33.69504 54.03695 15.74588 2
44 8 34.99931 53.96500 18.00533 2
45 10 37.61963 54.05684 22.43516 2
46 11 38.93493 54.21969 24.60049 2
47 12 40.25315 54.45963 26.73027 2
48 13 41.57397 54.77581 28.82348 2
49 14 42.89710 55.16727 30.87982 2
50 16 45.54954 56.17193 34.88453 2
51 17 46.87877 56.78325 36.83632 2
52 18 48.21025 57.46656 38.75807 2
53 19 49.54461 58.22266 40.65330 2
54 20 50.88313 59.05509 42.52505 2
55 21 52.22789 59.97318 44.36944 2
56 24 56.24397 63.21832 49.26963 2
57 25 57.55394 64.33850 50.76938 2
58 28 61.45282 68.05043 54.85522 2
59 32 66.44875 72.85234 60.04517 2
60 35 69.96560 76.06171 63.86949 2
61 36 71.09268 77.06821 65.11714 2
62 37 72.19743 78.04559 66.34927 2
63 38 73.28041 78.99518 67.56565 2
64 40 75.37861 80.81593 69.94129 2
65 44 79.29028 84.20275 74.37780 2
66 45 80.20272 85.00888 75.39656 2
67 46 81.08645 85.80180 76.37110 2
68 47 81.93696 86.57689 77.29704 2
69 48 82.75920 87.34100 78.17739 2
70 49 83.55055 88.09165 79.00945 2
71 50 84.30962 88.82357 79.79567 2
72 51 85.03743 89.53669 80.53817 2
73 52 85.73757 90.23223 81.24291 2
74 53 86.41419 90.91607 81.91232 2
75 54 87.05716 91.58632 82.52800 2
76 59 89.75923 94.58218 84.93629 2
77 60 90.18557 95.05573 85.31541 2
78 61 90.58166 95.51469 85.64864 2
79 63 91.27115 96.31107 86.23124 2
80 71 92.40983 98.35031 86.46934 2
81 72 92.36362 98.52258 86.20465 2
82 73 92.27734 98.67161 85.88308 2
83 84 88.66150 98.84699 78.47602 2
84 85 88.08846 98.73625 77.44067 2", header = TRUE)
NVIQ_predict$id <- NULL
Make sure the group column is a factor variable, so we can use it as a line type.
NVIQ_predict$group <- as.factor(NVIQ_predict$group)
Then build the plot.
library(ggplot2)
g1 <- ggplot(NVIQ_predict, aes(cogn.age, predict, color=group)) +
geom_smooth(aes(x = cogn.age, y = upper, group=group), method = loess, se = FALSE) +
geom_smooth(aes(x = cogn.age, y = lower, group=group), method = loess, se = FALSE) +
geom_line(aes(linetype = group), size = 0.8)
Finally, extract the (x,ymin) and (x,ymax) coordinates of the curves for group 1 as well as group 2. These pairs have identical x-coordinates, so connecting those points mimics shading the areas between both curves. This was explained in Fill region between two loess-smoothed lines in R with ggplot. The only difference here is that we need to be a bit more careful to select and connect the points that belong to the correct curves...
gp <- ggplot_build(g1)
d1 <- gp$data[[1]]
d2 <- gp$data[[2]]
df1 <- data.frame(x = d1[d1$group == 1,]$x,
ymin = d2[d2$group == 1,]$y,
ymax = d1[d1$group == 1,]$y)
df2 <- data.frame(x = d1[d1$group == 2,]$x,
ymin = d2[d2$group == 2,]$y,
ymax = d1[d1$group == 2,]$y)
g1 + geom_ribbon(data = df1, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4) +
geom_ribbon(data = df2, aes(x = x, ymin = ymin, ymax = ymax), inherit.aes = FALSE, fill = "grey", alpha = 0.4)
The result looks like this:
I am using the package trees found here, by #jbaums and explained in this post.
My data are the following:
the tree is composed by
the trunk
Trunk
[1] 13.60415
and the branches
Tree
TreeBranchLength TreeBranchID
1 10.004269 1
2 7.994269 2
3 9.028834 11
4 10.817401 12
5 8.551311 111
6 10.599798 112
7 11.073243 121
8 13.367392 122
9 9.625431 1111
10 10.793569 1112
11 9.896499 11121
12 8.687741 11122
13 7.791180 1211
14 12.506105 1212
15 6.768478 1221
16 10.441796 1222
17 10.751892 1121
18 9.458651 1122
19 10.768509 11221
20 10.150673 11222
21 12.377448 111211
22 12.235136 111212
23 9.074079 11211
24 9.996334 11212
25 9.807019 112221
26 10.895809 112222
27 6.741274 1122211
28 15.841272 1122212
29 5.753920 11222111
30 8.846389 11222112
31 11.925961 112111
32 9.780776 112112
33 8.207965 12221
34 10.079375 12222
the 50 squirrel populations -
Populations
PopulationPositionOnBranch PopulationBranchID ID
1 10.6321655 112111 1
2 1.0644897 1 2
3 3.9315473 1 3
4 1.0310244 0 4
5 9.1768846 0 5
6 13.4267181 0 6
7 7.9461528 0 7
8 6.0533401 121 8
9 2.1227425 121 9
10 1.8256787 121 10
11 4.7332588 11222112 11
12 4.4837432 11222112 12
13 4.6200834 11222112 13
14 2.5622276 1221 14
15 1.2446683 1221 15
16 7.0674052 111 16
17 1.3854674 111 17
18 4.8735635 111 18
19 9.5007998 1222 19
20 6.6373468 1222 20
21 12.6757728 122 21
22 4.2685465 122 22
23 3.9806540 2 23
24 3.1025403 2 24
25 3.9119065 11122 25
26 1.5527653 11122 26
27 1.6687957 11122 27
28 8.0697456 1122 28
29 6.7871391 1122 29
30 9.8050713 111212 30
31 8.5226920 111212 31
32 3.6113379 111212 32
33 7.3184965 111211 33
34 8.6142984 111211 34
35 1.3550870 1211 35
36 8.3650639 12 36
37 4.6411446 112112 37
38 3.2985541 112112 38
39 12.2344148 1212 39
40 9.0290776 1212 40
41 1.3900249 1121 41
42 0.9261425 1122212 42
43 15.2522199 1122212 43
44 4.0253771 12222 44
45 8.7507678 11222 45
46 4.6289841 1122211 46
47 9.1799522 112 47
48 5.1293838 12221 48
49 1.1543080 12221 49
50 10.1014837 112222 50
the code to produce the plot
g <- germinate(list(trunk.height=Trunk,
branches=Tree$TreeBranchID,
lengths=Tree$TreeBranchLength),
left='1', right='2', angle=30))
xy <- squirrels(g, Populations$PopulationBranchID, pos=Populations$PopulationPositionOnBranch,
left='1', right='2', pch=21, bg='white', cex=3, lwd=2)
text(xy$x, xy$y, labels=seq_len(nrow(xy)), font=1)
, which produces
As you can see on the plot bellow population 43 (blue arrow) is out of the tree.. It seems that the length of the branches on the plot do not correspond to the data. For example the branch (left green arrow) on which are populations 38 and 37 is longer than the one where population 43 is (right green arrow), that is not the case in the data. What am I doing wrong? Have I understood correctly how to use trees?
On studying the germinate function it seems to me that the Tree values that you are passing to it needs to be sorted on TreeBranchId field in the ascending order.
The BranchID: 1122212 where you have placed 43 is not the actual 1122212 branch.
Due to the order in which you have fed the values in the Tree, the function is somehow messing the location of branch.
I was curious to see if I increase the length of Branch ID: 1122212, will it change the branch where 43 is placed, and guess what? it didn't. The branch which actually showed an increase in length was the branch where you have placed 37 and 38.
So this hint pointed out that something was wrong with germinate function. On further debugging I was able to make it work using the below code.
Tree<-read.csv("treeBranch.csv")
Tree<-Tree[order(Tree$TreeBranchID),]
g <- germinate(list(trunk.height=15,
branches=Tree$TreeBranchID,
lengths=Tree$TreeBranchLength),
left='1', right='2', angle=30)
xy <- squirrels(g, Populations$PopulationBranchID,pos=Populations$PopulationPositionOnBranch,
left='1', right='2', pch=21, bg='white', cex=3, lwd=2)
text(xy$x, xy$y, labels=seq_len(nrow(xy)), font=1)
I am totally lost with using ggplot. I've tried with various solutions, but none were successful. Using numbers below, I want to create a line graph where the three lines, each representing df$c, df$d, and df$e, the x-axis representing df$a, and the y-axis representing the cumulative probability where 95=100%.
a b c d e
1 0 18 0.047368421 0.036842105 0.005263158
2 1 20 0.047368421 0.036842105 0.010526316
13 2 26 0.052631579 0.031578947 0.026315789
20 3 35 0.084210526 0.036842105 0.031578947
22 4 41 0.068421053 0.052631579 0.047368421
24 5 88 0.131578947 0.068421053 0.131578947
26 7 90 0.131578947 0.068421053 0.136842105
27 8 93 0.126315789 0.068421053 0.147368421
28 9 96 0.126315789 0.073684211 0.152631579
3 10 115 0.105263158 0.078947368 0.210526316
4 11 116 0.105263158 0.084210526 0.210526316
5 12 120 0.094736842 0.084210526 0.226315789
6 13 128 0.105263158 0.073684211 0.247368421
7 14 129 0.100000000 0.073684211 0.252631579
8 15 154 0.031578947 0.042105263 0.368421053
9 16 155 0.031578947 0.036842105 0.373684211
10 17 158 0.036842105 0.036842105 0.378947368
11 18 161 0.036842105 0.031578947 0.389473684
12 19 163 0.026315789 0.031578947 0.400000000
14 20 169 0.026315789 0.021052632 0.421052632
15 21 171 0.015789474 0.021052632 0.431578947
16 22 174 0.010526316 0.021052632 0.442105263
17 24 176 0.010526316 0.021052632 0.447368421
18 25 186 0.005263158 0.005263158 0.484210526
19 26 187 0.005263158 0.000000000 0.489473684
21 35 188 0.005263158 0.005263158 0.489473684
23 40 189 0.005263158 0.000000000 0.494736842
25 60 190 0.000000000 0.000000000 0.500000000
I was somewhat successful with using R base coding
plot(df$a, df$c, type="l",col="red")
lines(df$a, df$d, col="green")
lines(df$a, df$e, col="blue")
You first need to melt your data so that you have one column that designates from which variables the data comes from (call it variable) and another column that lists actual value (call it value). Study the example below to fully understand what happens to the variables from the original data.frame you want to keep constant.
library(reshape2)
xymelt <- melt(xy, id.vars = "a")
library(ggplot2)
ggplot(xymelt, aes(x = a, y = value, color = variable)) +
theme_bw() +
geom_line()
ggplot(xymelt, aes(x = a, y = value)) +
theme_bw() +
geom_line() +
facet_wrap(~ variable)
This code is also drawing column from your data called "d". You can remove it prior to melting, after melting, prior to plotting... or plot it.
Hi everybody I am trying to make a ggplot graph with a special element to add value to the graphic. I have the next data frame:
Name x
1 33374984
2 33209955
3 30250000
4 25897811
5 14995677
6 8200000
7 7878540
8 7571275
9 7188469
10 6756588
11 6592099
12 6538284
13 6248395
14 6116285
15 6045148
16 5745422
17 5670931
18 5494094
19 5282317
20 4695526
21 4641434
22 4595977
23 4572251
24 4535916
25 4506470
26 4445051
27 4444925
28 4393253
29 4383303
30 4349352
31 4278734
32 4134470
33 4102491
34 4010763
35 3589238
36 3573466
37 3524591
38 3497869
39 3340105
40 3292353
41 3234672
42 3217727
43 3202363
44 3170645
45 3119682
46 3092471
47 3033967
48 3000145
49 2994306
50 2943389
51 2940969
52 2936803
53 2923212
54 2884965
55 2863525
56 2846172
57 2839577
58 2819895
59 2809083
60 2804884
61 2752059
62 2750962
63 2740161
64 2718946
65 2580859
66 2580822
67 2553712
68 2490213
69 2425135
70 2406000
71 2405486
72 2387143
73 2384597
74 2381402
75 2372381
76 2308623
77 2299046
78 2287879
79 2260205
80 2245436
81 2208582
82 2203883
83 2176060
84 2169769
85 2136766
86 2121242
87 2115891
88 2106713
89 2084986
90 2049367
91 2031410
92 2023409
93 2015622
94 1999140
95 1901045
96 1900000
97 1891783
98 1863410
99 1859789
100 1851046
I made a simple ggplot bar graphic using this code:
png(filename = "Delta.png", width = 800, height = 1000)
Delta=ggplot(DeltaPBO)+
geom_bar(aes(x=Name,y=x,fill=x),
stat='identity',position='dodge')+theme(axis.text.x=element_text(angle=80,hjust=1,vjust=1,colour="grey20",face="bold",size=12),axis.text.y=element_text(colour="grey20",face="bold",hjust=1,vjust=0.8),axis.title.x=element_text(colour="grey20",face="bold",size=15),axis.title.y=element_text(colour="grey20",face="bold",size=15))+xlab('Cliente')+ylab('Total')+ ggtitle("My graph")+theme(plot.title = element_text(lineheight=3, face="bold", color="black", size=29))
print(Delta)
dev.off()
And I got this graph:
My problem is the next x is in descending order and I want to show in the graphic different bar colors for each 25 observations, for example observation 1 to 25 should be in yellow because they have higher values than observations 26 to 50; observations 26 to 50 should have green colour due to they are higher than observations 51 to 75; observations 51 to 75 should have blue colour because they are higher than observations 76 to 100 and finally observations 76 to 100 should have red colour because they have the minor values.
I would like to make this in R but I don't have enough knowledge in ggplot. Thanks for your help.
You can add new variable that contains grouping names for every 25 observations in your data. Then use this new variable for the fill of bars. With scale_fill_manual() set fill colors as you need.
DeltaPBO$group<-rep(c("A","B","C","D"),each=25)
ggplot(DeltaPBO)+
geom_bar(aes(x=Name,y=x,fill=as.factor(group)),
stat='identity',position='dodge')+
scale_fill_manual(values=c("yellow","green","blue","red"))