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Attempt to set graph to R

This is my data set that I am trying to do is draw a graph of TAD Vs IPRE, DV, PRED as a whole, in which I have no problems creating them.

Second part: I'm trying to create a separate schedule for ID (n = 35), so schedule 35 for (TAD Vs IPRE, DV, PRED)

The third part is to try to create a graph based on Ward (TAD Vs IPRE, DV, PRED), which will have 5 graphs.

I tried several options and struggled to write code ... no luck (new to R). Can any body help me with this?

Thanks at Advance.

ID TIME TAD AMT BL WARD IPRE DV PRED RES WRES
1 0 0 0 0 0 0 0.01 0 0 0.95
1 0.01 0 20 0 0 0 0 0 0 0
1 1 0.99 0 0 0 0.11 0.13 0.02 0.11 3.85
1 2 1.99 0 0 0 0.11 0.12 0.02 0.1 4
1 4 3.99 0 0 0 0.1 0.07 0.02 0.05 -0.48
1 6 5.99 0 0 0 0.09 0.03 0.02 0.02 -3.07
1 8 7.99 0 0 0 0.08 0.04 0.02 0.03 -2.04
1 24 24 0 0 0 0.04 0.03 0.01 0.02 -0.32
1 48 0 20 0 0 0.02 0 0.01 0 0
1 49 1 0 0 0 0.12 0.25 0.02 0.23 3.07
1 50 2 0 0 0 0.12 0.26 0.02 0.23 3.33
1 52 4 0 0 0 0.11 0.17 0.02 0.15 1.99
1 54 6 0 0 0 0.1 0.09 0.02 0.07 0.7
1 56 8 0 0 0 0.09 0.05 0.02 0.04 -0.03
1 72 24 0 0 0 0.05 0.01 0.01 0 -1.09
2 0 0 20 0 0 0 0 0 0 0
2 1 1 0 0 0 0.01 0 0.02 -0.02 -1.09
2 2 2 0 0 0 0.01 0.01 0.02 -0.01 -0.55
2 4 4 0 0 0 0.01 0.01 0.02 -0.01 -0.58
2 6 6 0 0 0 0.01 0 0.01 -0.01 -0.71
2 8 8 0 0 0 0.01 0 0.01 -0.01 -0.63
2 24 24 0 0 0 0.01 0 0.01 0 -0.44
2 72 0 20 0 0 0 0 0 0 0
2 73 1 0 0 0 0.02 0.04 0.02 0.02 2.51
2 74 2 0 0 0 0.01 0.02 0.02 0 0.02
2 76 4 0 0 0 0.01 0.01 0.02 -0.01 -0.05
2 78 6 0 0 0 0.01 0.01 0.01 -0.01 -0.09
2 80 8 0 0 0 0.01 0.02 0.01 0 0.14
2 96 24 0 0 0 0.01 0 0.01 0 0
3 0 0 20 0 0 0 0 0 0 0
3 1 1 0 0 0 0.04 0.02 0.02 0 -0.61
3 2 2 0 0 0 0.04 0.03 0.02 0.01 -0.16
3 4 4 0 0 0 0.03 0.08 0.02 0.06 5.95
3 6 6 0 0 0 0.03 0.01 0.01 0 -1.29
3 8 8 0 0 0 0.03 0.01 0.01 0 -1.64
3 24 24 0 0 0 0.01 0 0.01 0 -2.32
4 0 0 20 0 0 0 0 0 0 0
4 1 1 0 0 0 0.08 0.1 0.02 0.09 1.45
4 2 2 0 0 0 0.07 0.13 0.02 0.12 4.73
4 4 4 0 0 0 0.07 0.13 0.02 0.12 5.94
4 6 6 0 0 0 0.06 0.07 0.01 0.06 -0.09
4 8 8 0 0 0 0.06 0.07 0.01 0.06 0.21
4 24 24 0 0 0 0.03 0.02 0.01 0.02 -3
4 72 72 0 0 0 0 0.01 0 0.01 0.98
4 72 0 20 0 0 0 0 0 0 0
4 73 0.99 0 0 0 0.08 0.09 0.02 0.07 0.51
4 74 1.99 0 0 0 0.08 0.06 0.02 0.04 0.02
4 76 3.99 0 0 0 0.07 0.04 0.02 0.02 -0.34
4 78 5.99 0 0 0 0.06 0.02 0.01 0 -0.71
4 80 7.99 0 0 0 0.06 0.02 0.01 0.01 -0.65
4 96 24 0 0 0 0.03 0.01 0.01 0 -1.02
5 0 0 20 0 0 0 0 0 0 0
5 1 1 0 0 0 0.06 0.08 0.02 0.07 2.05
5 2 2 0 0 0 0.06 0.07 0.02 0.05 1.17
5 4 4 0 0 0 0.05 0.06 0.02 0.04 0.56
5 6 6 0 0 0 0.05 0.08 0.01 0.07 3.47
5 24 24 0 0 0 0.02 0.01 0.01 0 -4.17
6 0 0 20 0 0 0 0 0 0 0
6 1 1 0 0 0 0.05 0 0.02 0 0
6 2 2 0 0 0 0.04 0.01 0.02 -0.01 -2.99
6 4 4 0 0 0 0.04 0.05 0.02 0.03 0.98
6 6 6 0 0 0 0.04 0.06 0.01 0.04 2.92
6 8 8 0 0 0 0.03 0.05 0.01 0.04 2.47
7 0 0 0 0 0 0 0 0 0 0.31
7 0 0 20 0 0 0 0 0 0 0
7 1 1 0 0 0 0.17 0.28 0.02 0.26 8.57
7 2 2 0 0 0 0.16 0.3 0.02 0.28 11
7 4 4 0 0 0 0.15 0.19 0.02 0.18 3.63
7 6 6 0 0 0 0.14 0.14 0.02 0.12 -0.72
7 8 8 0 0 0 0.13 0.11 0.02 0.1 -2.03
7 24 24 0 0 0 0.07 0.04 0.01 0.04 -4.53
8 0 0 20 0 0 0 0 0 0 0
8 1 1 0 0 0 0.02 0.03 0.02 0.02 1.64
8 2 2 0 0 0 0.02 0.01 0.02 -0.01 -0.66
8 4 4 0 0 0 0.01 0.02 0.02 0 0.16
8 6 6 0 0 0 0.01 0 0.01 -0.01 -1.17
8 8 8 0 0 0 0.01 0.01 0.01 0 -0.28
8 24 24 0 0 0 0.01 0.01 0.01 0 -0.07
9 0 0 20 0 3 0 0 0 0 0
9 1 1 0 0 3 0.05 0.09 0.02 0.07 4.59
9 2 2 0 0 3 0.04 0.06 0.02 0.04 2.24
9 4 4 0 0 3 0.04 0.03 0.02 0.01 -0.63
9 6 6 0 0 3 0.04 0.02 0.01 0 -1.64
9 8 8 0 0 3 0.03 0.01 0.01 -0.01 -2.88
9 24 24 0 0 3 0.02 0 0.01 0 0
10 0 0 20 0 3 0 0 0 0 0
10 1 1 0 0 3 0.02 0.04 0.02 0.02 1.78
10 2 2 0 0 3 0.02 0.01 0.02 -0.01 -0.54
10 4 4 0 0 3 0.02 0.01 0.02 -0.01 -0.86
10 6 6 0 0 3 0.01 0.02 0.01 0 0.37
10 8 8 0 0 3 0.01 0.01 0.01 -0.01 -1.04
10 24 24 0 0 3 0.01 0 0.01 0 0
11 0 0 0 0 3 0 0 0 0 -0.26
11 0.01 0 20 0 3 0 0 0 0 0
11 1 0.99 0 0 3 0.01 0.01 0.02 -0.01 -0.41
11 2 1.99 0 0 3 0.01 0.01 0.02 -0.01 -0.24
11 4 3.99 0 0 3 0.01 0.01 0.02 -0.01 -0.16
11 6 5.99 0 0 3 0.01 0.01 0.02 -0.01 -0.21
11 8 7.99 0 0 3 0.01 0.01 0.02 -0.01 -0.13
11 24 24 0 0 3 0 0 0.01 -0.01 -0.27
12 0 0 20 0 3 0 0 0 0 0
12 1 1 0 0 3 0.01 0.01 0.02 -0.01 0.02
12 2 2 0 0 3 0.01 0.01 0.02 -0.01 -0.08
12 4 4 0 0 3 0.01 0.01 0.02 -0.01 -0.23
12 6 6 0 0 3 0.01 0.01 0.01 -0.01 -0.45
12 8 8 0 0 3 0.01 0.01 0.01 0 -0.04
12 24 24 0 0 3 0 0 0.01 0 0
13 0 0 20 0 2 0 0 0 0 0
13 1 1 0 0 2 0.04 0.08 0.02 0.06 5.23
13 2 2 0 0 2 0.04 0.02 0.02 0.01 -0.42
13 4 4 0 0 2 0.04 0 0.02 -0.01 -2.33
13 6 6 0 0 2 0.03 0.01 0.01 -0.01 -2.01
13 8 8 0 0 2 0.03 0 0.01 0 0
13 24 24 0 0 2 0.02 0 0.01 0 0
14 0 0 20 0 3 0 0 0 0 0
14 1 1 0 0 3 0.01 0.01 0.02 -0.01 -0.39
14 2 2 0 0 3 0.01 0.01 0.02 -0.01 -0.49
14 4 4 0 0 3 0.01 0.01 0.02 0 0.33
14 6 6 0 0 3 0.01 0.01 0.01 0 -0.04
14 8 8 0 0 3 0.01 0.01 0.01 0 0.17
14 24 24 0 0 3 0 0 0.01 0 0
15 0 0 20 0 1 0 0 0 0 0
15 1 1 0 0 1 0.01 0.01 0.02 -0.01 -0.3
15 2 2 0 0 1 0.01 0.01 0.02 -0.01 -0.41
15 4 4 0 0 1 0 0 0.02 0 0
15 6 6 0 0 1 0 0 0.01 0 0
15 8 8 0 0 1 0 0 0.01 0 0
15 24 24 0 0 1 0 0 0.01 0 0
16 0 0 20 0 1 0 0 0 0 0
16 1 1 0 0 1 0 0 0.02 -0.02 -0.54
16 2 2 0 0 1 0 0 0.02 -0.01 -0.45
16 4 4 0 0 1 0 0 0.02 -0.01 -0.26
16 6 6 0 0 1 0 0.01 0.01 -0.01 0.05
16 8 8 0 0 1 0 0 0.01 -0.01 -0.12
16 24 24 0 0 1 0 0 0.01 0 -0.02
18 0 0 20 0 1 0 0 0 0 0
18 1 1 0 0 1 0.02 0.01 0.02 -0.01 -1.03
18 2 2 0 0 1 0.02 0.02 0.02 0 0.01
18 4 4 0 0 1 0.02 0 0.02 -0.01 -1.27
18 6 6 0 0 1 0.02 0.02 0.01 0 0.59
18 8 8 0 0 1 0.02 0.02 0.01 0 0.57
18 24 24 0 0 1 0.01 0.02 0.01 0.01 2.69
19 0 0 20 0 1 0 0 0 0 0
19 1 1 0 0 1 0.01 0 0.02 -0.02 -0.67
19 2 2 0 0 1 0.01 0 0.02 -0.01 -0.56
19 4 4 0 0 1 0.01 0.01 0.02 -0.01 -0.03
19 6 6 0 0 1 0.01 0.01 0.01 -0.01 -0.01
19 8 8 0 0 1 0.01 0.01 0.01 -0.01 0.15
19 24 24 0 0 1 0 0.01 0.01 0 0.49
20 0 0 20 0 1 0 0 0 0 0
20 1 1 0 0 1 0.01 0.03 0.02 0.01 0.98
20 2 2 0 0 1 0.01 0.02 0.02 0 0.31
20 4 4 0 0 1 0.01 0.01 0.02 -0.01 -0.49
20 6 6 0 0 1 0.01 0.01 0.01 -0.01 -0.53
20 8 8 0 0 1 0.01 0.01 0.01 -0.01 -0.59
20 24 24 0 0 1 0 0 0.01 0 -0.39
22 0 0 20 0 1 0 0 0 0 0
22 1 1 0 0 1 0.01 0.01 0.02 -0.01 -0.65
22 2 2 0 0 1 0.01 0.01 0.02 -0.01 -0.18
22 4 4 0 0 1 0.01 0.02 0.02 0 0.53
22 6 6 0 0 1 0.01 0.01 0.01 -0.01 -0.16
22 8 8 0 0 1 0.01 0.01 0.01 -0.01 -0.18
22 24 24 0 0 1 0 0 0.01 0 0
23 0 0 20 0 1 0 0 0 0 0
23 1 1 0 0 1 0.02 0.02 0.02 0 -0.47
23 2 2 0 0 1 0.02 0.05 0.02 0.03 2.6
23 4 4 0 0 1 0.02 0.02 0.02 0.01 0.21
23 6 6 0 0 1 0.02 0.01 0.01 0 -0.47
23 8 8 0 0 1 0.02 0.01 0.01 0 -0.86
23 24 24 0 0 1 0.01 0 0.01 0 -1.38
24 0 0 0 0 1 0 0 0 0 0.16
24 0.01 0 20 0 1 0 0 0 0 0
24 1 0.99 0 0 1 0.01 0.02 0.02 0 0.56
24 2 1.99 0 0 1 0.01 0.01 0.02 -0.01 -0.53
24 4 3.99 0 0 1 0.01 0.01 0.02 -0.01 -0.02
24 6 5.99 0 0 1 0.01 0.01 0.02 -0.01 -0.48
24 8 7.99 0 0 1 0.01 0.01 0.02 -0.01 -0.39
24 24 24 0 0 1 0.01 0 0.01 -0.01 -0.59
25 0 0 20 0 2 0 0 0 0 0
25 1 1 0 0 2 0.06 0.1 0.02 0.09 4.51
25 2 2 0 0 2 0.05 0.07 0.02 0.05 1.54
25 4 4 0 0 2 0.05 0.06 0.02 0.05 1.2
25 6 6 0 0 2 0.05 0.06 0.01 0.05 1.58
25 8 8 0 0 2 0.04 0.02 0.01 0.01 -2.52
25 24 24 0 0 2 0.02 0 0.01 0 -4.48
26 0 0 20 0 2 0 0 0 0 0
26 1 1 0 0 2 0.15 0.12 0.02 0.1 -2.99
26 2 2 0 0 2 0.15 0.24 0.02 0.22 9.42
26 4 4 0 0 2 0.14 0.22 0.02 0.21 9.35
26 6 6 0 0 2 0.12 0.17 0.01 0.16 5.55
26 8 8 0 0 2 0.11 0.14 0.01 0.12 2.46
26 24 24 0 0 2 0.06 0.01 0.01 0 -12.9
28 0 0 20 0 2 0 0 0 0 0
28 1 1 0 0 2 0.1 0.01 0.02 -0.01 -5.13
28 2 2 0 0 2 0.09 0.03 0.02 0.02 -2.94
28 4 4 0 0 2 0.08 0.18 0.02 0.17 13.7
28 6 6 0 0 2 0.08 0.07 0.01 0.06 2.22
28 8 8 0 0 2 0.07 0.04 0.01 0.03 -1.01
28 24 24 0 0 2 0.04 0 0.01 0 0
29 0 0 20 0 2 0 0 0 0 0
29 1 1 0 0 2 0.01 0.01 0.02 -0.01 -0.46
29 2 2 0 0 2 0.01 0.01 0.02 -0.01 -0.22
29 4 4 0 0 2 0.01 0 0.02 0 0
29 6 6 0 0 2 0 0 0.01 0 0
29 8 8 0 0 2 0 0 0.01 0 0
29 24 24 0 0 2 0 0 0.01 0 0
30 0 0 20 0 0 0 0 0 0 0
30 1 1 0 0 0 0.07 0 0.02 0 0
30 2 2 0 0 0 0.07 0.01 0.02 -0.01 -4.61
30 4 4 0 0 0 0.06 0.09 0.02 0.08 4.77
30 6 6 0 0 0 0.06 0.09 0.01 0.07 5.02
30 8 8 0 0 0 0.05 0.03 0.01 0.01 -1.98
30 24 24 0 0 0 0.03 0.04 0.01 0.03 3.14
31 0 0 20 0 4 0 0 0 0 0
31 1 1 0 0 4 0 0 0.02 -0.01 -0.26
31 2 2 0 0 4 0 0 0.02 -0.01 -0.46
31 4 4 0 0 4 0 0 0.02 -0.01 -0.27
31 6 6 0 0 4 0 0 0.01 -0.01 -0.15
31 8 8 0 0 4 0 0 0.01 -0.01 -0.21
31 24 24 0 0 4 0 0 0.01 0 0.06
32 0 0 20 0 4 0 0 0 0 0
32 1 1 0 0 4 0 0 0.02 0 0
32 2 2 0 0 4 0 0 0.02 -0.01 -0.23
32 4 4 0 0 4 0 0 0.02 -0.02 -0.62
32 6 6 0 0 4 0 0 0.01 -0.01 -0.38
32 24 24 0 0 4 0 0 0.01 0 0.05
33 0 0 20 0 4 0 0 0 0 0
33 1 1 0 0 4 0 0 0.02 -0.01 -0.32
33 2 2 0 0 4 0 0 0.02 -0.01 -0.29
33 4 4 0 0 4 0 0 0.02 -0.01 -0.37
33 6 6 0 0 4 0 0.01 0.01 -0.01 -0.08
33 8 8 0 0 4 0 0 0.01 -0.01 -0.29
33 24 24 0 0 4 0 0 0.01 0 0.06
34 0 0 20 0 4 0 0 0 0 0
34 1 1 0 0 4 0.01 0.01 0.02 -0.01 0.04
34 2 2 0 0 4 0.01 0 0.02 -0.01 -0.49
34 4 4 0 0 4 0.01 0 0.02 -0.01 -0.41
34 6 6 0 0 4 0.01 0 0.01 -0.01 -0.36
34 8 8 0 0 4 0.01 0.01 0.01 -0.01 -0.15
34 24 24 0 0 4 0 0.01 0.01 0 0.69
35 0 0 20 0 4 0 0 0 0 0
35 1 1 0 0 4 0 0 0.02 -0.02 -0.39
35 2 2 0 0 4 0 0.01 0.02 -0.01 -0.1
35 4 4 0 0 4 0 0 0.02 -0.01 -0.26
35 6 6 0 0 4 0 0 0.01 -0.01 -0.24
35 8 8 0 0 4 0 0 0.01 -0.01 -0.3
35 24 24 0 0 4 0 0 0.01 -0.01 -0.46

Yours faithfully

rcmoulirc

+3
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2 answers

You can easily multiply your data frame and only build

For example, let's say your data frame is called df:

df[df$ID==1,]

ID = 1

,

for (i in 1:35) # Or, even better 1:(max(df$ID))
    {
    X11()
    tmp <- df[df$ID == i,]
    # Plot whatever you need to plot
    }
+1

, ggplot2 , , . - Hadley, .

#Assume ID, and Ward are factors, and WRES. shouldn't be
pkso$ID<-as.factor(pkso$ID)
pkso$WARD<-as.factor(pkso$WARD)
pkso$WRES<-as.numeric(pkso$WRES.)

#display clean data for SO fans
dput(pkso)

library(ggplot2)

#draw a graph TAD Vs IPRE,DV,PRED as a whole which i have no issues in producing them.
#first melt the data into a narrow format, then plot
pksomelt<-melt(pkso, id.vars=c("ID","TIME","TAD","AMT","BL","WARD"))
pksomelt$value<-as.numeric(pksomelt$value)
#now subset that data for what we want
pksomelt<-subset(pksomelt,variable %in% c("IPRE","DV","PRED"))
#now plot, using facet_wrap to seperate variables
pkplot1<-ggplot(pksomelt, aes(TAD, value)) + 
        geom_point() +
        scale_shape(solid = FALSE) + 
        facet_wrap( ~ variable)

print(pkplot1)
#Second part is I am trying to produce an individual graph for ID (n=35) so 35 graph for (TAD Vs IPRE,DV,PRED)
#so, for an individual plot
pkplot2<-ggplot(subset(pksomelt, ID == "1"), aes(TAD, value)) + 
        geom_point() +
        scale_shape(solid = FALSE) + 
        facet_wrap( ~ variable)

print(pkplot2)
#  or, for presenting, you could do all 35 spread across 6 plots, but seperated, by using facet_grid
i<-ceiling(max(as.numeric(pksomelt$ID))/6)  #  Calculate number of ID per plot

pkplot3<-ggplot(subset(pksomelt, as.numeric(pksomelt$ID) > (i*0) & as.numeric(pksomelt$ID) < (i*1)+1), 
                aes(TAD, value)) + 
        geom_point() +
        scale_shape(solid = FALSE) + 
        facet_grid(ID ~ variable)

print(pkplot3)

pkplot4<-ggplot(subset(pksomelt, as.numeric(pksomelt$ID) > (i*1) & as.numeric(pksomelt$ID) < (i*2)+1), 
                aes(TAD, value)) + 
        geom_point() +
        scale_shape(solid = FALSE) + 
        facet_grid(ID ~ variable)

print(pkplot4) #  ect etc
#Third Part is to try and produce a graph based on Ward (TAD Vs IPRE,DV,PRED) which will have 5 graphs.
#Again, use Facet_grid to seperate different wards, let also add lines to show it possible
pkplot5<-ggplot(pksomelt, aes(TAD, value)) + 
        geom_point(aes(colour=ID)) + geom_line(aes(colour=ID)) + 
        scale_shape(solid = FALSE) + 
        facet_grid(WARD ~ variable, scales="free")

print(pkplot5)
+2

Source: https://habr.com/ru/post/1764996/

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