I am new to R Shiny and I am trying to build a Shiny Web App that produces a survival plot with two reactive inputs. The first input is the study (total=4). The second input is the groups (total=19) to compare survival curves. Ideally, these two inputs would allow me to see in a particular study, how group X survival curve compares to the survival curve of all other groups.
Here is a sample of my data:
ObsNum UniqueID Time Censored Group Group2 Study
1 523B95015 27 1 1 523 1
2 523B95014 27 1 1 523 1
3 523B85051 27 1 1 523 1
4 523B95009 27 1 1 523 1
5 523B85048 27 1 1 523 1
6 523B85050 27 1 1 523 1
7 675B89002 27 1 8 675 1
8 556B95006 27 1 12 556 1
9 556B85030 27 1 12 556 1
10 556B85044 27 1 12 556 1
11 556B95035 27 1 12 556 1
12 556B95000 27 1 12 556 1
13 556B95004 27 1 12 556 1
14 556B95002 27 1 12 556 1
15 756Y81172 27 1 17 756 1
16 741B95022 27 1 99 741 1
17 741B95020 27 1 99 741 1
18 619B92008 28 1 7 619 1
19 552B89003 28 1 10 552 1
20 101B94097 28 1 99 101 1
21 101B94098 28 1 99 101 1
22 618C84582 29 1 23 618 1
23 618C84580 29 1 23 618 1
24 618C84581 29 1 23 618 1
25 730B90003 29 1 99 730 1
26 646B42015 34 1 4 646 1
27 671B60009 35 1 17 671 1
28 612C80247 35 1 21 612 1
29 700C64500 35 1 99 791 1
30 101B89052 40 1 99 101 1
31 101B85047 40 1 99 101 1
32 101B95068 40 1 99 101 1
33 538B70011 51 1 10 538 1
34 689C85036 57 1 1 689 1
35 689C95450 57 1 1 689 1
36 556B85050 62 1 12 556 1
37 636B80005 62 1 23 636 1
38 636B92002 62 1 23 636 1
39 630B30005 70 1 2 630 1
40 642B80021 78 1 4 642 1
41 101B79173 86 1 99 101 1
42 523B81007 106 0 1 523 1
43 620B88003 106 0 2 620 1
44 642B40002 106 1 4 642 1
45 642B40001 106 1 4 642 1
46 581B81002 106 0 5 581 1
47 581B81001 106 0 5 581 1
48 573B95000 106 0 8 573 1
49 589B80015 106 0 15 589 1
50 589B80016 106 0 15 589 1
51 657B50013 106 0 15 657 1
52 657B43004 106 0 15 657 1
53 459B85085 106 0 21 459 1
54 459Y81171 106 0 21 459 1
55 101B75006 106 0 99 101 1
56 101SC8023 106 0 99 101 1
57 101B85122 106 0 99 101 1
58 101B55116 106 0 99 101 1
59 101B79086 106 0 99 101 1
60 101B95066 106 0 99 101 1
61 730B97005 106 0 99 730 1
62 741B85045 106 0 99 741 1
63 777B96001 106 0 99 777 1
64 556B85077 1 1 12 556 2
65 636B92003 1 1 23 636 2
66 101B94137 1 1 99 101 2
67 700C64500 5 1 99 791 2
68 463Y91171 6 1 20 463 2
69 618C84319 6 1 23 618 2
70 776C93046 6 1 99 776 2
71 556B95042 7 1 12 556 2
72 556B95043 7 1 12 556 2
73 556B97000 7 1 12 556 2
74 549B80069 7 1 17 549 2
75 573B95000 22 1 8 573 2
76 580B90024 22 1 16 580 2
77 523B81007 28 1 1 523 2
78 520B60012 32 1 16 520 2
79 520B70011 32 1 16 520 2
80 586B70008 33 1 16 586 2
81 586B80006 33 1 16 586 2
82 586B80011 33 1 16 586 2
83 586B80015 33 1 16 586 2
84 657B43004 34 1 15 657 2
85 636B99009 35 1 23 636 2
86 691B68018 36 1 22 691 2
87 657B50013 41 1 15 657 2
88 741B95031 42 1 99 741 2
89 620B88003 46 0 2 620 2
90 620B90008 46 0 2 620 2
91 581B81001 46 0 5 581 2
92 581B81002 46 0 5 581 2
93 552B99002 46 0 10 552 2
94 459B85085 46 0 21 459 2
95 459B95055 46 0 21 459 2
96 101B75006 46 0 99 101 2
97 101B55060 46 0 99 101 2
98 101B79086 46 0 99 101 2
99 101B79058 46 0 99 101 2
100 101B85122 46 0 99 101 2
101 101B89115 46 0 99 101 2
102 101B85047 46 0 99 101 2
103 101B94123 46 0 99 101 2
104 101B95091 46 0 99 101 2
105 101B95038 46 0 99 101 2
106 101D98001 46 0 99 101 2
107 730B97005 46 0 99 730 2
108 741B85045 46 0 99 741 2
Here is my code for the Shiny App:
library(shiny)
library(ggplot2)
library(survival)
library(survminer)
library(dplyr)
attach(tdata)
studychoices=unique(tdata$Study)
groupchoices=unique(tdata$Group)
# Define UI
ui <- fluidPage(
titlePanel("Survival Data"),
selectInput(inputId = "studyselector",label="Select a Study:", choices=studychoices),
selectInput(inputId = "groupselector",label="Select a Group:", choices=groupchoices),
plotOutput("p1")
)
# Define server logic
server <- function(input, output) {
filter=reactive({
filteredData=tdata[tdata$Study==input$studyselector,]
return(filteredData)
})
output$p1=renderPlot({
fit=survfit(Surv(Time,Censored)~input$groupselector,data=filter())
ggsurvplot(fit,data=filter(),pval=TRUE,xlim=c(0,max(Time)+1),
title=paste("Study","INSERT HERE STUDY #", "Survival Plot for Group","INSERT HERE GROUP #"),
xlab="Time (Days)",
ggtheme=theme(plot.title=element_text(hjust=0.5)))
})
}
# Run the application
shinyApp(ui = ui, server = server)
I have the following two questions:
1.) When I run the App, I get an error in an external window that reads, "Error: variable lengths differ (found for'input$groupselector'). There are no NAs in this data and I specified the data to be used is the filter() dataset based on the Study selection so I'm not sure why this error is popping up.
2.) How would I be able to dynamically change the Study # and the Group # in the title? I understand how to do that with a normal R function, but I'm a little lost with the Shiny set up.
Any help would be appreciated! Thank you!
Edited solution
This should now work - have edited the Group column to be a binary in/out when passed to your reactive dataframe, which should colour the lines appropriately:
library(tidyverse)
library(survival)
library(survminer)
library(shiny)
ui <- fluidPage(
titlePanel("Survival Data"),
selectInput(inputId = "studyselector",label="Select a Study:", choices=studychoices),
selectInput(inputId = "groupselector",label="Select a Group:", choices=groupchoices),
plotOutput("p1")
)
# Define server logic
server <- function(input, output) {
filter=reactive({
filteredData=data[data$Study==input$studyselector,]
filteredData['Group'] = ifelse(filteredData$Group==input$groupselector,
input$groupselector,
"Others")
return(filteredData)
})
output$p1=renderPlot({
fit=survfit(Surv(Time,Censored)~Group,data=filter()) # `Group` as variable to stratify by?
ggsurvplot(fit,data=filter(),pval=TRUE,xlim=c(0,max(filter()$Time)+1),
title=paste("Study",
input$studyselector, # paste these bits straight in
"Survival Plot for Group",
input$groupselector), # here too
xlab="Time (Days)",
ggtheme=theme(plot.title=element_text(hjust=0.5)))
})
}
# Run the application
shinyApp(ui = ui, server = server)
Do let me know in comments if there are any mistakes or further ideas/questions!
Related
My data looks like the example below. (sorry if it's too long, not sure what's acceptable/needed).
I have used the following code to calculate the median and IQR of each time difference (tdif) between tests (testno):
data %>% group_by(testno) %>% filter(type ==1) %>%
summarise(Median = median(tdif), IQR= IQR(tdif), n= n(), .groups = 'keep') -> result
I have done this for each category of 'type' (coded as 1 - 10), which brought me to the added table (bottom).
My question is, if it is possible to:
Do this an easier way (without the filters? So I can do this all in 1 run), and
Is it possible run a test for p-value with all the groups/filters?
data <- read.table(header=T, text= '
PID time tdif testno type
3 205 0 1 1
4 77 0 1 1
4 85 8 2 1
4 126 41 3 1
4 165 39 4 1
4 202 37 5 1
4 238 36 6 1
4 272 34 7 1
4 277 5 8 1
4 370 93 9 1
4 397 27 10 1
4 452 55 11 1
4 522 70 12 1
4 529 7 13 1
4 608 79 14 1
4 651 43 15 1
4 655 4 16 1
4 713 58 17 1
4 804 91 18 1
4 900 96 19 1
4 944 44 20 1
4 979 35 21 1
4 1015 36 22 1
4 1051 36 23 1
4 1077 26 24 1
4 1124 47 25 1
4 1162 38 26 1
4 1222 60 27 1
4 1334 112 28 1
4 1383 49 29 1
4 1457 74 30 1
4 1506 49 31 1
4 1590 84 32 1
4 1768 178 33 1
4 1838 70 34 1
4 1880 42 35 1
4 1915 35 36 1
4 1973 58 37 1
4 2017 44 38 1
4 2090 73 39 1
4 2314 224 40 1
4 2381 67 41 1
4 2433 52 42 1
4 2484 51 43 1
4 2694 210 44 1
4 2731 37 45 1
4 2792 61 46 1
4 2958 166 47 1
5 48 0 1 3
5 111 63 2 3
5 699 588 3 3
5 1077 378 4 3
6 -43 0 1 3
8 67 0 1 1
8 168 101 2 1
8 314 146 3 1
8 368 54 4 1
8 586 218 5 1
10 639 0 1 6
13 -454 0 1 3
13 -384 70 2 3
13 -185 199 3 3
13 193 378 4 3
13 375 182 5 3
13 564 189 6 3
13 652 88 7 3
13 669 17 8 3
13 718 49 9 3
14 704 0 1 8
15 -165 0 1 3
15 -138 27 2 3
15 1335 1473 3 3
16 168 0 1 6
18 -1329 0 1 3
18 -1177 152 2 3
18 -1071 106 3 3
18 -945 126 4 3
18 -834 111 5 3
18 -719 115 6 3
18 -631 88 7 3
18 -497 134 8 3
18 -376 121 9 3
18 -193 183 10 3
18 -78 115 11 3
18 -13 65 12 3
18 100 113 13 3
18 196 96 14 3
18 552 356 15 3
18 650 98 16 3
18 737 87 17 3
18 804 67 18 3
18 902 98 19 3
18 983 81 20 3
18 1119 136 21 3
19 802 0 1 1
19 1593 791 2 1
26 314 0 1 8
26 389 75 2 8
26 597 208 3 8
33 639 0 1 6
Added table (values differ from example data, because this isn't the complete set).
I have a list called res which includes 83 lists with the following format. I need to generate one sparse matrix out of these lists. Row and Columns are indecies for the row and column of the sparse matrix and freq is the entry for that corresponding index.
Example of format for res[82] and res[83]:
[[82]]
Row Columns Freq
2 82 33 1
3 82 173 1
4 82 211 1
5 82 247 2
6 82 480 2
7 82 541 1
8 82 974 1
9 82 1197 1
10 82 1416 1
11 82 1531 1
12 82 1797 7
13 82 2416 2
14 82 2530 1
15 82 2772 1
16 82 2970 2
17 82 3264 4
18 82 3416 1
19 82 3995 4
20 82 5593 1
21 82 6557 1
22 82 8141 1
23 82 9044 1
24 82 11889 1
25 82 12608 1
26 82 13352 1
27 82 13463 1
28 82 17937 1
29 82 29730 1
30 82 37712 1
31 82 258434 1
[[83]]
Row Columns Freq
2 83 309 1
3 83 447 1
4 83 480 2
5 83 487 1
6 83 619 1
7 83 651 1
8 83 913 1
9 83 1555 1
10 83 1874 1
11 83 2416 1
12 83 3101 1
13 83 3856 1
14 83 3964 1
15 83 3995 1
16 83 4017 1
17 83 4362 1
18 83 10551 1
19 83 17130 1
20 83 29730 1
We can use sparseMatrix from Matrix after rbinding the list elements.
library(Matrix)
d1 <- do.call(rbind, lst)
res <- sparseMatrix(d1[,1], d1[,2], x = d1[,3])
I am trying to plot a stacked bar plot of my dataset which is data.csv and which is as below.Apologies for posting large dataset.
degree Freq.x Freq.y
1 2978 0
2 1779 33
3 1390 22
4 919 19
5 787 16
6 676 22
7 578 16
8 513 23
9 460 11
10 376 17
11 345 13
12 292 17
13 291 14
14 286 8
15 269 15
16 216 10
17 192 18
18 183 10
19 184 7
20 190 10
21 157 9
22 155 14
23 127 9
24 151 15
25 119 10
26 102 6
27 113 7
28 99 6
29 98 4
30 103 7
31 94 11
32 79 7
33 76 5
34 73 8
35 76 11
36 59 5
37 58 5
38 61 5
39 63 7
40 68 9
41 63 4
42 57 8
43 45 6
44 45 4
45 39 3
46 40 6
47 42 6
48 30 3
49 36 7
50 28 5
51 33 1
52 32 6
53 34 5
54 43 4
55 35 6
56 29 2
57 27 4
58 35 6
59 25 4
60 24 4
61 32 4
62 15 2
63 24 5
64 25 4
65 23 9
66 25 7
67 27 7
68 22 7
69 23 7
70 17 6
71 19 4
72 19 4
73 19 2
74 18 2
75 19 6
76 12 3
77 25 6
78 23 9
79 20 4
80 17 6
81 15 5
82 13 4
83 14 4
84 13 5
85 15 1
86 13 1
87 12 5
88 14 5
89 16 4
90 12 3
91 10 3
92 12 5
93 12 7
94 10 0
95 11 4
96 12 3
97 6 5
98 20 7
99 5 3
100 8 3
101 11 2
102 11 3
103 8 0
104 14 4
105 15 2
106 7 0
107 7 1
108 6 0
109 9 2
110 10 1
111 8 1
112 6 1
113 8 1
114 8 2
115 7 4
116 3 1
117 4 2
118 5 0
120 5 0
121 1 0
122 9 2
123 7 3
124 4 1
125 3 0
126 3 2
127 7 3
128 5 3
129 3 1
130 3 0
131 5 1
132 5 2
133 2 0
134 5 2
135 10 1
136 5 2
137 3 1
138 7 2
139 6 2
140 3 1
141 5 1
142 9 4
143 3 1
144 2 1
145 4 2
146 2 0
147 2 2
148 3 1
149 1 0
150 1 0
151 2 1
152 3 1
153 3 1
154 2 1
155 3 1
156 6 4
157 4 2
158 3 1
159 4 1
160 2 1
161 2 1
163 3 1
164 5 2
165 2 1
166 3 0
167 4 4
168 2 1
169 1 0
170 2 2
171 3 2
172 1 0
173 4 3
174 3 2
175 1 1
177 3 3
178 3 2
179 1 0
180 3 1
181 2 0
182 1 1
183 3 1
184 2 2
185 2 1
186 3 1
187 2 1
188 1 1
191 1 0
192 1 0
193 1 0
195 4 2
196 2 2
197 4 1
198 1 0
199 2 1
200 1 0
201 2 2
202 1 0
204 2 0
206 3 1
207 1 0
208 1 0
209 2 1
211 1 1
212 2 1
213 2 2
214 1 1
215 1 1
218 2 2
220 2 1
222 3 1
223 2 2
224 1 1
225 1 1
226 1 1
227 2 1
228 2 1
230 3 1
231 1 1
233 2 2
234 3 1
235 1 1
236 1 1
237 1 1
239 2 2
241 1 1
242 1 0
243 1 0
244 1 1
245 1 1
246 1 1
247 2 0
250 2 1
251 3 2
252 1 1
253 2 2
254 1 1
256 1 1
258 2 1
260 1 1
262 1 1
264 1 0
267 1 1
268 1 1
269 1 1
270 1 1
271 2 1
272 1 1
275 2 1
276 1 1
277 2 2
278 1 0
280 1 1
283 1 0
285 2 1
290 1 1
291 1 1
294 1 1
299 1 1
301 4 3
303 1 1
304 2 0
305 1 1
307 1 1
311 1 1
314 2 1
317 1 1
318 1 1
319 1 1
321 1 1
323 1 1
329 2 1
330 1 1
333 1 0
334 1 1
335 1 1
337 1 1
339 1 1
342 1 1
343 1 0
350 2 2
356 1 1
368 1 0
370 2 2
377 1 1
390 1 1
392 1 1
394 1 1
406 1 1
408 1 1
409 1 1
419 1 1
424 1 1
427 1 1
451 1 1
459 1 1
461 1 1
462 1 0
478 1 1
479 1 0
488 1 1
530 1 1
550 1 1
553 1 1
568 1 0
594 1 1
608 1 1
622 1 1
625 1 1
626 1 1
628 1 1
646 1 1
648 1 1
652 1 1
655 1 1
656 1 1
660 1 0
688 1 1
723 1 1
732 1 1
740 1 1
761 1 1
769 1 0
845 1 1
865 1 1
1063 1 1
1105 1 1
1242 1 1
1737 1 1
1989 1 1
2456 1 1
9588 1 1
I want to plot stacked barplot in which i want to compare the degree in freq.x and freq.y field. That means on x axis there will be degree and on on y axis there will be frequency.I tried the ggplot2 function in r and plotted stacked bar plot. But the problem is my dataset is large so i want to combine bar limits. The code which i tried is as follow.
d_ap <- read.csv("data.csv")
l_nw <- data.frame(d_ap)
library(reshape2)
final_df <- melt(l_nw, id.var="Degree")
library(ggplot2)
ggplot(final_df, aes(x = Degree, y = value, fill = variable)) +
geom_bar(stat = "identity")
this will output a barplot but i want to set bar limits on x-axis and in my desired output of bar plot on x-axis i want to plot degree from 1 to 10 in individual bars. Then from degree 11 to 9588 i want to club it in bars like 11 to 20 then 20 to 30 and then 30 to 50 and 50 to 9588. How can i set bar limits on x-axis like this..?? So that by setting this bar limit i can better visualize my stacked bar plot.
Is that what you want?
final_df$cdegree=cut(final_df$degree,c(0,1,2,3,4,5,6,7,8,9,10,20,30,50,9590))
library(ggplot2)
ggplot(final_df, aes(x = cdegree, y = value, fill = variable)) +
geom_bar(stat = "identity")
I have a dataset that looks like this:
USER.ID avgfrequency orders group
1 3 3.7821782 101 3
2 7 14.7500000 8 3
3 9 13.4761905 21 3
4 13 5.1967213 61 3
5 16 6.7812500 64 3
6 26 41.7500000 4 2
7 49 13.6666667 3 2
8 50 7.0000000 1 1
9 51 1.0000000 1 1
10 52 17.7500000 4 2
11 69 4.5000000 2 1
12 75 9.9500000 20 3
13 91 84.2000000 5 2
14 98 8.0185185 54 3
15 138 14.2000000 5 2
16 139 34.7500000 4 2
17 149 7.6666667 21 3
18 155 35.3333333 9 3
19 167 24.0000000 1 1
20 170 7.3529412 34 3
21 171 4.4210526 76 3
22 174 4.5000000 2 1
23 175 6.5781250 64 3
24 176 19.2857143 21 3
25 177 10.4864865 37 3
26 178 28.0000000 15 3
27 180 4.8461538 39 3
28 183 25.5000000 2 1
29 184 13.0000000 1 1
30 210 32.0000000 1 1
31 215 13.4615385 13 3
32 220 11.3611111 36 3
33 223 26.2500000 8 3
34 224 40.5000000 8 3
35 230 15.4000000 10 3
36 232 14.6666667 3 2
37 234 34.5833333 12 3
38 238 138.5000000 2 1
39 240 7.0000000 3 2
40 243 35.0000000 3 2
41 246 6.7500000 4 2
42 247 8.5000000 50 3
43 258 17.6666667 3 2
44 283 23.5000000 2 1
45 295 19.5625000 16 3
46 300 81.6666667 3 2
47 311 34.4166667 12 3
48 338 64.0000000 1 1
49 342 113.3333333 3 2
50 343 197.0000000 1 1
51 347 3.6923077 13 3
52 350 4.6666667 3 2
53 360 177.5000000 2 1
54 361 39.0000000 10 3
55 362 1.4000000 5 2
56 365 15.0000000 24 3
57 366 59.2000000 5 2
58 367 5.0000000 4 2
59 369 27.9285714 14 3
60 372 63.6666667 3 2
61 375 9.3750000 8 3
62 377 13.3225806 31 3
63 380 169.5000000 2 1
64 383 23.2352941 17 3
65 391 0.0000000 1 1
I want to split avgfrequency into different bins of width 10 and plot it as x-axis and on y-axis I want to show the count of USER.ID as histograms and in each bar I want to show count of USER.ID of different group with different color. So, each histogram would have three different colors for each bin.
Is it possible to do it in R ?
It is possible. See below:
library(ggplot2) #load the ggplot2 graph package
data = data.frame(data) #make the dataset a R dataframe object
head(data,2) #just showing part of the data here.
USER.ID avgfrequency orders group
3 3.782178 101 3
7 14.750000 8 3
#build graph
ggplot(data, aes(x=avgfrequency,fill=factor(group))) +
geom_histogram(breaks=seq(0,200,by=10),colour='black') +
xlab("Average Frequency") + ylab("Count of USER.ID") +
scale_fill_manual("Group", breaks = c("1","2","3"), values = c("grey30","grey50", "grey70")) +
theme_bw()
I am trying to figure out how to label the boxplots that appear after I use the svyboxplot library for R.
I have tried the following:
svyboxplot(~ALCANYNO~factor(REGION), design=ihisDesign3, xlab='Region', ylab='Frequency', ylim=c(0,10), colnames=c("Northeast", "Midwest", "South", "West"));
SOLUTION: Add the following to factor:
labels = c('Northeast', 'Midwest', 'South', 'West')
This changes the example above to the following:
svyboxplot(~ALCANYNO~factor(REGION,
labels=c('Northeast', 'Midwest', 'South', 'West')),
design=ihisDesign3, xlab='Region', ylab='Frequency',
ylim =c (0, 10))
I am Creating a dataset to explain:
options(width = 120)
library (survey)
library (KernSmooth)
xd1<-
"xsmoke age_p psu stratum wt8
13601 3 22 2 20 356.5600
32966 3 38 2 45 434.3562
63493 1 32 1 87 699.9987
238175 3 46 1 338 982.8075
174162 3 40 1 240 273.6313
220206 3 33 2 308 1477.1688
118133 3 68 1 159 716.3012
142859 2 23 1 194 1100.9475
115253 2 35 2 155 444.3750
61675 3 31 1 85 769.5963
189813 3 37 1 263 328.5600
226274 1 47 2 318 605.8700
41969 3 71 2 58 597.0150
167667 3 40 2 230 1030.4637
225103 3 37 2 316 349.6825
49894 3 70 2 68 517.7862
98075 3 46 2 130 1428.7225
180771 3 50 1 250 652.4188
137057 3 42 1 186 590.2100
77705 2 23 1 105 1687.2450
89106 3 48 1 118 407.6513
208178 3 50 1 290 556.5000
100403 3 52 2 133 1481.8200
221571 1 27 2 310 833.5338
10823 2 72 1 16 1807.6425
108431 3 71 2 145 945.6263
68708 1 46 1 94 1989.3775
23874 3 23 2 33 1707.8775
150634 3 19 2 206 761.1500
231232 3 42 2 326 1487.4113
184654 2 42 2 255 1715.2375
215312 3 57 1 300 483.5663
40713 2 57 2 56 2042.2762
130309 3 23 1 177 948.5625
25515 2 55 1 35 2719.7525
235612 2 83 2 333 603.3537
13755 2 36 2 20 265.1938
2441 3 33 1 4 1062.1200
157327 3 77 1 215 2010.6600
66502 3 20 2 91 1122.9725
230778 1 55 2 325 1207.3025
74805 3 54 1 101 1028.5150
166556 1 50 1 229 1546.9450
91914 1 68 1 121 428.5350
89651 3 59 2 118 143.5437
149329 3 44 2 204 1064.7725
212700 2 59 2 295 1050.1163
454 1 79 1 1 275.5700
125639 1 27 1 170 785.1037
55442 3 47 1 76 950.3312
145132 3 77 1 197 1269.2287
123069 3 24 1 167 216.1937
188301 1 55 2 260 426.6313
852 2 66 2 1 1443.4887
3582 3 81 1 6 790.8412
235423 1 44 2 333 659.4238
42175 2 40 1 59 1089.6762
57033 3 43 1 78 226.8750
177273 2 85 1 244 392.7200
218558 3 40 2 305 1680.2700
27784 2 45 1 39 280.0550
81823 3 43 1 110 965.0438
76344 3 26 1 103 1095.6012
114916 3 56 2 154 436.8838
35563 3 78 1 49 333.2875
192279 3 30 2 267 722.0312
61315 1 48 2 84 1426.5725
219903 3 43 1 308 791.5738
42612 3 25 1 60 658.1387
178488 3 33 2 246 675.1912
9031 1 27 2 14 989.4863
145092 2 64 1 197 960.1912
71885 3 53 2 97 595.4050
38137 2 75 1 53 1004.0912
140149 1 21 1 190 1870.9350
162052 3 25 1 223 892.7775
89527 2 39 2 118 518.1050
59650 3 26 2 82 432.7837
24709 2 84 1 34 453.9013
18933 3 85 1 27 582.3288
24904 3 35 2 34 1027.5287
213668 3 39 1 298 3174.1925
110509 3 30 1 149 469.8188
72462 3 63 1 98 386.2163
152596 3 19 1 209 1328.2188
17014 4 62 1 24 294.9250
33467 2 50 1 46 1601.4575
5241 3 33 1 9 1651.0988
215094 3 23 1 300 427.6313
88885 1 21 1 118 1092.2613
204868 2 60 2 285 781.2325
157415 2 31 2 215 1323.5750
71081 2 44 2 96 1059.2088
25420 3 38 1 35 530.7413
144226 1 27 1 196 1126.3112
47888 3 46 2 66 965.4050
216179 3 29 2 301 1237.6463
29172 3 68 1 41 1025.9738
168786 1 47 1 232 680.6213
94035 2 23 2 124 330.4563
170542 1 25 2 234 757.2287
160331 2 33 2 220 636.3900
124163 3 80 2 167 287.6988
71442 2 37 1 97 442.2300
80191 2 74 2 107 871.0338
199309 3 29 2 277 485.2337
91293 3 35 2 120 138.3187
219524 2 68 1 307 609.5862
119336 3 85 2 160 149.7612
31814 3 68 1 44 396.6913
54920 1 28 2 75 532.7175
161034 3 29 2 221 791.0100
177037 1 50 1 244 626.2400
119963 1 54 1 162 374.1062
107972 2 58 1 145 944.8863
22932 3 60 1 32 310.6413
54197 3 23 2 74 931.2737
209598 3 23 1 292 1078.2950
213604 1 74 2 297 588.5000
146480 3 27 1 200 212.0588
162463 3 55 2 223 1202.0925
215534 3 33 2 300 430.3938
100703 1 53 1 134 463.6200
162588 3 27 1 224 612.0250
222676 1 35 1 312 292.7000
220052 3 84 1 308 1301.4738
131382 3 36 1 178 825.9512
102117 3 28 1 137 451.4075
70362 3 52 2 95 185.2562
188757 3 22 2 261 704.3913
215878 2 37 1 301 789.9837
45820 3 18 2 64 2019.4137
84860 3 47 1 113 149.0200
110581 3 37 1 149 526.0775
207650 3 51 2 289 688.0538
40723 3 59 2 56 497.6050
169663 3 19 2 233 845.0362
191955 1 36 1 267 735.7350
213816 3 18 2 298 2275.3513
120967 3 48 2 163 1055.3238
209430 2 42 2 291 1771.0225
21235 3 21 1 30 1204.5663
131326 3 29 1 178 331.9588
19667 1 57 1 28 638.9138
74743 2 48 1 101 1208.8763
178672 3 66 2 246 338.2013
100174 3 24 2 133 1733.6275
69046 3 24 2 94 542.4863
79960 1 41 2 107 567.6363
108591 2 42 1 146 978.3775
235635 3 24 1 334 1382.9437
187426 2 54 2 259 478.2362
28728 3 39 2 40 1165.6175
205348 3 32 2 286 1082.9913
218812 3 30 1 306 308.1037
168389 3 48 2 231 593.2475
145479 1 21 1 198 864.2663
105170 2 40 1 141 1016.7862
155753 2 78 2 212 1109.0025
169399 3 28 1 233 1467.1363
55664 1 63 1 76 904.3763
74024 2 51 1 100 547.5538
85558 1 25 1 114 893.8825
142684 3 54 2 193 1203.3212
198792 1 22 1 277 1800.3325
82603 3 70 2 110 827.3763
171036 2 50 2 235 2003.9725
1616 1 42 2 2 590.5662
57042 3 45 1 78 1021.7287
45100 2 38 2 63 1807.9288
134828 2 28 1 183 715.1187
91167 3 26 2 120 480.1950
170605 3 40 2 234 507.2763
175869 3 77 1 242 386.2987
81594 2 82 2 109 580.0838
37426 1 20 2 52 1159.1613
113799 3 85 1 153 459.5450
24721 3 18 2 34 2912.7575
26297 3 45 2 36 1304.4925
57074 1 51 1 78 602.2112
185000 3 34 1 256 583.5738
94196 3 44 2 124 2344.1087
80656 3 45 2 108 1340.9713
14849 1 46 1 22 967.2525
145730 2 73 1 198 418.8037
56633 3 34 2 77 1011.5488
273 2 54 1 1 786.2138
60567 1 40 2 83 315.2925
47788 1 38 2 66 1105.9188
76943 2 53 2 103 537.7062
165014 3 34 1 227 824.3125
188444 3 22 1 261 623.2225
29043 1 35 1 41 724.9025
165578 3 25 1 228 596.0275
50702 3 43 2 69 985.9662
197621 3 39 2 275 1310.1163
26267 3 41 2 36 1030.3900
29565 1 60 2 41 920.8550
20060 3 36 2 28 157.2188
119780 2 20 1 162 863.8100"
tor <- read.table(textConnection(xd1), header=TRUE, as.is=TRUE)
# Grouping variable "xsmoke" must be a factor
tor$xsmoke <- factor(tor$xsmoke,levels=c (1,2,3),
labels=c('Current SMK','Former SMK', 'Never Smk'), ordered=TRUE)
is.factor(tor$xsmoke)
# object with survey design variables and data
nhis <- svydesign (id=~psu,strat=~stratum, weights=~wt8, data=tor, nest=TRUE)
MyBreaks <- c(18, 25, 35, 45, 55, 65, 75, 85)
svyboxplot (age_p~xsmoke,
subset (nhis, age_p>=0),
col=c("red", "yellow", "green"), medcol="blue",
varwidth=TRUE, all.outliers=TRUE,
ylab="Age at Interview",
xlab=" "
)
The Factor variable xsmoke is coded as tor$xsmoke <- factor(tor$xsmoke,levels=c (1,2,3),
labels=c('Current SMK','Former SMK', 'Never Smk'), ordered=TRUE) which should be useful
__________________________________________enter code here