Stacked bar chart

[Michael Dan Arvig]

I want to create a stacked bar chart. I have a variable with five categories. This variable is measured at three time points. I want to make a stacked bar chart where the y-axis shows the percent and the x axis the three time points. The bar at each time point should then show the percentages of the five categories contained within the variable.


record_id time ef_fivelevels1 ef_fivelevels2 ef_fivelevels3
1 1 . . .
1 2 . . .
1 3 . . .
2 1 . . .
2 2 . . .
2 3 . . .
4 1 Severe Severe Severe
4 2 Severe Severe Severe
4 3 Severe Severe Severe
5 1 Normal Normal Normal
5 2 Normal Normal Normal
5 3 Normal Normal Normal
6 1 Severe Severe Severe
6 2 Severe Severe Severe
6 3 Severe Severe Severe
8 1 Normal Normal Normal
8 2 Normal Normal Normal
8 3 Normal Normal Normal
9 1 . . .
9 2 . . .
9 3 . . .
10 1 . . .
10 2 . . .
10 3 . . .
11 1 . . .
11 2 . . .
11 3 . . .
12 1 Moderate Moderate Moderate
12 2 Moderate Moderate Moderate
12 3 Moderate Moderate Moderate
13 1 . . .
13 2 . . .
13 3 . . .
14 1 . . .
14 2 . . .
14 3 . . .
15 1 . . .
15 2 . . .
15 3 . . .
16 1 . . .
16 2 . . .
16 3 . . .
17 1 Mild Mild Mild
17 2 Mild Mild Mild
17 3 Mild Mild Mild
18 1 Normal Normal Normal
18 2 Normal Normal Normal
18 3 Normal Normal Normal
19 1 Hyperdynamic Hyperdynamic Hyperdynamic
19 2 Hyperdynamic Hyperdynamic Hyperdynamic
19 3 Hyperdynamic Hyperdynamic Hyperdynamic
20 1 . . .
20 2 . . .
20 3 . . .
21 1 Mild Mild Mild
21 2 Mild Mild Mild
21 3 Mild Mild Mild
22 1 Normal Normal Normal
22 2 Normal Normal Normal
22 3 Normal Normal Normal
23 1 . . .
23 2 . . .
23 3 . . .
24 1 . . .
24 2 . . .
24 3 . . .
25 1 Moderate Moderate Moderate
25 2 Moderate Moderate Moderate
25 3 Moderate Moderate Moderate
26 1 . . .
26 2 . . .
26 3 . . .
27 1 . . .
27 2 . . .
27 3 . . .
28 1 . . .
28 2 . . .
28 3 . . .
29 1 Severe Severe Severe
29 2 Severe Severe Severe
29 3 Severe Severe Severe
30 1 . . .
30 2 . . .
30 3 . . .
31 1 Normal Normal Normal
31 2 Normal Normal Normal
31 3 Normal Normal Normal
32 1 Normal Normal Normal
32 2 Normal Normal Normal
32 3 Normal Normal Normal
33 1 Mild Normal Normal
33 2 Mild Normal Normal
33 3 Mild Normal Normal
34 1 . . .
34 2 . . .
34 3 . . .
35 1 . . .
35 2 . . .
35 3 . . .
36 1 Normal Severe Normal
36 2 Normal Severe Normal
36 3 Normal Severe Normal
37 1 Normal Normal Normal
37 2 Normal Normal Normal
37 3 Normal Normal Normal
38 1 . . .
38 2 . . .
38 3 . . .
39 1 Normal Normal Normal
39 2 Normal Normal Normal
39 3 Normal Normal Normal
40 1 Normal Normal Normal
40 2 Normal Normal Normal
40 3 Normal Normal Normal
41 1 . . .
41 2 . . .
41 3 . . .
42 1 . . .
42 2 . . .
42 3 . . .
43 1 Normal Normal Normal
43 2 Normal Normal Normal
43 3 Normal Normal Normal
44 1 . . .
44 2 . . .
44 3 . . .
45 1 . . .
45 2 . . .
45 3 . . .
46 1 Mild Mild Mild
46 2 Mild Mild Mild
46 3 Mild Mild Mild
47 1 Moderate Moderate Moderate
47 2 Moderate Moderate Moderate
47 3 Moderate Moderate Moderate
48 1 Severe Severe Severe
48 2 Severe Severe Severe
48 3 Severe Severe Severe
49 1 . . .
49 2 . . .
49 3 . . .
50 1 . . .
50 2 . . .
50 3 . . .
51 1 . . .
51 2 . . .
51 3 . . .
52 1 Mild Mild Mild
52 2 Mild Mild Mild
52 3 Mild Mild Mild
53 1 Normal Normal Normal
53 2 Normal Normal Normal
53 3 Normal Normal Normal
54 1 . . .
54 2 . . .
54 3 . . .
55 1 . . .
55 2 . . .
55 3 . . .
56 1 Mild Mild Mild
56 2 Mild Mild Mild
56 3 Mild Mild Mild
57 1 Mild Mild Mild
57 2 Mild Mild Mild
57 3 Mild Mild Mild
58 1 . . .
58 2 . . .
58 3 . . .
59 1 Normal Normal Normal
59 2 Normal Normal Normal
59 3 Normal Normal Normal
60 1 . . .
60 2 . . .
60 3 . . .
61 1 . . .
61 2 . . .
61 3 . . .
62 1 Normal Normal Normal
62 2 Normal Normal Normal
62 3 Normal Normal Normal
63 1 . . .
63 2 . . .
63 3 . . .
64 1 . . .
64 2 . . .
64 3 . . .
65 1 Normal Normal Normal
65 2 Normal Normal Normal
65 3 Normal Normal Normal
66 1 . . .
66 2 . . .
66 3 . . .
67 1 Moderate Moderate Mild
67 2 Moderate Moderate Mild
67 3 Moderate Moderate Mild
68 1 Normal Normal Normal
68 2 Normal Normal Normal
68 3 Normal Normal Normal
69 1 . . .
69 2 . . .
69 3 . . .
70 1 . . .
70 2 . . .
70 3 . . .
71 1 Mild Mild Mild
71 2 Mild Mild Mild
71 3 Mild Mild Mild
72 1 Normal Normal Normal
72 2 Normal Normal Normal
72 3 Normal Normal Normal
73 1 . . .
73 2 . . .
73 3 . . .
74 1 Severe Severe Severe
74 2 Severe Severe Severe
74 3 Severe Severe Severe
75 1 Severe Severe Severe
75 2 Severe Severe Severe
75 3 Severe Severe Severe
76 1 Moderate Moderate Moderate
76 2 Moderate Moderate Moderate
76 3 Moderate Moderate Moderate
77 1 . . .
77 2 . . .
77 3 . . .
78 1 . . .
78 2 . . .
78 3 . . .
79 1 . . .
79 2 . . .
79 3 . . .
80 1 Severe Severe Severe
80 2 Severe Severe Severe
80 3 Severe Severe Severe
81 1 Moderate Moderate Moderate
81 2 Moderate Moderate Moderate
81 3 Moderate Moderate Moderate
82 1 Moderate Moderate Moderate
82 2 Moderate Moderate Moderate
82 3 Moderate Moderate Moderate
83 1 . . .
83 2 . . .
83 3 . . .
84 1 . . .
84 2 . . .
84 3 . . .
85 1 . . .
85 2 . . .
85 3 . . .
86 1 . . .
86 2 . . .
86 3 . . .
87 1 . . .
87 2 . . .
87 3 . . .
88 1 Normal Normal Normal
88 2 Normal Normal Normal
88 3 Normal Normal Normal
89 1 Mild Moderate Mild
89 2 Mild Moderate Mild
89 3 Mild Moderate Mild
90 1 Normal Normal Normal
90 2 Normal Normal Normal
90 3 Normal Normal Normal
91 1 Severe Severe Severe
91 2 Severe Severe Severe
91 3 Severe Severe Severe
92 1 . . .
92 2 . . .
92 3 . . .
93 1 . . .
93 2 . . .
93 3 . . .
94 1 Normal Normal Normal
94 2 Normal Normal Normal
94 3 Normal Normal Normal
95 1 Normal Normal Normal
95 2 Normal Normal Normal
95 3 Normal Normal Normal
96 1 . . .
96 2 . . .
96 3 . . .
97 1 . . .
97 2 . . .
97 3 . . .
98 1 Normal Normal Normal
98 2 Normal Normal Normal
98 3 Normal Normal Normal
99 1 . . .
99 2 . . .
99 3 . . .
100 1 Moderate Moderate Moderate
100 2 Moderate Moderate Moderate
100 3 Moderate Moderate Moderate
101 1 . . .
101 2 . . .
101 3 . . .
102 1 . . .
102 2 . . .
102 3 . . .
103 1 Mild Mild Mild
103 2 Mild Mild Mild
103 3 Mild Mild Mild
104 1 . . .
104 2 . . .
104 3 . . .
105 1 Severe Severe Severe
105 2 Severe Severe Severe
105 3 Severe Severe Severe
106 1 Normal Normal Normal
106 2 Normal Normal Normal
106 3 Normal Normal Normal
107 1 Severe Severe Severe
107 2 Severe Severe Severe
107 3 Severe Severe Severe
108 1 Normal Normal Normal
108 2 Normal Normal Normal
108 3 Normal Normal Normal
109 1 . . .
109 2 . . .
109 3 . . .
110 1 Severe Severe Severe
110 2 Severe Severe Severe
110 3 Severe Severe Severe
111 1 . . .
111 2 . . .
111 3 . . .
112 1 . . .
112 2 . . .
112 3 . . .
113 1 . . .
113 2 . . .
113 3 . . .
114 1 Normal Normal Normal
114 2 Normal Normal Normal
114 3 Normal Normal Normal
115 1 Mild Mild Mild
115 2 Mild Mild Mild
115 3 Mild Mild Mild
116 1 . . .
116 2 . . .
116 3 . . .
117 1 Normal Normal Normal
117 2 Normal Normal Normal
117 3 Normal Normal Normal
118 1 . . .
118 2 . . .
118 3 . . .
119 1 . . .
119 2 . . .
119 3 . . .
120 1 Normal Normal Normal
120 2 Normal Normal Normal
120 3 Normal Normal Normal
121 1 . . .
121 2 . . .
121 3 . . .
122 1 . . .
122 2 . . .
122 3 . . .
123 1 Hyperdynamic Hyperdynamic Hyperdynamic
123 2 Hyperdynamic Hyperdynamic Hyperdynamic
123 3 Hyperdynamic Hyperdynamic Hyperdynamic
124 1 Normal Normal Normal
124 2 Normal Normal Normal
124 3 Normal Normal Normal
125 1 . . .
125 2 . . .
125 3 . . .
126 1 Mild Mild Mild
126 2 Mild Mild Mild
126 3 Mild Mild Mild
127 1 . . .
127 2 . . .
127 3 . . .
128 1 . . .
128 2 . . .
128 3 . . .
129 1 Moderate Moderate Moderate
129 2 Moderate Moderate Moderate
129 3 Moderate Moderate Moderate
130 1 Normal Normal Normal
130 2 Normal Normal Normal
130 3 Normal Normal Normal
131 1 . . .
131 2 . . .
131 3 . . .
132 1 Moderate Moderate Moderate
132 2 Moderate Moderate Moderate
132 3 Moderate Moderate Moderate
134 1 Normal Normal Normal
134 2 Normal Normal Normal
134 3 Normal Normal Normal
135 1 Normal Normal Normal
135 2 Normal Normal Normal
135 3 Normal Normal Normal
136 1 Normal Normal Normal
136 2 Normal Normal Normal
136 3 Normal Normal Normal
137 1 . . .
137 2 . . .
137 3 . . .
138 1 Mild Mild Mild
138 2 Mild Mild Mild
138 3 Mild Mild Mild
139 1 . . .
139 2 . . .
139 3 . . .
140 1 . . .
140 2 . . .
140 3 . . .
141 1 Normal Normal Normal
141 2 Normal Normal Normal
141 3 Normal Normal Normal
142 1 Normal Normal Normal
142 2 Normal Normal Normal
142 3 Normal Normal Normal
143 1 . . .
143 2 . . .
143 3 . . .
144 1 . . .
144 2 . . .
144 3 . . .
145 1 Mild Mild Mild
145 2 Mild Mild Mild
145 3 Mild Mild Mild
146 1 . . .
146 2 . . .
146 3 . . .
148 1 . . .
148 2 . . .
148 3 . . .
149 1 Normal Normal Normal
149 2 Normal Normal Normal
149 3 Normal Normal Normal
150 1 . . .
150 2 . . .
150 3 . . .
151 1 . . .
151 2 . . .
151 3 . . .
152 1 Normal Normal Normal
152 2 Normal Normal Normal
152 3 Normal Normal Normal
153 1 Severe Severe Severe
153 2 Severe Severe Severe
153 3 Severe Severe Severe
154 1 . . .
154 2 . . .
154 3 . . .
155 1 Normal Normal Normal
155 2 Normal Normal Normal
155 3 Normal Normal Normal
156 1 . . .
156 2 . . .
156 3 . . .
157 1 . . .
157 2 . . .
157 3 . . .
158 1 Mild Mild Mild
158 2 Mild Mild Mild
158 3 Mild Mild Mild
159 1 Normal Normal Normal
159 2 Normal Normal Normal
159 3 Normal Normal Normal
160 1 . . .
160 2 . . .
160 3 . . .
161 1 . . .
161 2 . . .
161 3 . . .
162 1 Mild Mild Mild
162 2 Mild Mild Mild
162 3 Mild Mild Mild
163 1 Normal Normal Normal
163 2 Normal Normal Normal
163 3 Normal Normal Normal
164 1 . . .
164 2 . . .
164 3 . . .
165 1 Normal Normal Normal
165 2 Normal Normal Normal
165 3 Normal Normal Normal
166 1 . . .
166 2 . . .
166 3 . . .
167 1 Mild Mild Mild
167 2 Mild Mild Mild
167 3 Mild Mild Mild
168 1 . . .
168 2 . . .
168 3 . . .
169 1 Normal Normal Normal
169 2 Normal Normal Normal
169 3 Normal Normal Normal
170 1 . . .
170 2 . . .
170 3 . . .
171 1 . . .
171 2 . . .
171 3 . . .
172 1 . . .
172 2 . . .
172 3 . . .
173 1 Moderate Moderate Moderate
173 2 Moderate Moderate Moderate
173 3 Moderate Moderate Moderate
174 1 Moderate Moderate Moderate
174 2 Moderate Moderate Moderate
174 3 Moderate Moderate Moderate
175 1 Mild Mild Mild
175 2 Mild Mild Mild
175 3 Mild Mild Mild
176 1 . . .
176 2 . . .
176 3 . . .
177 1 Mild Mild Mild
177 2 Mild Mild Mild
177 3 Mild Mild Mild
178 1 . . .
178 2 . . .
178 3 . . .
179 1 Severe Severe Severe
179 2 Severe Severe Severe
179 3 Severe Severe Severe
180 1 Normal Normal Normal
180 2 Normal Normal Normal
180 3 Normal Normal Normal
181 1 . . .
181 2 . . .
181 3 . . .
182 1 . . .
182 2 . . .
182 3 . . .
183 1 . . .
183 2 . . .
183 3 . . .
184 1 Normal Normal Normal
184 2 Normal Normal Normal
184 3 Normal Normal Normal
185 1 Mild Mild Mild
185 2 Mild Mild Mild
185 3 Mild Mild Mild
186 1 . . .
186 2 . . .
186 3 . . .
187 1 Normal Normal Normal
187 2 Normal Normal Normal
187 3 Normal Normal Normal
188 1 . . .
188 2 . . .
188 3 . . .
189 1 . . .
189 2 . . .
189 3 . . .
190 1 Moderate Mild Mild
190 2 Moderate Mild Mild
190 3 Moderate Mild Mild
191 1 Normal Normal Normal
191 2 Normal Normal Normal
191 3 Normal Normal Normal
192 1 Mild Mild Mild
192 2 Mild Mild Mild
192 3 Mild Mild Mild
193 1 . . .
193 2 . . .
193 3 . . .
194 1 . . .
194 2 . . .
194 3 . . .
195 1 Normal Normal Normal
195 2 Normal Normal Normal
195 3 Normal Normal Normal
196 1 . . .
196 2 . . .
196 3 . . .
197 1 Moderate Moderate Mild
197 2 Moderate Moderate Mild
197 3 Moderate Moderate Mild
198 1 . . .
198 2 . . .
198 3 . . .
199 1 . . .
199 2 . . .
199 3 . . .
200 1 Moderate Moderate Moderate
200 2 Moderate Moderate Moderate
200 3 Moderate Moderate Moderate
201 1 . . .
201 2 . . .
201 3 . . .
202 1 Normal Normal Normal
202 2 Normal Normal Normal
202 3 Normal Normal Normal
203 1 Severe Severe Severe
203 2 Severe Severe Severe
203 3 Severe Severe Severe
204 1 . . .
204 2 . . .
204 3 . . .
205 1 Normal Normal Normal
205 2 Normal Normal Normal
205 3 Normal Normal Normal
206 1 . . .
206 2 . . .
206 3 . . .

It should look like this:

[Nick Cox]

You have three variables each recording one of 5 categories at 3 time points, so your set-up doesn't seem analogous to the graph you show.

You didn't use dataex, despite our explicit request (FAQ Advice #12). So, your data need surgery before they are useful. The observations with missing values are presumably useless. Please confirm that I got the order right here.

* Example generated by -dataex-. For more info, type help dataex
clear
input int record_id byte time str12(ef_fivelevels1 ef_fivelevels2 ef_fivelevels3)
  4 1 "Severe"       "Severe"       "Severe"      
  4 2 "Severe"       "Severe"       "Severe"      
  4 3 "Severe"       "Severe"       "Severe"      
  5 1 "Normal"       "Normal"       "Normal"      
  5 2 "Normal"       "Normal"       "Normal"      
  5 3 "Normal"       "Normal"       "Normal"      
  6 1 "Severe"       "Severe"       "Severe"      
  6 2 "Severe"       "Severe"       "Severe"      
  6 3 "Severe"       "Severe"       "Severe"      
  8 1 "Normal"       "Normal"       "Normal"      
  8 2 "Normal"       "Normal"       "Normal"      
  8 3 "Normal"       "Normal"       "Normal"      
 12 1 "Moderate"     "Moderate"     "Moderate"    
 12 2 "Moderate"     "Moderate"     "Moderate"    
 12 3 "Moderate"     "Moderate"     "Moderate"    
 17 1 "Mild"         "Mild"         "Mild"        
 17 2 "Mild"         "Mild"         "Mild"        
 17 3 "Mild"         "Mild"         "Mild"        
 18 1 "Normal"       "Normal"       "Normal"      
 18 2 "Normal"       "Normal"       "Normal"      
 18 3 "Normal"       "Normal"       "Normal"      
 19 1 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
 19 2 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
 19 3 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
 21 1 "Mild"         "Mild"         "Mild"        
 21 2 "Mild"         "Mild"         "Mild"        
 21 3 "Mild"         "Mild"         "Mild"        
 22 1 "Normal"       "Normal"       "Normal"      
 22 2 "Normal"       "Normal"       "Normal"      
 22 3 "Normal"       "Normal"       "Normal"      
 25 1 "Moderate"     "Moderate"     "Moderate"    
 25 2 "Moderate"     "Moderate"     "Moderate"    
 25 3 "Moderate"     "Moderate"     "Moderate"    
 29 1 "Severe"       "Severe"       "Severe"      
 29 2 "Severe"       "Severe"       "Severe"      
 29 3 "Severe"       "Severe"       "Severe"      
 31 1 "Normal"       "Normal"       "Normal"      
 31 2 "Normal"       "Normal"       "Normal"      
 31 3 "Normal"       "Normal"       "Normal"      
 32 1 "Normal"       "Normal"       "Normal"      
 32 2 "Normal"       "Normal"       "Normal"      
 32 3 "Normal"       "Normal"       "Normal"      
 33 1 "Mild"         "Normal"       "Normal"      
 33 2 "Mild"         "Normal"       "Normal"      
 33 3 "Mild"         "Normal"       "Normal"      
 36 1 "Normal"       "Severe"       "Normal"      
 36 2 "Normal"       "Severe"       "Normal"      
 36 3 "Normal"       "Severe"       "Normal"      
 37 1 "Normal"       "Normal"       "Normal"      
 37 2 "Normal"       "Normal"       "Normal"      
 37 3 "Normal"       "Normal"       "Normal"      
 39 1 "Normal"       "Normal"       "Normal"      
 39 2 "Normal"       "Normal"       "Normal"      
 39 3 "Normal"       "Normal"       "Normal"      
 40 1 "Normal"       "Normal"       "Normal"      
 40 2 "Normal"       "Normal"       "Normal"      
 40 3 "Normal"       "Normal"       "Normal"      
 43 1 "Normal"       "Normal"       "Normal"      
 43 2 "Normal"       "Normal"       "Normal"      
 43 3 "Normal"       "Normal"       "Normal"      
 46 1 "Mild"         "Mild"         "Mild"        
 46 2 "Mild"         "Mild"         "Mild"        
 46 3 "Mild"         "Mild"         "Mild"        
 47 1 "Moderate"     "Moderate"     "Moderate"    
 47 2 "Moderate"     "Moderate"     "Moderate"    
 47 3 "Moderate"     "Moderate"     "Moderate"    
 48 1 "Severe"       "Severe"       "Severe"      
 48 2 "Severe"       "Severe"       "Severe"      
 48 3 "Severe"       "Severe"       "Severe"      
 52 1 "Mild"         "Mild"         "Mild"        
 52 2 "Mild"         "Mild"         "Mild"        
 52 3 "Mild"         "Mild"         "Mild"        
 53 1 "Normal"       "Normal"       "Normal"      
 53 2 "Normal"       "Normal"       "Normal"      
 53 3 "Normal"       "Normal"       "Normal"      
 56 1 "Mild"         "Mild"         "Mild"        
 56 2 "Mild"         "Mild"         "Mild"        
 56 3 "Mild"         "Mild"         "Mild"        
 57 1 "Mild"         "Mild"         "Mild"        
 57 2 "Mild"         "Mild"         "Mild"        
 57 3 "Mild"         "Mild"         "Mild"        
 59 1 "Normal"       "Normal"       "Normal"      
 59 2 "Normal"       "Normal"       "Normal"      
 59 3 "Normal"       "Normal"       "Normal"      
 62 1 "Normal"       "Normal"       "Normal"      
 62 2 "Normal"       "Normal"       "Normal"      
 62 3 "Normal"       "Normal"       "Normal"      
 65 1 "Normal"       "Normal"       "Normal"      
 65 2 "Normal"       "Normal"       "Normal"      
 65 3 "Normal"       "Normal"       "Normal"      
 67 1 "Moderate"     "Moderate"     "Mild"        
 67 2 "Moderate"     "Moderate"     "Mild"        
 67 3 "Moderate"     "Moderate"     "Mild"        
 68 1 "Normal"       "Normal"       "Normal"      
 68 2 "Normal"       "Normal"       "Normal"      
 68 3 "Normal"       "Normal"       "Normal"      
 71 1 "Mild"         "Mild"         "Mild"        
 71 2 "Mild"         "Mild"         "Mild"        
 71 3 "Mild"         "Mild"         "Mild"        
 72 1 "Normal"       "Normal"       "Normal"      
 72 2 "Normal"       "Normal"       "Normal"      
 72 3 "Normal"       "Normal"       "Normal"      
 74 1 "Severe"       "Severe"       "Severe"      
 74 2 "Severe"       "Severe"       "Severe"      
 74 3 "Severe"       "Severe"       "Severe"      
 75 1 "Severe"       "Severe"       "Severe"      
 75 2 "Severe"       "Severe"       "Severe"      
 75 3 "Severe"       "Severe"       "Severe"      
 76 1 "Moderate"     "Moderate"     "Moderate"    
 76 2 "Moderate"     "Moderate"     "Moderate"    
 76 3 "Moderate"     "Moderate"     "Moderate"    
 80 1 "Severe"       "Severe"       "Severe"      
 80 2 "Severe"       "Severe"       "Severe"      
 80 3 "Severe"       "Severe"       "Severe"      
 81 1 "Moderate"     "Moderate"     "Moderate"    
 81 2 "Moderate"     "Moderate"     "Moderate"    
 81 3 "Moderate"     "Moderate"     "Moderate"    
 82 1 "Moderate"     "Moderate"     "Moderate"    
 82 2 "Moderate"     "Moderate"     "Moderate"    
 82 3 "Moderate"     "Moderate"     "Moderate"    
 88 1 "Normal"       "Normal"       "Normal"      
 88 2 "Normal"       "Normal"       "Normal"      
 88 3 "Normal"       "Normal"       "Normal"      
 89 1 "Mild"         "Moderate"     "Mild"        
 89 2 "Mild"         "Moderate"     "Mild"        
 89 3 "Mild"         "Moderate"     "Mild"        
 90 1 "Normal"       "Normal"       "Normal"      
 90 2 "Normal"       "Normal"       "Normal"      
 90 3 "Normal"       "Normal"       "Normal"      
 91 1 "Severe"       "Severe"       "Severe"      
 91 2 "Severe"       "Severe"       "Severe"      
 91 3 "Severe"       "Severe"       "Severe"      
 94 1 "Normal"       "Normal"       "Normal"      
 94 2 "Normal"       "Normal"       "Normal"      
 94 3 "Normal"       "Normal"       "Normal"      
 95 1 "Normal"       "Normal"       "Normal"      
 95 2 "Normal"       "Normal"       "Normal"      
 95 3 "Normal"       "Normal"       "Normal"      
 98 1 "Normal"       "Normal"       "Normal"      
 98 2 "Normal"       "Normal"       "Normal"      
 98 3 "Normal"       "Normal"       "Normal"      
100 1 "Moderate"     "Moderate"     "Moderate"    
100 2 "Moderate"     "Moderate"     "Moderate"    
100 3 "Moderate"     "Moderate"     "Moderate"    
103 1 "Mild"         "Mild"         "Mild"        
103 2 "Mild"         "Mild"         "Mild"        
103 3 "Mild"         "Mild"         "Mild"        
105 1 "Severe"       "Severe"       "Severe"      
105 2 "Severe"       "Severe"       "Severe"      
105 3 "Severe"       "Severe"       "Severe"      
106 1 "Normal"       "Normal"       "Normal"      
106 2 "Normal"       "Normal"       "Normal"      
106 3 "Normal"       "Normal"       "Normal"      
107 1 "Severe"       "Severe"       "Severe"      
107 2 "Severe"       "Severe"       "Severe"      
107 3 "Severe"       "Severe"       "Severe"      
108 1 "Normal"       "Normal"       "Normal"      
108 2 "Normal"       "Normal"       "Normal"      
108 3 "Normal"       "Normal"       "Normal"      
110 1 "Severe"       "Severe"       "Severe"      
110 2 "Severe"       "Severe"       "Severe"      
110 3 "Severe"       "Severe"       "Severe"      
114 1 "Normal"       "Normal"       "Normal"      
114 2 "Normal"       "Normal"       "Normal"      
114 3 "Normal"       "Normal"       "Normal"      
115 1 "Mild"         "Mild"         "Mild"        
115 2 "Mild"         "Mild"         "Mild"        
115 3 "Mild"         "Mild"         "Mild"        
117 1 "Normal"       "Normal"       "Normal"      
117 2 "Normal"       "Normal"       "Normal"      
117 3 "Normal"       "Normal"       "Normal"      
120 1 "Normal"       "Normal"       "Normal"      
120 2 "Normal"       "Normal"       "Normal"      
120 3 "Normal"       "Normal"       "Normal"      
123 1 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
123 2 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
123 3 "Hyperdynamic" "Hyperdynamic" "Hyperdynamic"
124 1 "Normal"       "Normal"       "Normal"      
124 2 "Normal"       "Normal"       "Normal"      
124 3 "Normal"       "Normal"       "Normal"      
126 1 "Mild"         "Mild"         "Mild"        
126 2 "Mild"         "Mild"         "Mild"        
126 3 "Mild"         "Mild"         "Mild"        
129 1 "Moderate"     "Moderate"     "Moderate"    
129 2 "Moderate"     "Moderate"     "Moderate"    
129 3 "Moderate"     "Moderate"     "Moderate"    
130 1 "Normal"       "Normal"       "Normal"      
130 2 "Normal"       "Normal"       "Normal"      
130 3 "Normal"       "Normal"       "Normal"      
132 1 "Moderate"     "Moderate"     "Moderate"    
132 2 "Moderate"     "Moderate"     "Moderate"    
132 3 "Moderate"     "Moderate"     "Moderate"    
134 1 "Normal"       "Normal"       "Normal"      
134 2 "Normal"       "Normal"       "Normal"      
134 3 "Normal"       "Normal"       "Normal"      
135 1 "Normal"       "Normal"       "Normal"      
135 2 "Normal"       "Normal"       "Normal"      
135 3 "Normal"       "Normal"       "Normal"      
136 1 "Normal"       "Normal"       "Normal"      
136 2 "Normal"       "Normal"       "Normal"      
136 3 "Normal"       "Normal"       "Normal"      
138 1 "Mild"         "Mild"         "Mild"        
138 2 "Mild"         "Mild"         "Mild"        
138 3 "Mild"         "Mild"         "Mild"        
141 1 "Normal"       "Normal"       "Normal"      
141 2 "Normal"       "Normal"       "Normal"      
141 3 "Normal"       "Normal"       "Normal"      
142 1 "Normal"       "Normal"       "Normal"      
142 2 "Normal"       "Normal"       "Normal"      
142 3 "Normal"       "Normal"       "Normal"      
145 1 "Mild"         "Mild"         "Mild"        
145 2 "Mild"         "Mild"         "Mild"        
145 3 "Mild"         "Mild"         "Mild"        
149 1 "Normal"       "Normal"       "Normal"      
149 2 "Normal"       "Normal"       "Normal"      
149 3 "Normal"       "Normal"       "Normal"      
152 1 "Normal"       "Normal"       "Normal"      
152 2 "Normal"       "Normal"       "Normal"      
152 3 "Normal"       "Normal"       "Normal"      
153 1 "Severe"       "Severe"       "Severe"      
153 2 "Severe"       "Severe"       "Severe"      
153 3 "Severe"       "Severe"       "Severe"      
155 1 "Normal"       "Normal"       "Normal"      
155 2 "Normal"       "Normal"       "Normal"      
155 3 "Normal"       "Normal"       "Normal"      
158 1 "Mild"         "Mild"         "Mild"        
158 2 "Mild"         "Mild"         "Mild"        
158 3 "Mild"         "Mild"         "Mild"        
159 1 "Normal"       "Normal"       "Normal"      
159 2 "Normal"       "Normal"       "Normal"      
159 3 "Normal"       "Normal"       "Normal"      
162 1 "Mild"         "Mild"         "Mild"        
162 2 "Mild"         "Mild"         "Mild"        
162 3 "Mild"         "Mild"         "Mild"        
163 1 "Normal"       "Normal"       "Normal"      
163 2 "Normal"       "Normal"       "Normal"      
163 3 "Normal"       "Normal"       "Normal"      
165 1 "Normal"       "Normal"       "Normal"      
165 2 "Normal"       "Normal"       "Normal"      
165 3 "Normal"       "Normal"       "Normal"      
167 1 "Mild"         "Mild"         "Mild"        
167 2 "Mild"         "Mild"         "Mild"        
167 3 "Mild"         "Mild"         "Mild"        
169 1 "Normal"       "Normal"       "Normal"      
169 2 "Normal"       "Normal"       "Normal"      
169 3 "Normal"       "Normal"       "Normal"      
173 1 "Moderate"     "Moderate"     "Moderate"    
173 2 "Moderate"     "Moderate"     "Moderate"    
173 3 "Moderate"     "Moderate"     "Moderate"    
174 1 "Moderate"     "Moderate"     "Moderate"    
174 2 "Moderate"     "Moderate"     "Moderate"    
174 3 "Moderate"     "Moderate"     "Moderate"    
175 1 "Mild"         "Mild"         "Mild"        
175 2 "Mild"         "Mild"         "Mild"        
175 3 "Mild"         "Mild"         "Mild"        
177 1 "Mild"         "Mild"         "Mild"        
177 2 "Mild"         "Mild"         "Mild"        
177 3 "Mild"         "Mild"         "Mild"        
179 1 "Severe"       "Severe"       "Severe"      
179 2 "Severe"       "Severe"       "Severe"      
179 3 "Severe"       "Severe"       "Severe"      
180 1 "Normal"       "Normal"       "Normal"      
180 2 "Normal"       "Normal"       "Normal"      
180 3 "Normal"       "Normal"       "Normal"      
184 1 "Normal"       "Normal"       "Normal"      
184 2 "Normal"       "Normal"       "Normal"      
184 3 "Normal"       "Normal"       "Normal"      
185 1 "Mild"         "Mild"         "Mild"        
185 2 "Mild"         "Mild"         "Mild"        
185 3 "Mild"         "Mild"         "Mild"        
187 1 "Normal"       "Normal"       "Normal"      
187 2 "Normal"       "Normal"       "Normal"      
187 3 "Normal"       "Normal"       "Normal"      
190 1 "Moderate"     "Mild"         "Mild"        
190 2 "Moderate"     "Mild"         "Mild"        
190 3 "Moderate"     "Mild"         "Mild"        
191 1 "Normal"       "Normal"       "Normal"      
191 2 "Normal"       "Normal"       "Normal"      
191 3 "Normal"       "Normal"       "Normal"      
192 1 "Mild"         "Mild"         "Mild"        
192 2 "Mild"         "Mild"         "Mild"        
192 3 "Mild"         "Mild"         "Mild"        
195 1 "Normal"       "Normal"       "Normal"      
195 2 "Normal"       "Normal"       "Normal"      
195 3 "Normal"       "Normal"       "Normal"      
197 1 "Moderate"     "Moderate"     "Mild"        
197 2 "Moderate"     "Moderate"     "Mild"        
197 3 "Moderate"     "Moderate"     "Mild"        
200 1 "Moderate"     "Moderate"     "Moderate"    
200 2 "Moderate"     "Moderate"     "Moderate"    
200 3 "Moderate"     "Moderate"     "Moderate"    
202 1 "Normal"       "Normal"       "Normal"      
202 2 "Normal"       "Normal"       "Normal"      
202 3 "Normal"       "Normal"       "Normal"      
203 1 "Severe"       "Severe"       "Severe"      
203 2 "Severe"       "Severe"       "Severe"      
203 3 "Severe"       "Severe"       "Severe"      
205 1 "Normal"       "Normal"       "Normal"      
205 2 "Normal"       "Normal"       "Normal"      
205 3 "Normal"       "Normal"       "Normal"      
end

label def grade 1 Mild 2 Moderate 3 Normal 4 Severe 5 Hyperdynamic

foreach v of var ef* {
    encode `v', label(grade) gen(`v'_2)
    label var `v'_2 "`v'"
}

[Michael Dan Arvig]

Hi Nick.

Sorry for that. I've missed the FAQ #12. Here's the data again: And yes, the missing data point should not be included in the graph.

* Example generated by -dataex-. For more info, type help dataex
clear
input int record_id byte(ef_fivelevels1 ef_fivelevels2 ef_fivelevels3)
4 4 4 4
5 1 1 1
6 4 4 4
8 1 1 1
12 3 3 3
17 2 2 2
18 1 1 1
19 5 5 5
21 2 2 2
22 1 1 1
25 3 3 3
29 4 4 4
31 1 1 1
32 1 1 1
33 2 1 1
36 1 4 1
37 1 1 1
39 1 1 1
40 1 1 1
43 1 1 1
46 2 2 2
47 3 3 3
48 4 4 4
52 2 2 2
53 1 1 1
56 2 2 2
57 2 2 2
59 1 1 1
62 1 1 1
65 1 1 1
67 3 3 2
68 1 1 1
71 2 2 2
72 1 1 1
74 4 4 4
75 4 4 4
76 3 3 3
80 4 4 4
81 3 3 3
82 3 3 3
88 1 1 1
89 2 3 2
90 1 1 1
91 4 4 4
94 1 1 1
95 1 1 1
98 1 1 1
100 3 3 3
103 2 2 2
105 4 4 4
106 1 1 1
107 4 4 4
108 1 1 1
110 4 4 4
114 1 1 1
115 2 2 2
117 1 1 1
120 1 1 1
123 5 5 5
124 1 1 1
126 2 2 2
129 3 3 3
130 1 1 1
132 3 3 3
134 1 1 1
135 1 1 1
136 1 1 1
138 2 2 2
141 1 1 1
142 1 1 1
145 2 2 2
149 1 1 1
152 1 1 1
153 4 4 4
155 1 1 1
158 2 2 2
159 1 1 1
162 2 2 2
163 1 1 1
165 1 1 1
167 2 2 2
169 1 1 1
173 3 3 3
174 3 3 3
175 2 2 2
177 2 2 2
179 4 4 4
180 1 1 1
184 1 1 1
185 2 2 2
187 1 1 1
190 3 2 2
191 1 1 1
192 2 2 2
195 1 1 1
197 3 3 2
200 3 3 3
202 1 1 1
203 4 4 4
205 1 1 1
end
label values ef_fivelevels1 ef_fivelevels3
label values ef_fivelevels2 ef_fivelevels3
label values ef_fivelevels3 ef_fivelevels3
label def ef_fivelevels3 1 "Normal", modify
label def ef_fivelevels3 2 "Mild", modify
label def ef_fivelevels3 3 "Moderate", modify
label def ef_fivelevels3 4 "Severe", modify
label def ef_fivelevels3 5 "Hyperdynamic", modify
[/CODE]

In the meantime, I tried to reshape the data and used the graph hbar and it seems to have solved the problem.

reshape long ef_fivelevels, i(record_id) j(time)

graph hbar (count), over(ef_fivelevels) over(time) asyvars stack

[Nick Cox]

Looks like graph bar to me!

Excellent that you made progress, although I don't follow how 3 x 5 x 3 reduces to 5 x 3.

More interesting perhaps, compare what can be done with tabplot from the Stata Journal, particularly to show small changes and/or small proportions, which I guess carry most of the concern.

A good way in is through

https://www.statalist.org/forums/for...updated-on-ssc

while the code and help come from

. search tabplot, sj

Noting

latest update

SJ-22-2 gr0066_3  . . . . . . . . . . . . . . . .  Software update for tabplot
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q2/22   SJ 22(2):467
        bug fixed; help file updated to include further references

fullest write-up

SJ-16-2 gr0066  . . . . . .  Speaking Stata: Multiple bar charts in table form
        (help tabplot if installed) . . . . . . . . . . . . . . . .  N. J. Cox
        Q2/16   SJ 16(2):491--510
        provides multiple bar charts in table form representing
        contingency tables for one, two, or three categorical variables

[Michael Dan Arvig]

Thanks for the input. However, I'm not quit sure how you get 3 x 5 x 3 to 5 x 3. The graph depicts three time point on the x-axis and each bar represent the variable with 5 categories. I will take a look at the tabplot to see if it gives a bette visualization of the data. Again, thanks for the input.

[Nick Cox]

You had 3 variables, 5 categories and 3 time points in #1. What’s the reasoning behind the reshape?

[Nick Cox]

Duplicate in error

[Michael Dan Arvig]

One variable with 5 categories measured at three time slots (0, 2 and 4 h). Reshaped to show this variable at the three time slots.

[Nick Cox]

In Stata terms ef_fivelevels1 ef_fivelevels2 ef_fivelevels3 amount to 3 variables, hence your statement in #1 and mine.

I think I understand this: the information was just duplicated originally for some reason. Any way, you got what you wanted.

[Michael Dan Arvig]

Thx, for your feedback. The three variables, you mentioned are just the same parameter measured at the three time slots.