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R
Radiant
Commit d3861e30 authored by wuzekai's avatar wuzekai
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Initial commit

parent 1742ff44
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View file @ d3861e30
......@@ -6,21 +6,26 @@ RUN apt-get update && apt-get install -y \
libpng-dev libtiff5-dev libjpeg-dev \
&& rm -rf /var/lib/apt/lists/*
RUN R -e "install.packages(c('shiny', 'shinydashboard', 'remotes'), repos='https://cran.rstudio.com/')"
RUN R -e "install.packages(c('shiny', 'shinydashboard', 'remotes','readxl'), repos='https://cran.rstudio.com/',dependencies=TRUE)"
RUN echo 'options(radiant.shinyFiles = FALSE)' >> /usr/local/lib/R/etc/Rprofile.site
COPY . /srv/shiny-server/
COPY set_path.R /usr/local/lib/R/etc/Rprofile.site.d/00-radiant-path.R
# 安装所有子模块
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.data', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.basics', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.model', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.multivariate', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.design', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.data',dependencies=TRUE, type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.basics',dependencies=TRUE, type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.model',dependencies=TRUE, type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.multivariate',dependencies=TRUE, type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.design',dependencies=TRUE, type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen',dependencies=TRUE, type='source', upgrade='never')"
# 安装主 radiant 应用
RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master', type='source', upgrade='never')"
RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master',dependencies=TRUE, type='source', upgrade='never')"
WORKDIR /data
CMD ["R", "-e", "radiant::radiant(host='0.0.0.0', port=3838)"]
#docker images | grep radiant 查看镜像
#sudo systemctl restart shinyproxy 重启proxy
#sudo vim /etc/shinyproxy/application.yml 修改配置文件
#docker build -t radiant:latest . 构建镜像
\ No newline at end of file
data/cancer.csv 0 → 100644
View file @ d3861e30
ID,Hospital,Treatment,Age,Sex,Blood test,Obstruct,Perfor,Adhere,Differ,Extent,Effect,Occurrence,OS_status,OS_time,Objective Response
1,hospital_5,Lev,43,Male,3829,NA,No,No,Moderate,Serosa,Bad,5,1,1521,Yes
2,hospital_3,Lev,63,Male,3825,No,No,No,Moderate,Serosa,Good,1,0,3087,No
3,hospital_5,Obs,71,Female,3809,No,No,Yes,Moderate,Muscle,Bad,7,1,963,Yes
4,hospital_4,Lev,66,Female,3808,Yes,No,No,Moderate,Serosa,Bad,6,1,293,Yes
5,hospital_5,Obs,69,Male,3738,No,No,No,Moderate,Serosa,Bad,22,1,659,Yes
6,hospital_4,Lev,57,Female,3714,No,No,No,Moderate,Serosa,Bad,9,1,1767,Yes
7,hospital_2,Lev,77,Male,3692,No,No,No,Moderate,Serosa,Bad,5,1,420,Yes
8,hospital_2,Obs,54,Male,3685,No,No,No,Moderate,Serosa,Good,1,0,3192,No
9,hospital_2,Lev,46,Male,3673,No,No,Yes,Moderate,Serosa,Good,2,0,3173,Yes
10,hospital_2,Lev,68,Female,3587,No,No,No,Moderate,Serosa,Bad,1,0,3308,Yes
11,hospital_1,Lev,47,Female,3585,No,No,Yes,Moderate,Serosa,Bad,1,0,2908,No
12,hospital_1,Lev,52,Male,3578,No,No,No,Poor,Serosa,Bad,2,0,3309,Yes
13,hospital_3,Obs,64,Male,3530,No,No,No,Moderate,Serosa,Good,1,1,2085,Yes
14,hospital_4,Lev,68,Male,3524,Yes,No,No,Moderate,Serosa,Good,3,1,2910,Yes
15,hospital_1,Obs,46,Male,3519,Yes,No,No,Moderate,Serosa,Good,4,0,2754,Yes
16,hospital_1,Obs,68,Male,3517,No,No,No,Moderate,Serosa,Good,1,0,3214,Yes
17,hospital_4,Lev,62,Male,3500,Yes,No,Yes,Moderate,Serosa,Bad,6,1,406,Yes
18,hospital_5,Lev,79,Male,3485,Yes,No,No,Moderate,Serosa,Bad,1,1,522,Yes
19,hospital_1,Lev,34,Female,3469,Yes,No,No,Moderate,Muscle,Bad,1,1,887,No
20,hospital_1,Lev,50,Female,3469,No,No,Yes,Moderate,Serosa,Good,1,0,3329,No
21,hospital_2,Obs,64,Male,3458,NA,No,No,Moderate,Serosa,Good,1,1,2789,Yes
22,hospital_4,Lev,56,Male,3451,Yes,No,No,Moderate,Serosa,Bad,4,1,739,Yes
23,hospital_3,Lev,61,Female,3450,No,No,No,Moderate,Serosa,Bad,4,1,709,No
24,hospital_1,Lev,50,Female,3441,No,No,No,Moderate,Muscle,Good,1,0,2969,Yes
25,hospital_1,Lev,70,Female,3427,No,No,No,Moderate,Serosa,Good,4,0,2889,Yes
26,hospital_5,Obs,73,Male,3425,No,No,No,Moderate,Serosa,Bad,1,1,1772,No
27,hospital_5,Obs,70,Female,3418,No,No,Yes,Moderate,Serosa,Bad,13,1,384,Yes
28,hospital_5,Lev,68,Female,3416,No,No,No,Moderate,Serosa,Bad,1,1,968,Yes
29,hospital_4,Obs,56,Female,3415,Yes,No,No,Poor,Serosa,Bad,6,1,218,Yes
30,hospital_5,Lev,80,Male,3413,No,No,No,Poor,Serosa,Bad,1,1,133,No
31,hospital_2,Lev,64,Female,3410,No,No,No,Moderate,Submucosa,Good,1,0,3238,Yes
32,hospital_3,Lev,33,Female,3408,Yes,No,No,Moderate,Serosa,Good,3,0,3019,Yes
33,hospital_2,Obs,49,Female,3405,No,No,No,Moderate,Serosa,Bad,2,1,1745,No
34,hospital_5,Obs,47,Male,3401,No,No,No,Moderate,Serosa,Good,3,1,2527,Yes
35,hospital_4,Lev,56,Female,3399,No,No,No,Moderate,Serosa,Bad,1,1,1387,Yes
36,hospital_4,Lev,72,Female,3399,No,No,Yes,Poor,Serosa,Good,1,0,3024,Yes
37,hospital_5,Obs,79,Male,3389,No,No,No,Moderate,Serosa,Bad,19,1,570,Yes
38,hospital_2,Obs,64,Female,3383,No,No,No,Poor,Serosa,Good,1,0,2815,Yes
39,hospital_1,Lev,61,Male,3371,No,No,No,Poor,Serosa,Good,2,0,2901,Yes
40,hospital_2,Lev,58,Male,3369,No,No,No,Moderate,Serosa,Bad,7,1,553,No
41,hospital_3,Lev,75,Male,3362,No,No,No,Moderate,Serosa,Bad,2,1,905,Yes
42,hospital_3,Obs,39,Female,3349,Yes,No,No,Well,Serosa,Good,1,0,3030,Yes
43,hospital_3,Obs,71,Male,3342,Yes,No,No,Moderate,Muscle,Bad,3,1,685,Yes
44,hospital_2,Lev,58,Female,3340,Yes,No,No,Moderate,Serosa,Good,2,0,2740,Yes
45,hospital_3,Lev,40,Male,3335,No,No,No,Well,Serosa,Good,1,0,2899,No
46,hospital_1,Obs,63,Female,3328,No,Yes,No,Moderate,Serosa,Good,2,0,2598,No
47,hospital_1,Lev,59,Female,3326,No,No,No,Poor,Submucosa,Good,1,0,2840,Yes
48,hospital_1,Lev,59,Female,3321,Yes,No,No,Well,Serosa,Good,2,0,2802,Yes
49,hospital_4,Lev,59,Male,3320,No,No,No,Moderate,Serosa,Good,6,0,2781,Yes
50,hospital_3,Lev,60,Male,3315,No,No,No,Poor,Serosa,Bad,5,1,833,Yes
51,hospital_4,Obs,68,Female,3315,No,No,No,Moderate,Serosa,Bad,2,1,1290,No
52,hospital_3,Lev,70,Female,3302,No,No,No,Moderate,Serosa,Bad,3,1,1620,Yes
53,hospital_1,Obs,76,Male,3300,No,No,No,Moderate,Serosa,Good,2,0,2765,Yes
54,hospital_4,Lev,45,Female,3294,No,No,No,Moderate,Serosa,Bad,3,0,2708,Yes
55,hospital_3,Lev,61,Male,3289,No,No,No,Moderate,Muscle,Bad,2,0,2737,Yes
56,hospital_4,Obs,76,Male,3289,No,No,No,Well,Serosa,Bad,3,1,1178,No
57,hospital_3,Lev,53,Male,3281,No,No,No,Moderate,Serosa,Good,2,0,2765,Yes
58,hospital_1,Lev,63,Female,3279,No,No,No,Moderate,Serosa,Good,5,0,2883,Yes
59,hospital_2,Obs,65,Female,3275,No,No,No,Poor,Serosa,Good,6,0,2679,Yes
60,hospital_3,Obs,57,Female,3272,Yes,No,No,Poor,Serosa,Good,4,0,2925,No
61,hospital_4,Lev,80,Male,3265,No,Yes,No,Moderate,Serosa,Bad,4,1,472,Yes
62,hospital_1,Lev,58,Male,3265,No,No,No,Well,Muscle,Good,1,0,2772,Yes
63,hospital_5,Obs,53,Female,3265,No,No,No,Poor,Serosa,Bad,15,1,474,No
64,hospital_3,Lev,53,Female,3264,Yes,No,No,NA,Serosa,Good,1,0,2739,Yes
65,hospital_4,Obs,71,Female,3261,No,No,No,Well,Serosa,Bad,1,1,365,Yes
66,hospital_1,Lev,66,Female,3261,No,No,No,Moderate,Serosa,Good,2,0,2653,Yes
67,hospital_4,Lev,49,Male,3260,No,No,No,Poor,Serosa,Good,6,0,2726,No
68,hospital_5,Obs,60,Male,3254,No,No,No,Moderate,Serosa,Bad,10,1,774,Yes
69,hospital_3,Obs,70,Female,3254,No,No,No,Moderate,Submucosa,Good,3,0,3078,Yes
70,hospital_4,Obs,40,Female,3247,No,No,No,Moderate,Serosa,Good,2,1,2133,No
71,hospital_2,Lev,55,Female,3247,No,No,No,Moderate,Serosa,Good,1,1,2127,No
72,hospital_4,Lev,56,Female,3243,No,No,No,Moderate,Serosa,Bad,15,1,400,Yes
73,hospital_5,Lev,54,Male,3240,No,No,No,Moderate,Serosa,Good,9,0,3185,Yes
74,hospital_5,Obs,49,Female,3239,Yes,No,No,Poor,Serosa,Bad,5,1,1237,Yes
75,hospital_5,Lev,71,Female,3237,No,No,No,Moderate,Serosa,Bad,7,1,1219,No
76,hospital_4,Obs,67,Female,3232,No,No,No,Moderate,Serosa,Good,4,0,3017,Yes
77,hospital_2,Lev,68,Female,3231,No,No,No,Moderate,Serosa,Bad,5,1,806,Yes
78,hospital_1,Lev,63,Male,3230,No,No,No,Moderate,Serosa,Good,2,0,2985,Yes
79,hospital_2,Lev,50,Male,3230,Yes,No,Yes,Moderate,Serosa,Good,1,0,2969,Yes
80,hospital_5,Lev,45,Female,3226,No,No,No,Moderate,Muscle,Good,7,1,1995,Yes
81,hospital_3,Lev,66,Male,3226,No,No,No,Moderate,Muscle,Good,1,0,2958,Yes
82,hospital_4,Obs,46,Female,3225,No,No,No,Poor,Serosa,Bad,1,1,1133,No
83,hospital_5,Lev,70,Male,3224,No,No,No,NA,Muscle,Bad,3,1,1548,Yes
84,hospital_1,Lev,63,Male,3223,Yes,No,No,Moderate,Muscle,Good,2,0,3085,Yes
85,hospital_4,Lev,45,Male,3223,No,No,No,Poor,Serosa,Bad,8,1,736,No
86,hospital_1,Lev,49,Female,3220,No,No,No,Moderate,Serosa,Good,1,0,2789,Yes
87,hospital_2,Lev,62,Female,3218,No,No,No,Moderate,Muscle,Good,2,0,3325,Yes
88,hospital_4,Obs,59,Male,3216,No,No,No,Moderate,Serosa,Bad,4,1,381,No
89,hospital_1,Lev,80,Female,3213,NA,No,No,Well,Serosa,Good,4,0,2724,Yes
90,hospital_3,Lev,73,Male,3211,Yes,No,No,NA,Serosa,Bad,4,1,997,Yes
91,hospital_5,Obs,46,Male,3209,No,No,No,Moderate,Serosa,Bad,11,1,413,Yes
92,hospital_2,Lev,64,Male,3208,No,No,No,Moderate,Serosa,Bad,4,1,708,Yes
93,hospital_3,Lev,45,Male,3206,No,No,No,Moderate,Serosa,Good,9,0,2765,No
94,hospital_3,Lev,26,Female,3203,No,No,Yes,Moderate,Adjacent structures,Bad,NA,0,2869,No
95,hospital_1,Lev,66,Male,3199,NA,No,No,Moderate,Serosa,Bad,1,0,2918,Yes
96,hospital_3,Lev,67,Male,3197,No,No,Yes,Moderate,Adjacent structures,Bad,2,1,2458,Yes
97,hospital_5,Obs,65,Female,3195,No,No,No,Moderate,Adjacent structures,Bad,20,1,459,Yes
98,hospital_2,Lev,74,Male,3191,No,No,Yes,Moderate,Serosa,Good,1,1,2318,Yes
99,hospital_2,Lev,71,Male,3190,No,No,Yes,Moderate,Adjacent structures,Bad,NA,1,569,Yes
100,hospital_5,Obs,58,Female,3188,No,No,Yes,Moderate,Muscle,Good,8,1,2077,Yes
101,hospital_5,Obs,59,Male,3183,No,No,No,Moderate,Serosa,Bad,6,1,1216,Yes
102,hospital_5,Lev,61,Male,3182,NA,No,No,Poor,Serosa,Bad,5,1,171,Yes
103,hospital_4,Lev,49,Male,3179,No,No,No,Moderate,Serosa,Bad,2,1,712,Yes
104,hospital_5,Lev,50,Female,3179,No,No,No,Moderate,Serosa,Bad,6,1,614,No
105,hospital_1,Obs,75,Female,3178,No,No,No,Moderate,Serosa,Good,1,0,2849,No
106,hospital_3,Obs,68,Male,3176,No,No,No,Poor,Serosa,Bad,3,1,1530,Yes
107,hospital_4,Lev,60,Female,3174,No,No,Yes,Moderate,Serosa,Bad,10,1,402,Yes
108,hospital_5,Lev,65,Female,3172,No,No,Yes,Moderate,Serosa,Bad,2,1,1511,Yes
109,hospital_4,Lev,74,Female,3168,No,No,No,Moderate,Serosa,Good,5,0,2828,No
110,hospital_2,Lev,52,Female,3163,No,No,No,Poor,Serosa,Bad,3,1,23,Yes
111,hospital_1,Lev,64,Male,3156,No,No,Yes,Moderate,Serosa,Good,6,0,2941,Yes
112,hospital_3,Lev,47,Male,3156,No,No,Yes,Moderate,Serosa,Bad,1,1,755,Yes
113,hospital_1,Obs,45,Female,3153,No,No,No,Moderate,Submucosa,Good,1,0,2815,Yes
114,hospital_5,Obs,61,Male,3151,Yes,No,Yes,Poor,Serosa,Bad,5,1,761,Yes
115,hospital_2,Lev,53,Male,3148,No,No,No,Poor,Serosa,Good,2,1,1839,No
116,hospital_4,Obs,60,Male,3131,Yes,No,No,Moderate,Serosa,Good,5,1,2351,Yes
117,hospital_1,Lev,60,Female,3130,No,No,No,Moderate,Serosa,Good,1,0,2760,Yes
118,hospital_1,Lev,51,Female,3128,No,No,No,Moderate,Serosa,Good,5,0,2951,No
119,hospital_1,Lev,60,Female,3128,No,No,Yes,Moderate,Serosa,Good,1,0,2820,No
120,hospital_5,Obs,64,Female,3121,No,No,No,Moderate,Serosa,Bad,1,1,1327,Yes
121,hospital_2,Lev,52,Female,3118,No,No,No,Moderate,Serosa,Good,1,0,2678,Yes
122,hospital_3,Lev,65,Female,3118,No,No,No,Poor,Serosa,Bad,2,1,226,No
123,hospital_3,Lev,70,Female,3113,No,No,No,Moderate,Serosa,Good,1,0,2913,Yes
124,hospital_2,Obs,55,Male,3100,No,No,No,Moderate,Serosa,Good,2,0,2862,Yes
125,hospital_5,Lev,71,Male,3098,No,No,No,Poor,Serosa,Bad,5,1,454,Yes
126,hospital_3,Obs,30,Male,3093,No,No,No,Moderate,Muscle,Good,1,0,2743,Yes
127,hospital_2,Obs,40,Female,3092,No,No,No,Moderate,Serosa,Bad,3,1,1679,No
128,hospital_5,Obs,31,Male,3092,Yes,No,No,Poor,Serosa,Bad,11,1,924,No
129,hospital_3,Obs,53,Male,3090,No,No,No,Moderate,Muscle,Bad,1,1,721,No
130,hospital_4,Obs,67,Male,3088,No,No,No,Moderate,Serosa,Bad,1,1,709,No
131,hospital_3,Lev,58,Female,3080,No,No,No,Moderate,Serosa,Bad,1,1,961,No
132,hospital_4,Lev,79,Female,3077,Yes,No,No,Poor,Serosa,Bad,2,1,326,Yes
133,hospital_4,Lev,55,Female,3077,No,No,No,Poor,Muscle,Good,1,0,2703,Yes
134,hospital_3,Obs,63,Female,3074,No,No,Yes,Moderate,Serosa,Bad,2,1,289,Yes
135,hospital_3,Lev,55,Male,3072,Yes,No,No,Moderate,Serosa,Good,2,0,2905,No
136,hospital_5,Lev,42,Male,3068,Yes,No,No,Moderate,Serosa,Bad,5,1,206,No
137,hospital_2,Obs,56,Male,3065,No,No,No,Moderate,Serosa,Good,1,0,2618,Yes
138,hospital_3,Lev,64,Female,3062,No,No,No,Moderate,Serosa,Bad,8,1,283,No
139,hospital_2,Lev,55,Male,3058,Yes,No,No,Moderate,Serosa,Good,2,1,2718,Yes
140,hospital_1,Obs,25,Female,3058,Yes,No,No,Moderate,Serosa,Good,7,0,2826,Yes
141,hospital_4,Lev,59,Male,3055,No,No,No,Moderate,Serosa,Good,1,0,2916,Yes
142,hospital_5,Lev,28,Male,3052,No,No,No,Poor,Serosa,Bad,9,1,499,No
143,hospital_2,Lev,49,Female,3051,No,No,No,Well,Serosa,Good,NA,0,2950,Yes
144,hospital_4,Obs,52,Female,3048,No,No,No,Moderate,Submucosa,Good,1,0,2899,Yes
145,hospital_2,Obs,64,Female,3047,No,No,No,Poor,Serosa,Bad,5,1,673,Yes
146,hospital_1,Obs,33,Female,3045,Yes,No,Yes,Well,Serosa,Good,1,0,3000,No
147,hospital_4,Obs,76,Male,3044,No,No,No,Moderate,Serosa,Good,1,1,2284,No
148,hospital_5,Lev,38,Male,3042,No,No,No,Well,Serosa,Bad,2,1,362,Yes
149,hospital_2,Lev,53,Female,3041,Yes,Yes,No,Moderate,Serosa,Good,1,1,2152,Yes
150,hospital_1,Obs,72,Female,3040,Yes,No,No,Moderate,Serosa,Good,2,0,2562,Yes
151,hospital_1,Lev,46,Male,3038,No,No,No,Moderate,Serosa,Good,1,0,2730,Yes
152,hospital_5,Lev,69,Female,3032,No,No,No,Moderate,Serosa,Bad,13,1,802,Yes
153,hospital_5,Obs,43,Male,3030,Yes,No,No,Poor,Serosa,Bad,0,1,1166,Yes
154,hospital_4,Lev,72,Male,3030,Yes,No,Yes,Moderate,Serosa,Bad,2,1,1193,No
155,hospital_4,Lev,73,Male,3028,Yes,No,No,Moderate,Serosa,Bad,2,1,45,Yes
156,hospital_1,Obs,68,Male,3027,No,No,No,Moderate,Serosa,Good,3,0,2577,Yes
157,hospital_5,Obs,52,Female,3027,No,Yes,No,Well,Serosa,Bad,6,1,1136,No
158,hospital_2,Lev,73,Female,3027,Yes,No,No,Moderate,Serosa,Good,2,0,2497,Yes
159,hospital_3,Lev,66,Male,3025,Yes,No,No,Moderate,Serosa,Good,1,0,2648,Yes
160,hospital_1,Obs,75,Female,3020,No,No,No,Poor,Serosa,Good,2,0,2458,No
161,hospital_5,Lev,43,Male,3020,Yes,No,No,NA,Serosa,Bad,6,1,1135,Yes
162,hospital_5,Lev,57,Female,3017,No,No,No,Moderate,Serosa,Bad,1,1,580,Yes
163,hospital_4,Obs,74,Male,3017,Yes,No,No,Moderate,Serosa,Bad,6,1,485,Yes
164,hospital_1,Lev,67,Female,3017,No,No,No,Moderate,Serosa,Good,1,0,2656,Yes
165,hospital_4,Lev,66,Female,3013,No,No,Yes,Moderate,Serosa,Bad,2,1,806,Yes
166,hospital_4,Obs,57,Male,3013,Yes,No,No,Moderate,Serosa,Good,3,0,2435,Yes
167,hospital_2,Lev,81,Female,3009,No,No,No,Moderate,Serosa,Bad,3,1,1439,Yes
168,hospital_3,Obs,50,Male,3007,No,No,No,Poor,Serosa,Bad,4,1,717,Yes
169,hospital_4,Lev,62,Male,3006,Yes,Yes,No,Moderate,Serosa,Good,2,0,2568,Yes
170,hospital_1,Lev,48,Female,3006,No,No,No,Well,Serosa,Good,4,0,2631,Yes
171,hospital_3,Lev,45,Male,3005,No,No,No,Moderate,Muscle,NA,2,0,1474,No
172,hospital_4,Lev,46,Male,3003,No,No,Yes,Moderate,Serosa,Good,2,1,2593,Yes
173,hospital_3,Lev,72,Female,3001,No,No,No,Moderate,Serosa,Bad,2,1,1550,Yes
174,hospital_2,Obs,55,Female,2997,No,No,No,Moderate,Serosa,Bad,4,1,670,Yes
175,hospital_4,Lev,67,Male,2997,Yes,No,No,Moderate,Serosa,Bad,1,1,430,Yes
176,hospital_3,Lev,41,Female,2996,No,No,No,Moderate,Serosa,Good,2,0,2821,Yes
177,hospital_4,Lev,56,Male,2995,No,No,No,Moderate,Submucosa,Good,2,0,2672,Yes
178,hospital_2,Lev,50,Male,2990,No,No,No,Moderate,Submucosa,Good,4,0,2699,No
179,hospital_1,Lev,65,Female,2988,No,No,No,Well,Muscle,Good,1,0,2927,Yes
180,hospital_2,Lev,74,Male,2988,No,No,Yes,Moderate,Serosa,Bad,1,1,993,No
181,hospital_4,Obs,74,Female,2987,No,No,No,Moderate,Serosa,Good,2,0,2580,No
182,hospital_4,Obs,58,Female,2987,No,No,No,Moderate,Serosa,Good,4,0,2621,Yes
183,hospital_1,Lev,56,Female,2986,No,No,No,Poor,Serosa,Good,4,0,2779,Yes
184,hospital_2,Obs,75,Female,2985,No,No,No,Moderate,Muscle,Good,1,0,2794,No
185,hospital_1,Lev,67,Male,2984,No,No,No,Moderate,Serosa,Good,4,0,2656,Yes
186,hospital_2,Lev,58,Female,2982,No,No,No,Well,Serosa,Good,6,0,2513,No
187,hospital_1,Lev,58,Female,2981,No,No,No,Moderate,Serosa,Good,2,0,2488,No
188,hospital_5,Lev,41,Male,2974,No,No,No,Poor,Serosa,Bad,33,1,1112,Yes
189,hospital_1,Lev,72,Male,2972,No,No,No,Moderate,Serosa,Good,NA,0,2618,No
190,hospital_4,Lev,48,Male,2972,No,No,Yes,NA,Serosa,Bad,2,1,795,Yes
191,hospital_5,Lev,69,Female,2972,No,No,No,Moderate,Serosa,Bad,1,1,363,Yes
192,hospital_5,Obs,57,Male,2967,Yes,No,Yes,Moderate,Serosa,Bad,1,1,949,No
193,hospital_1,Lev,45,Female,2961,Yes,No,Yes,Moderate,Serosa,Bad,1,1,127,Yes
194,hospital_2,Lev,76,Female,2960,No,No,No,Moderate,Serosa,Good,4,0,2871,No
195,hospital_3,Obs,59,Male,2958,No,No,Yes,Moderate,Serosa,Good,2,0,2558,No
196,hospital_2,Obs,66,Female,2958,No,No,No,Moderate,Serosa,Good,1,1,2083,No
197,hospital_2,Obs,65,Female,2956,No,No,No,Poor,Muscle,Good,1,0,2747,Yes
198,hospital_4,Lev,69,Male,2950,No,No,No,Well,Serosa,Bad,1,1,1105,Yes
199,hospital_4,Lev,32,Female,2949,No,No,No,Poor,Serosa,Bad,NA,1,490,No
200,hospital_2,Obs,56,Female,2949,No,No,No,NA,Muscle,Good,1,0,2507,Yes
201,hospital_2,Lev,71,Male,2947,No,No,No,Well,Serosa,Bad,1,1,961,Yes
202,hospital_5,Lev,75,Male,2945,No,No,No,NA,Serosa,Good,8,1,1879,Yes
203,hospital_1,Lev,58,Male,2944,No,No,No,Poor,Serosa,Good,7,0,2592,Yes
204,hospital_4,Obs,40,Female,2942,No,No,No,Moderate,Serosa,Good,4,1,2552,Yes
205,hospital_4,Lev,39,Female,2941,No,No,No,Well,Submucosa,Good,2,0,2474,No
206,hospital_1,Lev,42,Female,2939,No,No,No,Moderate,Serosa,Good,4,0,2349,No
207,hospital_3,Lev,53,Female,2935,No,No,No,Well,Submucosa,Good,4,0,2835,Yes
208,hospital_2,Obs,43,Male,2929,No,No,No,Moderate,Serosa,Good,2,0,2761,Yes
209,hospital_3,Obs,51,Female,2923,No,No,No,Moderate,Serosa,Bad,4,1,411,Yes
210,hospital_1,Lev,77,Male,2923,No,No,No,Well,Serosa,Good,2,0,2720,Yes
211,hospital_2,Lev,67,Male,2922,Yes,No,No,Moderate,Serosa,Good,1,0,2495,Yes
212,hospital_3,Obs,41,Male,2916,No,No,No,Moderate,Serosa,Bad,7,1,462,No
213,hospital_1,Lev,63,Male,2914,Yes,Yes,Yes,Poor,Adjacent structures,Good,2,0,2497,Yes
214,hospital_1,Lev,63,Male,2913,No,No,Yes,Moderate,Muscle,Good,3,0,2343,Yes
215,hospital_4,Lev,63,Female,2910,No,No,No,Moderate,Serosa,Good,6,0,2630,Yes
216,hospital_4,Lev,58,Female,2908,Yes,No,No,Moderate,Serosa,Good,8,0,2461,Yes
217,hospital_3,Lev,67,Female,2896,No,No,No,Moderate,Adjacent structures,Good,4,0,2747,No
218,hospital_5,Lev,55,Male,2894,Yes,No,No,Moderate,Serosa,Good,7,1,2482,Yes
219,hospital_2,Lev,63,Male,2893,Yes,No,No,Poor,Serosa,Bad,5,1,580,Yes
220,hospital_4,Lev,77,Male,2893,No,No,Yes,Moderate,Serosa,Bad,1,1,1215,Yes
221,hospital_2,Lev,43,Male,2891,Yes,Yes,Yes,Poor,Serosa,Good,4,0,2525,Yes
222,hospital_1,Lev,78,Female,2891,No,No,No,Moderate,Muscle,Good,2,0,2842,Yes
223,hospital_5,Obs,51,Female,2887,No,No,Yes,Moderate,Serosa,Bad,5,1,1272,Yes
224,hospital_2,Obs,67,Male,2886,Yes,No,No,Moderate,Serosa,Bad,1,1,438,No
225,hospital_2,Lev,66,Male,2886,No,No,No,Moderate,Serosa,Good,1,0,2423,Yes
226,hospital_4,Obs,64,Female,2885,No,No,Yes,Moderate,Adjacent structures,Bad,1,1,599,Yes
227,hospital_2,Obs,75,Female,2884,Yes,No,Yes,Moderate,Serosa,Good,1,0,2706,Yes
228,hospital_5,Lev,53,Male,2881,No,No,No,Moderate,Serosa,Good,1,1,2683,Yes
229,hospital_3,Lev,48,Male,2880,Yes,Yes,No,Moderate,Serosa,Good,2,0,2723,Yes
230,hospital_3,Obs,59,Female,2878,No,No,Yes,Moderate,Adjacent structures,Bad,1,1,1230,No
231,hospital_3,Obs,60,Female,2875,No,No,No,Moderate,Serosa,Good,1,0,2731,Yes
232,hospital_2,Obs,42,Male,2865,No,No,No,Moderate,Serosa,Good,2,0,2313,Yes
233,hospital_3,Lev,60,Male,2864,No,No,No,Moderate,Muscle,Good,1,0,2484,No
234,hospital_1,Lev,58,Male,2862,NA,No,No,Well,Serosa,Good,1,0,2754,Yes
235,hospital_3,Lev,59,Male,2860,NA,No,No,Moderate,Muscle,Good,2,1,2725,Yes
236,hospital_5,Obs,70,Female,2856,No,No,No,Moderate,Serosa,Bad,2,1,1295,Yes
237,hospital_1,Lev,75,Female,2855,No,No,No,Moderate,Serosa,Good,3,0,2506,Yes
238,hospital_3,Lev,73,Female,2853,No,No,No,Moderate,Serosa,Good,3,0,2713,Yes
239,hospital_4,Obs,76,Male,2852,No,No,No,Moderate,Serosa,Good,1,0,2695,Yes
240,hospital_5,Lev,68,Female,2852,No,No,No,Moderate,Serosa,Bad,3,1,1276,Yes
241,hospital_3,Lev,61,Male,2851,No,No,No,Moderate,Adjacent structures,Good,2,0,2600,No
242,hospital_2,Lev,76,Male,2850,NA,No,No,Moderate,Muscle,Good,1,0,2352,No
243,hospital_1,Lev,56,Female,2850,No,Yes,Yes,Moderate,Serosa,Good,3,0,2775,Yes
244,hospital_2,Lev,55,Female,2849,Yes,No,No,NA,Serosa,Good,2,0,2442,Yes
245,hospital_3,Obs,60,Female,2847,Yes,No,Yes,Poor,Serosa,Bad,4,1,437,No
246,hospital_3,Lev,72,Male,2845,No,No,No,Moderate,Serosa,Bad,9,1,1709,Yes
247,hospital_3,Lev,74,Female,2843,Yes,No,No,Moderate,Serosa,Bad,11,1,1607,Yes
248,hospital_3,Lev,62,Female,2839,No,No,No,Moderate,Serosa,Bad,3,1,1637,No
249,hospital_4,Lev,76,Female,2832,No,No,No,Well,Serosa,Bad,1,1,1117,Yes
250,hospital_2,Lev,72,Male,2832,No,No,No,Moderate,Serosa,Good,2,0,2732,Yes
251,hospital_4,Lev,57,Male,2831,No,No,No,Poor,Serosa,Bad,2,1,222,Yes
252,hospital_3,Obs,39,Female,2831,No,No,No,Moderate,Adjacent structures,Good,1,0,2688,No
253,hospital_3,Lev,61,Male,2831,No,No,No,Moderate,Serosa,Good,1,0,2520,Yes
254,hospital_5,Obs,62,Female,2830,No,No,No,Poor,Serosa,Bad,5,1,595,Yes
255,hospital_4,Lev,70,Male,2826,No,No,No,Moderate,Serosa,Bad,2,1,343,No
256,hospital_4,Obs,70,Female,2825,No,No,No,Moderate,Serosa,Bad,5,1,874,Yes
257,hospital_5,Lev,57,Female,2824,Yes,No,No,Moderate,Serosa,Bad,8,1,232,Yes
258,hospital_3,Lev,65,Male,2823,No,No,No,Moderate,Muscle,Good,3,0,2558,Yes
259,hospital_3,Lev,65,Female,2821,No,No,No,Moderate,Serosa,Good,1,0,2711,Yes
260,hospital_5,Obs,71,Female,2818,No,No,No,Moderate,Serosa,Bad,15,1,113,Yes
261,hospital_5,Lev,70,Female,2816,Yes,No,No,Moderate,Serosa,Bad,3,1,643,Yes
262,hospital_2,Obs,36,Female,2813,No,No,No,Moderate,Serosa,Good,1,0,2413,No
263,hospital_1,Lev,48,Male,2813,No,No,No,Moderate,Submucosa,Good,2,0,2509,Yes
264,hospital_1,Lev,53,Male,2812,No,No,No,Well,Serosa,Good,1,0,2356,Yes
265,hospital_5,Lev,54,Male,2810,No,No,No,Moderate,Serosa,Bad,3,1,1279,Yes
266,hospital_3,Lev,58,Male,2809,No,No,No,Well,Serosa,Bad,3,1,938,Yes
267,hospital_3,Obs,67,Male,2804,No,No,No,Moderate,Serosa,Good,3,0,2761,No
268,hospital_3,Lev,59,Male,2803,Yes,No,No,Moderate,Serosa,Good,2,0,2386,No
269,hospital_2,Obs,67,Female,2800,No,No,Yes,Moderate,Serosa,Bad,4,1,433,Yes
270,hospital_2,Lev,60,Female,2799,Yes,No,No,Poor,Serosa,Good,1,0,2445,Yes
271,hospital_1,Lev,70,Female,2799,No,No,No,Moderate,Muscle,Good,1,0,2280,Yes
272,hospital_3,Obs,76,Male,3194,No,No,No,Well,Serosa,Bad,3,1,311,Yes
273,hospital_4,Lev,60,Male,3192,Yes,No,No,Well,Serosa,Bad,1,1,977,No
274,hospital_5,Obs,67,Female,3190,Yes,No,No,Well,Serosa,Bad,12,1,833,Yes
275,hospital_4,Lev,53,Male,3189,No,No,No,Moderate,Serosa,Bad,3,1,582,Yes
276,hospital_4,Lev,76,Female,3187,No,No,No,Well,Serosa,Good,1,0,2541,Yes
277,hospital_4,Obs,74,Male,3184,No,No,No,Moderate,Serosa,Bad,2,1,238,Yes
278,hospital_2,Obs,56,Female,3184,Yes,No,No,Moderate,Adjacent structures,Good,6,1,2257,No
279,hospital_4,Lev,65,Female,3180,No,No,No,Moderate,Muscle,Good,1,0,2501,Yes
280,hospital_2,Lev,63,Male,3179,No,No,No,Moderate,Serosa,Bad,2,1,1798,Yes
281,hospital_2,Obs,56,Male,3178,No,No,No,Moderate,Serosa,Bad,2,1,883,Yes
282,hospital_3,Lev,70,Female,3178,No,No,Yes,Poor,Serosa,Good,4,0,2548,No
283,hospital_3,Lev,48,Female,3177,No,No,No,Moderate,Serosa,Good,9,0,2527,Yes
284,hospital_2,Lev,45,Male,3174,Yes,No,No,Moderate,Serosa,Good,2,0,2263,Yes
285,hospital_4,Lev,68,Female,3170,No,No,No,Moderate,Serosa,Bad,8,1,706,No
286,hospital_1,Lev,57,Female,3169,No,No,No,Moderate,Muscle,Good,1,0,2588,Yes
287,hospital_5,Lev,70,Male,3167,No,No,No,Moderate,Muscle,Good,1,1,2542,Yes
288,hospital_1,Lev,39,Male,3165,No,No,No,Moderate,Serosa,NA,1,0,1279,Yes
289,hospital_1,Obs,46,Male,3164,No,No,Yes,Poor,Adjacent structures,Bad,1,1,1304,No
290,hospital_2,Obs,77,Female,3163,Yes,No,No,Moderate,Serosa,Good,4,0,2324,Yes
291,hospital_1,Lev,59,Male,3163,NA,No,No,Well,Serosa,Good,1,0,2488,Yes
292,hospital_4,Lev,58,Female,3161,No,No,No,Moderate,Serosa,Good,1,0,2628,No
293,hospital_2,Obs,56,Male,3157,No,No,No,Moderate,Serosa,Good,8,0,2800,No
294,hospital_1,Lev,68,Male,3157,No,No,No,NA,Serosa,Good,2,0,2456,Yes
295,hospital_2,Obs,57,Female,3155,No,Yes,Yes,Moderate,Serosa,Good,1,0,2503,Yes
296,hospital_4,Lev,44,Female,3154,No,No,No,Moderate,Serosa,Bad,3,1,1446,Yes
297,hospital_5,Lev,56,Male,3152,No,No,Yes,Moderate,Serosa,Bad,3,1,641,No
298,hospital_5,Obs,67,Male,3150,No,No,No,Moderate,Serosa,Bad,4,1,1788,Yes
299,hospital_3,Lev,62,Male,3150,No,No,No,Moderate,Muscle,Good,6,0,2304,Yes
300,hospital_3,Lev,68,Female,3145,No,No,No,Moderate,Serosa,Bad,7,1,944,Yes
301,hospital_3,Lev,61,Male,3144,No,No,No,Moderate,Muscle,Good,2,0,2679,Yes
302,hospital_3,Lev,44,Female,3142,Yes,No,No,Moderate,Serosa,Bad,5,1,559,Yes
303,hospital_2,Lev,52,Female,3140,No,No,No,Moderate,Serosa,Good,7,0,2663,Yes
304,hospital_3,Lev,51,Male,3137,Yes,No,No,Poor,Serosa,Bad,1,1,647,No
305,hospital_1,Obs,59,Female,3135,No,No,No,Moderate,Serosa,Good,9,0,2697,Yes
306,hospital_3,Lev,61,Male,3134,No,No,No,Poor,Serosa,Good,1,0,2538,Yes
307,hospital_2,Obs,57,Female,3132,No,No,No,Poor,Serosa,Good,2,0,2219,No
308,hospital_3,Lev,65,Male,3131,No,No,No,Moderate,Serosa,Good,1,0,2487,Yes
309,hospital_4,Lev,73,Female,3131,Yes,No,No,Moderate,Serosa,Good,1,0,2709,Yes
310,hospital_5,Obs,69,Female,3129,No,No,No,Poor,Serosa,Bad,6,1,840,No
311,hospital_1,Lev,63,Male,3129,No,No,No,Moderate,Serosa,Good,1,0,2267,Yes
312,hospital_3,Lev,75,Male,3127,No,No,No,Moderate,Muscle,Good,2,0,2444,Yes
313,hospital_2,Lev,64,Female,3127,No,No,No,Well,Serosa,Good,5,0,2393,Yes
314,hospital_1,Obs,56,Female,3122,No,No,No,Well,Submucosa,Good,1,0,2237,Yes
315,hospital_2,Lev,69,Male,3121,No,No,No,Moderate,Serosa,Good,2,0,2915,No
316,hospital_2,Lev,64,Female,3119,No,No,No,Moderate,Serosa,Good,2,0,2467,No
317,hospital_2,Lev,74,Female,3119,Yes,No,No,Moderate,Serosa,Bad,2,1,1092,Yes
318,hospital_3,Lev,72,Male,3113,No,No,No,Moderate,Muscle,Good,4,0,2726,Yes
319,hospital_4,Lev,58,Male,3113,Yes,No,No,Moderate,Serosa,Good,9,0,2764,Yes
320,hospital_3,Lev,74,Female,3112,No,No,Yes,Poor,Adjacent structures,Good,15,0,2592,Yes
321,hospital_1,Obs,61,Female,3112,No,No,No,Well,Muscle,Good,2,0,2730,Yes
322,hospital_1,Obs,74,Female,3110,NA,No,No,Moderate,Muscle,Good,1,1,2287,Yes
323,hospital_3,Lev,64,Male,3110,No,No,No,Moderate,Serosa,Good,5,0,2117,Yes
324,hospital_5,Lev,71,Female,3109,No,No,No,Moderate,Serosa,Bad,20,1,1668,Yes
325,hospital_1,Obs,66,Female,3107,No,No,Yes,NA,Adjacent structures,Bad,1,1,528,Yes
326,hospital_1,Obs,53,Female,3104,Yes,No,No,Well,Serosa,Good,2,0,2651,No
327,hospital_1,Lev,76,Female,3104,Yes,No,No,Moderate,Serosa,Good,1,0,2676,No
328,hospital_5,Lev,75,Female,3103,Yes,No,Yes,Moderate,Muscle,Good,3,1,1885,Yes
329,hospital_4,Lev,64,Male,3102,No,No,No,NA,Serosa,Good,3,0,2350,Yes
330,hospital_5,Lev,65,Male,3100,No,No,No,Moderate,Serosa,Bad,8,1,475,Yes
331,hospital_3,Obs,57,Female,3100,No,No,No,Moderate,Serosa,Bad,5,1,1246,No
332,hospital_3,Lev,65,Male,3098,No,No,Yes,Poor,Serosa,Good,1,0,2204,Yes
333,hospital_1,Obs,53,Male,3098,No,No,No,Moderate,Adjacent structures,Good,2,0,2716,Yes
334,hospital_2,Lev,74,Male,3097,No,No,No,Moderate,Serosa,Bad,4,1,797,Yes
335,hospital_2,Lev,74,Male,3095,No,No,No,Moderate,Serosa,Good,1,0,2527,Yes
336,hospital_4,Lev,74,Female,3094,No,No,Yes,Poor,Serosa,Good,1,1,2021,Yes
337,hospital_3,Lev,48,Female,3092,No,No,No,Moderate,Serosa,Bad,6,1,498,No
338,hospital_3,Lev,58,Male,3091,No,No,No,Poor,Serosa,Bad,NA,1,885,Yes
339,hospital_4,Lev,74,Female,3091,No,No,No,Moderate,Serosa,Bad,3,1,802,Yes
340,hospital_1,Lev,71,Female,3091,No,No,No,Moderate,Serosa,Good,1,0,2674,No
341,hospital_2,Obs,54,Female,3091,No,No,No,Moderate,Serosa,Bad,2,1,743,No
342,hospital_4,Lev,80,Male,3090,No,No,No,Moderate,Muscle,Good,4,0,2517,Yes
343,hospital_5,Lev,70,Male,3090,No,No,Yes,Moderate,Serosa,Bad,8,1,349,Yes
344,hospital_3,Lev,70,Male,3089,Yes,No,No,Moderate,Serosa,Good,1,0,2723,No
345,hospital_2,Obs,70,Male,3089,NA,No,No,Poor,Serosa,Bad,1,1,775,Yes
346,hospital_2,Obs,71,Male,3088,No,No,No,Moderate,Serosa,Good,4,0,2326,Yes
347,hospital_4,Obs,59,Male,3087,No,No,No,Moderate,Serosa,Bad,2,1,1134,Yes
348,hospital_3,Lev,47,Male,3087,No,No,No,NA,Serosa,Bad,2,1,602,Yes
349,hospital_1,Lev,63,Male,3086,No,No,No,Poor,Serosa,Good,2,0,2547,No
350,hospital_3,Lev,69,Female,3085,No,No,No,Moderate,Serosa,Bad,7,1,940,No
351,hospital_1,Lev,73,Female,3084,No,No,No,Moderate,Serosa,Good,1,0,2496,No
352,hospital_1,Obs,67,Male,3083,No,No,No,Moderate,Serosa,Good,1,0,2628,No
353,hospital_1,Lev,55,Female,3083,No,No,No,Moderate,Serosa,Good,1,0,2353,No
354,hospital_2,Obs,45,Male,3081,No,No,No,Moderate,Serosa,Good,2,0,2184,Yes
355,hospital_3,Lev,78,Female,3080,Yes,No,No,Moderate,Serosa,Good,3,0,2219,Yes
356,hospital_2,Lev,80,Male,3079,No,No,No,Moderate,Serosa,Bad,3,1,191,Yes
357,hospital_3,Obs,55,Male,3076,No,No,No,Moderate,Serosa,Bad,8,1,421,No
358,hospital_2,Lev,64,Female,3076,Yes,No,No,Moderate,Serosa,Good,NA,0,2362,No
359,hospital_5,Lev,60,Female,3074,No,Yes,No,Moderate,Serosa,Bad,10,1,952,Yes
360,hospital_5,Lev,74,Female,3074,No,No,No,Moderate,Serosa,Bad,5,1,911,No
361,hospital_5,Lev,61,Female,3073,No,No,No,Moderate,Serosa,Good,1,1,1851,Yes
362,hospital_1,Obs,64,Male,3071,No,No,No,Moderate,Muscle,Good,4,0,2423,Yes
363,hospital_4,Obs,48,Female,3071,No,No,Yes,Poor,Serosa,Bad,1,1,464,Yes
364,hospital_5,Lev,76,Male,3070,No,No,No,Moderate,Serosa,Bad,2,1,443,No
365,hospital_3,Lev,76,Female,3070,No,No,No,Poor,Serosa,Bad,NA,1,186,Yes
366,hospital_1,Lev,50,Female,3070,No,No,No,Moderate,Serosa,Good,4,0,2545,Yes
367,hospital_5,Lev,56,Male,3069,No,No,No,Moderate,Adjacent structures,Bad,3,1,356,Yes
368,hospital_4,Lev,42,Female,3067,No,No,No,Poor,Serosa,Bad,10,1,484,No
369,hospital_2,Lev,72,Female,3067,No,No,No,Moderate,Serosa,Bad,1,1,382,No
370,hospital_5,Obs,68,Female,3065,No,No,Yes,Moderate,Serosa,Bad,9,1,1021,Yes
371,hospital_2,Lev,49,Male,3064,No,No,No,Moderate,Muscle,Good,1,0,2472,Yes
372,hospital_5,Lev,64,Male,3064,No,No,No,Moderate,Serosa,Good,12,0,2352,Yes
373,hospital_2,Obs,70,Female,3062,NA,No,No,Well,Muscle,Good,1,0,2486,Yes
374,hospital_2,Lev,50,Male,3062,No,No,Yes,Moderate,Adjacent structures,Bad,2,1,324,Yes
375,hospital_1,Obs,74,Male,3061,No,No,Yes,Poor,Serosa,Good,3,0,2517,Yes
376,hospital_4,Lev,37,Female,3058,No,No,No,Moderate,Serosa,Good,1,0,2613,No
377,hospital_2,Lev,75,Female,3057,NA,No,No,Poor,Serosa,Good,1,0,2450,Yes
378,hospital_5,Obs,56,Female,3056,No,No,No,Poor,Serosa,Bad,2,1,770,Yes
379,hospital_2,Lev,74,Male,3054,No,No,No,Well,Serosa,Good,3,0,2490,No
380,hospital_5,Obs,63,Male,3053,No,No,No,Moderate,Serosa,Bad,2,1,1548,Yes
381,hospital_3,Lev,56,Male,3052,No,No,No,Moderate,Muscle,Good,2,0,2449,Yes
382,hospital_5,Lev,42,Male,3052,No,No,No,Moderate,Serosa,Good,13,0,2299,No
383,hospital_2,Lev,63,Male,3049,No,No,No,Moderate,Serosa,Bad,NA,1,1252,Yes
384,hospital_2,Lev,62,Female,3048,No,No,No,Moderate,Serosa,Bad,2,1,730,Yes
385,hospital_1,Lev,33,Female,3047,No,No,No,Moderate,Serosa,Good,2,0,2460,Yes
386,hospital_5,Obs,50,Female,3047,No,No,No,Moderate,Serosa,Bad,8,1,499,Yes
387,hospital_5,Lev,73,Male,3044,No,No,No,Poor,Serosa,Bad,6,1,129,Yes
388,hospital_5,Lev,61,Female,3042,No,No,No,Poor,Serosa,Bad,9,1,52,Yes
389,hospital_3,Obs,70,Male,3039,Yes,Yes,No,Moderate,Serosa,Good,1,1,2171,Yes
390,hospital_3,Lev,38,Female,3038,No,No,No,Moderate,Serosa,Bad,3,1,1178,Yes
391,hospital_4,Obs,64,Female,3038,Yes,No,No,Moderate,Serosa,Good,3,1,1915,Yes
392,hospital_1,Lev,39,Female,3038,No,No,No,Moderate,Serosa,Good,2,0,2572,Yes
393,hospital_1,Lev,47,Female,3036,No,No,No,Moderate,Serosa,Good,1,0,2590,No
394,hospital_4,Obs,76,Male,3033,No,No,Yes,Poor,Serosa,Bad,13,1,215,Yes
395,hospital_2,Obs,65,Male,3032,No,No,No,Moderate,Serosa,Good,2,0,2325,Yes
396,hospital_3,Lev,43,Female,3030,Yes,No,No,Moderate,Serosa,Bad,4,1,961,Yes
397,hospital_3,Lev,72,Male,3030,No,No,No,Moderate,Serosa,Good,1,0,2691,Yes
398,hospital_3,Obs,33,Male,3029,No,No,No,Moderate,Serosa,Good,2,0,2574,Yes
399,hospital_4,Lev,66,Male,3028,No,No,No,Moderate,Serosa,Good,7,0,2540,Yes
400,hospital_1,Lev,74,Female,3027,No,No,No,Moderate,Serosa,Good,2,0,2364,No
401,hospital_1,Lev,54,Male,3026,No,No,No,Moderate,Serosa,Good,1,0,2472,Yes
402,hospital_5,Obs,44,Male,3022,No,No,No,Moderate,Serosa,Bad,14,1,409,No
403,hospital_5,Obs,75,Male,3022,Yes,No,No,Moderate,Muscle,Bad,27,1,961,No
404,hospital_4,Lev,58,Male,3020,No,No,No,Moderate,Serosa,Good,6,0,2449,Yes
405,hospital_1,Lev,48,Male,3020,No,No,No,Well,Muscle,Good,6,0,2520,Yes
406,hospital_3,Lev,64,Female,3018,No,No,No,Moderate,Serosa,Bad,1,1,916,No
407,hospital_2,Lev,49,Male,3017,NA,No,No,Well,Serosa,Good,1,0,2227,Yes
408,hospital_2,Obs,55,Female,3014,No,No,No,Moderate,Serosa,Good,1,0,2394,No
409,hospital_1,Lev,60,Male,3013,No,No,No,Moderate,Serosa,Bad,3,0,2029,No
410,hospital_3,Obs,67,Male,3011,No,No,No,Moderate,Serosa,Bad,2,0,2487,Yes
411,hospital_3,Lev,79,Male,3011,No,No,No,Moderate,Muscle,Bad,1,1,2052,No
412,hospital_1,Lev,57,Male,3011,No,No,No,Moderate,Serosa,Good,1,0,2690,Yes
413,hospital_2,Obs,22,Female,3010,No,No,No,Moderate,Serosa,Good,3,0,2234,Yes
414,hospital_5,Obs,59,Male,3007,Yes,No,No,Well,Serosa,Bad,4,1,537,No
415,hospital_2,Lev,66,Female,3003,Yes,No,No,Well,Serosa,Good,1,0,2277,Yes
416,hospital_2,Lev,54,Male,3001,No,No,Yes,Moderate,Adjacent structures,Bad,2,1,729,No
417,hospital_4,Obs,58,Male,3000,No,No,No,Moderate,Muscle,Good,1,0,2194,No
418,hospital_1,Obs,36,Female,2999,Yes,No,No,Moderate,Serosa,Good,7,0,2551,No
419,hospital_5,Obs,52,Male,2999,No,No,No,Moderate,Serosa,Bad,4,1,1692,Yes
420,hospital_5,Obs,61,Female,2997,No,No,No,Moderate,Serosa,Bad,1,1,758,Yes
421,hospital_4,Lev,76,Female,2993,No,No,No,Well,Serosa,Good,2,1,2079,No
422,hospital_3,Lev,58,Female,2993,No,No,Yes,Well,Serosa,Bad,6,1,720,Yes
423,hospital_3,Lev,52,Male,2985,No,No,No,Well,Serosa,Good,5,0,2380,Yes
424,hospital_4,Obs,72,Male,2984,No,No,No,Moderate,Serosa,Bad,1,1,845,Yes
425,hospital_4,Lev,74,Male,2983,No,No,No,Well,Serosa,Good,1,0,2416,Yes
426,hospital_4,Obs,62,Male,2979,No,No,No,Moderate,Muscle,Bad,6,1,863,Yes
427,hospital_2,Obs,46,Female,2977,No,No,Yes,Moderate,Serosa,Bad,1,1,1363,No
428,hospital_2,Obs,66,Male,2976,No,No,No,Moderate,Serosa,Good,1,0,2528,No
429,hospital_2,Lev,52,Male,2976,No,No,No,Moderate,Serosa,Bad,5,1,1145,Yes
430,hospital_5,Lev,38,Female,2976,No,No,No,NA,Serosa,Bad,24,1,499,Yes
431,hospital_4,Obs,61,Male,2976,No,No,No,Moderate,Serosa,Bad,1,1,242,Yes
432,hospital_1,Obs,60,Female,2972,No,No,No,Well,Serosa,Good,1,0,2294,Yes
433,hospital_2,Lev,68,Female,2970,No,No,Yes,Well,Serosa,Bad,2,1,858,Yes
434,hospital_5,Lev,53,Female,2966,No,No,No,Moderate,Serosa,Bad,6,1,122,No
435,hospital_3,Lev,58,Male,2966,No,No,No,Moderate,Serosa,Bad,1,0,2682,Yes
436,hospital_3,Obs,64,Male,2966,No,No,No,Moderate,Serosa,Bad,4,1,1818,Yes
437,hospital_1,Obs,68,Male,2965,Yes,No,Yes,Moderate,Serosa,Good,3,0,2532,Yes
438,hospital_2,Obs,62,Male,2958,No,No,Yes,Moderate,Adjacent structures,Bad,6,1,331,Yes
439,hospital_2,Obs,70,Male,2955,No,No,No,Well,Serosa,Bad,1,1,1048,No
440,hospital_3,Lev,57,Male,2952,No,No,No,Moderate,Serosa,Good,1,0,2513,Yes
441,hospital_1,Lev,66,Female,2950,No,No,Yes,Moderate,Adjacent structures,Bad,1,1,340,Yes
442,hospital_3,Lev,59,Female,2947,NA,No,No,Moderate,Serosa,Good,11,1,1850,Yes
443,hospital_1,Lev,75,Male,2943,No,No,No,Well,Serosa,Good,3,0,2332,Yes
444,hospital_3,Lev,77,Male,2930,No,No,No,Moderate,Serosa,Good,2,0,2279,Yes
445,hospital_5,Lev,57,Male,2929,No,No,No,Moderate,Serosa,Bad,6,1,1041,Yes
446,hospital_3,Obs,72,Male,2925,No,No,No,Well,Muscle,Bad,2,1,1790,No
447,hospital_3,Lev,64,Female,2923,Yes,No,No,Moderate,Serosa,Bad,3,1,304,Yes
448,hospital_2,Lev,33,Male,2923,No,No,No,Poor,Serosa,Bad,2,0,2517,Yes
449,hospital_3,Lev,62,Female,2921,No,No,No,Moderate,Serosa,Bad,2,0,2156,Yes
450,hospital_4,Lev,70,Male,2920,No,No,No,Moderate,Serosa,Bad,1,1,1388,Yes
451,hospital_2,Lev,55,Female,2918,Yes,No,No,Well,Serosa,Good,4,0,2544,Yes
452,hospital_2,Obs,32,Male,2908,Yes,No,Yes,Moderate,Serosa,Good,11,0,1852,No
453,hospital_2,Obs,64,Female,2908,Yes,No,No,Moderate,Muscle,Good,2,0,2122,Yes
454,hospital_1,Obs,48,Female,2906,No,No,No,Moderate,Adjacent structures,Good,1,0,2577,Yes
455,hospital_4,Lev,68,Male,2906,No,No,Yes,Moderate,Serosa,Good,3,1,2023,No
456,hospital_2,Lev,59,Female,2901,No,No,No,Moderate,Serosa,Bad,1,1,1201,Yes
457,hospital_4,Obs,68,Male,2897,No,No,Yes,Poor,Serosa,Bad,9,1,901,Yes
458,hospital_3,Lev,50,Female,2895,No,No,Yes,Well,Serosa,Good,1,0,2385,Yes
459,hospital_2,Obs,41,Female,2895,Yes,No,No,Moderate,Serosa,Bad,4,1,663,Yes
460,hospital_2,Lev,69,Female,2892,No,No,Yes,Moderate,Serosa,Good,1,0,2506,Yes
461,hospital_1,Lev,46,Male,2890,No,No,No,Well,Muscle,Good,2,0,2332,Yes
462,hospital_4,Obs,43,Male,2885,No,No,No,Moderate,Serosa,Good,1,0,2176,Yes
463,hospital_3,Lev,68,Male,2881,No,No,No,Poor,Serosa,Bad,3,1,1212,No
464,hospital_4,Lev,73,Male,2880,No,No,No,Moderate,Serosa,Bad,3,1,890,No
465,hospital_5,Lev,66,Female,2876,No,No,Yes,Poor,Adjacent structures,Bad,4,1,302,Yes
466,hospital_1,Obs,60,Female,2876,No,No,Yes,Moderate,Serosa,Good,1,0,2396,Yes
467,hospital_1,Lev,74,Male,2871,No,No,No,Moderate,Serosa,Good,2,0,2481,No
468,hospital_5,Lev,68,Male,2869,No,No,No,Well,Serosa,Bad,1,1,316,Yes
469,hospital_1,Lev,60,Female,2868,Yes,No,No,Moderate,Serosa,Good,1,0,2245,Yes
470,hospital_2,Obs,71,Male,2864,No,No,Yes,Moderate,Adjacent structures,Good,1,0,2447,No
471,hospital_3,Obs,76,Male,2850,No,No,No,Moderate,Submucosa,Good,2,0,2213,Yes
472,hospital_5,Obs,57,Male,2844,No,No,No,Poor,Muscle,Bad,8,1,1070,Yes
473,hospital_4,Lev,33,Female,3102.4,No,No,No,Poor,Serosa,Bad,7,1,439,Yes
474,hospital_3,Lev,54,Male,3096,No,No,No,Moderate,Serosa,Bad,4,1,905,No
475,hospital_5,Obs,68,Male,3091.2,No,No,No,Moderate,Serosa,Bad,13,1,1723,Yes
476,hospital_5,Obs,70,Female,3086.4,No,No,No,Poor,Muscle,Bad,6,1,241,Yes
477,hospital_5,Obs,66,Male,3086.4,No,No,No,Moderate,Serosa,Bad,5,1,1314,No
478,hospital_5,Lev,45,Female,3068.8,Yes,No,No,Moderate,Serosa,Bad,3,1,438,Yes
479,hospital_1,Lev,61,Female,3068.8,No,No,No,Moderate,Serosa,Good,2,0,1981,Yes
480,hospital_1,Obs,74,Female,3064,No,No,Yes,Poor,Serosa,Good,1,0,2195,Yes
481,hospital_4,Obs,47,Male,3060.8,Yes,Yes,Yes,Moderate,Serosa,Bad,4,1,1101,Yes
482,hospital_5,Lev,58,Female,3051.2,Yes,No,No,Poor,Serosa,Bad,5,1,79,Yes
483,hospital_5,Obs,73,Male,3048,No,No,No,Moderate,Serosa,Bad,1,1,976,Yes
484,hospital_5,Lev,61,Male,3048,No,No,No,Moderate,Serosa,Bad,17,1,664,No
485,hospital_4,Obs,44,Female,3043.2,No,No,No,Moderate,Serosa,Good,4,0,2299,Yes
486,hospital_4,Lev,56,Male,3043.2,No,No,No,Moderate,Serosa,Bad,1,1,279,Yes
487,hospital_5,Lev,66,Female,3033.6,No,No,No,Moderate,Serosa,Bad,7,1,1037,Yes
488,hospital_5,Obs,30,Female,3025.6,No,No,No,Poor,Serosa,Bad,6,1,692,Yes
489,hospital_5,Lev,67,Female,3016,No,No,Yes,Moderate,Serosa,Good,10,1,2174,No
490,hospital_1,Lev,74,Female,3014.4,No,No,No,Moderate,Serosa,Good,2,0,2144,No
491,hospital_4,Lev,74,Female,3006.4,No,No,Yes,Poor,Serosa,Good,1,0,2058,Yes
492,hospital_4,Obs,56,Male,3006.4,No,No,No,Moderate,Serosa,Good,4,0,2485,Yes
493,hospital_1,Lev,60,Female,3000,No,No,No,Moderate,Serosa,Good,2,0,2410,Yes
494,hospital_1,Lev,54,Female,3000,Yes,No,Yes,Poor,Muscle,Good,2,0,2565,Yes
495,hospital_5,Lev,74,Female,2992,No,No,No,Moderate,Muscle,Bad,3,1,884,No
496,hospital_3,Lev,45,Female,2982.4,No,No,No,Moderate,Serosa,Bad,3,1,389,Yes
497,hospital_5,Lev,81,Female,2977.6,No,No,No,Moderate,Serosa,Bad,1,1,692,Yes
498,hospital_4,Lev,73,Male,2969.6,Yes,No,No,Moderate,Serosa,Bad,1,1,642,Yes
499,hospital_3,Lev,71,Male,2969.6,Yes,No,No,Moderate,Serosa,Bad,1,1,846,Yes
500,hospital_1,Lev,70,Male,2968,No,No,No,NA,Serosa,Good,3,0,2203,No
501,hospital_2,Lev,66,Male,2964.8,No,No,No,Moderate,Serosa,Bad,1,0,2164,Yes
502,hospital_3,Lev,71,Female,2963.2,No,No,No,Moderate,Serosa,Bad,NA,1,1273,Yes
503,hospital_3,Obs,48,Male,2961.6,No,No,No,Poor,Muscle,Bad,1,0,2393,Yes
504,hospital_4,Lev,55,Female,2960,No,No,No,Moderate,Serosa,Bad,4,1,862,No
505,hospital_1,Lev,72,Female,2942.4,No,No,No,Poor,Muscle,Good,2,0,2331,Yes
506,hospital_4,Lev,57,Male,2940.8,No,No,No,NA,Serosa,Good,3,0,2264,Yes
507,hospital_5,Lev,57,Male,2929.6,No,No,No,Poor,Serosa,Bad,11,1,144,No
508,hospital_1,Obs,38,Male,2926.4,No,No,No,Well,Serosa,Good,2,0,2429,Yes
509,hospital_2,Lev,52,Male,2924.8,Yes,No,No,Moderate,Serosa,Bad,0,1,723,Yes
510,hospital_2,Obs,57,Male,2923.2,No,No,No,Moderate,Serosa,Good,1,0,2530,Yes
511,hospital_4,Lev,33,Female,2923.2,No,No,No,Moderate,Serosa,Bad,2,1,1568,No
512,hospital_1,Obs,39,Female,2916.8,No,No,No,Moderate,Muscle,Good,2,0,2292,Yes
513,hospital_2,Lev,61,Male,2916.8,No,No,No,Moderate,Serosa,Good,1,0,2252,Yes
514,hospital_1,Lev,71,Female,2912,No,No,No,Poor,Serosa,Good,1,0,2408,No
515,hospital_3,Lev,58,Male,2910.4,No,No,No,Moderate,Serosa,Good,4,0,2555,No
516,hospital_3,Lev,66,Female,2908.8,No,No,No,Poor,Serosa,Bad,7,1,276,Yes
517,hospital_3,Obs,59,Female,2902.4,No,No,No,Moderate,Muscle,Good,4,1,1896,Yes
518,hospital_3,Lev,43,Female,2902.4,Yes,No,No,Moderate,Serosa,Good,1,0,2147,Yes
519,hospital_3,Obs,66,Male,2899.2,No,No,No,Moderate,Serosa,Good,3,0,2269,No
520,hospital_3,Obs,48,Female,2884.8,No,No,No,Moderate,Serosa,Good,1,0,2391,Yes
521,hospital_5,Lev,68,Male,2880,No,No,No,Moderate,Serosa,Bad,1,1,1424,Yes
522,hospital_2,Obs,72,Female,2876.8,No,No,No,Moderate,Serosa,Good,NA,0,2257,Yes
523,hospital_3,Lev,77,Female,2867.2,NA,No,No,Poor,Adjacent structures,Good,10,0,2381,Yes
524,hospital_1,Lev,68,Female,2864,Yes,No,No,Moderate,Serosa,Good,1,0,2103,Yes
525,hospital_4,Obs,44,Female,2860.8,No,No,Yes,Moderate,Adjacent structures,Good,1,0,2210,Yes
526,hospital_1,Lev,63,Female,2852.8,No,No,No,Moderate,Serosa,Good,1,0,2530,No
527,hospital_1,Lev,66,Male,2835.2,No,No,No,Moderate,Serosa,Good,1,0,2386,Yes
528,hospital_5,Lev,50,Male,2828.8,No,No,No,Poor,Serosa,Bad,3,1,219,Yes
529,hospital_3,Lev,72,Male,2827.2,No,No,No,Moderate,Serosa,Bad,1,1,486,No
530,hospital_4,Lev,40,Male,2803.2,No,No,No,Well,Serosa,Bad,5,1,1246,Yes
531,hospital_4,Lev,49,Male,2792,No,No,No,Moderate,Serosa,Good,10,0,2138,Yes
532,hospital_1,Lev,56,Female,2756.8,No,No,No,Poor,Serosa,Good,2,0,2439,No
533,hospital_4,Obs,61,Male,2734.4,No,No,No,Moderate,Muscle,Good,2,0,2231,Yes
534,hospital_5,Obs,65,Male,2707.2,No,No,No,Moderate,Serosa,Bad,6,1,384,Yes
535,hospital_3,Lev,65,Male,2686.4,No,No,No,Moderate,Muscle,Good,2,0,2505,Yes
536,hospital_5,Lev,72,Female,2673.6,No,No,No,Well,Submucosa,Bad,1,1,1652,Yes
537,hospital_1,Lev,68,Female,2668.8,No,No,Yes,Moderate,Serosa,Good,1,0,2441,No
538,hospital_4,Obs,59,Female,2649.6,No,No,No,Moderate,Serosa,Bad,4,1,687,No
539,hospital_4,Lev,59,Male,2643.2,No,No,No,Moderate,Serosa,Good,4,0,2179,Yes
540,hospital_2,Lev,71,Female,2619.2,Yes,No,No,Moderate,Serosa,Good,6,0,2189,Yes
541,hospital_3,Lev,49,Male,2592,No,No,No,Moderate,Serosa,Good,8,0,2472,Yes
542,hospital_1,Lev,70,Male,2571.2,No,No,No,Moderate,Serosa,Good,3,0,2111,Yes
543,hospital_3,Lev,55,Male,2508.8,No,No,No,Poor,Serosa,Good,7,0,2020,Yes
544,hospital_4,Lev,46,Female,2480,No,Yes,No,Moderate,Serosa,Bad,7,1,1161,Yes
545,hospital_1,Obs,77,Male,2476.8,No,No,No,Moderate,Serosa,Good,1,0,2231,Yes
546,hospital_2,Lev,57,Male,2476.8,No,No,No,Moderate,Serosa,Good,4,0,2300,Yes
547,hospital_3,Obs,68,Male,2464,No,No,No,Moderate,Serosa,Good,1,0,2391,Yes
548,hospital_2,Lev,62,Male,2459.2,No,No,Yes,Moderate,Serosa,Bad,1,1,759,No
549,hospital_2,Obs,44,Male,2448,No,No,No,Poor,Serosa,Bad,7,1,166,No
550,hospital_1,Lev,58,Female,2433.6,No,No,Yes,Moderate,Serosa,Good,1,0,2323,Yes
551,hospital_3,Lev,75,Female,2417.6,No,No,No,Well,Serosa,Good,2,0,2321,Yes
552,hospital_1,Obs,70,Female,2414.4,Yes,No,No,Moderate,Serosa,Good,7,0,1976,Yes
553,hospital_4,Obs,82,Male,2392,No,No,No,Moderate,Serosa,Good,6,0,2229,No
554,hospital_3,Lev,61,Female,2371.2,No,No,No,Moderate,Serosa,Bad,3,1,1671,Yes
555,hospital_2,Obs,52,Male,2358.4,No,No,No,Moderate,Serosa,Good,2,1,1907,Yes
556,hospital_4,Lev,47,Male,2315.2,No,No,No,Poor,Serosa,Bad,7,1,1783,Yes
557,hospital_4,Lev,61,Female,2313.6,No,No,No,Poor,Serosa,Bad,7,1,684,Yes
558,hospital_3,Lev,39,Male,2302.4,No,No,No,Moderate,Serosa,Bad,9,1,441,Yes
559,hospital_2,Lev,69,Female,2299.2,No,No,No,Moderate,Muscle,Bad,3,1,902,No
560,hospital_4,Lev,55,Male,2294.4,No,No,No,Poor,Serosa,Good,7,0,2244,Yes
561,hospital_1,Obs,56,Female,2294.4,Yes,No,No,Moderate,Serosa,Good,1,0,2170,Yes
562,hospital_3,Obs,80,Male,2278.4,No,No,No,Poor,Serosa,Good,1,0,1870,No
563,hospital_3,Lev,43,Male,2273.6,No,No,No,Moderate,Serosa,Good,4,0,2066,No
564,hospital_4,Lev,83,Male,2248,No,No,No,Moderate,Serosa,Good,2,0,2030,Yes
565,hospital_4,Obs,85,Female,2238.4,No,No,No,Well,Serosa,Bad,1,1,253,Yes
566,hospital_1,Lev,61,Male,2220.8,No,No,No,Moderate,Serosa,Good,1,0,2047,No
567,hospital_3,Lev,47,Female,2219.2,No,No,No,Moderate,Adjacent structures,Bad,9,1,1768,Yes
568,hospital_4,Lev,76,Female,2200,No,No,No,Moderate,Serosa,Good,1,0,2043,Yes
569,hospital_2,Lev,58,Male,2184,No,No,No,Moderate,Serosa,Good,1,0,2162,Yes
570,hospital_1,Obs,59,Male,2180.8,No,No,No,Moderate,Muscle,Good,2,0,2093,Yes
571,hospital_4,Lev,60,Female,2123.2,No,No,No,Moderate,Serosa,Good,1,0,2310,No
572,hospital_4,Lev,32,Male,2120,No,No,No,Moderate,Serosa,Good,2,1,2128,No
573,hospital_5,Obs,56,Male,2102.4,No,No,No,NA,Adjacent structures,Good,1,1,1884,No
574,hospital_5,Lev,75,Male,2100.8,No,No,No,Moderate,Serosa,Bad,2,1,219,No
575,hospital_4,Lev,62,Male,2089.6,No,No,No,Well,Serosa,Good,3,0,2309,No
576,hospital_4,Obs,69,Female,2086.4,No,No,No,Moderate,Serosa,Bad,12,1,313,Yes
577,hospital_5,Lev,72,Male,2083.2,No,No,Yes,Moderate,Serosa,Bad,4,1,546,Yes
578,hospital_2,Obs,54,Female,2072,No,No,No,Moderate,Serosa,Good,1,1,2213,Yes
579,hospital_5,Lev,72,Male,2072,No,No,No,Moderate,Serosa,Good,1,1,1856,No
580,hospital_2,Lev,27,Female,2064,No,No,No,Moderate,Serosa,NA,1,0,1421,No
581,hospital_4,Lev,62,Female,2046.4,No,No,No,Moderate,Adjacent structures,Bad,4,1,460,Yes
582,hospital_1,Lev,39,Female,2046.4,No,No,No,Moderate,Submucosa,Good,2,0,2173,No
583,hospital_5,Lev,60,Female,2041.6,No,No,No,Moderate,Serosa,Bad,4,1,283,Yes
584,hospital_2,Lev,55,Male,2041.6,Yes,No,No,Poor,Serosa,Bad,5,1,34,Yes
585,hospital_1,Obs,64,Male,2036.8,No,No,No,Moderate,Serosa,Good,1,0,2191,Yes
586,hospital_3,Lev,68,Female,2035.2,No,No,No,Poor,Serosa,Good,12,0,2186,No
587,hospital_3,Obs,65,Female,2019.2,No,No,No,Moderate,Serosa,Bad,2,1,1159,Yes
588,hospital_4,Lev,66,Female,2019.2,Yes,No,No,Well,Serosa,Bad,3,1,616,Yes
589,hospital_1,Lev,39,Male,2003.2,No,No,No,Poor,Serosa,Good,2,0,2270,Yes
590,hospital_1,Lev,54,Male,1993.6,No,No,Yes,Moderate,Serosa,Bad,NA,1,1061,No
591,hospital_5,Obs,34,Male,1993.6,Yes,No,No,Moderate,Serosa,Bad,1,1,591,No
592,hospital_4,Lev,74,Female,1979.2,No,No,No,Moderate,Serosa,Good,4,0,2065,Yes
593,hospital_2,Obs,81,Female,1968,Yes,No,Yes,Moderate,Serosa,Bad,3,1,208,Yes
594,hospital_1,Lev,38,Male,1950.4,No,No,No,Moderate,Serosa,Good,1,0,2360,Yes
595,hospital_2,Lev,59,Female,1945.6,No,No,No,Moderate,Serosa,Good,3,0,2312,Yes
596,hospital_2,Lev,62,Female,1944,No,No,No,Moderate,Serosa,Good,1,0,1902,Yes
597,hospital_2,Lev,61,Male,1939.2,Yes,No,No,Well,Muscle,NA,5,0,1814,Yes
598,hospital_1,Obs,46,Male,1934.4,Yes,No,No,Moderate,Serosa,Good,2,0,2111,No
599,hospital_1,Lev,55,Male,1931.2,No,No,No,Moderate,Serosa,Good,1,0,2330,Yes
600,hospital_4,Obs,57,Male,1921.6,No,No,No,Moderate,Serosa,Good,5,0,2202,Yes
601,hospital_4,Lev,38,Female,1916.8,No,No,No,Moderate,Serosa,Bad,3,1,1295,No
602,hospital_5,Lev,69,Female,1912,No,No,Yes,Moderate,Serosa,Good,19,0,1918,Yes
603,hospital_4,Obs,79,Female,1908.8,No,No,Yes,Moderate,Muscle,Good,6,0,2066,Yes
604,hospital_4,Lev,71,Female,1905.6,No,No,No,Moderate,Serosa,Good,2,0,2136,No
605,hospital_2,Lev,74,Male,1897.6,No,No,No,Poor,Serosa,Good,1,0,2289,Yes
606,hospital_4,Lev,30,Female,1884.8,Yes,No,No,Moderate,Serosa,Bad,12,1,576,Yes
607,hospital_1,Obs,72,Female,1884.8,No,No,No,Poor,Serosa,Good,1,0,2232,Yes
608,hospital_5,Lev,68,Female,1865.6,NA,No,No,Moderate,Serosa,Bad,3,1,1034,Yes
609,hospital_2,Lev,66,Male,1857.6,Yes,No,No,Poor,Submucosa,Bad,NA,1,583,Yes
610,hospital_5,Obs,63,Male,1854.4,No,No,No,Moderate,Serosa,Bad,4,1,417,Yes
611,hospital_3,Lev,52,Female,1846.4,No,No,Yes,Moderate,Serosa,Good,1,0,1905,Yes
612,hospital_4,Lev,64,Male,1841.6,No,No,No,Moderate,Serosa,Bad,4,1,875,Yes
613,hospital_5,Lev,68,Male,1832,No,No,No,Moderate,Serosa,Bad,1,1,674,Yes
614,hospital_3,Lev,74,Male,1832,No,No,No,Moderate,Serosa,Good,2,0,2185,Yes
615,hospital_5,Lev,32,Male,1822.4,No,No,No,Moderate,Serosa,Bad,2,1,1151,No
616,hospital_4,Lev,60,Female,1820.8,Yes,No,No,Moderate,Serosa,Bad,6,1,342,Yes
617,hospital_5,Obs,39,Female,1817.6,No,No,No,Moderate,Serosa,Good,8,0,2187,Yes
618,hospital_5,Obs,76,Female,1816,No,No,Yes,Moderate,Serosa,Bad,5,1,1434,Yes
619,hospital_5,Lev,62,Male,1814.4,No,No,No,Poor,Serosa,Bad,14,1,430,Yes
620,hospital_4,Lev,41,Female,1812.8,No,No,Yes,Moderate,Serosa,Good,2,0,2339,Yes
621,hospital_5,Lev,35,Male,1795.2,No,No,Yes,Moderate,Serosa,Bad,2,1,1365,Yes
622,hospital_4,Obs,59,Female,1787.2,No,No,No,Moderate,Serosa,Good,1,0,2313,No
623,hospital_3,Obs,52,Male,1779.2,No,No,No,Moderate,Serosa,Good,2,0,2153,Yes
624,hospital_2,Lev,65,Male,1768,No,No,No,Moderate,Serosa,Good,1,0,2164,No
625,hospital_3,Lev,61,Male,1764.8,No,No,No,Poor,Serosa,Good,3,0,2180,No
626,hospital_4,Obs,61,Male,1761.6,No,No,No,Well,Serosa,Good,8,0,2110,Yes
627,hospital_1,Obs,59,Male,1747.2,No,No,No,Well,Serosa,Good,1,0,2139,Yes
628,hospital_1,Lev,59,Male,1732.8,No,No,No,NA,Serosa,Good,2,0,2120,No
629,hospital_3,Lev,71,Female,1726.4,No,No,No,Moderate,Muscle,Good,5,1,2197,Yes
630,hospital_2,Obs,39,Male,1712,No,No,No,Poor,Adjacent structures,NA,4,0,453,No
631,hospital_4,Obs,65,Male,1697.6,No,No,No,Poor,Serosa,Bad,7,1,928,No
632,hospital_2,Lev,34,Female,1688,Yes,No,No,Well,Serosa,Bad,3,1,675,Yes
633,hospital_1,Obs,51,Male,1676.8,Yes,No,No,Moderate,Serosa,Good,4,0,2170,No
634,hospital_3,Obs,55,Female,1673.6,No,No,No,Moderate,Serosa,Good,3,0,2138,Yes
635,hospital_4,Lev,65,Male,1665.6,No,Yes,No,Poor,Serosa,Good,3,0,2255,Yes
636,hospital_1,Lev,71,Female,1659.2,No,No,No,Well,Serosa,Good,NA,0,2290,No
637,hospital_2,Lev,68,Female,1654.4,Yes,No,No,Moderate,Serosa,Bad,1,1,1055,Yes
638,hospital_2,Lev,59,Female,1649.6,No,No,No,Moderate,Serosa,Good,1,0,2347,No
639,hospital_4,Lev,66,Male,1635.2,No,No,No,Poor,Serosa,Bad,2,1,573,No
640,hospital_4,Obs,62,Male,1633.6,No,No,No,Moderate,Muscle,Good,2,0,2200,No
641,hospital_5,Lev,40,Female,1628.8,No,No,No,Moderate,Serosa,Bad,8,1,550,Yes
642,hospital_5,Lev,64,Male,1595.2,No,No,No,Poor,Serosa,Bad,3,1,1405,Yes
643,hospital_4,Lev,40,Male,1595.2,Yes,No,No,Poor,Serosa,Good,2,0,2355,No
644,hospital_4,Lev,70,Male,1588.8,No,No,No,Moderate,Serosa,Bad,9,1,1186,Yes
645,hospital_2,Lev,66,Male,1577.6,No,No,Yes,NA,Adjacent structures,Bad,1,1,1540,Yes
646,hospital_3,Lev,73,Male,1563.2,Yes,No,Yes,Moderate,Muscle,Good,1,0,2229,Yes
647,hospital_3,Lev,69,Male,1561.6,No,No,No,Well,Serosa,Bad,1,1,969,Yes
648,hospital_3,Obs,58,Male,1550.4,No,No,No,Moderate,Serosa,Bad,1,1,465,Yes
649,hospital_4,Lev,55,Female,1548.8,No,No,No,Moderate,Serosa,Bad,6,1,603,No
650,hospital_5,Lev,53,Female,1545.6,No,No,Yes,Moderate,Serosa,Bad,6,1,997,No
651,hospital_1,Lev,52,Male,1540.8,No,No,No,Moderate,Serosa,Good,2,0,2274,Yes
652,hospital_3,Obs,62,Female,1537.6,No,No,No,Moderate,Serosa,Bad,3,1,563,Yes
653,hospital_3,Lev,61,Male,1537.6,No,No,Yes,Moderate,Serosa,Bad,2,1,1434,Yes
654,hospital_4,Lev,69,Female,1537.6,No,No,No,Moderate,Serosa,Good,3,0,2316,Yes
655,hospital_5,Lev,41,Female,1537.6,Yes,No,No,Moderate,Serosa,Bad,3,1,692,Yes
656,hospital_5,Obs,64,Male,1531.2,No,No,No,Moderate,Serosa,Bad,3,1,1447,No
657,hospital_5,Lev,59,Female,1523.2,No,No,No,Poor,Serosa,Bad,3,1,428,Yes
658,hospital_2,Lev,65,Female,1518.4,No,No,No,Well,Muscle,Good,1,0,2152,Yes
659,hospital_4,Obs,66,Male,1510.4,No,No,No,Moderate,Serosa,Bad,3,1,854,Yes
660,hospital_1,Lev,60,Male,1507.2,No,No,No,Moderate,Serosa,Good,2,0,2093,Yes
661,hospital_2,Lev,43,Male,1504,No,No,Yes,Moderate,Serosa,Good,1,0,2198,No
662,hospital_2,Obs,74,Male,1502.4,No,No,No,Poor,Serosa,Good,1,0,2126,Yes
663,hospital_1,Lev,67,Male,1500.8,No,No,No,Poor,Muscle,Good,2,0,2189,Yes
664,hospital_1,Lev,46,Male,1497.6,Yes,No,No,Moderate,Serosa,Good,1,0,2142,Yes
665,hospital_2,Lev,47,Male,1486.4,No,No,No,Moderate,Muscle,Good,3,0,2278,Yes
666,hospital_3,Obs,66,Male,1484.8,Yes,No,No,Moderate,Serosa,Bad,4,1,622,Yes
667,hospital_2,Lev,36,Female,1478.4,Yes,No,No,Moderate,Serosa,Good,6,0,2192,Yes
668,hospital_3,Obs,62,Male,1465.6,Yes,No,No,Poor,Serosa,Bad,2,1,413,No
669,hospital_5,Lev,69,Female,1457.6,No,No,No,Moderate,Serosa,Bad,2,1,1191,Yes
670,hospital_3,Obs,76,Female,1454.4,No,No,No,Well,Serosa,Bad,3,1,1139,Yes
671,hospital_5,Lev,36,Male,1448,Yes,No,No,Poor,Serosa,Bad,2,1,165,Yes
672,hospital_1,Obs,73,Male,1448,No,No,Yes,NA,Muscle,Good,3,0,2210,Yes
673,hospital_3,Obs,72,Male,1443.2,No,No,No,Moderate,Muscle,Good,5,0,2227,Yes
674,hospital_4,Lev,61,Male,1441.6,No,No,No,Moderate,Muscle,Good,1,0,2076,No
675,hospital_1,Obs,58,Male,1424,No,No,No,Moderate,Serosa,Good,2,0,2132,Yes
676,hospital_2,Lev,52,Female,1419.2,Yes,No,No,Well,Serosa,Good,3,0,2084,Yes
677,hospital_3,Obs,66,Male,1419.2,No,No,No,Moderate,Adjacent structures,Good,2,0,1905,No
678,hospital_3,Lev,43,Female,1419.2,No,No,No,Moderate,Serosa,Bad,7,1,608,Yes
679,hospital_1,Lev,60,Male,1416,No,No,No,Moderate,Muscle,Good,1,0,2066,Yes
680,hospital_3,Obs,61,Female,1414.4,No,No,No,Moderate,Serosa,Good,4,0,2006,Yes
681,hospital_4,Lev,61,Male,1412.8,No,No,No,Moderate,Muscle,Good,3,0,2183,Yes
682,hospital_2,Lev,46,Male,1400,No,No,No,Moderate,Submucosa,Good,1,0,2099,Yes
683,hospital_3,Lev,73,Male,1398.4,No,No,No,Moderate,Serosa,Good,1,0,1879,Yes
684,hospital_4,Obs,65,Female,1380.8,Yes,No,No,Moderate,Serosa,Bad,3,1,264,Yes
685,hospital_2,Lev,61,Female,1379.2,Yes,No,No,Moderate,Serosa,Good,1,0,2107,No
686,hospital_5,Lev,65,Male,1372.8,No,No,No,Poor,Serosa,Bad,15,1,743,No
687,hospital_3,Obs,68,Male,1366.4,No,No,No,Moderate,Serosa,Bad,2,1,966,Yes
688,hospital_4,Lev,63,Male,1353.6,No,No,No,Moderate,Serosa,Good,1,0,2076,Yes
689,hospital_5,Lev,72,Male,1352,Yes,No,No,Poor,Serosa,Bad,4,1,24,No
690,hospital_4,Lev,74,Female,1350.4,No,Yes,No,Moderate,Serosa,Bad,2,1,448,Yes
691,hospital_5,Lev,45,Female,1344,No,No,No,Moderate,Serosa,Bad,9,1,909,Yes
692,hospital_3,Lev,66,Female,1332.8,No,No,No,Well,Serosa,Good,1,0,2188,No
693,hospital_3,Obs,38,Male,1332.8,NA,No,No,NA,Adjacent structures,Bad,2,1,469,Yes
694,hospital_2,Obs,46,Female,1331.2,No,Yes,Yes,Poor,Adjacent structures,Bad,2,1,594,Yes
695,hospital_1,Lev,51,Female,1297.6,No,No,No,Moderate,Serosa,Good,1,0,2008,Yes
696,hospital_2,Lev,65,Male,1289.6,No,No,No,Moderate,Adjacent structures,Bad,3,1,1276,No
697,hospital_3,Obs,55,Male,1289.6,No,No,No,Moderate,Serosa,Bad,2,1,957,Yes
698,hospital_4,Obs,58,Male,1283.2,No,No,No,Moderate,Serosa,Good,4,1,1950,Yes
699,hospital_1,Lev,65,Male,1283.2,No,No,No,Moderate,Serosa,Good,1,0,2668,No
700,hospital_1,Lev,46,Female,1275.2,Yes,No,No,Well,Serosa,Good,2,0,2250,Yes
701,hospital_1,Lev,57,Female,1272,No,No,No,Moderate,Serosa,Good,1,0,2191,Yes
702,hospital_2,Obs,67,Male,1240,No,No,Yes,Moderate,Serosa,Good,1,0,2365,Yes
703,hospital_1,Lev,75,Female,1238.4,No,No,No,Moderate,Serosa,Good,1,0,2331,Yes
704,hospital_5,Lev,64,Male,1232,No,No,Yes,Moderate,Serosa,Bad,5,1,1262,Yes
705,hospital_2,Lev,63,Male,1225.6,No,No,No,Moderate,Serosa,NA,2,0,1537,Yes
706,hospital_1,Lev,64,Male,1224,No,No,No,Moderate,Serosa,Good,3,0,2387,No
707,hospital_1,Obs,59,Male,1222.4,No,No,No,Moderate,Serosa,Bad,1,0,2235,Yes
708,hospital_3,Lev,78,Female,1217.6,No,No,No,Moderate,Serosa,Bad,3,0,1980,Yes
709,hospital_4,Lev,63,Male,1216,No,No,No,Well,Submucosa,Bad,2,1,578,Yes
710,hospital_3,Obs,57,Male,1214.4,No,No,No,Well,Serosa,Bad,4,1,1083,Yes
711,hospital_5,Obs,52,Male,1214.4,No,No,No,Moderate,Serosa,Good,3,1,1875,No
712,hospital_1,Obs,50,Female,1212.8,No,No,No,Moderate,Serosa,Good,3,0,2384,No
713,hospital_2,Obs,36,Male,1208,Yes,No,No,Moderate,Serosa,Bad,3,1,563,Yes
714,hospital_1,Lev,50,Female,1204.8,No,No,No,Poor,Serosa,Good,4,0,2076,Yes
715,hospital_1,Obs,53,Male,1188.8,No,No,No,Moderate,Serosa,Good,1,0,2025,Yes
716,hospital_4,Lev,48,Male,1188.8,No,No,No,Moderate,Serosa,Good,7,0,2055,Yes
717,hospital_3,Obs,55,Female,1182.4,No,No,No,Moderate,Serosa,Bad,2,1,1375,No
718,hospital_2,Lev,47,Male,1177.6,No,No,No,Moderate,Muscle,Good,2,0,2350,Yes
719,hospital_1,Lev,74,Female,1168,No,No,Yes,Moderate,Serosa,Good,1,0,2200,Yes
720,hospital_3,Lev,53,Male,1166.4,Yes,Yes,No,Moderate,Serosa,Good,3,0,2222,Yes
721,hospital_2,Obs,55,Male,1156.8,No,No,No,Moderate,Serosa,Good,7,0,2149,Yes
722,hospital_3,Lev,53,Male,1153.6,No,No,No,Moderate,Muscle,Good,4,0,2303,No
723,hospital_3,Obs,67,Male,1152,No,Yes,No,Moderate,Adjacent structures,Bad,4,1,322,Yes
724,hospital_5,Lev,56,Male,1148.8,No,No,No,Moderate,Serosa,Bad,3,1,764,Yes
725,hospital_3,Lev,30,Female,1147.2,Yes,No,Yes,Moderate,Serosa,Bad,1,1,765,Yes
726,hospital_5,Lev,56,Female,1145.6,No,No,No,Well,Serosa,Bad,8,1,93,No
727,hospital_1,Lev,46,Male,1139.2,Yes,No,No,Moderate,Serosa,Good,3,0,1918,Yes
728,hospital_4,Lev,60,Male,1134.4,No,No,No,Well,Muscle,Good,1,1,1932,Yes
729,hospital_1,Obs,60,Female,1134.4,No,No,No,Moderate,Muscle,Good,1,0,2278,No
730,hospital_5,Obs,57,Male,1132.8,Yes,No,No,Poor,Serosa,Bad,9,1,145,Yes
731,hospital_1,Lev,42,Female,1129.6,No,No,No,Poor,Serosa,Good,1,0,1913,Yes
732,hospital_2,Obs,58,Male,1113.6,No,No,No,Moderate,Serosa,Good,2,0,2176,Yes
733,hospital_3,Obs,53,Female,1108.8,No,No,No,Poor,Serosa,Good,1,0,1995,No
734,hospital_4,Lev,73,Male,1107.2,No,No,Yes,Moderate,Serosa,Good,1,0,2023,Yes
735,hospital_2,Obs,73,Male,1107.2,No,No,No,Moderate,Serosa,Good,7,0,1935,Yes
736,hospital_2,Obs,57,Male,1107.2,No,No,No,Moderate,Serosa,Good,NA,0,2191,No
737,hospital_2,Lev,77,Female,1099.2,No,No,No,Poor,Serosa,Good,1,0,2191,No
738,hospital_1,Obs,60,Male,1096,No,No,No,NA,Serosa,Good,2,0,1856,Yes
739,hospital_4,Lev,74,Male,1094.4,No,No,No,Moderate,Serosa,Bad,8,1,628,Yes
740,hospital_2,Lev,70,Male,1084.8,No,No,No,Moderate,Muscle,Good,3,0,1976,Yes
741,hospital_1,Obs,53,Female,1080,No,No,No,Moderate,Serosa,NA,5,0,1819,No
742,hospital_4,Lev,68,Female,1078.4,No,No,Yes,Moderate,Adjacent structures,Bad,8,1,141,Yes
743,hospital_4,Lev,59,Male,1076.8,Yes,No,No,Moderate,Serosa,Bad,2,1,422,Yes
744,hospital_3,Lev,29,Male,1072,Yes,No,No,Moderate,Serosa,Good,3,0,2006,Yes
745,hospital_1,Obs,57,Female,1070.4,No,No,No,Moderate,Muscle,Good,2,0,2113,Yes
746,hospital_3,Obs,61,Male,1065.6,No,No,No,Moderate,Muscle,NA,4,0,1823,Yes
747,hospital_2,Obs,68,Male,1064,Yes,No,No,Moderate,Serosa,Good,3,0,2170,Yes
748,hospital_3,Lev,49,Male,1062.4,No,No,No,Moderate,Serosa,Bad,11,1,1812,No
749,hospital_1,Lev,70,Female,1060.8,No,No,No,Moderate,Serosa,Good,2,0,1838,Yes
750,hospital_1,Obs,53,Male,1054.4,Yes,No,No,Moderate,Serosa,Good,2,0,1855,Yes
751,hospital_2,Lev,57,Female,1035.2,Yes,No,No,Poor,Serosa,Bad,2,1,269,No
752,hospital_2,Lev,76,Male,1028.8,No,No,No,Moderate,Serosa,Good,1,0,2414,Yes
753,hospital_2,Lev,59,Male,1028.8,No,No,Yes,Moderate,Muscle,Good,2,0,2174,Yes
754,hospital_3,Lev,56,Male,1027.2,No,No,No,Moderate,Muscle,Good,1,0,2263,No
755,hospital_4,Lev,38,Male,1027.2,No,No,No,Moderate,Serosa,Bad,7,1,1495,Yes
756,hospital_4,Lev,61,Female,1025.6,No,No,No,Poor,Serosa,Bad,10,1,314,Yes
757,hospital_3,Obs,49,Male,1006.4,No,No,No,Moderate,Serosa,Good,2,0,2261,Yes
758,hospital_5,Obs,69,Female,1004.8,No,No,No,Moderate,Serosa,Bad,12,1,716,Yes
759,hospital_3,Obs,53,Male,995.2,No,No,No,Moderate,Serosa,Good,1,0,2190,No
760,hospital_2,Lev,72,Female,985.6,No,No,No,Poor,Serosa,Good,6,0,2331,No
761,hospital_1,Lev,57,Female,982.4,No,No,No,Moderate,Serosa,Good,3,0,2187,Yes
762,hospital_5,Lev,73,Male,979.2,No,No,Yes,Moderate,Adjacent structures,Bad,1,1,602,Yes
763,hospital_4,Lev,58,Female,974.4,No,No,Yes,Well,Serosa,Bad,2,1,666,Yes
764,hospital_5,Lev,69,Male,972.8,No,No,No,Moderate,Serosa,Bad,5,1,138,Yes
765,hospital_1,Obs,69,Female,964.8,Yes,No,No,Poor,Serosa,Good,3,0,2001,Yes
766,hospital_5,Lev,47,Female,963.2,No,No,No,Moderate,Serosa,Bad,5,1,887,Yes
767,hospital_2,Lev,80,Female,963.2,No,Yes,No,Well,Muscle,Bad,2,1,1207,Yes
768,hospital_1,Lev,67,Male,961.6,No,No,No,Moderate,Serosa,Good,2,0,1997,Yes
769,hospital_1,Lev,54,Female,958.4,No,No,No,Moderate,Serosa,Good,1,0,1864,Yes
770,hospital_3,Lev,63,Male,952,NA,No,Yes,Poor,Serosa,Good,7,0,2422,No
771,hospital_4,Lev,65,Female,950.4,Yes,No,No,Poor,Serosa,Bad,NA,1,444,No
772,hospital_4,Lev,65,Female,947.2,No,Yes,Yes,Moderate,Serosa,Good,3,0,2375,Yes
773,hospital_5,Obs,48,Female,945.6,Yes,No,No,Moderate,Serosa,Bad,2,1,718,Yes
774,hospital_1,Obs,52,Male,942.4,No,No,No,Moderate,Serosa,Good,2,0,2204,Yes
775,hospital_1,Obs,67,Male,939.2,No,No,Yes,Well,Serosa,Good,1,0,2120,No
776,hospital_5,Lev,70,Female,932.8,No,Yes,Yes,Well,Serosa,Bad,5,1,766,Yes
777,hospital_5,Lev,51,Female,931.2,Yes,No,Yes,Poor,Serosa,Bad,6,1,366,Yes
778,hospital_5,Lev,32,Female,928,No,No,No,Poor,Muscle,Bad,14,1,1752,Yes
779,hospital_1,Lev,63,Male,928,No,No,Yes,Poor,Serosa,Good,7,0,2183,Yes
780,hospital_1,Obs,81,Female,924.8,No,No,Yes,Moderate,Serosa,Good,1,0,1902,Yes
781,hospital_3,Obs,66,Male,921.6,No,No,No,Moderate,Adjacent structures,Bad,7,1,259,No
782,hospital_4,Lev,60,Female,921.6,No,Yes,Yes,Poor,Serosa,Bad,4,1,609,Yes
783,hospital_5,Obs,81,Female,916.8,Yes,No,No,Well,Serosa,Bad,1,1,832,Yes
784,hospital_2,Obs,66,Male,912,Yes,No,Yes,Moderate,Serosa,Bad,4,1,164,No
785,hospital_2,Lev,78,Male,910.4,No,No,Yes,Poor,Serosa,Good,2,0,1828,No
786,hospital_2,Lev,46,Female,900.8,No,No,No,Moderate,Serosa,Good,3,0,2147,Yes
787,hospital_4,Obs,57,Female,900.8,No,No,No,Moderate,Serosa,Bad,NA,1,201,Yes
788,hospital_1,Lev,57,Female,894.4,No,No,No,Moderate,Submucosa,Good,3,0,2221,No
789,hospital_2,Lev,67,Female,884.8,No,No,No,Poor,Serosa,Bad,2,1,503,Yes
790,hospital_5,Lev,68,Male,880,No,No,No,Moderate,Serosa,Bad,9,1,376,Yes
791,hospital_2,Lev,38,Female,873.6,No,No,No,Moderate,Serosa,Bad,2,1,811,Yes
792,hospital_1,Lev,70,Female,859.2,No,No,No,Moderate,Serosa,Good,3,0,1985,Yes
793,hospital_1,Obs,66,Male,846.4,No,No,No,Moderate,Serosa,Good,2,0,2076,No
794,hospital_4,Lev,63,Male,844.8,No,No,No,Moderate,Serosa,Bad,8,1,1154,No
795,hospital_5,Lev,72,Male,835.2,No,No,No,Moderate,Muscle,Bad,5,1,844,No
796,hospital_2,Obs,61,Male,819.2,Yes,No,Yes,Well,Serosa,Bad,1,1,1031,No
797,hospital_5,Lev,46,Male,816,No,No,No,Moderate,Serosa,Bad,3,1,1302,No
798,hospital_2,Lev,36,Female,809.6,Yes,No,No,Moderate,Serosa,Good,3,0,2169,Yes
799,hospital_2,Obs,69,Female,804.8,No,No,No,Moderate,Muscle,Good,1,0,1929,Yes
800,hospital_2,Lev,75,Female,798.4,No,No,No,Moderate,Serosa,NA,1,0,1800,Yes
801,hospital_5,Lev,64,Female,798.4,No,No,No,Moderate,Serosa,Bad,12,1,355,No
802,hospital_1,Lev,77,Male,798.4,No,No,No,Moderate,Serosa,Bad,1,1,669,No
803,hospital_3,Obs,51,Female,796.8,NA,No,No,Moderate,Muscle,Bad,7,1,465,Yes
804,hospital_4,Obs,36,Male,784,No,No,No,Moderate,Serosa,Bad,6,0,2157,No
805,hospital_5,Obs,56,Male,777.6,No,No,No,Moderate,Serosa,Bad,7,1,929,Yes
806,hospital_3,Lev,63,Female,776,No,No,No,Moderate,Serosa,Good,2,0,2242,Yes
807,hospital_2,Lev,64,Male,774.4,No,No,No,Moderate,Serosa,Good,2,0,2212,Yes
808,hospital_1,Lev,44,Male,760,No,No,No,Moderate,Serosa,Good,1,0,2148,No
809,hospital_5,Lev,73,Male,758.4,Yes,No,No,Moderate,Serosa,Bad,7,1,171,Yes
810,hospital_3,Lev,54,Male,755.2,Yes,No,No,Moderate,Adjacent structures,Bad,1,1,629,Yes
811,hospital_2,Lev,44,Male,750.4,No,No,No,Moderate,Muscle,Good,2,1,1829,Yes
812,hospital_2,Lev,30,Male,744,No,Yes,No,Moderate,Serosa,Good,9,0,1939,No
813,hospital_5,Lev,65,Female,744,No,No,No,Moderate,Serosa,Bad,7,1,529,No
814,hospital_2,Obs,68,Female,742.4,Yes,No,No,Moderate,Serosa,Good,2,0,2130,Yes
815,hospital_1,Obs,56,Female,739.2,No,No,No,NA,Submucosa,Good,2,0,1990,Yes
816,hospital_3,Obs,62,Male,736,No,No,No,Poor,Serosa,Bad,5,1,587,Yes
817,hospital_4,Lev,36,Female,734.4,No,No,No,Well,Muscle,Bad,7,1,1145,Yes
818,hospital_1,Lev,57,Male,726.4,No,No,No,Well,Serosa,Good,2,0,2162,Yes
819,hospital_5,Lev,31,Male,724.8,Yes,No,No,Well,Serosa,Bad,NA,1,643,Yes
820,hospital_4,Lev,39,Male,716.8,No,No,No,Moderate,Serosa,Good,11,0,2129,No
821,hospital_5,Obs,53,Male,710.4,No,No,No,Moderate,Serosa,Bad,3,1,1437,Yes
822,hospital_5,Lev,60,Male,708.8,Yes,No,No,Moderate,Adjacent structures,Bad,14,1,512,Yes
823,hospital_1,Obs,65,Female,705.6,Yes,No,No,Moderate,Serosa,Good,1,0,2008,No
824,hospital_1,Lev,61,Female,702.4,No,No,No,Moderate,Serosa,NA,1,0,1814,Yes
825,hospital_4,Lev,77,Female,700.8,Yes,No,Yes,Moderate,Serosa,Good,3,0,2161,Yes
826,hospital_5,Obs,48,Female,700.8,Yes,No,No,Moderate,Serosa,Bad,5,1,506,No
827,hospital_2,Obs,77,Male,699.2,No,No,No,Well,Serosa,Bad,3,1,1482,Yes
828,hospital_3,Lev,69,Male,692.8,No,No,No,Poor,Serosa,Good,10,0,1964,Yes
829,hospital_5,Lev,36,Female,688,No,No,No,Poor,Serosa,Bad,17,1,693,Yes
830,hospital_3,Lev,64,Female,688,No,No,Yes,Moderate,Serosa,Bad,4,1,592,Yes
831,hospital_2,Lev,70,Male,684.8,No,No,No,Moderate,Muscle,Good,1,0,2207,Yes
832,hospital_1,Lev,74,Female,675.2,No,No,No,Well,Serosa,Good,3,0,2181,Yes
833,hospital_5,Lev,63,Male,673.6,No,No,No,Poor,Serosa,Bad,9,1,696,Yes
834,hospital_5,Lev,52,Female,672,No,No,No,Poor,Serosa,Bad,10,1,257,Yes
835,hospital_5,Lev,54,Female,667.2,No,No,No,Moderate,Serosa,Good,3,1,2171,Yes
836,hospital_4,Lev,54,Female,660.8,Yes,No,No,Moderate,Serosa,Good,4,0,2209,Yes
837,hospital_5,Lev,41,Female,660.8,Yes,No,No,Well,Serosa,Bad,1,1,1122,No
838,hospital_5,Obs,61,Female,657.6,No,No,No,Moderate,Serosa,Bad,4,1,936,Yes
839,hospital_5,Lev,34,Female,654.4,Yes,No,No,Poor,Serosa,Bad,10,1,601,Yes
840,hospital_1,Obs,59,Female,649.6,Yes,No,No,Moderate,Serosa,Good,3,0,2265,Yes
841,hospital_1,Lev,57,Female,643.2,No,No,No,Moderate,Serosa,Good,3,0,2198,Yes
842,hospital_5,Obs,39,Female,640,No,No,No,Moderate,Muscle,Bad,6,1,275,Yes
843,hospital_3,Obs,74,Male,624,No,No,No,Poor,Serosa,Bad,3,1,1195,Yes
844,hospital_4,Lev,45,Male,622.4,Yes,No,No,Moderate,Serosa,Good,3,0,2099,No
845,hospital_1,Lev,66,Female,614.4,No,No,No,Moderate,Serosa,Good,1,0,2165,Yes
846,hospital_4,Lev,60,Male,614.4,No,No,No,Moderate,Serosa,Good,2,0,2212,No
847,hospital_5,Lev,73,Male,611.2,No,No,No,Moderate,Serosa,Bad,1,1,355,No
848,hospital_1,Obs,76,Female,609.6,No,No,No,Poor,Muscle,Good,1,0,2097,Yes
849,hospital_2,Lev,79,Female,601.6,Yes,No,No,Moderate,Serosa,Bad,1,1,56,Yes
850,hospital_5,Lev,47,Male,595.2,Yes,No,No,Moderate,Serosa,Bad,4,1,589,No
851,hospital_5,Obs,60,Male,585.6,No,No,No,Moderate,Serosa,Bad,9,1,510,Yes
852,hospital_1,Lev,62,Female,584,No,No,No,Moderate,Serosa,Good,3,0,2250,No
853,hospital_5,Obs,18,Male,580.8,Yes,No,Yes,Poor,Muscle,Bad,4,1,612,No
854,hospital_4,Lev,75,Female,579.2,No,No,No,Moderate,Serosa,Good,5,0,2284,Yes
855,hospital_2,Lev,56,Female,569.6,No,No,No,Well,Serosa,Good,3,0,1992,No
856,hospital_4,Lev,70,Male,568,Yes,No,No,Moderate,Muscle,Bad,5,1,251,Yes
857,hospital_2,Lev,54,Female,568,No,No,No,NA,Muscle,Good,3,0,2070,Yes
858,hospital_5,Obs,64,Female,558.4,No,No,No,Moderate,Serosa,Bad,1,1,1209,No
859,hospital_3,Lev,63,Female,548.8,No,No,No,Moderate,Serosa,Good,1,0,2122,Yes
860,hospital_3,Lev,52,Female,547.2,Yes,No,No,Well,Serosa,Good,2,0,2111,No
861,hospital_5,Lev,71,Female,544,No,No,Yes,Moderate,Serosa,Bad,11,1,274,No
862,hospital_3,Lev,54,Female,529.6,No,No,No,Well,Serosa,Bad,1,1,1306,No
863,hospital_3,Lev,27,Female,521.6,No,No,No,Poor,Muscle,Bad,1,1,1325,Yes
864,hospital_2,Obs,68,Male,518.4,No,No,Yes,Moderate,Serosa,Good,10,0,1968,Yes
865,hospital_4,Obs,49,Female,516.8,No,No,No,Moderate,Serosa,Bad,2,1,753,No
866,hospital_4,Obs,61,Male,515.2,No,No,Yes,Moderate,Adjacent structures,Bad,2,1,760,Yes
867,hospital_2,Lev,40,Male,505.6,No,No,No,Moderate,Serosa,Good,16,0,1861,Yes
868,hospital_5,Lev,38,Male,502.4,No,No,No,Well,Serosa,Bad,3,1,1138,Yes
869,hospital_1,Obs,56,Female,502.4,No,No,No,Moderate,Muscle,Good,2,0,2167,Yes
870,hospital_4,Lev,79,Male,500.8,Yes,No,No,Moderate,Serosa,Good,1,0,1944,Yes
871,hospital_4,Obs,55,Female,497.6,No,No,No,Poor,Serosa,Bad,13,1,372,No
872,hospital_4,Obs,59,Female,486.4,No,No,No,Moderate,Submucosa,NA,1,0,1792,No
873,hospital_5,Lev,36,Male,483.2,No,No,Yes,Moderate,Serosa,Bad,4,1,986,Yes
874,hospital_4,Lev,63,Female,468.8,Yes,No,No,Moderate,Serosa,Good,2,0,2378,Yes
875,hospital_5,Obs,27,Male,462.4,No,Yes,Yes,Moderate,Serosa,Bad,12,1,390,Yes
876,hospital_5,Lev,66,Female,452.8,No,No,Yes,Moderate,Muscle,Bad,2,1,642,Yes
877,hospital_3,Lev,63,Male,452.8,No,No,No,Moderate,Serosa,Good,3,0,2345,Yes
878,hospital_4,Lev,64,Male,446.4,No,No,No,Moderate,Muscle,Good,2,0,2254,No
879,hospital_1,Obs,78,Female,441.6,No,No,No,Moderate,Serosa,Good,1,0,2118,Yes
880,hospital_5,Lev,66,Female,440,Yes,No,No,Moderate,Serosa,Good,2,1,1831,Yes
881,hospital_2,Obs,64,Female,438.4,No,No,No,Poor,Serosa,Good,3,0,2167,Yes
882,hospital_4,Obs,68,Male,433.6,No,No,No,Moderate,Serosa,Bad,2,1,1198,Yes
883,hospital_2,Lev,35,Male,430.4,No,No,Yes,Moderate,Serosa,Good,2,0,2050,No
884,hospital_4,Lev,64,Male,422.4,No,No,Yes,Poor,Serosa,Bad,2,1,271,Yes
885,hospital_1,Lev,60,Female,414.4,No,No,No,Moderate,Muscle,Good,2,0,2138,Yes
886,hospital_3,Obs,55,Female,414.4,Yes,No,No,Moderate,Serosa,Good,1,0,1891,Yes
887,hospital_5,Obs,39,Male,411.2,No,No,No,Moderate,Serosa,Bad,6,1,1313,Yes
888,hospital_3,Lev,67,Male,404.8,No,No,No,Moderate,Serosa,Good,3,0,1853,Yes
889,hospital_1,Lev,55,Male,401.6,Yes,No,No,Poor,Serosa,Good,3,0,2130,Yes
890,hospital_5,Lev,61,Male,387.2,No,No,No,Moderate,Serosa,Bad,8,1,150,No
891,hospital_5,Lev,67,Female,385.6,No,No,No,Moderate,Serosa,Bad,6,1,759,Yes
892,hospital_3,Lev,62,Male,380.8,No,No,No,Moderate,Serosa,Good,1,0,2100,Yes
893,hospital_1,Lev,52,Female,371.2,No,No,No,Moderate,Serosa,Good,4,0,2101,Yes
894,hospital_5,Lev,80,Male,361.6,No,No,No,Moderate,Serosa,Bad,1,1,942,Yes
895,hospital_4,Obs,51,Female,355.2,No,No,No,Moderate,Serosa,NA,11,0,1823,Yes
896,hospital_5,Obs,74,Male,350.4,No,No,No,Poor,Submucosa,Bad,1,1,665,No
897,hospital_5,Obs,70,Male,350.4,Yes,Yes,Yes,Moderate,Serosa,Bad,4,1,1656,Yes
898,hospital_1,Lev,73,Male,348.8,No,No,No,Moderate,Serosa,Good,4,0,1969,Yes
899,hospital_3,Lev,56,Female,344,No,No,No,Poor,Adjacent structures,Good,3,0,2190,No
900,hospital_5,Obs,70,Female,332.8,Yes,No,No,Moderate,Serosa,Bad,15,1,576,Yes
901,hospital_5,Lev,68,Male,329.6,No,No,No,Moderate,Serosa,Bad,12,1,1103,Yes
902,hospital_5,Lev,51,Male,321.6,No,No,Yes,Poor,Serosa,Bad,2,1,1046,Yes
903,hospital_1,Lev,56,Female,305.6,No,No,No,Moderate,Serosa,Good,3,0,1929,Yes
904,hospital_5,Obs,71,Male,299.2,No,No,No,Well,Serosa,Bad,10,1,125,Yes
905,hospital_2,Obs,63,Female,297.6,No,No,No,Well,Serosa,Bad,2,1,187,Yes
906,hospital_2,Lev,82,Male,273.6,NA,No,No,Moderate,Serosa,Bad,3,1,939,Yes
907,hospital_4,Obs,68,Female,273.6,No,No,No,Poor,Serosa,Bad,12,1,887,No
908,hospital_2,Obs,55,Female,265.6,No,No,No,Moderate,Serosa,NA,2,0,1803,No
909,hospital_1,Lev,71,Male,264,No,No,No,Moderate,Serosa,Good,1,0,2018,Yes
910,hospital_5,Lev,68,Female,262.4,No,No,Yes,Moderate,Serosa,Bad,12,1,1509,Yes
911,hospital_5,Lev,60,Male,240,No,No,No,Moderate,Serosa,Bad,13,1,314,No
912,hospital_1,Lev,72,Male,232,No,No,No,Well,Serosa,Good,3,0,1971,Yes
913,hospital_2,Lev,69,Male,230.4,Yes,Yes,Yes,Moderate,Serosa,Good,3,0,1827,Yes
914,hospital_5,Obs,46,Female,225.6,Yes,No,No,Moderate,Muscle,Bad,3,1,1079,No
915,hospital_4,Lev,42,Female,220.8,No,No,No,Well,Serosa,Good,6,0,2114,Yes
916,hospital_5,Obs,37,Female,212.8,No,No,No,Moderate,Serosa,Bad,8,1,1262,Yes
917,hospital_4,Obs,71,Female,206.4,No,No,No,Poor,Serosa,Bad,5,1,259,Yes
918,hospital_3,Lev,50,Male,203.2,No,No,No,Moderate,Muscle,Bad,7,1,323,No
919,hospital_5,Lev,70,Female,200,Yes,No,Yes,Moderate,Muscle,Bad,1,1,678,Yes
920,hospital_4,Lev,70,Female,195.2,No,No,Yes,Moderate,Muscle,Good,5,0,1827,Yes
921,hospital_5,Lev,58,Male,180.8,Yes,No,No,Moderate,Adjacent structures,Bad,3,1,1399,No
922,hospital_4,Lev,70,Female,148.8,No,No,No,Moderate,Serosa,Bad,1,1,1022,Yes
923,hospital_4,Lev,55,Male,126.4,Yes,No,No,Poor,Serosa,Good,2,0,2240,Yes
924,hospital_1,Obs,64,Male,89.6,Yes,No,Yes,Poor,Serosa,Good,2,0,2158,Yes
925,hospital_4,Lev,71,Male,83.2,No,No,Yes,Moderate,Serosa,Good,4,0,1875,Yes
926,hospital_2,Lev,72,Female,72,No,No,No,Moderate,Serosa,Good,1,0,2154,Yes
927,hospital_2,Lev,76,Male,54.4,No,No,Yes,Poor,Serosa,Bad,1,1,1018,Yes
928,hospital_2,Lev,48,Female,38.4,Yes,No,No,Moderate,Serosa,Good,4,0,2072,No
929,hospital_2,Lev,66,Female,36.8,Yes,No,No,Moderate,Serosa,NA,1,0,1820,Yes
radiant-master/DESCRIPTION
View file @ d3861e30
......@@ -14,7 +14,8 @@ Depends:
radiant.design (>= 1.6.6),
radiant.basics (>= 1.6.6),
radiant.model (>= 1.6.6),
radiant.multivariate (>= 1.6.6)
radiant.multivariate (>= 1.6.6),
radiant.quickgen
Imports:
shiny (>= 1.8.1),
import (>= 1.1.0),
......
radiant-master/inst/app/global.R
View file @ d3861e30
......@@ -42,7 +42,6 @@ source(file.path(getOption("radiant.path.design"), "app/init.R"), encoding = get
source(file.path(getOption("radiant.path.basics"), "app/init.R"), encoding = getOption("radiant.encoding"), local = TRUE)
source(file.path(getOption("radiant.path.model"), "app/init.R"), encoding = getOption("radiant.encoding"), local = TRUE)
source(file.path(getOption("radiant.path.multivariate"), "app/init.R"), encoding = getOption("radiant.encoding"), local = TRUE)
# 添加quickgen模块的init.R
source(file.path(getOption("radiant.path.quickgen"), "app/init.R"), encoding = getOption("radiant.encoding"), local = TRUE)
options(radiant.url.patterns = make_url_patterns())
......
radiant-master/inst/translations/translation_zh.csv
View file @ d3861e30
......@@ -20,7 +20,7 @@ Sample size:,样本大小：,"clt_ui.R,sampling_ui.R"
# of samples:,样本数量：,clt_ui.R
Number of bins:,分箱数量：,"clt_ui.R,visualize_ui.R"
Central Limit Theorem,中心极限定理,"clt_ui.R,init.R"
Basics > Probability,基础 > 概率,clt_ui.R
Basics > Probability,基础统计 > 概率,clt_ui.R
Please choose a sample size larger than 2,请选择一个大于 2 的样本大小,clt_ui.R
Please choose 2 or more samples,请选择 2 个或更多样本,clt_ui.R
Please choose a minimum value for the uniform distribution,请为均匀分布选择一个最小值,clt_ui.R
......@@ -61,9 +61,9 @@ Wilcox,Wilcoxon 检验,compare_means_ui.R
Select plots:,选择绘图类型：,compare_means_ui.R
Select plots,选择绘图,compare_means_ui.R
Compare means,均值比较,"compare_means_ui.R,init.R"
Summary,摘要,"compare_means_ui.R,doe_ui.R, randomizer_ui.R, sample_size_comp.R, sample_size_ui.R, sampling_ui.R,gbt_ui.R,conjoint_ui.R"
Plot,图形,"compare_means_ui.R,sample_size_comp.R,dtree_ui.R, gbt_ui.R,conjoint_ui.R"
Basics > Means,基础 > 均值,compare_means_ui.R
Summary,摘要,"compare_means_ui.R,doe_ui.R, randomizer_ui.R, sample_size_comp.R, sample_size_ui.R, sampling_ui.R,gbt_ui.R,conjoint_ui.R,crtree_ui.R,crs_ui.R"
Plot,图形,"compare_means_ui.R,sample_size_comp.R,dtree_ui.R, gbt_ui.R,conjoint_ui.R,crtree_ui.R,crs_ui.R"
Basics > Means,基础统计 > 均值,compare_means_ui.R
"This analysis requires at least two variables. The first can be of type
factor, numeric, or interval. The second must be of type numeric or interval.
If these variable types are not available please select another dataset.
......@@ -72,7 +72,7 @@ If these variable types are not available please select another dataset.
",compare_means_ui.R
Nothing to plot. Please select a plot type,没有可绘制的内容，请选择绘图类型,compare_means_ui.R
Save compare means plot,保存均值比较图,compare_means_ui.R
Basics > Proportions,基础 > 比例,compare_props_ui.R
Basics > Proportions,基础统计 > 比例,compare_props_ui.R
Compare proportions,比较比例,"compare_props_ui.R,init.R"
"This analysis requires two categorical variables. The first must have
two or more levels. The second can have only two levels. If these
......@@ -88,7 +88,7 @@ Pearson,皮尔逊积矩相关,correlation_ui.R
Spearman,斯皮尔曼秩相关,correlation_ui.R
Kendall,肯德尔秩相关,correlation_ui.R
Calculate correlation,计算相关性,correlation_ui.R
Basics > Tables,基础 > 表格,correlation_ui.R
Basics > Tables,基础统计 > 表格,correlation_ui.R
Correlation,相关性,"correlation_ui.R,init.R"
Adjust for {factor} variables,针对 {factor} 变量进行调整,"correlation_ui.R,full_factor_ui.R"
Calculate adjusted p.values,计算调整后的 p 值,correlation_ui.R
......@@ -252,7 +252,7 @@ Load data,加载数据,manage_ui.R
Description,描述,manage_ui.R
Paste,粘贴,manage_ui.R
Copy data,复制数据,manage_ui.R
Save,保存,"manage_ui.R,logistic_ui.R,radiant.R"
Save,保存,"manage_ui.R,logistic_ui.R,radiant.R,quickgen_ai_ui.R"
Save data,保存数据,manage_ui.R
Save radiant state file,保存 Radiant 状态文件,"manage_ui.R,global.R"
Add/edit data description,添加/编辑数据描述,manage_ui.R
......@@ -608,7 +608,7 @@ Estimate model,估计模型,"crs_ui.R, crtree_ui.R, gbt_ui.R, logistic_ui.R,conj
Re-estimate model,重新估计模型,"crs_ui.R, crtree_ui.R, gbt_ui.R, logistic_ui.R,conjoint_ui.R"
<label>Store recommendations:</label>,<label>保存推荐结果:</label>,crs_ui.R
Collaborative Filtering,协同过滤,"crs_ui.R,init.R"
Model > Recommend,模型 > 推荐,crs_ui.R
Model > Recommend,模型方法 > 推荐,crs_ui.R
"This analysis requires a user id, a product id, and product ratings.
If these variables are not available please select another dataset.
......@@ -675,7 +675,7 @@ Save decision tree plot,保存决策树图,crtree_ui.R
Generating predictions,正在生成预测,"crtree_ui.R, gbt_ui.R, logistic_ui.R,conjoint_ui.R"
Generating prediction plot,正在生成预测图,"crtree_ui.R, gbt_ui.R, logistic_ui.R,conjoint_ui.R"
Generating tree diagramm,正在生成树图,crtree_ui.R
Model > Estimate,模型 > 估计,"crtree_ui.R, logistic_ui.R"
Model > Estimate,模型方法 > 估计,"crtree_ui.R, logistic_ui.R"
** Press the Estimate button to estimate the model **,** 点击“估计模型”按钮来估计模型 **,"crtree_ui.R, gbt_ui.R,conjoint_ui.R"
Please select one or more explanatory variables.,请选择一个或多个自变量。,"crtree_ui.R, gbt_ui.R, nn_ui.R"
Max,最大化,dtree_ui.R
......@@ -689,7 +689,7 @@ Select decisions to evaluate,选择要评估的决策,dtree_ui.R
<i id='dtree_sense_del' title='Remove variable' href='#' class='action-button fa fa-minus-circle'></i></label>","<label>添加变量: <i id='dtree_sense_add' title='添加变量' href='#' class='action-button fa fa-plus-circle'></i>
<i id='dtree_sense_del' title='删除变量' href='#' class='action-button fa fa-minus-circle'></i></label>",dtree_ui.R
Step:,步长:,dtree_ui.R
Model,模型,"dtree_ui.R,init.R"
Model,模型方法,"dtree_ui.R,init.R"
Decision analysis,决策分析,"dtree_ui.R,init.R"
<i title='Report results' class='fa fa-edit action-button' href='#dtree_report1' id='dtree_report1'></i>,<i title='报告结果' class='fa fa-edit action-button' href='#dtree_report1' id='dtree_report1'></i>,dtree_ui.R
<i title='Report results & Screenshot' class='fa fa-camera action-button aligncenter' href='#dtree_screenshot1' id='dtree_screenshot1' onclick='generate_screenshot();'></i>,<i title='报告结果并截图' class='fa fa-camera action-button aligncenter' href='#dtree_screenshot1' id='dtree_screenshot1' onclick='generate_screenshot();'></i>,dtree_ui.R
......@@ -757,7 +757,7 @@ Save model evaluation plot,保存模型评估图,"evalbin_ui.R, evalreg_ui.R"
Save confusion plots,保存混淆图,evalbin_ui.R
Save uplift plots,保存提升图,evalbin_ui.R
Evaluate,评估,"evalbin_ui.R,init.R"
Model > Evaluate,模型 > 评估,"evalbin_ui.R, evalreg_ui.R"
Model > Evaluate,模型方法 > 评估,"evalbin_ui.R, evalreg_ui.R"
** Press the Evaluate button to evaluate models **,** 请点击“评估”按钮以评估模型 **,"evalbin_ui.R, evalreg_ui.R"
<label>More than 50 levels. Please choose another response variable</label>,<label>超过 50 个水平。请选择其他响应变量</label>,evalbin_ui.R
"This analysis requires a response variable of type factor and one or more
......@@ -784,7 +784,7 @@ Sub-sample:,子样本比例：,gbt_ui.R
# rounds:,迭代轮数：,gbt_ui.R
Early stopping:,提前停止：,gbt_ui.R
Gradient Boosted Trees,梯度提升树,"gbt_ui.R,init.R"
Model > Trees,模型 > 树模型,gbt_ui.R
Model > Trees,模型方法 > 树模型,gbt_ui.R
** Select prediction input **,** 请选择预测输入 **,"gbt_ui.R, logistic_ui.R,conjoint_ui.R"
** Select data for prediction **,** 请选择用于预测的数据 **,"gbt_ui.R, logistic_ui.R,conjoint_ui.R"
** Enter prediction commands **,** 请输入预测命令 **,"gbt_ui.R, logistic_ui.R,conjoint_ui.R"
......@@ -796,7 +796,7 @@ Save gradient boosted trees prediction plot,保存梯度提升树预测图,gbt_u
Save gradient boosted trees plot,保存梯度提升树图,gbt_ui.R
This analysis requires a response variable with two levels and one\nor more explanatory variables. If these variables are not available\nplease select another dataset.\n\n,此分析需要一个具有两个水平的响应变量和一个\n或多个解释变量。如果这些变量不可用\n请选择其他数据集。\n\n,gbt_ui.R
This analysis requires a response variable of type integer\nor numeric and one or more explanatory variables.\nIf these variables are not available please select another dataset.\n\n,此分析需要一个整数类型的响应变量\n或数值型，以及一个或多个解释变量。\n如果这些变量不可用，请选择其他数据集。\n\n,gbt_ui.R
Predict,预测,"gbt_ui.R,conjoint_ui.R"
Predict,预测,"gbt_ui.R,conjoint_ui.R,crtree_ui.R"
Storing residuals,存储残差,logistic_ui.R
Save coefficients,保存系数,logistic_ui.R
Save logistic prediction plot,保存逻辑回归预测图,logistic_ui.R
......@@ -1090,7 +1090,7 @@ Radiant docs,Radiant文档,global.R
Report issue,报告问题,global.R
Design,设计,init.R
Sample,样本,init.R
Basics,基础,init.R
Basics,基础统计,init.R
Probability,概率,init.R
Means,均值,init.R
Proportions,比例,init.R
......@@ -1127,12 +1127,12 @@ Base dir:,根目录：,global.R
Generate descriptive statistics with one click,一键生成描述性统计,"quickgen_basic_ui.R,init.R"
Oneclick generation > Generate descriptive statistics,一键生成 > 生成描述性统计,quickgen_basic_ui.R
Oneclick generation,一键生成,init.R
LLM generates descriptive statistics,大模型生成描述性统计,init.R
AI generates descriptive statistics,大模型生成描述性统计,"init.R,quickgen_ai_ui.R"
Select All,全选,quickgen_basic_ui.R
Deselect All,全不选,quickgen_basic_ui.R
Invert,反选,quickgen_basic_ui.R
Radiant screenshot,截图,radiant.R
Cancel,取消,radiant.R
Cancel,取消,"radiant.R,quickgen_ai_ui.R"
Table,表格,quickgen_basic_ui.R
OneClick table generation,一键成表,quickgen_basic_ui.R
Select numeric variables,选择数值变量,quickgen_basic_ui.R
......@@ -1143,3 +1143,46 @@ Select X variable(s),选择X变量,quickgen_basic_ui.R
No data available,暂无数据,quickgen_basic_ui.R
No numerical variable available,无可用数值变量,quickgen_basic_ui.R
### Current data overview,### 当前数据概况,quickgen_basic_ui.R
Describe your analysis request:,描述您的分析请求：,quickgen_ai_ui.R
Oneclick generation > AI generates descriptive statistics,一键生成 > 大模型生成描述性统计,quickgen_ai_ui.R
AI assistant,AI助手,quickgen_ai_ui.R
Run code,运行,quickgen_ai_ui.R
Edit code,编辑,quickgen_ai_ui.R
Generated R code,生成的R代码,quickgen_ai_ui.R
Send,发送,quickgen_ai_ui.R
e. g. Please help me draw a scatter plot of diamonds prices and carats,e.g. 请帮我画一张 diamonds 价格与克拉的散点图,quickgen_ai_ui.R
Please enter an analysis request,请输入分析请求,quickgen_ai_ui.R
Save Changes,保存,quickgen_ai_ui.R
Edit R Code,编辑R代码,quickgen_ai_ui.R
Save AI-generated plot,保存大模型生成的图表,quickgen_ai_ui.R
Edit the generated R code here...,在此处编辑生成的R代码...,quickgen_ai_ui.R
Normality test,正态性检验,init.R
Homogeneity of variance test,方差齐性检验,init.R
Basics > Normality,基础统计 > 正态性,normality_test_ui.R
Shapiro-Wilk,SW 检验,normality_test_ui.R
Kolmogorov-Smirnov,K-S 检验,normality_test_ui.R
Anderson-Darling,AD 检验,normality_test_ui.R
Basics > Homogeneity,基础统计 > 方差齐性,homo_variance_test_ui.R
Grouping variable:,分组变量：,homo_variance_test_ui.R
Test method:,检验方法：,homo_variance_test_ui.R
Levene,莱文检验,homo_variance_test_ui.R
Bartlett,巴特利特检验,homo_variance_test_ui.R
Fligner,弗林格检验,homo_variance_test_ui.R
Cox Proportional Hazards Regression,比例风险回归,init.R
Support Vector Machine (SVM),支持向量机（SVM）,init.R
Kernel:,核函数：,svm_ui.R
Linear,线性,svm_ui.R
Polynomial,多项式,svm_ui.R
Radial,径向基,svm_ui.R
Cost (C):,惩罚系数 (C)：,svm_ui.R
Gamma:,伽马系数：,svm_ui.R
Coef0:,常数项：,svm_ui.R
Epsilon (tube):,Epsilon 间隔带：,svm_ui.R
Estimate class probabilities,估计类别概率,svm_ui.R
Choose Excel file:,选择Excel文件：,manage_ui.R
Sheet index (1-based):,工作表索引（从1开始）：,manage_ui.R
First row as header,第一行为表头,manage_ui.R
Time variable:,生存时间变量：,cox_ui.R
Status variable:,事件状态变量：,cox_ui.R
AI running...,大模型运行中...,quickgen_ai_ui.R
Warning:Please enter a request related to descriptive statistics or visualization.,警告：请输入与描述性统计或可视化相关的请求。,quickgen_ai_ui.R
radiant.quickgen/DESCRIPTION
View file @ d3861e30
......@@ -2,3 +2,21 @@ Package: radiant.quickgen
Version: 0.0.1
Title: Quick Generator table and chart for Radiant
Description: Provides a simple data generator for the Radiant interface.
Depends:
R (>= 4.3.0),
radiant.data (>= 1.6.6)
Imports:
shiny (>= 1.8.1),
shiny.i18n,
httr2 (>= 1.0.0),
shinyjs,
shinyAce,
shinyWidgets(>= 0.8.0),
patchwork
Suggests:
testthat (>= 2.0.0),
pkgdown (>= 1.1.0),
markdown (>= 1.3)
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.3.2
radiant.quickgen/NAMESPACE 0 → 100644
View file @ d3861e30
# Generated by roxygen2: do not edit by hand
S3method(dtab,explore)
S3method(store,explore)
S3method(summary,explore)
export(ai_generate)
export(ai_get_data_call)
export(ai_run_code)
export(build_r_prompt)
export(chat_completion)
export(cv)
export(does_vary)
export(empty_level)
export(explore)
export(flip)
export(ln)
export(me)
export(meprop)
export(modal)
export(n_missing)
export(n_obs)
export(p01)
export(p025)
export(p05)
export(p10)
export(p25)
export(p75)
export(p90)
export(p95)
export(p975)
export(p99)
export(prop)
export(qscatter)
export(sdpop)
export(sdprop)
export(se)
export(seprop)
export(varpop)
export(varprop)
export(visualize)
radiant.quickgen/R/quickgen_ai.R
View file @ d3861e30
#' 一键生成模块服务器逻辑
# === 配置 ===
MODELSCOPE_OPENAI_URL <- "https://api-inference.modelscope.cn/v1"
MODELSCOPE_API_KEY <- Sys.getenv("MODELSCOPE_API_KEY", "ms-b2746d72-f897-4faf-8089-89e5e511ed5a")
MODEL_ID <- "deepseek-ai/DeepSeek-V3.1"
# === 低层封装：单次对话 ===
#' @export
chat_completion <- function(user_prompt,
max_tokens = 1500,
temperature = 0.3) {
req <- httr2::request(paste0(MODELSCOPE_OPENAI_URL, "/chat/completions")) %>%
httr2::req_headers(
"Authorization" = paste("Bearer", MODELSCOPE_API_KEY),
"Content-Type" = "application/json"
) %>%
httr2::req_body_json(list(
model = MODEL_ID,
messages = list(list(role = "user", content = user_prompt)),
temperature = temperature,
max_tokens = max_tokens,
stream = FALSE
))
resp <- httr2::req_perform(req)
body <- httr2::resp_body_json(resp)
if (is.null(body$choices[[1]]$message$content))
stop("ModelScope API 返回空内容：", body)
body$choices[[1]]$message$content
}
# === 构造发给模型的 Prompt ===
#' @export
build_r_prompt <- function(user_prompt, data_call) {
sprintf(
"你是 R 语言专家，必须严格遵守以下规则：
〓 输出格式 〓
- 只返回可运行 R 代码，用 ```r 包裹，禁止任何解释、注释、空行。
- 若用户请求不符合下方【白名单】，一律返回空代码块（仅 ```r\n``` ），不对话。
〓 白名单关键词（必须至少出现 1 个）〓
箱线图|柱状图|条形图|散点图|折线图|密度图|直方图|热图|森林图|瀑布图|饼图|气泡图|生存曲线|KM 曲线|ggsurvplot|tbl_summary|tableone|CreateTableOne|描述性统计|基线表|相关性|group comparison|distribution|ggplot|geom_|patchwork
〓 否定示例（立即返回空块）〓
- 仅输入：“图表”“表格”“画图”“来张图”
- 非医学/统计描述：笑话、故事、计算、翻译、写文章、写代码注释、解释概念、生成非 ggplot 图形（base、lattice）
〓 技术细节 〓
1. 数据集已读入：%s
2. 主题函数带括号：theme_minimal()、theme_bw() ...
3. 多张图用 patchwork 拼页。
4. 包函数写全名，不得省略括号。
用户请求：%s",
data_call, user_prompt
)
}
#' @export
ai_generate <- function(prompt, dataset, envir = parent.frame()) {
data_call <- ai_get_data_call(dataset, envir)
sys_prompt <- build_r_prompt(prompt, data_call)
r_code <- try(chat_completion(sys_prompt), silent = TRUE)
if (inherits(r_code, "try-error"))
stop("AI API error: ", attr(r_code, "condition")$message)
r_code <- gsub("(?s)```r\\s*|```", "", r_code, perl = TRUE)
r_code <- trimws(r_code)
if (r_code == "") {
return(list(r_code = "",
type = "empty",
auto_run = FALSE))
}
r_code <- gsub("(theme_minimal|theme_bw|theme_classic|theme_gray|theme_void|theme_dark)\\b(?!\\s*\\()",
"\\1()", r_code, perl = TRUE)
r_code <- paste0(data_call, "\n", r_code)
has_gg <- grepl("ggplot\\(|geom_", r_code)
has_tbl <- grepl("data\\.frame\\(|tibble\\(|tbl_summary|tableOne|CreateTableOne", r_code)
type <- if (has_gg) "plot" else if (has_tbl) "table" else "text"
list(r_code = r_code, type = type, auto_run = TRUE)
}
#' @export
quickgen_server <- function(input, output, session, r_data, r_info, r_state) {
# 为模块创建命名空间
ns <- session$ns
ai_get_data_call <- function(dataset, envir) {
df_name <- if (is_string(dataset)) dataset else deparse1(substitute(dataset))
paste0("df <- eval(quote(get_data(\"", df_name, "\", envir = ",
deparse1(substitute(envir)), ")), envir = parent.frame())")
}
# 空函数，确保模块能正确加载
# 后续开发时可在此添加实际逻辑
#' @export
ai_run_code <- function(r_code, envir = parent.frame()) {
eval(parse(text = r_code), envir = envir)
}
\ No newline at end of file
radiant.quickgen/R/quickgen_basic.R
View file @ d3861e30
#' @export
explore <- function(dataset, vars = "", byvar = "", fun = c("mean", "sd"),
top = "fun", tabfilt = "", tabsort = "", tabslice = "",
nr = Inf, data_filter = "", arr = "", rows = NULL,
......@@ -171,7 +171,7 @@ explore <- function(dataset, vars = "", byvar = "", fun = c("mean", "sd"),
) %>% add_class("explore")
}
#' @export
summary.explore <- function(object, dec = 3, ...) {
cat("Explore\n")
cat("Data :", object$df_name, "\n")
......@@ -209,7 +209,7 @@ summary.explore <- function(object, dec = 3, ...) {
invisible()
}
#' @export
store.explore <- function(dataset, object, name, ...) {
if (missing(name)) {
object$tab
......@@ -225,7 +225,7 @@ store.explore <- function(dataset, object, name, ...) {
}
}
#' @export
flip <- function(expl, top = "fun") {
cvars <- expl$byvar %>%
(function(x) if (is.empty(x[1])) character(0) else x)
......@@ -245,7 +245,7 @@ flip <- function(expl, top = "fun") {
expl$tab
}
#' @export
dtab.explore <- function(object, dec = 3, searchCols = NULL,
order = NULL, pageLength = NULL,
caption = NULL, ...) {
......@@ -330,31 +330,31 @@ dtab.explore <- function(object, dec = 3, searchCols = NULL,
## turn functions below into functional ...
###########################################
#' @export
n_obs <- function(x, ...) length(x)
#' @export
n_missing <- function(x, ...) sum(is.na(x))
#' @export
p01 <- function(x, na.rm = TRUE) quantile(x, .01, na.rm = na.rm)
#' @export
p025 <- function(x, na.rm = TRUE) quantile(x, .025, na.rm = na.rm)
#' @export
p05 <- function(x, na.rm = TRUE) quantile(x, .05, na.rm = na.rm)
#' @export
p10 <- function(x, na.rm = TRUE) quantile(x, .1, na.rm = na.rm)
#' @export
p25 <- function(x, na.rm = TRUE) quantile(x, .25, na.rm = na.rm)
#' @export
p75 <- function(x, na.rm = TRUE) quantile(x, .75, na.rm = na.rm)
#' @export
p90 <- function(x, na.rm = TRUE) quantile(x, .90, na.rm = na.rm)
#' @export
p95 <- function(x, na.rm = TRUE) quantile(x, .95, na.rm = na.rm)
#' @export
p975 <- function(x, na.rm = TRUE) quantile(x, .975, na.rm = na.rm)
#' @export
p99 <- function(x, na.rm = TRUE) quantile(x, .99, na.rm = na.rm)
#' @export
cv <- function(x, na.rm = TRUE) {
m <- mean(x, na.rm = na.rm)
if (m == 0) {
......@@ -364,17 +364,17 @@ cv <- function(x, na.rm = TRUE) {
sd(x, na.rm = na.rm) / m
}
}
#' @export
se <- function(x, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
sd(x) / sqrt(length(x))
}
#' @export
me <- function(x, conf_lev = 0.95, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
se(x) * qt(conf_lev / 2 + .5, length(x) - 1, lower.tail = TRUE)
}
#' @export
prop <- function(x, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
if (is.numeric(x)) {
......@@ -387,36 +387,36 @@ prop <- function(x, na.rm = TRUE) {
NA
}
}
#' @export
varprop <- function(x, na.rm = TRUE) {
p <- prop(x, na.rm = na.rm)
p * (1 - p)
}
#' @export
sdprop <- function(x, na.rm = TRUE) sqrt(varprop(x, na.rm = na.rm))
#' @export
seprop <- function(x, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
sqrt(varprop(x, na.rm = FALSE) / length(x))
}
#' @export
meprop <- function(x, conf_lev = 0.95, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
seprop(x) * qnorm(conf_lev / 2 + .5, lower.tail = TRUE)
}
#' @export
varpop <- function(x, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
n <- length(x)
var(x) * ((n - 1) / n)
}
#' @export
sdpop <- function(x, na.rm = TRUE) sqrt(varpop(x, na.rm = na.rm))
#' @export
ln <- function(x, na.rm = TRUE) {
if (na.rm) log(na.omit(x)) else log(x)
}
#' @export
does_vary <- function(x, na.rm = TRUE) {
## based on http://stackoverflow.com/questions/4752275/test-for-equality-among-all-elements-of-a-single-vector
if (length(x) == 1L) {
......@@ -429,7 +429,7 @@ does_vary <- function(x, na.rm = TRUE) {
}
}
}
#' @export
empty_level <- function(x) {
if (!is.factor(x)) x <- as.factor(x)
levs <- levels(x)
......@@ -443,7 +443,7 @@ empty_level <- function(x) {
}
x
}
#' @export
modal <- function(x, na.rm = TRUE) {
if (na.rm) x <- na.omit(x)
unv <- unique(x)
......@@ -451,7 +451,7 @@ modal <- function(x, na.rm = TRUE) {
}
#—————————————————————————————————————————————————绘图部分———————————————————————————————————————————
#' @export
visualize <- function(dataset, xvar, yvar = "", comby = FALSE, combx = FALSE,
type = ifelse(is.empty(yvar), "dist", "scatter"), nrobs = -1,
facet_row = ".", facet_col = ".", color = "none", fill = "none",
......@@ -1131,7 +1131,7 @@ visualize <- function(dataset, xvar, yvar = "", comby = FALSE, combx = FALSE,
(function(x) if (isTRUE(shiny)) x else print(x))
}
}
#' @export
qscatter <- function(dataset, xvar, yvar, lev = "", fun = "mean", bins = 20) {
if (is.character(dataset[[yvar]])) {
dataset <- mutate_at(dataset, .vars = yvar, .funs = as.factor)
......
radiant.quickgen/inst/app/init.R
View file @ d3861e30
......@@ -15,7 +15,7 @@ options(
tags$script(src = "www_quickgen/js/run_return.js")
),
tabPanel(i18n$t("Generate descriptive statistics with one click"), uiOutput("quickgen_basic")),
tabPanel(i18n$t("LLM generates descriptive statistics"), uiOutput("quickgen_ai"))
tabPanel(i18n$t("AI generates descriptive statistics"), uiOutput("quickgen_ai"))
)
)
)
\ No newline at end of file
radiant.quickgen/inst/app/tools/analysis/quickgen_ai_ui.R
View file @ d3861e30
#' 大模型生成描述性统计界面
quickgen_ai_ui <- function() {
# quickgen_ai_ui.R
library(shinyjs)
library(shinyAce)
## ==================== 右下角浮框 ====================
ui_ai_progress <- tags$div(
id = "ai_progress_box",
style = "display:none;
position:fixed;
bottom:15px; right:15px;
width:220px; z-index:9999;
background:#f5f5f5; color:#333;
border:1px solid #337ab7; border-radius:4px;
padding:10px 15px; box-shadow:0 2px 8px rgba(0,0,0,.25);",
tags$strong(i18n$t("AI running...")),
tags$div(class = "progress",
tags$div(class = "progress-bar progress-bar-striped active",
style = "width:100%"))
)
## ======== 警告弹窗========
ui_ai_warn <- tags$div(
id = "ai_warn_box",
style = "display:none;
position:fixed;
bottom:15px; right:15px;
width:220px; z-index:9999;
background:#fff3cd; color:#856404;
border:1px solid #ffeaa7; border-radius:4px;
padding:10px 15px; box-shadow:0 2px 8px rgba(0,0,0,.25);",
tags$strong(i18n$t("Warning:Please enter a request related to descriptive statistics or visualization."))
)
## ==================== 统一入口 ====================
output$quickgen_ai <- renderUI({
stat_tab_panel(
menu = i18n$t("Oneclick generation > AI generates descriptive statistics"),
tool = i18n$t("AI generates descriptive statistics"),
tool_ui = "ai_main_ui",
output_panels = tabPanel(
title = i18n$t("AI assistant"),
value = "ai_panel",
uiOutput("ai_result_area")
)
)
})
## ==================== 右侧 AI 面板 ====================
output$ai_main_ui <- renderUI({
tagList(
useShinyjs(),
ui_ai_progress,
ui_ai_warn,
wellPanel(
i18n$t("Describe your analysis request"),
returnTextAreaInput("ai_prompt",
label = NULL,
placeholder = i18n$t("e. g. Please help me draw a scatter plot of diamonds prices and carats"),
rows = 4,
value = state_init("ai_prompt", "")),
fluidRow(
column(6, uiOutput("ui_ai_submit")),
column(6, uiOutput("ai_loading"))
)
),
wellPanel(
i18n$t("Generated R code"),
uiOutput("ai_r_code_block"),
fluidRow(
column(6, actionButton("ai_run_code", i18n$t("Run code"), icon = icon("play"), class = "btn-success")),
column(6, actionButton("ai_edit_code", i18n$t("Edit code"), icon = icon("edit"), class = "btn-default"))
)
),
help_and_report(
modal_title = i18n$t("AI generates descriptive statistics"),
fun_name = "quickgen_ai",
help_file = inclMD(file.path(getOption("radiant.path.quickgen"), "app/tools/help/quickgen_ai.md")),
lic = "by-sa"
)
)
})
## ==================== 控件渲染 ====================
output$ui_ai_submit <- renderUI({
req(input$dataset)
actionButton("ai_submit", i18n$t("Send"), icon = icon("magic"), class = "btn-primary")
})
output$ai_loading <- renderUI({
if (isTRUE(r_values$ai_loading))
tags$div(class = "progress",
tags$div(class = "progress-bar progress-bar-striped active",
style = "width:100%",
i18n$t("Calling AI model...")))
})
## ==================== reactiveValues ====================
r_values <- reactiveValues(
ai_r_code = "",
ai_result_type = "text",
ai_result_ready = FALSE,
ai_loading = FALSE
)
## ==================== 生成代码 ====================
observeEvent(input$ai_submit, {
if (is.empty(input$ai_prompt))
return(showNotification(i18n$t("Please enter an analysis request"), type = "error"))
r_values$ai_loading <- TRUE
shinyjs::show("ai_progress_box") # 显示右下角进度框
on.exit({
r_values$ai_loading <- FALSE
shinyjs::hide("ai_progress_box") # 无论成功失败都隐藏
})
res <- try(do.call(ai_generate,
list(prompt = input$ai_prompt,
dataset = input$dataset,
envir = r_data)),
silent = TRUE)
if (inherits(res, "try-error")) {
showNotification(paste(i18n$t("AI API error:"), res), type = "error")
return()
}
r_values$ai_r_code <- res$r_code
r_values$ai_result_type <- res$type
r_values$ai_result_ready <- FALSE
r_values$auto_run <- res$auto_run
if (res$type == "empty") {
shinyjs::show("ai_warn_box")
shinyjs::delay(3000, shinyjs::hide("ai_warn_box"))
return()
}
if (isTRUE(r_values$auto_run))
shinyjs::click("ai_run_code")
})
## ==================== 显示 R 代码 ====================
output$ai_r_code_block <- renderUI({
codes <- r_values$ai_r_code
tags$pre(codes, style = "background:#f5f5f5; padding:10px; border-radius:4px;
font-family:monospace; white-space:pre-wrap; min-height:100px;")
})
## ==================== 运行代码 ====================
observeEvent(input$ai_run_code, {
shinyjs::hide("ai_progress_box")
if (is.empty(r_values$ai_r_code)) return()
if (trimws(r_values$ai_r_code) == "" || identical(r_values$ai_result_type, "empty")) {
shinyjs::show("ai_warn_box")
shinyjs::delay(3000, shinyjs::hide("ai_warn_box"))
return()
}
tryCatch({
result <- do.call(ai_run_code,
list(r_code = r_values$ai_r_code, envir = r_data))
r_data$ai_temp_result <- result
if (inherits(result, "gg") || inherits(result, "ggplot")) {
r_values$ai_result_type <- "plot"
output$ai_result_plot <- renderPlot(print(result))
} else if (is.data.frame(result) || is.matrix(result)) {
r_values$ai_result_type <- "table"
output$ai_result_table <- DT::renderDataTable(
DT::datatable(result, options = list(scrollX = TRUE, pageLength = 10)))
} else {
r_values$ai_result_type <- "text"
output$ai_result_text <- renderText(capture.output(print(result)))
}
r_values$ai_result_ready <- TRUE
}, error = function(e) {
r_values$ai_result_type <- "error"
output$ai_result_error <- renderText(paste0(i18n$t("Error: "), e$message))
r_values$ai_result_ready <- TRUE
showNotification(paste0(i18n$t("Run code error: "), e$message),
type = "error", duration = NULL)
})
}, ignoreInit = TRUE)
## ======== 结果展示区========
output$ai_result_area <- renderUI({
req(r_values$ai_result_ready)
tagList(
h3("大模型生成描述性统计"),
p("正在开发中...")
conditionalPanel(
condition = "output.ai_result_type == 'plot'",
download_link("dlp_ai_plot"), br(),
plotOutput("ai_result_plot", width = "100%", height = "500px")
),
conditionalPanel(
condition = "output.ai_result_type == 'table'",
download_link("dlp_ai_table"), br(),
DT::dataTableOutput("ai_result_table")
),
conditionalPanel(
condition = "output.ai_result_type == 'text' || output.ai_result_type == 'error'",
verbatimTextOutput("ai_result_text")
)
)
})
output$ai_result_type <- reactive({
r_values$ai_result_type
})
outputOptions(output, "ai_result_type", suspendWhenHidden = FALSE)
## ==================== 编辑代码模态框 ====================
observeEvent(input$ai_edit_code, {
showModal(
modalDialog(
title = i18n$t("Edit R Code"),
size = "l",
footer = tagList(
actionButton("ai_save_code", i18n$t("Save Changes"), class = "btn-primary"),
modalButton(i18n$t("Cancel"))
),
aceEditor(
"ai_code_editor",
mode = "r",
theme = getOption("radiant.ace_theme", "tomorrow"),
wordWrap = TRUE,
value = r_values$ai_r_code,
placeholder = i18n$t("Edit the generated R code here..."),
vimKeyBinding = getOption("radiant.ace_vim.keys", FALSE),
tabSize = getOption("radiant.ace_tabSize", 2),
useSoftTabs = getOption("radiant.ace_useSoftTabs", TRUE),
showInvisibles = getOption("radiant.ace_showInvisibles", FALSE),
autoScrollEditorIntoView = TRUE,
minLines = 15,
maxLines = 30
)
)
)
})
## ==================== 保存代码 ====================
observeEvent(input$ai_save_code, {
r_values$ai_r_code <- input$ai_code_editor
r_values$auto_run <- FALSE
removeModal()
})
## ==================== PNG 下载处理器 ====================
dlp_ai_plot <- function(path) {
result <- r_data$ai_temp_result
if (inherits(result, "gg") || inherits(result, "ggplot")) {
png(path, width = 800, height = 500, res = 96)
print(result)
dev.off()
} else {
png(path, width = 400, height = 400)
plot(1, type = "n", axes = FALSE, xlab = "", ylab = "")
text(1, 1, "No plot available", cex = 1.5)
dev.off()
}
}
download_handler(
id = "dlp_ai_plot",
fun = dlp_ai_plot,
fn = function() paste0("plot_", Sys.Date()),
type = "png",
caption = i18n$t("Save AI-generated plot")
)
# ======== 表格 CSV 下载处理器 ========
dlp_ai_table <- function(path) {
result <- r_data$ai_temp_result
if (is.data.frame(result)) {
write.csv(result, file = path, row.names = FALSE)
} else {
write.csv(data.frame(msg = "No table available"), file = path, row.names = FALSE)
}
}
download_handler(
id = "dlp_ai_table",
fun = dlp_ai_table,
fn = function() paste0("table_", Sys.Date()),
type = "csv",
caption = i18n$t("Save AI-generated table")
)
quickgen_ai_ui()
\ No newline at end of file
## ==================== 报告 / 截图 ====================
ai_report <- function() {}
observeEvent(input$ai_report, ai_report())
observeEvent(input$ai_screenshot, radiant_screenshot_modal("modal_ai_screenshot"))
observeEvent(input$modal_ai_screenshot, { ai_report(); removeModal() })
\ No newline at end of file
radiant.quickgen/inst/app/tools/analysis/quickgen_basic_ui .txt deleted 100644 → 0
View file @ 1742ff44
make_desc_text <- function(df) {
if (is.null(df) || nrow(df) == 0) return(i18n$t("No data available"))
## 只保留数值列
num_cols <- sapply(df, is.numeric)
if (sum(num_cols) == 0) return("No numerical variable available")
df_num <- df[, num_cols, drop = FALSE]
buf <- c(i18n$t("### Current data overview"))
buf <- c(buf, sprintf("- **Sample size**：%d records", nrow(df)))
## 连续变量
for (v in names(df_num)) {
x <- df_num[[v]]
x <- x[!is.na(x)]
if (length(x) == 0) next
buf <- c(buf, sprintf(
"- **%s**: mean %.2f ± %.2f; median %.2f; range [%.2f, %.2f]; missing %.1f%%",
v,
mean(x), sd(x), median(x), min(x), max(x),
100 * (1 - length(x) / nrow(df))
))
}
## 分类变量
cat_cols <- sapply(df, function(z) is.factor(z) || is.character(z))
for (v in names(df)[cat_cols]) {
tbl <- table(df[[v]], useNA = "ifany")
lev_txt <- paste(sprintf("%s (%d, %.1f%%)",
names(tbl), tbl,
100 * tbl / sum(tbl)), collapse = "、")
buf <- c(buf, sprintf("- **%s**：%s", v, lev_txt))
}
paste(buf, collapse = "\n")
}
## quickgen_basic 的形参列表
default_funs <- c("n_obs", "mean", "sd", "min", "max")
qib_type <- c(
"分布图（dist）" = "dist", "密度图（density）" = "density", "散点图（scatter）" = "scatter",
"曲面图（surface）" = "surface", "折线图（line）" = "line", "条形图（bar）" = "bar", "箱线图（box）" = "box"
)
qib_check <- c(
"直线（line）" = "line", "局部加权回归（loess）" = "loess",
"抖动（jitter）" = "jitter",
"插值（interpolate）" = "interpolate"
)
qib_axes <- c(
"翻转坐标轴（flip）" = "flip", "X轴对数变换（log_x）" = "log_x", "Y轴对数变换（log_y）" = "log_y",
"Y轴缩放（scale_y）" = "scale_y", "密度（density）" = "density", "排序（sort）" = "sort"
)
qib_theme <- c(
"灰色主题（gray）" = "theme_gray", "黑白主题（bw）" = "theme_bw",
"明亮主题（light）" = "theme_light", "暗黑主题（dark）" = "theme_dark",
"极简主题（minimal）" = "theme_minimal", "经典主题（classic）" = "theme_classic"
)
os_type <- Sys.info()["sysname"]
if (os_type == "Windows") {
fnt <- names(windowsFonts())
names(fnt) <- tools::toTitleCase(fnt)
qib_base_family <- c("Theme default" = "", fnt)
} else {
qib_base_family <- c(
"Theme default" = "", "Helvetica" = "Helvetica", "Serif" = "serif",
"Sans" = "sans", "Mono" = "mono", "Courier" = "Courier", "Times" = "Times"
)
}
qib_labs <- c(i18n$t("title"), i18n$t("subtitle"), i18n$t("caption"), i18n$t("x"), i18n$t("y"))
qib_add_labs <- function() {
lab_list <- list()
for (l in qib_labs) {
inp <- input[[paste0("qib_labs_", l)]]
if (!is.empty(inp)) lab_list[[l]] <- inp
}
lab_list
}
qgb_args <- as.list(formals(explore))
qib_args <- as.list(formals(visualize))
## 收集用户输入的 reactive 列表
qgb_inputs <- reactive({
qgb_args$data_filter <- if (input$show_filter) input$data_filter else ""
qgb_args$arr <- if (input$show_filter) input$data_arrange else ""
qgb_args$rows <- if (input$show_filter) input$data_rows else ""
qgb_args$dataset <- input$dataset
for (i in r_drop(names(qgb_args))) {
qgb_args[[i]] <- input[[paste0("qgb_", i)]]
}
qgb_args
})
qgb_sum_args <- as.list(if (exists("summary.explore")) {
formals(summary.explore)
} else {
formals(radiant.data:::summary.explore)
})
qib_inputs <- reactive({
qib_args$data_filter <- if (isTRUE(input$show_filter)) input$data_filter else ""
qib_args$arr <- if (isTRUE(input$show_filter)) input$data_arrange else ""
qib_args$rows <- if (isTRUE(input$show_filter)) input$data_rows else ""
qib_args$dataset <- input$dataset
qib_args$shiny <- input$shiny
qib_args$labs <- qib_add_labs()
for (i in r_drop(names(qib_args), drop = c(i18n$t("dataset"), i18n$t("data_filter"), i18n$t("arr"), i18n$t("rows"), i18n$t("labs")))) {
qib_args[[i]] <- input[[paste0("qib_", i)]]
}
qib_args
})
## list of function inputs selected by user
qgb_sum_inputs <- reactive({
## loop needed because reactive values don't allow single bracket indexing
for (i in names(qgb_sum_args)) {
qgb_sum_args[[i]] <- input[[paste0("qgb_", i)]]
}
qgb_sum_args
})
## UI-elements
output$ui_qgb_vars <- renderUI({
vars <- varnames()
req(available(vars))
selectizeInput(
"qgb_vars",
label = i18n$t("Numeric variable(s):"),
choices = vars,
selected = state_multiple("qgb_vars", vars, isolate(input$qgb_vars)),
multiple = TRUE,
options = list(
placeholder = i18n$t("Select numeric variables"),
plugins = list("remove_button", "drag_drop")
)
)
})
output$ui_qgb_byvar <- renderUI({
withProgress(message = i18n$t("Acquiring variable information"), value = 1, {
vars <- groupable_vars()
})
req(available(vars))
if (any(vars %in% input$qgb_vars)) {
vars <- base::setdiff(vars, input$qgb_vars)
names(vars) <- varnames() %>%
(function(x) x[match(vars, x)]) %>%
names()
}
isolate({
## if nothing is selected expl_byvar is also null
if ("qgb_byvar" %in% names(input) && is.null(input$qgb_byvar)) {
r_state$qgb_byvar <<- NULL
} else {
if (available(r_state$qgb_byvar) && all(r_state$qgb_byvar %in% vars)) {
vars <- unique(c(r_state$qgb_byvar, vars))
names(vars) <- varnames() %>%
(function(x) x[match(vars, x)]) %>%
names()
}
}
})
selectizeInput(
"qgb_byvar",
label = i18n$t("Group by:"), choices = vars,
selected = state_multiple("qgb_byvar", vars, isolate(input$qgb_byvar)),
multiple = TRUE,
options = list(
placeholder = i18n$t("Select group-by variable"),
plugins = list("remove_button", "drag_drop")
)
)
})
output$ui_qgb_fun <- renderUI({
r_funs <- getOption("radiant.functions")
selected <- isolate(
if (is.empty(input$qgb_fun)) default_funs else input$qgb_fun
)
tagList(
div(
style = "margin-top: 5px; display: flex; gap: 3px; flex-wrap: wrap;",
actionButton("qgb_select_all", i18n$t("Select All"),
class = "btn-xs btn-primary", icon = icon("check-square")),
actionButton("qgb_deselect_all", i18n$t("Deselect All"),
class = "btn-xs btn-primary", icon = icon("square")),
actionButton("qgb_invert_selection", i18n$t("Invert"),
class = "btn-xs btn-primary", icon = icon("undo"))
),
checkboxGroupInput(
inputId = "qgb_fun",
label = i18n$t("Apply function(s):"),
choices = r_funs,
selected = selected
)
)
})
output$ui_qgb_top <- renderUI({
if (is.empty(input$qgb_vars)) {
return()
}
top_var <- setNames(
c("fun", "var", "byvar"),
c(i18n$t("Function"), i18n$t("Variables"), i18n$t("Group by"))
)
if (is.empty(input$qgb_byvar)) top_var <- top_var[1:2]
selectizeInput(
"qgb_top",
label = i18n$t("Column header:"),
choices = top_var,
selected = state_single("qgb_top", top_var, isolate(input$qgb_top)),
multiple = FALSE
)
})
output$ui_qgb_name <- renderUI({
req(input$dataset)
textInput("qgb_name", i18n$t("Store as:"), "", placeholder = i18n$t("Provide a table name"))
})
output$ui_qgb_run <- renderUI({
## updates when dataset changes
req(input$dataset)
actionButton("qgb_run", i18n$t("OneClick table generation"), width = "100%", icon = icon("play", verify_fa = FALSE), class = "btn-success")
})
## add a spinning refresh icon if the table needs to be (re)calculated
run_refresh(qgb_args, "qgb", init = "vars", label = i18n$t("OneClick table generation"), relabel = i18n$t("Update table"))
#######################################
# Visualize data
#######################################
output$ui_qib_type <- renderUI({
selectInput(
inputId = "qib_type", label = i18n$t("Plot-type:"), choices = qib_type,
selected = state_single("qib_type", qib_type),
multiple = FALSE
)
})
output$ui_qib_nrobs <- renderUI({
req(input$qib_type == "scatter")
nrobs <- nrow(.get_data())
choices <- c("1,000" = 1000, "5,000" = 5000, "10,000" = 10000, "All" = -1) %>%
.[. < nrobs]
selectInput(
"qib_nrobs", i18n$t("Number of data points plotted:"),
choices = choices,
selected = state_single("qib_nrobs", choices, 1000)
)
})
## Y - variable
output$ui_qib_yvar <- renderUI({
req(input$qib_type)
vars <- varying_vars()
req(available(vars))
vars <- vars["date" != .get_class()[vars]]
if (input$qib_type %in% c("line", "bar", "scatter", "surface", "box")) {
vars <- vars["character" != .get_class()[vars]]
}
if (input$qib_type %in% c("box", "scatter")) {
vars <- vars["factor" != .get_class()[vars]]
}
selectizeInput(
inputId = "qib_yvar",
label = i18n$t("Y-variable:"),
choices = vars,
selected = state_multiple("qib_yvar", vars, isolate(input$qib_yvar)),
multiple = TRUE,
options = list(
placeholder = i18n$t("Select Y variable(s)"),
plugins = list("remove_button", "drag_drop")
)
)
})
## X - variable
output$ui_qib_xvar <- renderUI({
req(input$qib_type)
vars <- varying_vars()
req(available(vars))
if (input$qib_type == "dist") vars <- vars["date" != .get_class()[vars]]
if (input$qib_type == "density") vars <- vars["factor" != .get_class()[vars]]
if (input$qib_type %in% c("box", "bar")) vars <- groupable_vars_nonum()
selectizeInput(
inputId = "qib_xvar",
label = i18n$t("X-variable:"),
choices = vars,
selected = state_multiple("qib_xvar", vars, isolate(input$qib_xvar)),
multiple = TRUE,
options = list(
placeholder = i18n$t("Select X variable(s)"),
plugins = list("remove_button", "drag_drop")
)
)
})
output$ui_qib_comby <- renderUI({
checkboxInput(
"qib_comby", i18n$t("Combine Y-variables in one plot"),
state_init("qib_comby", FALSE)
)
})
output$ui_qib_combx <- renderUI({
checkboxInput(
"qib_combx", i18n$t("Combine X-variables in one plot"),
state_init("qib_combx", FALSE)
)
})
observeEvent(length(input$qib_xvar) < 2, {
updateCheckboxInput(session, "qib_combx", value = FALSE)
})
observeEvent(length(input$qib_yvar) < 2, {
updateCheckboxInput(session, "qib_comby", value = FALSE)
})
observeEvent(input$qib_type, {
if (input$qib_type %in% c("dist", "density")) {
updateCheckboxInput(session, "qib_comby", value = FALSE)
} else {
updateCheckboxInput(session, "qib_combx", value = FALSE)
}
})
observeEvent(input$qib_check, {
if (!"loess" %in% input$qib_check && input$qib_smooth != 1) {
updateSliderInput(session, "qib_smooth", value = 1)
}
})
# output$ui_qib_facet_row <- renderUI({
# vars <- c("None" = ".", groupable_vars_nonum())
# selectizeInput(
# "qib_facet_row", i18n$t("Facet row:"), vars,
# selected = state_single("qib_facet_row", vars, init = "."),
# multiple = FALSE
# )
# })
# output$ui_qib_facet_col <- renderUI({
# vars <- c("None" = ".", groupable_vars_nonum())
# selectizeInput(
# "qib_facet_col", i18n$t("Facet column:"), vars,
# selected = state_single("qib_facet_col", vars, init = "."),
# multiple = FALSE
# )
# })
# output$ui_qib_color <- renderUI({
# req(input$qib_type)
# if (input$qib_type == "line") {
# vars <- c("None" = "none", groupable_vars())
# } else {
# vars <- c("None" = "none", varnames())
# }
#
# if (isTRUE(input$qib_comby) && length(input$qib_yvar) > 1) vars <- c("None" = "none")
# selectizeInput(
# "qib_color", i18n$t("Color:"), vars,
# multiple = FALSE,
# selected = state_single("qib_color", vars, init = "none")
# )
# })
# output$ui_qib_fill <- renderUI({
# vars <- c("None" = "none", groupable_vars())
# if (isTRUE(input$qib_combx) && length(input$qib_xvar) > 1) vars <- vars[1]
# selectizeInput(
# "qib_fill", i18n$t("Fill:"), vars,
# multiple = FALSE,
# selected = state_single("qib_fill", vars, init = "none")
# )
# })
# output$ui_qib_size <- renderUI({
# req(input$qib_type)
# isNum <- .get_class() %in% c("integer", "numeric", "ts")
# vars <- c("None" = "none", varnames()[isNum])
# if (isTRUE(input$qib_comby) && length(input$qib_yvar) > 1) vars <- c("None" = "none")
# selectizeInput(
# "qib_size", i18n$t("Size:"), vars,
# multiple = FALSE,
# selected = state_single("qib_size", vars, init = "none")
# )
# })
output$ui_qib_axes <- renderUI({
req(input$qib_type)
ind <- 1
if (input$qib_type %in% c("line", "scatter", "surface")) {
ind <- 1:3
} else if (input$qib_type == "dist") {
ind <- c(1:2, 5)
} else if (input$qib_type == "density") {
ind <- 1:2
} else if (input$qib_type %in% c("bar", "box")) {
ind <- c(1, 3)
}
if (!is.empty(input$qib_facet_row, ".") || !is.empty(input$qib_facet_col, ".")) ind <- c(ind, 4)
if (input$qib_type == "bar") ind <- c(ind, 6)
checkboxGroupInput(
"qib_axes", NULL, qib_axes[ind],
selected = state_group("qib_axes", ""),
inline = TRUE
)
})
output$ui_qib_check <- renderUI({
req(input$qib_type)
if (input$qib_type == "scatter") {
ind <- 1:3
} else if (input$qib_type == "box") {
ind <- 3
} else if (input$qib_type == "surface") {
ind <- 4
} else {
ind <- c()
}
if (!input$qib_type %in% c("scatter", "box")) {
r_state$qib_check <<- gsub("jitter", "", r_state$qib_check)
}
if (input$qib_type != "scatter") {
r_state$qib_check <<- gsub("line", "", r_state$qib_check)
r_state$qib_check <<- gsub("loess", "", r_state$qib_check)
}
checkboxGroupInput(
"qib_check", NULL, qib_check[ind],
selected = state_group("qib_check", ""),
inline = TRUE
)
})
output$ui_qib_run <- renderUI({
req(input$dataset)
actionButton("qib_run", i18n$t("OneClick chart generation"), width = "100%", icon = icon("play", verify_fa = FALSE), class = "btn-success")
})
# output$ui_qib_labs <- renderUI({
# ## updates when dataset changes
# req(input$dataset)
# wellPanel(
# textAreaInput("qib_labs_title", NULL, "", placeholder = i18n$t("Title"), rows = 1),
# textAreaInput("qib_labs_subtitle", NULL, "", placeholder = i18n$t("Subtitle"), rows = 1),
# textAreaInput("qib_labs_caption", NULL, "", placeholder = i18n$t("Caption"), rows = 1),
# textAreaInput("qib_labs_y", NULL, "", placeholder = i18n$t("Y-label"), rows = 1),
# textAreaInput("qib_labs_x", NULL, "", placeholder = i18n$t("X-label"), rows = 1)
# )
# })
output$ui_qib_colors <- renderUI({
tagList(
conditionalPanel(
condition = "input.qib_type == 'bar' ||
input.qib_type == 'dist' ||
input.qib_type == 'box' ||
input.qib_type == 'density'",
selectInput(
"qib_fillcol", i18n$t("Fill color:"),
choices = colors(),
selected = state_single("qib_fillcol", colors(), "blue")
)
),
conditionalPanel(
condition = "input.qib_type == 'dist' ||
input.qib_type == 'density' ||
input.qib_type == 'box' ||
input.qib_type == 'scatter' ||
input.qib_type == 'line'",
selectInput(
"qib_linecol", i18n$t("Line color:"),
choices = colors(),
selected = state_single("qib_linecol", colors(), "black")
)
),
conditionalPanel(
condition = "input.qib_type == 'scatter' ||
input.qib_type == 'line' ||
input.qib_type == 'box'",
selectInput(
"qib_pointcol", i18n$t("Point color:"),
choices = colors(),
selected = state_single("qib_pointcol", colors(), "black")
)
)
)
})
run_refresh(
qib_args, "qib",
init = c("xvar", "yvar"), label = i18n$t("OneClick chart generation"), relabel = i18n$t("Update plot"),
inputs = c("labs_title", "labs_subtitle", "labs_caption", "labs_y", "labs_x")
)
output$ui_quickgen_basic <- renderUI({
tagList(
wellPanel(
uiOutput("ui_qgb_run")
),
checkboxInput("qgb_details_table", i18n$t("Table"), state_init("qgb_details_table", FALSE)),
conditionalPanel(
"input.qgb_details_table == true",
wellPanel(
uiOutput("ui_qgb_vars"),
uiOutput("ui_qgb_byvar"),
uiOutput("ui_qgb_top"),
# returnTextAreaInput("qgb_tab_slice",
# label = i18n$t("Table slice (rows):"),
# rows = 1,
# value = state_init("qgb_tab_slice"),
# placeholder = i18n$t("e.g., 1:5 and press return")
# ),
uiOutput("ui_qgb_fun"),
numericInput("qgb_dec", label = i18n$t("Decimals:"), value = state_init("qgb_dec", 3), min = 0),
tags$table(
tags$td(uiOutput("ui_qgb_name")),
tags$td(actionButton("qgb_store", i18n$t("Store"), icon = icon("plus", verify_fa = FALSE)), class = "top")
)
),
),
wellPanel(
uiOutput("ui_qib_run")
),
checkboxInput("qib_details_chart", i18n$t("Chart"), state_init("qib_details_chart", FALSE)),
conditionalPanel(
"input.qib_details_chart == true",
wellPanel(
uiOutput("ui_qib_type"),
conditionalPanel(
"input.qib_type == 'scatter'",
uiOutput("ui_qib_nrobs")
),
conditionalPanel(
condition = "input.qib_type != 'dist' && input.qib_type != 'density'",
uiOutput("ui_qib_yvar"),
conditionalPanel(
"input.qib_yvar != undefined && input.qib_yvar != null && input.qib_yvar.length > 1",
uiOutput("ui_qib_comby")
)
),
uiOutput("ui_qib_xvar"),
conditionalPanel(
"input.qib_type == 'dist' || input.qib_type == 'density'",
conditionalPanel(
"input.qib_xvar != undefined && input.qib_xvar != null && input.qib_xvar.length > 1",
uiOutput("ui_qib_combx")
)
),
# uiOutput("ui_qib_facet_row"),
# uiOutput("ui_qib_facet_col"),
# conditionalPanel(
# condition = "input.qib_type == 'bar' ||
# input.qib_type == 'dist' ||
# input.qib_type == 'density' ||
# input.qib_type == 'surface'",
# uiOutput("ui_qib_fill")
# ),
# conditionalPanel(
# condition = "input.qib_type == 'scatter' ||
# input.qib_type == 'line' ||
# input.qib_type == 'box'",
# uiOutput("ui_qib_color")
# ),
# conditionalPanel(
# condition = "input.qib_type == 'scatter'",
# uiOutput("ui_qib_size")
# ),
conditionalPanel(
condition = "input.qib_type == 'bar' ||
input.qib_type == 'scatter' ||
input.qib_type == 'line'",
selectInput(
"qib_fun", i18n$t("Function:"),
choices = getOption("radiant.functions"),
selected = state_single("qib_fun", getOption("radiant.functions"), "mean")
)
),
# conditionalPanel(
# condition = "input.qib_type == 'scatter' ||
# input.qib_type == 'line' ||
# input.qib_type == 'surface' ||
# input.qib_type == 'box'",
# uiOutput("ui_qib_check")
# ),
# uiOutput("ui_qib_axes"),
conditionalPanel(
condition = "input.qib_type == 'dist'",
sliderInput(
"qib_bins",
label = i18n$t("Number of bins:"),
value = state_init("qib_bins", 10),
min = 2, max = 50, step = 1
)
),
conditionalPanel(
"input.qib_type == 'density' ||
input.qib_type == 'dist' && (input.qib_axes && input.qib_axes.indexOf('density')) >= 0 ||
(input.qib_type == 'scatter' && (input.qib_check && input.qib_check.indexOf('loess') >= 0))",
sliderInput(
"qib_smooth",
label = i18n$t("Smooth:"),
value = state_init("qib_smooth", 1),
min = 0.1, max = 3, step = .1
)
)
)
),
# checkboxInput("qib_details_labels", i18n$t("Labels"), state_init("qib_details_labels", FALSE)),
# conditionalPanel(
# "input.qib_details_labels == true",
# uiOutput("ui_qib_labs")
# ),
checkboxInput("qib_details_style", i18n$t("Style"), state_init("qib_details_style", FALSE)),
conditionalPanel(
"input.qib_details_style == true",
wellPanel(
selectInput(
"qib_theme", i18n$t("Plot theme:"),
choices = qib_theme,
selected = state_single("qib_theme", qib_theme, "theme_gray")
),
numericInput(
"qib_base_size", i18n$t("Base font size:"),
value = state_init("qib_base_size", 11)
),
selectInput(
"qib_base_family", i18n$t("Font family:"),
choices = qib_base_family,
selected = state_single("qib_base_family", qib_base_family, "helvetica")
),
uiOutput("ui_qib_colors"),
sliderInput(
"qib_alpha",
label = i18n$t("Opacity:"),
value = state_init("qib_alpha", .5),
min = 0, max = 1, step = .01
),
tags$table(
tags$td(
numericInput(
"qib_plot_height",
label = i18n$t("Plot height:"), min = 100,
max = 2000, step = 50,
value = state_init("qib_plot_height", r_info[["plot_height"]]),
width = "117px"
)
),
tags$td(
numericInput(
"qib_plot_width",
label = i18n$t("Plot width:"), min = 100,
max = 2000, step = 50,
value = state_init("qib_plot_width", r_info[["plot_width"]]),
width = "117px"
),
width = "100%"
)
)
)
),
help_and_report(
modal_title = i18n$t("Generate descriptive statistics with one click"), fun_name = "quickgen_basic",
help_file = inclMD(file.path(getOption("radiant.path.quickgen"), "app/tools/help/quickgen_basic.md")),
lic = "by-sa"
)
)
})
qib_plot_width <- reactive({
if (is.empty(input$qib_plot_width)) r_info[["plot_width"]] else input$qib_plot_width
})
qib_plot_height <- eventReactive(
{
input$qib_run
input$qib_plot_height
input$qib_plot_width
},
{
if (is.empty(input$qib_plot_height)) {
r_info[["plot_height"]]
} else {
lx <- ifelse(not_available(input$qib_xvar) || isTRUE(input$qib_combx), 1, length(input$qib_xvar))
ly <- ifelse(not_available(input$qib_yvar) || input$qib_type %in% c("dist", "density") ||
isTRUE(input$qib_comby), 1, length(input$qib_yvar))
nr <- lx * ly
if (nr > 1) {
(input$qib_plot_height / 2) * ceiling(nr / 2)
} else {
input$qib_plot_height
}
}
}
)
.quickgen <- eventReactive(input$qgb_run, {
if (not_available(input$qgb_vars) || is.null(input$qgb_top)) {
return()
} else if (!is.empty(input$qgb_byvar) && not_available(input$qgb_byvar)) {
return()
} else if (available(input$qgb_byvar) && any(input$qgb_byvar %in% input$qgb_vars)) {
return()
}
qgbi <- qgb_inputs()
qgbi$envir <- r_data
sshhr(do.call(explore, qgbi))
})
observeEvent(input$qgb_search_columns, {
r_state$qgb_search_columns <<- input$qgb_search_columns
})
observeEvent(input$qgb_state, {
r_state$qgb_state <<- input$qgb_state
})
qgb_reset <- function(var, ncol) {
if (!identical(r_state[[var]], input[[var]])) {
r_state[[var]] <<- input[[var]]
r_state$qgb_state <<- list()
r_state$qgb_search_columns <<- rep("", ncol)
}
}
output$qgb_tab <- DT::renderDataTable({
input$qgb_run
withProgress(message = i18n$t("Generating explore table"), value = 1, {
isolate({
qgb <- .quickgen()
req(!is.null(qgb))
qgb$shiny <- TRUE
## resetting DT when changes occur
nc <- ncol(qgb$tab)
qgb_reset("qgb_vars", nc)
qgb_reset("qgb_byvar", nc)
qgb_reset("qgb_fun", nc)
if (!is.null(r_state$qgb_top) &&
!is.null(input$qgb_top) &&
!identical(r_state$qgb_top, input$qgb_top)) {
r_state$qgb_top <<- input$qgb_top
r_state$qgb_state <<- list()
r_state$qgb_search_columns <<- rep("", nc)
}
searchCols <- lapply(r_state$qgb_search_columns, function(x) list(search = x))
order <- r_state$qgb_state$order
pageLength <- r_state$qgb_state$length
})
caption <- if (is.empty(input$qgb_tab_slice)) NULL else glue("Table slice {input$expl_tab_slice} will be applied on Download, Store, or Report")
dtab(
qgb,
dec = input$qgb_dec, searchCols = searchCols, order = order,
pageLength = pageLength,
caption = caption
)
})
})
dl_qgb_tab <- function(path) {
dat <- try(.quickgen(), silent = TRUE)
if (inherits(dat, "try-error") || is.null(dat)) {
write.csv(tibble::tibble("Data" = "[Empty]"), path, row.names = FALSE)
return()
}
rows <- input$qgb_rows_all
tmp <- if (is.null(rows)) dat$tab else dat$tab[rows, , drop = FALSE]
if (is.null(tmp) || nrow(tmp) == 0) {
write.csv(tibble::tibble("Data" = "[Empty]"), path, row.names = FALSE)
} else {
write.csv(tmp, path, row.names = FALSE)
}
}
output$qib_chart <- renderPlot(
{
req(input$qib_type)
if (not_available(input$qib_xvar)) {
if (!input$qib_type %in% c("box", "line")) {
return(
plot(
x = 1, type = "n",
main = " ",
axes = FALSE, xlab = "", ylab = "", cex.main = .9
)
)
}
}
.qib_chart() %>%
(function(x) {
if (is.empty(x) || is.character(x)) {
plot(x = 1, type = "n", main = paste0("\n", x), axes = FALSE, xlab = "", ylab = "", cex.main = .9)
} else if (length(x) > 0) {
print(x)
}
})
},
width = qib_plot_width,
height = qib_plot_height,
res = 96
)
.qib_chart <- eventReactive(input$qib_run, {
req(input$qib_type)
if (input$qib_type == "scatter") req(input$qib_nrobs)
## need dependency on ..
req(input$qib_plot_height && input$qib_plot_width)
if (not_available(input$qib_xvar) && !input$qib_type %in% c("box", "line")) {
return()
} else if (input$qib_type %in% c("scatter", "line", "box", "bar", "surface") && not_available(input$qib_yvar)) {
return(i18n$t("No Y-variable provided for a plot that requires one"))
} else if (input$qib_type == "box" && !all(input$qib_xvar %in% groupable_vars())) {
return()
}
## waiting for comby and/or combx to be updated
if (input$qib_type %in% c("dist", "density")) {
if (isTRUE(input$qib_comby)) {
return()
}
if (length(input$qib_xvar) > 1 && is.null(input$qib_combx)) {
return()
}
} else {
if (isTRUE(input$qib_combx)) {
return()
}
if (length(input$qib_yvar) > 1 && is.null(input$qib_comby)) {
return()
}
}
#req(!is.null(input$qib_color) || !is.null(input$qib_fill))
qibi <- qib_inputs()
qibi$dataset <- input$dataset
qibi$shiny <- TRUE
qibi$envir <- r_data
qibi$color <- "none"
qibi$fill <- "none"
qibi$facet_row <- "."
qibi$facet_col <- "."
withProgress(message = i18n$t("Making plot"), value = 1, {
do.call(visualize, qibi)
})
})
observeEvent(input$qgb_store, {
req(input$qgb_name)
dat <- .quickgen()
if (is.null(dat)) {
return()
}
dataset <- fix_names(input$qgb_name)
if (input$qgb_name != dataset) {
updateTextInput(session, inputId = "qgb_name", value = dataset)
}
rows <- input$qgb_rows_all
tmp <- if (is.null(rows)) dat$tab else dat$tab[rows, , drop = FALSE]
r_data[[dataset]] <- tmp
register(dataset)
updateSelectInput(session, "dataset", selected = input$dataset)
showModal(
modalDialog(
title = i18n$t("Data Stored"),
span(
i18n$t("Dataset was successfully added to the datasets dropdown. Add code to Report > Rmd or Report > R to (re)create the results by clicking the report icon on the bottom left of your screen.")
),
footer = modalButton(i18n$t("OK")),
size = "m",
easyClose = TRUE
)
)
})
qgb_report <- function() {
## get the state of the dt table
ts <- dt_state("qgb_tab")
xcmd <- "# summary(result)\ndtab(result"
if (!is.empty(input$qgb_dec, 3)) {
xcmd <- paste0(xcmd, ", dec = ", input$qgb_dec)
}
if (!is.empty(r_state$qgb_state$length, 10)) {
xcmd <- paste0(xcmd, ", pageLength = ", r_state$qgb_state$length)
}
xcmd <- paste0(xcmd, ", caption = \"\") %>% render()")
if (!is.empty(input$qgb_name)) {
dataset <- fix_names(input$qgb_name)
if (input$qgb_name != dataset) {
updateTextInput(session, inputId = "qgb_name", value = dataset)
}
xcmd <- paste0(xcmd, "\n", dataset, " <- result$tab\nregister(\"", dataset, "\")")
}
inp_main <- clean_args(qgb_inputs(), qgb_args)
if (ts$tabsort != "") inp_main <- c(inp_main, tabsort = ts$tabsort)
if (ts$tabfilt != "") inp_main <- c(inp_main, tabfilt = ts$tabfilt)
if (is.empty(inp_main$rows)) {
inp_main$rows <- NULL
}
if (is.empty(input$qgb_tab_slice)) {
inp_main <- c(inp_main, nr = Inf)
} else {
inp_main$tabslice <- input$qgb_tab_slice
}
inp_out <- list(clean_args(qgb_sum_inputs(), qgb_sum_args[-1]))
update_report(
inp_main = inp_main,
fun_name = "qgb",
inp_out = inp_out,
outputs = c(),
figs = FALSE,
xcmd = xcmd
)
}
qib_report <- function() {
## resetting hidden elements to default values
vi <- qib_inputs()
if (input$qib_type != "dist") {
vi$bins <- qib_args$bins
}
if (input$qib_type %in% c("dist", "density")) {
vi$yvar <- qib_args$yvar
}
if (!input$qib_type %in% c("density", "scatter", "dist") ||
!("loess" %in% input$qib_check || "density" %in% input$qib_axes || input$qib_type == "density")) {
vi$smooth <- qib_args$smooth
}
if (!input$qib_type %in% c("scatter", "box") && "jitter" %in% input$qib_check) {
vi$check <- base::setdiff(vi$check, "jitter")
}
if (input$qib_type != "scatter") {
vi$size <- "none"
vi$nrobs <- NULL
} else {
vi$nrobs <- as_integer(vi$nrobs)
}
if (!input$qib_type %in% c("scatter", "line", "box")) {
vi$color <- NULL
}
if (!input$qib_type %in% c("bar", "dist", "density", "surface")) {
vi$fill <- NULL
}
if (!input$qib_type %in% c("bar", "dist", "box", "density")) {
vi$fillcol <- "blue"
}
if (!input$qib_type %in% c("dist", "density", "box", "scatter", "line")) {
vi$linecol <- "black"
}
if (!input$qib_type %in% c("box", "scatter", "line")) {
vi$pointcol <- "black"
}
if (!input$qib_type %in% c("bar", "line", "scatter")) {
vi$fun <- "mean"
}
if (is.empty(input$data_rows)) {
vi$rows <- NULL
}
inp_main <- c(clean_args(vi, qib_args), custom = FALSE)
update_report(
inp_main = inp_main,
fun_name = "qib_chart",
outputs = character(0),
pre_cmd = "",
figs = TRUE,
fig.width = qib_plot_width(),
fig.height = qib_plot_height()
)
}
download_handler(
id = "dl_qgb_tab",
fun = dl_qgb_tab,
fn = function() paste0(input$dataset, "_tab"),
type = "csv"
)
download_handler(
id = "dlp_qib_chart",
fun = download_handler_plot,
fn = function() paste0(input$dataset, "_chart"),
type = "png",
caption = i18n$t("Save visualize plot"),
plot = .qib_chart,
width = qib_plot_width,
height = qib_plot_height
)
observeEvent(input$qgb_report, {
r_info[["latest_screenshot"]] <- NULL
qgb_report()
})
observeEvent(input$qgb_screenshot, {
r_info[["latest_screenshot"]] <- NULL
radiant_screenshot_modal("modal_qgb_screenshot")
})
observeEvent(input$modal_qgb_screenshot, {
qgb_report()
removeModal()
})
observeEvent(input$qib_report, {
r_info[["latest_screenshot"]] <- NULL
qib_report()
})
observeEvent(input$qib_screenshot, {
r_info[["latest_screenshot"]] <- NULL
radiant_screenshot_modal("modal_qib_screenshot")
})
observeEvent(input$modal_qib_screenshot, {
qib_report()
removeModal()
})
# 全选功能
observeEvent(input$qgb_select_all, {
r_funs <- getOption("radiant.functions")
if (!is.null(r_funs)) {
updateCheckboxGroupInput(session, "qgb_fun", selected = r_funs)
}
})
# 全不选功能
observeEvent(input$qgb_deselect_all, {
updateCheckboxGroupInput(session, "qgb_fun", selected = character(0))
})
# 反选功能
observeEvent(input$qgb_invert_selection, {
current <- input$qgb_fun %||% character(0)
r_funs <- getOption("radiant.functions")
if (!is.null(r_funs)) {
new_selection <- setdiff(r_funs, current)
updateCheckboxGroupInput(session, "qgb_fun", selected = new_selection)
}
})
output$quickgen_basic <- renderUI({
stat_tab_panel(
menu = i18n$t("Oneclick generation > Generate descriptive statistics"),
tool = i18n$t("Generate descriptive statistics with one click"),
tool_ui = "ui_quickgen_basic",
output_panels = tagList(
tabPanel(
title = i18n$t("Table"),
download_link("dl_qgb_tab"),br(),
DT::dataTableOutput("qgb_tab"),
tags$hr(),
htmlOutput("qgb_desc_text", inline = FALSE)
),
tabPanel(
title = i18n$t("Chart"),
download_link("dlp_qib_chart"), br(),
plotOutput("qib_chart", width = "100%", height = "auto"),
tags$hr(),
htmlOutput("qib_desc_text", inline = FALSE)
)
)
)
})
## ---------- 表格的描述文字 ----------
output$qgb_desc_text <- renderUI({
dat <- tryCatch(.quickgen(), error = function(e) NULL)
txt <- if (!is.null(dat) && !is.null(dat$tab)) {
make_desc_text(dat$tab)
} else {
" "
}
HTML(markdown::markdownToHTML(text = txt, fragment.only = TRUE))
})
## ---------- 图表的描述文字 ----------
output$qib_desc_text <- renderUI({
df <- tryCatch({
qibi <- qib_inputs()
qibi$envir <- r_data
qibi$dataset <- input$dataset
qibi$shiny <- TRUE
res <- do.call(visualize, qibi)
res$data
}, error = function(e) NULL)
txt <- if (!is.null(df)) make_desc_text(df) else " "
HTML(markdown::markdownToHTML(text = txt, fragment.only = TRUE))
})
\ No newline at end of file
radiant.quickgen/inst/app/tools/analysis/quickgen_basic_ui.R
View file @ d3861e30
safe_is_empty <- function(x) {
if (is.null(x) || !is.character(x)) return(TRUE)
is.empty(x)
}
make_desc_text <- function(df) {
if (is.null(df) || nrow(df) == 0) return(i18n$t("No data available"))
num_cols <- sapply(df, is.numeric)
......@@ -68,7 +73,7 @@ qib_add_labs <- function() {
lab_list <- list()
for (l in qib_labs) {
inp <- input[[paste0("qib_labs_", l)]]
if (!is.empty(inp)) lab_list[[l]] <- inp
if (!safe_is_empty(inp)) lab_list[[l]] <- inp
}
lab_list
}
......@@ -402,7 +407,7 @@ output$ui_qib_axes <- renderUI({
} else if (input$qib_type %in% c("bar", "box")) {
ind <- c(1, 3)
}
if (!is.empty(input$qib_facet_row, ".") || !is.empty(input$qib_facet_col, ".")) ind <- c(ind, 4)
if (!safe_is_empty(input$qib_facet_row, ".") || !safe_is_empty(input$qib_facet_col, ".")) ind <- c(ind, 4)
if (input$qib_type == "bar") ind <- c(ind, 6)
checkboxGroupInput(
......@@ -676,7 +681,7 @@ output$ui_quickgen_basic <- renderUI({
})
qib_plot_width <- reactive({
if (is.empty(input$qib_plot_width)) r_info[["plot_width"]] else input$qib_plot_width
if (safe_is_empty(input$qib_plot_width)) r_info[["plot_width"]] else input$qib_plot_width
})
qib_plot_height <- eventReactive(
......@@ -686,7 +691,7 @@ qib_plot_height <- eventReactive(
input$qib_plot_width
},
{
if (is.empty(input$qib_plot_height)) {
if (safe_is_empty(input$qib_plot_height)) {
r_info[["plot_height"]]
} else {
lx <- ifelse(not_available(input$qib_xvar) || isTRUE(input$qib_combx), 1, length(input$qib_xvar))
......@@ -786,67 +791,37 @@ dl_qgb_tab <- function(path) {
}
output$qib_chart <- renderPlot(
{
output$qib_chart <- renderPlot({
req(input$qib_type)
if (not_available(input$qib_xvar)) {
if (!input$qib_type %in% c("box", "line")) {
return(
plot(
x = 1, type = "n",
main = " ",
axes = FALSE, xlab = "", ylab = "", cex.main = .9
)
)
}
}
.qib_chart() %>%
(function(x) {
if (is.empty(x) || is.character(x)) {
plot(x = 1, type = "n", main = paste0("\n", x), axes = FALSE, xlab = "", ylab = "", cex.main = .9)
} else if (length(x) > 0) {
print(x)
}
})
},
width = qib_plot_width,
height = qib_plot_height,
res = 96
)
p <- .qib_chart()
if (is.null(p)) return(NULL)
print(p)
}, width = qib_plot_width, height = qib_plot_height, res = 96)
.qib_chart <- eventReactive(input$qib_run, {
req(input$qib_type)
if (input$qib_type == "scatter") req(input$qib_nrobs)
## need dependency on ..
req(input$qib_plot_height && input$qib_plot_width)
if (not_available(input$qib_xvar) && !input$qib_type %in% c("box", "line")) {
return()
} else if (input$qib_type %in% c("scatter", "line", "box", "bar", "surface") && not_available(input$qib_yvar)) {
return(i18n$t("No Y-variable provided for a plot that requires one"))
} else if (input$qib_type == "box" && !all(input$qib_xvar %in% groupable_vars())) {
return()
return(NULL)
}
## waiting for comby and/or combx to be updated
if (input$qib_type %in% c("dist", "density")) {
if (isTRUE(input$qib_comby)) {
return()
if (input$qib_type %in% c("scatter", "line", "box", "bar", "surface") && not_available(input$qib_yvar)) {
return(NULL)
}
if (length(input$qib_xvar) > 1 && is.null(input$qib_combx)) {
return()
if (input$qib_type == "box" && !all(input$qib_xvar %in% groupable_vars())) {
return(NULL)
}
## 等待 combx / comby 更新
if (input$qib_type %in% c("dist", "density")) {
if (isTRUE(input$qib_comby)) return(NULL)
if (length(input$qib_xvar) > 1 && is.null(input$qib_combx)) return(NULL)
} else {
if (isTRUE(input$qib_combx)) {
return()
}
if (length(input$qib_yvar) > 1 && is.null(input$qib_comby)) {
return()
}
if (isTRUE(input$qib_combx)) return(NULL)
if (length(input$qib_yvar) > 1 && is.null(input$qib_comby)) return(NULL)
}
#req(!is.null(input$qib_color) || !is.null(input$qib_fill))
qibi <- qib_inputs()
qibi$dataset <- input$dataset
qibi$shiny <- TRUE
......@@ -855,8 +830,10 @@ output$qib_chart <- renderPlot(
qibi$fill <- "none"
qibi$facet_row <- "."
qibi$facet_col <- "."
withProgress(message = i18n$t("Making plot"), value = 1, {
do.call(visualize, qibi)
p <- do.call(visualize, qibi)
if (is.character(p)) return(NULL) else p
})
})
......@@ -889,154 +866,95 @@ observeEvent(input$qgb_store, {
)
})
qgb_report <- function() {
## get the state of the dt table
ts <- dt_state("qgb_tab")
xcmd <- "# summary(result)\ndtab(result"
if (!is.empty(input$qgb_dec, 3)) {
xcmd <- paste0(xcmd, ", dec = ", input$qgb_dec)
}
if (!is.empty(r_state$qgb_state$length, 10)) {
xcmd <- paste0(xcmd, ", pageLength = ", r_state$qgb_state$length)
}
download_handler(
id = "dl_qgb_tab",
fun = dl_qgb_tab,
fn = function() paste0(input$dataset, "_tab"),
type = "csv"
)
download_handler(
id = "dlp_qib_chart",
fun = download_handler_plot,
fn = function() paste0(input$dataset, "_chart"),
type = "png",
caption = i18n$t("Save visualize plot"),
plot = .qib_chart,
width = qib_plot_width,
height = qib_plot_height
)
quickgen_basic_report <- function() {
req(input$qgb_vars, input$qib_type)
##-------------- 表 格 部 分 --------------
tabsort <- if (!is.null(input$qgb_state$order))
paste0("order = ", jsonlite::toJSON(input$qgb_state$order)) else ""
tabfilt <- if (!is.null(input$qgb_search_columns))
paste0("searchCols = ", jsonlite::toJSON(lapply(input$qgb_search_columns, function(x) list(search = x)))) else ""
xcmd <- "# quickgen table: descriptive statistics\ndtab(result"
if (!is.empty(input$qgb_dec, 3)) xcmd <- paste0(xcmd, ", dec = ", input$qgb_dec)
if (!is.empty(r_state$qgb_state$length, 10)) xcmd <- paste0(xcmd, ", pageLength = ", r_state$qgb_state$length)
if (nzchar(tabsort)) xcmd <- paste0(xcmd, ", ", tabsort)
if (nzchar(tabfilt)) xcmd <- paste0(xcmd, ", ", tabfilt)
xcmd <- paste0(xcmd, ", caption = \"\") %>% render()")
if (!is.empty(input$qgb_name)) {
dataset <- fix_names(input$qgb_name)
if (input$qgb_name != dataset) {
if (input$qgb_name != dataset)
updateTextInput(session, inputId = "qgb_name", value = dataset)
}
xcmd <- paste0(xcmd, "\n", dataset, " <- result$tab\nregister(\"", dataset, "\")")
}
inp_main <- clean_args(qgb_inputs(), qgb_args)
if (ts$tabsort != "") inp_main <- c(inp_main, tabsort = ts$tabsort)
if (ts$tabfilt != "") inp_main <- c(inp_main, tabfilt = ts$tabfilt)
if (is.empty(inp_main$rows)) {
inp_main$rows <- NULL
}
if (is.empty(input$qgb_tab_slice)) {
inp_main <- c(inp_main, nr = Inf)
} else {
inp_main$tabslice <- input$qgb_tab_slice
}
inp_out <- list(clean_args(qgb_sum_inputs(), qgb_sum_args[-1]))
update_report(
inp_main = inp_main,
fun_name = "qgb",
inp_out = inp_out,
outputs = c(),
figs = FALSE,
xcmd = xcmd
)
}
if (is.empty(inp_main$rows)) inp_main$rows <- NULL
if (!is.empty(input$qgb_tab_slice)) inp_main$tabslice <- input$qgb_tab_slice
qib_report <- function() {
## resetting hidden elements to default values
##-------------- 图 形 部 分 --------------
vi <- qib_inputs()
if (input$qib_type != "dist") {
vi$bins <- qib_args$bins
}
if (input$qib_type %in% c("dist", "density")) {
vi$yvar <- qib_args$yvar
}
if (input$qib_type != "dist") vi$bins <- qib_args$bins
if (input$qib_type %in% c("dist", "density")) vi$yvar <- qib_args$yvar
if (!input$qib_type %in% c("density", "scatter", "dist") ||
!("loess" %in% input$qib_check || "density" %in% input$qib_axes || input$qib_type == "density")) {
!("loess" %in% input$qib_check || "density" %in% input$qib_axes || input$qib_type == "density"))
vi$smooth <- qib_args$smooth
}
if (!input$qib_type %in% c("scatter", "box") && "jitter" %in% input$qib_check) {
if (!input$qib_type %in% c("scatter", "box") && "jitter" %in% input$qib_check)
vi$check <- base::setdiff(vi$check, "jitter")
}
if (input$qib_type != "scatter") {
vi$size <- "none"
vi$nrobs <- NULL
} else {
vi$nrobs <- as_integer(vi$nrobs)
}
if (!input$qib_type %in% c("scatter", "line", "box")) {
vi$color <- NULL
}
if (!input$qib_type %in% c("bar", "dist", "density", "surface")) {
vi$fill <- NULL
}
if (!input$qib_type %in% c("scatter", "line", "box")) vi$color <- NULL
if (!input$qib_type %in% c("bar", "dist", "density", "surface")) vi$fill <- NULL
if (!input$qib_type %in% c("bar", "dist", "box", "density")) vi$fillcol <- "blue"
if (!input$qib_type %in% c("dist", "density", "box", "scatter", "line")) vi$linecol <- "black"
if (!input$qib_type %in% c("box", "scatter", "line")) vi$pointcol <- "black"
if (!input$qib_type %in% c("bar", "line", "scatter")) vi$fun <- "mean"
if (safe_is_empty(input$data_rows)) vi$rows <- NULL
if (!input$qib_type %in% c("bar", "dist", "box", "density")) {
vi$fillcol <- "blue"
}
if (!input$qib_type %in% c("dist", "density", "box", "scatter", "line")) {
vi$linecol <- "black"
}
if (!input$qib_type %in% c("box", "scatter", "line")) {
vi$pointcol <- "black"
}
if (!input$qib_type %in% c("bar", "line", "scatter")) {
vi$fun <- "mean"
}
if (is.empty(input$data_rows)) {
vi$rows <- NULL
}
inp_main <- c(clean_args(vi, qib_args), custom = FALSE)
inp_main <- c(inp_main, clean_args(vi, qib_args), custom = FALSE)
##-------------- 一次性写进 Report --------------
update_report(
inp_main = inp_main,
fun_name = "qib_chart",
fun_name = "quickgen_basic", # 统一事件名
outputs = character(0),
pre_cmd = "",
figs = TRUE,
figs = TRUE, # 需要插图
fig.width = qib_plot_width(),
fig.height = qib_plot_height()
)
}
download_handler(
id = "dl_qgb_tab",
fun = dl_qgb_tab,
fn = function() paste0(input$dataset, "_tab"),
type = "csv"
)
download_handler(
id = "dlp_qib_chart",
fun = download_handler_plot,
fn = function() paste0(input$dataset, "_chart"),
type = "png",
caption = i18n$t("Save visualize plot"),
plot = .qib_chart,
width = qib_plot_width,
height = qib_plot_height
)
observeEvent(input$qgb_report, {
r_info[["latest_screenshot"]] <- NULL
qgb_report()
})
observeEvent(input$qgb_screenshot, {
r_info[["latest_screenshot"]] <- NULL
radiant_screenshot_modal("modal_qgb_screenshot")
})
observeEvent(input$modal_qgb_screenshot, {
qgb_report()
removeModal()
})
observeEvent(input$qib_report, {
observeEvent(input$quickgen_basic_report, {
r_info[["latest_screenshot"]] <- NULL
qib_report()
quickgen_basic_report()
})
observeEvent(input$qib_screenshot, {
observeEvent(input$quickgen_basic_screenshot, {
r_info[["latest_screenshot"]] <- NULL
radiant_screenshot_modal("modal_qib_screenshot")
radiant_screenshot_modal("modal_quickgen_basic_screenshot")
})
observeEvent(input$modal_qib_screenshot, {
qib_report()
observeEvent(input$modal_quickgen_basic_screenshot, {
quickgen_basic_report()
removeModal()
})
......
radiant.quickgen/inst/app/tools/help/quickgen_ai.md
View file @ d3861e30
> 大模型生成描述性统计
## 使用方法
以下是 `大模型生成描述性统计`的使用方法。
1.`数据对象`必须要有，且必须和数据集中的名字一致（大小写也一致）
2.`图像类型`必须要有，比如分布图、散点图等。可以要求模型返回多张图表，需向模型明确。
3.`生成代码`如果有误，或者想要修改，可以点击`编辑`按钮对R代码进行修改，保存后点击`运行`按钮即可。
## 示例
**1. 散点图**
请用diamonds画一个散点图，X轴是carat，Y轴是price，用color来区分颜色，并加上theme_bw()。
**2. 箱线图**
请用diamonds画箱线图，把price按cut分组，看看不同切工的价格分布。
**3. 直方图**
请用diamonds画carat的直方图，分面按clarity排布，bin宽度取0.1。
**4. 柱状图**
请用diamonds统计各color等级的数量，画一个柱状图，颜色按实际颜色填充。
**5. 密度图**
请用diamonds画出0.5~2克拉范围内，不同color的carat密度曲线，要求半透明重叠。
**6. 分组均值表**
请用diamonds按cut分组，计算每组price与carat的平均值、标准差，输出成表格。
**7. 价格对数-克拉线性拟合图**
请用diamonds画log(price)和carat的散点图，并加上回归直线，颜色按clarity区分，用theme_minimal()。
\ No newline at end of file
set_path.R
View file @ d3861e30
# 创建文件 /home/wuzekai/radiant/set_path.R
cat <<'EOF' | sudo tee /home/wuzekai/radiant/set_path.R
options(radiant.launch_dir = "/data")
options(radiant.project_dir = "/data")
......
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