RShinyProject/server.R at master · kellyho15/RShinyProject · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172


shinyServer(function(input, output){

  # heat map tab #################################
    # show map using googleVis 2013 (youth)
    output$Y_map2013 <- renderGvis({
        gvisGeoChart(y_heatmap_2013, "LocationDesc", input$Y_selectedMap2015,
                     options=list(region="US", displayMode="regions",
                                  resolution="provinces",
                                  width="auto", height="auto"))
    })
    # show map using googleVis 2013 (adult)
    output$A_map2013 <- renderGvis({
      gvisGeoChart(a_heatmap_2013, "LocationDesc", input$A_selectedMap2015,
                   options=list(region="US", displayMode="regions",
                                resolution="provinces",
                                width="auto", height="auto"))
    })


  # linear reg model tab #################################
    # lin reg general trend of Youth from state data across years
    # youth state data over 2007, 2009, 2011, 2013, 2015
    output$Y_trend_fru <- renderPlot(
      ggplot(y_spread, aes(x=`Fruit consumption`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +   # obesity vs fruit
        labs(x= "Fruit consumption population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$Y_trend_veg <- renderPlot(
      ggplot(y_spread, aes(x=`Vegetable consumption`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +   # obesity vs veggie
        labs(x= "Vegetable consumption population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$Y_trend_pa1 <- renderPlot(
      ggplot(y_spread, aes(x=`>1 hr daily physical activity`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs PA
        labs(x= ">1 hr daily physical activity population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$Y_trend_pa2 <- renderPlot(
      ggplot(y_spread, aes(x=`Daily PE class`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs PA
        labs(x= "Daily PE class population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )

    # lin reg general trend of Youth and Adult from state data across years
    # adult state data over 2011, 2013, 2015
    output$A_trend_fru <- renderPlot(
      ggplot(a_spread, aes(x=`Fruit consumption`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs fruit
        labs(x= "Fruit consumption population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_veg <- renderPlot(
      ggplot(a_spread, aes(x=`Vegetable consumption`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs veggie
        labs(x= "Vegetable consumption population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_pa1 <- renderPlot(
      ggplot(a_spread, aes(x=`Short duration aerobic`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +   # obesity vs PA
        labs(x= "Short duration aerobic population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_pa2 <- renderPlot(
      ggplot(a_spread, aes(x=`Short duration aerobic and strengthening`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs PA
        labs(x= "Short duration aerobic and strengthening population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_pa3 <- renderPlot(
      ggplot(a_spread, aes(x=`Long duration aerobic`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs PA
        labs(x= "Long duration aerobic population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_pa4 <- renderPlot(
      ggplot(a_spread, aes(x=`Strengthening`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +  # obesity vs PA
        labs(x= "Strengthening population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_trend_pa5 <- renderPlot(
      ggplot(a_spread, aes(x=`No physical activity`, y=Obesity)) + geom_point() + geom_smooth(method = 'lm') +   # obesity vs PA
        labs(x= "No physical activity population (%)", y="Obese population (%)") +
        theme(axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )


  # demographic tab #################################
    # show plot obesity vs demographic (youth) from state/national data across years
    output$Y_ob_stra <- renderPlot(
      ay_combine %>%
        filter(!Stratification == "Total", Question == "Obesity", Data == "youth",
               LocationDesc == input$Y_Selected_LocationDesc, StratificationCategory  == input$Y_Selected_cat) %>%
        ggplot(aes(x=Year, y=Data_Value)) + labs(y="Obese population (%)") +
        scale_y_continuous(limits = c(0, 40)) + scale_x_continuous(breaks = seq(2001, 2015, by = 2)) +
        geom_bar(stat ="identity", position = "dodge", fill="tomato1") + facet_grid(~ Stratification) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14, angle=90), axis.text.y = element_text(size=14))
    )
    # show plot obesity vs demographic (adult) from state/national data across years
    output$A_ob_stra <- renderPlot(
        if (input$A_Selected_cat == "Education"){
        edu %>%
        filter(Question == "Obesity") %>%
        ggplot(aes(x=Year, y=Data_Value)) + labs(y="Obese population (%)") +
        scale_y_continuous(limits = c(0, 40)) + scale_x_continuous(breaks = seq(2011, 2016, by = 1)) +
        geom_bar(stat ="identity", position = "dodge", fill="tomato1") + facet_grid(~ Stratification) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14, angle=90), axis.text.y = element_text(size=14))
        }else{
        ay_combine %>%
        filter(!Stratification == "Total", Question == "Obesity", Data == "adult",
               LocationDesc == input$A_Selected_LocationDesc, StratificationCategory == input$A_Selected_cat) %>%
        ggplot(aes(x=Year, y=Data_Value)) + labs(y="Obese population (%)") +
        scale_y_continuous(limits = c(0, 40)) + scale_x_continuous(breaks = seq(2011, 2016, by = 1)) +
        geom_bar(stat ="identity", position = "dodge", fill="tomato1") + facet_grid(~ Stratification) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14, angle=90), axis.text.y = element_text(size=14))
        }
    )


  # comparison tab #################################
    # box plot Obesity across age (Youth and Adult) from state data across years
    output$AY_ob_age <- renderPlot(
      ay_combine %>%
        filter(!Stratification == "Total", !LocationDesc == "National", Year == 2015,
               StratificationCategory == "Age", Question == "Obesity") %>%   #filter obesity, age data
        ggplot(aes(x=Stratification, y=Data_Value)) + labs(x= "Age", y="Obese population (%)") +
        geom_boxplot() + geom_point(alpha = 0.15) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    # box plot Youth and Adult Behavior Factor (Food) from state data across years
    output$AY_ob_food <- renderPlot(
     ay_combine %>%
        filter(!Stratification == "Total", !LocationDesc == "National", Year == 2015,
               StratificationCategory == "Age", Question == input$AY_Selected_food) %>%
        ggplot(aes(x=Stratification, y=Data_Value)) + labs(x= "Age", y="Population (%)") +
        geom_boxplot() + geom_point(alpha = 0.15) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    # box plot Youth and Adult Behavior Factor (PA) from state data across years
    output$Y_ob_pa <- renderPlot(
      ay_combine %>%
        filter(!Stratification == "Total", !LocationDesc == "National", Year == 2015,
               StratificationCategory == "Age", Question == input$Y_Selected_pa) %>%
        ggplot(aes(x=Stratification, y=Data_Value)) + labs(x= "Age", y="Population (%)") + scale_y_continuous(limits = c(10, 75)) +
        geom_boxplot() + geom_point(alpha = 0.05) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
    output$A_ob_pa <- renderPlot(
      ay_combine %>%
        filter(!Stratification == "Total", !LocationDesc == "National", Year == 2015,
               StratificationCategory == "Age", Question == input$A_Selected_pa) %>%
        ggplot(aes(x=Stratification, y=Data_Value)) + labs(x= "Age", y="Population (%)") + scale_y_continuous(limits = c(10, 75)) +
        geom_boxplot() + geom_point(alpha = 0.05) +
        theme(strip.text = element_text(size=15), axis.title = element_text(size=13),
              axis.text.x = element_text(size=14), axis.text.y = element_text(size=14))
    )
})