primera versión de un scatter de IMC vs algunos químicos

estadisticas.py

@@ -51,6 +51,29 @@ def popdist(datos):
     )
     return fig
 
+def popcorr(datos):
+    medidas = [ "glucose", "hba1c", "ct", "hdl", "insuline"]
+    # removemos outliers
+    fig = make_subplots(rows=2, cols=3, shared_yaxes=False, subplot_titles=medidas)
+    lcol = 1
+    lrow = 1
+
+    for medida in medidas:
+        datosfil = datos[np.abs(stats.zscore(datos[medida], nan_policy='omit')) < 3]
+        fig.add_trace(
+            go.Scatter(x=datosfil["imc"], y=datosfil[medida], name=medida, mode="markers"),
+            row=lrow, col=lcol
+        )
+        lrow = lrow + (lcol // 3)
+        lcol = lcol + 1 if lcol < 3 else 1
+
+    fig.update_layout(
+        bargap=0.05, # gap between bars of adjacent location coordinates
+        bargroupgap=0.05, # gap between bars of the same location coordinates
+        showlegend = False
+    )
+    return fig
+
 
 
 def correlaciones(datos, controlado):
@@ -445,6 +468,12 @@ app.layout = html.Div(children=[
         html.Div(children= [ html.H1("Distribuciones de la población", className= "header-title2"),], ),
         dcc.Graph(figure=popdist(visitas_incluidos))
     ]),
+    # correlaciones entre indices de la poblacion
+    html.Div(children=[
+        html.Div(children= [ html.H1("Correlaciones entre medidas", className= "header-title2"),], ),
+        dcc.Graph(figure=popcorr(visitas_incluidos))
+    ]),
+
     # ppgr a controlados
     html.Div(children= [
         html.Div(children= [html.H1("iAUC/Controlados ", className = "header-title2")]),