diff --git a/app.py b/app.py
index 78dd6f5..8fa263c 100644
--- a/app.py
+++ b/app.py
@@ -296,16 +296,17 @@ def sanitize(string):
     with col4:
         col4.plotly_chart(plot.plot_fornitore(vaccines.deliveries, area), use_container_width=True)
     st.subheader(f"Dettaglio andamenti {area}")
-    col1, _, col2, _ = st.beta_columns([1, 2, 1, 2])
+    col1, _, col2, col3, _ = st.beta_columns([1, 2, 1, 1, 1])
     status = col1.selectbox('', ['Cumulato', 'Giornaliero'])
     fascia_anagrafica = col2.selectbox('Seleziona fascia anagrafica', ['16-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+'], index=7)
+    dose = col3.selectbox('Seleziona dose', ['seconda dose', 'prima dose'])
     if status == 'Cumulato':
         cumulate = True
     else:
         cumulate = False
     col1, col2 = st.beta_columns(2)
     col1.plotly_chart(plot.ages_timeseries(vaccines.raw, area, cumulate=cumulate), use_container_width=True)
-    col2.plotly_chart(plot.age_timeseries(vaccines.raw, area, fascia_anagrafica, demography, cumulate=cumulate), use_container_width=True)
+    col2.plotly_chart(plot.age_timeseries(vaccines.raw, area, fascia_anagrafica, demography, dose=dose, cumulate=cumulate), use_container_width=True)
 
     col1, col2 = st.beta_columns(2)
     col2.plotly_chart(plot.fornitori_timeseries(vaccines.raw, area, cumulate=cumulate), use_container_width=True)
diff --git a/plot.py b/plot.py
index 711b63c..682dd2f 100644
--- a/plot.py
+++ b/plot.py
@@ -172,7 +172,7 @@ def test_positivity_rate(data_in, country, rule):
     PALETTE_ALPHA = get_default_palette(True)
     next(PALETTE_ALPHA)
     next(PALETTE)
-    fig = make_subplots(1, 1, subplot_titles=[rule], specs=[[{"secondary_y": True}]])
+    fig = make_subplots(1, 1, subplot_titles=[f'{country}: {rule}'], specs=[[{"secondary_y": True}]])
     # plot_data = region.nuovi_positivi.rolling(7).mean() / region.tamponi.diff().rolling(
     #     7).mean() * 100
     if PERCENTAGE_RULE[rule] == 'tamponi':
@@ -755,7 +755,7 @@ def categories_timeseries(vaccines, area, cumulate=False):
         'xanchor': "right",
         'x': .4,
     }
-    return plot_fill([data_list[i] for i in order], [names[i] for i in order], cumulate=cumulate, subplot_title='Somministrazioni vaccino per categoria', legend=legend)
+    return plot_fill([data_list[i] for i in order], [names[i] for i in order], cumulate=cumulate, subplot_title=f'{area}: Somministrazioni vaccino per categoria', legend=legend)
 
 
 def sum_doses(data):
@@ -779,12 +779,13 @@ def fornitori_timeseries(vaccines, area, cumulate=False):
         'xanchor': "left",
         'x': .0,
     }
-    return plot_fill(plot_data.values(), plot_data.keys(), subplot_title="Somministrazioni vaccino per fornitore", cumulate=cumulate, legend=legend)
+    return plot_fill(plot_data.values(), plot_data.keys(), subplot_title=f"{area}: Somministrazioni vaccino per fornitore", cumulate=cumulate, legend=legend)
 
 
-def age_timeseries(vaccines, area, fascia_anagrafica, demography, unita=100, cumulate=False):
+def age_timeseries(vaccines, area, fascia_anagrafica, demography, dose, unita=100, cumulate=False):
     PALETTE = itertools.cycle(plotly.colors.qualitative.Plotly)#itertools.cycle(get_matplotlib_cmap('tab10', bins=8))
-    PALETTE_ALPHA = itertools.cycle(plotly.colors.qualitative.Plotly)#itertools.cycle(get_matplotlib_cmap('tab10', bins=8, alpha=1))
+    title = f'{area}: Percentuale popolazione che ha ricevuto la {dose}<br>nella fascia {fascia_anagrafica}'
+    dose = dose.replace(' ', '_')
     if area == 'Italia':
         plot_data = vaccines[vaccines.fascia_anagrafica == fascia_anagrafica]
     else:
@@ -795,10 +796,10 @@ def cum(data):
             return data.cumsum()
         else:
             return data
-    fig = make_subplots(1, subplot_titles=[f'Percentuale popolazione che ha ricevuto la seconda dose<br>nella fascia {fascia_anagrafica}'], specs=[[{"secondary_y": True}]])
+    fig = make_subplots(1, subplot_titles=[title], specs=[[{"secondary_y": True}]])
     maxs_perc = []
     for fornitore in np.unique(sorted(plot_data.fornitore)):
-        fornitore_data = cum(plot_data[plot_data.fornitore == fornitore].seconda_dose.groupby('data_somministrazione').sum())
+        fornitore_data = cum(getattr(plot_data[plot_data.fornitore == fornitore], dose).groupby('data_somministrazione').sum())
         percentage = fornitore_data / demography[area].loc[fascia_anagrafica] * unita
         bar_perc = go.Bar(
             x=percentage.index,
@@ -809,7 +810,7 @@ def cum(data):
         )
         fig.add_trace(bar_perc, secondary_y=True)
         maxs_perc.append(percentage.max())
-    total = cum(plot_data.groupby('data_somministrazione').sum().rolling(7).mean().seconda_dose)
+    total = cum(getattr(plot_data.groupby('data_somministrazione').sum().rolling(7).mean(), dose))
     total_perc = total / demography[area].loc[fascia_anagrafica] * unita
     ax_perc = go.Scatter(
         x=total_perc.index,
@@ -887,7 +888,7 @@ def ages_timeseries(vaccines, area, cumulate=False):
         'xanchor': "left",
         'x': 0,
     }
-    return plot_fill(data_list, names, subplot_title="Somministrazioni vaccino per fascia d'età", cumulate=cumulate, legend=legend)
+    return plot_fill(data_list, names, subplot_title=f"{area}: Somministrazioni vaccino per fascia d'età", cumulate=cumulate, legend=legend)
 
 
 @st.cache(allow_output_mutation=True, show_spinner=False)