ess4


Internet use and religion in Europe, part four

This notebook presents explorations of the association between Internet use and religion in Europe, using data from the European Social Survey (http://www.europeansocialsurvey.org).

Copyright 2015 Allen Downey

MIT License: http://opensource.org/licenses/MIT


In [43]:
from __future__ import print_function, division

import string
import random
import cPickle as pickle

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf

import thinkstats2
import thinkplot
import matplotlib.pyplot as plt

import ess

# colors by colorbrewer2.org
BLUE1 = '#a6cee3'
BLUE2 = '#1f78b4'
GREEN1 = '#b2df8a'
GREEN2 = '#33a02c'
PINK = '#fb9a99'
RED = '#e31a1c'
ORANGE1 = '#fdbf6f'
ORANGE2 = '#ff7f00'
PURPLE1 = '#cab2d6'
PURPLE2 = '#6a3d9a'
YELLOW = '#ffff99'
BROWN = '#b15928'

%matplotlib inline

Open the store containing resampled DataFrames.


In [11]:
store.close()
store = pd.HDFStore('ess.resamples.h5')

Make the country objects


In [12]:
reload(ess)
country_map = ess.make_countries(store)


Austria
Belgium
Bulgaria
Switzerland
Cyprus
Czech Rep
Germany
Denmark
Estonia
Spain
Finland
France
UK
Greece
Croatia
Hungary
Ireland
Israel
Iceland
Italy
Lithuania
Luxembourg
Latvia
Netherlands
Norway
Poland
Portugal
Romania
Russia
Sweden
Slovenia
Slovakia
Turkey
Ukraine

For each resampled frame, run both models and store the results in the Country objects


In [15]:
keys = store.keys()
len(keys)


Out[15]:
204

In [16]:
reload(ess)
FORMULA1 = ('hasrelig_f ~ inwyr07_f + yrbrn60_f + yrbrn60_f2 + '
            'edurank_f + hincrank_f +'
            'tvtot_f + rdtot_f + nwsptot_f + netuse_f')

num = 101
ess.process_all_frames(store, country_map, num,
                       smf.logit, FORMULA1, model_num=1)


0 /AAVZWa
1 /ADbUvD
2 /AJEDdF
3 /AOacJP
4 /AsSyrK
5 /BIXejR
6 /Blwttj
7 /BytXnJ
8 /CuiQgF
9 /CxkVBv
10 /DOKcxz
11 /DSSzPM
12 /DdpHTg
13 /EBHNWn
14 /EHuhuk
15 /EIaigX
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17 /EdeAYH
18 /EiftYh
19 /EoHBcy
20 /Evkitq
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22 /FWawby
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24 /GPBBMj
25 /GYhuaT
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34 /HujYDN
35 /IKLjEu
36 /IORbkE
37 /IXYMov
38 /InEXbB
39 /JKBolS
40 /JVSJPq
41 /JofMZK
42 /JomohW
43 /JznRlw
44 /KEthFz
45 /KFwczR
46 /KUVnJc
47 /KnKXTR
48 /KuGUhG
49 /KudtCP
50 /LaUmLC
51 /LissvE
52 /LmraEV
53 /MCmopN
54 /MIdmWa
55 /MgSdJx
56 /NJjQrX
57 /NfzPAX
58 /OJZEtt
59 /Oaksmf
60 /OdhAjf
61 /PJETsk
62 /PXxSpS
63 /PiWfGA
64 /PptHII
65 /PvfGpy
66 /QTTYTa
67 /QbhbQt
68 /QoHLXF
69 /QskeUe
70 /QtkeEX
71 /RHVBHl
72 /RRpxwc
73 /RYtpJo
74 /RuCVox
75 /RwJMYt
76 /SHnJcB
77 /ScbnLb
78 /TOcaLi
79 /TRVSRU
80 /TaHTXL
81 /UKzbGY
82 /UVvNeb
83 /UfXGIO
84 /VHIVpS
85 /VcRwRL
86 /VgqgVe
87 /VlUfcv
88 /VzZAXk
89 /WczOWP
90 /WkLtrX
91 /WkfCQW
92 /WlHtRg
93 /WwTDDj
94 /WxWlWp
95 /XGmIIH
96 /XOxJQN
97 /XhgvtL
98 /YMsFSK
99 /YeASVz
100 /YoxGxL

In [17]:
reload(ess)
FORMULA2 = ('rlgdgr_f ~ inwyr07_f + yrbrn60_f  + yrbrn60_f2 + '
            'edurank_f + hincrank_f +'
            'tvtot_f + rdtot_f + nwsptot_f + netuse_f')

ess.process_all_frames(store, country_map, num,
                       smf.ols, FORMULA2, model_num=2)


0 /AAVZWa
1 /ADbUvD
2 /AJEDdF
3 /AOacJP
4 /AsSyrK
5 /BIXejR
6 /Blwttj
7 /BytXnJ
8 /CuiQgF
9 /CxkVBv
10 /DOKcxz
11 /DSSzPM
12 /DdpHTg
13 /EBHNWn
14 /EHuhuk
15 /EIaigX
16 /EOOBpB
17 /EdeAYH
18 /EiftYh
19 /EoHBcy
20 /Evkitq
21 /FJboqX
22 /FWawby
23 /GIKXkG
24 /GPBBMj
25 /GYhuaT
26 /GdTLTY
27 /GeUlsB
28 /GeolrR
29 /GkMwBV
30 /GownbC
31 /GrCTmE
32 /HGSBFA
33 /HemGKU
34 /HujYDN
35 /IKLjEu
36 /IORbkE
37 /IXYMov
38 /InEXbB
39 /JKBolS
40 /JVSJPq
41 /JofMZK
42 /JomohW
43 /JznRlw
44 /KEthFz
45 /KFwczR
46 /KUVnJc
47 /KnKXTR
48 /KuGUhG
49 /KudtCP
50 /LaUmLC
51 /LissvE
52 /LmraEV
53 /MCmopN
54 /MIdmWa
55 /MgSdJx
56 /NJjQrX
57 /NfzPAX
58 /OJZEtt
59 /Oaksmf
60 /OdhAjf
61 /PJETsk
62 /PXxSpS
63 /PiWfGA
64 /PptHII
65 /PvfGpy
66 /QTTYTa
67 /QbhbQt
68 /QoHLXF
69 /QskeUe
70 /QtkeEX
71 /RHVBHl
72 /RRpxwc
73 /RYtpJo
74 /RuCVox
75 /RwJMYt
76 /SHnJcB
77 /ScbnLb
78 /TOcaLi
79 /TRVSRU
80 /TaHTXL
81 /UKzbGY
82 /UVvNeb
83 /UfXGIO
84 /VHIVpS
85 /VcRwRL
86 /VgqgVe
87 /VlUfcv
88 /VzZAXk
89 /WczOWP
90 /WkLtrX
91 /WkfCQW
92 /WlHtRg
93 /WwTDDj
94 /WxWlWp
95 /XGmIIH
96 /XOxJQN
97 /XhgvtL
98 /YMsFSK
99 /YeASVz
100 /YoxGxL

In [18]:
store.close()

In [19]:
with open('ess4.pkl', 'wb') as fp:
    pickle.dump(country_map, fp)

In [20]:
with open('ess4.pkl', 'rb') as fp:
    country_map = pickle.load(fp)

In [72]:
plot_counter = 1

def save_plot(flag=True):
    """Saves plots in png format.
    
    flag: boolean, whether to save or not
    """
    global plot_counter
    if flag:
        root = 'ess4.%2.2d' % plot_counter
        thinkplot.Save(root=root, formats=['png'])
        plot_counter += 1

Make a plot showing confidence interval for the given parameters


In [73]:
xlabel1 = 'Difference in percentage points of hasrelig'
xlabel2 = 'Difference in religiosity (0-10 scale)'

In [74]:
xlim = [-25, 15]

First let's check on the estimated parameters for the age variables.


In [75]:
t = ess.extract_ranges(country_map, 'yrbrn60_f', 'hasrelig_f')    
ess.plot_cis(t, GREEN2)
thinkplot.Config(title='Year born',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.01.png
<matplotlib.figure.Figure at 0x7f9efe9d6710>

In almost every country, year born is associated with less religiosity.


In [76]:
t = ess.extract_ranges(country_map, 'inwyr07_f', 'hasrelig_f')    
ess.plot_cis(t, GREEN1)
thinkplot.Config(title='Interview year',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.02.png
<matplotlib.figure.Figure at 0x7f9edf072610>

In [77]:
t = ess.extract_ranges(country_map, 'edurank_f', 'hasrelig_f')
ess.plot_cis(t, ORANGE2)
thinkplot.Config(title='Education (relative rank)',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.03.png
<matplotlib.figure.Figure at 0x7f9ede9d0d50>

In [78]:
t = ess.extract_ranges(country_map, 'hincrank_f', 'hasrelig_f')
ess.plot_cis(t, ORANGE1)
thinkplot.Config(title='Income (relative rank)',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.04.png
<matplotlib.figure.Figure at 0x7f9edc38e710>

In [79]:
t = ess.extract_ranges(country_map, 'tvtot_f', 'hasrelig_f')
ess.plot_cis(t, RED)
thinkplot.Config(title='Television watching',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.05.png
<matplotlib.figure.Figure at 0x7f9ee4d5dc50>

In [80]:
t = ess.extract_ranges(country_map, 'rdtot_f', 'hasrelig_f')
ess.plot_cis(t, BLUE1)
thinkplot.Config(title='Radio listening',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.06.png
<matplotlib.figure.Figure at 0x7f9f04f371d0>

In [81]:
t = ess.extract_ranges(country_map, 'nwsptot_f', 'hasrelig_f')
ess.plot_cis(t, BLUE2)
thinkplot.Config(title='Newspaper reading',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.07.png
<matplotlib.figure.Figure at 0x7f9efc2d6090>

In [82]:
t = ess.extract_ranges(country_map, 'netuse_f', 'hasrelig_f')
ess.plot_cis(t, PURPLE2)
thinkplot.Config(title='Internet use',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess4.08.png
<matplotlib.figure.Figure at 0x7f9efe2d6690>

In [83]:
reload(ess)
cdfnames = ['yrbrn60_f', 'netuse_f', 'edurank_f', 'tvtot_f', 'hincrank_f',
            'rdtot_f', 'nwsptot_f',
            'inwyr07_f' ]
ess.plot_cdfs(country_map, ess.extract_ranges, cdfnames=cdfnames)
thinkplot.Config(xlabel='Difference in percentage points',
                 xlim=[-20, 10],
                 ylabel='CDF',
                 legend=True,
                 loc='upper left')
save_plot()


-7.6100903746 -7.99861220321
-3.47246804305 -3.39621681489
-1.56600401422 -1.95101119651
-0.948475855492 -1.04940487676
0.283111223687 -0.100804047038
-0.128763052394 0.188254315036
0.248812913673 0.625585601944
1.30795708007 0.426758433373
Writing ess4.09.png
<matplotlib.figure.Figure at 0x7f9f005c7250>

In [84]:
t = ess.extract_ranges(country_map, 'netuse_f', 'hasrelig_f')
ess.plot_scatter(t, BLUE)
thinkplot.Config(title='',
                 xlabel=xlabel1,
                 ylabel='Fraction affiliated',
                 xlim=[-10, 5], ylim=[0, 1])
save_plot()


0.30172553685
Writing ess4.10.png
<matplotlib.figure.Figure at 0x7f9edf8c6550>

In [85]:
t = ess.extract_ranges(country_map, 'netuse_f', 'rlgdgr_f')
ess.plot_scatter(t, BLUE)
thinkplot.Config(title='',
                 xlabel=xlabel1,
                 ylabel='Mean religiosity',
                 xlim=[-10, 5], ylim=[0, 7.5])
save_plot()


0.299832107839
Writing ess4.11.png
<matplotlib.figure.Figure at 0x7f9ed421d490>

In [86]:
t = ess.extract_ranges(country_map, 'netuse_f', 'netuse_f')
ess.plot_scatter(t, BLUE)
thinkplot.Config(title='',
                 xlabel=xlabel1,
                 ylabel='Mean Internet use',
                 xlim=[-10, 5], ylim=[0, 7.5])
save_plot()


-0.377644186914
Writing ess4.12.png
<matplotlib.figure.Figure at 0x7f9ede54ca50>

Make similar figures for the second model, with degree of religiosity as the dependent variable.


In [87]:
xlim = [-2.5, 1.0]

In [88]:
t = ess.extract_ranges2(country_map, 'yrbrn60_f', 'rlgdgr_f')
ess.plot_cis(t, GREEN2)
thinkplot.Config(title='Year born',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.13.png
<matplotlib.figure.Figure at 0x7f9f00a4a550>

In [89]:
t = ess.extract_ranges2(country_map, 'inwyr07_f', 'rlgdgr_f')
ess.plot_cis(t, GREEN1)
thinkplot.Config(title='Education rank',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.14.png
<matplotlib.figure.Figure at 0x7f9f00a4a050>

In [90]:
t = ess.extract_ranges2(country_map, 'edurank_f', 'rlgdgr_f')
ess.plot_cis(t, ORANGE2)
thinkplot.Config(title='Education rank',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.15.png
<matplotlib.figure.Figure at 0x7f9edfb70e50>

In [91]:
t = ess.extract_ranges2(country_map, 'hincrank_f', 'hasrelig_f')
ess.plot_cis(t, ORANGE1)
thinkplot.Config(title='Income rank',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.16.png
<matplotlib.figure.Figure at 0x7f9edf92d710>

In [92]:
t = ess.extract_ranges2(country_map, 'tvtot_f', 'hasrelig_f')
ess.plot_cis(t, RED)
thinkplot.Config(title='Television watching',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.17.png
<matplotlib.figure.Figure at 0x7f9efeafff90>

In [93]:
t = ess.extract_ranges2(country_map, 'rdtot_f', 'hasrelig_f')
ess.plot_cis(t, BLUE1)
thinkplot.Config(title='Radio listening',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.18.png
<matplotlib.figure.Figure at 0x7f9ee55f3f90>

In [94]:
t = ess.extract_ranges2(country_map, 'nwsptot_f', 'hasrelig_f')
ess.plot_cis(t, BLUE2)
thinkplot.Config(title='Newspaper reading',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.19.png
<matplotlib.figure.Figure at 0x7f9efe256d50>

In [95]:
t = ess.extract_ranges2(country_map, 'netuse_f', 'hasrelig_f')
ess.plot_cis(t, PURPLE2)
thinkplot.Config(title='Internet use',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess4.20.png
<matplotlib.figure.Figure at 0x7f9f005c7450>

In [96]:
cdfnames = ['netuse_f', 'edurank_f', 'tvtot_f', 'hincrank_f',
            'rdtot_f', 'nwsptot_f',
            'inwyr07_f', 'yrbrn60_f']
ess.plot_cdfs(country_map, ess.extract_ranges2, cdfnames=cdfnames)
thinkplot.Config(xlabel=xlabel2,
                 xlim=[-2, 0.7],
                 ylabel='CDF',
                 loc='upper left')
save_plot()


-0.361527956195 -0.334836169355
-0.185573584648 -0.201108610193
-0.217090335159 -0.223876985972
-0.113349234086 -0.125387887266
-0.0348431722332 -0.0408627145833
-0.0198243358803 -0.0057449815362
0.0265003098879 0.0239471272503
-0.529880792947 -0.598870219159
Writing ess4.21.png
<matplotlib.figure.Figure at 0x7f9ee5ce4490>

Here's the scatter plot of effect size on rlgdgr versus mean value of rlgdgr

rlgdgr is on a 0 to 10 scale, so it is mildly astonishing that national means vary as much as they do, from 2.5 to 7.


In [97]:
t = ess.extract_ranges2(country_map, 'netuse_f', 'hasrelig_f')
ess.plot_scatter(t, BLUE)
thinkplot.Config(title='',
                 xlabel=xlabel2,
                 ylabel='Fraction affiliated',
                 xlim=[-2.5, 0.5], ylim=[0, 1]
                )
save_plot()


-0.224489530769
Writing ess4.22.png
<matplotlib.figure.Figure at 0x7f9eec1b6750>

In [98]:
t = ess.extract_ranges2(country_map, 'netuse_f', 'rlgdgr_f')
ess.plot_scatter(t, BLUE)
thinkplot.Config(title='',
                 xlabel=xlabel2,
                 ylabel='Mean religiosity',
                 xlim=[-2.5, 0.5], ylim=[0, 7.5]
                )
save_plot()


-0.229662125758
Writing ess4.23.png
<matplotlib.figure.Figure at 0x7f9efc2d6590>

In [99]:
t = ess.extract_ranges2(country_map, 'netuse_f', 'netuse_f')
ess.plot_scatter(t, PURPLE2)
thinkplot.Config(title='',
                 xlabel=xlabel2,
                 ylabel='Mean Internet use',
                 xlim=[-2.5, 0.5], ylim=[0, 7.5])
save_plot()


-0.065890035769
Writing ess4.24.png
<matplotlib.figure.Figure at 0x7f9e9182f610>

In [101]:
reload(ess)
varnames = ['inwyr07_f', 'yrbrn60_f', 'netuse_f', 'edurank_f', 
            'tvtot_f', 'hincrank_f', 'rdtot_f', 'nwsptot_f']

ts = ess.make_table(country_map, varnames, ess.extract_ranges)
ess.print_table(ts)


varname  	neg*  	neg  	pos  	pos*
---------  	----  	---  	---  	----
yrbrn60_f  	27  	3  	2  	2  	34
netuse_f  	24  	6  	4  	0  	34
edurank_f  	19  	7  	3  	5  	34
tvtot_f  	14  	10  	4  	6  	34
nwsptot_f  	10  	4  	11  	9  	34
rdtot_f  	8  	7  	13  	6  	34
hincrank_f  	4  	15  	11  	4  	34
inwyr07_f  	2  	9  	11  	12  	34

In [102]:
ts = ess.make_table(country_map, varnames, ess.extract_ranges2)
ess.print_table(ts)


varname  	neg*  	neg  	pos  	pos*
---------  	----  	---  	---  	----
yrbrn60_f  	29  	3  	1  	1  	34
netuse_f  	24  	7  	3  	0  	34
tvtot_f  	22  	6  	3  	3  	34
edurank_f  	21  	4  	5  	4  	34
hincrank_f  	16  	14  	3  	1  	34
rdtot_f  	13  	7  	7  	7  	34
nwsptot_f  	12  	5  	13  	4  	34
inwyr07_f  	4  	10  	12  	8  	34

In [ ]:


In [ ]: