ess5


Internet use and religion in Europe, part five

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 [36]:
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 [2]:
store = pd.HDFStore('ess.resamples.h5')

Make the country objects


In [3]:
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 [4]:
FORMULA1 = ('netuse_f ~ inwyr07_f + yrbrn60_f + yrbrn60_f2 + '
            'edurank_f + hincrank_f +'
            'tvtot_f + rdtot_f + nwsptot_f + hasrelig_f')

FORMULA2 = ('netuse_f ~ inwyr07_f + yrbrn60_f + yrbrn60_f2 + '
            'edurank_f + hincrank_f +'
            'tvtot_f + rdtot_f + nwsptot_f + rlgdgr_f')

In [5]:
num = 201
ess.process_all_frames(store, country_map, num, 
                       smf.ols, FORMULA1, 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
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
101 /YvdfEk
102 /ZEEBve
103 /ZXovwc
104 /ZgSZAY
105 /ZjGafB
106 /ZxNahg
107 /bJIOjl
108 /bNKOFy
109 /bOZZkd
110 /blIIdK
111 /btCIZx
112 /bvntaM
113 /cEzhky
114 /cYiUkH
115 /cgKsnt
116 /czQkEF
117 /dcjEvm
118 /dqGBQR
119 /dyuBXv
120 /dzpDVu
121 /eDVvJf
122 /ewfhTI
123 /fBFUGB
124 /fEkGRW
125 /ggYbXH
126 /gnJSCF
127 /hIQegI
128 /hOQHWV
129 /hTfXDB
130 /hlJZff
131 /hmmXxf
132 /iABzcU
133 /iGVZEK
134 /iWltCV
135 /iaTUMA
136 /iiSwHC
137 /ilWxnR
138 /jNqZpZ
139 /kVTeXb
140 /kfAnDn
141 /kuZzaN
142 /lHDxRr
143 /lfUmXq
144 /lsXAWo
145 /mbTfIj
146 /mtyzJg
147 /nOsmSf
148 /ncvQcP
149 /neEVfl
150 /nnERGx
151 /nnUXHn
152 /nvZcGU
153 /olGyuX
154 /pezcXZ
155 /qRQqmc
156 /qWZfql
157 /qchGUz
158 /qfZVHF
159 /qqYojL
160 /rCrtjG
161 /rSravW
162 /rUWSeP
163 /rYeOLP
164 /rnYDRv
165 /sPKzmv
166 /shBLMW
167 /siHRLd
168 /ssTuqu
169 /tIBOEC
170 /tJaytt
171 /taosRR
172 /tazMjo
173 /teuTPZ
174 /tiUoXr
175 /tjiIAT
176 /tjjdLV
177 /tuLFXm
178 /uHhvHu
179 /uOAidw
180 /uWhsWV
181 /uZGRbW
182 /vPnlcH
183 /vktdCG
184 /vqFufP
185 /vqxuWx
186 /wGQrTR
187 /wahSMf
188 /wsvHYt
189 /xfKUcU
190 /xgAfWd
191 /xjbtHM
192 /xtqhXa
193 /xwCakd
194 /xxGtEc
195 /yImumW
196 /yKuLlN
197 /ybmXrn
198 /yfOjqX
199 /ylSawW
200 /zBUKWF

In [6]:
ess.process_all_frames(store, country_map, num,
                       smf.ols, FORMULA2, 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
101 /YvdfEk
102 /ZEEBve
103 /ZXovwc
104 /ZgSZAY
105 /ZjGafB
106 /ZxNahg
107 /bJIOjl
108 /bNKOFy
109 /bOZZkd
110 /blIIdK
111 /btCIZx
112 /bvntaM
113 /cEzhky
114 /cYiUkH
115 /cgKsnt
116 /czQkEF
117 /dcjEvm
118 /dqGBQR
119 /dyuBXv
120 /dzpDVu
121 /eDVvJf
122 /ewfhTI
123 /fBFUGB
124 /fEkGRW
125 /ggYbXH
126 /gnJSCF
127 /hIQegI
128 /hOQHWV
129 /hTfXDB
130 /hlJZff
131 /hmmXxf
132 /iABzcU
133 /iGVZEK
134 /iWltCV
135 /iaTUMA
136 /iiSwHC
137 /ilWxnR
138 /jNqZpZ
139 /kVTeXb
140 /kfAnDn
141 /kuZzaN
142 /lHDxRr
143 /lfUmXq
144 /lsXAWo
145 /mbTfIj
146 /mtyzJg
147 /nOsmSf
148 /ncvQcP
149 /neEVfl
150 /nnERGx
151 /nnUXHn
152 /nvZcGU
153 /olGyuX
154 /pezcXZ
155 /qRQqmc
156 /qWZfql
157 /qchGUz
158 /qfZVHF
159 /qqYojL
160 /rCrtjG
161 /rSravW
162 /rUWSeP
163 /rYeOLP
164 /rnYDRv
165 /sPKzmv
166 /shBLMW
167 /siHRLd
168 /ssTuqu
169 /tIBOEC
170 /tJaytt
171 /taosRR
172 /tazMjo
173 /teuTPZ
174 /tiUoXr
175 /tjiIAT
176 /tjjdLV
177 /tuLFXm
178 /uHhvHu
179 /uOAidw
180 /uWhsWV
181 /uZGRbW
182 /vPnlcH
183 /vktdCG
184 /vqFufP
185 /vqxuWx
186 /wGQrTR
187 /wahSMf
188 /wsvHYt
189 /xfKUcU
190 /xgAfWd
191 /xjbtHM
192 /xtqhXa
193 /xwCakd
194 /xxGtEc
195 /yImumW
196 /yKuLlN
197 /ybmXrn
198 /yfOjqX
199 /ylSawW
200 /zBUKWF

In [37]:
store.close()

In [7]:
with open('ess5.pkl', 'wb') as fp:
    pickle.dump(country_map, fp)

In [34]:
with open('ess5.pkl', 'rb') as fp:
    country_map = pickle.load(fp)

In [75]:
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 = 'ess5.%2.2d' % plot_counter
        thinkplot.Save(root=root, formats=['png'])
        plot_counter += 1

Make a plot showing confidence interval of effect size for the given parameters


In [76]:
xlabel1 = 'Difference in level of Internet use (0-7 scale)'
xlabel2 = 'Difference in level of Internet use (0-7 scale)'

In [77]:
xlim = [-1.0, 3.5]

In [78]:
reload(ess)
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 ess5.01.png
<matplotlib.figure.Figure at 0x7f910bbec450>

In [79]:
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 ess5.02.png
<matplotlib.figure.Figure at 0x7f914ead4dd0>

In [80]:
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 ess5.03.png
<matplotlib.figure.Figure at 0x7f9176de5a50>

In [81]:
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 ess5.04.png
<matplotlib.figure.Figure at 0x7f911fb88f50>

In [82]:
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 ess5.05.png
<matplotlib.figure.Figure at 0x7f910c871d50>

In [83]:
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 ess5.06.png
<matplotlib.figure.Figure at 0x7f9147deebd0>

In [84]:
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 ess5.07.png
<matplotlib.figure.Figure at 0x7f9115ae6f10>

In [85]:
reload(ess)
t = ess.extract_ranges(country_map, 'hasrelig_f', 'hasrelig_f')
ess.plot_cis(t, BROWN)
thinkplot.Config(title='Religious affiliation',
                 xlabel=xlabel1, xlim=xlim)
save_plot()


Writing ess5.08.png
<matplotlib.figure.Figure at 0x7f9145e68190>

Plot the CDFs of effect size across countries


In [86]:
reload(ess)
cdfnames = ['tvtot_f', 'hasrelig_f', 'rdtot_f', 
            'nwsptot_f', 'hincrank_f', 'edurank_f',
            'inwyr07_f', 'yrbrn60_f']
ess.plot_cdfs(country_map, ess.extract_ranges, cdfnames=cdfnames)
thinkplot.Config(xlabel=xlabel1,
                 xlim=xlim,
                 legend=True,
                 loc='lower right')
save_plot()


-0.245125284714 -0.26345511532
-0.0925992328957 -0.0395775212147
0.0421007185354 0.0565209201039
0.117305836288 0.0693131635635
0.568379267708 0.612963693385
1.36588360845 1.3269043995
0.795743263863 0.910541867449
1.91490040499 1.83416921284
Writing ess5.09.png
<matplotlib.figure.Figure at 0x7f914506b990>

Make similar figures for the second model.


In [87]:
xlim = [-1.5, 3.5]

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


Writing ess5.10.png
<matplotlib.figure.Figure at 0x7f9145f45ed0>

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


Writing ess5.11.png
<matplotlib.figure.Figure at 0x7f9148560e50>

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 ess5.12.png
<matplotlib.figure.Figure at 0x7f914380f410>

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 ess5.13.png
<matplotlib.figure.Figure at 0x7f9138cc0690>

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 ess5.14.png
<matplotlib.figure.Figure at 0x7f9135735cd0>

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 ess5.15.png
<matplotlib.figure.Figure at 0x7f914db5ef50>

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 ess5.16.png
<matplotlib.figure.Figure at 0x7f91446fe850>

In [95]:
t = ess.extract_ranges2(country_map, 'rlgdgr_f', 'hasrelig_f')
ess.plot_cis(t, BROWN)
thinkplot.Config(title='Degree of religiosity',
                 xlabel=xlabel2,
                 xlim=xlim)
save_plot()


Writing ess5.17.png
<matplotlib.figure.Figure at 0x7f911f8c41d0>

In [96]:
reload(ess)
cdfnames = ['tvtot_f', 'rlgdgr_f', 'rdtot_f', 
            'nwsptot_f', 'hincrank_f', 'edurank_f',
            'inwyr07_f', 'yrbrn60_f']
ess.plot_cdfs(country_map, ess.extract_ranges2, cdfnames=cdfnames)
thinkplot.Config(xlabel=xlabel2,
                 xlim=xlim,
                 ylabel='CDF',
                 loc='lower right')
save_plot()


-0.256137175641 -0.263852615538
-0.189269580411 -0.138621548651
0.0414823196446 0.060188890896
0.115147176451 0.0686866084364
0.55960831952 0.611396961511
1.35342846531 1.29659404412
0.791596015686 0.907234081272
1.90361837892 1.84010356899
Writing ess5.18.png
<matplotlib.figure.Figure at 0x7f9116b419d0>

In [97]:
reload(ess)
varnames = ['inwyr07_f', 'yrbrn60_f', 'hasrelig_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*
---------  	----  	---  	---  	----
tvtot_f  	28  	5  	0  	1  	34
hasrelig_f  	15  	2  	17  	0  	34
rdtot_f  	8  	5  	7  	14  	34
inwyr07_f  	1  	2  	3  	28  	34
yrbrn60_f  	0  	0  	0  	34  	34
edurank_f  	0  	0  	0  	34  	34
hincrank_f  	0  	0  	1  	33  	34
nwsptot_f  	0  	2  	9  	23  	34

In [98]:
varnames = ['inwyr07_f', 'yrbrn60_f', 'rlgdgr_f', 'edurank_f', 
            'tvtot_f', 'hincrank_f', 'rdtot_f', 'nwsptot_f']

ts = ess.make_table(country_map, varnames, ess.extract_ranges2)
ess.print_table(ts)


varname  	neg*  	neg  	pos  	pos*
---------  	----  	---  	---  	----
tvtot_f  	29  	4  	1  	0  	34
rlgdgr_f  	24  	8  	2  	0  	34
rdtot_f  	8  	5  	7  	14  	34
inwyr07_f  	1  	2  	3  	28  	34
yrbrn60_f  	0  	0  	0  	34  	34
edurank_f  	0  	0  	0  	34  	34
hincrank_f  	0  	0  	1  	33  	34
nwsptot_f  	0  	2  	10  	22  	34

In [ ]:


In [ ]: