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Ben Anderson
energy-storage-dtc
Commits
32ec7e74
Commit
32ec7e74
authored
10 years ago
by
Ben Anderson
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ons-2005-data-exploration.R
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# Header ###########################################
# ONS Time Use 2005 data
#
# Explorations using R
#
# Script for use as part of: The Social Science of Energy Storage (PSY6018)
#
# Sheffield/Southampton Centre for Doctoral Training in Energy Storage and its Applications
# http://www.southampton.ac.uk/engineering/postgraduate/research_degrees/energy_storage_cdt.page
#
# Copyright (C) 2014 University of Southampton
#
# Author: Ben Anderson (b.anderson@soton.ac.uk, @dataknut, https://github.com/dataknut)
# [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
#
# end header
# To do: -----------------------------------------------------------------
# Prelims -----------------------------------------------------------------
# clear out all old objects etc to avoid confusion
rm
(
list
=
ls
())
# load required packages
x
<-
c
(
"foreign"
,
"ggplot2"
,
"plyr"
)
lapply
(
x
,
require
,
character.only
=
T
)
# if this breaks/fails because they haven't been installed use
# install.packages("x")
# path to data
# You will need to have downloaded the data from Dropbox/MOLE
# You will need to change this!!
dpath
<-
"/Users/ben/Documents/Dropbox/energy-storage-dtc/data/"
# Load data -----------------------------------------------------------------
# First: time use data in long form - this has data in 10 minute time slots
onstu2005_longcsv
<-
read.csv
(
paste0
(
dpath
,
"/UK-2005-TU-long.csv"
))
# Now stop to check what's in it and make sure we understand the format!
head
(
onstu2005_longcsv
,
n
=
4
)
# check values of main acts (the things people reported doing)
table
(
"Main acts"
=
onstu2005_longcsv
$
pact
)
# check values of location variable (where they reported doing them)
table
(
"Location"
=
onstu2005_longcsv
$
lact
)
# NB: quite a lot of missing
# check values of months variable (so we see that seasons are represented)
table
(
"Month"
=
onstu2005_longcsv
$
t_month
)
# Second: survey data
onstu2005_surveycsv
<-
read.csv
(
paste0
(
dpath
,
"/UK-2005-TU-survey-reduced.csv"
))
# check what's in it
head
(
onstu2005_surveycsv
,
n
=
2
)
# Test: Sleep -----------------------------------------------------------------
onstu2005_longcsv
$
sleep_p
<-
0
onstu2005_longcsv
$
sleep_p
[
onstu2005_longcsv
$
pact
==
"sleeping"
]
<-
1
onstu2005_longcsv
$
sleep_s
<-
0
onstu2005_longcsv
$
sleep_s
[
onstu2005_longcsv
$
sact
==
"sleeping"
]
<-
1
onstu2005_longcsv
$
sleep_all
<-
onstu2005_longcsv
$
sleep_p
+
onstu2005_longcsv
$
sleep_s
sleeping
<-
ddply
(
onstu2005_longcsv
,
c
(
"s_dow"
,
"s_halfhour"
),
summarise
,
propn
=
mean
(
sleep_all
))
ggplot
(
sleeping
,
aes
(
x
=
s_halfhour
,
y
=
propn
,
colour
=
s_dow
,
group
=
s_dow
))
+
geom_line
()
# Practice: Preparing food -----------------------------------------------------------------
# Might not require energy of course...
onstu2005_longcsv
$
cooking_p
<-
0
onstu2005_longcsv
$
cooking_p
[
onstu2005_longcsv
$
pact
==
"preparing food"
&
onstu2005_longcsv
$
lact
!=
"elsewhere"
]
<-
1
onstu2005_longcsv
$
cooking_s
<-
0
onstu2005_longcsv
$
cooking_s
[
onstu2005_longcsv
$
sact
==
"preparing food"
&
onstu2005_longcsv
$
lact
!=
"elsewhere"
]
<-
1
onstu2005_longcsv
$
cooking_all
<-
onstu2005_longcsv
$
cooking_p
+
onstu2005_longcsv
$
cooking_s
# Need to try to calculate the se somehow if we want error bars?
cooking_all
<-
ddply
(
onstu2005_longcsv
,
c
(
"s_halfhour"
),
summarise
,
propn
=
mean
(
cooking_all
),
sd
=
sd
(
cooking_all
),
n
=
obs
(
cooking_all
))
ggplot
(
cooking_all
,
aes
(
x
=
s_halfhour
,
y
=
propn
))
+
geom_line
(
aes
(
group
=
1
))
+
geom_point
(
size
=
4
)
+
geom_errorbar
(
aes
(
ymin
=
propn
-
sd
,
ymax
=
propn
+
sd
),
width
=
.2
)
cooking_dow
<-
ddply
(
onstu2005_longcsv
,
c
(
"s_dow"
,
"s_halfhour"
),
summarise
,
propn
=
mean
(
cooking_all
))
ggplot
(
cooking_dow
,
aes
(
x
=
s_halfhour
,
y
=
propn
,
colour
=
s_dow
,
group
=
s_dow
))
+
geom_line
()
# Practice: Car use -----------------------------------------------------------------
onstu2005_longcsv
$
car_enj_p
<-
0
onstu2005_longcsv
$
car_enj_p
[
onstu2005_longcsv
$
pact
==
"travel - car/enjoyment"
]
<-
1
onstu2005_longcsv
$
car_enj_s
<-
0
onstu2005_longcsv
$
car_enj_s
[
onstu2005_longcsv
$
sact
==
"travel - car/enjoyment"
]
<-
1
onstu2005_longcsv
$
car_enj_all
<-
onstu2005_longcsv
$
car_enj_p
+
onstu2005_longcsv
$
car_enj_s
onstu2005_longcsv
$
car_oth_p
<-
0
onstu2005_longcsv
$
car_oth_p
[
onstu2005_longcsv
$
pact
==
"travel - car/other"
]
<-
1
onstu2005_longcsv
$
car_oth_s
<-
0
onstu2005_longcsv
$
car_oth_s
[
onstu2005_longcsv
$
sact
==
"travel - car/other"
]
<-
1
onstu2005_longcsv
$
car_oth_all
<-
onstu2005_longcsv
$
car_oth_p
+
onstu2005_longcsv
$
car_oth_s
onstu2005_longcsv
$
car_shop_p
<-
0
onstu2005_longcsv
$
car_shop_p
[
onstu2005_longcsv
$
pact
==
"travel - car/shopping"
]
<-
1
onstu2005_longcsv
$
car_shop_s
<-
0
onstu2005_longcsv
$
car_shop_s
[
onstu2005_longcsv
$
sact
==
"travel - car/shopping"
]
<-
1
onstu2005_longcsv
$
car_shop_all
<-
onstu2005_longcsv
$
car_shop_p
+
onstu2005_longcsv
$
car_shop_s
car_use
<-
ddply
(
onstu2005_longcsv
,
c
(
"s_halfhour"
),
summarise
,
shop
=
mean
(
car_shop_all
),
enj
=
mean
(
car_enj_all
),
other
=
mean
(
car_oth_all
))
ggplot
(
car_use
,
aes
(
x
=
s_halfhour
,
y
=
shop
))
+
aes
(
group
=
1
)
+
geom_line
()
ggplot
(
car_use
,
aes
(
x
=
s_halfhour
,
y
=
enj
))
+
aes
(
group
=
1
)
+
geom_line
()
# needs to have home as location afterwards for relevance to EV?
# merge them
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