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Tom Rushby
woRkflow
Commits
68b7344b
Commit
68b7344b
authored
4 years ago
by
Tom Rushby
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Extract code from app to develop plots.
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howTo/openGeogAPI/local_auth_ghg_plots.R
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howTo/openGeogAPI/local_auth_ghg_plots.R
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68b7344b
# rsconnect::deployApp('/Users/twr1m15/SotonGitLab/woRkflow/howTo/openGeogAPI', appName = "LAemissions",appTitle = "Hampshire local authority ghg emissions by sector")
# Load libraries ----
library
(
readxl
)
library
(
ggplot2
)
library
(
plotly
)
library
(
dplyr
)
library
(
reshape2
)
library
(
RColorBrewer
)
# Construct colour palette ----
industry_pal
<-
brewer.pal
(
n
=
8
,
name
=
"Greys"
)[
4
:
8
]
# industry greys, 5 categories
domestic_pal
<-
brewer.pal
(
n
=
4
,
name
=
"Blues"
)[
2
:
4
]
# domestic blues, 3 categories
transport_pal
<-
brewer.pal
(
n
=
6
,
name
=
"Oranges"
)[
2
:
6
]
# transport oranges, 5 categories
lulucf_pal
<-
brewer.pal
(
n
=
9
,
name
=
"Greens"
)[
4
:
9
]
# lulucf greens, 6 categories
# for details
detailed_pal
<-
c
(
industry_pal
,
domestic_pal
,
transport_pal
,
lulucf_pal
)
# for totals/outlines
totals_pal
<-
c
(
brewer.pal
(
n
=
9
,
name
=
"Greys"
)[
8
],
brewer.pal
(
n
=
9
,
name
=
"Blues"
)[
8
],
brewer.pal
(
n
=
9
,
name
=
"Oranges"
)[
8
],
brewer.pal
(
n
=
9
,
name
=
"Greens"
)[
8
])
# List local authority areas to load
# These used to filter emissions data
# and construct Open Geog API query (geo_query ... to do)
las_to_load
<-
c
(
"Southampton"
,
"Portsmouth"
,
"Winchester"
,
"Eastleigh"
,
"Isle of Wight"
,
"Fareham"
,
"Gosport"
,
"Test Valley"
,
"East Hampshire"
,
"Havant"
,
"New Forest"
,
"Hart"
,
"Basingstoke and Deane"
)
# Load GHG emissions data ----
url_to_get
<-
"https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/894787/2005-18-uk-local-regional-co2-emissions.xlsx"
tempf
<-
tempfile
(
fileext
=
".xlsx"
)
download.file
(
url_to_get
,
tempf
,
method
=
"curl"
)
dt
<-
readxl
::
read_xlsx
(
tempf
,
sheet
=
"Full dataset"
,
skip
=
1
)
x_min
<-
min
(
dt
$
Year
)
x_max
<-
max
(
dt
$
Year
)
# Functions ----
lvl_detail
<-
"high"
filter_detail
<-
function
(
lvl_detail
=
lvl_detail
){
if
(
lvl_detail
==
"low"
){
dt
<-
dt
%>%
filter
(
Name
%in%
las_to_load
)
%>%
select
(
`CTRY18NM/RGN18NM`
,
`Second Tier Authority`
,
Name
,
Code
,
Year
,
`Industry and Commercial Total`
,
`Domestic Total`
,
`Transport Total`
,
`LULUCF Net Emissions`
)
}
if
(
lvl_detail
==
"high"
){
dt
<-
dt
%>%
filter
(
Name
%in%
las_to_load
)
%>%
select
(
`CTRY18NM/RGN18NM`
,
`Second Tier Authority`
,
Name
,
Code
,
Year
,
`A. Industry and Commercial Electricity`
,
`B. Industry and Commercial Gas`
,
`C. Large Industrial Installations`
,
`D. Industrial and Commercial Other Fuels`
,
`E. Agriculture`
,
`F. Domestic Electricity`
,
`G. Domestic Gas`
,
`H. Domestic 'Other Fuels'`
,
`I. Road Transport (A roads)`
,
`J. Road Transport (Motorways)`
,
`K. Road Transport (Minor roads)`
,
`L. Diesel Railways`
,
`M. Transport Other`
,
`LULUCF Net Emissions`
)
}
ghg_emissions
<-
melt
(
dt
,
id.vars
=
c
(
"CTRY18NM/RGN18NM"
,
"Second Tier Authority"
,
"Name"
,
"Code"
,
"Year"
))
return
(
ghg_emissions
)
}
# Process data ----
per_capita_dt
<-
dt
%>%
filter
(
Name
%in%
las_to_load
)
%>%
rename
(
Population
=
`Population ('000s, mid-year estimate)`
)
%>%
mutate
(
Population
=
Population
*
1000
)
per_cap_fun
<-
function
(
x
)
x
*
1000
/
per_capita_dt
$
Population
per_capita_totals
<-
data.frame
(
per_capita_dt
[
c
(
1
:
5
,
30
)],
lapply
(
per_capita_dt
[
c
(
11
,
15
,
21
,
28
,
29
,
32
,
33
)],
per_cap_fun
)
)
per_capita_detail
<-
data.frame
(
per_capita_dt
[
c
(
1
:
5
,
30
)],
lapply
(
per_capita_dt
[
c
(
6
:
10
,
12
:
14
,
16
:
20
,
22
:
27
,
29
,
32
,
33
)],
per_cap_fun
)
)
# Note that for hampshire LAs there are no emissions in categories Q or S
# Reshape data
pc_totals_plot
<-
data.frame
(
per_capita_totals
[
c
(
1
:
5
,
7
:
10
)])
pc_totals_plot
<-
melt
(
pc_totals_plot
,
id.vars
=
c
(
"CTRY18NM.RGN18NM"
,
"Second.Tier.Authority"
,
"Name"
,
"Code"
,
"Year"
))
pc_details_plot
<-
data.frame
(
per_capita_totals
[
c
(
1
:
5
,
7
:
10
)])
pc_details_plot
<-
melt
(
pc_totals_plot
,
id.vars
=
c
(
"CTRY18NM.RGN18NM"
,
"Second.Tier.Authority"
,
"Name"
,
"Code"
,
"Year"
))
# Construct plots ----
dt2
<-
dt
%>%
filter
(
Name
%in%
las_to_load
)
%>%
rename
(
Population
=
`Population ('000s, mid-year estimate)`
)
%>%
mutate
(
Population
=
Population
*
1000
,
`Industry per capita`
=
`Industry and Commercial Total`
*
1000
/
Population
,
`Domestic per capita`
=
`Domestic Total`
*
1000
/
Population
,
`Transport per capita`
=
`Transport Total`
*
1000
/
Population
,
`LULUCF per capita`
=
`LULUCF Net Emissions`
*
1000
/
Population
,
`Total per capita chk`
=
`Industry per capita`
+
`Domestic per capita`
+
`Transport per capita`
+
`LULUCF per capita`
,
`Total per capita`
=
`Grand Total`
*
1000
/
Population
)
dt3
<-
dt
%>%
filter
(
Name
%in%
las_to_load
)
%>%
rename
(
Population
=
`Population ('000s, mid-year estimate)`
)
%>%
mutate
(
Population
=
Population
*
1000
,
`Industry (elec) per capita`
=
`A. Industry and Commercial Electricity`
*
1000
/
Population
,
`Industry (gas) per capita`
=
`B. Industry and Commercial Gas`
*
1000
/
Population
,
`Industry (lge) per capita`
=
`C. Large Industrial Installations`
*
1000
/
Population
,
`Industry (other fuels) per capita`
=
`D. Industrial and Commercial Other Fuels`
*
1000
/
Population
,
`Domestic (elec) per capita`
=
`F. Domestic Electricity`
*
1000
/
Population
,
`Domestic (gas) per capita`
=
`G. Domestic Gas`
*
1000
/
Population
,
`Domestic (other fuels) per capita`
=
`H. Domestic 'Other Fuels'`
*
1000
/
Population
,
`Transport (A roads) per capita`
=
`I. Road Transport (A roads)`
*
1000
/
Population
,
`Transport (Motorways) per capita`
=
`J. Road Transport (Motorways)`
*
1000
/
Population
,
`Transport (Minor roads) per capita`
=
`K. Road Transport (Minor roads)`
*
1000
/
Population
,
`Transport (Diesel rail) per capita`
=
`L. Diesel Railways`
*
1000
/
Population
,
`Transport (other) per capita`
=
`M. Transport Other`
*
1000
/
Population
)
# ghg_emissions <- filter_detail(lvl_detail = "low")
ghg_emissions
<-
filter_detail
(
lvl_detail
=
"high"
)
rm
(
dt
)
## Subset data - Southampton by default
ghg_subset
<-
function
(
auth_area
=
"Southampton"
){
ghg_emissions_sub
<-
ghg_emissions
%>%
filter
(
ghg_emissions
$
Name
%in%
auth_area
)
# add filter for categories with input$
}
ghg_emissions_sub
<-
ghg_subset
()
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