library(tidyverse)
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## v tibble 3.0.4 v dplyr 1.0.2
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## v readr 1.4.0 v forcats 0.5.0
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library(mosaic)
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## method from
## fortify.SpatialPolygonsDataFrame ggplot2
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## Attaching package: 'mosaic'
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library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(rgdal)
## Loading required package: sp
## rgdal: version: 1.5-18, (SVN revision 1082)
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## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
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## project
library(tmap)
IMD2019 = read_csv("NewcastleUponTyne.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## Code = col_character(),
## Region = col_character(),
## CodeRegion = col_character(),
## SCORE = col_double(),
## Decile = col_double()
## )
LTHP = read_csv("long-term_health_problem_or_disability_(LTHP).csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## Name = col_character(),
## LSOA = col_character(),
## LTHP = col_double(),
## Perc_LTHP = col_double()
## )
NOCAR = read_csv("no_car_data.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## Name = col_character(),
## LSOA = col_character(),
## NoCars = col_double(),
## Perc_NoCars = col_double()
## )
favstats(~SCORE,data=IMD2019)
## min Q1 median Q3 max mean sd n missing
## 32 3120 10560 22504.75 32714 12921.83 10612.5 166 0
round(favstats(~SCORE,data=IMD2019),digits=1)
## min Q1 median Q3 max mean sd n missing
## 32 3120 10560 22504.8 32714 12921.8 10612.5 166 0
gf_histogram(~SCORE,data=IMD2019,title="IMD Scores Newcastle")
gf_dhistogram(~SCORE,data=IMD2019,title="IMD Scores Newcastle") %>% gf_dens(color="red")
round(favstats(~Perc_NoCars,data=NOCAR),digits=1)
## min Q1 median Q3 max mean sd n missing
## 5.3 25 39.4 55.2 78.3 40.5 18.7 175 0
gf_dhistogram(~Perc_NoCars,data=NOCAR,title="Percentage Households with No Car",) %>% gf_dens(color = "red") %>% gf_labs(x="Percentage without Cars")
stem(NOCAR$Perc_NoCars)
##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 55899
## 1 | 22333444
## 1 | 55566778888888999
## 2 | 0011222333444
## 2 | 556666777779999
## 3 | 0011223344
## 3 | 55666677777888899999
## 4 | 01122222233444
## 4 | 5566666677888999
## 5 | 00000011122
## 5 | 5556667778999
## 6 | 0001123334
## 6 | 555567788
## 7 | 0222244
## 7 | 5556678
NEWCASTLE = read_sf("england_lsoa_2011.shp")
plot(NEWCASTLE["name"])
head(IMD2019)
## # A tibble: 6 x 5
## Code Region CodeRegion SCORE Decile
## <chr> <chr> <chr> <dbl> <dbl>
## 1 E01008288 Newcastle upon Tyne 027A E08000021 9092 3
## 2 E01008289 Newcastle upon Tyne 027B E08000021 1985 1
## 3 E01008290 Newcastle upon Tyne 027C E08000021 1567 1
## 4 E01008291 Newcastle upon Tyne 027D E08000021 137 1
## 5 E01008292 Newcastle upon Tyne 027E E08000021 26799 9
## 6 E01008293 Newcastle upon Tyne 004A E08000021 15011 5
head(NEWCASTLE)
## Simple feature collection with 6 features and 3 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 419707.3 ymin: 563103.6 xmax: 426470.8 ymax: 571146
## projected CRS: OSGB 1936 / British National Grid
## # A tibble: 6 x 4
## label name code geometry
## <chr> <chr> <chr> <POLYGON [m]>
## 1 E08000021E02~ Newcastle u~ E01033~ ((425400 564505, 425421 564502, 425444 564~
## 2 E08000021E02~ Newcastle u~ E01033~ ((425276 564514.2, 425273.3 564494.7, 4252~
## 3 E08000021E02~ Newcastle u~ E01033~ ((424797 564019.3, 424797.1 564018.3, 4247~
## 4 E08000021E02~ Newcastle u~ E01033~ ((420966.4 564478.5, 420966.1 564469.5, 42~
## 5 E08000021E02~ Newcastle u~ E01033~ ((426198.4 564384.2, 426193 564323.5, 4261~
## 6 E08000021E02~ Newcastle u~ E01033~ ((423911 571142, 423927 571131, 423935.6 5~
NEWCASTLE = merge(NEWCASTLE,IMD2019,by.x="code",by.y="Code")
names(NEWCASTLE)
## [1] "code" "label" "name" "Region" "CodeRegion"
## [6] "SCORE" "Decile" "geometry"
qtm(NEWCASTLE,fill="SCORE",title="IMD SCORES")
qtm(NEWCASTLE,fill="SCORE",title="IMD SCORES", format="NLD_wide",style="classic")
CENSUS = merge(LTHP,NOCAR)
head(CENSUS)
## Name LSOA LTHP Perc_LTHP NoCars Perc_NoCars
## 1 Newcastle upon Tyne 001A E01008294 115 18.6 120 19.4
## 2 Newcastle upon Tyne 001B E01008306 219 30.4 152 21.1
## 3 Newcastle upon Tyne 001C E01008307 140 28.0 190 38.0
## 4 Newcastle upon Tyne 001D E01008308 166 26.9 177 28.7
## 5 Newcastle upon Tyne 001E E01008311 124 18.0 91 13.2
## 6 Newcastle upon Tyne 001F E01008312 101 20.1 81 16.1
NEWCASTLE = merge(NEWCASTLE,CENSUS,by.x="code",by.y="LSOA")
qtm(NEWCASTLE,fill="Perc_NoCars",title="NO-CAR HOUSEHOLDS", format="NLD_wide",style="classic")
qtm(NEWCASTLE,fill="Perc_NoCars",fill.style="jenks",fill.n=5,title="NO-CAR HOUSEHOLDS", title.position=c("right","TOP"),format="NLD_wide",style="classic") + tm_compass(north=0,type="arrow",position=c("right","top")) + tm_scale_bar(position=c("left","bottom"))
tm_shape(NEWCASTLE) + tm_fill("SCORE",style="jenks",n=5) + tm_borders() + tm_layout(title="IMD SCORES", title.position=c("centre","TOP")) + tm_legend(legend.format=list(digits=1)) + tm_compass(north=0,type="arrow",position=c("right","top")) + tm_scale_bar(position=c("right","bottom"))
