GIS Blog Post 3
Jyoti Nepal, MSW
19 April, 2022
Read Data
v19_Profile <- load_variables(2019,
"acs5/profile",
cache = TRUE)
v14_Profile <- load_variables(2014,
"acs5/profile",
cache = TRUE)#demographic
#Search for variables by using grep()
v19_Profile[grep(x = v19_Profile$label,
"Educational Attainment",
ignore.case = TRUE),
c("name", "label")]v19_Profile[grep(x = v19_Profile$label,
"Hispanic and Race",
ignore.case = TRUE),
c("name", "label")]v19_Profile[grep(x = v19_Profile$label,
"Sex and Age",
ignore.case = TRUE),
c("name", "label")]v14_Profile[grep(x = v14_Profile$label,
"Educational Attainment",
ignore.case = TRUE),
c("name", "label")]v14_Profile[grep(x = v14_Profile$label,
"Hispanic and Race",
ignore.case = TRUE),
c("name", "label")]v14_Profile[grep(x = v14_Profile$label,
"Sex and Age",
ignore.case = TRUE),
c("name", "label")]educ14<-get_acs(geography = 'county',
state ='TX',
year = 2014,
variables = c(peduc14 = "DP02_0066P",
male = "DP05_0002P",
hispanic = "DP05_0066P",
nh_white = "DP05_0072P",
nh_black = "DP05_0073P"),
geometry = T, output = "wide")## Getting data from the 2010-2014 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile##
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|======================================================================| 100%#rename variables and filter missing cases
educ14 <- educ14%>%
mutate(peduc14 = peduc14E,
peduc14_er = peduc14M/1.645,
peduc14_cv =100* (peduc14_er/peduc14),
male = maleE,
female = 100 - male,
hispanic = hispanicE,
nh_white = nh_whiteE,
nh_black = nh_blackE) %>%
filter(complete.cases(peduc14), is.finite(peduc14_cv)==T)%>%
select(GEOID, NAME, peduc14, peduc14_er,peduc14_cv, male, female, hispanic,nh_white,nh_black)
head(educ14)educ19<-get_acs(geography = 'county',
state ='TX',
year = 2019,
variables = c(peduc19 = "DP02_0067P",
male = "DP05_0002P",
hispanic = "DP05_0071P",
nh_white = "DP05_0077P",
nh_black = "DP05_0078P"),
geometry = T, output = "wide")## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile##
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|======================================================================| 100%#rename variables and filter missing cases
educ19 <- educ19%>%
mutate(peduc19 = peduc19E,
peduc19_er = peduc19M/1.645,
peduc19_cv =100* (peduc19_er/peduc19),
male = maleE,
female = 100 - male,
hispanic = hispanicE,
nh_white = nh_whiteE,
nh_black = nh_blackE) %>%
filter(complete.cases(peduc19), is.finite(peduc19_cv)==T)%>%
select(GEOID, NAME, peduc19, peduc19_er,peduc19_cv, male, female, hispanic,nh_white,nh_black)
head(educ19)counties <- educ19 %>%
filter(NAME =='Bexar County, Texas'| NAME == 'Dallas County, Texas'| NAME == 'Harris County, Texas'| NAME == 'Tarrant County, Texas'| NAME == 'Travis County, Texas')
counties$NAME <- str_sub(counties$NAME, end=-14)
cts <- educ14 %>%
filter(NAME =='Bexar County, Texas'| NAME == 'Dallas County, Texas'| NAME == 'Harris County, Texas'| NAME == 'Tarrant County, Texas'| NAME == 'Travis County, Texas')
cts$NAME <- str_sub(cts$NAME, end=-14)p1<-tm_shape(educ14)+
tm_polygons(c("peduc14"),
title=c("% Population with HS degree or higher"),
palette="Reds",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'blue')+
#tm_format("World", legend.outside=T, title.size =4)+
tm_scale_bar(width = 0.1)+
tm_layout(title="Population percent with HS degree or higher
ACS 2014 : 5 year estimates",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))
p2<-tm_shape(educ14)+
tm_polygons(c("peduc14_cv"),
title=c("CV % pop with HS degree or higher
"),
palette="Reds",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'blue')+
#tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
tm_layout(title="CV - Population percent with HS degree or higher
ACS 2014 : 5 year estimates",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_scale_bar(width = 0.1)+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))
map14<-tmap_arrange(p1, p2)
map14
bar1 <- ggplot(cts)+
geom_col(aes(NAME,peduc14), fill = 'red')+
labs(x = "Counties", y = "Percent population with HS degree or higher", title = "Percent of population with HS degree or higher across Texas counties", subtitle = "ACS 2010-2014")
bar1
p3<-tm_shape(educ19)+
tm_polygons(c("peduc19"),
title=c("% Population with HS degree or higher"),
palette="Blues",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'blue')+
#tm_format("World", legend.outside=T, title.size =4)+
tm_scale_bar(width = 0.1)+
tm_layout(title="Population percent with HS degree or higher
ACS 2019 : 5 year estimates",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))
p4<-tm_shape(educ19)+
tm_polygons(c("peduc19_cv"),
title=c("CV % pop with HS degree or higher"),
palette="Blues",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'blue')+
#tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
tm_layout(title="CV - Population percent with HS degree or higher
ACS 2019 : 5 year estimates",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_scale_bar(width = 0.1)+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))
map19<- tmap_arrange(p3, p4)
map19
bar2<- ggplot(counties)+
geom_col(aes(NAME,peduc19), fill = 'skyblue')+
labs(x = "Counties", y = "Percent population with HS degree or higher", title = "Percent of population with HS degree or higher across Texas counties", subtitle = "ACS 2015-2019")
bar2
acstest<-function(names,geoid, est1, err1, est2, err2, alpha, yr1, yr2, span){
se1<-err1/qnorm(.90)
se2<-err2/qnorm(.90)
yrs1<-seq(yr1, to=yr1-span)
yrs2<-seq(yr2, to=yr2-span)
C<-mean(yrs2%in%yrs1)
diff<- (est1-est2)
test<-(est1-est2) / (sqrt(1-C)*sqrt(se1^2+se2^2))
crit<-qnorm(1-alpha/2)
pval<-1-pnorm(abs(test))
result<-NULL
result[pval > alpha]<-"No Significant Change "
result[pval < alpha & test < 0]<- "Significant Increase"
result[pval < alpha & test > 0]<-"Significant Decrease"
data.frame(name=names,geoid=geoid, est1=est1, est2=est2, se1=se1, se2=se2,difference=diff, test=test, result=result, pval=pval)
}#merge the two years worth of data
st_geometry(educ19)<-NULL #strip the geometry from the 2019 data
mdat<-left_join(educ14, educ19, by=c("GEOID"="GEOID"))
head(mdat)diff1419 <- acstest(names = mdat$GEOID,
geoid = mdat$GEOID,
est1 = mdat$peduc14,
est2 = mdat$peduc19,
err1 = mdat$peduc14_er,
err2 = mdat$peduc19_er,
alpha = .1,
yr1 = 2014, yr2=2019,
span = 5)mdat$signif<- significance(est1=mdat$peduc14,
est2=mdat$peduc19,
moe1=mdat$peduc14_er,
moe2 = mdat$peduc19_er,
clevel = .9)table(mdat$signif) ###
## FALSE TRUE
## 105 149acs_merge<-left_join(mdat, diff1419, by=c("GEOID"="geoid"))
tmap_mode("plot")## tmap mode set to plottingp5<-tm_shape(acs_merge)+
tm_polygons(c("peduc14"),
title=c("% in Poverty 2010"),
palette="Blues",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'red')+
#tm_format("World", legend.outside=T, title.size =4)+
tm_scale_bar(width = 0.1)+
tm_layout(title="San Antonio Poverty Rate Estimates 2010",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_compass()+
tm_format("World",
legend.position = c("LEFT", "BOTTOM"),
main.title.position =c("TOP","CENTERr"))
p6<-tm_shape(acs_merge)+
tm_polygons(c("peduc19"),
title=c("% in Poverty 2019"),
palette="Blues",
style="quantile",
n=5)+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'red')+
#tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
tm_layout(title="San Antonio Poverty Rate Estimate 2019",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_scale_bar(width = 0.1)+
tm_compass()+
tm_format("World",
legend.position = c("LEFT", "BOTTOM"),
main.title.position =c("TOP","CENTER"))
p7 <- tm_shape(acs_merge)+
tm_polygons(c("result"),
title=c("Change in % population with HS degree or higher"),
palette = "Set2")+
tm_shape(counties)+
tm_dots(size = 0.3, col = 'blue')+
#tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
tm_layout(main.title=
"Change in Percentage of Population with HS degree or higher across Texas counties
ACS 2014 and ACS 2019 - 5 year estimates",
main.title.size =0.7,
legend.frame = TRUE,
main.title.position = c('center'))+
tm_scale_bar(width = 0.1)+
tm_compass()+
tm_format("World",
legend.position = c("LEFT", "BOTTOM"),
main.title.position =c("TOP","CENTERr"))
mapdiff<- p7
mapdiff
---
title: "GIS Blog Post 3"
author: "Jyoti Nepal, MSW"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    df_print: paged
    fig_height: 7
    fig_width: 7
    toc: yes
    toc_float: yes
    code_download: yes
  word_document:
    toc: yes
---


```{r, message=FALSE, quietly =T, include=F}
library(survey, quietly = T)
library(dplyr, quietly = T)
library(car, quietly = T)
library(ggplot2, quietly = T)
library(tigris, quietly = T)
library(classInt, quietly = T)
library(srvyr, quietly = T)
library(reldist, quietly = T)
library(sf, quietly = T)
library(mapview, quietly = T)
library(janitor, quietly = T)
library(tidyverse, quietly = T)
library(tmap, quietly = T)
library(patchwork, quietly = T)
library(tmaptools, quietly = T)
library(ggsn, quietly = T)
library(tidycensus, quietly = T)
library(stringr)

```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

### Read Data
```{r}


v19_Profile <- load_variables(2019,
                              "acs5/profile",
                              cache = TRUE)

v14_Profile <- load_variables(2014,
                              "acs5/profile",
                              cache = TRUE)#demographic 

#Search for variables by using grep()


v19_Profile[grep(x = v19_Profile$label,
                 "Educational Attainment",
                 ignore.case = TRUE),
            c("name", "label")]

v19_Profile[grep(x = v19_Profile$label,
                 "Hispanic and Race",
                 ignore.case = TRUE),
            c("name", "label")]
v19_Profile[grep(x = v19_Profile$label,
                 "Sex and Age",
                 ignore.case = TRUE),
            c("name", "label")]
v14_Profile[grep(x = v14_Profile$label,
                 "Educational Attainment",
                 ignore.case = TRUE),
            c("name", "label")]

v14_Profile[grep(x = v14_Profile$label,
                 "Hispanic and Race",
                 ignore.case = TRUE),
            c("name", "label")]
v14_Profile[grep(x = v14_Profile$label,
                 "Sex and Age",
                 ignore.case = TRUE),
            c("name", "label")]


```


```{r}
educ14<-get_acs(geography = 'county',
                state ='TX',
                year = 2014,
                variables = c(peduc14 = "DP02_0066P",
                              male = "DP05_0002P",
                              hispanic = "DP05_0066P",
                              nh_white = "DP05_0072P",
                              nh_black = "DP05_0073P"), 
              geometry = T, output = "wide")


#rename variables and filter missing cases
educ14 <- educ14%>%
  mutate(peduc14 = peduc14E, 
         peduc14_er = peduc14M/1.645,
         peduc14_cv =100* (peduc14_er/peduc14),
         male = maleE,
         female = 100 - male,
         hispanic = hispanicE,
         nh_white = nh_whiteE,
         nh_black = nh_blackE) %>%
  filter(complete.cases(peduc14), is.finite(peduc14_cv)==T)%>%
  select(GEOID, NAME, peduc14, peduc14_er,peduc14_cv, male, female, hispanic,nh_white,nh_black)

head(educ14)



educ19<-get_acs(geography = 'county',
                state ='TX',
                year = 2019,
                variables = c(peduc19 = "DP02_0067P",
                              male = "DP05_0002P",
                              hispanic = "DP05_0071P",
                              nh_white = "DP05_0077P",
                              nh_black = "DP05_0078P"), 
              geometry = T, output = "wide")


#rename variables and filter missing cases
educ19 <- educ19%>%
  mutate(peduc19 = peduc19E, 
         peduc19_er = peduc19M/1.645,
         peduc19_cv =100* (peduc19_er/peduc19),
         male = maleE,
         female = 100 - male,
         hispanic = hispanicE,
         nh_white = nh_whiteE,
         nh_black = nh_blackE) %>%
  filter(complete.cases(peduc19), is.finite(peduc19_cv)==T)%>%
  select(GEOID, NAME, peduc19, peduc19_er,peduc19_cv, male, female, hispanic,nh_white,nh_black)

head(educ19)

```



```{r}

counties <- educ19 %>% 
  
  filter(NAME =='Bexar County, Texas'| NAME == 'Dallas County, Texas'| NAME == 'Harris County, Texas'| NAME == 'Tarrant County, Texas'| NAME == 'Travis County, Texas')

counties$NAME <- str_sub(counties$NAME, end=-14)

cts <- educ14 %>% 
  
  filter(NAME =='Bexar County, Texas'| NAME == 'Dallas County, Texas'| NAME == 'Harris County, Texas'| NAME == 'Tarrant County, Texas'| NAME == 'Travis County, Texas')

cts$NAME <- str_sub(cts$NAME, end=-14)

```


```{r}
p1<-tm_shape(educ14)+
  tm_polygons(c("peduc14"),
              title=c("% Population with HS degree or higher"),
              palette="Reds",
              style="quantile",
              n=5)+
  tm_shape(counties)+
  tm_dots(size = 0.3, col = 'blue')+
  #tm_format("World", legend.outside=T, title.size =4)+
  tm_scale_bar(width = 0.1)+
  tm_layout(title="Population percent with HS degree or higher 
ACS 2014 : 5 year estimates",
            title.size =1.5, 
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))


p2<-tm_shape(educ14)+
  tm_polygons(c("peduc14_cv"),
              title=c("CV % pop with HS degree or higher
"),
              palette="Reds",
              style="quantile",
              n=5)+
   tm_shape(counties)+
  tm_dots(size = 0.3, col = 'blue')+
  #tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
  tm_layout(title="CV - Population percent with HS degree or higher
ACS 2014 : 5 year estimates",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_scale_bar(width = 0.1)+
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))

map14<-tmap_arrange(p1, p2)
map14

```
```{r}
bar1 <- ggplot(cts)+
geom_col(aes(NAME,peduc14), fill = 'red')+
  labs(x = "Counties", y = "Percent population with HS degree or higher", title = "Percent of population with HS degree or higher across Texas counties", subtitle = "ACS 2010-2014")
bar1
```



```{r}

p3<-tm_shape(educ19)+
  tm_polygons(c("peduc19"),
              title=c("% Population with HS degree or higher"),
              palette="Blues",
              style="quantile",
              n=5)+
  tm_shape(counties)+
  tm_dots(size = 0.3, col = 'blue')+
  #tm_format("World", legend.outside=T, title.size =4)+
  tm_scale_bar(width = 0.1)+
  tm_layout(title="Population percent with HS degree or higher 
ACS 2019 : 5 year estimates",
            title.size =1.5, 
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))


p4<-tm_shape(educ19)+
  tm_polygons(c("peduc19_cv"),
              title=c("CV % pop with HS degree or higher"),
              palette="Blues",
              style="quantile",
              n=5)+
   tm_shape(counties)+
  tm_dots(size = 0.3, col = 'blue')+
  #tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
  tm_layout(title="CV - Population percent with HS degree or higher
ACS 2019 : 5 year estimates",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_scale_bar(width = 0.1)+
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))

map19<- tmap_arrange(p3, p4)
map19

```


```{r}
bar2<- ggplot(counties)+
geom_col(aes(NAME,peduc19), fill = 'skyblue')+
  labs(x = "Counties", y = "Percent population with HS degree or higher", title = "Percent of population with HS degree or higher across Texas counties", subtitle = "ACS 2015-2019")
bar2
```

```{r}
acstest<-function(names,geoid, est1, err1, est2, err2, alpha, yr1, yr2, span){
  
  se1<-err1/qnorm(.90)
  se2<-err2/qnorm(.90)
  yrs1<-seq(yr1, to=yr1-span)
  yrs2<-seq(yr2, to=yr2-span)

  C<-mean(yrs2%in%yrs1)
  diff<- (est1-est2)
  test<-(est1-est2) / (sqrt(1-C)*sqrt(se1^2+se2^2))
  crit<-qnorm(1-alpha/2)
  pval<-1-pnorm(abs(test))
  result<-NULL
  result[pval > alpha]<-"No Significant Change "
  result[pval < alpha & test < 0]<- "Significant Increase"
  result[pval < alpha & test > 0]<-"Significant Decrease" 
  
  data.frame(name=names,geoid=geoid, est1=est1, est2=est2, se1=se1, se2=se2,difference=diff, test=test, result=result, pval=pval)
}
```


```{r}

#merge the two years worth of data
st_geometry(educ19)<-NULL #strip the geometry from the 2019 data

mdat<-left_join(educ14, educ19, by=c("GEOID"="GEOID"))

head(mdat)
```


```{r}
diff1419 <- acstest(names = mdat$GEOID,
                    geoid = mdat$GEOID,
                    est1 = mdat$peduc14,
                    est2 = mdat$peduc19,
                    err1 = mdat$peduc14_er,
                    err2 = mdat$peduc19_er,
                    alpha = .1,
                    yr1 = 2014, yr2=2019,
                    span = 5)
```


```{r}
mdat$signif<- significance(est1=mdat$peduc14,
                           est2=mdat$peduc19,
                           moe1=mdat$peduc14_er,
                           moe2 = mdat$peduc19_er,
                           clevel = .9)
```



```{r}
table(mdat$signif) #

```

```{r}
acs_merge<-left_join(mdat, diff1419, by=c("GEOID"="geoid"))

tmap_mode("plot")
p5<-tm_shape(acs_merge)+
  tm_polygons(c("peduc14"),
              title=c("% in Poverty  2010"),
              palette="Blues",
              style="quantile",
              n=5)+
  tm_shape(counties)+
  tm_dots(size = 0.3, col = 'red')+
  #tm_format("World", legend.outside=T, title.size =4)+
  tm_scale_bar(width = 0.1)+
  tm_layout(title="San Antonio Poverty Rate Estimates 2010",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_compass()+
  tm_format("World",
            legend.position =  c("LEFT", "BOTTOM"),
            main.title.position =c("TOP","CENTERr"))

p6<-tm_shape(acs_merge)+
  tm_polygons(c("peduc19"),
              title=c("% in Poverty 2019"),
              palette="Blues", 
              style="quantile",
              n=5)+
  tm_shape(counties)+
  tm_dots(size = 0.3, col = 'red')+
  #tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
  tm_layout(title="San Antonio Poverty Rate Estimate 2019",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_scale_bar(width = 0.1)+
  tm_compass()+
  tm_format("World",
            legend.position =  c("LEFT", "BOTTOM"),
            main.title.position =c("TOP","CENTER"))


p7  <- tm_shape(acs_merge)+
  tm_polygons(c("result"),
              title=c("Change in % population with HS degree or higher"),
              palette = "Set2")+
  tm_shape(counties)+
  tm_dots(size = 0.3, col = 'blue')+
  #tm_format("World", title="San Antonio Poverty Rate CV", legend.outside=T)+
  tm_layout(main.title=
"Change in Percentage of Population with HS degree or higher across Texas counties 
ACS 2014 and ACS 2019 - 5 year estimates", 
            main.title.size =0.7, 
            legend.frame = TRUE,
            main.title.position = c('center'))+
  tm_scale_bar(width = 0.1)+
  tm_compass()+
  tm_format("World",
            legend.position =  c("LEFT", "BOTTOM"),
            main.title.position =c("TOP","CENTERr"))

  

mapdiff<- p7
mapdiff

```
