Guy Abel guyabel

## un_stock.R
library(tidyverse)
library(readxl)

download.file(url = "https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx",
              destfile = "undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx",  mode = "wb")

d <- read_excel(path = "undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx",
                sheet = 2, skip = 10, na = "..", guess_max = 1e5) %>%
  select(-1, -3, -5) %>%
  pivot_longer(cols = -(1:4), names_to = "year", values_to = "stock") %>%

## pop_total.R
library(tidyverse)
library(treemapify)
library(countrycode)
library(gganimate)
library(magick)
# devtools::install_github("PPgp/wpp2022")
library(wpp2022)

# load data
data(pop1dt)

## migrant_sankey.R
library(tidyverse)
library(readxl)
library(tweenr)
library(ggforce)
library(gridExtra)
library(animation)
library(magick)

# download the migrant stock origin-destination xlsx file from the UN site
x <- tempfile(fileext = ".xlsx")

## china_flow_2010_2015.csv

          
            orig
            dest
            flow

            
              Beijing
              Beijing
              0

            
              Tianjin
              Beijing
              67355

            
              Hebei
              Beijing
              864387

            
              Shanxi
              Beijing
              225032

            
              Inner Mongolia
              Beijing
              103355

            
              Liaoning
              Beijing
              155032

            
              Jilin
              Beijing
              104774

            
              Heilongjiang
              Beijing
              194387

            
              Shanghai
              Beijing
              26581

## china_region_details.csv

          
            name
            region
            order
            colour
            gap
            name_zh

            
              Beijing
              East
              1
              #B61416
              2
              北京

            
              Tianjin
              East
              2
              #C01517
              2
              天津

            
              Hebei
              East
              3
              #CA1718
              2
              河北

            
              Shandong
              East
              4
              #D4181A
              2
              山东

            
              Jiangsu
              East
              5
              #DE191B
              2
              江苏

            
              Shanghai
              East
              6
              #E91A1C
              2
              上海

            
              Zhejiang
              East
              7
              #F31B1D
              2
              浙江

            
              Fujian
              East
              8
              #FD1C1F
              2
              福建

            
              Guangdong
              East
              9
              #FF1E20
              2
              广东

## ipums_clean_dta.R
ipums_clean_dta <- function(
  d = NULL, numeric_convert = NULL, string_as_factors = TRUE, clean_labels = FALSE,
  mis_lab = c("unknown", "niu (not in universe)", "not reported/missing",
              "unknown/missing", "response suppressed", "unclassifiable")
  ){
  d %>%
    {if(clean_labels) dplyr::mutate_if(is.labelled, ipumsr::lbl_clean) else .} %>%
    dplyr::mutate_if(is.labelled, ~ipumsr::lbl_na_if(., ~.lbl %in% mis_lab)) %>%
    dplyr::mutate_at({numeric_convert}, haven::zap_labels) %>%
    dplyr::mutate_if(is.labelled, haven::as_factor) %>%

## gist:7b31e43002ff4229eab3e896e51d4f6d
<!DOCTYPE html>
<html>
<head>
</head>

<body>

<table>
<tbody>
  <tr>

## cd.R
## ----setup, include=FALSE------------------------------------------------
library(knitr)
knitr::opts_chunk$set(prompt= TRUE, collapse = TRUE, comment = NA)
knitr::opts_chunk$set(fig.width = 5, fig.height = 5)
knitr::knit_hooks$set(purl = hook_purl)

## ---- message=FALSE------------------------------------------------------
# install.packages("migest")
library(tidyverse)
d0 <- read_csv(system.file("imr", "reg_flow.csv", package = "migest"))

## thesis.tex

\documentclass[11pt]{report}
\usepackage[a4paper,margin=2cm, bindingoffset=2cm]{geometry}
\usepackage{appendix}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{threeparttable}
\usepackage{natbib}
\bibliographystyle{chicago}

## sweden1993.csv

          
            age
            x
            Nx_m
            Nx_f
            Lx_m
            Lx_f
            Fx
            Mx_f

            
              00--05
              0
              310189
              293395
              496754
              497487
              0
              6840

            
              05--10
              5
              261963
              248369
              496297
              497138
              0
              4150

            
              10--15
              10
              252046
              240012
              495989
              496901
              0
              3365

            
              15--20
              15
              274711
              261346
              495113
              496531
              0.012
              5270

            
              20--25
              20
              296679
              285209
              493460
              495902
              0.0908
              9240

            
              25--30
              25
              333726
              314388
              491475
              495168
              0.1499
              8230

            
              30--35
              30
              296774
              281290
              489325
              494213
              0.1125
              5470

            
              35--40
              35
              299391
              286923
              486487
              492760
              0.0441
              3155

            
              40--45
              40
              314295
              304108
              482392
              490447
              0.0074
              1770
	library(tidyverse)
	library(readxl)

	download.file(url = "https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx",
	destfile = "undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx", mode = "wb")

	d <- read_excel(path = "undesa_pd_2020_ims_stock_by_sex_destination_and_origin.xlsx",
	sheet = 2, skip = 10, na = "..", guess_max = 1e5) %>%
	select(-1, -3, -5) %>%
	pivot_longer(cols = -(1:4), names_to = "year", values_to = "stock") %>%
	library(tidyverse)
	library(treemapify)
	library(countrycode)
	library(gganimate)
	library(magick)
	# devtools::install_github("PPgp/wpp2022")
	library(wpp2022)

	# load data
	data(pop1dt)
	library(tidyverse)
	library(readxl)
	library(tweenr)
	library(ggforce)
	library(gridExtra)
	library(animation)
	library(magick)

	# download the migrant stock origin-destination xlsx file from the UN site
	x <- tempfile(fileext = ".xlsx")
orig	dest	flow
Beijing	Beijing	0
Tianjin	Beijing	67355
Hebei	Beijing	864387
Shanxi	Beijing	225032
Inner Mongolia	Beijing	103355
Liaoning	Beijing	155032
Jilin	Beijing	104774
Heilongjiang	Beijing	194387
Shanghai	Beijing	26581
name	region	order	colour	gap	name_zh
Beijing	East	1	#B61416	2	北京
Tianjin	East	2	#C01517	2	天津
Hebei	East	3	#CA1718	2	河北
Shandong	East	4	#D4181A	2	山东
Jiangsu	East	5	#DE191B	2	江苏
Shanghai	East	6	#E91A1C	2	上海
Zhejiang	East	7	#F31B1D	2	浙江
Fujian	East	8	#FD1C1F	2	福建
Guangdong	East	9	#FF1E20	2	广东
	ipums_clean_dta <- function(
	d = NULL, numeric_convert = NULL, string_as_factors = TRUE, clean_labels = FALSE,
	mis_lab = c("unknown", "niu (not in universe)", "not reported/missing",
	"unknown/missing", "response suppressed", "unclassifiable")
	){
	d %>%
	{if(clean_labels) dplyr::mutate_if(is.labelled, ipumsr::lbl_clean) else .} %>%
	dplyr::mutate_if(is.labelled, ~ipumsr::lbl_na_if(., ~.lbl %in% mis_lab)) %>%
	dplyr::mutate_at({numeric_convert}, haven::zap_labels) %>%
	dplyr::mutate_if(is.labelled, haven::as_factor) %>%
	## ----setup, include=FALSE------------------------------------------------
	library(knitr)
	knitr::opts_chunk$set(prompt= TRUE, collapse = TRUE, comment = NA)
	knitr::opts_chunk$set(fig.width = 5, fig.height = 5)
	knitr::knit_hooks$set(purl = hook_purl)

	## ---- message=FALSE------------------------------------------------------
	# install.packages("migest")
	library(tidyverse)
	d0 <- read_csv(system.file("imr", "reg_flow.csv", package = "migest"))

	\documentclass[11pt]{report}
	\usepackage[a4paper,margin=2cm, bindingoffset=2cm]{geometry}
	\usepackage{appendix}
	\usepackage{amsmath}
	\usepackage{booktabs}
	\usepackage{threeparttable}
	\usepackage{natbib}
	\bibliographystyle{chicago}
age	x	Nx_m	Nx_f	Lx_m	Lx_f	Fx	Mx_f
00--05	0	310189	293395	496754	497487	0	6840
05--10	5	261963	248369	496297	497138	0	4150
10--15	10	252046	240012	495989	496901	0	3365
15--20	15	274711	261346	495113	496531	0.012	5270
20--25	20	296679	285209	493460	495902	0.0908	9240
25--30	25	333726	314388	491475	495168	0.1499	8230
30--35	30	296774	281290	489325	494213	0.1125	5470
35--40	35	299391	286923	486487	492760	0.0441	3155
40--45	40	314295	304108	482392	490447	0.0074	1770