View pop_total.R
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library(tidyverse) | |
library(treemapify) | |
library(countrycode) | |
library(gganimate) | |
library(magick) | |
# devtools::install_github("PPgp/wpp2022") | |
library(wpp2022) | |
# load data | |
data(pop1dt) |
View migrant_sankey.R
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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") |
View china_flow_2010_2015.csv
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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 |
View china_region_details.csv
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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 |
View ipums_clean_dta.R
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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) %>% |
View gist:7b31e43002ff4229eab3e896e51d4f6d
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<!DOCTYPE html> | |
<html> | |
<head> | |
</head> | |
<body> | |
<table> | |
<tbody> | |
<tr> |
View cd.R
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## ----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")) |
View thesis.tex
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\documentclass[11pt]{report} | |
\usepackage[a4paper,margin=2cm, bindingoffset=2cm]{geometry} | |
\usepackage{appendix} | |
\usepackage{amsmath} | |
\usepackage{booktabs} | |
\usepackage{threeparttable} | |
\usepackage{natbib} | |
\bibliographystyle{chicago} |
View sweden1993.csv
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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 |