wgoitg01.Rmd

---
title: "WGOITG practice"
author: "Jilung Hsieh"
date: "`r Sys.Date()`"
output: html_document
---

# Set up

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width = 4, fig.asp = 0.4)
```

```{r loading-libs}
library(tidyverse)
options(stringsAsFactors = F)

# install.packages("gghighlight")
library(gghighlight)
```

# Median of net worth by age group

The news: <https://www.nytimes.com/2021/05/11/learning/lesson-plans/teach-about-inequality-with-these-28-new-york-times-graphs.html>

1.  `names(toplot)` should be `[1] "year"     "Category" "Net_worth"       "increase"`
2.  Select `year`, `Category`, and `Net_Worth`
3.  Grouped by `Category`
    1.  Sort by `year` (using `arrange`)

    2.  Mutate a new column `increase`
4.  ungroup() then plotting

```{r}
toplot <- read_csv("data/nytdata/interactive_bulletin_charts_agecl_median.csv")
# YOUR CODE SHOULD PIPELINE BEGINING HERE 



```

```{r vis-net-worth}
toplot %>%    
    ggplot() + aes(year, increase, color = Category) + 
    geom_line() + 
    gghighlight(Category %in% c("65-74", "35-44")) + 
    theme_minimal() + 
    scale_x_continuous(breaks = NULL) + 
    theme(panel.background = element_rect(fill = "whitesmoke",
                                colour = "whitesmoke",
                                size = 0.5, linetype = "solid"))
```

# UNICEF-Optimistic

<https://www.nytimes.com/2021/11/17/upshot/global-survey-optimism.html> <https://changingchildhood.unicef.org/about>

1.  relabel by mutate and `ordered()` with levels and labels\
    `mutate(var1 = ordered(var2, levels = c(…), labels = c(…))`
2.  `count()` and `spread()` then `mutate()` a new var `perc` standing for answering "world will be better!" proportion.
3.  `spread()` by `age` group and its `perc`
4.  `rename` columns
5.  then plotting

```{r young-people-optimistic-scatter, fig.cap = '(ref:young-people-optimistic)'}

toplot <- read_csv("data/nytdata/unicef-changing-childhood-data.csv") %>% 
    select(country = WP5, age = WP22140, bw = WP22092) %>%
    mutate(country = ordered(country, 
                             levels=c(1, 3, 4, 10, 11, 12, 13, 14, 17, 29, 
                                      31, 33, 35, 36, 60, 61, 77, 79, 81, 87, 165), 
                             labels=c("USA", "Morocco", "Lebanon", "Indonesia",
                                      "Bangladesh", "UK", "France", "Germany",
                                      "Spain", "Japan", "India", "Brazil", 
                                      "Nigeria", "Kenya", "Ethiopia", "Mali", 
                                      "Ukraine", "Cameroon", "Zimbabwe",
                                      "Argentina", "Peru")))
# YOUR CODE SHOULD PIPELINE BEGINING HERE 





```

```{r}
toplot %>% 
    ggplot() + aes(`40+y`, `15-24y`, label = country) + 
    geom_point(color = "skyblue", size = 2) + 
    xlim(0, 1) + ylim(0,1) + 
    geom_text(hjust = -0.1, vjust = -0.5) + 
    geom_abline(intercept = 0, slop = 1, 
                color="lightgrey", alpha=0.5, linetype="dashed") + 
    theme_minimal() + 
    theme(aspect.ratio=1)
```

# Global Carbon Projects

[Who Has The Most Historical Responsibility for Climate Change? - The New York Times (nytimes.com)](https://www.nytimes.com/interactive/2021/11/12/climate/cop26-emissions-compensation.html?campaign_id=29&emc=edit_up_20211112&instance_id=45236&nl=the-upshot&regi_id=52022771&segment_id=74222&te=1&user_id=7cc6d9cd8f523e256ae41958ee8a9cb5)

```{r}

totreemap <- read_csv("WGOITG/nytdata/GCB2021v34_MtCO2_flat.csv") %>% 
    drop_na(`Total`) %>%
    filter(!Country %in% c("Global", "International Transport")) %>%
    filter(Year==2020) %>%
    arrange(desc(`Total`)) %>%
    mutate(perc = Total/sum(Total)) %>%
    slice(1:20)
library(treemapify)
totreemap %>%
    ggplot() + aes(area = perc, fill=`Per Capita`, label=Country) +
    geom_treemap() + 
    geom_treemap_text(color="white", 
                      place="centre", 
                      grow=TRUE
                      )
```