w29_technology.Rmd

---
title: "w29 Technology"
output: html_document
---


## Cross-country Historical Adoption of Technology (CHAT)

Data contains information on the adoption of over 100 technologies in more than 150 countries since 1800.
Described is technology usage primarily based on per capita measures and divide technologies into the two broad categories of production and consumption.

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
library(tidyverse)
library(knitr)
knitr::opts_chunk$set(cache = TRUE, warning = FALSE, message = FALSE, 
                      comment = "",
                      echo = TRUE, dpi = 300, cache.lazy = FALSE,
                      tidy = "styler", fig.width = 8, fig.height = 5)
```



```{r}
# technology <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-19/technology.csv')

```


```{r}
# saveRDS(technology,"technology.rds")
technology <- readRDS("technology.rds")

technology_df<-technology%>%select(-variable,-label)

technology_df %>% count(group)
```


```{r}
library(ggtext)
```


```{r}
technology_df %>%
  filter(group%in%c("Consumption","Production")) %>%
  filter(iso3c=="ITA") %>%
  group_by(iso3c,year,group) %>%
  summarize(Average=round(mean(value)),
            Median=round(median(value)),.groups="drop")%>%
  ungroup() %>%
  pivot_longer(cols = c("Average","Median"),names_to="metric",values_to="values_metrics") %>%
  filter(values_metrics>0) %>% 
  ggplot(aes(x=factor(year), y= values_metrics)) +
  geom_line(size=0.2,color="gray")+
  geom_line(aes(group=factor(group),
                color=factor(group)),
            size=0.8)+
  geom_point(aes(color=factor(group)),shape=21,stroke=0.6,size=2)+
  scale_y_log10(labels=scales::comma_format())+
  scale_x_discrete(breaks=seq(1839,2020,10))+
  ggthemes::scale_color_fivethirtyeight() +
  ggthemes::theme_fivethirtyeight() +
  labs(title="Cross-country Historical Adoption of Technology (CHAT)",
       subtitle="<span style='color:#0072B2;font-size:14pt'>Production vs Consuption</span><br><br>Data contains information on the adoption of over 100 technologies in more than 150 countries since 1800.<br>Described is technology usage primarily based on per capita measures and divide technologies into the two broad categories<br>of production and consumption. Evidences of average and median values differences explain distribution skewness and outliers.<br>The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency<br>when the distribution is not symmetrical.<br>Values are obtained grouping data by country, year and between production and consuption. ",
       caption="DataSource: #TidyTuesday 2022 week29 | Technology Adoption |	data.nber.org	| www.cgdev.org\nDataViz: Federica Gazzelloni (@fgazzelloni)",
       color="")+
  facet_wrap(~metric,scales="free_y",nrow = 2) +
  theme(plot.title = element_text(face = "bold",size=18),
        plot.title.position = "plot",
        plot.subtitle = element_markdown(lineheight = 1),
        axis.line.x = element_line(color="gray",size=1),
        axis.ticks.x = element_line(size=2))
```


```{r}
ggsave("w29_technology.png",
       dpi=320,
       width = 10,
       height = 8)
```