# Load some packages: 
library(tidyverse)
library(lubridate)

# Data links (source: https://github.com/CSSEGISandData/COVID-19): 
rm(list = ls())
link1 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
link2 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
link3 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"

# Clean and prepare data: 

link1 %>% 
  read_csv() %>% 
  rename(Province = `Province/State`, Country = `Country/Region`) %>% 
  gather(Date, Confirmed, -Province, -Country, -Lat, -Long) %>% 
  mutate(Date = mdy(Date)) %>% 
  filter(Country == "Vietnam", Date <= ymd("2020-04-04")) -> df_confirmed

df_confirmed %>% 
  mutate(before = lag(Confirmed)) %>% 
  mutate(NewConfirmed = Confirmed - before) %>% 
  slice(-1) -> df_for_g1

end_stage1 <- dmy("13-02-2020")
start_stage2 <- dmy("06-03-2020")
end_stage2 <- dmy("21-03-2020")

df_for_g1 %>% 
  mutate(Stage = case_when(Date <= end_stage1 ~ "Stage 1", 
                           Date > end_stage1 & Date < start_stage2 ~ "Standby", 
                           Date >= start_stage2 & Date <= end_stage2 ~ "Stage 2", 
                           TRUE ~ "Stage 3")) -> df

df %>% 
  select(TotalConfirmed = Confirmed, NewConfirmed, Date) %>% 
  gather(Type, Value, -Date) -> df_plot

df_plot$Date %>% unique() -> my_date

df_labels <- data.frame(Date = my_date)

df_labels %>% 
  mutate(label_x = case_when(Date == min(Date) ~ as.character(Date), 
                             Date == end_stage1 ~ as.character(end_stage1), 
                             Date == start_stage2 ~ as.character(start_stage2), 
                             Date == end_stage2 ~ as.character(end_stage2), 
                             Date == max(Date) ~ as.character(Date), 
                             TRUE ~ "")) %>% 
  mutate(Stage = case_when(Date <= end_stage1 ~ "Stage 1", 
                           Date > end_stage1 & Date < start_stage2 ~ "Standby", 
                           Date >= start_stage2 & Date <= end_stage2 ~ "Stage 2", 
                           TRUE ~ "Stage 3")) %>% 
  mutate(label_x = case_when(label_x == "2020-01-23" ~ "23-01", 
                             label_x == "2020-02-13" ~ "13-02", 
                             label_x == "2020-03-06" ~ "06-03", 
                             label_x == "2020-03-21" ~ "21-03", 
                             label_x == "2020-04-04" ~ "04-04", 
                             TRUE ~ label_x)) -> df_labels



df_for_g1 %>% 
  filter(Date == max(Date)) %>% 
  mutate(Date = Date + 2) %>% 
  select(TotalConfirmed = Confirmed, NewConfirmed, Date) %>% 
  gather(Type, Value, -Date) %>% 
  mutate(label = case_when(Value == 4 ~ paste0(Value, "  "), 
                           TRUE ~ as.character(Value))) -> df_an 


fill_colors <- c("#fff5f0", "#fcbba1", "#fc9272", "#fb6a4a")
line_colors <- c("#e41a1c", "#1f78b4")
h <- 250
k <- -0.5
my_font <- "Ubuntu Condensed"

ggplot() + 
  theme_minimal() + 
  geom_rect(aes(xmin = min(my_date), xmax = end_stage1, ymin = -Inf, ymax = Inf, fill = "Stage 1"), fill = "#a1d99b", alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = end_stage1, xmax = start_stage2, ymin = -Inf, ymax = Inf, fill = "Standby"), fill = fill_colors[2], alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = start_stage2, xmax = end_stage2, ymin = -Inf, ymax = Inf, fill = "Stage 2"), fill = fill_colors[3], alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = end_stage2, xmax = max(my_date), ymin = -Inf, ymax = Inf, fill = "Stage 3"), fill = fill_colors[4], alpha = 0.3, show.legend = FALSE) + 
  geom_line(data = df_plot, aes(Date, Value, color = Type), size = 1.5) + 
  scale_color_manual(values = line_colors, name = "", labels = c("New", "Total")) + 
  scale_y_continuous(breaks = seq(0, 250, 25), limits = c(0, 250)) + 
  theme(panel.grid.minor.y = element_blank()) + 
  theme(legend.position = "top") + 
  scale_x_date(limits = c(min(my_date), max(my_date) + 3), breaks = seq(min(my_date), max(my_date), 1), 
               labels = df_labels$label_x, position = "top", expand = c(0, 0)) + 
  theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank()) + 
  geom_text(data = df_an, aes(Date, Value, label = label), show.legend = FALSE, color = line_colors[2:1], size = 4.5, 
            fontface = "bold", hjust = 0.6, family = my_font) +
  labs(x = NULL, y = NULL, 
       title = "Figure 1: Coronavirus cases in Vietnam, as of 04-04-2020", 
       caption = "Source: Ministry of Health of Vietnam") + 
  geom_text(data = data.frame(Date = ymd("2020-01-23") + 10, Value = h), aes(Date, Value), label = "Stage 1", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-02-13") + 11, Value = h), aes(Date, Value), label = "Standby", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-03-06") + 7, Value = h), aes(Date, Value), label = "Stage 2", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-03-21") + 7, Value = h), aes(Date, Value), label = "Stage 3", size = 4, vjust = k, family = my_font) + 
  theme(axis.text.x = element_text(size = 10, family = my_font)) + 
  theme(axis.text.y = element_text(size = 13, family = my_font)) + 
  theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) + 
  theme(plot.title = element_text(family = my_font, size = 20)) + 
  theme(plot.caption = element_text(family = my_font, size = 10, color = "grey30")) + 
  theme(legend.text = element_text(family = my_font, size = 10, face = "bold", color = "grey30"))

---
title: 'Coronavirus cases in Vietnam'
author: 'Author: Nguyen Chi Dung'
subtitle: "Daily Graph Series"
output:
  html_document: 
    code_download: true
    # code_folding: hide
    highlight: zenburn
    # number_sections: yes
    theme: "flatly"
    toc: TRUE
    toc_float: TRUE
---

```{r setup,include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.width = 10, fig.height = 6)
```


# Motivations

Chức năng của Graph là truyền tải các insights cho người tiếp nhận thông tin. Khi nào chức năng này mà không làm được, hoặc gây hiểu lầm, hoặc não người mất quá nhiều thời gian để hiểu insight trong graph được trình bày, thì coi như graph chưa làm được chức năng của nó. Một Graph tốt thì nên là self-explanatory (tự cái hình đã giải thích mọi thứ). Một trong những ví dụ như vậy là hình số 2 của báo cáo có tên *ĐÁNH GIÁ TÁC ĐỘNG CỦA COVID-19 ĐẾN NỀN KINH TẾ VÀ CÁC KHUYẾN NGHỊ CHÍNH SÁCH* (có thể xem [tại đây](https://neu.edu.vn/Resources/Docs/SubDomain/HomePage/ThongBao/2020/2020_4/FormatFactory%20PDF%20Joiner%20BIA%201%20bao%20cao_1.pdf?fbclid=IwAR1f6YzdfSaDH69lfa05HnCwpAuEF25HSD0qkhnGJmSTMx-LZ2eADFqyWxs)). Chúng ta có thể cải tiến lại như sau: 

![](C:\\Users\\ADMIN\\Desktop\\corona.jpg)

# R codes 

R codes để thực hiện hình này như sau: 


```{r, eval=FALSE}
# Load some packages: 
library(tidyverse)
library(lubridate)

# Data links (source: https://github.com/CSSEGISandData/COVID-19): 
rm(list = ls())
link1 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
link2 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
link3 <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"

# Clean and prepare data: 

link1 %>% 
  read_csv() %>% 
  rename(Province = `Province/State`, Country = `Country/Region`) %>% 
  gather(Date, Confirmed, -Province, -Country, -Lat, -Long) %>% 
  mutate(Date = mdy(Date)) %>% 
  filter(Country == "Vietnam", Date <= ymd("2020-04-04")) -> df_confirmed

df_confirmed %>% 
  mutate(before = lag(Confirmed)) %>% 
  mutate(NewConfirmed = Confirmed - before) %>% 
  slice(-1) -> df_for_g1

end_stage1 <- dmy("13-02-2020")
start_stage2 <- dmy("06-03-2020")
end_stage2 <- dmy("21-03-2020")

df_for_g1 %>% 
  mutate(Stage = case_when(Date <= end_stage1 ~ "Stage 1", 
                           Date > end_stage1 & Date < start_stage2 ~ "Standby", 
                           Date >= start_stage2 & Date <= end_stage2 ~ "Stage 2", 
                           TRUE ~ "Stage 3")) -> df

df %>% 
  select(TotalConfirmed = Confirmed, NewConfirmed, Date) %>% 
  gather(Type, Value, -Date) -> df_plot

df_plot$Date %>% unique() -> my_date

df_labels <- data.frame(Date = my_date)

df_labels %>% 
  mutate(label_x = case_when(Date == min(Date) ~ as.character(Date), 
                             Date == end_stage1 ~ as.character(end_stage1), 
                             Date == start_stage2 ~ as.character(start_stage2), 
                             Date == end_stage2 ~ as.character(end_stage2), 
                             Date == max(Date) ~ as.character(Date), 
                             TRUE ~ "")) %>% 
  mutate(Stage = case_when(Date <= end_stage1 ~ "Stage 1", 
                           Date > end_stage1 & Date < start_stage2 ~ "Standby", 
                           Date >= start_stage2 & Date <= end_stage2 ~ "Stage 2", 
                           TRUE ~ "Stage 3")) %>% 
  mutate(label_x = case_when(label_x == "2020-01-23" ~ "23-01", 
                             label_x == "2020-02-13" ~ "13-02", 
                             label_x == "2020-03-06" ~ "06-03", 
                             label_x == "2020-03-21" ~ "21-03", 
                             label_x == "2020-04-04" ~ "04-04", 
                             TRUE ~ label_x)) -> df_labels



df_for_g1 %>% 
  filter(Date == max(Date)) %>% 
  mutate(Date = Date + 2) %>% 
  select(TotalConfirmed = Confirmed, NewConfirmed, Date) %>% 
  gather(Type, Value, -Date) %>% 
  mutate(label = case_when(Value == 4 ~ paste0(Value, "  "), 
                           TRUE ~ as.character(Value))) -> df_an 


fill_colors <- c("#fff5f0", "#fcbba1", "#fc9272", "#fb6a4a")
line_colors <- c("#e41a1c", "#1f78b4")
h <- 250
k <- -0.5
my_font <- "Ubuntu Condensed"

ggplot() + 
  theme_minimal() + 
  geom_rect(aes(xmin = min(my_date), xmax = end_stage1, ymin = -Inf, ymax = Inf, fill = "Stage 1"), fill = "#a1d99b", alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = end_stage1, xmax = start_stage2, ymin = -Inf, ymax = Inf, fill = "Standby"), fill = fill_colors[2], alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = start_stage2, xmax = end_stage2, ymin = -Inf, ymax = Inf, fill = "Stage 2"), fill = fill_colors[3], alpha = 0.3, show.legend = FALSE) + 
  geom_rect(aes(xmin = end_stage2, xmax = max(my_date), ymin = -Inf, ymax = Inf, fill = "Stage 3"), fill = fill_colors[4], alpha = 0.3, show.legend = FALSE) + 
  geom_line(data = df_plot, aes(Date, Value, color = Type), size = 1.5) + 
  scale_color_manual(values = line_colors, name = "", labels = c("New", "Total")) + 
  scale_y_continuous(breaks = seq(0, 250, 25), limits = c(0, 250)) + 
  theme(panel.grid.minor.y = element_blank()) + 
  theme(legend.position = "top") + 
  scale_x_date(limits = c(min(my_date), max(my_date) + 3), breaks = seq(min(my_date), max(my_date), 1), 
               labels = df_labels$label_x, position = "top", expand = c(0, 0)) + 
  theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank()) + 
  geom_text(data = df_an, aes(Date, Value, label = label), show.legend = FALSE, color = line_colors[2:1], size = 4.5, 
            fontface = "bold", hjust = 0.6, family = my_font) +
  labs(x = NULL, y = NULL, 
       title = "Figure 1: Coronavirus cases in Vietnam, as of 04-04-2020", 
       caption = "Source: Ministry of Health of Vietnam") + 
  geom_text(data = data.frame(Date = ymd("2020-01-23") + 10, Value = h), aes(Date, Value), label = "Stage 1", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-02-13") + 11, Value = h), aes(Date, Value), label = "Standby", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-03-06") + 7, Value = h), aes(Date, Value), label = "Stage 2", size = 4, vjust = k, family = my_font) + 
  geom_text(data = data.frame(Date = ymd("2020-03-21") + 7, Value = h), aes(Date, Value), label = "Stage 3", size = 4, vjust = k, family = my_font) + 
  theme(axis.text.x = element_text(size = 10, family = my_font)) + 
  theme(axis.text.y = element_text(size = 13, family = my_font)) + 
  theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) + 
  theme(plot.title = element_text(family = my_font, size = 20)) + 
  theme(plot.caption = element_text(family = my_font, size = 10, color = "grey30")) + 
  theme(legend.text = element_text(family = my_font, size = 10, face = "bold", color = "grey30"))

```





