Motivations

Plot của Financial Times được John Burn-Murdoch trích dẫn như sau:

Bằng cách sử dụng R chúng ta có thể “tái tạo” lại plot này như sau:

# R Codes

Dưới đây là R Codes cho area plot ở trên:

# Load original plot from: https://mobile.twitter.com/jburnmurdoch/status/1541097879984537602/photo/1
# Extract color codes from downloaded image by https://imagecolorpicker.com/en


# Clear R Environment: 

rm(list = ls())

# Create a data set for ploting: 

my_time <- 1:7

a_lot <- c(0.3, 0.29, 0.31, 0.32, 0.35, 0.45, 0.6)

a_little <- c(1, 0.95, 1.1, 1.2, 1.25, 1.7, 2.2)

not_limited <- c(1.7, 1.6, 1.75, 1.8, 2.1, 2.6, 3.1)

library(tidyverse)

df_wide <- tibble(my_time = my_time, a_lot = a_lot, a_little = a_little, not_limited = not_limited)

#----------------
#  Prepare data
#----------------

my_levels <- c("a_lot", "a_little", "not_limited")

df_wide %>% 
  gather(type, value, -my_time) %>% 
  mutate(type = factor(type, level = my_levels[3:1])) -> df_long

label_on_x <- c("May\n2021", "Jul", "Sep", "Nov", "Jan\n2022", "Mar", "May")


a_lot_color <- "#c94456"

a_little_color <- "#ea9077"

not_limited_color <- "#dfd2c8"

grid_color <- "#eaded2"

text_color_grey <- "#908b86"

bgr_color <- "#fff1e5"

p_title <- "Rates of self-reported long Covid continue rise in the UK, with 2% of\npopulation now stating the condition is limiting their daily activities"

p_subtittle <- "Estimated prevalence of self-reported long Covid in the UK, by level of limitation\n(% of total population)"

p_caption <- "Source: ONS Covid-19 infection survey\nFT graphic: Nguyen Chi Dung"


library(showtext) # -> Package for using extra fonts. 

my_font <- "Roboto Condensed" # -> Use Roboto Condensed font for our plot. 

font_add_google(name = my_font, family = my_font) # -> Load font. 

showtext_auto() # -> Automatically render text: 

library(ggtext)

df_long %>% 
  ggplot(aes(x = my_time, y = value, fill = type)) + 
  geom_area(position = "dodge", show.legend = FALSE, alpha = 0.8) + 
  theme_minimal() +  
  labs(title = p_title, subtitle = p_subtittle, caption = p_caption) +   
  scale_x_continuous(limits = c(1, 7), breaks = seq(1, 7, 1), expand = c(0, 0), labels = label_on_x) + 
  scale_y_continuous(position = "right", expand = c(0, 0)) + 
  scale_fill_manual(values = c(not_limited_color, a_little_color, a_lot_color)) + 
  theme(axis.title = element_blank()) + 
  theme(plot.margin = unit(rep(0.7, 4), "cm")) + 
  theme(panel.grid.minor = element_blank()) + 
  theme(panel.grid.major.x = element_blank()) + 
  theme(panel.grid.major.y = element_line(size = 0.8, color = grid_color)) + 
  theme(plot.background = element_rect(color = NA, fill = bgr_color)) + 
  theme(panel.background = element_rect(color = NA, fill = NA)) + 
  theme(axis.ticks.length.x = unit(0.2, "cm")) + 
  theme(axis.ticks.x = element_line(color = "grey50", size = 0.8)) + 
  theme(axis.text.x = element_text(family = my_font, size = 12, color = "grey40", hjust = 0.13)) + 
  theme(axis.text.y = element_text(family = my_font, size = 12, color = "grey40")) + 
  theme(plot.title = element_text(family = my_font, size = 19, color = "black")) + 
  theme(plot.subtitle = element_text(family = my_font, size = 15, color = "grey30")) + 
  theme(plot.caption = element_text(family = my_font, size = 10, color = "grey30", hjust = 0)) + 
  theme(axis.line.x = element_line(size = 0.8, color = "grey40")) + 
  annotate("text", 
           label = "Daily activities limited a alot", 
           family = my_font, 
           x = 4.2, 
           y = 0.12, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "white") + 
  annotate("text", 
           label = "Daily activities limited a little", 
           family = my_font, 
           x = 4.2, 
           y = 0.6, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "white", 
           angle = "7") + 
  annotate("text", 
           label = "Daily activities not limited", 
           family = my_font, 
           x = 4.2, 
           y = 1.25, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "grey50", 
           angle = "20")

# Make FT icon: 

library(grid)

grid.rect(x = 0, y = 1, width = 0.06, height = 0.01, just = c("left", "top"), gp = gpar(fill = "black", col = "black"))

Notes

  1. Làm chữ béo (bold) cho một số từ trong text của plot là vấn đề còn bỏ trống. Chúng ta có thể sử dụng gói ggtext để xử lí vấn đề này.
  2. Có thể tham khảo thêm một số plot của FT tại đây hoặc tại đây.
---
title: 'R for Data Visualization: Replicating Financial Times Graph (naive version)'
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, cache = TRUE, fig.showtext = TRUE)

```

# Motivations

Plot của Financial Times được John Burn-Murdoch trích dẫn như sau: 


![](C:/Users/Admin/Documents/area_plot_original.jpg)

Bằng cách sử dụng R chúng ta có thể "tái tạo" lại plot này như sau: 


![](C:/Users/Admin/Documents/area2.jpg)
# R Codes

Dưới đây là R Codes cho area plot ở trên: 

```{r, eval=FALSE}

# Load original plot from: https://mobile.twitter.com/jburnmurdoch/status/1541097879984537602/photo/1
# Extract color codes from downloaded image by https://imagecolorpicker.com/en


# Clear R Environment: 

rm(list = ls())

# Create a data set for ploting: 

my_time <- 1:7

a_lot <- c(0.3, 0.29, 0.31, 0.32, 0.35, 0.45, 0.6)

a_little <- c(1, 0.95, 1.1, 1.2, 1.25, 1.7, 2.2)

not_limited <- c(1.7, 1.6, 1.75, 1.8, 2.1, 2.6, 3.1)

library(tidyverse)

df_wide <- tibble(my_time = my_time, a_lot = a_lot, a_little = a_little, not_limited = not_limited)

#----------------
#  Prepare data
#----------------

my_levels <- c("a_lot", "a_little", "not_limited")

df_wide %>% 
  gather(type, value, -my_time) %>% 
  mutate(type = factor(type, level = my_levels[3:1])) -> df_long

label_on_x <- c("May\n2021", "Jul", "Sep", "Nov", "Jan\n2022", "Mar", "May")


a_lot_color <- "#c94456"

a_little_color <- "#ea9077"

not_limited_color <- "#dfd2c8"

grid_color <- "#eaded2"

text_color_grey <- "#908b86"

bgr_color <- "#fff1e5"

p_title <- "Rates of self-reported long Covid continue rise in the UK, with 2% of\npopulation now stating the condition is limiting their daily activities"

p_subtittle <- "Estimated prevalence of self-reported long Covid in the UK, by level of limitation\n(% of total population)"

p_caption <- "Source: ONS Covid-19 infection survey\nFT graphic: Nguyen Chi Dung"


library(showtext) # -> Package for using extra fonts. 

my_font <- "Roboto Condensed" # -> Use Roboto Condensed font for our plot. 

font_add_google(name = my_font, family = my_font) # -> Load font. 

showtext_auto() # -> Automatically render text: 

library(ggtext)

df_long %>% 
  ggplot(aes(x = my_time, y = value, fill = type)) + 
  geom_area(position = "dodge", show.legend = FALSE, alpha = 0.8) + 
  theme_minimal() +  
  labs(title = p_title, subtitle = p_subtittle, caption = p_caption) +   
  scale_x_continuous(limits = c(1, 7), breaks = seq(1, 7, 1), expand = c(0, 0), labels = label_on_x) + 
  scale_y_continuous(position = "right", expand = c(0, 0)) + 
  scale_fill_manual(values = c(not_limited_color, a_little_color, a_lot_color)) + 
  theme(axis.title = element_blank()) + 
  theme(plot.margin = unit(rep(0.7, 4), "cm")) + 
  theme(panel.grid.minor = element_blank()) + 
  theme(panel.grid.major.x = element_blank()) + 
  theme(panel.grid.major.y = element_line(size = 0.8, color = grid_color)) + 
  theme(plot.background = element_rect(color = NA, fill = bgr_color)) + 
  theme(panel.background = element_rect(color = NA, fill = NA)) + 
  theme(axis.ticks.length.x = unit(0.2, "cm")) + 
  theme(axis.ticks.x = element_line(color = "grey50", size = 0.8)) + 
  theme(axis.text.x = element_text(family = my_font, size = 12, color = "grey40", hjust = 0.13)) + 
  theme(axis.text.y = element_text(family = my_font, size = 12, color = "grey40")) + 
  theme(plot.title = element_text(family = my_font, size = 19, color = "black")) + 
  theme(plot.subtitle = element_text(family = my_font, size = 15, color = "grey30")) + 
  theme(plot.caption = element_text(family = my_font, size = 10, color = "grey30", hjust = 0)) + 
  theme(axis.line.x = element_line(size = 0.8, color = "grey40")) + 
  annotate("text", 
           label = "Daily activities limited a alot", 
           family = my_font, 
           x = 4.2, 
           y = 0.12, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "white") + 
  annotate("text", 
           label = "Daily activities limited a little", 
           family = my_font, 
           x = 4.2, 
           y = 0.6, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "white", 
           angle = "7") + 
  annotate("text", 
           label = "Daily activities not limited", 
           family = my_font, 
           x = 4.2, 
           y = 1.25, 
           size = 5.5, 
           hjust = -0.5, 
           vjust = 0, 
           color = "grey50", 
           angle = "20")

# Make FT icon: 

library(grid)

grid.rect(x = 0, y = 1, width = 0.06, height = 0.01, just = c("left", "top"), gp = gpar(fill = "black", col = "black"))
```

# Notes

1. Làm chữ béo (bold) cho một số từ trong text của plot là vấn đề còn bỏ trống. Chúng ta có thể sử dụng gói ggtext để xử lí vấn đề này. 
2. Có thể tham khảo thêm một số plot của FT [tại đây](https://www.google.com/search?q=Replicating+Financial+Times+Graph&rlz=1C1CHBF_enVN870VN870&tbm=isch&source=iu&ictx=1&vet=1&fir=H6ETH_xtNoDHUM%252CygItsaiDIJZupM%252C_%253BthatZUSUP6phTM%252CkQ-mx2TBkTrDMM%252C_%253BR9AzSQZt2MKrNM%252CikhtKRVjpPZX9M%252C_%253BBxUcwKnMl2YkfM%252C_fvbtATBvqqdJM%252C_%253B_5pU_9f_b10SxM%252CyUHn5Pm8aaO_uM%252C_%253BcRr2of99wSm47M%252CYllFp9Cwg3R1OM%252C_%253Bn-dSxPcqqzKQOM%252C_fvbtATBvqqdJM%252C_%253Bl2hfe29DTLnq6M%252CEp4tm0JdQJg8wM%252C_%253BDh9ZIQwmD1dVTM%252C_fvbtATBvqqdJM%252C_%253BorRheRLON_Pn8M%252CkMT3U9Cz1hdgMM%252C_&usg=AI4_-kTx2LiNRnCG1368MM_JfLPGSHJXEg&sa=X&ved=2ahUKEwjyrqfyxeP4AhWIqFYBHYXhCHIQ9QF6BAgGEAE) hoặc [tại đây](https://www.google.com/search?q=Replicating+Financial+Times+Graph&rlz=1C1CHBF_enVN870VN870&tbm=isch&source=iu&ictx=1&vet=1&fir=H6ETH_xtNoDHUM%252CygItsaiDIJZupM%252C_%253BthatZUSUP6phTM%252CkQ-mx2TBkTrDMM%252C_%253BR9AzSQZt2MKrNM%252CikhtKRVjpPZX9M%252C_%253BBxUcwKnMl2YkfM%252C_fvbtATBvqqdJM%252C_%253B_5pU_9f_b10SxM%252CyUHn5Pm8aaO_uM%252C_%253BcRr2of99wSm47M%252CYllFp9Cwg3R1OM%252C_%253Bn-dSxPcqqzKQOM%252C_fvbtATBvqqdJM%252C_%253Bl2hfe29DTLnq6M%252CEp4tm0JdQJg8wM%252C_%253BDh9ZIQwmD1dVTM%252C_fvbtATBvqqdJM%252C_%253BorRheRLON_Pn8M%252CkMT3U9Cz1hdgMM%252C_&usg=AI4_-kTx2LiNRnCG1368MM_JfLPGSHJXEg&sa=X&ved=2ahUKEwjyrqfyxeP4AhWIqFYBHYXhCHIQ9QF6BAgUEAE#imgrc=thatZUSUP6phTM&imgdii=HmfDQAKNRGGayM
# https://twitter.com/jburnmurdoch/status/1541097879984537602). 

