R语言formattable表格按行奇偶修改Average列color_bar颜色

要实现Average列按行自定义条形颜色，你可以直接在formattable的列格式化规则中加入条件判断，通过行号或者指标名称匹配对应颜色，具体修改方案如下：

首先新增自定义蓝色变量，然后修改Average列的格式化逻辑即可，完整可运行代码如下：

library(data.table)
library(dplyr)
library(formattable)
library(tidyr)
customGreen0 = "#DeF7E9"
customGreen = "#71CA97"
customRed = "#ff7f7f"
# 新增自定义蓝色
customBlue = "#4287f5"

austinData= fread('https://raw.githubusercontent.com/lgellis/MiscTutorial/master/Austin/Imagine_Austin_Indicators.csv', data.table=FALSE, header = TRUE, stringsAsFactors = FALSE)
attach(austinData)

i1 <- austinData %>%
  filter(`Indicator Name` %in% 
           c('Prevalence of Obesity', 'Prevalence of Tobacco Use', 
             'Prevalence of Cardiovascular Disease', 'Prevalence of Diabetes')) %>%
  select(c(`Indicator Name`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`)) %>%
  mutate (Average = round(rowMeans(
    cbind(`2011`, `2012`, `2013`, `2014`, `2015`, `2016`), na.rm=T),2), 
    `Improvement` = round((`2011`-`2016`)/`2011`*100,2))

formattable(i1, align =c("l","c","c","c","r", "c", "l", "l", "r"), list(
  `Indicator Name` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
  `2011`= color_tile(customGreen, customGreen0),
  `2012`= color_tile(customGreen, customGreen0),
  `2013`= color_tile(customGreen, customGreen0),
  `2014`= color_bar(customRed),
  `2015`= color_tile(customGreen, customGreen0),
  `2016`= color_bar(customGreen),
  # 修改Average列的格式化逻辑，按指标名称匹配颜色
  `Average` = formatter("span", style = function(x, index) {
    cur_name <- i1$`Indicator Name`[index]
    bar_color <- ifelse(cur_name %in% c('Prevalence of Tobacco Use', 'Prevalence of Diabetes'), customBlue, customRed)
    style(display = "inline-block",
          `border-radius` = "5px", `padding-left` = "3px",
          `background-color` = csscolor(bar_color),
          width = percent(proportion(as.numeric(gsub(",", "", x)))))
  })
))

以上实现用指标名称匹配颜色比按行号匹配更稳定，就算后续调整了i1的行顺序，颜色也不会匹配错误。如果习惯按行号匹配，只需要把bar_color的判断逻辑改成bar_color <- ifelse(index %in% c(2,4), customBlue, customRed)即可。

内容的提问来源于stack exchange，提问作者JK Lambert