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？胡扯

創建華夫餅圖視覺化

描述

方形圓餅圖（又稱華夫餅圖）可用於傳達分類數量的整體部分。要模擬餅圖的百分比視圖，應使用 10x10 網格，每個方格代表總數的 1%。華夫餅圖的現代使用不一定遵守此規則，可以使用任何矩形形狀的網格來建立。最佳實踐建議保持較小的類別數量，就像創建餅圖時應該做的那樣。提供了工具來創建華夫餅圖並將它們縫合在一起，並使用字形來製作同型象形圖。

它使用 ggplot2 並傳回 ggplot2 物件。

罐頭裡面有什麼

實現了以下功能：

  • waffle ：製作華夫餅（方形餅圖）圖表

  • draw_key_pictogram ：象形圖的圖例產生器

  • fa_grep ：搜尋模式的 Font Awesome 字形名稱

  • fa_list ：列出所有 Font Awesome 字形

  • fa5_brand : Font Awesome 5 品牌

  • fa5_solid : Font Awesome 5 固體

  • geom_pictogram : 象形圖 Geom

  • geom_waffle : 華夫餅（方形餅圖）Geom

  • install_fa_fonts : 安裝 Font Awesome 5 字型

  • iron ：華夫餅圖的垂直左對齊佈局

  • scale_label_pictogram ：與 geom_pictogram() 一起使用將 Font Awesome 字體對應到標籤

  • theme_enhance_waffle ：華夫餅圖主題污垢去除器，可與任何其他主題一起使用

安裝

install.packages( " waffle " ) # NOTE: CRAN version is 0.7.0
# or
remotes :: install_github( " hrbrmstr/waffle " )

注意：要使用「remotes」安裝選項，您需要安裝 {remotes} 軟體包。

用法

library( waffle )
library( magrittr )
library( hrbrthemes )
library( ggplot2 )
library( dplyr )
library( waffle )

# current verison
packageVersion( " waffle " )
# # [1] '1.0.2'

首先是一些新的部分

 data.frame (
  parts = factor (rep( month.abb [ 1 : 3 ], 3 ), levels = month.abb [ 1 : 3 ]),
  vals = c( 10 , 20 , 30 , 6 , 14 , 40 , 30 , 20 , 10 ),
  col = rep(c( " navy " , " black " , " maroon " ), 3 ),
  fct = c(
    rep( " Thing 1 " , 3 ),
    rep( " Thing 2 " , 3 ),
    rep( " Thing 3 " , 3 )
  )
) - > xdf

xdf % > %
  count( parts , wt = vals ) % > %
  ggplot(
    aes( fill = parts , values = n )
  ) +
  geom_waffle(
    n_rows = 20 ,
    size = 0.33 , 
    colour = " white " ,
    flip = TRUE
  ) +
  scale_fill_manual(
    name = NULL ,
    values = c( " #a40000 " , " #c68958 " , " #ae6056 " ),
    labels = c( " Fruit " , " Sammich " , " Pizza " )
  ) +
  coord_equal() +
  theme_ipsum_rc( grid = " " ) +
  theme_enhance_waffle()

 xdf % > %
  count( parts , wt = vals ) % > %
  ggplot(
    aes( label = parts , values = n )
  ) +
  geom_pictogram(
    n_rows = 10 , 
    aes( colour = parts ), 
    flip = TRUE , 
    make_proportional = TRUE
  ) +
  scale_color_manual(
    name = NULL ,
    values = c( " #a40000 " , " #c68958 " , " #ae6056 " ),
    labels = c( " Fruit " , " Sammich " , " Pizza " )
  ) +
  scale_label_pictogram(
    name = NULL ,
    values = c( " apple-alt " , " bread-slice " , " pizza-slice " ),
    labels = c( " Fruit " , " Sammich " , " Pizza " )
  ) +
  coord_equal() +
  theme_ipsum_rc( grid = " " ) +
  theme_enhance_waffle() +
  theme(
    legend.key.height = unit( 2.25 , " line " ),
    legend.text = element_text( size = 10 , hjust = 0 , vjust = 0.75 )
  ) 

 xdf % > %
  count( parts , wt = vals ) % > %
  ggplot(
    aes( label = parts , values = n )
  ) +
  geom_pictogram(
    n_rows = 20 , 
    size = 6 , 
    aes( colour = parts ), 
    flip = TRUE ,
    family = " FontAwesome5Brands-Regular "
  ) +
  scale_color_manual(
    name = NULL ,
    values = c( " #073f9c " , " black " , " #f34323 " ),
    labels = c( " BitBucket " , " GitHub " , " Other " )
  ) +
  scale_label_pictogram(
    name = NULL ,
    values = c( " bitbucket " , " github " , " git-alt " ),
    labels = c( " BitBucket " , " GitHub " , " Other " )
  ) +
  coord_equal() +
  theme_ipsum_rc( grid = " " ) +
  theme_enhance_waffle() +
  theme(
    legend.text = element_text( hjust = 0 , vjust = 1 )
  )

幾何！ 

library( hrbrthemes )
library( waffle )
library( tidyverse )

tibble(
  parts = factor (rep( month.abb [ 1 : 3 ], 3 ), levels = month.abb [ 1 : 3 ]),
  values = c( 10 , 20 , 30 , 6 , 14 , 40 , 30 , 20 , 10 ),
  fct = c(rep( " Thing 1 " , 3 ), rep( " Thing 2 " , 3 ), rep( " Thing 3 " , 3 ))
) - > xdf

ggplot(
  data = xdf , 
  aes( fill = parts , values = values )
) +
  geom_waffle(
    color = " white " , 
    size = 1.125 , 
    n_rows = 6
  ) +
  facet_wrap( ~ fct , ncol = 1 ) +
  scale_x_discrete(
    expand = c( 0 , 0 , 0 , 0 )
  ) +
  scale_y_discrete(
    expand = c( 0 , 0 , 0 , 0 )
  ) +
  ggthemes :: scale_fill_tableau( name = NULL ) +
  coord_equal() +
  labs(
    title = " Faceted Waffle Geoms "
  ) +
  theme_ipsum_rc( grid = " " ) +
  theme_enhance_waffle()

帶刻度的華夫餅條形圖！ 

library( dplyr )
library( waffle )

storms % > % 
  filter( year > = 2010 ) % > % 
  count( year , status ) - > storms_df

ggplot(
  data = storms_df , 
  aes( fill = status , values = n )
) +
  geom_waffle(
    color = " white " , 
    size = .25 , 
    n_rows = 10 , 
    flip = TRUE
  ) +
  facet_wrap(
    ~ year , 
    nrow = 1 , 
    strip.position = " bottom "
  ) +
  scale_x_discrete() + 
  scale_y_continuous(
    labels = function ( x ) x * 10 , # make this multiplier the same as n_rows
    expand = c( 0 , 0 )
  ) +
  ggthemes :: scale_fill_tableau( name = NULL ) +
  coord_equal() +
  labs(
    x = " Year " , y = " Count " ,
    title = " Faceted Waffle Bar Chart " ,
    subtitle = " {dplyr} storms data "
  ) +
  theme_minimal(
    base_family = " Roboto Condensed "
  ) +
  theme(
    panel.grid = element_blank(), 
    axis.ticks.y = element_line()
  ) +
  guides(
    fill = guide_legend( reverse = TRUE )
  )

基本範例

 parts <- c( 80 , 30 , 20 , 10 )
waffle( parts , rows = 8 )

使用數據框

 parts <- data.frame (
  names = LETTERS [ 1 : 4 ],
  vals = c( 80 , 30 , 20 , 10 )
)

waffle( parts , rows = 8 )

稍微複雜一點的例子

c(
  `Un-breachednUS Population` = ( 318 - 11 - 79 ), 
  `Premera` = 11 , 
  `Anthem` = 79
) - > parts 
waffle(
  parts = parts , 
  rows = 8 , 
  size = 1 , 
  colors = c( " #969696 " , " #1879bf " , " #009bda " ),
  legend_pos = " bottom "
)

健康記錄洩漏占美國人口的比例

一平方 == 1m 人

waffle(
  parts = parts / 10 , 
  rows = 3 ,
  colors = c( " #969696 " , " #1879bf " , " #009bda " )
)

健康記錄洩漏占美國人口的比例

（一平方 == 10m 人） 

library( extrafont )

waffle(
  parts = parts / 10 , 
  rows = 3 , 
  colors = c( " #969696 " , " #1879bf " , " #009bda " ),
  use_glyph = " medkit " , 
  size = 8
) + 
  expand_limits(
    y = c( 0 , 4 )
  )

複製舊的最愛

資料來源：https://www.nytimes.com/2008/07/20/business/20debt.html 

c(
  `Mortgagen($84,911)` = 84911 , 
  `Auto andntuition loansn($14,414)` = 14414 ,
  `Home equity loansn($10,062)` = 10062 , 
  `Credit Cardsn($8,565)` = 8565
) - > savings 
waffle(
  parts = savings / 392 , 
  rows = 7 , 
  size = 0.5 , 
  legend_pos = " bottom " ,
  colors = c( " #c7d4b6 " , " #a3aabd " , " #a0d0de " , " #97b5cf " )
)

每年平均家庭儲蓄

（1 平方 == 392 美元）

更多複製

類似 https://eagereyes.org/techniques/square-pie-charts 

 professional <- c( `Male` = 44 , `Female (56%)` = 56 )
waffle(
  parts = professional , 
  rows = 10 , 
  size = 0.5 ,
  colors = c( " #af9139 " , " #544616 " )
)

現在將因子保留為預設水平

和： 

iron(
  waffle(
    parts = c( thing1 = 0 , thing2 = 100 ), 
    rows = 5
  ),
  waffle(
    parts = c( thing1 = 25 , thing2 = 75 ), 
    rows = 5
  )
)

沒有（您現在可以透過keep參數停用它）： 

iron(
  waffle(
    parts = c( thing1 = 0 , thing2 = 100 ), 
    rows = 5 , 
    keep = FALSE
  ),
  waffle(
    parts = c( thing1 = 25 , thing2 = 75 ), 
    rows = 5 , 
    keep = FALSE
  )
)

專業勞動力彩妝

Iron 範例（多個圖表的左對齊和填充） 

 pain.adult.1997 <- c( `YOY (406)` = 406 , `Adult (24)` = 24 )

waffle(
  parts = pain.adult.1997 / 2 ,
  rows = 7 , 
  size = 0.5 ,
  colors = c( " #c7d4b6 " , " #a3aabd " ),
  title = " Paine Run Brook Trout Abundance (1997) " ,
  xlab = " 1 square = 2 fish " , pad = 3
) - > A

pine.adult.1997 <- c( `YOY (221)` = 221 , `Adult (143)` = 143 )

waffle(
  parts = pine.adult.1997 / 2 , 
  rows = 7 , 
  size = 0.5 ,
  colors = c( " #c7d4b6 " , " #a3aabd " ),
  title = " Piney River Brook Trout Abundance (1997) " ,
  xlab = " 1 square = 2 fish " , pad = 8
) - > B

stan.adult.1997 <- c( `YOY (270)` = 270 , `Adult (197)` = 197 )

waffle(
  parts = stan.adult.1997 / 2 , 
  rows = 7 , 
  size = 0.5 ,
  colors = c( " #c7d4b6 " , " #a3aabd " ),
  title = " Staunton River Trout Abundance (1997) " ,
  xlab = " 1 square = 2 fish "
) - > C

iron( A , B , C )

包代碼指標

 cloc :: cloc_pkg_md()
郎# 文件(%)控制點(%)空行(%) # 行(%)
右14 0.44 624 0.35 218 0.36第439章0.38
平均數2 0.06 255 0.15 88 0.14 139 0.12
和16 0.50第879章0.50 306 0.50第578章0.50

{克洛克}？華夫餅的指標

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