In databrew/sfi:

# Packages
library(sfi)
library(webshot)
library(ggplot2)
library(dplyr)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(knitr)
library(Hmisc)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)

# webshot::install_phantomjs()

loadfonts()
## Global options
options(max.print="75")
opts_chunk$set(echo=FALSE,
            cache=FALSE,
              prompt=FALSE,
              tidy=TRUE,
              comment=NA,
              message=FALSE,
              warning=FALSE,
              dpi = 300,
              # dev = "cairo_pdf",
              dev = c("png", "cairo_pdf"),
              fig.pos="!h",
              fig.path = 'figures/')
opts_knit$set(width=75)
options(xtable.comment = FALSE)

This markdown is for alternative (as compared to the doughnut charts) Alexander et al figures.

\newpage

Alexander 4

# Get data
  data <- alexander$f4

  # Flag the groups
  data$group <-
    ifelse(data$key %in% 
             c('Exemption',
               'Color',
               'Race',
               'National origin',
               'Religion',
               'Sex',
               'Retaliation'), 
           'Claim type',
           'National origin')

  # Get percentage
  data$value <- data$value * 100

  # subset to claim type 
  dat_claim <- data[data$group == 'Claim type',]

  # subset to National origin
  dat_origin <- data[data$group == 'National origin',]

  # get a color vector
  colors_claim  <- make_colors(length(unique(dat_claim$key)), bw = TRUE)
  colors_origin  <- make_colors(length(unique(dat_origin$key)), bw = TRUE)

  # sort data
  dat_claim <- dat_claim[order(dat_claim$value, decreasing = TRUE),]
  dat_origin <- dat_origin[order(dat_origin$value, decreasing = TRUE),]

  g1 <- ggplot(dat_claim,
               aes(reorder(x = key, -value),
                   y = value)) +
    geom_bar(stat = 'identity',
             color = 'grey',
             fill = 'black', 
             alpha = 0.7) +
    coord_flip() +
    labs(x = '',
         y = '',
         title = 'Figure 1',
         subtitle = paste0('Figure 4a', '\n', 'Title VII Claim types', '\n', 'from complaint text'),
         caption = '') +
    theme_sfi() +
    theme(axis.title = element_text(size = 10, hjust = 1),
          axis.text = element_text(size = 10, hjust = 1))

    g1

  # candlestick flipped
  g2 <-ggplot(dat_origin,
               aes(reorder(x = key, -value),
                   y = value)) +  
    geom_segment(aes(x=reorder(key, value), 
                     xend=key, 
                     y=0, 
                     yend=value),
                 size = 2,
                 color = 'black',
                 alpha = 0.5) + 
    coord_flip() +
    geom_point(size= 3, 
               alpha = 1) + 
    labs(x = '',
         y = '',
         title = 'Figure 1',
         subtitle = paste0('Figure 4b', '\n', "Title VII Plaintiff's race", '\n', 'from complaint text'),
         caption = '') +
    theme_sfi() +
    theme(axis.title = element_text(size = 10, hjust = 1),
          axis.text = element_text(size = 10, hjust = 1))

  g2

Alexander 7

# Get data
  data <- alexander$f7
  data$value <- data$value * 100

  # get colors
  colors  <- make_colors(length(unique(data$key)), bw = TRUE)

  # sort data
  data <- data[order(data$value, decreasing = TRUE),]

  # add id
  data$id <- 1:nrow(data)


# ----- This section prepare a dataframe for labels ---- #
# Get the name and the y position of each label

label_data=data

# calculate the ANGLE of the labels
number_of_bar=nrow(label_data)
angle= 90 - 360 * (label_data$id-0.5) /number_of_bar     # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)

# calculate the alignment of labels: right or left
# If I am on the left part of the plot, my labels have currently an angle < -90
label_data$hjust<-ifelse( angle < -90, 1, 0)


# flip angle BY to make them readable
label_data$angle<-ifelse(angle < -90, angle+180, angle)
# ----- ------------------------------------------- ---- #

data$value_2 <- data$value + 8

# Start the plot
p = ggplot(data, aes(x=as.factor(id), y=value_2)) +       # Note that id is a factor. If x is numeric, there is some space between the first bar

  # This add the bars with a blue color
  geom_bar(stat="identity", fill=alpha("black", 0.7)) +

  # Limits of the plot = very important. The negative value controls the size of the inner circle, the positive one is useful to add size over each bar
  ylim(-100,120) +

  # Custom the theme: no axis title and no cartesian grid
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm")      # Adjust the margin to make in sort labels are not truncated!
  ) +

  # This makes the coordinate polar instead of cartesian.
  coord_polar(start = 0) +

  # Add the labels, using the label_data dataframe that we have created before
  geom_text(data=label_data, aes(x=id, y=value+10, label=key, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +
    geom_text(data=label_data, aes(x=id, y=value+15, label=value, vjust=-2), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) 


p

databrew/sfi documentation built on May 29, 2019, 1:52 a.m.