Giorgia Lupi是视觉叙事信息的大师-通过可视化讲故事。她的作品集包括为在意大利最流通报纸La Lettura上发布的一系列探索性数据可视化。她的工作会引导那些不太熟悉数据可视化的人，同时组织数据的复杂性，为读者带来深刻的体验。 让我们品味她的作品之一，诺贝尔奖。
自己细细品味喜欢请关注个人公众号R语言数据分析指南持续分享更多优质资源
d <- read.csv("archive.csv", stringsAsFactors = F, encoding = "UTF-8")
# create array identifying each Nobel prize type
cats <- c("Chemistry", "Economics", "Literature",
"Medicine", "Peace", "Physics")
# load library
library(dplyr)
# clean the data types and calculate winner ages from birth and year of prize
d <- d %>%
mutate(Category = factor(Category,
levels = cats,
ordered = T)) %>%
mutate(Sex = factor(Sex)) %>%
mutate(Birth.Year = as.integer(substring(Birth.Date, 1, 4))) %>%
mutate(Age = Year - Birth.Year)
# aggregaate overall average age of winners
davg <- d %>% group_by(Category) %>%
summarise(avg_age = mean(Age, na.rm = T)) %>%
ungroup()
cols <- c("#cc5b47", "#488595", "#96c17c",
"#decd7c", "#924855", "#e79275")
library(ggplot2); library(ggthemes)
ggplot() +
geom_point(aes(x = 1:6, y = 0),
shape = 21, size = 5, fill = cols, color = cols) +
theme_void()
p1 <- ggplot(d, aes(color = Category)) +
theme_minimal(base_family = "sans", base_size = 8) +
geom_hline(yintercept = mean(d$Age, na.rm = T),
lwd = .2, color = "black", linetype = "dashed") +
geom_hline(data=davg, mapping = aes(yintercept=(avg_age),color = Category)) +
geom_line(aes(Year, Age, color = Category), lwd = .2) +
geom_point(aes(Year, Age, color = Category), size = 1.5, alpha = .5) +
geom_point(data = filter(d, Sex == "Female"),
aes(Year, Age),
color = "#da87bd", shape = 21, size = 4) +
facet_wrap(Category ~ ., nrow=6, strip.position="left") +
scale_color_manual(values = cols) +
scale_x_continuous(breaks = c(1901, 1931, 1961, 1991, 2016),
minor_breaks = seq(1911, 2016, by = 10),
position = "top") +
theme(legend.position = "") +
labs(y = "", x = "")
p1
dedu <- read.table(text = "
Category Doctor Master Bachelor None
Chemistry 98 1 1 0
Economics 95 1 4 0
Literature 20 20 25 35
Medicine 95 4 1 0
Peace 32 25 20 23
Physics 99.1 .3 .3 .3
", header = T)
# changes the data from wide form, above, to long format
# where each degree is identified in the variable Education
dedu <- reshape2::melt(dedu,
variable.name = "Education",
value.name = "Percent")
# transform charater types to ordered factors
dedu <- dedu %>%
mutate(Category = factor(Category,
levels = cats,
ordered = T),
Education = factor(Education,
levels = c("None", "Bachelor",
"Master", "Doctor"),
ordered = T))
p2 <-
ggplot(dedu) +
facet_wrap(~Category, ncol = 1) +
geom_bar(aes(Education, Percent, fill = Category), stat = "identity") +
coord_flip() +
scale_fill_manual(values = cols) +
theme_minimal(base_family = "sans", base_size = 8) +
theme(legend.position = "",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank(),
axis.title = element_blank())
cities <- c("Chicago, IL", "Washington, DC", "New York, NY",
"Boston, MA", "London", "Paris", "Munich",
"Berlin", "Vienna", "Budapest", "Moscow")
# aggregate totals by era, birth city, and category of prize
d3 <- d %>%
filter(Birth.City %in% cities) %>%
mutate(Birth.City = factor(Birth.City,
levels = cities[11:1], ordered = T)) %>%
mutate(era = case_when(Year < 1931 ~ 1901,
Year < 1961 ~ 1931,
Year < 1991 ~ 1961,
TRUE ~ 1991)) %>%
group_by(era, Birth.City, Category) %>%
summarise(n = n()) %>%
ungroup()
# aggregate totals by era and birth city
d4 <- d3 %>%
group_by(era, Birth.City) %>%
summarise(Total = sum(n)) %>%
ungroup()
p3 <- ggplot(d3) +
facet_wrap(era~., nrow = 1) +
geom_bar(aes(Birth.City, n, fill = Category),
stat = 'identity', width = 0.2) +
geom_text(data = d4,
mapping = aes(Birth.City, Total + 2, label = Total),
size = 2.5) +
coord_flip() +
scale_fill_manual(values = cols) +
theme_minimal(base_family = "sans") +
theme(legend.position = "",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_blank(),
axis.title = element_blank())
universities <- c("Harvard", "MIT", "Stanford", "Caltech",
"Columbia", "Cambridge", "Berkeley")
d5 <- d %>%
mutate(Organization.Name =
ifelse(grepl("Berkeley", Organization.City, perl = T),
"Berkeley", Organization.Name)) %>%
mutate(University = NA) %>%
mutate(University =
ifelse(grepl("Harvard", Organization.Name, perl = T, useBytes = T),
"Harvard", University)) %>%
mutate(University =
ifelse(grepl("MIT", Organization.Name, perl = T, useBytes = T),
"MIT", University)) %>%
mutate(University =
ifelse(grepl("Stanford", Organization.Name, perl = T, useBytes = T),
"Stanford", University)) %>%
mutate(University =
ifelse(grepl("Caltech", Organization.Name, perl = T, useBytes = T),
"Caltech", University)) %>%
mutate(University =
ifelse(grepl("Columbia", Organization.Name, perl = T, useBytes = T),
"Columbia", University)) %>%
mutate(University =
ifelse(grepl("Cambridge", Organization.Name, perl = T, useBytes = T),
"Cambridge", University)) %>%
mutate(University =
ifelse(grepl("Berkeley", Organization.Name, perl = T, useBytes = T),
"Berkeley", University))
d5 <- d5 %>%
filter(University %in% universities) %>%
group_by(Category, University, .drop = F) %>%
summarise(n = n())
# setup data for plot
library(ggforce)
# use helper function to change tidy data into new format
data <- gather_set_data(d5, 1:2)
# Note a slight hack: to add litrature back in (there are zero
# literature prize winners at schools of interest
data <- data %>%
mutate(University = ifelse(Category == "Literature", "Harvard", University)) %>%
mutate(y = ifelse(Category == "Literature" & x == "University", "Harvard", y)) %>%
mutate(y = ifelse(Category == "Literature" & x == "Category", "Literature", y))
p4 <- ggplot(data, aes(x, id=id, split = y, value = n)) +
geom_parallel_sets(aes(fill = Category),
alpha = 0.6, axis.width = 0.05, sep = .1) +
geom_parallel_sets_axes(axis.width = 0.01, fill = "gray80", sep = .1) +
geom_parallel_sets_labels(size = 2, angle = 0,
position = position_nudge(x = c(rep(-.1, 6), rep(.1, 7)) ),
sep = .1) +
scale_fill_manual(values = cols) +
theme_void() +
theme(legend.position = "")
library(patchwork)
p <- (p1|p2|p4)+plot_layout(widths = c(3,1,3))
p/p3+plot_layout(ncol = 1)+
plot_layout(heights = c(2,1))
个人公众号R语言数据分析指南持续分析更多优质案例
