I found this picture by chance when I was looking for information. This picture comes from the paper
Russian periphery is dying in movement: a cohort assessment of internal youth migration in Central Russia。The link to the paper is
https://link.springer.com/article/10.1007%2Fs10708-018-9953-5，
Links to data and code storage
https://gist.github.com/ikashnitsky/2f3e2b2af6f50911bb775bbce6eb0fb8
https://ikashnitsky.github.io/2019/dotplot/
I feel that this picture is very beautiful, and the data code is still public, so let's repeat it
The abscissa of this graph is the rate of change, the ordinate is the region, and each ordinate corresponds to two 1980-84 1988-92 variables, each of which corresponds to a solid point and a hollow point, census and stat record
I still can't think of how to apply this graph to my own data. It can be used to represent a certain value, such as processing and comparison.
The previous code for sorting data will not be introduced here. If you are interested, you can run it yourself and study the function of each line of code.
library(tidyverse)
df<-read.csv("20210822_raw.csv")
head(df)
# relevel regions ascending
df_plot <- df %>%
select(cohort, region, change_cens) %>%
spread(cohort, change_cens) %>%
arrange(`Cohort 1988-1992`) %>%
mutate(
region = region %>%
as_factor %>%
fct_relevel("CFD TOTAL", after = 0)
) %>%
arrange(region) %>%
gather("cohort", "value", 2:3) %>%
left_join(df, by = c("region", "cohort"))
df_plot %>%
# calculate y positioning values
mutate(region = region %>% as_factor,
y = region %>% as.numeric,
adjust = ifelse(cohort=="Cohort 1988-1992", .15, -.15),
ypos = y - adjust) %>%
write.csv(file="20210822.csv",quote = F,row.names = F)
The final drawing data used
df_plot_1<-read.csv("20210822.csv")
head(df_plot_1)
drawing code
library(ggplot2)
library(tidyverse)
library(extrafont)
df_plot_1 %>% pull(region) %>% unique() -> labels
breaks<-1:length(labels)
breaks
pal <- c("#8C510A", "#003C30")
df_plot_1 %>%
ggplot(aes(color = cohort, y = ypos))+
geom_vline(xintercept = 0, size = 2,
alpha = .5, color = "grey50")+
geom_segment(aes(x = change_cens,
xend = change_rolling,
yend = ypos))+
geom_point(aes(x = change_cens),
shape = 16, size = 2)+
geom_point(aes(x = change_rolling),
shape = 21, size = 2,
fill = "white")+
scale_color_manual(values = pal)+
scale_y_continuous(breaks = breaks,
labels = labels,
expand = c(.01, .01))+
theme_minimal(base_family = "Times New Roman",
base_size = 12)+
theme(legend.position = "none",
panel.grid.minor.y = element_blank(),
panel.grid.major.y = element_line(size = 4, color = "grey95"),
axis.text.y = element_text(vjust = .3, size = 12))+
labs(x = "Change in cohort size, 2003-2010, %", y = NULL)
Compared with the original code, I have modified the font here, because hrbrthemesthis theme package is used in the original code, and I have never understood the font involved.
Next is the legend
The method he uses here is to use the annotate()function to manually add
font_rc <- "Times New Roman"
p1+
annotate("rect", xmin = 29, xmax = 63,
ymin = 2.5, ymax = 9.5,
color = "grey50", fill = "white")+
annotate("text", x = 45, y = 8.5,
label = "LEGEND",
size = 5, hjust = .5,
family = font_rc, color = "grey20")+
annotate("text", x = 45, y = 7,
label = "Change in cohort size by",
size = 4.5, hjust = .5,
family = font_rc, color = "grey20")+
annotate("point", x = c(32.5, 47.5), y = 6,
pch = c(16, 21), size = 2, color = 1)+
annotate("text", x = c(35, 50), y = 6,
label = c("census", "stat record"),
size = 4.5, hjust = 0,
family = font_rc, color = "grey20")+
annotate("text", x = 45, y = 4.5,
label = "Cohorts born in",
size = 4.5, hjust = .5,
family = font_rc, color = "grey20")+
annotate("segment", x = c(32, 47), xend = c(34, 49),
y = 3.5, yend = 3.5,
pch = c(16, 21), size = 2, color = pal)+
annotate("text", x = c(35, 50), y = 3.5,
label = c("1980-84", "1988-92"),
size = 4.5, hjust = 0,
family = font_rc, color = "grey20")
Final result
The download link of sample data and code can be obtained in the comment area of today's next tweet, the next tweet is an advertisement
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