[data analysis and visualization] key points of data drawing 3- spaghetti map

Key points of data drawing 3- Spaghetti map

Line charts with too many lines usually become unreadable , This kind of picture is generally called spaghetti picture . So this kind of graph can hardly provide information about the data .

Drawing examples

Let's start with the United States 1880 Year to 2015 Take the evolution of female baby names in .

# Libraries
library(tidyverse)
library(hrbrthemes)
library(kableExtra)
library(babynames)
library(viridis)
library(DT)
library(plotly)

#  Display data 
data <- babynames
head(data)
nrow(data)

A tibble: 6 × 5

year	sex	name	n	prop
<dbl>	<chr>	<chr>	<int>	<dbl>
1880	F	Mary	7065	0.07238359
1880	F	Anna	2604	0.02667896
1880	F	Emma	2003	0.02052149
1880	F	Elizabeth	1939	0.01986579
1880	F	Minnie	1746	0.01788843
1880	F	Margaret	1578	0.01616720

1924665

#  Pick data for certain names 
data = filter(data,name %in% c("Mary","Emma", "Ida", "Ashley", "Amanda", "Jessica", "Patricia", "Linda", "Deborah",   "Dorothy", "Betty", "Helen"))
head(data)
nrow(data)

A tibble: 6 × 5

year	sex	name	n	prop
<dbl>	<chr>	<chr>	<int>	<dbl>
1880	F	Mary	7065	0.07238359
1880	F	Emma	2003	0.02052149
1880	F	Ida	1472	0.01508119
1880	F	Helen	636	0.00651606
1880	F	Amanda	241	0.00246914
1880	F	Betty	117	0.00119871

2599

#  As long as the female data 
data= filter(data,sex=="F")
head(data)
nrow(data)

A tibble: 6 × 5

year	sex	name	n	prop
<dbl>	<chr>	<chr>	<int>	<dbl>
1880	F	Mary	7065	0.07238359
1880	F	Emma	2003	0.02052149
1880	F	Ida	1472	0.01508119
1880	F	Helen	636	0.00651606
1880	F	Amanda	241	0.00246914
1880	F	Betty	117	0.00119871

1593

#  mapping 
ggplot(data,aes(x=year, y=n, group=name, color=name)) +
geom_line() +
scale_color_viridis(discrete = TRUE) +
theme(
  plot.title = element_text(size=14)
) +
ggtitle("A spaghetti chart of baby names popularity")

As you can see from the diagram, it is difficult to understand the evolution of the popularity of a particular name according to a line . in addition , Even if you try to follow a line to show the results , You also need to associate it with more difficult illustrations . Let's try to find some solutions to improve this graph .

How to improve

For specific groups

Suppose you draw many groups , But the actual reason is to explain the characteristics of a particular group compared with other groups . Then a good solution is to highlight the Group ： Make it look different , And give it an appropriate comment . ad locum ,Amanda The evolution of popularity is obvious . It is important to keep other names , Because it allows you to Amanda Compare with all other names

#  Add data items 
data =  mutate( data, highlight=ifelse(name=="Amanda", "Amanda", "Other"))
head(data)

A tibble: 6 × 6

year	sex	name	n	prop	highlight
<dbl>	<chr>	<chr>	<int>	<dbl>	<chr>
1880	F	Mary	7065	0.07238359	Other
1880	F	Emma	2003	0.02052149	Other
1880	F	Ida	1472	0.01508119	Other
1880	F	Helen	636	0.00651606	Other
1880	F	Amanda	241	0.00246914	Amanda
1880	F	Betty	117	0.00119871	Other

ggplot(data,aes(x=year, y=n, group=name, color=highlight, size=highlight)) +
geom_line() +
scale_color_manual(values = c("#69b3a2", "lightgrey")) +
scale_size_manual(values=c(1.5,0.2)) +
theme(legend.position="none") +
ggtitle("Popularity of American names in the previous 30 years") +
geom_label( x=1990, y=55000, label="Amanda reached 3550\nbabies in 1970", size=4, color="#69b3a2") +
theme(,
  plot.title = element_text(size=14)
)

Using subgraphs

Area maps can be used to provide a more comprehensive overview of the dataset , Especially when used with subgraphs . In the chart below , You can easily glimpse the evolution of any name ：

ggplot(data,aes(x=year, y=n, group=name, fill=name)) +
geom_area() +
scale_fill_viridis(discrete = TRUE) +
theme(legend.position="none") +
ggtitle("Popularity of American names in the previous 30 years") +
theme(
  panel.spacing = unit(0.1, "lines"),
  strip.text.x = element_text(size = 8),
  plot.title = element_text(size=14)
) +
#  Map by name 
facet_wrap(~name)

As you can see from the picture ,Linda This name is a very popular name in a very short time . On the other hand ,Ida Never very popular , Less used in decades .

Combination method

If you want to compare the evolution of each line with other lines , You can combine targeting specific groups with using subgraphs

#  Duplicate column ,name/name2 They have different uses , One is used to display the data in the sub graph , One for sorting 
tmp <- data %>%
  mutate(name2=name)
head(tmp)

A tibble: 6 × 7

year	sex	name	n	prop	highlight	name2
<dbl>	<chr>	<chr>	<int>	<dbl>	<chr>	<chr>
1880	F	Mary	7065	0.07238359	Other	Mary
1880	F	Emma	2003	0.02052149	Other	Emma
1880	F	Ida	1472	0.01508119	Other	Ida
1880	F	Helen	636	0.00651606	Other	Helen
1880	F	Amanda	241	0.00246914	Amanda	Amanda
1880	F	Betty	117	0.00119871	Other	Betty

tmp %>%
ggplot( aes(x=year, y=n)) +
#  use name2 Display the data 
geom_line( data=tmp %>% dplyr::select(-name), aes(group=name2), color="grey", size=0.5, alpha=0.5) +
geom_line( aes(color=name), color="#69b3a2", size=1.2 )+
scale_color_viridis(discrete = TRUE) +
theme(
  legend.position="none",
  plot.title = element_text(size=14),
  panel.grid = element_blank()
) +
ggtitle("A spaghetti chart of baby names popularity") +
#  use name Subgraph 
facet_wrap(~name)

Reference resources

• THE SPAGHETTI PLOT