I have created the following plot:

From a bigger version (5 rows, 58 columns) of this df:

df <- data.frame(row.names = c("ROBERT", "FRANK", "MICHELLE", "KATE"), `1` = c(31, 87, 22, 12), `2` = c(37, 74, 33, 20), `3` = c(35, 32, 44, 14))
colnames(df) <- c("1", "2", "3")

In the following manner:

df = df %>% 
  rownames_to_column("Name") %>% 
  as.data.frame()

df <- melt(df ,  id.vars = 'Name', variable.name = 'ep')
ggplot(df, aes(ep,value))   geom_line(aes(colour = Name, group=Name))

The plot kind of shows what I'd like to, but it really is a mess. Does anyone have a suggestion that would help me increasing its readability? Any help is very much appreciated!

CodePudding user response：

The best approach will really depend on what you're trying to understand or communicate with your plot. But here are a few options for visualizing lots of datapoints across a smallish number of cases. These are illustrated with a subset of the txhousing data included with ggplot2.

Solution 1: Faceting

As @rdelrossi suggested, one solution is to facet by Name:

library(ggplot2)

ggplot(df, aes(ep,value))   
  geom_line(aes(colour = Name, group=Name), show.legend = FALSE)  
  scale_x_continuous(expand = c(0,0))  
  facet_wrap(vars(Name), ncol = 1, scales = "free_x")  
  theme_bw()

Solution 2: Smoothing

Another option is to use geom_smooth() to smooth out local fluctuations to see larger longer-term trends:

ggplot(df, aes(ep,value))   
  geom_smooth(
    aes(colour = Name, group=Name), 
    se = FALSE,
    span = 1,    # higher number = smoother
    size = 1.25
  )  
  scale_x_date(expand = c(0,0))  
  theme_bw()

Solution 3: Lasagna

Sometimes called a "lasagna plot," this is a heatmap with cases on the y axis, time (or whatever) on the x axis, and values mapped to color. It's a different way of comparing changes within (left to right) and between (up and down) individuals.

ggplot(df, aes(ep, Name, colour = value, fill = value))   
  geom_tile(size = .5)  
  scale_fill_viridis_c(option = "B", aesthetics = c("colour", "fill"))  
  coord_cartesian(expand = FALSE)  
  theme(
    axis.text.y = element_text(size = 12, face = "bold"),
    axis.title.y = element_blank()
  )

Data prep:

library(dplyr)
library(lubridate)

df <- txhousing %>% 
  filter(
    city %in% c("Beaumont", "Amarillo", "Arlington", "Corpus Christi", "El Paso"),
    between(year, 2004, 2012)
  ) %>% 
  group_by(city) %>% 
  mutate(
    Name = city, 
    value = scale(sales),
    ep = ym(str_c(year, month))
  ) %>% 
  ungroup()

CodePudding user response：

If your readability concern is just the x axis labels, then I think the main issue is that when you use reshape2::melt() the result is that the column ep is a factor which means that the x axis of your plot will show all the levels and get crowded. The solution is to convert it to numeric and then it will adjust the labels in a sensible way.

I replace your use of reshape2::melt() with tidyr::pivot_longer() which has superseded it within the {tidyverse} but your original code would still work.

library(tidyverse)

df <- structure(list(`1` = c(31, 87, 22, 12), `2` = c(37, 74, 33, 20), `3` = c(35, 32, 44, 14)), class = "data.frame", row.names = c("ROBERT", "FRANK", "MICHELLE", "KATE"))

df %>% 
  rownames_to_column("Name") %>% 
  pivot_longer(-Name, names_to = "ep") %>%
  mutate(ep = as.numeric(ep)) %>% 
  ggplot(aes(ep, value, color = Name))  
  geom_line()

^{Created on 2022-03-07 by the}

Also you can add facet_grid(~Name)

Also you can add

geom_text(aes(label=value), position = position_stack(vjust = .5))