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Plotting a Multiple Line Plot

Jose M Sallan 2023-03-15 6 min read

It is frequent that we need to visualize the temporal evolution along time of one or several variables using a line plot. Doing multiple line plots with ggplot might not be easy at first, as usually we have each variable in a column. Here I will illustrate how to do that with the economics dataset, included in the tidyverse. I will also plot a variable and its rolling mean obtained with zoo.

library(tidyverse)
library(zoo)
data("economics")

economics was produced from US economic time series data available from https://fred.stlouisfed.org/:

economics
## # A tibble: 574 × 6
##    date         pce    pop psavert uempmed unemploy
##    <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
##  1 1967-07-01  507. 198712    12.6     4.5     2944
##  2 1967-08-01  510. 198911    12.6     4.7     2945
##  3 1967-09-01  516. 199113    11.9     4.6     2958
##  4 1967-10-01  512. 199311    12.9     4.9     3143
##  5 1967-11-01  517. 199498    12.8     4.7     3066
##  6 1967-12-01  525. 199657    11.8     4.8     3018
##  7 1968-01-01  531. 199808    11.7     5.1     2878
##  8 1968-02-01  534. 199920    12.3     4.5     3001
##  9 1968-03-01  544. 200056    11.7     4.1     2877
## 10 1968-04-01  544  200208    12.3     4.6     2709
## # … with 564 more rows

We see that we have multiple series of macroeconomic aggregates, presented on a monthly basis.

A Single Line Plot

Let’s start plotting the evolution of the personal savings rate psavert. This is straightforward with geom_line().

economics |>
  ggplot(aes(date, psavert)) +
  geom_line()

We can remove some clutter by applying theme_minimal and replacing axis labels with a descriptive title.

economics |>
  ggplot(aes(date, psavert)) +
  geom_line() +
  theme_minimal() +
  labs(title = "Temporal evolution of personal savings rate", x = NULL, y = NULL)

Two or More Lines

Let’s do a plot including personal savings rate psavert and median duration of unemployment uempmed. Each variable is presented in its column, so we need to transform data in two steps.

First, let’s select the variables included in the plot:

  • y-axis variables psavert and uempmed
  • the x-axis time variable date
economics |>
  select(date, psavert, uempmed)
## # A tibble: 574 × 3
##    date       psavert uempmed
##    <date>       <dbl>   <dbl>
##  1 1967-07-01    12.6     4.5
##  2 1967-08-01    12.6     4.7
##  3 1967-09-01    11.9     4.6
##  4 1967-10-01    12.9     4.9
##  5 1967-11-01    12.8     4.7
##  6 1967-12-01    11.8     4.8
##  7 1968-01-01    11.7     5.1
##  8 1968-02-01    12.3     4.5
##  9 1968-03-01    11.7     4.1
## 10 1968-04-01    12.3     4.6
## # … with 564 more rows

Second, apply pivot_longer to have a long table excluding the time variable.

economics |>
  select(date, psavert, uempmed) |>
  pivot_longer(-date)
## # A tibble: 1,148 × 3
##    date       name    value
##    <date>     <chr>   <dbl>
##  1 1967-07-01 psavert  12.6
##  2 1967-07-01 uempmed   4.5
##  3 1967-08-01 psavert  12.6
##  4 1967-08-01 uempmed   4.7
##  5 1967-09-01 psavert  11.9
##  6 1967-09-01 uempmed   4.6
##  7 1967-10-01 psavert  12.9
##  8 1967-10-01 uempmed   4.9
##  9 1967-11-01 psavert  12.8
## 10 1967-11-01 uempmed   4.7
## # … with 1,138 more rows

No matter how many variables had, now we have three columns: the x axis variable, value for the y axis and name to define the color of each line. Now we can do the plot.

economics |>
  select(date, psavert, uempmed) |>
  pivot_longer(-date) |>
  ggplot(aes(date, value, color = name)) +
  geom_line()

Here is an improved version. I have defined line colors and legend labels with scale_color_manual, and placed the legend below the plot with theme(legend.position = "bottom").

economics |>
  select(date, psavert, uempmed) |>
  pivot_longer(-date) |>
  ggplot(aes(date, value, color = name)) +
  geom_line() +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_color_manual(values = c("#FF8000", "#0080FF"), name = "variable", labels = c("savings", "unemployment")) +
  labs(title = "Temporal evolution of savings and unemployment", x= NULL ,y = NULL)

Variable and Rolling Mean

A special case of a two-line plot is presenting a variable and its rolling mean. We can obtain that mean with rollmean from the zoo package.

economics |>
  mutate(psavert_roll = rollmean(psavert, k = 12, fill = NA, align = "right")) |>
  select(date, psavert, psavert_roll) |>
  print(n = 15)
## # A tibble: 574 × 3
##    date       psavert psavert_roll
##    <date>       <dbl>        <dbl>
##  1 1967-07-01    12.6         NA  
##  2 1967-08-01    12.6         NA  
##  3 1967-09-01    11.9         NA  
##  4 1967-10-01    12.9         NA  
##  5 1967-11-01    12.8         NA  
##  6 1967-12-01    11.8         NA  
##  7 1968-01-01    11.7         NA  
##  8 1968-02-01    12.3         NA  
##  9 1968-03-01    11.7         NA  
## 10 1968-04-01    12.3         NA  
## 11 1968-05-01    12           NA  
## 12 1968-06-01    11.7         12.2
## 13 1968-07-01    10.7         12.0
## 14 1968-08-01    10.5         11.9
## 15 1968-09-01    10.6         11.8
## # … with 559 more rows

After that, let’s select the variables included in the plot. Then, we use apply pivot_longer to have a long table.

economics |>
  mutate(psavert_roll = rollmean(psavert, k = 12, fill = NA, align = "right")) |>
  select(date, psavert, psavert_roll) |>
  pivot_longer(-date) 
## # A tibble: 1,148 × 3
##    date       name         value
##    <date>     <chr>        <dbl>
##  1 1967-07-01 psavert       12.6
##  2 1967-07-01 psavert_roll  NA  
##  3 1967-08-01 psavert       12.6
##  4 1967-08-01 psavert_roll  NA  
##  5 1967-09-01 psavert       11.9
##  6 1967-09-01 psavert_roll  NA  
##  7 1967-10-01 psavert       12.9
##  8 1967-10-01 psavert_roll  NA  
##  9 1967-11-01 psavert       12.8
## 10 1967-11-01 psavert_roll  NA  
## # … with 1,138 more rows

Now we are ready to do the plot:

economics |>
  mutate(psavert_roll = rollmean(psavert, k = 12, fill = NA, align = "right")) |>
  select(date, psavert, psavert_roll) |>
  pivot_longer(-date) |>
  ggplot(aes(date, value, color = name)) +
  geom_line()
## Warning: Removed 11 rows containing missing values (`geom_line()`).

If we want to avoid the warning thrown by the NA of the rolling mean, we can remove these rows with filter(!is.na(value)). To plot the variable and its rolling mean, I have selected two colors with similar hue.

economics |>
  mutate(psavert_roll = rollmean(psavert, k = 24, fill = NA, align = "right")) |>
  select(date, psavert, psavert_roll) |>
  pivot_longer(-date) |>
  filter(!is.na(value)) |>
  ggplot(aes(date, value, color = name)) +
  geom_line() +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_color_manual(values = c("#99CCFF", "#0066CC"), name = "savings", labels = c("raw", "detrended")) +
  labs(title = "Temporal evolution of savings (raw and detrended)", x= NULL ,y = NULL)

Whenever you need to do a multi line plot in ggplot, do no forget the two steps:

  • select the variables included in the plot
  • apply pivot_longer to have a long table
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