Understanding the Problem and Goal
The problem presented involves creating a plot using the ggplot2 package in R, where four different lines are plotted against time. Each line corresponds to a specific variable (State.1_day, State.1_night, State.2_day, and State.2_night). The goal is to display a legend that indicates which variable each line represents.
Using geom_line() with Different Lines
The code provided uses geom_line() to plot the four different lines. Each line is assigned a color using the colour argument outside of the aes() function. This approach works for individual lines but does not provide a clear legend that links each line to its corresponding variable.
Using scale_color_discrete()
The problem statement mentions trying to use scale_color_discrete() to create a legend. However, it seems this approach did not yield any results.
Converting the Data Frame into Long Format
One potential solution involves converting the data frame into long format using the pivot_longer() function from the tidyr package. This allows for creating a single column that specifies the color and another column that maps to the corresponding variable.
Adding a Color Column and Mapping Variables
To create the desired legend, we need to add a new column called “color” that maps to the specific variable (State.1_day, State.1_night, State.2_day, or State.2_night) for each row in the data frame. This is achieved using the mutate() function with a case_when() statement.
Creating the Plot
After converting the data frame into long format and adding the color column, we can create the plot using ggplot(). The geom_line() function will be used to plot each line, while the scale_x_date() function is used for date formatting. The scale_colour_manual() function is used to map colors to specific variables.
Final Plot with Legend
The final plot includes a legend that indicates which variable each line represents. The colors are manually set using line_colors_a and line_colors_b.
## Step-by-Step Solution
### Step 1: Load Required Libraries
First, we need to load the required libraries. In this case, we require `ggplot2`, `dplyr`, `tidyr`, and `tibble`.
```r
library(ggplot2)
library(dplyr)
library(tidyr)
library(tibble)
Step 2: Create Data Frame
Next, we create a data frame with the required columns (date, State.1_day, State.2_day, State.1_night, and State.2_night).
df <- data.frame(date = as.Date(c("2020-08-05","2020-08-06","2020-08-07","2020-08-08","2020-08-09","2020-08-10","2020-08-11","2020-08-12")),
State.1_day=c(0.8,0.3,0.2,0.5,0.6,0.7,0.8,0.7),
State.2_day=c(0.4,0.2,0.1,0.2,0.3,0.4,0.5,0.6),
State.1_night=c(0.7,0.8,0.5,0.4,0.3,0.2,0.3,0.2),
State.2_night=c(0.5,0.6,0.7,0.4,0.3,0.5,0.6,0.7))
Step 3: Convert Data Frame into Long Format
We then convert the data frame into long format using pivot_longer() from the tidyr package.
df1 <- df %>%
pivot_longer(-date) %>%
mutate(colour = case_when(
name == "State.1_day" ~ line_colors_a[1],
name == "State.1_night" ~ line_colors_b[2],
name == "State.2_day" ~ line_colors_a[3],
name == "State.2_night" ~ line_colors_b[4]
))
Step 4: Create Plot
Finally, we create the plot using ggplot() and customize its appearance.
ggplot(df1, aes(x = date, y = value, colour = name)) +
geom_line(size = 1) +
scale_x_date(date_labels = "%Y-%m-%d") +
scale_colour_manual(values = tibble::deframe(distinct(df1, colour, name))) +
theme_bw() +
labs(y = "% time", x = "Date") +
theme(strip.text = element_text(face="bold", size=18),
strip.background=element_rect(fill="white", colour="black",size=2),
axis.title.x =element_text(margin = margin(t = 10, r = 0, b = 0, l = 0),size = 20),
axis.title.y =element_text(margin = margin(t = 0, r = 10, b = 0, l = 0),size = 20),
axis.text.x = element_text(angle = 70, hjust = 1,size = 15),
axis.text.y = element_text(angle = 0, hjust = 0.5,size = 15),
axis.line = element_line(),
panel.grid.major= element_blank(),
panel.grid.minor = element_blank(),
legend.text=element_text(size=18),
legend.title = element_text(size=19, face = "bold"),
legend.key=element_blank(),
legend.position = "top",
panel.border = element_blank(),
strip.placement = "outside")
The final plot includes a legend that indicates which variable each line represents.
## Example Use Cases
* The provided code snippet can be used as an example of how to create a line plot with multiple lines and a custom color scheme.
* This approach can be extended to accommodate additional variables or lines by modifying the `case_when()` statement within the `mutate()` function.
### Step 5: Advice for Further Improvement
* To further improve this solution, consider using more advanced visualization techniques such as faceting or grouping.
* Additionally, you may want to explore other color palettes or mapping methods to enhance the visual appeal of your plots.
Last modified on 2023-12-06