Plotting average of multiple groups across time in ggplot2

Question

I'm sure this question has been asked before, but I'm having trouble finding a solution that works:

I have a data frame comprising two groups of 5 samples each, where each sample has ten observations spaced equally across time. I would like to plot this dataset as a time series with two lines linking the average of each group at each time point. At each time point I would like to have some measure of variability (e.g. 95% confidence interval).

For example, the data set is:

group_a <- data.frame(runif(50, min=80, max=100), 1:10, rep("a", 10), c(rep("i", 10), rep("ii", 10), rep("iii", 10), rep("iv", 10), rep("v", 10)))

names(group_a) <- c("yvar", "xvar", "group", "sample")

group_b <- data.frame(runif(50, min=60, max=80), 1:10, rep("b", 10), c(rep("vi", 10), rep("vii", 10), rep("viii", 10), rep("ix", 10), rep("x", 10)))

names(group_b) <- c("yvar", "xvar", "group", "sample")

sample_data <- rbind(group_a, group_b)

So each time point (xvar) has 10 cases (sample) of observations (yvar), split equally into two groups (group). The closest I have come to the answer I'm looking for is by the following:

require(ggplot2)

p <- ggplot(sample_data, aes(x = xvar, y = yvar)) + geom_line(aes(color = group, linetype = group))

print(p)

Which produces something like:

So the line is split by group, but at each time point it follows each individual case vertically, rather than as a mean.

What I'm looking for is something more like what's suggested in this other answer: Plot time series with ggplot with confidence interval, but with multiple lines on the graph, and not necessarily a continuous ribbon plot.

Does anyone have any suggestions? I know this should be really simple, but I'm relatively new to R and ggplot and apparently can't find the right search terms (or am missing something really obvious). Any help is very much appreciated!

Answer 1

Instead of using rep(), you can implement gl() function for indicating each sample. I think it can simplify your columns.

Here, use gl(n = 10, k = 1, length = 50, labels = 1:10). Then the factor with labels = 1:10 is made as

#> [1] 1  2  3  4  5  6  7  8  9  10 1  2  3  4  5 
#> [16] 6  7  8  9  10 1  2  3  4  5  6  7  8  9  10
#> [31] 1  2  3  4  5  6  7  8  9  10 1  2  3  4  5 
#> [46] 6  7  8  9  10
#> Levels: 1 2 3 4 5 6 7 8 9 10

Just adding this to yvar, the problem can be solved.

library(tidyverse)

set.seed(10)
(group_a <-
  data_frame(
    yvar = runif(50, min = 80, max = 100),
    gl = gl(n = 10, k = 1, length = 50, labels = 1:10)
  ))
#> # A tibble: 50 x 2
#>     yvar gl   
#>    <dbl> <fct>
#>  1  90.1 1    
#>  2  86.1 2    
#>  3  88.5 3    
#>  4  93.9 4    
#>  5  81.7 5    
#>  6  84.5 6    
#>  7  85.5 7    
#>  8  85.4 8    
#>  9  92.3 9    
#> 10  88.6 10   
#> # ... with 40 more rows

(group_a_mean <-
  group_a %>%
  group_by(gl) %>% # for each group, calculate mean, standard deviation
  summarise(sample_mean = mean(yvar),
            lower = sample_mean - 1.96 * sd(yvar), # lower CI
            upper = sample_mean + 1.96 * sd(yvar))) # upper CI
#> # A tibble: 10 x 4
#>    gl    sample_mean lower upper
#>    <fct>       <dbl> <dbl> <dbl>
#>  1 1            91.3  82.9  99.8
#>  2 2            87.2  78.5  96.0
#>  3 3            86.0  74.0  98.0
#>  4 4            93.1  85.3 101. 
#>  5 5            86.1  80.6  91.6
#>  6 6            89.1  78.5  99.6
#>  7 7            88.0  72.2 104. 
#>  8 8            88.9  77.0 101. 
#>  9 9            90.3  79.8 101. 
#> 10 10           91.7  83.1 100.

same for group_b

(group_b <-
  data_frame(
    yvar = runif(50, min = 60, max = 80),
    gl = gl(n = 10, k = 1, length = 50, labels = 1:10)
  ))
#> # A tibble: 50 x 2
#>     yvar gl   
#>    <dbl> <fct>
#>  1  67.1 1    
#>  2  78.7 2    
#>  3  64.9 3    
#>  4  69.5 4    
#>  5  63.8 5    
#>  6  71.7 6    
#>  7  69.2 7    
#>  8  69.3 8    
#>  9  68.0 9    
#> 10  70.1 10   
#> # ... with 40 more rows

group_b_mean <-
  group_b %>%
  group_by(gl) %>%
  summarise(sample_mean = mean(yvar),
            lower = sample_mean - 1.96 * sd(yvar),
            upper = sample_mean + 1.96 * sd(yvar))

---

After that, if two data frame is binded with each group idicator such as "a" and "b", you can draw what you want.

group_a_mean %>%
  mutate(gr = "a") %>% # "a" indicator
  bind_rows(group_b_mean %>% mutate(gr = "b")) %>% # "b" indicator and bind row
  ggplot() +
  aes(x = as.numeric(gl), colour = gr) + # since gl variable is factor, you should conduct as.numeric()
  geom_line(aes(y = sample_mean)) +
  geom_line(aes(y = lower), linetype = "dashed") +
  geom_line(aes(y = upper), linetype = "dashed")

You can also use geom_ribbon():

group_a_mean %>%
  mutate(gr = "a") %>%
  bind_rows(group_b_mean %>% mutate(gr = "b")) %>%
  ggplot() +
  aes(x = as.numeric(gl), colour = gr) +
  geom_ribbon(aes(ymin = lower, ymax = upper, fill = gr), alpha = .3) +
  geom_line(aes(y = sample_mean))

Answer 2

Here are two variations. I'd recommend pre-calculating your summary stats and feeding that into ggplot.

sample_sum <- sample_data %>%
  group_by(xvar, group) %>%
  summarize(mean = mean(yvar),
            sd   = sd(yvar),
            mean_p2sd = mean + 2 * sd,
            mean_m2sd = mean - 2 * sd) %>%
  ungroup()

This first approach gathers mean, mean minus 2 SD, and mean plus 2 SD into the same columns, with "stat" marking which stat it is, and yvar storing the value. (I picked those because +/- 2 SD captures ~95% of a normal distribution.) Then we can plot them together in a single call to geom_line.

p <- ggplot(sample_sum %>%
              gather(stat, yvar, mean, mean_p2sd:mean_m2sd), 
            aes(x = xvar, y = yvar)) + 
  geom_line(aes(color = group, linetype = stat))
p

Alternatively, we can keep them apart and plot the +/- 2 SD area using geom_ribbon.

p <- ggplot(sample_sum, aes(x = xvar,  color = group, fill = group)) + 
  geom_ribbon(aes(ymin = mean_m2sd, ymax = mean_p2sd), alpha = 0.1) +
  geom_line(aes(y= mean))

p

Answer 3

I think you want it like this:

p <- ggplot(sample_data, aes(x = xvar, y = yvar, shape = sample)) + 
geom_line(aes(color = group, linetype = sample))
print(p)