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In a recent project, I was looking to plot data from different variables along the same time axis. The difficulty was, that some of these variables I wanted to have as point plots, while others I wanted as box-plots.

Because I work with the tidyverse, I wanted to produce these plots with ggplot2. Faceting was the obvious first step but it took me quite a while to figure out how to best combine facets with point plots (where I have one value per time point) with and box-plots (where I have multiple values per time point).

The reason why this isn’t trivial is that box plots require groups or factors on the x-axis, while points can be plotted over a continuous range of x-values. If your alarm bells are ringing right now, you are absolutely right: before you try to combine plots with different x-axis properties, you should think long and hard whether this is an accurate representation of the data and if its a good idea to do so! Here, I had multiple values per time point for one variable and I wanted to make the median + variation explicitly clear, while also showing the continuous changes of other variables over the same range of time.

So, I am writing this short tutorial here in hopes that it saves the next person trying to do something similar from spending an entire morning on stackoverflow. 😉

For this demonstration, I am creating some fake data:

```library(tidyverse)
dates <- seq(as.POSIXct("2017-10-01 07:00"), as.POSIXct("2017-10-01 10:30"), by = 180) # 180 seconds == 3 minutes
fake_data <- data.frame(time = dates,
var1_1 = runif(length(dates)),
var1_2 = runif(length(dates)),
var1_3 = runif(length(dates)),
var2 = runif(length(dates))) %>%
sample_frac(size = 0.33)
##                   time    var1_1    var1_2    var1_3       var2
## 8  2017-10-01 07:21:00 0.2359625 0.6121708 0.4114921 0.03327728
## 27 2017-10-01 08:18:00 0.5592436 0.3834683 0.8025474 0.44557932
## 29 2017-10-01 08:24:00 0.7667775 0.4636693 0.7642972 0.97718507
## 18 2017-10-01 07:51:00 0.2819686 0.3995273 0.9127757 0.42115579
## 1  2017-10-01 07:00:00 0.5940754 0.1599054 0.7287677 0.91953437
## 71 2017-10-01 10:30:00 0.2159290 0.2853349 0.7817291 0.57598897```

Here, variable 1 (`var1`) has three measurements per time point, while variable 2 (`var2`) has one.

First, for plotting with ggplot2 we want our data in a tidy long format. I also add another column for faceting that groups the variables from `var1` together.

```fake_data_long <- fake_data %>%
gather(x, y, var1_1:var2) %>%
mutate(facet = ifelse(x %in% c("var1_1", "var1_2", "var1_3"), "var1", x))
##                  time      x         y facet
## 1 2017-10-01 07:21:00 var1_1 0.2359625  var1
## 2 2017-10-01 08:18:00 var1_1 0.5592436  var1
## 3 2017-10-01 08:24:00 var1_1 0.7667775  var1
## 4 2017-10-01 07:51:00 var1_1 0.2819686  var1
## 5 2017-10-01 07:00:00 var1_1 0.5940754  var1
## 6 2017-10-01 10:30:00 var1_1 0.2159290  var1```

Now, we can plot this the following way:

• facet by variable
• subset data to facets for point plots and give aesthetics in `geom_point()`
• subset data to facets for box plots and give aesthetics in `geom_boxplot()`. Here we also need to set the `group` aesthetic; if we don’t specifically give that, we will get a plot with one big box, instead of a box for every time point.
```fake_data_long %>%
ggplot() +
facet_grid(facet ~ ., scales = "free") +
geom_point(data = subset(fake_data_long, facet == "var2"),
aes(x = time, y = y),
size = 1) +
geom_line(data = subset(fake_data_long, facet == "var2"),
aes(x = time, y = y)) +
geom_boxplot(data = subset(fake_data_long, facet == "var1"),
aes(x = time, y = y, group = time))```

```sessionInfo()
## R version 3.4.2 (2017-09-28)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
##
## attached base packages:
## [1] methods   stats     graphics  grDevices utils     datasets  base
##
## other attached packages:
##  [1] bindrcpp_0.2    forcats_0.2.0   stringr_1.2.0   dplyr_0.7.4
##  [5] purrr_0.2.4     readr_1.1.1     tidyr_0.7.2     tibble_1.3.4
##  [9] ggplot2_2.2.1   tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.3 reshape2_1.4.2   haven_1.1.0      lattice_0.20-35
##  [5] colorspace_1.3-2 htmltools_0.3.6  yaml_2.1.14      rlang_0.1.4
##  [9] foreign_0.8-69   glue_1.2.0       modelr_0.1.1     readxl_1.0.0
## [13] bindr_0.1        plyr_1.8.4       munsell_0.4.3    blogdown_0.3
## [17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      psych_1.7.8
## [21] evaluate_0.10.1  labeling_0.3     knitr_1.17       parallel_3.4.2
## [25] broom_0.4.2      Rcpp_0.12.13     scales_0.5.0     backports_1.1.1
## [29] jsonlite_1.5     mnormt_1.5-5     hms_0.3          digest_0.6.12
## [33] stringi_1.1.5    bookdown_0.5     grid_3.4.2       rprojroot_1.2
## [37] cli_1.0.0        tools_3.4.2      magrittr_1.5     lazyeval_0.2.1
## [41] crayon_1.3.4     pkgconfig_2.0.1  xml2_1.1.1       lubridate_1.7.1
## [45] assertthat_0.2.0 rmarkdown_1.7    httr_1.3.1       rstudioapi_0.7
## [49] R6_2.2.2         nlme_3.1-131     compiler_3.4.2```