Why this CSV data complicates with ggplot2 whisker plot?

Question

I can reproduce a working ggplot2 boxplot with the test data but not with CSV data in R. Data visually with single point about the events (sleep and awake)

"Vars"    , "Sleep", "Awake"
"Average" , 7      , 12
"Min"     , 4      , 5
"Max"     , 10     , 15

Data in real life about sleep

"Vars"    , "Sleep1", "Sleep2", ...
"Average" , 7       , 5
"Min"     , 4       , 3
"Max"     , 10      , 8

Data in real life about Awake

"Vars"    , "Awake1", "Awake2", ...
"Average" , 12      , 14
"Min"     , 10      , 7
"Max"     , 15      , 17

Code where data integrated

# only single point!
dat.m <- structure(list(Vars = structure(c(1L, 3L, 2L), .Label = c("Average ", 
"Max     ", "Min     "), class = "factor"), Sleep = c(7, 4, 10
), Awake = c(12L, 5L, 15L)), .Names = c("Vars", "Sleep", "Awake"
), class = "data.frame", row.names = c(NA, -3L))

library('ggplot2')    
# works:
str(mpg)
#mpg$class
#mpg$hwy
ggplot(mpg, aes(x = class, y = hwy)) +
    geom_boxplot()

# http://stackoverflow.com/a/44031194/54964
m <- t(dat.m)    
dat.m <- data.frame(m[2:nrow(m),])
names(dat.m) <- m[1,]
dat.m$Vars <- rownames(m)[2:nrow(m)]
dat.m <- melt(dat.m, id.vars = "Vars")

# TODO complicates here although should not
ggplot(dat.m, aes(x = Vars, y = value, fill=variable)) + #
    geom_boxplot() 

Test data output in Fig. 1 and Output in Fig. 2.

Fig. 1 Test data output, Fig. 2 Output of the code

Assumption made below for the quartiles:

Code

 # http://stackoverflow.com/a/44043313/54964
 quartiles <- data.frame(Vars = c("Q1","Q3"), Sleep = c(6,8), 
               Awake = c(9,13))

I want to set Q1 <- 0.25 * average and Q3 <- 0.75 * average. Assume you have any amount of the main fields (here Sleep and Awake). How can you request the data (here dat.m) to get min and max of each main field?

R: 3.3.3
OS: Debian 8.7

Answer 1

There is base R function to make boxplots using the quartiles: bxp(), but you need 25th, 50th and 75th percentiles known as well as the lower quartile (Q1), the median (Q2) and upper quartile (Q3).

For example:

bxp(list(stats = matrix(c( 4,6,7,9,10, 10,11,12,14,15), nrow = 5,
 ncol = 2), n = c(30,30), names = c("Sleep", "Awake")))

Now using your data: (Edited)

Let us use the first dataset that you introduced:

dat.m <- structure(list(Vars = structure(c(1L, 3L, 2L), .Label = c("Average ", 
"Max     ", "Min     "), class = "factor"), Sleep = c(7, 4, 10
), Awake = c(12L, 5L, 15L)), .Names = c("Vars", "Sleep", "Awake"
), class = "data.frame", row.names = c(NA, -3L))

> dat.m
      Vars Sleep Awake
1 Average      7    12
2 Min          4     5
3 Max         10    15


> str(dat.m)
'data.frame':   3 obs. of  3 variables:
 $ Vars : Factor w/ 3 levels "Average ","Max     ",..: 1 3 2
 $ Sleep: num  7 4 10
 $ Awake: int  12 5 15

In you data, the first and third quartiles are missing. The second is also needed, which is the median, but let us assume that it is equal to the mean. I will assume that you have all of them e.g.:

quartiles <- data.frame(Vars = c("Q1","Q3"), Sleep = c(6,8), 
                    Awake = c(9,13))

> str(quartiles)
'data.frame':   2 obs. of  3 variables:
 $ Vars : Factor w/ 2 levels "Q1","Q3": 1 2
 $ Sleep: num  6 8
 $ Awake: num  9 13


data <- rbind(dat.m ,quartiles)

      Vars Sleep Awake
1 Average      7    12
2 Min          4     5
3 Max         10    15
4 Q1           6     9
5 Q3           8    13

Then sorting your variables:

library(dplyr)
## Disable this line if you want to use the universal approach
data <-  dplyr::arrange(data, Sleep, Awake)
## Enable the following for more universal approach
# data <- arrange_(data, .dots = as.list(strsplit(colnames(data)[2:ncol(data)], ', '))) 

bxp(list(stats = as.matrix(data[,2:3]), n = c(30,30), names = names(data[,2:3]))) # assuming n = 30.

With ggplot2

We first convert the dataset from 'wide' to 'long' format with reshape2::melt().

library(reshape2)
library(ggplot2)
(data2 <- melt(data))

       Vars variable value
1  Min         Sleep     4
2  Q1          Sleep     6
3  Average     Sleep     7
4  Q3          Sleep     8
5  Max         Sleep    10
6  Min         Awake     5
7  Q1          Awake     9
8  Average     Awake    12
9  Q3          Awake    13
10 Max         Awake    15

Then:

ggplot(data2, aes(x = variable, y = value)) +
  geom_boxplot()

You might find interesting these articles:

1. Points of Significance: Visualizing samples with box plots (http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2813.html)
2. The Box Plot: A Simple Visual Method to Interpret Data (http://annals.org/aim/article/703149/box-plot-simple-visual-method-interpret-data)
3. Variations of box plots (http://amstat.tandfonline.com/doi/abs/10.1080/00031305.1978.10479236)