Why does CSV data complicate ggplot2 graphics?
I can reproduce a work block ggplot2
with test data but not with CSV data in R. Data visually with one point about events (sleep and wake)
"Vars" , "Sleep", "Awake"
"Average" , 7 , 12
"Min" , 4 , 5
"Max" , 10 , 15
Real life data about sleep
"Vars" , "Sleep1", "Sleep2", ...
"Average" , 7 , 5
"Min" , 4 , 3
"Max" , 10 , 8
Real life data about Awake
"Vars" , "Awake1", "Awake2", ...
"Average" , 12 , 14
"Min" , 10 , 7
"Max" , 15 , 17
The code in which the data is 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()
The test data output in Figure 1 and the output in Figure 2.
fig. 1 Test data output, Figure 2 Code output
Assumption made below for 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 install Q1 <- 0.25 * average
and Q3 <- 0.75 * average
. Suppose you have any number of main fields (here Sleep
and Awake
). How can you query the data (here dat.m
) to get min
and max
each main field?
R: 3.3.3
OS: Debian 8.7
source to share
There is a function base R
to create boxes using quartiles: bxp()
but you need the 25th, 50th and 75th percentiles, as well as the lower quartile (Q1), the median (Q2) and the 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's use the first dataset you entered:
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
Your data is missing the first and third quartiles. The second is also needed, which is the median, but let's assume it is equal to the mean. I assume you have everything, for example:
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 the 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
First, we convert the dataset from wide to long 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 can find interesting articles:
- Pros: Visualizing samples with square plots ( http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2813.html )
- Box: A Simple Visual Method to Interpret Data ( http://annals.org/aim/article/703149/box-plot-simple-visual-method-interpret-data )
- Variations of box fields ( http://amstat.tandfonline.com/doi/abs/10.1080/00031305.1978.10479236 )
source to share