A good table can be visualised quickly

The more I work on data visualization using R, the more I realized that a good table has a clean structure and requires much less effort to be plotted out.

In ggplot, this often means that if a table requires more than a melt() in {reshape2} package then the table is not good enough - needs some more cleaning.

Working example:

d1 <- iris
d1 %>% head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

d1_melted <- d1 %>% melt(id.vars = c("Species"))
d1_melted %>% head()

##   Species     variable value
## 1  setosa Sepal.Length   5.1
## 2  setosa Sepal.Length   4.9
## 3  setosa Sepal.Length   4.7
## 4  setosa Sepal.Length   4.6
## 5  setosa Sepal.Length   5.0
## 6  setosa Sepal.Length   5.4

The facet_wrap() function can create subplots according to defined attributes. Here, I’d like to:

• display different Species vertically, and
• along each row to plot out length and with of sepal and petal. Now that these data are stored in variable and value from the melt(), they can be specified in ggplot expression.

d1_melted %>% ggplot(aes(x=value)) + geom_histogram() +
        facet_wrap(Species ~ variable) +
        theme_bw()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Take home message: Be goal-oriented and thinking backward. Better-engineered data allows visualization and decision-makingm more efficiently.