In [1]:

# DAL ToolBox
# version 1.1.727

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform

In [2]:

load_library("ggplot2")
load_library("RColorBrewer")

#color palette
colors <- brewer.pal(4, 'Set1')

# setting the font size for all charts
font <- theme(text = element_text(size=16))

Loading required package: ggplot2

Loading required package: RColorBrewer

Examples using data distribution¶

The following examples use random variables so that different data distribution can be better viewed.

In [3]:

# example: dataset to be plotted  
example <- data.frame(exponential = rexp(100000, rate = 1), 
                     uniform = runif(100000, min = 2.5, max = 3.5), 
                     normal = rnorm(100000, mean=5))
head(example)

A data.frame: 6 × 3
	exponential	uniform	normal
	<dbl>	<dbl>	<dbl>
1	0.09155231	2.612075	5.085059
2	2.01816829	3.294267	4.457824
3	0.58176393	2.636868	4.982670
4	0.12511421	3.344929	6.220225
5	0.02524745	2.573040	5.234677
6	0.31813066	2.620384	3.070939

Histogram¶

Visualize the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. Histograms (geom_histogram()) display the counts with bars. More information: ?geom_histogram (R documentation)

In [4]:

load_library("dplyr")

grf <- plot_hist(example |> select(exponential), 
                  label_x = "exponential", color=colors[1]) + font
options(repr.plot.width=5, repr.plot.height=4)
plot(grf)

Loading required package: dplyr


Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


Using  as id variables

Arrangement of graphs¶

Function $grid.arrange$ is used to position previously computed charts

In [5]:

grfe <- plot_hist(example |> select(exponential), 
                  label_x = "exponential", color=colors[1]) + font
grfu <- plot_hist(example |> select(uniform), 
                  label_x = "uniform", color=colors[1]) + font  
grfn <- plot_hist(example |> select(normal), 
                  label_x = "normal", color=colors[1]) + font

Using  as id variables

Using  as id variables

Using  as id variables

In [6]:

load_library("gridExtra") 

options(repr.plot.width=15, repr.plot.height=4)
grid.arrange(grfe, grfu, grfn, ncol=3)

Loading required package: gridExtra


Attaching package: ‘gridExtra’


The following object is masked from ‘package:dplyr’:

    combine

In [ ]: