Analises

Retornando ao conjunto de dados de diamantes:

library(ggplot2)
ggplot(data = diamonds) +
 geom_bar(mapping = aes(x = cut))

## Contando os tipos

library(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
diamonds %>%
 count(cut)
## # A tibble: 5 x 2
##   cut           n
##   <ord>     <int>
## 1 Fair       1610
## 2 Good       4906
## 3 Very Good 12082
## 4 Premium   13791
## 5 Ideal     21551

Ajustando

ggplot(data = diamonds) +
 geom_histogram(mapping = aes(x = carat), binwidth = 0.5)

diamonds %>%
 count(cut_width(carat, 0.5))
## # A tibble: 11 x 2
##    `cut_width(carat, 0.5)`     n
##    <fct>                   <int>
##  1 [-0.25,0.25]              785
##  2 (0.25,0.75]             29498
##  3 (0.75,1.25]             15977
##  4 (1.25,1.75]              5313
##  5 (1.75,2.25]              2002
##  6 (2.25,2.75]               322
##  7 (2.75,3.25]                32
##  8 (3.25,3.75]                 5
##  9 (3.75,4.25]                 4
## 10 (4.25,4.75]                 1
## 11 (4.75,5.25]                 1
smaller <- diamonds %>%
 filter(carat < 3)
ggplot(data = smaller, mapping = aes(x = carat)) +
 geom_histogram(binwidth = 0.1)

ggplot(data = smaller, mapping = aes(x = carat, color = cut)) +
 geom_freqpoly(binwidth = 0.1)

Valores tipicos

ggplot(data = smaller, mapping = aes(x = carat)) +
 geom_histogram(binwidth = 0.01)

Valores sobrepostos:

ggplot(mpg, aes(displ, fill = drv)) +
geom_histogram(binwidth = 0.5)

Facets:

ggplot(mpg, aes(displ, fill = drv)) +
geom_histogram(binwidth = 0.5) +
facet_wrap(~drv, ncol = 1)

Variáveis categóricas x Contínuas

ggplot(data = diamonds, mapping = aes(x = price)) +
 geom_freqpoly(mapping = aes(color = cut), binwidth = 500)

Bar plot

ggplot(diamonds) +
 geom_bar(mapping = aes(x = cut))

ggplot(
 data = diamonds,
 mapping = aes(x = price, y = ..density..)
 ) +
 geom_freqpoly(mapping = aes(color = cut), binwidth = 500)

Localizando a média e amplitudes

ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
 geom_boxplot()

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
 geom_boxplot()

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
 geom_boxplot() + 
 geom_point() 

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
 geom_boxplot() + 
 geom_point() + 
 geom_jitter()

Mediana

ggplot(data = mpg) +
 geom_boxplot(
 mapping = aes(
 x = reorder(class, hwy, FUN = median),
 y = hwy
 )
 )

Rotacionando

ggplot(data = mpg) +
 geom_boxplot(
 mapping = aes(
 x = reorder(class, hwy, FUN = median),
 y = hwy
 )
 ) +
 coord_flip()

Trabalhando com duas variáveis categóricas

ggplot(data = diamonds) +
 geom_count(mapping = aes(x = cut, y = color, color = ..n.. ))+
 guides(color = 'legend')

diamonds %>%
 count(color, cut) %>%
 ggplot(mapping = aes(x = color, y = cut)) +
 geom_tile(mapping = aes(fill = n))

Criando intervalos regulares

ggplot(data = smaller, mapping = aes(x = carat, y = price)) +
 geom_boxplot(mapping = aes(group = cut_width(carat, 0.1)))

Criando intervalos com o mesmo numero de ocorrências

ggplot(data = smaller, mapping = aes(x = carat, y = price)) +
 geom_boxplot(mapping = aes(group = cut_number(carat, 20)))

year <- function(x) as.POSIXlt(x)$year + 1900

head(economics)
## # A tibble: 6 x 6
##   date         pce    pop psavert uempmed unemploy
##   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 1967-07-01  507. 198712    12.6     4.5     2944
## 2 1967-08-01  510. 198911    12.6     4.7     2945
## 3 1967-09-01  516. 199113    11.9     4.6     2958
## 4 1967-10-01  512. 199311    12.9     4.9     3143
## 5 1967-11-01  517. 199498    12.8     4.7     3066
## 6 1967-12-01  525. 199657    11.8     4.8     3018
ggplot(economics, aes(unemploy / pop, uempmed)) +
geom_path(colour = "grey50") +
geom_point(aes(colour = year(date)))

year <- function(x) as.POSIXlt(x)$year + 1900
economics
## # A tibble: 574 x 6
##    date         pce    pop psavert uempmed unemploy
##    <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
##  1 1967-07-01  507. 198712    12.6     4.5     2944
##  2 1967-08-01  510. 198911    12.6     4.7     2945
##  3 1967-09-01  516. 199113    11.9     4.6     2958
##  4 1967-10-01  512. 199311    12.9     4.9     3143
##  5 1967-11-01  517. 199498    12.8     4.7     3066
##  6 1967-12-01  525. 199657    11.8     4.8     3018
##  7 1968-01-01  531. 199808    11.7     5.1     2878
##  8 1968-02-01  534. 199920    12.3     4.5     3001
##  9 1968-03-01  544. 200056    11.7     4.1     2877
## 10 1968-04-01  544  200208    12.3     4.6     2709
## # … with 564 more rows
ggplot(economics, aes(unemploy / pop, uempmed)) +
geom_path(colour = "grey50") +
geom_point(aes(colour = year(date)))

ggplot(economics, aes(date, unemploy, colour = pop)) +
  geom_line()

ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line()

ggplot(economics) +
geom_rect(
aes(xmin = start, xmax = end, fill = party),
ymin = -Inf, ymax = Inf, alpha = 0.2,
data = presidential
) +
geom_vline(
aes(xintercept = as.numeric(start)),
data = presidential,
colour = "grey50", alpha = 0.5
) +
geom_text(
aes(x = start, y = 2500, label = name),
data = presidential,
size = 3, vjust = 0, hjust = 0, nudge_x = 50
) +
geom_line(aes(date, unemploy)) +
scale_fill_manual(values = c("orange", "green"))+ labs(fill = "Partido no Governo", title = "Desempregados", xlab="aaa") + ylab("Nº de desempregados") + xlab("Período")

Viasuaizando distribuições

Distribuição populacional

ggplot(diamonds, aes(depth)) +
geom_density(na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")

Gerando escalas

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
p <- plot_ly(d, x = ~carat, y = ~price, color = ~carat,
        size = ~carat, text = ~paste("Clarity: ", clarity),
        type = "scatter", mode = "markers")
p
## Warning: `line.width` does not currently support multiple values.

Distribuição por extratos

ggplot(diamonds, aes(depth, fill = cut, colour = cut)) +
geom_density(alpha = 0.2, na.rm = TRUE) +
xlim(58, 68) +
theme(legend.position = "none")

Utilizando linhas para dados enumeráveis

ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
geom_line() +
theme(legend.position = "none")

Melorando as linhas

ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
geom_point() +
geom_smooth(method = "lm")

ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
geom_point() +
geom_line() +  
geom_smooth(method = "lm")

ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth() +
facet_wrap(~year)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Regressão Local (loess)

ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(aes(colour = "loess"), method = "loess", se = FALSE) +
geom_smooth(aes(colour = "lm"), method = "lm", se = FALSE) +
labs(colour = "Method")