Probabilidade e Estatística - Administração - 2º ano - AEDB - 2018

Resultado da avaliação de Probabilidade e Estatística - Administração - AEDB - 2018

1º Bimestre

---

library(readr)
notas <- read_delim("notas.CSV", 
    ";", escape_double = FALSE, col_types = cols(notas = col_number()), 
    trim_ws = TRUE)
notas$notas <-notas$notas/10

library(psych)
table(notas)

     sexo
notas Feminino Masculino
  7          3         1
  7.1        2         0
  7.2        1         0
  7.3        3         0
  7.4        1         0
  7.5        1         3
  7.6        1         0
  7.7        1         0
  7.8        3         2
  7.9        0         1
  8          4         1
  8.1        2         2
  8.2        2         1
  8.3        2         1
  8.4        0         1
  8.5        1         0
  8.6        1         2
  8.8        1         0
  8.9        3         0
  9          2         1
  9.1        1         0
  9.4        1         0
  9.5        4         0
  10         7         1

library("graphics")
### Mosaic plot of observed values
mosaicplot(table(notas),  las=2, col="steelblue",
           main = "Tabulação das notas")

describeBy(notas$notas, notas$sexo)

 Descriptive statistics by group 
group: Feminino
   vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 47 8.43 0.99    8.2    8.41 1.19   7  10     3 0.22    -1.27 0.14
------------------------------------------------------------------ 
group: Masculino
   vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
X1    1 17 8.14 0.69    8.1    8.09 0.44   7  10     3 0.91     0.93 0.17

library(ggplot2)

Attaching package: 㤼㸱ggplot2㤼㸲

The following objects are masked from 㤼㸱package:psych㤼㸲:

    %+%, alpha

a <- ggplot(notas, aes(x = notas))
#histogram Eng
# Position adjustment: "identity" (overlaid)
a + geom_histogram(breaks=seq(0,10,1),aes(color = sexo), fill = "white", alpha = 0.4,position="identity", closed = c("left"))+
  scale_x_continuous(limits = c(0,10), breaks=seq(0,10,1))+
  theme_minimal()+
  xlab("Notas")+
  ylab("Frequência")+
  ggtitle("Histograma - Sexo")

  
#histogram Eng
plot <- ggplot(data=notas, aes(x=notas)) + geom_histogram(breaks=seq(0,10,1),fill="royalblue", colour="black", alpha=.4, closed = c("left"))
plot <- plot + xlab("Notas")+
  ylab("Frequência")+
  ggtitle("Histograma")+
  theme(plot.title=element_text(size=rel(1), lineheight=.9,face="bold.italic", colour="black"))+
  theme(axis.title=element_text(size=12, lineheight=.9, face="bold", colour="black"))+
  stat_bin(bins=10, binwidth = 1,breaks=seq(0,10,1), geom="text", aes(label=..count..), vjust=-1, closed = c("left"))+ scale_x_continuous(limits = c(0,10), breaks=seq(0,10,1))+
  scale_y_continuous(expand = c(0,0),limits = c(0,max(ggplot_build(plot)$data[[1]]$count)*1.1),  breaks=seq(0,max(ggplot_build(plot)$data[[1]]$count)*1.1,10)) 
plot

a + geom_dotplot(aes(fill = sexo),binpositions = "all")+
  theme_minimal()

# Box plot with mean points
e <- ggplot(notas, aes(x = sexo, y = notas))
e + geom_boxplot(aes(color = sexo,fill = sexo)) +
stat_summary(fun.y = mean, geom = "point",
shape = 18, size = 4, color = "blue")+
  scale_color_brewer(palette="Dark2")+
  theme_minimal()

# Change point colors by dose (groups)
e + geom_jitter(aes(color = sexo), position = position_jitter(0.3)) +
theme_minimal()