Probabilidade e Estatística - Ciências Contábeis - 2º ano - AEDB

Resultado da avaliação de Ciências Contábeis - AEDB

1º Bimestre

---

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

Tabulação das Notas

library(psych)
table(notas)

     sexo
notas Feminino Masculino
  1.3        1         0
  4.5        0         1
  4.7        0         1
  5          1         0
  5.9        1         0
  6          3         0
  6.5        0         1
  7          2         0
  7.1        0         1
  7.2        1         0
  7.3        2         0
  7.7        0         1
  7.9        1         0
  8          2         1
  8.2        1         0
  8.4        1         0
  8.5        1         0
  8.6        0         1

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

describeBy(notas$notas, notas$sexo)

 Descriptive statistics by group 
group: Feminino
   vars  n mean   sd median trimmed  mad min max range skew
X1    1 17 6.76 1.74    7.2    7.01 1.48 1.3 8.5   7.2 -1.7
   kurtosis   se
X1     2.97 0.42
------------------------------------------------ 
group: Masculino
   vars n mean  sd median trimmed  mad min max range  skew
X1    1 7 6.73 1.6    7.1    6.73 1.33 4.5 8.6   4.1 -0.34
   kurtosis  se
X1    -1.74 0.6

library(ggplot2)
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 por sexo")

  
#histogram 
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 - Notas de Probabilidade e Estatística")+
  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()