Probabilidade e Estatística - Engenharia 2º ano - AEDB

Resultado da avaliação de Probabilidade e Estatística - Engenharia 2A e 2B - AEDB

3º Bimestre - 2017

---

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

Tabulação das Notas por Turma

library(psych)
table(notas)

     turma
notas ENG_2A ENG_2B
  0        2      0
  2        1      1
  3        3      3
  3.5      1      0
  4        8      8
  4.5      1      1
  5        2      2
  5.5      7      7
  6        3      3
  6.5      3      4
  7       11      8
  7.5      2      2
  8        6      5
  8.5      8      6
  9        3      1
  9.5      1      2
  10      11      9

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

Sumário - Turmas 2A e 2B

describeBy(notas$notas, notas$turma)

 Descriptive statistics by group 
group: ENG_2A
   vars  n mean   sd median trimmed  mad min max range  skew
X1    1 73 6.73 2.43      7     6.9 2.22   0  10    10 -0.58
   kurtosis   se
X1    -0.14 0.28
------------------------------------------------ 
group: ENG_2B
   vars  n mean   sd median trimmed  mad min max range  skew
X1    1 62 6.77 2.19      7    6.82 2.22   2  10     8 -0.13
   kurtosis   se
X1       -1 0.28

Sumário - Todos os alunos

psych::describe(notas$notas)

   vars   n mean   sd median trimmed  mad min max range  skew
X1    1 135 6.75 2.31      7    6.86 2.22   0  10    10 -0.42
   kurtosis  se
X1    -0.37 0.2

library(ggplot2)
a <- ggplot(notas, aes(x = notas))
#histogram Eng
# Position adjustment: "identity" (overlaid)
a + geom_histogram(breaks=seq(0,10,1),aes(color = turma), 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 turmas (Eng 2A e 2B)")

  
#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 das turmas Eng 2A e 2B")+
  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 = turma),binpositions = "all")+
  theme_minimal()

# Box plot with mean points
e <- ggplot(notas, aes(x = turma, y = notas))
e + geom_boxplot(aes(color = turma,fill = turma)) +
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 = turma), position = position_jitter(0.3)) +
theme_minimal()