Probabilidade e Estatística - Engenharia 2º ano - AEDB

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

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)

     turma
notas ENG_2A ENG_2B
  0        0      1
  1        1      1
  2        0      2
  2.5      1      2
  3        2      4
  3.5      3      3
  4        4      6
  4.5      2      8
  5        6      4
  5.5     10      9
  6        5      5
  6.5      7      7
  7        4      4
  7.5      7      3
  7.7      1      0
  8        3      6
  8.5      2      4
  9        4      0
  9.5      3      1
  10      11      4

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

describeBy(notas$notas, notas$turma)

 Descriptive statistics by group 
group: ENG_2A
   vars  n mean  sd median trimmed  mad min max range  skew kurtosis   se
X1    1 76  6.7 2.2    6.5    6.76 2.22   1  10     9 -0.09    -0.75 0.25
-------------------------------------------------------- 
group: ENG_2B
   vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
X1    1 74 5.67 2.19    5.5    5.67 2.22   0  10    10 -0.02    -0.35 0.25

library(ggplot2)

Need help getting started? Try the cookbook for R:
http://www.cookbook-r.com/Graphs/

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 = 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()