library(tidyverse)
library(readxl)
nota <- read_excel("nota.xlsx")
nota %>% 
  arrange(classif) %>% 
    filter(local=="C Bas") 
nota %>% 
  arrange(classif) %>% 
    filter(local=="EsPCEx") 

Nota e Classificação dos cad sexo feminino com a nota média (EsPCEx e C Bas)

ggplot(nota)+ 
  geom_point(aes(x=cadete, y=classif)) +
  geom_point(size=3, aes(x=cadete, y=classif, col=local, shape=local)) +
  xlab("Cadete") + ylab("Notas") + ggtitle("Classificação - Cad Sexo Feminino - Turma C Bas 2018") +
  scale_color_brewer(palette="Dark2") + theme(legend.position="top")+
#  scale_y_continuous(limits = c(6.5,10))+
  theme(axis.text.x = element_text(angle=90))

NA
ggplot(nota)+ 
  geom_point(aes(x=cadete, y=nota)) +
  geom_point(size=3, aes(x=cadete, y=nota, col=local, shape=local)) +
  xlab("Cadete") + ylab("Notas") + ggtitle("Notas - Cad Sexo Feminino - Turma C Bas 2018") +
  scale_color_brewer(palette="Dark2") + theme(legend.position="top")+
  scale_y_continuous(limits = c(6.5,10))+
  theme(axis.text.x = element_text(angle=90))

  # geom_point(size=3, aes(x=cadete, y=classif, col=local, shape=local))
  
  

Nota e Classificação dos cad sexo feminino sem a nota média (EsPCEx e C Bas)

nota <- read_excel("nota.xlsx")
nota <- nota %>% filter(local !="Nota Média")
ggplot(nota)+ 
  geom_point(aes(x=cadete, y=nota)) +
  geom_point(size=3, aes(x=cadete, y=nota, col=local, shape=local)) +
  xlab("Cadete") + ylab("Notas") + ggtitle("Notas - Cad Sexo Feminino - Turma C Bas 2018") +
  scale_color_brewer(palette="Dark2") + theme(legend.position="top")+
  scale_y_continuous(limits = c(6.5,10))+
  theme(axis.text.x = element_text(angle=90))

  # geom_point(size=3, aes(x=cadete, y=classif, col=local, shape=local))
  
  
ggplot(nota)+ 
  geom_point(aes(x=cadete, y=classif)) +
  geom_point(size=3, aes(x=cadete, y=classif, col=local, shape=local)) +
  xlab("Cadete") + ylab("Notas") + ggtitle("Classificação - Cad Sexo Feminino - Turma C Bas 2018") +
  scale_color_brewer(palette="Dark2") + theme(legend.position="top")+
#  scale_y_continuous(limits = c(6.5,10))+
  theme(axis.text.x = element_text(angle=90))

NA
LS0tDQp0aXRsZTogIkNsYXNzaWZpY2HDp8OjbyBDYWRldGVzIGRvIFNleG8gRmVtaW5pbm8gLSBUdXJtYSBDIEJhcyAyMDE4Ig0Kb3V0cHV0OiBodG1sX25vdGVib29rDQotLS0NCg0KYGBge3J9DQpsaWJyYXJ5KHRpZHl2ZXJzZSkNCmBgYA0KDQoNCmBgYHtyfQ0KbGlicmFyeShyZWFkeGwpDQpub3RhIDwtIHJlYWRfZXhjZWwoIm5vdGEueGxzeCIpDQoNCm5vdGEgJT4lIA0KICBhcnJhbmdlKGNsYXNzaWYpICU+JSANCiAgICBmaWx0ZXIobG9jYWw9PSJDIEJhcyIpIA0KDQpub3RhICU+JSANCiAgYXJyYW5nZShjbGFzc2lmKSAlPiUgDQogICAgZmlsdGVyKGxvY2FsPT0iRXNQQ0V4IikgDQpgYGANCg0KDQoNCiMgIE5vdGEgZSBDbGFzc2lmaWNhw6fDo28gZG9zIGNhZCBzZXhvIGZlbWluaW5vIGNvbSBhIG5vdGEgbcOpZGlhIChFc1BDRXggZSBDIEJhcykNCg0KYGBge3IgZmlnLmhlaWdodD05LCBmaWcud2lkdGg9OX0NCmdncGxvdChub3RhKSsgDQogIGdlb21fcG9pbnQoYWVzKHg9Y2FkZXRlLCB5PWNsYXNzaWYpKSArDQogIGdlb21fcG9pbnQoc2l6ZT0zLCBhZXMoeD1jYWRldGUsIHk9Y2xhc3NpZiwgY29sPWxvY2FsLCBzaGFwZT1sb2NhbCkpICsNCiAgeGxhYigiQ2FkZXRlIikgKyB5bGFiKCJOb3RhcyIpICsgZ2d0aXRsZSgiQ2xhc3NpZmljYcOnw6NvIC0gQ2FkIFNleG8gRmVtaW5pbm8gLSBUdXJtYSBDIEJhcyAyMDE4IikgKw0KICBzY2FsZV9jb2xvcl9icmV3ZXIocGFsZXR0ZT0iRGFyazIiKSArIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbj0idG9wIikrDQojICBzY2FsZV95X2NvbnRpbnVvdXMobGltaXRzID0gYyg2LjUsMTApKSsNCiAgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGU9OTApKQ0KICANCmBgYA0KDQoNCmBgYHtyIGZpZy5oZWlnaHQ9OSwgZmlnLndpZHRoPTl9DQpnZ3Bsb3Qobm90YSkrIA0KICBnZW9tX3BvaW50KGFlcyh4PWNhZGV0ZSwgeT1ub3RhKSkgKw0KICBnZW9tX3BvaW50KHNpemU9MywgYWVzKHg9Y2FkZXRlLCB5PW5vdGEsIGNvbD1sb2NhbCwgc2hhcGU9bG9jYWwpKSArDQogIHhsYWIoIkNhZGV0ZSIpICsgeWxhYigiTm90YXMiKSArIGdndGl0bGUoIk5vdGFzIC0gQ2FkIFNleG8gRmVtaW5pbm8gLSBUdXJtYSBDIEJhcyAyMDE4IikgKw0KICBzY2FsZV9jb2xvcl9icmV3ZXIocGFsZXR0ZT0iRGFyazIiKSArIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbj0idG9wIikrDQogIHNjYWxlX3lfY29udGludW91cyhsaW1pdHMgPSBjKDYuNSwxMCkpKw0KICB0aGVtZShheGlzLnRleHQueCA9IGVsZW1lbnRfdGV4dChhbmdsZT05MCkpDQogICMgZ2VvbV9wb2ludChzaXplPTMsIGFlcyh4PWNhZGV0ZSwgeT1jbGFzc2lmLCBjb2w9bG9jYWwsIHNoYXBlPWxvY2FsKSkNCiAgDQoNCiAgDQpgYGANCg0KIyAgTm90YSBlIENsYXNzaWZpY2HDp8OjbyBkb3MgY2FkIHNleG8gZmVtaW5pbm8gc2VtIGEgbm90YSBtw6lkaWEgKEVzUENFeCBlIEMgQmFzKQ0KDQoNCg0KYGBge3J9DQpub3RhIDwtIHJlYWRfZXhjZWwoIm5vdGEueGxzeCIpDQpub3RhIDwtIG5vdGEgJT4lIGZpbHRlcihsb2NhbCAhPSJOb3RhIE3DqWRpYSIpDQpgYGANCg0KDQpgYGB7ciBmaWcuaGVpZ2h0PTksIGZpZy53aWR0aD05fQ0KZ2dwbG90KG5vdGEpKyANCiAgZ2VvbV9wb2ludChhZXMoeD1jYWRldGUsIHk9bm90YSkpICsNCiAgZ2VvbV9wb2ludChzaXplPTMsIGFlcyh4PWNhZGV0ZSwgeT1ub3RhLCBjb2w9bG9jYWwsIHNoYXBlPWxvY2FsKSkgKw0KICB4bGFiKCJDYWRldGUiKSArIHlsYWIoIk5vdGFzIikgKyBnZ3RpdGxlKCJOb3RhcyAtIENhZCBTZXhvIEZlbWluaW5vIC0gVHVybWEgQyBCYXMgMjAxOCIpICsNCiAgc2NhbGVfY29sb3JfYnJld2VyKHBhbGV0dGU9IkRhcmsyIikgKyB0aGVtZShsZWdlbmQucG9zaXRpb249InRvcCIpKw0KICBzY2FsZV95X2NvbnRpbnVvdXMobGltaXRzID0gYyg2LjUsMTApKSsNCiAgdGhlbWUoYXhpcy50ZXh0LnggPSBlbGVtZW50X3RleHQoYW5nbGU9OTApKQ0KICAjIGdlb21fcG9pbnQoc2l6ZT0zLCBhZXMoeD1jYWRldGUsIHk9Y2xhc3NpZiwgY29sPWxvY2FsLCBzaGFwZT1sb2NhbCkpDQogIA0KDQogIA0KYGBgDQoNCmBgYHtyIGZpZy5oZWlnaHQ9OSwgZmlnLndpZHRoPTl9DQpnZ3Bsb3Qobm90YSkrIA0KICBnZW9tX3BvaW50KGFlcyh4PWNhZGV0ZSwgeT1jbGFzc2lmKSkgKw0KICBnZW9tX3BvaW50KHNpemU9MywgYWVzKHg9Y2FkZXRlLCB5PWNsYXNzaWYsIGNvbD1sb2NhbCwgc2hhcGU9bG9jYWwpKSArDQogIHhsYWIoIkNhZGV0ZSIpICsgeWxhYigiTm90YXMiKSArIGdndGl0bGUoIkNsYXNzaWZpY2HDp8OjbyAtIENhZCBTZXhvIEZlbWluaW5vIC0gVHVybWEgQyBCYXMgMjAxOCIpICsNCiAgc2NhbGVfY29sb3JfYnJld2VyKHBhbGV0dGU9IkRhcmsyIikgKyB0aGVtZShsZWdlbmQucG9zaXRpb249InRvcCIpKw0KIyAgc2NhbGVfeV9jb250aW51b3VzKGxpbWl0cyA9IGMoNi41LDEwKSkrDQogIHRoZW1lKGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KGFuZ2xlPTkwKSkNCiAgDQpgYGANCg0K