Histogramas
ggplot(data = data, aes(x = raisedhands)) + geom_histogram(color = "black") +
scale_x_continuous(breaks = seq(0,100,5)) +
labs(x = "Raised Hands", y = "Student Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = data, aes(x = VisITedResources)) + geom_histogram(color = "black") +
scale_x_continuous(breaks = seq(0,100,5)) +
labs(x = "Visited Resources", y = "Student Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = data, aes(x = AnnouncementsView)) + geom_histogram(color = "black") +
scale_x_continuous(breaks = seq(0,100,5)) +
labs(x = "Announcements View", y = "Student Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = data, aes(x = Discussion)) + geom_histogram(color = "black") +
scale_x_continuous(breaks = seq(0,100,5)) +
labs(x = "Discussion Participation", y = "Student Count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Graficos de barras
ggplot(data = data, aes(x = gender)) + geom_bar() +
labs(x = "Gender", y = "Student Count") +
scale_y_continuous(breaks = seq(0,300,30)) + coord_flip()
ggplot(data = data, aes(x = NationalITy)) + geom_bar() +
labs(x = "Nationality", y = "Student Count") +
scale_y_continuous(breaks = seq(0,200,20)) + coord_flip()
Jordania y Kw tienen el mayor numero de estudiantes
ggplot(data = data, aes(x = PlaceofBirth)) + geom_bar(aes(fill = NationalITy)) +
labs(x = "Birth Place", y = "Student Count") + coord_flip() # usa is a mix of nationalities
ggplot(data = data, aes(x = GradeID, fill = Class)) + geom_bar() +
labs(x = "Grade ID", y = "Student Count") + coord_flip() # g-06 has students with only low grades
ggplot(data = data, aes(x = GradeID, fill = gender)) + geom_bar() +
labs(x = "Grade ID", y = "Student Count") + coord_flip() # g-10 has no females
ggplot(data = data, aes(x = SectionID, fill = Topic)) + geom_bar() +
labs(x = "Section ID", y = "Student Count") +
coord_flip()
la clase c solo tiene estudiantes de IT y ciencias
ggplot(data = data, aes(x = Topic, fill = gender)) + geom_bar() +
labs(x = "Topic", y = "Student Count") +
scale_y_continuous(breaks = seq(0,100,4)) + coord_flip()
Español tiene el peor relacion hombre-mujer mientras que ciencia,
quimica, ingles y francés tienen una buena relación
ggplot(data = data, aes(x = Topic, fill = NationalITy)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
La mayoria de las personas de IT son de Kw, quimica es la que posee
menos diversidad y francés la que tiene mayor.
ggplot(data = data, aes(x = Topic, fill = StageID)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
La sección c solo tiene estudiantes de español y IT
ggplot(data = data, aes(x = Topic, fill = Semester)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
IT tiene la mayoria de los estudiantes en primer semestre.
ggplot(data = data, aes(x = Topic, fill = Relation)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
La mayoria de los estudiantes de francés tiene a su madre como
tutora mientras que la mayoria de estudiantes de IT tiene a su padre
como tutor
ggplot(data = data, aes(x = Topic, fill = Class)) + geom_bar() +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
ggplot(data = data, aes(x = Topic, fill = Class)) + geom_bar(position = "fill") +
labs(x = "Topic", y = "Student Count") + coord_flip() +
scale_y_continuous(breaks = seq(0,100,4))
Geología no tiene estudiantes de clases bajas
ggplot(data = data, aes(x = Semester)) + geom_bar() +
labs(x = "Semester", y = "Student Count")
ggplot(data = data, aes(x = Relation, fill = Semester)) + geom_bar() +
labs(x = "Gaurdian", y = "Student Count")
ggplot(data = data, aes(x = ParentAnsweringSurvey, fill = ParentschoolSatisfaction)) +
geom_bar() +
labs(x = "Does parent answer survey ?", y = "Student Count")
La mayoria de los padres que no estan satisfechos con la escuela no
respondieron la encuesta.
ggplot(data = data, aes(x = ParentschoolSatisfaction)) +
geom_bar() +
labs(x = "Is the parent satified with the school ?", y = "Student Count")
ggplot(data = data, aes(x = StudentAbsenceDays)) + geom_bar() +
labs(x = "Is the student absent for more than seven days", y = "Student Count")
ggplot(data = data, aes(x = Class, fill = gender)) + geom_bar() +
labs(x = "Class", y = "Student Count")
Muy pocas chicas en clases bajas
ggplot(data = data, aes(x = Class, fill = Relation)) + geom_bar() +
labs(x = "Class", y = "Student Count")
Los estudiantes que tienen a madres como tutoras tienen mas
posibilidades de obtener mayores notas.
ggplot(data = data, aes(x = Class, fill = ParentAnsweringSurvey)) + geom_bar() +
labs(x = "Class", y = "Student Count")
Los estudiantes cuyos padres respondieron la encuesta son los que
poseen buenas notas.
ggplot(data = data, aes(x = Class, fill = StudentAbsenceDays)) + geom_bar() +
labs(x = "Class", y = "Student Count")
Estudiantes que faltan son los que tienen menores notas.
Diagramas de caja
ggplot(data = data, aes(x = gender, y = raisedhands)) + geom_boxplot()
Las chicas son las que levantan mas la mano.
ggplot(data = data, aes(x = gender, y = VisITedResources)) + geom_boxplot()
Las chicas visitaron mas recursos academicos
ggplot(data = data, aes(x = NationalITy, y = raisedhands)) + geom_boxplot()
Jordania tiene mas manoos levantadas que Kw, Libia es el que menos
tiene e Irak y Palestina son los que mas tienen.
ggplot(data = data, aes(x = StageID, y = raisedhands)) + geom_boxplot()
Hay mas discuciones en la preparatoria.
ggplot(data = data, aes(x = GradeID, y = raisedhands)) + geom_boxplot()
El sexto grado tiene la mayoria de manos levantadas en
promedio.
ggplot(data = data, aes(x = SectionID, y = Discussion)) + geom_boxplot()
La seccion C tiene las menores discuciones
ggplot(data = data, aes(x = Topic, y = raisedhands)) + geom_boxplot()
IT tiene muy pocas manos levantadas a pesar de que la mayoria de los
estudiantes estudian ahí.
ggplot(data = data, aes(x = Semester, y = raisedhands)) + geom_boxplot()
El segundo semestre es el que mas levannta la mano.
ggplot(data = data, aes(x = Relation, y = raisedhands)) + geom_boxplot()
Los estudiantes con madres tutoras levantan mas la mano.
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = raisedhands)) + geom_boxplot()
Los que respondieron que si a la encuesta levantan mas la mano.
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = VisITedResources)) + geom_boxplot()
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = AnnouncementsView)) + geom_boxplot()
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = Discussion)) + geom_boxplot()
ggplot(data = data, aes(x = ParentschoolSatisfaction, y = raisedhands)) + geom_boxplot()
Los que repondieron a la satisfaccion como bien levantan mas la
mano.
ggplot(data = data, aes(x = ParentschoolSatisfaction, y = VisITedResources)) + geom_boxplot()
ggplot(data = data, aes(x = ParentschoolSatisfaction, y = AnnouncementsView)) + geom_boxplot()
ggplot(data = data, aes(x = ParentschoolSatisfaction, y = Discussion)) + geom_boxplot()
ggplot(data = data, aes(x = StudentAbsenceDays, y = raisedhands)) + geom_boxplot()
Entre mas se van del salon menos levantan las manos.
ggplot(data = data, aes(x = StudentAbsenceDays, y = VisITedResources)) + geom_boxplot()
ggplot(data = data, aes(x = StudentAbsenceDays, y = AnnouncementsView)) + geom_boxplot()
ggplot(data = data, aes(x = StudentAbsenceDays, y = Discussion)) + geom_boxplot()
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = raisedhands)) + geom_boxplot()
Si respondieron a la encuesta que si levantan mas la mano.
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = VisITedResources)) + geom_boxplot()
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = AnnouncementsView)) + geom_boxplot()
ggplot(data = data, aes(x = ParentAnsweringSurvey, y = Discussion)) + geom_boxplot()
Graficos de cajas por clases
ggplot(data = data, aes(x = Class, y = raisedhands)) + geom_boxplot()
Entre mejores notas tengan mas levantan la mano.
ggplot(data = data, aes(x = Class, y = VisITedResources)) + geom_boxplot()
Entre mejores notas tenga mas recursos academicos tienen.
ggplot(data = data, aes(x = Class, y = AnnouncementsView)) + geom_boxplot()
Mejores notas mas vistas de anuncios
ggplot(data = data, aes(x = Class, y = Discussion)) + geom_boxplot()
Mayores notas mas discusiones.
Graficos de dispersión
ggplot(data = data, aes( x = raisedhands, y = VisITedResources)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data = data, aes( x = raisedhands, y = AnnouncementsView)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data = data, aes( x = raisedhands, y = Discussion)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data = data, aes( x = VisITedResources, y = AnnouncementsView)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data = data, aes( x = VisITedResources, y = Discussion)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
ggplot(data = data, aes( x = AnnouncementsView, y = Discussion)) + geom_point() +
geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
Graficos de densidad
ggplot(data = data, aes(x = raisedhands, color = gender)) + geom_density()
ggplot(data = data, aes(x = raisedhands, color = Topic)) + geom_density()
ggplot(data = data, aes(x = raisedhands, color = SectionID)) + geom_density()
ggplot(data = data, aes(x = raisedhands, color = Semester)) + geom_density()
ggplot(data = data, aes(x = raisedhands, color = Class)) + geom_density()
Tile Map
tile.map <- data %>% group_by(gender, NationalITy) %>%
summarise(Count = n()) %>% arrange(desc(Count))
## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.
ggplot(data = tile.map, aes(x = gender, NationalITy, fill = Count)) + geom_tile()
