Seminar C4

Exercitii

Cu tabelul pseudo_facebook:

Reprezentati printr-un nor de puncte variabilele friend_count si friendships_initiated.

qplot(x=friend_count, y=friendships_initiated, data=pf)

Testati 3 valori pentru alpha in graficul de mai sus.

qplot(x=friend_count, y=friendships_initiated, data=pf, alpha=1/20)

ggplot(aes(x=friend_count, y=friendships_initiated), data=pf)+
  geom_point(alpha=1/30)

Colorati punctele din nor intr-o culoare la alegere.

ggplot(aes(x=friend_count, y=friendships_initiated), data=pf)+
  geom_point(alpha=1/30, color="lightcoral")

Colorati punctele din nor in functie de sex.

ggplot(aes(x=friend_count, y=friendships_initiated), data=subset(pf, !is.na(pf$gender)))+
  geom_point(alpha=1/5, aes(color=gender))

Reprezentati printr-un nor de puncte variabilele dob_year (orizontal) si mobile_likes (vertical).

ggplot(aes(x=dob_year, y=mobile_likes), data=pf)+
  geom_point()

Testati 3 valori pentru alpha in graficul de mai sus.

ggplot(aes(x=dob_year, y=mobile_likes), data=pf)+
  geom_point(alpha=1/10)

Colorati punctele din nor intr-o culoare la alegere.

ggplot(aes(x=dob_year, y=mobile_likes), data=pf)+
  geom_point(alpha=1/10, color="deepskyblue4")

Colorati punctele din nor in functie de sex.

ggplot(aes(x=dob_year, y=mobile_likes), data=subset(pf,!is.na(gender)))+
  geom_point(alpha=1/10, aes(color=gender))

Grupati datele din pf in functie de anul nasterii.

library(dplyr)

package <U+393C><U+3E31>dplyr<U+393C><U+3E32> was built under R version 3.5.3
Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:gridExtra<U+393C><U+3E32>:

    combine

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union

pfAn <- group_by(pf, dob_year)
str(pfAn)

Pentru fiecare an al nasterii salvati intr-un tabel nou media, mediana si numarul de observatii pentru variabila mobile_likes.

pfAn.sumar <- summarise(pfAn,
                        MediaML=mean(mobile_likes),
                        MedianaML=median(mobile_likes),
                        Nobs=n())
head(pfAn.sumar)

Reprezentati intr-un grafic media si mediana pentru mobile_likes pentru fiecare an (printr-o linie poligonala).

G1 <- ggplot(aes(dob_year), data=pfAn.sumar)+
  geom_line(aes(y=MediaML), color="blue")+
  geom_line(aes(y=MedianaML), color="red")+
  geom_line(aes(y=Nobs), color="green")+
  ylab("Media, mediana si Nobs Mobile_likes")
G1

Reprezentati intr-un grafic numarul de mobile_likes pentru fiecare an.

G2 <- ggplot(aes(x=dob_year, y=Nobs), data=pfAn.sumar)+
  geom_line(color="firebrick3")
G2

Grupati ultimele doua grafice impreuna.

library(gridExtra)

grid.arrange(G1,G2)

Reprezentati printr-un nor de puncte dob_year si likes_received.

ggplot(aes(x=dob_year, y=likes_received), data=pf)+
  geom_point()

Adaugati pe graficul de mai sus media likes_received pentru fiecare dob_year (printr-o linie poligonala).

ggplot(aes(x=dob_year, y=likes_received), data=pf)+
  geom_point(color='chocolate1', alpha=1/10)+
  geom_line(stat="summary", fun.y=mean)+
  coord_cartesian(ylim=c(0,5000))

Adaugati pe graficul de mai sus mediana, quantilele 10% si 90%.

ggplot(aes(x=dob_year, y=likes_received), data=pf)+
  geom_point(color='chocolate1', alpha=1/10)+
  geom_line(stat="summary", fun.y=mean)+
  geom_line(stat="summary", fun.y=median, linetype=2)+
  geom_line(stat="summary", fun.y=quantile, fun.args=list(probs=0.9), linetype=3, color="darkblue")+
  geom_line(stat="summary", fun.y=quantile, fun.args=list(probs=0.1), linetype=3, color="darkblue")+
  coord_cartesian(ylim=c(0,750))

Reveniti la toate graficele de mai sus si adaugati etichete potrivite pentru axe, titluri, subtitluri, si explicatii (caption).

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Exercitii:

1. Grupati datele din pf in functie de dob_month.

2. Pentru fiecare valoare a dob_month calculati pentru variabilele likes, mobile_likes si www_likes valoare medie, mediana, abaterea medie patratica, suma si numarul de observatii.

3. Reprezentati mediile celor 3 variabile ca si linii poligonale.

4. Reprezentati medianele celor 3 variabile ca si linii poligonale.

5. Grupati datele din pf in functie de tenure.

6. Pentru fiecare valoare a tenure calculati pentru variabilele likes, mobile_likes si www_likes valoare medie, mediana, abaterea medie patratica, suma si numarul de observatii.

7. Reprezentati mediile celor 3 variabile ca si linii poligonale.

8. Reprezentati medianele celor 3 variabile ca si linii poligonale.