Manipulare de date
## Var1 Var2
## 1 A 1
## 2 A 2
## 3 B 3
## 4 B 4
## 5 B 5
## 6 C 6
## 7 C 7
## 8 C 8
## 9 C 9
## 10 D 10
## 11 D 11
c4_grupat <- group_by(c4, Var1)
c4_grupat
## # A tibble: 11 × 2
## # Groups: Var1 [4]
## Var1 Var2
## <chr> <int>
## 1 A 1
## 2 A 2
## 3 B 3
## 4 B 4
## 5 B 5
## 6 C 6
## 7 C 7
## 8 C 8
## 9 C 9
## 10 D 10
## 11 D 11
## gropd_df [11 × 2] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Var1: chr [1:11] "A" "A" "B" "B" ...
## $ Var2: int [1:11] 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "groups")= tibble [4 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Var1 : chr [1:4] "A" "B" "C" "D"
## ..$ .rows: list<int> [1:4]
## .. ..$ : int [1:2] 1 2
## .. ..$ : int [1:3] 3 4 5
## .. ..$ : int [1:4] 6 7 8 9
## .. ..$ : int [1:2] 10 11
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
grupe_continent <- group_by(gapminder, continent)
str(grupe_continent)
## gropd_df [1,704 × 9] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent : Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap : num [1:1704] 779 821 853 836 740 ...
## $ tara_mica : logi [1:1704] FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ tara_mica_f : Factor w/ 2 levels "mare","mica": 1 1 1 1 1 1 1 1 1 1 ...
## $ tara_mica_f_o: Ord.factor w/ 2 levels "mica"<"mare": 2 2 2 2 2 2 2 2 2 2 ...
## - attr(*, "groups")= tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ continent: Factor w/ 5 levels "Africa","Americas",..: 1 2 3 4 5
## ..$ .rows : list<int> [1:5]
## .. ..$ : int [1:624] 25 26 27 28 29 30 31 32 33 34 ...
## .. ..$ : int [1:300] 49 50 51 52 53 54 55 56 57 58 ...
## .. ..$ : int [1:396] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..$ : int [1:360] 13 14 15 16 17 18 19 20 21 22 ...
## .. ..$ : int [1:24] 61 62 63 64 65 66 67 68 69 70 ...
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
c4_sumar <- summarise(c4_grupat,
MediaVar2 = mean(Var2),
MedianaVar2 = median(Var2),
n=n()
)
c4_sumar
## # A tibble: 4 × 4
## Var1 MediaVar2 MedianaVar2 n
## <chr> <dbl> <dbl> <int>
## 1 A 1.5 1.5 2
## 2 B 4 4 3
## 3 C 7.5 7.5 4
## 4 D 10.5 10.5 2
continente_sumar <- summarise(grupe_continent,
minLifeExp=min(lifeExp),
maxLifeExp=max(lifeExp),
MediaPop=mean(pop),
n=n()
)
continente_sumar
## # A tibble: 5 × 5
## continent minLifeExp maxLifeExp MediaPop n
## <fct> <dbl> <dbl> <dbl> <int>
## 1 Africa 23.6 76.4 9916003. 624
## 2 Americas 37.6 80.7 24504795. 300
## 3 Asia 28.8 82.6 77038722. 396
## 4 Europe 43.6 81.8 17169765. 360
## 5 Oceania 69.1 81.2 8874672. 24
c4_ordonat <- arrange(c4_sumar, n)
c4_ordonat
## # A tibble: 4 × 4
## Var1 MediaVar2 MedianaVar2 n
## <chr> <dbl> <dbl> <int>
## 1 A 1.5 1.5 2
## 2 D 10.5 10.5 2
## 3 B 4 4 3
## 4 C 7.5 7.5 4
## # A tibble: 4 × 4
## Var1 MediaVar2 MedianaVar2 n
## <chr> <dbl> <dbl> <int>
## 1 C 7.5 7.5 4
## 2 B 4 4 3
## 3 A 1.5 1.5 2
## 4 D 10.5 10.5 2
continente_ordonate <- arrange(continente_sumar, desc(minLifeExp))
continente_ordonate
## # A tibble: 5 × 5
## continent minLifeExp maxLifeExp MediaPop n
## <fct> <dbl> <dbl> <dbl> <int>
## 1 Oceania 69.1 81.2 8874672. 24
## 2 Europe 43.6 81.8 17169765. 360
## 3 Americas 37.6 80.7 24504795. 300
## 4 Asia 28.8 82.6 77038722. 396
## 5 Africa 23.6 76.4 9916003. 624
## # A tibble: 5 × 5
## continent minLifeExp maxLifeExp MediaPop n
## <fct> <dbl> <dbl> <dbl> <int>
## 1 Oceania 69.1 81.2 8874672. 24
## 2 Americas 37.6 80.7 24504795. 300
## 3 Europe 43.6 81.8 17169765. 360
## 4 Asia 28.8 82.6 77038722. 396
## 5 Africa 23.6 76.4 9916003. 624