Linguagem R na Ciência de Dados

Aula 04 - Manipulando objetos em R

Ben Dêivide | UFSJ

Filosofia de publicação (Selo DC)

🎯 Objetivo da Aula

Compreender estruturas de dados no R
Manipular dados usando base R
Aplicar funções da família apply
Utilizar estruturas de controle
Trabalhar com dados reais (penguins)

📦 Estruturas Fundamentais

Vetores

Estrutura básica do R
Elementos do mesmo tipo

Exemplo:

x <- c(1, 2, 3, 4); x

[1] 1 2 3 4

Data Frames

Estrutura tabular
Colunas podem ter tipos diferentes

df <- data.frame(a = 1:3, b = 5:7); df

🔍 Indexação

Formas de indexação

x[1]

[1] 1

df[1, ]

  a b
1 1 5

df[, "a"]

[1] 1 2 3

[1] 1 2 3 4

df

Indexação lógica

x[x > 2]

[1] 3 4

🔧 Subconjuntos

subset(df, a > 1)

  a b
2 2 6
3 3 7

df

Filtra linhas
Pode selecionar colunas

🔁 Família apply

Ideia central

“Aplicar uma função sobre dados”

Função `apply()`

apply(df[,1:2], 1, mean)

[1] 3 4 5

apply(df[,1:2], 2, mean)

a b 
2 6

df

Funções `lapply()` e `sapply()`

lapply(df, mean)

$a
[1] 2

$b
[1] 6

sapply(df, mean)

a b 
2 6

df

lapply → lista
sapply → simplifica

Função `tapply()`

df <- data.frame(a = 1:6, 
                 b = rep(letters[1:3], 
                         each = 2))
tapply(df$a, df$b, mean)

  a   b   c 
1.5 3.5 5.5

df

Aplica função por grupos

🔄 Outras funções importantes

Função	Ideia principal
`with()`	Evitar repetir `df$`
`within()`	Modificar/criar variáveis no data frame
`by()`	Aplicar função por grupos (similar ao `tapply`, porém mais geral)
`ave()`	Retornar resultado por grupo no mesmo tamanho do vetor

Função `with()`

with(df, mean(a))

[1] 3.5

df

mean(df$a)

[1] 3.5

Função `by()`

by(df$a, df$b, mean)

df$b: a
[1] 1.5
------------------------------------------------------------ 
df$b: b
[1] 3.5
------------------------------------------------------------ 
df$b: c
[1] 5.5

# Equivalente
tapply(df$a, df$b, mean)

  a   b   c 
1.5 3.5 5.5

df

mean(df$a[df$b == "a"])

[1] 1.5

mean(df$a[df$b == "b"])

[1] 3.5

mean(df$a[df$b == "c"])

[1] 5.5

Diferença entre `by()` e `tapply()`

# datasets::penguins
dados <- na.omit(penguins); head(dados)

  species    island bill_len bill_dep flipper_len body_mass    sex year
1  Adelie Torgersen     39.1     18.7         181      3750   male 2007
2  Adelie Torgersen     39.5     17.4         186      3800 female 2007
3  Adelie Torgersen     40.3     18.0         195      3250 female 2007
5  Adelie Torgersen     36.7     19.3         193      3450 female 2007
6  Adelie Torgersen     39.3     20.6         190      3650   male 2007
7  Adelie Torgersen     38.9     17.8         181      3625 female 2007

# Funcao tapply()
tapply(dados$body_mass, dados$species, mean)

   Adelie Chinstrap    Gentoo 
 3706.164  3733.088  5092.437

Diferença entre `by()` e `tapply()`

# Funcao by()
by(dados$body_mass, dados$species, mean)

dados$species: Adelie
[1] 3706.164
------------------------------------------------------------ 
dados$species: Chinstrap
[1] 3733.088
------------------------------------------------------------ 
dados$species: Gentoo
[1] 5092.437

Diferença entre `by()` e `tapply()`

# Funcao by()
by(dados$body_mass, dados[, c("species", "sex")], mean)

species: Adelie
sex: female
[1] 3368.836
------------------------------------------------------------ 
species: Chinstrap
sex: female
[1] 3527.206
------------------------------------------------------------ 
species: Gentoo
sex: female
[1] 4679.741
------------------------------------------------------------ 
species: Adelie
sex: male
[1] 4043.493
------------------------------------------------------------ 
species: Chinstrap
sex: male
[1] 3938.971
------------------------------------------------------------ 
species: Gentoo
sex: male
[1] 5484.836

Diferença entre `by()` e `tapply()`

# Funcao tapply()
tapply(dados$body_mass, dados[, c("species", "sex")], mean)

           sex
species       female     male
  Adelie    3368.836 4043.493
  Chinstrap 3527.206 3938.971
  Gentoo    4679.741 5484.836

Diferença entre `by()` e `tapply()`

# Funcao by()
by(dados$body_mass, dados[, c("species", "sex", "year")], mean)

species: Adelie
sex: female
year: 2007
[1] 3389.773
------------------------------------------------------------ 
species: Chinstrap
sex: female
year: 2007
[1] 3569.231
------------------------------------------------------------ 
species: Gentoo
sex: female
year: 2007
[1] 4618.75
------------------------------------------------------------ 
species: Adelie
sex: male
year: 2007
[1] 4038.636
------------------------------------------------------------ 
species: Chinstrap
sex: male
year: 2007
[1] 3819.231
------------------------------------------------------------ 
species: Gentoo
sex: male
year: 2007
[1] 5552.941
------------------------------------------------------------ 
species: Adelie
sex: female
year: 2008
[1] 3386
------------------------------------------------------------ 
species: Chinstrap
sex: female
year: 2008
[1] 3472.222
------------------------------------------------------------ 
species: Gentoo
sex: female
year: 2008
[1] 4627.273
------------------------------------------------------------ 
species: Adelie
sex: male
year: 2008
[1] 4098
------------------------------------------------------------ 
species: Chinstrap
sex: male
year: 2008
[1] 4127.778
------------------------------------------------------------ 
species: Gentoo
sex: male
year: 2008
[1] 5410.87
------------------------------------------------------------ 
species: Adelie
sex: female
year: 2009
[1] 3334.615
------------------------------------------------------------ 
species: Chinstrap
sex: female
year: 2009
[1] 3522.917
------------------------------------------------------------ 
species: Gentoo
sex: female
year: 2009
[1] 4786.25
------------------------------------------------------------ 
species: Adelie
sex: male
year: 2009
[1] 3995.192
------------------------------------------------------------ 
species: Chinstrap
sex: male
year: 2009
[1] 3927.083
------------------------------------------------------------ 
species: Gentoo
sex: male
year: 2009
[1] 5510.714

Diferença entre `by()` e `tapply()`

# Funcao tapply()
tapply(dados$body_mass, dados[, c("species", "sex", "year")], mean)

, , year = 2007

           sex
species       female     male
  Adelie    3389.773 4038.636
  Chinstrap 3569.231 3819.231
  Gentoo    4618.750 5552.941

, , year = 2008

           sex
species       female     male
  Adelie    3386.000 4098.000
  Chinstrap 3472.222 4127.778
  Gentoo    4627.273 5410.870

, , year = 2009

           sex
species       female     male
  Adelie    3334.615 3995.192
  Chinstrap 3522.917 3927.083
  Gentoo    4786.250 5510.714

Diferença entre `by()` e `tapply()`

# Funcao tapply()
by(dados[, c("bill_len", "flipper_len")],
       dados$species,
       function(x) summary(x))

dados$species: Adelie
    bill_len      flipper_len   
 Min.   :32.10   Min.   :172.0  
 1st Qu.:36.73   1st Qu.:186.0  
 Median :38.85   Median :190.0  
 Mean   :38.82   Mean   :190.1  
 3rd Qu.:40.77   3rd Qu.:195.0  
 Max.   :46.00   Max.   :210.0  
------------------------------------------------------------ 
dados$species: Chinstrap
    bill_len      flipper_len   
 Min.   :40.90   Min.   :178.0  
 1st Qu.:46.35   1st Qu.:191.0  
 Median :49.55   Median :196.0  
 Mean   :48.83   Mean   :195.8  
 3rd Qu.:51.08   3rd Qu.:201.0  
 Max.   :58.00   Max.   :212.0  
------------------------------------------------------------ 
dados$species: Gentoo
    bill_len      flipper_len   
 Min.   :40.90   Min.   :203.0  
 1st Qu.:45.35   1st Qu.:212.0  
 Median :47.40   Median :216.0  
 Mean   :47.57   Mean   :217.2  
 3rd Qu.:49.60   3rd Qu.:221.5  
 Max.   :59.60   Max.   :231.0

Diferença entre `by()` e `tapply()`

# Funcao tapply()
tapply(dados[, c("bill_len", "flipper_len")],
       dados$species,
       function(x) summary(x))

$Adelie
    bill_len      flipper_len   
 Min.   :32.10   Min.   :172.0  
 1st Qu.:36.73   1st Qu.:186.0  
 Median :38.85   Median :190.0  
 Mean   :38.82   Mean   :190.1  
 3rd Qu.:40.77   3rd Qu.:195.0  
 Max.   :46.00   Max.   :210.0  

$Chinstrap
    bill_len      flipper_len   
 Min.   :40.90   Min.   :178.0  
 1st Qu.:46.35   1st Qu.:191.0  
 Median :49.55   Median :196.0  
 Mean   :48.83   Mean   :195.8  
 3rd Qu.:51.08   3rd Qu.:201.0  
 Max.   :58.00   Max.   :212.0  

$Gentoo
    bill_len      flipper_len   
 Min.   :40.90   Min.   :203.0  
 1st Qu.:45.35   1st Qu.:212.0  
 Median :47.40   Median :216.0  
 Mean   :47.57   Mean   :217.2  
 3rd Qu.:49.60   3rd Qu.:221.5  
 Max.   :59.60   Max.   :231.0

Diferença entre `by()` e `tapply()`

# Funcao by()
# Ajuste de regressão por espécie
resultado_by <- by(dados,
                   dados[, c("species", "sex")],
                   function(df) {
                     modelo <- lm(body_mass ~ flipper_len, data = dados)
                     coef(modelo)
                   }); resultado_by

species: Adelie
sex: female
(Intercept) flipper_len 
-5872.09268    50.15327 
------------------------------------------------------------ 
species: Chinstrap
sex: female
(Intercept) flipper_len 
-5872.09268    50.15327 
------------------------------------------------------------ 
species: Gentoo
sex: female
(Intercept) flipper_len 
-5872.09268    50.15327 
------------------------------------------------------------ 
species: Adelie
sex: male
(Intercept) flipper_len 
-5872.09268    50.15327 
------------------------------------------------------------ 
species: Chinstrap
sex: male
(Intercept) flipper_len 
-5872.09268    50.15327 
------------------------------------------------------------ 
species: Gentoo
sex: male
(Intercept) flipper_len 
-5872.09268    50.15327

Diferença entre `by()` e `tapply()`

# Funcao by()
# Ajuste de regressão por espécie
resultado_tapply <- tapply(dados,
                   dados[, c("species", "sex")],
                   function(df) {
                     modelo <- lm(body_mass ~ flipper_len, data = dados)
                     coef(modelo)
                   }); resultado_tapply

           sex
species     female    male     
  Adelie    numeric,2 numeric,2
  Chinstrap numeric,2 numeric,2
  Gentoo    numeric,2 numeric,2

Diferença entre `by()` e `tapply()`

⚠️ Constatação:
- Realizando alguns testes tapply() e by() apresentam resultado idênticos
- Entretanto, quando o segundo argumento é um quadro de dados (Data frame) e não um vetor; situações mais complexas não funcionam com tapply()

Função `within()`

Permitir criar, modificar ou remover variáveis dentro de um objeto (geralmente data frame) de forma organizada e legível.

# Imprimindo df
df

df2 <- within(df, {
  nova <- a * 2
}); df2

Função `ave()`

Calcular estatísticas por grupo e retornar um vetor do mesmo tamanho do original, com o resultado repetido para cada elemento do grupo

# Imprimindo df
df

ave(df$a, df$b, FUN = mean)

[1] 1.5 1.5 3.5 3.5 5.5 5.5

Função `aggregate()`

Resumir dados por grupo, retornando um data frame agregado

# Exemplo 1
aggregate(body_mass ~ species,
          data = dados,
          FUN = mean)

    species body_mass
1    Adelie  3706.164
2 Chinstrap  3733.088
3    Gentoo  5092.437

# Exemplo 2
aggregate(body_mass ~ species + sex,
          data = dados,
          FUN = mean)

    species    sex body_mass
1    Adelie female  3368.836
2 Chinstrap female  3527.206
3    Gentoo female  4679.741
4    Adelie   male  4043.493
5 Chinstrap   male  3938.971
6    Gentoo   male  5484.836

Função `split()`

split(dados, dados$species)

$Adelie
    species    island bill_len bill_dep flipper_len body_mass    sex year
1    Adelie Torgersen     39.1     18.7         181      3750   male 2007
2    Adelie Torgersen     39.5     17.4         186      3800 female 2007
3    Adelie Torgersen     40.3     18.0         195      3250 female 2007
5    Adelie Torgersen     36.7     19.3         193      3450 female 2007
6    Adelie Torgersen     39.3     20.6         190      3650   male 2007
7    Adelie Torgersen     38.9     17.8         181      3625 female 2007
8    Adelie Torgersen     39.2     19.6         195      4675   male 2007
13   Adelie Torgersen     41.1     17.6         182      3200 female 2007
14   Adelie Torgersen     38.6     21.2         191      3800   male 2007
15   Adelie Torgersen     34.6     21.1         198      4400   male 2007
16   Adelie Torgersen     36.6     17.8         185      3700 female 2007
17   Adelie Torgersen     38.7     19.0         195      3450 female 2007
18   Adelie Torgersen     42.5     20.7         197      4500   male 2007
19   Adelie Torgersen     34.4     18.4         184      3325 female 2007
20   Adelie Torgersen     46.0     21.5         194      4200   male 2007
21   Adelie    Biscoe     37.8     18.3         174      3400 female 2007
22   Adelie    Biscoe     37.7     18.7         180      3600   male 2007
23   Adelie    Biscoe     35.9     19.2         189      3800 female 2007
24   Adelie    Biscoe     38.2     18.1         185      3950   male 2007
25   Adelie    Biscoe     38.8     17.2         180      3800   male 2007
26   Adelie    Biscoe     35.3     18.9         187      3800 female 2007
27   Adelie    Biscoe     40.6     18.6         183      3550   male 2007
28   Adelie    Biscoe     40.5     17.9         187      3200 female 2007
29   Adelie    Biscoe     37.9     18.6         172      3150 female 2007
30   Adelie    Biscoe     40.5     18.9         180      3950   male 2007
31   Adelie     Dream     39.5     16.7         178      3250 female 2007
32   Adelie     Dream     37.2     18.1         178      3900   male 2007
33   Adelie     Dream     39.5     17.8         188      3300 female 2007
34   Adelie     Dream     40.9     18.9         184      3900   male 2007
35   Adelie     Dream     36.4     17.0         195      3325 female 2007
36   Adelie     Dream     39.2     21.1         196      4150   male 2007
37   Adelie     Dream     38.8     20.0         190      3950   male 2007
38   Adelie     Dream     42.2     18.5         180      3550 female 2007
39   Adelie     Dream     37.6     19.3         181      3300 female 2007
40   Adelie     Dream     39.8     19.1         184      4650   male 2007
41   Adelie     Dream     36.5     18.0         182      3150 female 2007
42   Adelie     Dream     40.8     18.4         195      3900   male 2007
43   Adelie     Dream     36.0     18.5         186      3100 female 2007
44   Adelie     Dream     44.1     19.7         196      4400   male 2007
45   Adelie     Dream     37.0     16.9         185      3000 female 2007
46   Adelie     Dream     39.6     18.8         190      4600   male 2007
47   Adelie     Dream     41.1     19.0         182      3425   male 2007
49   Adelie     Dream     36.0     17.9         190      3450 female 2007
50   Adelie     Dream     42.3     21.2         191      4150   male 2007
51   Adelie    Biscoe     39.6     17.7         186      3500 female 2008
52   Adelie    Biscoe     40.1     18.9         188      4300   male 2008
53   Adelie    Biscoe     35.0     17.9         190      3450 female 2008
54   Adelie    Biscoe     42.0     19.5         200      4050   male 2008
55   Adelie    Biscoe     34.5     18.1         187      2900 female 2008
56   Adelie    Biscoe     41.4     18.6         191      3700   male 2008
57   Adelie    Biscoe     39.0     17.5         186      3550 female 2008
58   Adelie    Biscoe     40.6     18.8         193      3800   male 2008
59   Adelie    Biscoe     36.5     16.6         181      2850 female 2008
60   Adelie    Biscoe     37.6     19.1         194      3750   male 2008
61   Adelie    Biscoe     35.7     16.9         185      3150 female 2008
62   Adelie    Biscoe     41.3     21.1         195      4400   male 2008
63   Adelie    Biscoe     37.6     17.0         185      3600 female 2008
64   Adelie    Biscoe     41.1     18.2         192      4050   male 2008
65   Adelie    Biscoe     36.4     17.1         184      2850 female 2008
66   Adelie    Biscoe     41.6     18.0         192      3950   male 2008
67   Adelie    Biscoe     35.5     16.2         195      3350 female 2008
68   Adelie    Biscoe     41.1     19.1         188      4100   male 2008
69   Adelie Torgersen     35.9     16.6         190      3050 female 2008
70   Adelie Torgersen     41.8     19.4         198      4450   male 2008
71   Adelie Torgersen     33.5     19.0         190      3600 female 2008
72   Adelie Torgersen     39.7     18.4         190      3900   male 2008
73   Adelie Torgersen     39.6     17.2         196      3550 female 2008
74   Adelie Torgersen     45.8     18.9         197      4150   male 2008
75   Adelie Torgersen     35.5     17.5         190      3700 female 2008
76   Adelie Torgersen     42.8     18.5         195      4250   male 2008
77   Adelie Torgersen     40.9     16.8         191      3700 female 2008
78   Adelie Torgersen     37.2     19.4         184      3900   male 2008
79   Adelie Torgersen     36.2     16.1         187      3550 female 2008
80   Adelie Torgersen     42.1     19.1         195      4000   male 2008
81   Adelie Torgersen     34.6     17.2         189      3200 female 2008
82   Adelie Torgersen     42.9     17.6         196      4700   male 2008
83   Adelie Torgersen     36.7     18.8         187      3800 female 2008
84   Adelie Torgersen     35.1     19.4         193      4200   male 2008
85   Adelie     Dream     37.3     17.8         191      3350 female 2008
86   Adelie     Dream     41.3     20.3         194      3550   male 2008
87   Adelie     Dream     36.3     19.5         190      3800   male 2008
88   Adelie     Dream     36.9     18.6         189      3500 female 2008
89   Adelie     Dream     38.3     19.2         189      3950   male 2008
90   Adelie     Dream     38.9     18.8         190      3600 female 2008
91   Adelie     Dream     35.7     18.0         202      3550 female 2008
92   Adelie     Dream     41.1     18.1         205      4300   male 2008
93   Adelie     Dream     34.0     17.1         185      3400 female 2008
94   Adelie     Dream     39.6     18.1         186      4450   male 2008
95   Adelie     Dream     36.2     17.3         187      3300 female 2008
96   Adelie     Dream     40.8     18.9         208      4300   male 2008
97   Adelie     Dream     38.1     18.6         190      3700 female 2008
98   Adelie     Dream     40.3     18.5         196      4350   male 2008
99   Adelie     Dream     33.1     16.1         178      2900 female 2008
100  Adelie     Dream     43.2     18.5         192      4100   male 2008
101  Adelie    Biscoe     35.0     17.9         192      3725 female 2009
102  Adelie    Biscoe     41.0     20.0         203      4725   male 2009
103  Adelie    Biscoe     37.7     16.0         183      3075 female 2009
104  Adelie    Biscoe     37.8     20.0         190      4250   male 2009
105  Adelie    Biscoe     37.9     18.6         193      2925 female 2009
106  Adelie    Biscoe     39.7     18.9         184      3550   male 2009
107  Adelie    Biscoe     38.6     17.2         199      3750 female 2009
108  Adelie    Biscoe     38.2     20.0         190      3900   male 2009
109  Adelie    Biscoe     38.1     17.0         181      3175 female 2009
110  Adelie    Biscoe     43.2     19.0         197      4775   male 2009
111  Adelie    Biscoe     38.1     16.5         198      3825 female 2009
112  Adelie    Biscoe     45.6     20.3         191      4600   male 2009
113  Adelie    Biscoe     39.7     17.7         193      3200 female 2009
114  Adelie    Biscoe     42.2     19.5         197      4275   male 2009
115  Adelie    Biscoe     39.6     20.7         191      3900 female 2009
116  Adelie    Biscoe     42.7     18.3         196      4075   male 2009
117  Adelie Torgersen     38.6     17.0         188      2900 female 2009
118  Adelie Torgersen     37.3     20.5         199      3775   male 2009
119  Adelie Torgersen     35.7     17.0         189      3350 female 2009
120  Adelie Torgersen     41.1     18.6         189      3325   male 2009
121  Adelie Torgersen     36.2     17.2         187      3150 female 2009
122  Adelie Torgersen     37.7     19.8         198      3500   male 2009
123  Adelie Torgersen     40.2     17.0         176      3450 female 2009
124  Adelie Torgersen     41.4     18.5         202      3875   male 2009
125  Adelie Torgersen     35.2     15.9         186      3050 female 2009
126  Adelie Torgersen     40.6     19.0         199      4000   male 2009
127  Adelie Torgersen     38.8     17.6         191      3275 female 2009
128  Adelie Torgersen     41.5     18.3         195      4300   male 2009
129  Adelie Torgersen     39.0     17.1         191      3050 female 2009
130  Adelie Torgersen     44.1     18.0         210      4000   male 2009
131  Adelie Torgersen     38.5     17.9         190      3325 female 2009
132  Adelie Torgersen     43.1     19.2         197      3500   male 2009
133  Adelie     Dream     36.8     18.5         193      3500 female 2009
134  Adelie     Dream     37.5     18.5         199      4475   male 2009
135  Adelie     Dream     38.1     17.6         187      3425 female 2009
136  Adelie     Dream     41.1     17.5         190      3900   male 2009
137  Adelie     Dream     35.6     17.5         191      3175 female 2009
138  Adelie     Dream     40.2     20.1         200      3975   male 2009
139  Adelie     Dream     37.0     16.5         185      3400 female 2009
140  Adelie     Dream     39.7     17.9         193      4250   male 2009
141  Adelie     Dream     40.2     17.1         193      3400 female 2009
142  Adelie     Dream     40.6     17.2         187      3475   male 2009
143  Adelie     Dream     32.1     15.5         188      3050 female 2009
144  Adelie     Dream     40.7     17.0         190      3725   male 2009
145  Adelie     Dream     37.3     16.8         192      3000 female 2009
146  Adelie     Dream     39.0     18.7         185      3650   male 2009
147  Adelie     Dream     39.2     18.6         190      4250   male 2009
148  Adelie     Dream     36.6     18.4         184      3475 female 2009
149  Adelie     Dream     36.0     17.8         195      3450 female 2009
150  Adelie     Dream     37.8     18.1         193      3750   male 2009
151  Adelie     Dream     36.0     17.1         187      3700 female 2009
152  Adelie     Dream     41.5     18.5         201      4000   male 2009

$Chinstrap
      species island bill_len bill_dep flipper_len body_mass    sex year
277 Chinstrap  Dream     46.5     17.9         192      3500 female 2007
278 Chinstrap  Dream     50.0     19.5         196      3900   male 2007
279 Chinstrap  Dream     51.3     19.2         193      3650   male 2007
280 Chinstrap  Dream     45.4     18.7         188      3525 female 2007
281 Chinstrap  Dream     52.7     19.8         197      3725   male 2007
282 Chinstrap  Dream     45.2     17.8         198      3950 female 2007
283 Chinstrap  Dream     46.1     18.2         178      3250 female 2007
284 Chinstrap  Dream     51.3     18.2         197      3750   male 2007
285 Chinstrap  Dream     46.0     18.9         195      4150 female 2007
286 Chinstrap  Dream     51.3     19.9         198      3700   male 2007
287 Chinstrap  Dream     46.6     17.8         193      3800 female 2007
288 Chinstrap  Dream     51.7     20.3         194      3775   male 2007
289 Chinstrap  Dream     47.0     17.3         185      3700 female 2007
290 Chinstrap  Dream     52.0     18.1         201      4050   male 2007
291 Chinstrap  Dream     45.9     17.1         190      3575 female 2007
292 Chinstrap  Dream     50.5     19.6         201      4050   male 2007
293 Chinstrap  Dream     50.3     20.0         197      3300   male 2007
294 Chinstrap  Dream     58.0     17.8         181      3700 female 2007
295 Chinstrap  Dream     46.4     18.6         190      3450 female 2007
296 Chinstrap  Dream     49.2     18.2         195      4400   male 2007
297 Chinstrap  Dream     42.4     17.3         181      3600 female 2007
298 Chinstrap  Dream     48.5     17.5         191      3400   male 2007
299 Chinstrap  Dream     43.2     16.6         187      2900 female 2007
300 Chinstrap  Dream     50.6     19.4         193      3800   male 2007
301 Chinstrap  Dream     46.7     17.9         195      3300 female 2007
302 Chinstrap  Dream     52.0     19.0         197      4150   male 2007
303 Chinstrap  Dream     50.5     18.4         200      3400 female 2008
304 Chinstrap  Dream     49.5     19.0         200      3800   male 2008
305 Chinstrap  Dream     46.4     17.8         191      3700 female 2008
306 Chinstrap  Dream     52.8     20.0         205      4550   male 2008
307 Chinstrap  Dream     40.9     16.6         187      3200 female 2008
308 Chinstrap  Dream     54.2     20.8         201      4300   male 2008
309 Chinstrap  Dream     42.5     16.7         187      3350 female 2008
310 Chinstrap  Dream     51.0     18.8         203      4100   male 2008
311 Chinstrap  Dream     49.7     18.6         195      3600   male 2008
312 Chinstrap  Dream     47.5     16.8         199      3900 female 2008
313 Chinstrap  Dream     47.6     18.3         195      3850 female 2008
314 Chinstrap  Dream     52.0     20.7         210      4800   male 2008
315 Chinstrap  Dream     46.9     16.6         192      2700 female 2008
316 Chinstrap  Dream     53.5     19.9         205      4500   male 2008
317 Chinstrap  Dream     49.0     19.5         210      3950   male 2008
318 Chinstrap  Dream     46.2     17.5         187      3650 female 2008
319 Chinstrap  Dream     50.9     19.1         196      3550   male 2008
320 Chinstrap  Dream     45.5     17.0         196      3500 female 2008
321 Chinstrap  Dream     50.9     17.9         196      3675 female 2009
322 Chinstrap  Dream     50.8     18.5         201      4450   male 2009
323 Chinstrap  Dream     50.1     17.9         190      3400 female 2009
324 Chinstrap  Dream     49.0     19.6         212      4300   male 2009
325 Chinstrap  Dream     51.5     18.7         187      3250   male 2009
326 Chinstrap  Dream     49.8     17.3         198      3675 female 2009
327 Chinstrap  Dream     48.1     16.4         199      3325 female 2009
328 Chinstrap  Dream     51.4     19.0         201      3950   male 2009
329 Chinstrap  Dream     45.7     17.3         193      3600 female 2009
330 Chinstrap  Dream     50.7     19.7         203      4050   male 2009
331 Chinstrap  Dream     42.5     17.3         187      3350 female 2009
332 Chinstrap  Dream     52.2     18.8         197      3450   male 2009
333 Chinstrap  Dream     45.2     16.6         191      3250 female 2009
334 Chinstrap  Dream     49.3     19.9         203      4050   male 2009
335 Chinstrap  Dream     50.2     18.8         202      3800   male 2009
336 Chinstrap  Dream     45.6     19.4         194      3525 female 2009
337 Chinstrap  Dream     51.9     19.5         206      3950   male 2009
338 Chinstrap  Dream     46.8     16.5         189      3650 female 2009
339 Chinstrap  Dream     45.7     17.0         195      3650 female 2009
340 Chinstrap  Dream     55.8     19.8         207      4000   male 2009
341 Chinstrap  Dream     43.5     18.1         202      3400 female 2009
342 Chinstrap  Dream     49.6     18.2         193      3775   male 2009
343 Chinstrap  Dream     50.8     19.0         210      4100   male 2009
344 Chinstrap  Dream     50.2     18.7         198      3775 female 2009

$Gentoo
    species island bill_len bill_dep flipper_len body_mass    sex year
153  Gentoo Biscoe     46.1     13.2         211      4500 female 2007
154  Gentoo Biscoe     50.0     16.3         230      5700   male 2007
155  Gentoo Biscoe     48.7     14.1         210      4450 female 2007
156  Gentoo Biscoe     50.0     15.2         218      5700   male 2007
157  Gentoo Biscoe     47.6     14.5         215      5400   male 2007
158  Gentoo Biscoe     46.5     13.5         210      4550 female 2007
159  Gentoo Biscoe     45.4     14.6         211      4800 female 2007
160  Gentoo Biscoe     46.7     15.3         219      5200   male 2007
161  Gentoo Biscoe     43.3     13.4         209      4400 female 2007
162  Gentoo Biscoe     46.8     15.4         215      5150   male 2007
163  Gentoo Biscoe     40.9     13.7         214      4650 female 2007
164  Gentoo Biscoe     49.0     16.1         216      5550   male 2007
165  Gentoo Biscoe     45.5     13.7         214      4650 female 2007
166  Gentoo Biscoe     48.4     14.6         213      5850   male 2007
167  Gentoo Biscoe     45.8     14.6         210      4200 female 2007
168  Gentoo Biscoe     49.3     15.7         217      5850   male 2007
169  Gentoo Biscoe     42.0     13.5         210      4150 female 2007
170  Gentoo Biscoe     49.2     15.2         221      6300   male 2007
171  Gentoo Biscoe     46.2     14.5         209      4800 female 2007
172  Gentoo Biscoe     48.7     15.1         222      5350   male 2007
173  Gentoo Biscoe     50.2     14.3         218      5700   male 2007
174  Gentoo Biscoe     45.1     14.5         215      5000 female 2007
175  Gentoo Biscoe     46.5     14.5         213      4400 female 2007
176  Gentoo Biscoe     46.3     15.8         215      5050   male 2007
177  Gentoo Biscoe     42.9     13.1         215      5000 female 2007
178  Gentoo Biscoe     46.1     15.1         215      5100   male 2007
180  Gentoo Biscoe     47.8     15.0         215      5650   male 2007
181  Gentoo Biscoe     48.2     14.3         210      4600 female 2007
182  Gentoo Biscoe     50.0     15.3         220      5550   male 2007
183  Gentoo Biscoe     47.3     15.3         222      5250   male 2007
184  Gentoo Biscoe     42.8     14.2         209      4700 female 2007
185  Gentoo Biscoe     45.1     14.5         207      5050 female 2007
186  Gentoo Biscoe     59.6     17.0         230      6050   male 2007
187  Gentoo Biscoe     49.1     14.8         220      5150 female 2008
188  Gentoo Biscoe     48.4     16.3         220      5400   male 2008
189  Gentoo Biscoe     42.6     13.7         213      4950 female 2008
190  Gentoo Biscoe     44.4     17.3         219      5250   male 2008
191  Gentoo Biscoe     44.0     13.6         208      4350 female 2008
192  Gentoo Biscoe     48.7     15.7         208      5350   male 2008
193  Gentoo Biscoe     42.7     13.7         208      3950 female 2008
194  Gentoo Biscoe     49.6     16.0         225      5700   male 2008
195  Gentoo Biscoe     45.3     13.7         210      4300 female 2008
196  Gentoo Biscoe     49.6     15.0         216      4750   male 2008
197  Gentoo Biscoe     50.5     15.9         222      5550   male 2008
198  Gentoo Biscoe     43.6     13.9         217      4900 female 2008
199  Gentoo Biscoe     45.5     13.9         210      4200 female 2008
200  Gentoo Biscoe     50.5     15.9         225      5400   male 2008
201  Gentoo Biscoe     44.9     13.3         213      5100 female 2008
202  Gentoo Biscoe     45.2     15.8         215      5300   male 2008
203  Gentoo Biscoe     46.6     14.2         210      4850 female 2008
204  Gentoo Biscoe     48.5     14.1         220      5300   male 2008
205  Gentoo Biscoe     45.1     14.4         210      4400 female 2008
206  Gentoo Biscoe     50.1     15.0         225      5000   male 2008
207  Gentoo Biscoe     46.5     14.4         217      4900 female 2008
208  Gentoo Biscoe     45.0     15.4         220      5050   male 2008
209  Gentoo Biscoe     43.8     13.9         208      4300 female 2008
210  Gentoo Biscoe     45.5     15.0         220      5000   male 2008
211  Gentoo Biscoe     43.2     14.5         208      4450 female 2008
212  Gentoo Biscoe     50.4     15.3         224      5550   male 2008
213  Gentoo Biscoe     45.3     13.8         208      4200 female 2008
214  Gentoo Biscoe     46.2     14.9         221      5300   male 2008
215  Gentoo Biscoe     45.7     13.9         214      4400 female 2008
216  Gentoo Biscoe     54.3     15.7         231      5650   male 2008
217  Gentoo Biscoe     45.8     14.2         219      4700 female 2008
218  Gentoo Biscoe     49.8     16.8         230      5700   male 2008
220  Gentoo Biscoe     49.5     16.2         229      5800   male 2008
221  Gentoo Biscoe     43.5     14.2         220      4700 female 2008
222  Gentoo Biscoe     50.7     15.0         223      5550   male 2008
223  Gentoo Biscoe     47.7     15.0         216      4750 female 2008
224  Gentoo Biscoe     46.4     15.6         221      5000   male 2008
225  Gentoo Biscoe     48.2     15.6         221      5100   male 2008
226  Gentoo Biscoe     46.5     14.8         217      5200 female 2008
227  Gentoo Biscoe     46.4     15.0         216      4700 female 2008
228  Gentoo Biscoe     48.6     16.0         230      5800   male 2008
229  Gentoo Biscoe     47.5     14.2         209      4600 female 2008
230  Gentoo Biscoe     51.1     16.3         220      6000   male 2008
231  Gentoo Biscoe     45.2     13.8         215      4750 female 2008
232  Gentoo Biscoe     45.2     16.4         223      5950   male 2008
233  Gentoo Biscoe     49.1     14.5         212      4625 female 2009
234  Gentoo Biscoe     52.5     15.6         221      5450   male 2009
235  Gentoo Biscoe     47.4     14.6         212      4725 female 2009
236  Gentoo Biscoe     50.0     15.9         224      5350   male 2009
237  Gentoo Biscoe     44.9     13.8         212      4750 female 2009
238  Gentoo Biscoe     50.8     17.3         228      5600   male 2009
239  Gentoo Biscoe     43.4     14.4         218      4600 female 2009
240  Gentoo Biscoe     51.3     14.2         218      5300   male 2009
241  Gentoo Biscoe     47.5     14.0         212      4875 female 2009
242  Gentoo Biscoe     52.1     17.0         230      5550   male 2009
243  Gentoo Biscoe     47.5     15.0         218      4950 female 2009
244  Gentoo Biscoe     52.2     17.1         228      5400   male 2009
245  Gentoo Biscoe     45.5     14.5         212      4750 female 2009
246  Gentoo Biscoe     49.5     16.1         224      5650   male 2009
247  Gentoo Biscoe     44.5     14.7         214      4850 female 2009
248  Gentoo Biscoe     50.8     15.7         226      5200   male 2009
249  Gentoo Biscoe     49.4     15.8         216      4925   male 2009
250  Gentoo Biscoe     46.9     14.6         222      4875 female 2009
251  Gentoo Biscoe     48.4     14.4         203      4625 female 2009
252  Gentoo Biscoe     51.1     16.5         225      5250   male 2009
253  Gentoo Biscoe     48.5     15.0         219      4850 female 2009
254  Gentoo Biscoe     55.9     17.0         228      5600   male 2009
255  Gentoo Biscoe     47.2     15.5         215      4975 female 2009
256  Gentoo Biscoe     49.1     15.0         228      5500   male 2009
258  Gentoo Biscoe     46.8     16.1         215      5500   male 2009
259  Gentoo Biscoe     41.7     14.7         210      4700 female 2009
260  Gentoo Biscoe     53.4     15.8         219      5500   male 2009
261  Gentoo Biscoe     43.3     14.0         208      4575 female 2009
262  Gentoo Biscoe     48.1     15.1         209      5500   male 2009
263  Gentoo Biscoe     50.5     15.2         216      5000 female 2009
264  Gentoo Biscoe     49.8     15.9         229      5950   male 2009
265  Gentoo Biscoe     43.5     15.2         213      4650 female 2009
266  Gentoo Biscoe     51.5     16.3         230      5500   male 2009
267  Gentoo Biscoe     46.2     14.1         217      4375 female 2009
268  Gentoo Biscoe     55.1     16.0         230      5850   male 2009
270  Gentoo Biscoe     48.8     16.2         222      6000   male 2009
271  Gentoo Biscoe     47.2     13.7         214      4925 female 2009
273  Gentoo Biscoe     46.8     14.3         215      4850 female 2009
274  Gentoo Biscoe     50.4     15.7         222      5750   male 2009
275  Gentoo Biscoe     45.2     14.8         212      5200 female 2009
276  Gentoo Biscoe     49.9     16.1         213      5400   male 2009

Linguagem R na Ciência de Dados

Filosofia de publicação (Selo DC)

🎯 Objetivo da Aula

📦 Estruturas Fundamentais

Vetores

Data Frames

🔍 Indexação

🔧 Subconjuntos

🔁 Família apply

Função apply()

Funções lapply() e sapply()

Função tapply()

🔄 Outras funções importantes

Função with()

Função by()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Diferença entre by() e tapply()

Função within()

Função ave()

Função aggregate()

Função split()

Função `apply()`

Funções `lapply()` e `sapply()`

Função `tapply()`

Função `with()`

Função `by()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Diferença entre `by()` e `tapply()`

Função `within()`

Função `ave()`

Função `aggregate()`

Função `split()`