Data manipulation - solutions

class: center, middle, title-slide

# Data manipulation - solutions
### Olivier Gimenez
### last updated: 2022-05-16

---

## Question 1a

.tiny-font[

```r
penguins # display data
```

```
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
```
]

---

## Question 1a

.tiny-font[

```r
penguins %>% glimpse() # display data
```

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
```
]

---

## Question 1b

```r
dat <- penguins %>%
# filter out missing sex
  filter(!is.na(sex)) %>% 
# select variables
  select(species, island, bill_length_mm, body_mass_g) 
```

---

## Question 2a

.tiny-font[

```r
dat # nb of penguins
```

```
## # A tibble: 333 × 4
##    species island    bill_length_mm body_mass_g
##    <fct>   <fct>              <dbl>       <int>
##  1 Adelie  Torgersen           39.1        3750
##  2 Adelie  Torgersen           39.5        3800
##  3 Adelie  Torgersen           40.3        3250
##  4 Adelie  Torgersen           36.7        3450
##  5 Adelie  Torgersen           39.3        3650
##  6 Adelie  Torgersen           38.9        3625
##  7 Adelie  Torgersen           39.2        4675
##  8 Adelie  Torgersen           41.1        3200
##  9 Adelie  Torgersen           38.6        3800
## 10 Adelie  Torgersen           34.6        4400
## # … with 323 more rows
```
]

---

## Questions 2b and 2d

```r
# nb of species, and penguins per species
dat %>% count(species) 
```

```
## # A tibble: 3 × 2
##   species       n
##   <fct>     <int>
## 1 Adelie      146
## 2 Chinstrap    68
## 3 Gentoo      119
```

---

## Questions 2b and 2d

```r
dat %>% count(species, sort = TRUE) # idem, arranged by n
```

```
## # A tibble: 3 × 2
##   species       n
##   <fct>     <int>
## 1 Adelie      146
## 2 Gentoo      119
## 3 Chinstrap    68
```

---

## Question 2c

```r
dat %>% count(island) # nb of island, and penguins per island
```

```
## # A tibble: 3 × 2
##   island        n
##   <fct>     <int>
## 1 Biscoe      163
## 2 Dream       123
## 3 Torgersen    47
```

---

## Question 2e

```r
# penguins per species and island
dat %>% count(species, island) 
```

```
## # A tibble: 5 × 3
##   species   island        n
##   <fct>     <fct>     <int>
## 1 Adelie    Biscoe       44
## 2 Adelie    Dream        55
## 3 Adelie    Torgersen    47
## 4 Chinstrap Dream        68
## 5 Gentoo    Biscoe      119
```

---

## Question 3a: mean body mass

.tiny-font[

```r
# option 1
dat %>%
  mutate(mean_bm = mean(body_mass_g))
```

```
## # A tibble: 333 × 5
##    species island    bill_length_mm body_mass_g mean_bm
##    <fct>   <fct>              <dbl>       <int>   <dbl>
##  1 Adelie  Torgersen           39.1        3750   4207.
##  2 Adelie  Torgersen           39.5        3800   4207.
##  3 Adelie  Torgersen           40.3        3250   4207.
##  4 Adelie  Torgersen           36.7        3450   4207.
##  5 Adelie  Torgersen           39.3        3650   4207.
##  6 Adelie  Torgersen           38.9        3625   4207.
##  7 Adelie  Torgersen           39.2        4675   4207.
##  8 Adelie  Torgersen           41.1        3200   4207.
##  9 Adelie  Torgersen           38.6        3800   4207.
## 10 Adelie  Torgersen           34.6        4400   4207.
## # … with 323 more rows
```
]

---

## Question 3a: mean body mass

```r
# option 2
dat %>%
  summarise(mean_bm = mean(body_mass_g))
```

```
## # A tibble: 1 × 1
##   mean_bm
##     <dbl>
## 1   4207.
```

---

## Question 3b: mean body mass per species

.tiny-font[

```r
# option 1
dat %>%
  group_by(species) %>% 
  mutate(mean_bm = mean(body_mass_g))
```

```
## # A tibble: 333 × 5
## # Groups:   species [3]
##    species island    bill_length_mm body_mass_g mean_bm
##    <fct>   <fct>              <dbl>       <int>   <dbl>
##  1 Adelie  Torgersen           39.1        3750   3706.
##  2 Adelie  Torgersen           39.5        3800   3706.
##  3 Adelie  Torgersen           40.3        3250   3706.
##  4 Adelie  Torgersen           36.7        3450   3706.
##  5 Adelie  Torgersen           39.3        3650   3706.
##  6 Adelie  Torgersen           38.9        3625   3706.
##  7 Adelie  Torgersen           39.2        4675   3706.
##  8 Adelie  Torgersen           41.1        3200   3706.
##  9 Adelie  Torgersen           38.6        3800   3706.
## 10 Adelie  Torgersen           34.6        4400   3706.
## # … with 323 more rows
```
]

---

## Question 3b: mean body mass per species

```r
# option 2
dat %>% 
  group_by(species) %>% 
  summarize(mean_bm = mean(body_mass_g))
```

```
## # A tibble: 3 × 2
##   species   mean_bm
##   <fct>       <dbl>
## 1 Adelie      3706.
## 2 Chinstrap   3733.
## 3 Gentoo      5092.
```

---

## Question 3c: mean traits

```r
# all at once, through column selection
dat %>% 
  group_by(species) %>% 
  summarize(across(bill_length_mm:body_mass_g, mean))
```

```
## # A tibble: 3 × 3
##   species   bill_length_mm body_mass_g
##   <fct>              <dbl>       <dbl>
## 1 Adelie              38.8       3706.
## 2 Chinstrap           48.8       3733.
## 3 Gentoo              47.6       5092.
```

---

## Question 3c: mean traits

```r
# all at once, through column format selection
dat %>% 
  group_by(species) %>% 
  summarize(across(where(is.numeric), mean))
```