slides.utf8

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# <div class="line-block">Getting Started in R<br />
an introduction to data analysis and visualisation</div>
## Transform
### Réka Solymosi & Sam Langton
### 3 July 2019

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![dplyr](img/dplyr.png)
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# Subsetting, sorting and summarising data

---
### Data

```r
starwars
```

```
## # A tibble: 87 x 13
##    name  height  mass hair_color skin_color eye_color birth_year gender
##    <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
##  1 Luke~    172    77 blond      fair       blue            19   male  
##  2 C-3PO    167    75 <NA>       gold       yellow         112   <NA>  
##  3 R2-D2     96    32 <NA>       white, bl~ red             33   <NA>  
##  4 Dart~    202   136 none       white      yellow          41.9 male  
##  5 Leia~    150    49 brown      light      brown           19   female
##  6 Owen~    178   120 brown, gr~ light      blue            52   male  
##  7 Beru~    165    75 brown      light      blue            47   female
##  8 R5-D4     97    32 <NA>       white, red red             NA   <NA>  
##  9 Bigg~    183    84 black      light      brown           24   male  
## 10 Obi-~    182    77 auburn, w~ fair       blue-gray       57   male  
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>, starships <list>
```

---
### `select()`

```r
# Pick columns
select(starwars, name, species)
```

```
## # A tibble: 87 x 2
##    name               species
##    <chr>              <chr>  
##  1 Luke Skywalker     Human  
##  2 C-3PO              Droid  
##  3 R2-D2              Droid  
##  4 Darth Vader        Human  
##  5 Leia Organa        Human  
##  6 Owen Lars          Human  
##  7 Beru Whitesun lars Human  
##  8 R5-D4              Droid  
##  9 Biggs Darklighter  Human  
## 10 Obi-Wan Kenobi     Human  
## # ... with 77 more rows
```

```r
# Pick a single column and return a vector
pull(starwars, name)
```

---
### `slice()`

```r
# Pick rows
slice(starwars, c(1,3,5))
```

```
## # A tibble: 3 x 13
##   name  height  mass hair_color skin_color eye_color birth_year gender
##   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
## 1 Luke~    172    77 blond      fair       blue              19 male  
## 2 R2-D2     96    32 <NA>       white, bl~ red               33 <NA>  
## 3 Leia~    150    49 brown      light      brown             19 female
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```

---
### `filter()`

```r
# Subset rows
filter(starwars, species == "Droid")
```

```
## # A tibble: 5 x 13
##   name  height  mass hair_color skin_color eye_color birth_year gender
##   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
## 1 C-3PO    167    75 <NA>       gold       yellow           112 <NA>  
## 2 R2-D2     96    32 <NA>       white, bl~ red               33 <NA>  
## 3 R5-D4     97    32 <NA>       white, red red               NA <NA>  
## 4 IG-88    200   140 none       metal      red               15 none  
## 5 BB8       NA    NA none       none       black             NA none  
## # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## #   vehicles <list>, starships <list>
```

---
### `arrange()`

```r
# Reorder rows
arrange(starwars, desc(mass))
```

```
## # A tibble: 87 x 13
##    name  height  mass hair_color skin_color eye_color birth_year gender
##    <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
##  1 Jabb~    175  1358 <NA>       green-tan~ orange         600   herma~
##  2 Grie~    216   159 none       brown, wh~ green, y~       NA   male  
##  3 IG-88    200   140 none       metal      red             15   none  
##  4 Dart~    202   136 none       white      yellow          41.9 male  
##  5 Tarf~    234   136 brown      brown      blue            NA   male  
##  6 Owen~    178   120 brown, gr~ light      blue            52   male  
##  7 Bossk    190   113 none       green      red             53   male  
##  8 Chew~    228   112 brown      unknown    blue           200   male  
##  9 Jek ~    180   110 brown      fair       blue            NA   male  
## 10 Dext~    198   102 none       brown      yellow          NA   male  
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>, starships <list>
```

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![magrittr](img/magrittr.png)

---
### `%>%` or the piping operator

- combines multiple operations
- passes a value / expression result forward into the next function call / expression
- makes R code more readable

---
### Inserting the `%>%` operator
Windows: Ctrl+Shift+M ; macOS: CmdShift+M)
<br>

```r
# select()
starwars %>% select(name, species)
```

```r
# pull()
starwars %>% pull(name)
```

```r
# slice()
starwars %>% slice(c(1,3,5))
```

```r
# filter()
starwars %>% filter(species == "Droid")
```

```r
# arrange()
starwars %>% arrange(desc(mass))
```

---
### `mutate()`

```r
# Add a column
starwars %>% 
  mutate(bmi = mass / ((height / 100)  ^ 2)) %>% 
  select(name:mass, bmi)
```

```
## # A tibble: 87 x 4
##    name               height  mass   bmi
##    <chr>               <int> <dbl> <dbl>
##  1 Luke Skywalker        172    77  26.0
##  2 C-3PO                 167    75  26.9
##  3 R2-D2                  96    32  34.7
##  4 Darth Vader           202   136  33.3
##  5 Leia Organa           150    49  21.8
##  6 Owen Lars             178   120  37.9
##  7 Beru Whitesun lars    165    75  27.5
##  8 R5-D4                  97    32  34.0
##  9 Biggs Darklighter     183    84  25.1
## 10 Obi-Wan Kenobi        182    77  23.2
## # ... with 77 more rows
```

---
### `group_by()` and `count()`

```r
# Group rows with same value and calculate frequency
starwars %>% 
  group_by(species) %>% 
  count(species, sort = TRUE)
```

```
## # A tibble: 38 x 2
## # Groups:   species [38]
##    species      n
##    <chr>    <int>
##  1 Human       35
##  2 <NA>         5
##  3 Droid        5
##  4 Gungan       3
##  5 Kaminoan     2
##  6 Mirialan     2
##  7 Twi'lek      2
##  8 Wookiee      2
##  9 Zabrak       2
## 10 Aleena       1
## # ... with 28 more rows
```

---
### `summarise()`

```r
# Return summary statistics
starwars %>%
  group_by(species) %>%
  summarise(n = n(),
            mass = mean(mass, na.rm = TRUE)) %>%
  filter(n > 1)
```

```
## # A tibble: 9 x 3
##   species      n  mass
##   <chr>    <int> <dbl>
## 1 <NA>         5  48  
## 2 Droid        5  69.8
## 3 Gungan       3  74  
## 4 Human       35  82.8
## 5 Kaminoan     2  88  
## 6 Mirialan     2  53.1
## 7 Twi'lek      2  55  
## 8 Wookiee      2 124  
## 9 Zabrak       2  80
```

---
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# Merging data

---
### Data

```r
band_members 
```

```
## # A tibble: 3 x 2
##   name  band   
##   <chr> <chr>  
## 1 Mick  Stones 
## 2 John  Beatles
## 3 Paul  Beatles
```

```r
band_instruments
```

```
## # A tibble: 3 x 2
##   name  plays 
##   <chr> <chr> 
## 1 John  guitar
## 2 Paul  bass  
## 3 Keith guitar
```

---
### `left_join()`

```r
band_members %>% left_join(band_instruments, by = "name")
```

```
## # A tibble: 3 x 3
##   name  band    plays 
##   <chr> <chr>   <chr> 
## 1 Mick  Stones  <NA>  
## 2 John  Beatles guitar
## 3 Paul  Beatles bass
```