Part 5

class: center, middle, inverse, title-slide

# Part 5
## R Programming
### Jessica Minnier
### 2022-02-02

---

class: center, middle

# Saving objects

---

# Penguins data

```r
library(tidyverse)
library(janitor)
library(palmerpenguins)
data(penguins)
glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie,…
## $ island            <fct> Torgersen, Torgersen, Torgersen…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.…
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.…
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 18…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 365…
## $ sex               <fct> male, female, female, NA, femal…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2…
```

---

# When we filter/clean/mutate the data

- If you're changing the data and you want to use that data later, save your work
- Save as new object, *or*,
- Save as the same object if you want to overwrite that data frame within R

```r
# save as new data frame penguins_f
penguins_f <- penguins %>%
  filter(sex=="female") %>%
  mutate(bill_ratio = bill_length_mm/bill_depth_mm)

# change the ordering of penguins_f and re-save it
penguins_f <- penguins_f %>% arrange(bill_ratio)

# print out that data frame, but don't save it
penguins_f %>% select(sex, contains("bill")) 
```

```
## # A tibble: 165 × 4
##    sex    bill_length_mm bill_depth_mm bill_ratio
##    <fct>           <dbl>         <dbl>      <dbl>
##  1 female           33.5          19         1.76
##  2 female           35.3          18.9       1.87
##  3 female           34.4          18.4       1.87
##  4 female           35.9          19.2       1.87
##  5 female           36.7          19.3       1.90
##  6 female           34.5          18.1       1.91
##  7 female           39.6          20.7       1.91
##  8 female           36            18.5       1.95
##  9 female           37.6          19.3       1.95
## 10 female           36.7          18.8       1.95
## # … with 155 more rows
```

---

# Summarizing data (i.e. tabyl, summarize)

- `tabyl()` and `summarize()` create new tibbles/data.frames that have summary info
- You can save it if you want, but not usually necessary unless you want to use it later!
- From `?tabyl`, Value = "Returns a data.frame with frequencies and percentages of the tabulated variable(s). A 3-way tabulation returns a list of data.frames."

.pull-left-40[

```r
penguins_f %>% tabyl(species)
```

```
##    species  n   percent
##     Adelie 73 0.4424242
##  Chinstrap 34 0.2060606
##     Gentoo 58 0.3515152
```
]
.pull-right-60[

```r
# save output as a tibble
penguins_f_table <- penguins_f %>% tabyl(species)

# we can see this is a tibble!
glimpse(penguins_f_table) 
```

```
## Rows: 3
## Columns: 3
## $ species <fct> Adelie, Chinstrap, Gentoo
## $ n       <dbl> 73, 34, 58
## $ percent <dbl> 0.4424242, 0.2060606, 0.3515152
```

```r
class(penguins_f_table)
```

```
## [1] "tabyl"      "data.frame"
```
]

---

# Using summaries later

```r
penguins_f_table %>% arrange(n)
```

```
##    species  n   percent
##  Chinstrap 34 0.2060606
##     Gentoo 58 0.3515152
##     Adelie 73 0.4424242
```

Nice printing:

```r
penguins_f_table %>% gt::gt()
```

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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">species</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">n</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">percent</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_center">Adelie</td>
<td class="gt_row gt_right">73</td>
<td class="gt_row gt_right">0.4424242</td></tr>
    <tr><td class="gt_row gt_center">Chinstrap</td>
<td class="gt_row gt_right">34</td>
<td class="gt_row gt_right">0.2060606</td></tr>
    <tr><td class="gt_row gt_center">Gentoo</td>
<td class="gt_row gt_right">58</td>
<td class="gt_row gt_right">0.3515152</td></tr>
  </tbody>
  
  
</table>
</div>

---

# Using summaries later

You can also save your summaries to excel or csv files:

```r
# one long pipe
penguins_f_table %>% 
  arrange(n) %>%
  write_csv(file = "table_of_species_in_females.csv")

# or save as tibble object and then write tibble to csv file
penguins_f_table <- penguins_f_table %>% arrange(n)

write_csv(penguins_f_table, file = "table_of_species_in_females.csv")
```

---

# "Printing" or "viewing"

Some things you *__don't__* usually want to save

- `glimpse()` prints a snapshot of your data, not useful to save
- `head()` or `tail()`, there's almost no reason to save just the first few lines of your data, except for educational purposes!
- Similarly, you can pipe on `nrow()` or `dim()` or `colnames()` or `class()` and so on, these are pulling out meta-data/information from your data; you can save this as an object but make sure you know what the output is! (probably a vector of information)
- `skim()` prints a visual summary of your data, you can save it if you want to do fancy things with it
- `view()`/`View()` pops up a window that shows your data, don't save this and don't keep this code in your Rmd

---

### glimpse

```r
# when you save this, it prints it, and then you could print it again
glimpse_out <- glimpse(penguins_f)
```

```
## Rows: 165
## Columns: 9
## $ species           <fct> Adelie, Adelie, Adelie, Adelie,…
## $ island            <fct> Torgersen, Biscoe, Torgersen, B…
## $ bill_length_mm    <dbl> 33.5, 35.3, 34.4, 35.9, 36.7, 3…
## $ bill_depth_mm     <dbl> 19.0, 18.9, 18.4, 19.2, 19.3, 1…
## $ flipper_length_mm <int> 190, 187, 184, 189, 193, 187, 1…
## $ body_mass_g       <int> 3600, 3800, 3325, 3800, 3450, 2…
## $ sex               <fct> female, female, female, female,…
## $ year              <int> 2008, 2007, 2007, 2007, 2007, 2…
## $ bill_ratio        <dbl> 1.763158, 1.867725, 1.869565, 1…
```

Print it now (but really, don't do this, there's no point):

```r
glimpse_out
```

```
## # A tibble: 165 × 9
##    species island    bill_length_mm bill_depth_mm
##    <fct>   <fct>              <dbl>         <dbl>
##  1 Adelie  Torgersen           33.5          19  
##  2 Adelie  Biscoe              35.3          18.9
##  3 Adelie  Torgersen           34.4          18.4
##  4 Adelie  Biscoe              35.9          19.2
##  5 Adelie  Torgersen           36.7          19.3
##  6 Adelie  Biscoe              34.5          18.1
##  7 Adelie  Biscoe              39.6          20.7
##  8 Adelie  Dream               36            18.5
##  9 Adelie  Dream               37.6          19.3
## 10 Adelie  Torgersen           36.7          18.8
## # … with 155 more rows, and 5 more variables:
## #   flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## #   year <int>, bill_ratio <dbl>
```

---

### skim

from `?skim`: Value = "A skim_df object, which also inherits the class(es) of the input data. In many ways, the object behaves like a `tibble::tibble()`."

```r
# when you save this, it doesn't print it, but you can print it later
skim_out <- skimr::skim(penguins_f)
```

Now you can print it (this gets cut off on the slide)

```r
skim_out
```

Table: Data summary

|                         |           |
|:------------------------|:----------|
|Name                     |penguins_f |
|Number of rows           |165        |
|Number of columns        |9          |
|_______________________  |           |
|Column type frequency:   |           |
|factor                   |3          |
|numeric                  |6          |
|________________________ |           |
|Group variables          |None       |

**Variable type: factor**

|skim_variable | n_missing| complete_rate|ordered | n_unique|top_counts                |
|:-------------|---------:|-------------:|:-------|--------:|:-------------------------|
|species       |         0|             1|FALSE   |        3|Ade: 73, Gen: 58, Chi: 34 |
|island        |         0|             1|FALSE   |        3|Bis: 80, Dre: 61, Tor: 24 |
|sex           |         0|             1|FALSE   |        1|fem: 165, mal: 0          |

**Variable type: numeric**

|skim_variable     | n_missing| complete_rate|    mean|     sd|      p0|     p25|     p50|     p75|    p100|hist  |
|:-----------------|---------:|-------------:|-------:|------:|-------:|-------:|-------:|-------:|-------:|:-----|
|bill_length_mm    |         0|             1|   42.10|   4.90|   32.10|   37.60|   42.80|   46.20|   58.00|▅▅▇▂▁ |
|bill_depth_mm     |         0|             1|   16.43|   1.80|   13.10|   14.50|   17.00|   17.80|   20.70|▇▃▇▇▁ |
|flipper_length_mm |         0|             1|  197.36|  12.50|  172.00|  187.00|  193.00|  210.00|  222.00|▂▇▃▃▃ |
|body_mass_g       |         0|             1| 3862.27| 666.17| 2700.00| 3350.00| 3650.00| 4550.00| 5200.00|▃▇▂▃▃ |
|year              |         0|             1| 2008.04|   0.81| 2007.00| 2007.00| 2008.00| 2009.00| 2009.00|▇▁▇▁▇ |
|bill_ratio        |         0|             1|    2.61|   0.50|    1.76|    2.16|    2.54|    3.12|    3.49|▇▇▅▆▇ |

---

You can `glimpse()` the skim output because it's deep down a tibble!

```r
class(skim_out)
```

```
## [1] "skim_df"    "tbl_df"     "tbl"        "data.frame"
```

```r
glimpse(skim_out)
```

```
## Rows: 9
## Columns: 15
## $ skim_type         <chr> "factor", "factor", "factor", "…
## $ skim_variable     <chr> "species", "island", "sex", "bi…
## $ n_missing         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0
## $ complete_rate     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1
## $ factor.ordered    <lgl> FALSE, FALSE, FALSE, NA, NA, NA…
## $ factor.n_unique   <int> 3, 3, 1, NA, NA, NA, NA, NA, NA
## $ factor.top_counts <chr> "Ade: 73, Gen: 58, Chi: 34", "B…
## $ numeric.mean      <dbl> NA, NA, NA, 42.096970, 16.42545…
## $ numeric.sd        <dbl> NA, NA, NA, 4.9034759, 1.795680…
## $ numeric.p0        <dbl> NA, NA, NA, 32.100000, 13.10000…
## $ numeric.p25       <dbl> NA, NA, NA, 37.600000, 14.50000…
## $ numeric.p50       <dbl> NA, NA, NA, 42.800000, 17.00000…
## $ numeric.p75       <dbl> NA, NA, NA, 46.200000, 17.80000…
## $ numeric.p100      <dbl> NA, NA, NA, 58.000000, 20.70000…
## $ numeric.hist      <chr> NA, NA, NA, "▅▅▇▂▁", "▇▃▇▇▁", "…
```

---

See `?skim` for examples on what you can do with `skim()` output, as well as the [skimr vignette](https://cran.r-project.org/web/packages/skimr/vignettes/skimr.html)

```r
skim_out %>% filter(n_missing > 0)
```

```
## # A tibble: 0 × 15
## # … with 15 variables: skim_type <chr>,
## #   skim_variable <chr>, n_missing <int>,
## #   complete_rate <dbl>, factor.ordered <lgl>,
## #   factor.n_unique <int>, factor.top_counts <chr>,
## #   numeric.mean <dbl>, numeric.sd <dbl>,
## #   numeric.p0 <dbl>, numeric.p25 <dbl>,
## #   numeric.p50 <dbl>, numeric.p75 <dbl>, …
```

```r
skim_out %>% 
  filter(skim_type == "numeric") %>%
  select(skim_variable, numeric.mean)
```

```
## # A tibble: 6 × 2
##   skim_variable     numeric.mean
##   <chr>                    <dbl>
## 1 bill_length_mm           42.1 
## 2 bill_depth_mm            16.4 
## 3 flipper_length_mm       197.  
## 4 body_mass_g            3862.  
## 5 year                   2008.  
## 6 bill_ratio                2.61
```

---

### view

- Use `View` or `view()` interactively, don't include this code in your Rmd, and don't save it as an object!

```r
View(penguins_f) # this will pop up every time you knit, not necessary
pview <- penguins_f %>% View() # this is not helpful
```

pview is `NULL` because `View()` produces NO output (no value)

```r
pview 
```

```
## NULL
```

---

# Saving ggplot

- Save a ggplot *as an object* if you want to "call it" later to add more layers, or if you want to save it *as an image file* (i.e .png, .pdf, .tif, etc) on your computer
- `ggsave()` by default saves the last plot printed to a file, but to be extra careful you can use the object name

```r
# save as object p_boxplot; will not print out plot
p_boxplot <- ggplot(penguins, 
                    aes(x = species,
                        y = bill_length_mm,
                        fill = sex)) +
  geom_boxplot()

# it's a ggplot object
class(p_boxplot)
```

```
## [1] "gg"     "ggplot"
```

```r
# print the plot in output
p_boxplot
```