data.table

class: left, top, inverse
background-image: url(https://images.unsplash.com/photo-1530018607912-eff2daa1bac4?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1502&q=80)
background-size: cover

# .large[data.table]
### Walk-throughs with {flipbookr} and {xaringan}

#### .right[Gina Reynolds Photo Credit: Abel Y Costa]

---

# Set up

Okay. Let's load the the `reveal for xaringan` functions for "flipbooking" and the `data.table`.

```r
source(file = "https://raw.githubusercontent.com/EvaMaeRey/little_flipbooks_library/master/xaringan_reveal_parentheses_balanced.R")
```

# Where we are headed

![](data.table_files/figure-html/pipe_data.table-1.png)

---

# The pipeline

---

class: split-40
count: false
.column[.content[

```r
*library(data.table)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
*library(gapminder)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
library(gapminder)  
*library(magrittr)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
library(gapminder)  
library(magrittr)  
*library(ggplot2)
```
]]
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
library(gapminder)  
library(magrittr)  
library(ggplot2)  
*gapminder
```
]]
.column[.content[

```
# A tibble: 1,704 x 6
 country continent year lifeExp pop gdpPercap
 <fct> <fct> <int> <dbl> <int> <dbl>
 1 Afghanistan Asia 1952 28.8 8425333 779.
 2 Afghanistan Asia 1957 30.3 9240934 821.
 3 Afghanistan Asia 1962 32.0 10267083 853.
 4 Afghanistan Asia 1967 34.0 11537966 836.
 5 Afghanistan Asia 1972 36.1 13079460 740.
 6 Afghanistan Asia 1977 38.4 14880372 786.
 7 Afghanistan Asia 1982 39.9 12881816 978.
 8 Afghanistan Asia 1987 40.8 13867957 852.
 9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
library(gapminder)  
library(magrittr)  
library(ggplot2)  
gapminder %>%  
* data.table()
```
]]
.column[.content[

```r
library(data.table)  
library(gapminder)  
library(magrittr)  
library(ggplot2)  
gapminder %>%  
  data.table() %>%  
*  .[year > 1980]
```
]]
.column[.content[

```
         country continent year lifeExp      pop gdpPercap
  1: Afghanistan      Asia 1982  39.854 12881816  978.0114
  2: Afghanistan      Asia 1987  40.822 13867957  852.3959
  3: Afghanistan      Asia 1992  41.674 16317921  649.3414
  4: Afghanistan      Asia 1997  41.763 22227415  635.3414
  5: Afghanistan      Asia 2002  42.129 25268405  726.7341
 ---                                                      
848:    Zimbabwe    Africa 1987  62.351  9216418  706.1573
849:    Zimbabwe    Africa 1992  60.377 10704340  693.4208
850:    Zimbabwe    Africa 1997  46.809 11404948  792.4500
851:    Zimbabwe    Africa 2002  39.989 11926563  672.0386
852:    Zimbabwe    Africa 2007  43.487 12311143  469.7093
```
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table)  
library(gapminder)  
library(magrittr)  
library(ggplot2)  
gapminder %>%  
  data.table() %>%  
   .[year > 1980] %>%  
*  .[                       ,
*    mean(gdpPercap)        ,
*    by = .(continent, year) ]
```
]]
.column[.content[

```
    continent year        V1
 1:      Asia 1982  7434.135
 2:      Asia 1987  7608.227
 3:      Asia 1992  8639.690
 4:      Asia 1997  9834.093
 5:      Asia 2002 10174.090
 6:      Asia 2007 12473.027
 7:    Europe 1982 15617.897
 8:    Europe 1987 17214.311
 9:    Europe 1992 17061.568
10:    Europe 1997 19076.782
11:    Europe 2002 21711.732
12:    Europe 2007 25054.482
13:    Africa 1982  2481.593
14:    Africa 1987  2282.669
15:    Africa 1992  2281.810
16:    Africa 1997  2378.760
17:    Africa 2002  2599.385
18:    Africa 2007  3089.033
19:  Americas 1982  7506.737
20:  Americas 1987  7793.400
21:  Americas 1992  8044.934
22:  Americas 1997  8889.301
23:  Americas 2002  9287.677
24:  Americas 2007 11003.032
25:   Oceania 1982 18554.710
26:   Oceania 1987 20448.040
27:   Oceania 1992 20894.046
28:   Oceania 1997 24024.175
29:   Oceania 2002 26938.778
30:   Oceania 2007 29810.188
    continent year        V1
```
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
* ggplot()
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_11-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
* aes(x = year)
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_12-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
 aes(x = year) + 
* aes(y = V1)
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_13-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
 aes(x = year) + 
 aes(y = V1) + 
* labs(y = "Average GDP per cap")
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_14-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
 aes(x = year) + 
 aes(y = V1) + 
 labs(y = "Average GDP per cap") + 
* geom_point()
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_15-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
 aes(x = year) + 
 aes(y = V1) + 
 labs(y = "Average GDP per cap") + 
 geom_point() + 
* geom_line()
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_16-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
library(data.table) 
library(gapminder) 
library(magrittr) 
library(ggplot2) 
gapminder %>% 
 data.table() %>% 
 .[year > 1980] %>% 
 .[ , 
 mean(gdpPercap) , 
 by = .(continent, year) ] %>% 
 ggplot() + 
 aes(x = year) + 
 aes(y = V1) + 
 labs(y = "Average GDP per cap") + 
 geom_point() + 
 geom_line() + 
* aes(color = continent)
```
]]
.column[.content[
![](data.table_files/figure-html/output_pipe_data.table_17-1.png)
]]

---

class: split-40
count: false
.column[.content[

```r
*gapminder
```
]]
.column[.content[

```r
gapminder %>%  
* data.table()
```
]]
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
* .[continent == "Asia", ]
```
]]
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
  .[continent == "Asia", ] %>%  
* .[1:30,]
```
]]
.column[.content[

```
        country continent year lifeExp      pop  gdpPercap
 1: Afghanistan      Asia 1952  28.801  8425333   779.4453
 2: Afghanistan      Asia 1957  30.332  9240934   820.8530
 3: Afghanistan      Asia 1962  31.997 10267083   853.1007
 4: Afghanistan      Asia 1967  34.020 11537966   836.1971
 5: Afghanistan      Asia 1972  36.088 13079460   739.9811
 6: Afghanistan      Asia 1977  38.438 14880372   786.1134
 7: Afghanistan      Asia 1982  39.854 12881816   978.0114
 8: Afghanistan      Asia 1987  40.822 13867957   852.3959
 9: Afghanistan      Asia 1992  41.674 16317921   649.3414
10: Afghanistan      Asia 1997  41.763 22227415   635.3414
11: Afghanistan      Asia 2002  42.129 25268405   726.7341
12: Afghanistan      Asia 2007  43.828 31889923   974.5803
13:     Bahrain      Asia 1952  50.939   120447  9867.0848
14:     Bahrain      Asia 1957  53.832   138655 11635.7995
15:     Bahrain      Asia 1962  56.923   171863 12753.2751
16:     Bahrain      Asia 1967  59.923   202182 14804.6727
17:     Bahrain      Asia 1972  63.300   230800 18268.6584
18:     Bahrain      Asia 1977  65.593   297410 19340.1020
19:     Bahrain      Asia 1982  69.052   377967 19211.1473
20:     Bahrain      Asia 1987  70.750   454612 18524.0241
21:     Bahrain      Asia 1992  72.601   529491 19035.5792
22:     Bahrain      Asia 1997  73.925   598561 20292.0168
23:     Bahrain      Asia 2002  74.795   656397 23403.5593
24:     Bahrain      Asia 2007  75.635   708573 29796.0483
25:  Bangladesh      Asia 1952  37.484 46886859   684.2442
26:  Bangladesh      Asia 1957  39.348 51365468   661.6375
27:  Bangladesh      Asia 1962  41.216 56839289   686.3416
28:  Bangladesh      Asia 1967  43.453 62821884   721.1861
29:  Bangladesh      Asia 1972  45.252 70759295   630.2336
30:  Bangladesh      Asia 1977  46.923 80428306   659.8772
        country continent year lifeExp      pop  gdpPercap
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
  .[continent == "Asia", ] %>%  
  .[1:30,] %>%  
* .[, c(1,3:6)]
```
]]
.column[.content[

```
        country year lifeExp      pop  gdpPercap
 1: Afghanistan 1952  28.801  8425333   779.4453
 2: Afghanistan 1957  30.332  9240934   820.8530
 3: Afghanistan 1962  31.997 10267083   853.1007
 4: Afghanistan 1967  34.020 11537966   836.1971
 5: Afghanistan 1972  36.088 13079460   739.9811
 6: Afghanistan 1977  38.438 14880372   786.1134
 7: Afghanistan 1982  39.854 12881816   978.0114
 8: Afghanistan 1987  40.822 13867957   852.3959
 9: Afghanistan 1992  41.674 16317921   649.3414
10: Afghanistan 1997  41.763 22227415   635.3414
11: Afghanistan 2002  42.129 25268405   726.7341
12: Afghanistan 2007  43.828 31889923   974.5803
13:     Bahrain 1952  50.939   120447  9867.0848
14:     Bahrain 1957  53.832   138655 11635.7995
15:     Bahrain 1962  56.923   171863 12753.2751
16:     Bahrain 1967  59.923   202182 14804.6727
17:     Bahrain 1972  63.300   230800 18268.6584
18:     Bahrain 1977  65.593   297410 19340.1020
19:     Bahrain 1982  69.052   377967 19211.1473
20:     Bahrain 1987  70.750   454612 18524.0241
21:     Bahrain 1992  72.601   529491 19035.5792
22:     Bahrain 1997  73.925   598561 20292.0168
23:     Bahrain 2002  74.795   656397 23403.5593
24:     Bahrain 2007  75.635   708573 29796.0483
25:  Bangladesh 1952  37.484 46886859   684.2442
26:  Bangladesh 1957  39.348 51365468   661.6375
27:  Bangladesh 1962  41.216 56839289   686.3416
28:  Bangladesh 1967  43.453 62821884   721.1861
29:  Bangladesh 1972  45.252 70759295   630.2336
30:  Bangladesh 1977  46.923 80428306   659.8772
        country year lifeExp      pop  gdpPercap
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
  .[continent == "Asia", ] %>%  
  .[1:30,] %>%  
  .[, c(1,3:6)] %>%  
* .[, .(pop, year, gdpPercap)]
```
]]
.column[.content[

```
         pop year  gdpPercap
 1:  8425333 1952   779.4453
 2:  9240934 1957   820.8530
 3: 10267083 1962   853.1007
 4: 11537966 1967   836.1971
 5: 13079460 1972   739.9811
 6: 14880372 1977   786.1134
 7: 12881816 1982   978.0114
 8: 13867957 1987   852.3959
 9: 16317921 1992   649.3414
10: 22227415 1997   635.3414
11: 25268405 2002   726.7341
12: 31889923 2007   974.5803
13:   120447 1952  9867.0848
14:   138655 1957 11635.7995
15:   171863 1962 12753.2751
16:   202182 1967 14804.6727
17:   230800 1972 18268.6584
18:   297410 1977 19340.1020
19:   377967 1982 19211.1473
20:   454612 1987 18524.0241
21:   529491 1992 19035.5792
22:   598561 1997 20292.0168
23:   656397 2002 23403.5593
24:   708573 2007 29796.0483
25: 46886859 1952   684.2442
26: 51365468 1957   661.6375
27: 56839289 1962   686.3416
28: 62821884 1967   721.1861
29: 70759295 1972   630.2336
30: 80428306 1977   659.8772
         pop year  gdpPercap
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
  .[continent == "Asia", ] %>%  
  .[1:30,] %>%  
  .[, c(1,3:6)] %>%  
  .[, .(pop, year, gdpPercap)] %>%  
* # .[, .(world_pop = sum(pop)), by = year] %>%  # .[, .(world_pop = sum(pop)), by = year] %>%
* .[, gdp := gdpPercap * pop] %>%
* .[year %in% 1952:1967, my_var := 2] %>%
* .[]
```
]]
.column[.content[

```
         pop year  gdpPercap         gdp my_var
 1:  8425333 1952   779.4453  6567086330      2
 2:  9240934 1957   820.8530  7585448670      2
 3: 10267083 1962   853.1007  8758855797      2
 4: 11537966 1967   836.1971  9648014150      2
 5: 13079460 1972   739.9811  9678553274     NA
 6: 14880372 1977   786.1134 11697659231     NA
 7: 12881816 1982   978.0114 12598563401     NA
 8: 13867957 1987   852.3959 11820990309     NA
 9: 16317921 1992   649.3414 10595901589     NA
10: 22227415 1997   635.3414 14121995875     NA
11: 25268405 2002   726.7341 18363410424     NA
12: 31889923 2007   974.5803 31079291949     NA
13:   120447 1952  9867.0848  1188460759      2
14:   138655 1957 11635.7995  1613361773      2
15:   171863 1962 12753.2751  2191816125      2
16:   202182 1967 14804.6727  2993238336      2
17:   230800 1972 18268.6584  4216406356     NA
18:   297410 1977 19340.1020  5751939724     NA
19:   377967 1982 19211.1473  7261179715     NA
20:   454612 1987 18524.0241  8421243626     NA
21:   529491 1992 19035.5792 10079167850     NA
22:   598561 1997 20292.0168 12146009862     NA
23:   656397 2002 23403.5593 15362026094     NA
24:   708573 2007 29796.0483 21112675360     NA
25: 46886859 1952   684.2442 32082059995      2
26: 51365468 1957   661.6375 33985317661      2
27: 56839289 1962   686.3416 39011165929      2
28: 62821884 1967   721.1861 45306268650      2
29: 70759295 1972   630.2336 44594887096     NA
30: 80428306 1977   659.8772 53072807954     NA
         pop year  gdpPercap         gdp my_var
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  data.table() %>%  
  .[continent == "Asia", ] %>%  
  .[1:30,] %>%  
  .[, c(1,3:6)] %>%  
  .[, .(pop, year, gdpPercap)] %>%  
  # .[, .(world_pop = sum(pop)), by = year] %>%  # .[, .(world_pop = sum(pop)), by = year] %>%
  .[, gdp := gdpPercap * pop] %>%  
  .[year %in% 1952:1967, my_var := 2] %>%  
  .[] %>%  
* .[, year := NULL] %>%
* .[]
```
]]
.column[.content[

```
         pop  gdpPercap         gdp my_var
 1:  8425333   779.4453  6567086330      2
 2:  9240934   820.8530  7585448670      2
 3: 10267083   853.1007  8758855797      2
 4: 11537966   836.1971  9648014150      2
 5: 13079460   739.9811  9678553274     NA
 6: 14880372   786.1134 11697659231     NA
 7: 12881816   978.0114 12598563401     NA
 8: 13867957   852.3959 11820990309     NA
 9: 16317921   649.3414 10595901589     NA
10: 22227415   635.3414 14121995875     NA
11: 25268405   726.7341 18363410424     NA
12: 31889923   974.5803 31079291949     NA
13:   120447  9867.0848  1188460759      2
14:   138655 11635.7995  1613361773      2
15:   171863 12753.2751  2191816125      2
16:   202182 14804.6727  2993238336      2
17:   230800 18268.6584  4216406356     NA
18:   297410 19340.1020  5751939724     NA
19:   377967 19211.1473  7261179715     NA
20:   454612 18524.0241  8421243626     NA
21:   529491 19035.5792 10079167850     NA
22:   598561 20292.0168 12146009862     NA
23:   656397 23403.5593 15362026094     NA
24:   708573 29796.0483 21112675360     NA
25: 46886859   684.2442 32082059995      2
26: 51365468   661.6375 33985317661      2
27: 56839289   686.3416 39011165929      2
28: 62821884   721.1861 45306268650      2
29: 70759295   630.2336 44594887096     NA
30: 80428306   659.8772 53072807954     NA
         pop  gdpPercap         gdp my_var
```
]]

---

# Create a data.table

```r
data.table(a = c(1, 2), 
           b = c("a", "b"))
```

```
   a b
1: 1 a
2: 2 b
```

---

```r
gapminder %>% #REVEAL
  data.table() ->
gapminder_dt
```

---

# Subset rows using i

```r
gapminder_dt %>% 
  .[1:25, ] %>% 
  .[year > 1980, ]
```

```
        country continent year lifeExp      pop gdpPercap
 1: Afghanistan      Asia 1982  39.854 12881816  978.0114
 2: Afghanistan      Asia 1987  40.822 13867957  852.3959
 3: Afghanistan      Asia 1992  41.674 16317921  649.3414
 4: Afghanistan      Asia 1997  41.763 22227415  635.3414
 5: Afghanistan      Asia 2002  42.129 25268405  726.7341
 6: Afghanistan      Asia 2007  43.828 31889923  974.5803
 7:     Albania    Europe 1982  70.420  2780097 3630.8807
 8:     Albania    Europe 1987  72.000  3075321 3738.9327
 9:     Albania    Europe 1992  71.581  3326498 2497.4379
10:     Albania    Europe 1997  72.950  3428038 3193.0546
11:     Albania    Europe 2002  75.651  3508512 4604.2117
12:     Albania    Europe 2007  76.423  3600523 5937.0295
```

---

# Manipulate columns with j

```r
gapminder_dt %>% 
  .[, c(1,3:6)] %>% #REVEAL
  .[, .(pop, year, gdpPercap)] 
```

```
           pop year gdpPercap
   1:  8425333 1952  779.4453
   2:  9240934 1957  820.8530
   3: 10267083 1962  853.1007
   4: 11537966 1967  836.1971
   5: 13079460 1972  739.9811
  ---                        
1700:  9216418 1987  706.1573
1701: 10704340 1992  693.4208
1702: 11404948 1997  792.4500
1703: 11926563 2002  672.0386
1704: 12311143 2007  469.7093
```

---

# Summarize

```r
gapminder_dt %>% 
  .[, .(x = sum(pop))] 
```

```
             x
1: 50440465801
```

---

# COMPUTE COLUMNS

```r
knitr::opts_chunk$set(eval = F)
```

```r
gapminder_dt[, c := 1 + 2] 
gapminder_dt[a == 1, c := 1 + 2] 
gapminder_dt[, `:=`(c = 1 , d = 2)]
```

```r
data.table(a = c(1, 2, 5, 11), 
           b = c("a", "b", "d", "x"),
           c = 1:4) ->
  dt
```

```r
dt %>% 
  .[1:2, ] %>% 
  .[a > 1, ]
```

```r
dt %>% 
  .[, c(2)]  %>% 
  .[, .(b, c)] 
```