El uso de múltiples lenguajes en Rmarkdown

# El uso de múltiples lenguajes en Rmarkdown
## ︿<br/>R-Ladies Puebla
### Karina Bartolomé

---

# ¿Quién soy?

<br>

<img src="images/imagen.jpg" width="50%" style="display: block; margin: auto;" />
]

.pull-right[
### Karina Bartolomé
- Economista
- Especialista en métodos cuantitativos
- Data Scientist en Ualá (Fintech)
]

---

background-position: 50% 50%
class: center, inverse

<img src="images/multilingual.png" width="500px" height="500px" style="position:center;">
 
# Múltiples idiomas

---

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class: center, inverse

<img src="images/multilingual_code.png" width="500px" height="500px" style="position:center;">
 
#  Múltiples lenguajes

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---

# ¿Qué vamos a ver hoy?

- El uso de **`python en Rmarkdown`**

- Similitudes / diferencias entre R y python, haciendo énfasis en `{tidyverse}`📦 y `{pandas}`📦

- `{reticulate}` 📦 para utilizar objetos de python en R y de R en python

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# { espacio de memes }

.pull-left[
<img src="memes/r_python.jpg" width="100%" height="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="memes/byelingual.jpg" width="100%" height="100%" style="display: block; margin: auto;" />
]

---

# Antes de empezar

¿se puede ejecutar código R o python sin tener que instalar nada en nuestra computadora?

---

# ☁️ Trabajar en la nube ☁️

Existen **recursos gratuitos**:

🔹 `RStudio Cloud`: https://rstudio.cloud/

🔹 `Google Colab`: https://colab.research.google.com/

🔹 `AWS Sagemaker Studio Lab`: https://studiolab.sagemaker.aws/

También hay **recursos pagos** si se busca mayor capacidad de cómputo o almacenamiento

---

# El uso de diversos lenguajes en Rmarkdown

📝 Para trabajar con distintos lenguajes en Rmarkdown, se utilizan **`chunks`** que indiquen el lenguaje que se quiere utilizar:

````
```{r}
# Acá se escribe código R 
```
````
````
```{python}
# Acá se escribe código Python
```
````
````
```{julia}
# Acá se escribe código Julia
```
````
````
```{sql}
# Acá se escribe sql
```
````

Etc.

---

# R en Rmarkdown

🔹 En **Rmarkdown**, se utiliza un `chunk R` para cargar los paquetes R 📦

```r
library(reticulate) # python
library(tidyverse)  # manipulación de datos
library(gt)         # tablas
```

---

# Python en Rmarkdown

🔹 Definir un `conda environment` a utilizar:

```r
reticulate::conda_create(envname='rladies', 
                         python_version="3.8.8")
```

-------------------------------------

🔹 Desde la terminal, ejecutar los siguientes comandos:

Se visualizan los environments con `conda env list`

]

Se activa con: `conda activate rladies`

]

Si se quiere remover el environment, primero es necesario activar el environment base, y luego se remueve el environment creado:

- Se activa el environment base: `conda activate`

- Se remueve el environment: `conda remove --name rladies --all`

]

---

# {cómo se ve lo anterior desde RStudio}

👉 [🔗 Archivo .Rmd para ejecutar python en rmarkdown desde un conda environment](https://github.com/karbartolome/rladies_charlas/blob/main/01_r_python/03_condaenvs/03_conda_envs.md)

---

# Python en Rmarkdown

🔹 Se instalan los paquetes 📦 a utilizar. En este caso, lo hice desde la terminal, teniendo activado el environment:

-   [x] ➕ numpy:       conda install -c conda-forge numpy

-   [x] 🐼 pandas:      conda install -c conda-forge pandas 
    
    -   [x] 📊 seaborn:     conda install -c conda-forge seaborn
    
    -   [x] 🔬 statsmodels: conda install -c conda-forge statsmodels
    
También es posible realizar la instalación de paquetes con la función `conda_install` de {reticulate} 📦 , aunque yo siempre lo hice desde la terminal.

---

🔹Se define que el environment a utilizar es el que ha sido creado:

```r
reticulate::use_condaenv(condaenv = 'rladies', 
                         required = TRUE)
```

🔹Se utiliza un `chunk python` para cargar los paquetes `python` 📦:

```python
import pandas as pd             # manipulación de datos
import numpy as np              # manipulación de datos
import matplotlib.pyplot as plt # visualización
import seaborn as sns           # visualización
```

---

# 📝 Diferentes objetos:

| R                      | Python            | Examples                                         |
|:-----------------------|:------------------|:-------------------------------------------------|
| Single-element vector  | Scalar            | `1`, `1L`, `TRUE`, `"foo"`                       |
| Multi-element vector   | List              | `c(1.0, 2.0, 3.0)`, `c(1L, 2L, 3L)`              |
| List of multiple types | Tuple             | `list(1L, TRUE, "foo")`                          |
| Named list             | Dict              | `list(a = 1L, b = 2.0)`, `dict(x = x_data)`      |
| Matrix/Array           | NumPy ndarray     | `matrix(c(1,2,3,4), nrow = 2, ncol = 2)`         |
| Data Frame             | Pandas DataFrame  | `data.frame(x = c(1,2), y = c("a", "b"))`        |
| Function               | Python function   | `function(x) x + 1`                              |
| NULL, TRUE, FALSE      | None, True, False | `NULL`, `TRUE`, `FALSE`                          |

---

# 0. Datos

Se utilizarán datos del comercio bilateral entre Argentina y México

---

# Aclaraciones

🔹 Los datos provienen del paquete 🔗 [{opentradestatistics}](https://docs.ropensci.org/tradestatistics/index.html).

🔹 Los ejemplos pueden no tener sentido real (agrupaciones de ramas de actividades que podrían no tener relación, etc.).

🔹 El objetivo es utilizar datos reales para mostrar diferentes funcionalidades, **no el análisis de los datos en sí**.

---

# Lectura de datos con R

Se realiza la **lectura de 3 dataframes** 📂. Los dataframes de secciones y commodities contienen las descripciones de los ids que aparecen en el dataframe de expo / impo.

```r
df_comercio  <- readr::read_csv('data/df_arg_mx.csv')
 
df_secciones <- readr::read_csv('data/df_secciones.csv')

df_commodities <- readr::read_csv('data/df_commodities.csv')
```

# Lectura de datos con python

```python
df_comercio_pd = pd.read_csv('data/df_arg_mx.csv')
```

---

Se observan las primeras 2 observaciones:

```r
df_comercio %>% head(2)
```

```
# A tibble: 2 x 7
   year reporter  partner commodity_code section_code trade_value_usd_exp
  <dbl> <chr>     <chr>   <chr>          <chr>                      <dbl>
1  2017 Argentina Mexico  010121         01                         15000
2  2019 Argentina Mexico  010121         01                             0
# ... with 1 more variable: trade_value_usd_imp <dbl>
```

⚡ Objetos cross lenguaje  **R -> Python** ⚡

```python
r.df_comercio.head(2)
```

```
     year   reporter partner commodity_code section_code  trade_value_usd_exp  \
0 2017.00  Argentina  Mexico         010121           01             15000.00   
1 2019.00  Argentina  Mexico         010121           01                 0.00

trade_value_usd_imp  
0                 0.00  
1             37091.00  
```

---

Se crea una lista de variables relevantes en un chunk python:

```python
variables = ['year','commodity_code','trade_value_usd_exp']
```

-------------------------------------------

🔹 Dos chunks que generan el mismo output:

````
```{r}
py$df_comercio_pd %>% 
  select(all_of(py$variables)) %>% 
  head(2)
```
````

```
  year commodity_code trade_value_usd_exp
1 2017          10121               15000
2 2019          10121                   0
```

]

````
```{python}
(r.df_comercio
  .filter(variables)
  .head(2))
```
````

```
     year commodity_code  trade_value_usd_exp
0 2017.00         010121             15000.00
1 2019.00         010121                 0.00
```

]

---

# 1. Estadística descriptiva

---
.panelset[
.panel[.panel-name[R summary]

```r
df_comercio %>% summary()
```

```
      year        reporter           partner          commodity_code    
 Min.   :2017   Length:5687        Length:5687        Length:5687       
 1st Qu.:2017   Class :character   Class :character   Class :character  
 Median :2018   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2018                                                           
 3rd Qu.:2019                                                           
 Max.   :2020                                                           
 section_code       trade_value_usd_exp trade_value_usd_imp
 Length:5687        Min.   :        0   Min.   :        0  
 Class :character   1st Qu.:        0   1st Qu.:     1892  
 Mode  :character   Median :        0   Median :    25909  
                    Mean   :   437500   Mean   :  1060187  
                    3rd Qu.:     5392   3rd Qu.:   239566  
                    Max.   :342756705   Max.   :450264324  
```

]

Variables que no son object:

```python
r.df_comercio.describe(exclude='object')
```

```
         year  trade_value_usd_exp  trade_value_usd_imp
count 5687.00              5687.00              5687.00
mean  2018.38            437500.39           1060187.19
std      1.13           6709261.81           9271817.03
min   2017.00                 0.00                 0.00
25%   2017.00                 0.00              1892.00
50%   2018.00                 0.00             25909.00
75%   2019.00              5392.00            239566.00
max   2020.00         342756705.00         450264324.00
```

Variables tipo object:

```python
r.df_comercio.describe(include='object')
```

```
         reporter partner commodity_code section_code
count        5687    5687           5687         5687
unique          1       1           2032           22
top     Argentina  Mexico         999999           16
freq         5687    5687              4         1401
```

]

```r
py$df_comercio_pd %>% 
  select(where(is.numeric)) %>% 
* skimr::skim() %>%
  select(-skim_type, -complete_rate) %>% 
  gt() %>% 
  tab_header(title=md('**Comercio exterior**: Argentina y México'), 
             subtitle='Estadística descriptiva') %>% 
  opt_align_table_header('left') %>% 
  fmt_number(columns=3:4) 
```

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</style>
<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="10" class="gt_heading gt_title gt_font_normal" style><strong>Comercio exterior</strong>: Argentina y México</th>
    </tr>
    <tr>
      <th colspan="10" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Estadística descriptiva</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">skim_variable</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">n_missing</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.mean</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.sd</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.p0</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.p25</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.p50</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.p75</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">numeric.p100</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">numeric.hist</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">year</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">2,018.38</td>
<td class="gt_row gt_right">1.13</td>
<td class="gt_row gt_right">2017</td>
<td class="gt_row gt_right">2017.0</td>
<td class="gt_row gt_right">2018</td>
<td class="gt_row gt_right">2019</td>
<td class="gt_row gt_right">2020</td>
<td class="gt_row gt_left">▇▆▁▆▆</td></tr>
    <tr><td class="gt_row gt_left">commodity_code</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">617,540.05</td>
<td class="gt_row gt_right">256,685.16</td>
<td class="gt_row gt_right">10121</td>
<td class="gt_row gt_right">381105.5</td>
<td class="gt_row gt_right">720230</td>
<td class="gt_row gt_right">848280</td>
<td class="gt_row gt_right">999999</td>
<td class="gt_row gt_left">▁▆▁▃▇</td></tr>
    <tr><td class="gt_row gt_left">section_code</td>
<td class="gt_row gt_right">4</td>
<td class="gt_row gt_right">11.86</td>
<td class="gt_row gt_right">5.04</td>
<td class="gt_row gt_right">1</td>
<td class="gt_row gt_right">6.0</td>
<td class="gt_row gt_right">15</td>
<td class="gt_row gt_right">16</td>
<td class="gt_row gt_right">21</td>
<td class="gt_row gt_left">▂▆▃▇▂</td></tr>
    <tr><td class="gt_row gt_left">trade_value_usd_exp</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">437,500.39</td>
<td class="gt_row gt_right">6,709,261.81</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">0.0</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">5392</td>
<td class="gt_row gt_right">342756705</td>
<td class="gt_row gt_left">▇▁▁▁▁</td></tr>
    <tr><td class="gt_row gt_left">trade_value_usd_imp</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">1,060,187.19</td>
<td class="gt_row gt_right">9,271,817.03</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">1892.0</td>
<td class="gt_row gt_right">25909</td>
<td class="gt_row gt_right">239566</td>
<td class="gt_row gt_right">450264324</td>
<td class="gt_row gt_left">▇▁▁▁▁</td></tr>
  </tbody>
  
  
</table>
</div>

]

---

# 2. Unión de dataframes

---
.panelset[
.panel[.panel-name[R]

```r
df <- df_comercio %>% 
  
* # Left join con datos de commodities
* left_join(df_commodities %>%
*             select(commodity_code,
*                    commodity_fullname_english,
*                    group_fullname_english),
*           by='commodity_code') %>%
*  
* # Left join con secciones
* left_join(df_secciones) %>%
  
  # Selección de variables y renombrarlas
  select(
    año             = year,
    pais            = reporter,
    socio           = partner, 
    commodity       = commodity_fullname_english,
    grupo           = group_fullname_english,
    seccion         = section_shortname_english, 
    expo            = trade_value_usd_exp,
    impo            = trade_value_usd_imp
  )
```

```
Joining, by = "section_code"
```

]

```python
df_py = (r.df_comercio

* # Left join con datos de commodities
* .merge(r.df_commodities[['commodity_code',
*                          'commodity_fullname_english',
*                          'group_fullname_english']],
*        on='commodity_code', how='left')
*  
* # Left join con secciones
* .merge(r.df_secciones, on='section_code', how='left')
  
  # Renombrar variables
  .rename({
    'year'                       : 'año',
    'reporter'                   : 'pais',
    'partner'                    : 'socio',
    'commodity_fullname_english' : 'commodity',
    'group_fullname_english'     : 'grupo',
    'section_shortname_english'  : 'seccion',
    'trade_value_usd_exp'        : 'expo',
    'trade_value_usd_imp'        : 'impo'
  }, axis=1)
  
  # Selección de las columnas relevantes (mismas que df anterior en r)
  [r.df.columns]
)
```

]

🔹 R dataframe:

```r
df %>% head(1) %>% gt::gt()
```

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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2017</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Horses; live, pure-bred breeding animals</td>
<td class="gt_row gt_left">Animals; live</td>
<td class="gt_row gt_left">Animal Products</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$df_py %>% head(1) %>% gt::gt()
```

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}

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}

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}

#keowjeiutv .gt_first_summary_row {
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}

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  background-color: #FFFFFF;
  text-transform: inherit;
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  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

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  padding-bottom: 8px;
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#keowjeiutv .gt_striped {
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#keowjeiutv .gt_table_body {
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  border-top-color: #D3D3D3;
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  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#keowjeiutv .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#keowjeiutv .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#keowjeiutv .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#keowjeiutv .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#keowjeiutv .gt_left {
  text-align: left;
}

#keowjeiutv .gt_center {
  text-align: center;
}

#keowjeiutv .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#keowjeiutv .gt_font_normal {
  font-weight: normal;
}

#keowjeiutv .gt_font_bold {
  font-weight: bold;
}

#keowjeiutv .gt_font_italic {
  font-style: italic;
}

#keowjeiutv .gt_super {
  font-size: 65%;
}

#keowjeiutv .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2017</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Horses; live, pure-bred breeding animals</td>
<td class="gt_row gt_left">Animals; live</td>
<td class="gt_row gt_left">Animal Products</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td></tr>
  </tbody>
  
  
</table>
</div>

]

Se utiliza el paquete waldo para comparar los filtros generados:

```r
waldo::compare(data.frame(df),
               data.frame(py$df_py))
```

```
v No differences
```

]

Cómo funciona el paquete {waldo}? (lo vamos a usar mucho). Se cuenta con un df_a y un df_b y se quiere verificar si son iguales:

```r
df_a <- data.frame(var1=c(1,2), var2=c(2,3))
df_a
```

```
  var1 var2
1    1    2
2    2    3
```

```r
df_b <- data.frame(var1=c('1','2'), var2=c(3,4))
df_b
```

```
  var1 var2
1    1    3
2    2    4
```

Waldo muestra las diferencias:

```r
waldo::compare(df_a, df_b)
```

```
`old$var1` is a double vector (1, 2)
`new$var1` is a character vector ('1', '2')

`old$var2`: 2 3  
`new$var2`:   3 4
```

]
]

---

# 3. Filtros

![](images/filter.gif)

---
.panelset[
.panel[.panel-name[R]

```r
filtro_r <- df %>% 
  
* filter(
*  
*   # Condición simple (equivalencia)
*   año == 2019 &
*  
*   # Condición OR con función quantile()
*   (expo   > quantile(df$expo,0.6) |
*    impo   > quantile(df$impo,0.6) ) &
*  
*   # Filtro por expresión regular
*   str_detect(tolower(commodity), 'metal')
  )
```

]

```python
filtro_py = (r.df

.query("""
         año == 2019 and                              \
                                                      \
         (expo   > expo.quantile(0.6) or              \
          impo   > impo.quantile(0.6)) and            \
                                                      \
         commodity.str.lower().str.contains('metal')
        """,  
        engine='python')                              
        
  .reset_index(drop=True)
)
```
]

🔹 R dataframe:

```r
filtro_r %>% head(1) %>% gt::gt()
```

<div id="ulmylafsno" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#ulmylafsno .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#ulmylafsno .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#ulmylafsno .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#ulmylafsno .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#ulmylafsno .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#ulmylafsno .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#ulmylafsno .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#ulmylafsno .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#ulmylafsno .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#ulmylafsno .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#ulmylafsno .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#ulmylafsno .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#ulmylafsno .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#ulmylafsno .gt_from_md > :first-child {
  margin-top: 0;
}

#ulmylafsno .gt_from_md > :last-child {
  margin-bottom: 0;
}

#ulmylafsno .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#ulmylafsno .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#ulmylafsno .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#ulmylafsno .gt_first_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
}

#ulmylafsno .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#ulmylafsno .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#ulmylafsno .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#ulmylafsno .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#ulmylafsno .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#ulmylafsno .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#ulmylafsno .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#ulmylafsno .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#ulmylafsno .gt_left {
  text-align: left;
}

#ulmylafsno .gt_center {
  text-align: center;
}

#ulmylafsno .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#ulmylafsno .gt_font_normal {
  font-weight: normal;
}

#ulmylafsno .gt_font_bold {
  font-weight: bold;
}

#ulmylafsno .gt_font_italic {
  font-style: italic;
}

#ulmylafsno .gt_super {
  font-size: 65%;
}

#ulmylafsno .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2019</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Alkali or alkali-earth metals; calcium</td>
<td class="gt_row gt_left">Inorganic chemicals; organic and inorganic compounds of precious metals; of rare earth metals, of radio-active elements and of isotopes</td>
<td class="gt_row gt_left">Chemical Products</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">105154</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$filtro_py %>% head(1) %>% gt::gt()
```

<div id="rcnnbxckwv" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#rcnnbxckwv .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#rcnnbxckwv .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#rcnnbxckwv .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#rcnnbxckwv .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#rcnnbxckwv .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#rcnnbxckwv .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#rcnnbxckwv .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#rcnnbxckwv .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#rcnnbxckwv .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#rcnnbxckwv .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#rcnnbxckwv .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#rcnnbxckwv .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#rcnnbxckwv .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#rcnnbxckwv .gt_from_md > :first-child {
  margin-top: 0;
}

#rcnnbxckwv .gt_from_md > :last-child {
  margin-bottom: 0;
}

#rcnnbxckwv .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#rcnnbxckwv .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#rcnnbxckwv .gt_summary_row {
  color: #333333;
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  font-style: italic;
  font-weight: normal;
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}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2019</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Alkali or alkali-earth metals; calcium</td>
<td class="gt_row gt_left">Inorganic chemicals; organic and inorganic compounds of precious metals; of rare earth metals, of radio-active elements and of isotopes</td>
<td class="gt_row gt_left">Chemical Products</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">105154</td></tr>
  </tbody>
  
  
</table>
</div>

]

Se utiliza el paquete waldo para comparar los filtros generados:

```r
waldo::compare(data.frame(filtro_r),
               data.frame(py$filtro_py))
```

```
v No differences
```

]

---

# 3. Nuevas columnas

---

```r
columnas_r <- df %>% 
  
  mutate( 
    
    # Cálculo
*   expo_netas = expo - impo,
*  
*   # If else
*   d_2020 = ifelse(año==2020,1,0),
*  
*   # Case when
*   categoria_extra = case_when(
*  
*     str_detect(commodity,'metal|machines') ~ 'Metal & maquinarias',
*     str_detect(commodity,'animal|food|vegetable') ~ 'Cultivos y animales',
*     TRUE ~ 'Otros'
      
    ) 
  )
```

]

```python
columnas_py = (r.df
  .assign(  
    
*   # Cálculo
*   expo_netas      = lambda x: x['expo'] - x['impo'],
*  
*   # If else
*   d_2020          = lambda x: np.where(x['año']==2020,1,0),
*  
*   # Case when
*   categoria_extra = lambda x: np.select(
*  
*     [x['commodity'].str.contains('metal|machines'),
*      x['commodity'].str.contains('animal|food|vegetable')],
*  
*     ['Metal & maquinarias',
*      'Cultivos y animales'],
*  
*     default = 'Otros')
    
  )
)
```

]

🔹 R dataframe:

```r
columnas_r %>% head(1) %>% 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_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_netas</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">d_2020</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">categoria_extra</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2017</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Horses; live, pure-bred breeding animals</td>
<td class="gt_row gt_left">Animals; live</td>
<td class="gt_row gt_left">Animal Products</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_left">Cultivos y animales</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$columnas_py %>% head(1) %>% gt::gt()
```

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  margin-top: 0;
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  margin-bottom: 0;
}

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}

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  border-right-style: solid;
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  border-right-color: #D3D3D3;
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}

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  padding: 4px;
}

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  text-align: left;
}

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  text-align: center;
}

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  text-align: right;
  font-variant-numeric: tabular-nums;
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  font-weight: normal;
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  font-weight: bold;
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  font-style: italic;
}

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  font-size: 65%;
}

#waijbiektq .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">pais</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">socio</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">commodity</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">grupo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_netas</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">d_2020</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">categoria_extra</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_right">2017</td>
<td class="gt_row gt_left">Argentina</td>
<td class="gt_row gt_left">Mexico</td>
<td class="gt_row gt_left">Horses; live, pure-bred breeding animals</td>
<td class="gt_row gt_left">Animals; live</td>
<td class="gt_row gt_left">Animal Products</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_right">15000</td>
<td class="gt_row gt_right">0</td>
<td class="gt_row gt_left">Cultivos y animales</td></tr>
  </tbody>
  
  
</table>
</div>

]

```r
waldo::compare(data.frame(columnas_r),
               data.frame(py$columnas_py))
```

```
v No differences
```

]

---

# 5. Agregación / Group by

---

```r
agg_r <- df %>% 
  
* group_by(seccion) %>%
*  
* summarise(
*   expo_total = sum(expo),
*   impo_total = sum(impo),
*   expo_prom = mean(expo),
*   impo_prom = mean(impo),
* ) %>%
*  
* ungroup()
```

]

```python
agg_py = (df_py

* .groupby('seccion', as_index=False)
*  
* .agg(
*    expo_total = ('expo','sum' ),
*    impo_total = ('impo','sum' ),
*    expo_prom  = ('expo','mean'),
*    impo_prom  = ('impo','mean')
*  )
)
```

]

🔹 R dataframe:

```r
agg_r %>% head(2) %>% gt::gt()
```

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}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_total</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo_total</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_prom</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo_prom</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Unspecified</td>
<td class="gt_row gt_right">769921184</td>
<td class="gt_row gt_right">48065</td>
<td class="gt_row gt_right">192480296</td>
<td class="gt_row gt_right">12016.25</td></tr>
    <tr><td class="gt_row gt_left">Transportation</td>
<td class="gt_row gt_right">397349013</td>
<td class="gt_row gt_right">1883795611</td>
<td class="gt_row gt_right">2631450</td>
<td class="gt_row gt_right">12475467.62</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$agg_py %>% head(2) %>% gt::gt()
```

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  border-right-color: #D3D3D3;
  vertical-align: middle;
}

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  padding: 0.5px;
  color: #333333;
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  border-top-width: 2px;
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  border-bottom-style: solid;
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}

#cirhajjcfk .gt_from_md > :first-child {
  margin-top: 0;
}

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}

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  padding-right: 5px;
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  border-top-color: #D3D3D3;
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  border-left-width: 1px;
  border-left-color: #D3D3D3;
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}

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  color: #333333;
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}

#cirhajjcfk .gt_footnotes {
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  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#cirhajjcfk .gt_footnote {
  margin: 0px;
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  padding: 4px;
}

#cirhajjcfk .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
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  font-size: 90%;
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#cirhajjcfk .gt_left {
  text-align: left;
}

#cirhajjcfk .gt_center {
  text-align: center;
}

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  text-align: right;
  font-variant-numeric: tabular-nums;
}

#cirhajjcfk .gt_font_normal {
  font-weight: normal;
}

#cirhajjcfk .gt_font_bold {
  font-weight: bold;
}

#cirhajjcfk .gt_font_italic {
  font-style: italic;
}

#cirhajjcfk .gt_super {
  font-size: 65%;
}

#cirhajjcfk .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_total</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo_total</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo_prom</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">impo_prom</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Unspecified</td>
<td class="gt_row gt_right">769921184</td>
<td class="gt_row gt_right">48065</td>
<td class="gt_row gt_right">192480296</td>
<td class="gt_row gt_right">12016.25</td></tr>
    <tr><td class="gt_row gt_left">Transportation</td>
<td class="gt_row gt_right">397349013</td>
<td class="gt_row gt_right">1883795611</td>
<td class="gt_row gt_right">2631450</td>
<td class="gt_row gt_right">12475467.62</td></tr>
  </tbody>
  
  
</table>
</div>

]

```r
waldo::compare(data.frame(agg_r),
               data.frame(py$agg_py))
```

```
v No differences
```

]
]

---

# 6. Formato wide a long / long a wide (pivot)

---

Las transformaciones del formato de los datos permiten visualizar la misma información de diferentes formas:

---

# Pivot wider

```r
wider_r <- df %>% 
  
  group_by(seccion, año) %>% 
  
  summarise(expo = sum(expo), .groups = 'keep') %>% 
  
  ungroup() %>% 
  
* pivot_wider(names_from=año,
*             names_prefix='y_',
*             values_from=expo)
```

]

```python
wider_py = (r.df

.assign(año=lambda x: ['y_'+str(int(i)) for i in x['año']])
 
 .groupby(['seccion','año'], as_index=False)
 
 .agg(expo = ('expo','sum'))
 
*.pivot(index='seccion',
*       columns='año',
*       values='expo')
)
```

]

🔹 R dataframe:

```r
wider_r %>% head(2) %>% gt::gt()
```

<div id="updoaughei" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
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}

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}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2017</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2018</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2019</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2020</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Unspecified</td>
<td class="gt_row gt_right">96332</td>
<td class="gt_row gt_right">225954813</td>
<td class="gt_row gt_right">342756705</td>
<td class="gt_row gt_right">201113334</td></tr>
    <tr><td class="gt_row gt_left">Chemical Products</td>
<td class="gt_row gt_right">114416816</td>
<td class="gt_row gt_right">73527737</td>
<td class="gt_row gt_right">65993570</td>
<td class="gt_row gt_right">65111224</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$wider_py %>% head(2) %>% gt::gt()
```

<div id="zfiuihvcmt" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#zfiuihvcmt .gt_from_md > :first-child {
  margin-top: 0;
}

#zfiuihvcmt .gt_from_md > :last-child {
  margin-bottom: 0;
}

#zfiuihvcmt .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#zfiuihvcmt .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#zfiuihvcmt .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#zfiuihvcmt .gt_first_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
}

#zfiuihvcmt .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#zfiuihvcmt .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#zfiuihvcmt .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#zfiuihvcmt .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#zfiuihvcmt .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#zfiuihvcmt .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#zfiuihvcmt .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#zfiuihvcmt .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#zfiuihvcmt .gt_left {
  text-align: left;
}

#zfiuihvcmt .gt_center {
  text-align: center;
}

#zfiuihvcmt .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#zfiuihvcmt .gt_font_normal {
  font-weight: normal;
}

#zfiuihvcmt .gt_font_bold {
  font-weight: bold;
}

#zfiuihvcmt .gt_font_italic {
  font-style: italic;
}

#zfiuihvcmt .gt_super {
  font-size: 65%;
}

#zfiuihvcmt .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2017</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2018</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2019</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">y_2020</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Unspecified</td>
<td class="gt_row gt_right">96332</td>
<td class="gt_row gt_right">225954813</td>
<td class="gt_row gt_right">342756705</td>
<td class="gt_row gt_right">201113334</td></tr>
    <tr><td class="gt_row gt_left">Chemical Products</td>
<td class="gt_row gt_right">114416816</td>
<td class="gt_row gt_right">73527737</td>
<td class="gt_row gt_right">65993570</td>
<td class="gt_row gt_right">65111224</td></tr>
  </tbody>
  
  
</table>
</div>

]

```r
waldo::compare(data.frame(wider_r),
               data.frame(py$wider_py))
```

```
v No differences
```

]
]

---

# Pivot longer

```r
longer_r <- wider_r %>% 
  
* pivot_longer(-seccion,
*              names_to='año',
*              values_to='expo') %>%
  
  mutate(año=str_replace(año, 'y_',''))
```

]

```python
longer_py = (wider_py

* .melt(
*   id_vars='seccion',
*   value_vars=['y_2017','y_2018','y_2019','y_2020'],
*   value_name='expo'
* )
  
  .assign(año = lambda x: [i.replace('y_','') for i in x['año']])
)
```

]

🔹 R dataframe:

```r
longer_r %>% head(2) %>% gt::gt()
```

<div id="msnuoocqtj" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#msnuoocqtj .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#msnuoocqtj .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#msnuoocqtj .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#msnuoocqtj .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#msnuoocqtj .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#msnuoocqtj .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#msnuoocqtj .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#msnuoocqtj .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#msnuoocqtj .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#msnuoocqtj .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#msnuoocqtj .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#msnuoocqtj .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#msnuoocqtj .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#msnuoocqtj .gt_from_md > :first-child {
  margin-top: 0;
}

#msnuoocqtj .gt_from_md > :last-child {
  margin-bottom: 0;
}

#msnuoocqtj .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#msnuoocqtj .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#msnuoocqtj .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#msnuoocqtj .gt_first_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
}

#msnuoocqtj .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#msnuoocqtj .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#msnuoocqtj .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#msnuoocqtj .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#msnuoocqtj .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#msnuoocqtj .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#msnuoocqtj .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#msnuoocqtj .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#msnuoocqtj .gt_left {
  text-align: left;
}

#msnuoocqtj .gt_center {
  text-align: center;
}

#msnuoocqtj .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#msnuoocqtj .gt_font_normal {
  font-weight: normal;
}

#msnuoocqtj .gt_font_bold {
  font-weight: bold;
}

#msnuoocqtj .gt_font_italic {
  font-style: italic;
}

#msnuoocqtj .gt_super {
  font-size: 65%;
}

#msnuoocqtj .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Animal and Vegetable Bi-Products</td>
<td class="gt_row gt_left">2017</td>
<td class="gt_row gt_right">17689911</td></tr>
    <tr><td class="gt_row gt_left">Animal and Vegetable Bi-Products</td>
<td class="gt_row gt_left">2018</td>
<td class="gt_row gt_right">90227077</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Pandas dataframe:

```r
py$longer_py %>% head(2) %>% gt::gt()
```

<div id="vbdqzonggi" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#vbdqzonggi .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#vbdqzonggi .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#vbdqzonggi .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#vbdqzonggi .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#vbdqzonggi .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#vbdqzonggi .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#vbdqzonggi .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#vbdqzonggi .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#vbdqzonggi .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#vbdqzonggi .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#vbdqzonggi .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#vbdqzonggi .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#vbdqzonggi .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#vbdqzonggi .gt_from_md > :first-child {
  margin-top: 0;
}

#vbdqzonggi .gt_from_md > :last-child {
  margin-bottom: 0;
}

#vbdqzonggi .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">seccion</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">año</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">expo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Animal and Vegetable Bi-Products</td>
<td class="gt_row gt_left">2017</td>
<td class="gt_row gt_right">17689911</td></tr>
    <tr><td class="gt_row gt_left">Animal and Vegetable Bi-Products</td>
<td class="gt_row gt_left">2018</td>
<td class="gt_row gt_right">90227077</td></tr>
  </tbody>
  
  
</table>
</div>

]

```r
waldo::compare(data.frame(longer_r),
               data.frame(py$longer_py))
```

```
v No differences
```

]
]

---

# 7. Visualización

![](images/viz.gif)

---

Se definen las **secciones relevantes**: Top 5️⃣ según monto de exportaciones:

```r
secciones_relevantes <- longer_r %>% 
  
  group_by(seccion) %>% 
  
  summarise(expo_4y=sum(expo)) %>% 
  
  ungroup() %>% 
  
  arrange(desc(expo_4y)) %>% 
  
* top_n(5) %>%
  
  pull(seccion)
```

```
Selecting by expo_4y
```

---

```r
longer_r %>% filter(seccion %in% secciones_relevantes) %>% 
  
* ggplot(aes(x=expo, y=reorder(seccion, expo)))+
*   geom_boxplot()+
    scale_x_continuous(labels = scales::unit_format(unit = "M", scale = 1e-6))+
    labs(title='Variación en las principales exportaciones entre 2017 y 2020', 
         x='Exportaciones (USD)', 
         y='Sección') + 
    theme_bw()
```

![](rladies_r_python_files/figure-html/unnamed-chunk-73-1.png)

]

```python
plt.figure(figsize=(13,4))
```

```{=html}
(longer_py[longer_py['seccion'].isin(r.secciones_relevantes)]
  
*  .pipe((sns.boxplot,"data"),
*       x="expo",  y="seccion", order=orden, color='white')
        
   .set(xlabel = "Exportaciones (USD)", ylabel='Sección', 
        title = 'Variación en las principales exportaciones entre 2017 y 2020')
)
plt.show()
```

]
]

---

# 8. Modelos

---

Se utiliza el paquete `{AER}` 📦 (Applied Econometric with R), que contiene un gran número de datasets econométricos.

En este caso, los datos contienen información sobre resultados educativos,Stock & Watson (2007) `$^1$`. Incluyen características sociodemográficas de estudiantes y escuelas en distritos de California.

<div style="position: absolute;left:60px;bottom:11px;color:gray;font-size:10px">
 `$^1$` Stock JH, Watson MW (2007). Introduction to Econometrics. 2nd edition. Addison-Wesley,
    Reading, MA. 
</div>

```r
library(AER) 
#data(package='AER') # Lista los datasets disponibles en el paquete AER
```

Se cargan los datos, generando una variable adicional de tamaño de la clase:

```r
data("CASchools")
CASchools <- CASchools %>% 
* mutate(tamaño = (students/teachers))
```

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<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="9" class="gt_heading gt_title gt_font_normal" style><strong>Tamaño de clase y desempeño en matemática</strong></th>
    </tr>
    <tr>
      <th colspan="9" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Distribución de las variables relevantes</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Variable</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Prom</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">SD</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Min</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">p25</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Mediana</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Max</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Distribución</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">N faltantes</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">math</td>
<td class="gt_row gt_right">653.34</td>
<td class="gt_row gt_right">18.75</td>
<td class="gt_row gt_right">605.40</td>
<td class="gt_row gt_right">639.38</td>
<td class="gt_row gt_right">652.45</td>
<td class="gt_row gt_right">709.50</td>
<td class="gt_row gt_left">▁▆▇▃▁</td>
<td class="gt_row gt_right">0</td></tr>
    <tr><td class="gt_row gt_left">tamaño</td>
<td class="gt_row gt_right">19.64</td>
<td class="gt_row gt_right">1.89</td>
<td class="gt_row gt_right">14.00</td>
<td class="gt_row gt_right">18.58</td>
<td class="gt_row gt_right">19.72</td>
<td class="gt_row gt_right">25.80</td>
<td class="gt_row gt_left">▁▃▇▃▁</td>
<td class="gt_row gt_right">0</td></tr>
  </tbody>
  
  
</table>
</div>

---

Se busca estimar el coeficiente asociado al tamaño de la clase, definido como la cantidad de estudiantes por la cantidad de profesores.

```r
reg_lineal <- lm(formula = math ~ tamaño, data = CASchools)
```

Con {equatiomatic} se visualiza la ecuación del modelo:

```r
equatiomatic::extract_eq(reg_lineal)
```

$$
\operatorname{math} = {\color{black}{\alpha}} + {\color{#e490e8}{\beta}}_{{\color{#e490e8}{1}}}(\operatorname{tamaño}) + {\color{black}{\epsilon}}
$$

También es posible visualizar la regresión lineal estimada (con los coeficientes correspondientes):

```r
equatiomatic::extract_eq(reg_lineal, use_coefs=TRUE)
```

$$
\operatorname{\widehat{math}} = 691.42 - 1.94(\operatorname{tamaño})
$$

El error de estimación es la diferencia entre el valor observado y el valor predicho:

$$
\operatorname{math} - \operatorname{\widehat{math}} = \epsilon
$$

---

# Visualmente:

![](rladies_r_python_files/figure-html/plot_reg-1.png)

]

![](rladies_r_python_files/figure-html/plot_reg_pred-1.png)
]
]

---

```python
import statsmodels.formula.api as smf

*reg = smf.ols ("math ~ tamaño", r.CASchools).fit()

modelo = reg.summary2()
```

```python
modelo.tables[0]
```

```
                     0                 1                    2          3
0               Model:               OLS      Adj. R-squared:      0.036
1  Dependent Variable:              math                 AIC:  3640.9217
2                Date:  2022-04-30 12:05                 BIC:  3649.0022
3    No. Observations:               420      Log-Likelihood:    -1818.5
4            Df Model:                 1         F-statistic:      16.62
5        Df Residuals:               418  Prob (F-statistic):   5.47e-05
6           R-squared:             0.038               Scale:     339.08
```
]

```python
modelo.tables[1]
```

```
           Coef.  Std.Err.     t  P>|t|  [0.025  0.975]
Intercept 691.42      9.38 73.69   0.00  672.97  709.86
tamaño     -1.94      0.48 -4.08   0.00   -2.87   -1.00
```
]

```python
modelo.tables[2]
```

```
                0      1                  2      3
0        Omnibus:  5.043     Durbin-Watson:  0.216
1  Prob(Omnibus):  0.080  Jarque-Bera (JB):  4.199
2           Skew:  0.155          Prob(JB):  0.123
3       Kurtosis:  2.620     Condition No.:    207
```
]

]

---

Comparando los `coeficientes` ajustando el modelo de regresión lineal con python o R:

```r
py$modelo$tables[[2]] %>% tibble::rownames_to_column('Variable') %>% 
  
  gt() %>% 
  
  fmt_number(where(is.numeric), decimals = 3) %>% 
  
  tab_header(title=md('**Tabla:** Modelo OLS'),
             subtitle = 'Statsmodels (python) a R') %>% 
  
  opt_align_table_header('left')
```

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<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="7" class="gt_heading gt_title gt_font_normal" style><strong>Tabla:</strong> Modelo OLS</th>
    </tr>
    <tr>
      <th colspan="7" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Statsmodels (python) a R</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Variable</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Coef.</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Std.Err.</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">t</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">P>|t|</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">[0.025</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">0.975]</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Intercept</td>
<td class="gt_row gt_right">691.417</td>
<td class="gt_row gt_right">9.382</td>
<td class="gt_row gt_right">73.692</td>
<td class="gt_row gt_right">0.000</td>
<td class="gt_row gt_right">672.975</td>
<td class="gt_row gt_right">709.860</td></tr>
    <tr><td class="gt_row gt_left">tamaño</td>
<td class="gt_row gt_right">−1.939</td>
<td class="gt_row gt_right">0.476</td>
<td class="gt_row gt_right">−4.077</td>
<td class="gt_row gt_right">0.000</td>
<td class="gt_row gt_right">−2.873</td>
<td class="gt_row gt_right">−1.004</td></tr>
  </tbody>
  
  
</table>
</div>
]

```r
*lm(formula = math ~ tamaño, data = CASchools) %>%
  
  gtsummary::tbl_regression(intercept=TRUE) %>% 
  
  gtsummary::modify_header(statistic="**Statistic**", std.error="**SE**") %>% 
  
  gtsummary::as_gt() %>% 
  
  tab_header(title=md('**Tabla:** Modelo OLS'), 
             subtitle='R Base') %>% 
  
  opt_align_table_header('left')
```

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    </tr>
    <tr>
      <th colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>R Base</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Característica</strong></th>
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<td class="gt_row gt_center">691</td>
<td class="gt_row gt_center">9.38</td>
<td class="gt_row gt_center">73.7</td>
<td class="gt_row gt_center">673, 710</td>
<td class="gt_row gt_center"><0.001</td></tr>
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<td class="gt_row gt_center">-1.9</td>
<td class="gt_row gt_center">0.476</td>
<td class="gt_row gt_center">-4.08</td>
<td class="gt_row gt_center">-2.9, -1.0</td>
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        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          SE = Standard Error, CI = Intervalo de confianza
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

🔹 Modelo python:

<div id="topcrmfsoy" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#topcrmfsoy .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#topcrmfsoy .gt_heading {
  background-color: #FFFFFF;
  text-align: left;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#topcrmfsoy .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#topcrmfsoy .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#topcrmfsoy .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#topcrmfsoy .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#topcrmfsoy .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#topcrmfsoy .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#topcrmfsoy .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#topcrmfsoy .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#topcrmfsoy .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#topcrmfsoy .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#topcrmfsoy .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#topcrmfsoy .gt_from_md > :first-child {
  margin-top: 0;
}

#topcrmfsoy .gt_from_md > :last-child {
  margin-bottom: 0;
}

#topcrmfsoy .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#topcrmfsoy .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#topcrmfsoy .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#topcrmfsoy .gt_first_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
}

#topcrmfsoy .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#topcrmfsoy .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#topcrmfsoy .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#topcrmfsoy .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#topcrmfsoy .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#topcrmfsoy .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#topcrmfsoy .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#topcrmfsoy .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#topcrmfsoy .gt_left {
  text-align: left;
}

#topcrmfsoy .gt_center {
  text-align: center;
}

#topcrmfsoy .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#topcrmfsoy .gt_font_normal {
  font-weight: normal;
}

#topcrmfsoy .gt_font_bold {
  font-weight: bold;
}

#topcrmfsoy .gt_font_italic {
  font-style: italic;
}

#topcrmfsoy .gt_super {
  font-size: 65%;
}

#topcrmfsoy .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="7" class="gt_heading gt_title gt_font_normal" style><strong>Tabla:</strong> Modelo OLS</th>
    </tr>
    <tr>
      <th colspan="7" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Statsmodels (python) a R</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">Variable</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Coef.</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">Std.Err.</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">t</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">P>|t|</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">[0.025</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">0.975]</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Intercept</td>
<td class="gt_row gt_right">691.417</td>
<td class="gt_row gt_right">9.382</td>
<td class="gt_row gt_right">73.692</td>
<td class="gt_row gt_right">0.000</td>
<td class="gt_row gt_right">672.975</td>
<td class="gt_row gt_right">709.860</td></tr>
    <tr><td class="gt_row gt_left">tamaño</td>
<td class="gt_row gt_right">−1.939</td>
<td class="gt_row gt_right">0.476</td>
<td class="gt_row gt_right">−4.077</td>
<td class="gt_row gt_right">0.000</td>
<td class="gt_row gt_right">−2.873</td>
<td class="gt_row gt_right">−1.004</td></tr>
  </tbody>
  
  
</table>
</div>

🔹 Modelo R:

<div id="mmhmmguhyt" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>html {
  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
}

#mmhmmguhyt .gt_table {
  display: table;
  border-collapse: collapse;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#mmhmmguhyt .gt_heading {
  background-color: #FFFFFF;
  text-align: left;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#mmhmmguhyt .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#mmhmmguhyt .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 0;
  padding-bottom: 4px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#mmhmmguhyt .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#mmhmmguhyt .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#mmhmmguhyt .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#mmhmmguhyt .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#mmhmmguhyt .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#mmhmmguhyt .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#mmhmmguhyt .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#mmhmmguhyt .gt_group_heading {
  padding: 8px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
}

#mmhmmguhyt .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#mmhmmguhyt .gt_from_md > :first-child {
  margin-top: 0;
}

#mmhmmguhyt .gt_from_md > :last-child {
  margin-bottom: 0;
}

#mmhmmguhyt .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#mmhmmguhyt .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 12px;
}

#mmhmmguhyt .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#mmhmmguhyt .gt_first_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
}

#mmhmmguhyt .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#mmhmmguhyt .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#mmhmmguhyt .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#mmhmmguhyt .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#mmhmmguhyt .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#mmhmmguhyt .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding: 4px;
}

#mmhmmguhyt .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#mmhmmguhyt .gt_sourcenote {
  font-size: 90%;
  padding: 4px;
}

#mmhmmguhyt .gt_left {
  text-align: left;
}

#mmhmmguhyt .gt_center {
  text-align: center;
}

#mmhmmguhyt .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#mmhmmguhyt .gt_font_normal {
  font-weight: normal;
}

#mmhmmguhyt .gt_font_bold {
  font-weight: bold;
}

#mmhmmguhyt .gt_font_italic {
  font-style: italic;
}

#mmhmmguhyt .gt_super {
  font-size: 65%;
}

#mmhmmguhyt .gt_footnote_marks {
  font-style: italic;
  font-weight: normal;
  font-size: 65%;
}
</style>
<table class="gt_table">
  <thead class="gt_header">
    <tr>
      <th colspan="6" class="gt_heading gt_title gt_font_normal" style><strong>Tabla:</strong> Modelo OLS</th>
    </tr>
    <tr>
      <th colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>R Base</th>
    </tr>
  </thead>
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Característica</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Beta</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>SE</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Statistic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>95% CI</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-valor</strong></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">(Intercept)</td>
<td class="gt_row gt_center">691</td>
<td class="gt_row gt_center">9.38</td>
<td class="gt_row gt_center">73.7</td>
<td class="gt_row gt_center">673, 710</td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left">tamaño</td>
<td class="gt_row gt_center">-1.9</td>
<td class="gt_row gt_center">0.476</td>
<td class="gt_row gt_center">-4.08</td>
<td class="gt_row gt_center">-2.9, -1.0</td>
<td class="gt_row gt_center"><0.001</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="6">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          SE = Standard Error, CI = Intervalo de confianza
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]
]

---
class: inverse, center, middle

# {Algunas cosas adicionales}

[1. 🔗 {rpy2}: paquete para ejecutar código R en python, notebook en Google Colab](https://colab.research.google.com/drive/11mIPeZQ_cDyECoR-57KUunDb_1av3NXJ#scrollTo=EiWnfQ7k0zad)

[2. 🔗 Renderizar Rmarkdowns parametrizados que incluyen código R y python](https://github.com/karbartolome/rladies_charlas/blob/main/01_r_python/05_rmarkdown_param/05_r_python_parametrizado.md)

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# Comentarios finales

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# Cosas que quiero que se lleven de este workshop

- R y python pueden ser complementarios, se puede usar lo mejor de ambos mundos 💜

- Tidyverse y pandas son bastante similares (:

- **El lenguaje utilizado no es un fin en sí mismo**, sino que es un medio para resolver algo

- 👉 Algunas configuraciones que mostré en segundos me llevaron tiempo de googlear y stackoverflow. Si quieren trabajar con conda environments en Rstudio de manera local no se frustren si no sale tan rápido

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# Links utiles

- 🔗 [TidyPandas: post que escribì con Rafael Zambrano detallando algunas similitudes entre tidyverse y pandas](https://karbartolome-blog.netlify.app/posts/tidypandas/)

- 🔗 [Guía de conversión entre R y python para manipulación de datos](https://www.mit.edu/~amidi/teaching/data-science-tools/conversion-guide/r-python-data-manipulation/)

- 🔗 [R & python: a love story](https://www.rstudio.com/resources/webinars/r-python-a-data-science-love-story/)

- 🔗 [Hilo de twitter sobre R y Python](https://twitter.com/AmeliaMN/status/1507484430113378307)

- [Otro link (:](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

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# Contacto

<a href="https://karbartolome-blog.netlify.com"><i class="fa fa-link fa-fw"></i>&nbsp; karbartolome-blog.netlify.com</a><br>
<a href="http://twitter.com/karbartolome"><i class="fa fa-twitter fa-fw"></i>&nbsp; @karbartolome</a><br>
<a href="http://github.com/karbartolome"><i class="fa fa-github fa-fw"></i>&nbsp; @karbartolome</a><br>

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# Muchas gracias!!

Las slides fueron creadas con el paquete [**xaringan**](https://github.com/yihui/xaringan), utilizando el template de Rladies