Generando tablas con R

1 Intro

Durante el curso vimos que se podían insertar tablas en nuestros documentos QMD de dos formas:

• usando Markdown

• usando R (o otro lenguaje de programación)

Solo vimos un ejemplo muy sencillo, concretamente este pero dije que se podían hacer tablas muy chulas con R. Vamos a profundizar en ello.

2 Datos

Para ello vamos a usar datos de inflación de la OCDE. Vamos a descargar los datos:

library(tidyverse)

#- Datos de inflación de la OCDE 
ruta_inflation <- "https://raw.githubusercontent.com/perezp44/archivos.download.2023/main/DP_LIVE_22032023113248591.csv"
inflation_orig <- readr::read_csv(ruta_inflation)
inflation_dicc <- pjpv.curso.R.2022::pjp_dicc(inflation_orig)

#- seleccionamos CPI mensual
inflation <- inflation_orig %>% 
  dplyr::filter(FREQUENCY == "M" & MEASURE == "AGRWTH") %>% 
  select(place = LOCATION, date = TIME, cpi = Value, tipo = SUBJECT) 

#- nos quedamos solo con el último dato: 2023-02 (y arreglamos)
inflation <- inflation %>% dplyr::filter(date == "2023-02") %>% 
  tidyr::pivot_wider(names_from = tipo, values_from = cpi) %>% 
  dplyr::arrange(desc(TOT))

3 Tablas básicas

Una vez tenemos los datos que queremos mostrar en una tabla, es muy fácil usar varios paquetes para mostrarlos en una tabla. Para saber un poco más sobre tablas en Quarto, puedes ir aquí

Con gt

gt::gt(inflation %>% slice(1:10))

place	date	ENRG	FOOD	TOT	TOT_FOODENRG
ARG	2023-02	NA	102.62940	102.50080	NA
TUR	2023-02	42.23681	69.33482	55.17983	51.902860
HUN	2023-02	39.45069	45.80613	25.40000	16.207950
LVA	2023-02	46.42958	25.33258	20.26292	10.511360
LTU	2023-02	29.20563	30.18281	18.73836	12.170210
EST	2023-02	30.27698	25.20833	17.54967	12.125680
CZE	2023-02	33.82126	23.91482	16.71949	11.444360
COL	2023-02	18.02995	24.13994	13.27902	10.150170
CHL	2023-02	13.22474	21.43148	11.94660	9.090909
AUT	2023-02	26.51685	16.15970	10.88180	8.612440

Con DT

DT::datatable(inflation)

Con reactable

reactable::reactable(inflation)

4 Tuneando

library(gt)
tt <- gt::gt(inflation %>% slice(1:10))

tt %>% tab_header(title = md("**Tasa de inflación**"),
                      subtitle = md("TOT: total; FOOD: alimentos; ENRG: energía")) %>% 
    tab_source_note(md("Fuente: datos de [OCDE](https://raw.githubusercontent.com/perezp44/archivos.download.2023/main/DP_LIVE_22032023113248591.csv)")) 

Tasa de inflación
TOT: total; FOOD: alimentos; ENRG: energía
place	date	ENRG	FOOD	TOT	TOT_FOODENRG
ARG	2023-02	NA	102.62940	102.50080	NA
TUR	2023-02	42.23681	69.33482	55.17983	51.902860
HUN	2023-02	39.45069	45.80613	25.40000	16.207950
LVA	2023-02	46.42958	25.33258	20.26292	10.511360
LTU	2023-02	29.20563	30.18281	18.73836	12.170210
EST	2023-02	30.27698	25.20833	17.54967	12.125680
CZE	2023-02	33.82126	23.91482	16.71949	11.444360
COL	2023-02	18.02995	24.13994	13.27902	10.150170
CHL	2023-02	13.22474	21.43148	11.94660	9.090909
AUT	2023-02	26.51685	16.15970	10.88180	8.612440
Fuente: datos de OCDE

Pongamos banderitas y alguna cosas más

#- data munging
inflation <- inflation_orig %>%
  dplyr::filter(FREQUENCY == "M") %>% 
  dplyr::filter(MEASURE == "AGRWTH") %>% 
  dplyr::mutate(fecha = lubridate::ym(TIME)) %>% 
  select(pais = LOCATION, fecha, inflacion = Value, tipo_inf = SUBJECT)

#- incorporar region para filtrar grupos de países
zz <- countrycode::codelist %>% select(iso.name.en, region, region23, wb, fips, ioc, imf, iso2c, iso3c, ecb, eurostat, continent, eu28, un, un.region.name)

inflation <- left_join(inflation, zz, by = join_by(pais == iso3c) ) 

#- solo Europa (q no sea Europa del Este) --------------------------------------
df <- inflation %>% 
  dplyr::filter(un.region.name %in% c("Europe")) %>% 
  dplyr::filter(!(region23 %in% c("Eastern Europe"))) 

my_title <- "Tasas de inflación interanual en Europa (febrero 2023)"
my_subtitle <- "Países europeos y <span style='color:#3569a4'> España </span> "

#- paso a ancho ----------------------------------------------------------------
df <- df %>% 
  select(pais, iso.name.en, fecha, inflacion, tipo_inf) %>% 
  pivot_wider(names_from = tipo_inf, values_from = inflacion)

#- banderas --------------------------------------------------------------------
df <- df %>% #- fips
  mutate(iso2 = countrycode::countrycode(sourcevar = pais, origin = "iso3c", destination = "iso2c", warn = FALSE)) %>% 
  mutate(iso2 = tolower(iso2)) %>% 
  mutate(flag_URL = glue::glue('https://hatscripts.github.io/circle-flags/flags/{iso2}.svg')) 
  
#- selecciono fecha ------------------------------------------------------------
my_fecha <-  max(inflation$fecha)
my_fecha <-  "2023-02-01"

df <- df %>% dplyr::filter(fecha == my_fecha) 

#- ordeno de + a - la inf. total -----------------------------------------------
df <- df %>% select(1,2, flag_URL, 3, 6, everything()) %>% arrange(desc(TOT), ENRG) %>% select(-iso2)

df <- df %>% mutate(FOOD = FOOD/max(FOOD))

Me he quedado solo con los países de Europa

#- con DT: https://rstudio.github.io/DT/
df_DT <- df %>% select(-flag_URL)
DT::datatable(df_DT, filter = 'top', extensions = "Scroller")

#- con reactable: https://glin.github.io/reactable/index.html
library(reactable)
reactable::reactable(df_DT, pagination = FALSE, height = 850, 
                     filterable = TRUE, searchable = TRUE, highlight = TRUE, 
                     columns = list( variable = colDef(
     # sticky = "left",
      # Add a right border style to visually distinguish the sticky column
      style = list(borderRight = "1px solid #eee"),
      headerStyle = list(borderRight = "1px solid #eee")
    )),
  defaultColDef = colDef(minWidth = 50)
)

tt_6 <- df %>%  
  gt(rowname_col = "pais") %>% 
  tab_header(title = my_title, subtitle = md(my_subtitle))  %>% 
  tab_source_note(md("Fuente: datos proveniente de la [OCDE](http://www.oecd.org/)")) %>%
  gtExtras::gt_theme_nytimes() %>%
  gtExtras::gt_img_rows(flag_URL, height = 25) %>% 
  gtExtras::gt_merge_stack(col2 = iso.name.en, col1 = pais) %>% 
  tab_footnote(footnote = "Tasa de inflación eliminando el efecto de la energía y los alimentos", 
               location = cells_column_labels(columns = TOT_FOODENRG)) %>% 
  cols_label(TOT = md("**Total**"), ENRG = "Energética", FOOD = "Alimentos", TOT_FOODENRG = "Total sin ..." )  %>% 
  cols_align(align = "center") %>% 
  cols_align(align = "left", columns = fecha) %>% 
  cols_width(columns = TOT ~ px(120)) %>% 
  fmt_percent(columns = c(TOT, ENRG, FOOD, TOT_FOODENRG), scale_values = FALSE) %>% 
  tab_style(style = cell_text(color = "#f97d64"),
            locations = cells_body(columns = TOT,
            rows = TOT >= mean(TOT))) %>% 
  tab_style(style = cell_text(color = "#f97d64"),
            locations = cells_body(columns = FOOD,
            rows = FOOD >= mean(FOOD))) %>% 
   tab_style(style = cell_text(color = "#f97d64"),
            locations = cells_body(columns = ENRG,
            rows = ENRG >= mean(ENRG))) %>% 
   tab_style(style = cell_text(color = "#3569a4"),
            locations = cells_body(rows = pais == "ESP")) %>% 
   data_color(columns = c(TOT_FOODENRG),
              colors = scales::col_numeric(palette = "Reds", domain = NULL)) 
  
tt_6  

Tasas de inflación interanual en Europa (febrero 2023)
Países europeos y España
	flag_URL	fecha	Total	Energética	Alimentos	Total sin ...1
LVA Latvia		2023-02-01	20.26%	46.43%	0.84%	10.51%
LTU Lithuania		2023-02-01	18.74%	29.21%	1.00%	12.17%
EST Estonia		2023-02-01	17.55%	30.28%	0.84%	12.13%
AUT Austria		2023-02-01	10.88%	26.52%	0.54%	8.61%
ISL Iceland		2023-02-01	10.20%	14.58%	0.40%	9.47%
SVN Slovenia		2023-02-01	9.28%	6.14%	0.61%	7.67%
ITA Italy		2023-02-01	9.15%	28.20%	0.44%	4.79%
FIN Finland		2023-02-01	8.80%	16.83%	0.54%	6.60%
DEU Germany		2023-02-01	8.68%	19.98%	0.69%	5.86%
IRL Ireland		2023-02-01	8.46%	29.30%	0.43%	5.56%
PRT Portugal		2023-02-01	8.25%	1.94%	0.71%	7.21%
NLD Netherlands (the)		2023-02-01	7.96%	−1.07%	0.59%	NA
DNK Denmark		2023-02-01	7.60%	13.97%	0.49%	5.80%
BEL Belgium		2023-02-01	6.62%	−7.93%	0.56%	6.90%
NOR Norway		2023-02-01	6.30%	12.44%	0.29%	5.60%
FRA France		2023-02-01	6.28%	13.95%	0.52%	3.57%
GRC Greece		2023-02-01	6.13%	−5.22%	0.49%	6.34%
ESP Spain		2023-02-01	6.03%	−8.87%	0.55%	5.19%
LUX Luxembourg		2023-02-01	4.31%	−1.98%	0.43%	3.63%
CHE Switzerland		2023-02-01	3.37%	16.65%	0.21%	2.03%
SWE Sweden		2023-02-01	NA	11.97%	0.69%	10.50%
Fuente: datos proveniente de la OCDE
1 Tasa de inflación eliminando el efecto de la energía y los alimentos

Algún día lo retomaré!!