Clase 1
2026-07-01
Clase 1 - Pasos previos
Ctrl + Atl + I para armar bloques de código o fragmentos.
Librerias
library(datos)## Warning: package 'datos' was built under R version 4.4.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.3library(gt)## Warning: package 'gt' was built under R version 4.4.3library(tidyverse)## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.2 ✔ tidyr 1.3.2
## ✔ readr 2.2.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(gridExtra)##
## Adjuntando el paquete: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combinelibrary(kableExtra)##
## Adjuntando el paquete: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rowslibrary(knitr)## Warning: package 'knitr' was built under R version 4.4.3options(scipen = 9999) # para ver numeros completosLevanto la base
Utilizo la convencionada como “datos” Hago algunos controles para ver qué tiene dentro
paises <- paises
str(paises)## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ pais : Factor w/ 142 levels "Afganistán","Albania",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continente : Factor w/ 5 levels "África","Américas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ anio : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ esperanza_de_vida: num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ poblacion : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ pib_per_capita : num [1:1704] 779 821 853 836 740 ...summary(paises)## pais continente anio esperanza_de_vida
## Afganistán: 12 África :624 Min. :1952 Min. :23.60
## Albania : 12 Américas:300 1st Qu.:1966 1st Qu.:48.20
## Argelia : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europa :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceanía : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## poblacion pib_per_capita
## Min. : 60011 Min. : 241.2
## 1st Qu.: 2793664 1st Qu.: 1202.1
## Median : 7023596 Median : 3531.8
## Mean : 29601212 Mean : 7215.3
## 3rd Qu.: 19585222 3rd Qu.: 9325.5
## Max. :1318683096 Max. :113523.1
## colnames(paises)## [1] "pais" "continente" "anio"
## [4] "esperanza_de_vida" "poblacion" "pib_per_capita"unique(paises$continente)## [1] Asia Europa África Américas Oceanía
## Levels: África Américas Asia Europa Oceaníaunique(paises$pais)## [1] Afganistán Albania
## [3] Argelia Angola
## [5] Argentina Australia
## [7] Austria Baréin
## [9] Bangladesh Bélgica
## [11] Benin Bolivia
## [13] Bosnia y Herzegovina Botswana
## [15] Brasil Bulgaria
## [17] Burkina Faso Burundi
## [19] Camboya Camerún
## [21] Canadá República Centroafricana
## [23] Chad Chile
## [25] China Colombia
## [27] Comoras República Democrática del Congo
## [29] Congo Costa Rica
## [31] Costa de Marfil Croacia
## [33] Cuba República Checa
## [35] Dinamarca Yibuti
## [37] República Dominicana Ecuador
## [39] Egipto El Salvador
## [41] Guinea Ecuatorial Eritrea
## [43] Etiopía Finlandia
## [45] Francia Gabón
## [47] Gambia Alemania
## [49] Ghana Grecia
## [51] Guatemala Guinea
## [53] Guinea Bissau Haití
## [55] Honduras Hong Kong, China
## [57] Hungría Islandia
## [59] India Indonesia
## [61] Irán Iraq
## [63] Irlanda Israel
## [65] Italia Jamaica
## [67] Japón Jordania
## [69] Kenia Corea, Rep. Dem.
## [71] Corea, Rep. Kuwait
## [73] Líbano Lesoto
## [75] Liberia Libia
## [77] Madagascar Malaui
## [79] Malasia Malí
## [81] Mauritania Mauricio
## [83] México Mongolia
## [85] Montenegro Marruecos
## [87] Mozambique Myanmar
## [89] Namibia Nepal
## [91] Países Bajos Nueva Zelanda
## [93] Nicaragua Niger
## [95] Nigeria Noruega
## [97] Omán Pakistán
## [99] Panamá Paraguay
## [101] Perú Filipinas
## [103] Polonia Portugal
## [105] Puerto Rico Reunión
## [107] Rumania Ruanda
## [109] Santo Tomé y Príncipe Arabia Saudita
## [111] Senegal Serbia
## [113] Sierra Leona Singapur
## [115] Eslovaquia Eslovenia
## [117] Somalia Sudáfrica
## [119] España Sri Lanka
## [121] Sudán Swazilandia
## [123] Suecia Suiza
## [125] Siria Taiwán, China
## [127] Tanzania Tailandia
## [129] Togo Trinidad y Tobago
## [131] Túnez Turquía
## [133] Uganda Reino Unido
## [135] Estados Unidos Uruguay
## [137] Venezuela Vietnam
## [139] Territorios Palestinos Yemen
## [141] Zambia Zimbabue
## 142 Levels: Afganistán Albania Argelia Angola Argentina Australia ... ZimbabueArmo los graficos
Vamos a elegir dos paises: Bolivia y Paraguay
seleccion <- paises |> # ctrl + shitf + M
filter(continente == "Américas",
pais %in% c("Bolivia", "Paraguay")) |>
ggplot() +
geom_point(aes(x = anio, y = esperanza_de_vida, colour = pais), size = 2) +
geom_line(aes(x = anio, y = esperanza_de_vida, group = pais,colour = pais), linewidth = 1) +
scale_x_continuous(
limits = c(1950,2010),
breaks = seq(1950,2010, by = 5) ) +
scale_color_manual(values = c("#880D1E","#F26A8D")) +
ylim(c(20,80)) +
labs(
title = "Esperanza de vida en Bolivia y Paraguay. Años 1952 a 2007",
subtitle = "evolución quinquenal",
caption = "Paquete datos, dataset paises",
x = "año",
y = "esperanza de vida",
color = NULL) +
theme_bw()+
theme(
legend.position = "bottom")
seleccion
Vamos con la tarea hacia el viernes…
Elegimos los paises del Mercosur.
1.- Graficamos en barras y líneas.
# Datos Mercosur seleccionados
datos_mercosur <- paises |>
filter(
pais %in% c("Argentina", "Brasil", "Uruguay")
)
ggplot(datos_mercosur,
aes(x = factor(anio),
y = esperanza_de_vida,
fill = pais)) +
geom_col(
width = 0.6,
position = position_dodge(width = 0.8)
) +
geom_text(
aes(label = round(esperanza_de_vida,1)),
position = position_dodge(width = 0.8),
vjust = -0.3,
size = 3
) +
coord_cartesian(ylim = c(40, 87)) +
scale_fill_manual(values = c(
"Argentina" = "#A8DADC",
"Brasil" = "#CDEAC0",
"Uruguay" = "#FFD6A5"
)) +
labs(
title = "Esperanza de vida",
subtitle = "Argentina, Brasil y Uruguay",
x = "Año",
y = "Esperanza de vida",
fill = "País"
) +
theme_bw()
ggplot(datos_mercosur,
aes(x = factor(anio),
y = esperanza_de_vida,
fill = pais)) +
geom_col(
width = 0.65,
position = position_dodge(width = 0.75)
) +
coord_cartesian(ylim = c(40, 85)) +
scale_fill_manual(values = c(
"Argentina" = "#5B8FD1",
"Brasil" = "#66C2A5",
"Uruguay" = "#F4A261"
)) +
labs(
title = "Esperanza de vida al nacer. Mercosur. (el cuadro mas lindo)",
subtitle = "Argentina, Brasil y Uruguay (estilo moderno)",
x = NULL,
y = "Años",
fill = NULL
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold", size = 18),
plot.subtitle = element_text(size = 12),
legend.position = "top",
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y = element_text(face = "bold"),
axis.text = element_text(color = "black")
)
ggplot(datos_mercosur,
aes(anio,
esperanza_de_vida,
color = pais,
group = pais)) +
geom_line(linewidth = 1.2) +
geom_point(size = 3) +
scale_color_manual(values = c(
"Argentina" = "#5B8FD1",
"Brasil" = "#66C2A5",
"Uruguay" = "#F4A261"
)) +
labs(
title = "Esperanza de vida al nacer. Mercosur.",
subtitle = "Argentina, Brasil y Uruguay. Gráfico de lineas muy bello.",
x = NULL,
y = "Años",
color = NULL
) +
theme_minimal(base_size = 13) +
theme(
legend.position = "top",
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
plot.title = element_text(face = "bold", size = 18)
)
Luego mostramos la tabla con los valores.
# Tabla
tabla <- datos_mercosur |>
select(anio, pais, esperanza_de_vida) |>
pivot_wider(
names_from = pais,
values_from = esperanza_de_vida
) |>
rename(Años = anio)
kable(
tabla,
caption = "Esperanza de vida por año",
digits = 1
) |>
kable_styling(
bootstrap_options = c("striped", "hover"),
full_width = FALSE,
font_size = 14
) |>
row_spec(0, bold = TRUE)| Años | Argentina | Brasil | Uruguay |
|---|---|---|---|
| 1952 | 62.5 | 50.9 | 66.1 |
| 1957 | 64.4 | 53.3 | 67.0 |
| 1962 | 65.1 | 55.7 | 68.3 |
| 1967 | 65.6 | 57.6 | 68.5 |
| 1972 | 67.1 | 59.5 | 68.7 |
| 1977 | 68.5 | 61.5 | 69.5 |
| 1982 | 69.9 | 63.3 | 70.8 |
| 1987 | 70.8 | 65.2 | 71.9 |
| 1992 | 71.9 | 67.1 | 72.8 |
| 1997 | 73.3 | 69.4 | 74.2 |
| 2002 | 74.3 | 71.0 | 75.3 |
| 2007 | 75.3 | 72.4 | 76.4 |