CLASE 2 parte 2
JUAN MARIA
2024-11-08
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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 errorsiris.es <- iris %>%
rename(LONG.SEPALOS = Sepal.Length ,
ANCHO.SEPALOS = Sepal.Width ,
LONG.PETALOS = Petal.Length ,
ANCHO.PETALOS = Petal.Width ,
ESPECIES = Species)LA ESTETICA (aesthethics)
ggplot(iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS))
GEOMETRIA ES LA SEGUNDA CAPA DEL GRAFICO
ggplot(data = iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS)) +
geom_point()
### 3 PASO ES ELABORAR EL FACETADO (VARIOS GRAFICOS DENTRO DEL MISMO
GRAFICO) permite mostrar la relacion entre las vbles ## NOS MUESTRA LA
RELACION ENTRE LAS DOS VBLES PERO CON UN GRAFICO PARA C/U
ggplot(data = iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS)) +
geom_point() + facet_grid(ESPECIES ~.)
EL MISMO GRAFICO PERO CAMBIAN LOS EJES
ggplot(data = iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS)) +
geom_point() + facet_grid(.~ESPECIES)
##EL TEMA (themes) permite cambiar la estetica
ggplot(data = iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS)) +
geom_point() + facet_grid(.~ESPECIES) + theme_dark() + theme_classic()
JUNTAMOS O COMBINAMOS TODAS LAS CAPAS
CON LA OPCION “COLOUR” CAMBIO EL COLOR DE LAS VBLES
ggplot(data = iris.es, aes(x = LONG.PETALOS, y = ANCHO.PETALOS, colour = ESPECIES)) +
geom_point() +
facet_grid(.~ESPECIES) +
theme_dark() +
theme_classic()
2. ¿CUAL DE LOS DOS (LOS PETALOS O LOS SEPALOS) PRESENTA UNA MAYOR AREA PROMEDIO? resolveremos usando el paquete dplyr
iris.es %>%
mutate(AREA_PETALO = LONG.PETALOS*ANCHO.PETALOS) %>%
summarise(MEDIA_PET = mean(AREA_PETALO))## MEDIA_PET
## 1 5.794067iris.es %>%
mutate(AREA_SEPALOS = LONG.SEPALOS*ANCHO.SEPALOS) %>%
summarise(MEDIA_SEP = mean(AREA_SEPALOS))## MEDIA_SEP
## 1 17.82287¿QUE ESPECIE TENDRA LA MAYOR LONGITUD PROMEDIO DE PETALOS?
iris.es %>%
group_by(ESPECIES) %>%
summarise(LONG_MEDIA_ESPECIE = mean(LONG.PETALOS))## # A tibble: 3 × 2
## ESPECIES LONG_MEDIA_ESPECIE
## <fct> <dbl>
## 1 setosa 1.46
## 2 versicolor 4.26
## 3 virginica 5.55CLASE NUMERO 3 08_11_2024
library("tidyverse")library(tidyverse)library(readxl)
(read_csv)## function (file, col_names = TRUE, col_types = NULL, col_select = NULL,
## id = NULL, locale = default_locale(), na = c("", "NA"), quoted_na = TRUE,
## quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf,
## guess_max = min(1000, n_max), name_repair = "unique", num_threads = readr_threads(),
## progress = show_progress(), show_col_types = should_show_types(),
## skip_empty_rows = TRUE, lazy = should_read_lazy())
## {
## if (edition_first()) {
## tokenizer <- tokenizer_csv(na = na, quoted_na = quoted_na,
## quote = quote, comment = comment, trim_ws = trim_ws,
## skip_empty_rows = skip_empty_rows)
## return(read_delimited(file, tokenizer, col_names = col_names,
## col_types = col_types, locale = locale, skip = skip,
## skip_empty_rows = skip_empty_rows, comment = comment,
## n_max = n_max, guess_max = guess_max, progress = progress,
## show_col_types = show_col_types))
## }
## if (!missing(quoted_na)) {
## lifecycle::deprecate_soft("2.0.0", "readr::read_csv(quoted_na = )")
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## vroom::vroom(file, delim = ",", col_names = col_names, col_types = col_types,
## col_select = {
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## }, id = id, .name_repair = name_repair, skip = skip,
## n_max = n_max, na = na, quote = quote, comment = comment,
## skip_empty_rows = skip_empty_rows, trim_ws = trim_ws,
## escape_double = TRUE, escape_backslash = FALSE, locale = locale,
## guess_max = guess_max, show_col_types = show_col_types,
## progress = progress, altrep = lazy, num_threads = num_threads)
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## [180] ".gb_Ea.gb_bc.not..gb_Id...gb_Bd.padding.left.0..gb_Ea.gb_bc..gb_Bd.gb_Hd.padding.right.0..gb_Ea.gb_bc..gb_Bd.gb_Hd..gb_Va.margin.left.10px..gb_2c.display.inline..gb_Ea.gb_Vc..gb_Bd.gb_Jd"
## [181] ".gb_Ea.gb_Id..gb_Bd.gb_Jd.padding.left.2px..gb_8c.display.inline.block..gb_Bd..moz.box.sizing.border.box..webkit.box.sizing.border.box.box.sizing.border.box.height.48px.line.height.normal.padding.0.4px.padding.left.30px..ms.flex.0.0.auto..webkit.flex.0.0.auto..webkit.box.flex.0..moz.box.flex.0.flex.0.0.auto..webkit.box.pack.flex.end..ms.flex.pack.end..webkit.justify.content.flex.end..moz.box.pack.flex.end.justify.content.flex.end..gb_Id.height.48px..gb_Ea.gb_Id.min.width.auto..gb_Id..gb_Bd.float.right.padding.left.32px..gb_Id..gb_Bd.gb_Kd.padding.left.0..gb_Ld.font.size.14px.max.width.200px.overflow.hidden.padding.0.12px.text.overflow.ellipsis.white.space.nowrap..moz.user.select.text..ms.user.select.text..webkit.user.select.text..gb_od..webkit.transition.background.color..4s..webkit.transition.background.color..4s..o.transition.background.color..4s.transition.background.color..4s..gb_Md.color.black..gb_Kc.color.white..gb_Ea.a"
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## [197] ".gb_Md..gb_9c.opacity.1..gb_Pd.position.relative..gb_L.font.family.arial"
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## [200] "X0.40"
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## [208] "X64.3"
## [209] "X67.3"
## [210] "X.15."
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## [227] "X67.8"
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## [230] "X64.9"
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## [297] "X.gb.gb_Kc.a.gb_pd.focus.hover.not..gb_F..background..f4f8ff.outline.1px.solid..c9ddfc..gb.a.gb_Ta.gb_F.gb_pd.focus"
## [298] "X.gb.a.gb_Ta.gb_F.gb_pd.focus.hover.background..a6c6fa.border.color.transparent..webkit.box.shadow.none..moz.box.shadow.none.box.shadow.none..gb..gb_Kc.a.gb_pd.active.not..gb_F."
## [299] "X.gb.gb_Kc.a.gb_pd.active.background..ecf3fe..gb.a.gb_Ta.gb_F.gb_pd.active.background..a1c3f9..webkit.box.shadow.0.1px.2px.rgba.60"
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## [669] "X..this.gbar_.this.gbar_......function._..var.window.this."ACÀ COPIE LOS CHUNKS QUE EL PROFE PUSO
# Instalamos y/o cargamos paquetes
library("tidyverse")
library("readxl")Función read_csv()
ALUMNOS_2024 <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vTwVt48G81FHfD_K9jcVmhjN3otTkR0-5Y8mC23oaEOTmhiG4So0kjw6I0b2tkU5ilpehvlhqBjId9i/pub?output=csv")## Rows: 24 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Marca temporal, Género, ¿Qué tan difícil le resulta el aprendizaje ...
## dbl (2): Edad, ¿Cuántas asignaturas rendiste y aprobaste este año?
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.names(ALUMNOS_2024)## [1] "Marca temporal"
## [2] "Género"
## [3] "Edad"
## [4] "¿Qué tan difícil le resulta el aprendizaje del software R y RStudio?"
## [5] "¿Cuántas asignaturas rendiste y aprobaste este año?"Función `read_excel()
MANDARINAS <- read_excel("MANDARINAS_2024.xlsx")glimpse(MANDARINAS)## Rows: 419
## Columns: 8
## $ N <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ GRUPO <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ VARIEDAD <chr> "Clementina", "Clementina", "Clementina", "Clementina", …
## $ N_DE_FRUTO <dbl> 19, 9, 21, 8, 4, 30, 22, 23, 17, 27, 29, 14, 16, 13, 25,…
## $ PESO <dbl> 101, 122, 127, 126, 37, 139, 140, 130, 138, 142, 121, 15…
## $ DIAM_ECUAT <dbl> 64.2, 64.2, 64.7, 64.9, 65.9, 66.4, 67.1, 67.5, 68.2, 68…
## $ NIVEL_DE_DAÑO <dbl> 1, 0, 3, 3, 2, 2, 3, 1, 2, 2, 2, 1, 1, 2, 1, 1, 0, 1, 0,…
## $ COLOR <dbl> 4, 5, 4, 1, 5, 4, 4, 3, 3, 4, 4, 1, 1, 3, 4, 1, 4, 1, 5,…Cargamos la base de datos “iris”
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaOrdenamos la base de datos
IRIS <- iris %>%
rename(Longitud.Sepalo = Sepal.Length,
Ancho.Sepalo = Sepal.Width,
Longitud.Petalo = Petal.Length, Ancho.Petalo = Petal.Width,
ESPECIE = Species)glimpse(IRIS)## Rows: 150
## Columns: 5
## $ Longitud.Sepalo <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4,…
## $ Ancho.Sepalo <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7,…
## $ Longitud.Petalo <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5,…
## $ Ancho.Petalo <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2,…
## $ ESPECIE <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa…Diagramas de dispersión
ggplot(IRIS, aes(Longitud.Petalo, Ancho.Petalo)) +
geom_point()
ggplot(IRIS, aes(Longitud.Petalo, Ancho.Petalo)) +
geom_point(shape = 25, fill = "yellow") # Para las formas 21 a 25, `fill =` controla el color de relleno de la forma.
También es posible modificar el tamaño de la forma seleccionada con
“size =”
ggplot(IRIS, aes(Longitud.Petalo, Ancho.Petalo)) +
geom_point(size = 4)
ggplot(IRIS, aes(Longitud.Petalo, Ancho.Petalo)) +
geom_point(color = "red")
Combinamos atributos
ggplot(IRIS, aes(Longitud.Petalo, Ancho.Petalo, shape = ESPECIE, color = ESPECIE)) +
geom_point(size= 3)
Gráfico de líneas
DATOS DE PRECIPITACIONES
PP_77_22 <- read_excel("PP_77_22.xlsx")glimpse(PP_77_22)## Rows: 46
## Columns: 2
## $ ANIO <dbl> 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,…
## $ PP <dbl> 666.4, 553.2, 675.7, 532.4, 528.7, 410.9, 569.2, 484.0, 610.2, 37…ggplot(PP_77_22, aes(ANIO, PP)) +
geom_line()
Renombramos variables
PP_77_22 <- PP_77_22 %>%
rename(AÑO = ANIO,
PRECIPITACION = PP)Visualizamos
PP_77_22## # A tibble: 46 × 2
## AÑO PRECIPITACION
## <dbl> <dbl>
## 1 1977 666.
## 2 1978 553.
## 3 1979 676.
## 4 1980 532.
## 5 1981 529.
## 6 1982 411.
## 7 1983 569.
## 8 1984 484
## 9 1985 610.
## 10 1986 379.
## # ℹ 36 more rowsCombinamos ambos argumentos…
ggplot(PP_77_22, aes(AÑO, PRECIPITACION)) +
geom_line(color = "blue", linetype = 3, lwd = 1 )
Histogramas
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Cambiamos color de los contenedores.
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram(color = "yellow")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Modificamos color y relleno de los contenedores.
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram(color = "yellow", fill = "tomato")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
A continuación pueden ver los nombres de algunos colores con la
función colors()
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"Ajuste de los contenedores
- Ajustando el número de bins a través del argumento
bins. Usando este método especificamos el número de contenedores.
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram(bins = 8, color = "gray", fill = "lightblue") +
theme_classic()
- Ajustando el ancho de los contenedores a través del argumento
binwidth. Con el segundo método, en lugar de especificar el número de bins, especificamos el ancho de los bins. Por ejemplo, establezcamos el ancho de cada contenedor en cien milímetros.
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram(binwidth = 100, color = "red", fill = "blue") +
theme_classic()
Polígono de frecuencias
#es limitado el poligono de frecuencia, por eso debo ponerlo con el histograma como en el chunk siguiente
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_freqpoly(binwidth = 100)
Combinamos un histograma con un polígono de frecuencias
ggplot(PP_77_22, aes(PRECIPITACION)) +
geom_histogram(binwidth = 100, color = "yellow") +
geom_freqpoly(binwidth = 100, color = "red", linewidth = 1.3)
Gráfico de cajas
MANDARINAS <- read_excel("MANDARINAS_2024.xlsx")str(MANDARINAS)## tibble [419 × 8] (S3: tbl_df/tbl/data.frame)
## $ N : num [1:419] 1 2 3 4 5 6 7 8 9 10 ...
## $ GRUPO : num [1:419] 1 1 1 1 1 1 1 1 1 1 ...
## $ VARIEDAD : chr [1:419] "Clementina" "Clementina" "Clementina" "Clementina" ...
## $ N_DE_FRUTO : num [1:419] 19 9 21 8 4 30 22 23 17 27 ...
## $ PESO : num [1:419] 101 122 127 126 37 139 140 130 138 142 ...
## $ DIAM_ECUAT : num [1:419] 64.2 64.2 64.7 64.9 65.9 66.4 67.1 67.5 68.2 68.2 ...
## $ NIVEL_DE_DAÑO: num [1:419] 1 0 3 3 2 2 3 1 2 2 ...
## $ COLOR : num [1:419] 4 5 4 1 5 4 4 3 3 4 ...#grafico del diametro ecuatorial en funcion de las variedades #los outlayers son valores que superan 1.5 veces los valores del rango intercuartilico
ggplot(MANDARINAS, aes(DIAM_ECUAT, VARIEDAD)) +
geom_boxplot()
ggplot(MANDARINAS, aes(DIAM_ECUAT, VARIEDAD, color = VARIEDAD)) +
geom_boxplot()
¿Y si agregamos la media muestral?
ggplot(MANDARINAS, aes(DIAM_ECUAT, VARIEDAD, color = VARIEDAD)) +
geom_boxplot() +
stat_summary(fun = mean, color = "black") # función para agregar la media muestral y asignarle color## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_segment()`).