Se muestran gráficos obtenidos del dataset iris usando ggplot2
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 virginica
Airquality es un dataset que viene dentro de Rstudio y podemos usarlo para practicar la visualización de datos de tipo ambiental.
Ozone Solar.R Wind Temp Month Day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6
7 23 299 8.6 65 5 7
8 19 99 13.8 59 5 8
9 8 19 20.1 61 5 9
10 NA 194 8.6 69 5 10
11 7 NA 6.9 74 5 11
12 16 256 9.7 69 5 12
13 11 290 9.2 66 5 13
14 14 274 10.9 68 5 14
15 18 65 13.2 58 5 15
16 14 334 11.5 64 5 16
17 34 307 12.0 66 5 17
18 6 78 18.4 57 5 18
19 30 322 11.5 68 5 19
20 11 44 9.7 62 5 20
21 1 8 9.7 59 5 21
22 11 320 16.6 73 5 22
23 4 25 9.7 61 5 23
24 32 92 12.0 61 5 24
25 NA 66 16.6 57 5 25
26 NA 266 14.9 58 5 26
27 NA NA 8.0 57 5 27
28 23 13 12.0 67 5 28
29 45 252 14.9 81 5 29
30 115 223 5.7 79 5 30
31 37 279 7.4 76 5 31
32 NA 286 8.6 78 6 1
33 NA 287 9.7 74 6 2
34 NA 242 16.1 67 6 3
35 NA 186 9.2 84 6 4
36 NA 220 8.6 85 6 5
37 NA 264 14.3 79 6 6
38 29 127 9.7 82 6 7
39 NA 273 6.9 87 6 8
40 71 291 13.8 90 6 9
41 39 323 11.5 87 6 10
42 NA 259 10.9 93 6 11
43 NA 250 9.2 92 6 12
44 23 148 8.0 82 6 13
45 NA 332 13.8 80 6 14
46 NA 322 11.5 79 6 15
47 21 191 14.9 77 6 16
48 37 284 20.7 72 6 17
49 20 37 9.2 65 6 18
50 12 120 11.5 73 6 19
51 13 137 10.3 76 6 20
52 NA 150 6.3 77 6 21
53 NA 59 1.7 76 6 22
54 NA 91 4.6 76 6 23
55 NA 250 6.3 76 6 24
56 NA 135 8.0 75 6 25
57 NA 127 8.0 78 6 26
58 NA 47 10.3 73 6 27
59 NA 98 11.5 80 6 28
60 NA 31 14.9 77 6 29
61 NA 138 8.0 83 6 30
62 135 269 4.1 84 7 1
63 49 248 9.2 85 7 2
64 32 236 9.2 81 7 3
65 NA 101 10.9 84 7 4
66 64 175 4.6 83 7 5
67 40 314 10.9 83 7 6
68 77 276 5.1 88 7 7
69 97 267 6.3 92 7 8
70 97 272 5.7 92 7 9
71 85 175 7.4 89 7 10
72 NA 139 8.6 82 7 11
73 10 264 14.3 73 7 12
74 27 175 14.9 81 7 13
75 NA 291 14.9 91 7 14
76 7 48 14.3 80 7 15
77 48 260 6.9 81 7 16
78 35 274 10.3 82 7 17
79 61 285 6.3 84 7 18
80 79 187 5.1 87 7 19
81 63 220 11.5 85 7 20
82 16 7 6.9 74 7 21
83 NA 258 9.7 81 7 22
84 NA 295 11.5 82 7 23
85 80 294 8.6 86 7 24
86 108 223 8.0 85 7 25
87 20 81 8.6 82 7 26
88 52 82 12.0 86 7 27
89 82 213 7.4 88 7 28
90 50 275 7.4 86 7 29
91 64 253 7.4 83 7 30
92 59 254 9.2 81 7 31
93 39 83 6.9 81 8 1
94 9 24 13.8 81 8 2
95 16 77 7.4 82 8 3
96 78 NA 6.9 86 8 4
97 35 NA 7.4 85 8 5
98 66 NA 4.6 87 8 6
99 122 255 4.0 89 8 7
100 89 229 10.3 90 8 8
101 110 207 8.0 90 8 9
102 NA 222 8.6 92 8 10
103 NA 137 11.5 86 8 11
104 44 192 11.5 86 8 12
105 28 273 11.5 82 8 13
106 65 157 9.7 80 8 14
107 NA 64 11.5 79 8 15
108 22 71 10.3 77 8 16
109 59 51 6.3 79 8 17
110 23 115 7.4 76 8 18
111 31 244 10.9 78 8 19
112 44 190 10.3 78 8 20
113 21 259 15.5 77 8 21
114 9 36 14.3 72 8 22
115 NA 255 12.6 75 8 23
116 45 212 9.7 79 8 24
117 168 238 3.4 81 8 25
118 73 215 8.0 86 8 26
119 NA 153 5.7 88 8 27
120 76 203 9.7 97 8 28
121 118 225 2.3 94 8 29
122 84 237 6.3 96 8 30
123 85 188 6.3 94 8 31
124 96 167 6.9 91 9 1
125 78 197 5.1 92 9 2
126 73 183 2.8 93 9 3
127 91 189 4.6 93 9 4
128 47 95 7.4 87 9 5
129 32 92 15.5 84 9 6
130 20 252 10.9 80 9 7
131 23 220 10.3 78 9 8
132 21 230 10.9 75 9 9
133 24 259 9.7 73 9 10
134 44 236 14.9 81 9 11
135 21 259 15.5 76 9 12
136 28 238 6.3 77 9 13
137 9 24 10.9 71 9 14
138 13 112 11.5 71 9 15
139 46 237 6.9 78 9 16
140 18 224 13.8 67 9 17
141 13 27 10.3 76 9 18
142 24 238 10.3 68 9 19
143 16 201 8.0 82 9 20
144 13 238 12.6 64 9 21
145 23 14 9.2 71 9 22
146 36 139 10.3 81 9 23
147 7 49 10.3 69 9 24
148 14 20 16.6 63 9 25
149 30 193 6.9 70 9 26
150 NA 145 13.2 77 9 27
151 14 191 14.3 75 9 28
152 18 131 8.0 76 9 29
153 20 223 11.5 68 9 30
Los Depósitos de Material Excedente (DME) son lugares designados para acopiar material sobrante de actividades, generalmente de construcción. A partir de puntos tomados en campo puede generarse polígonos y gracias a leaflet se pueden representar como mapas interactivos.
---
title: "FlexDashboards usando Rstudio"
author: "Bryan Quispe"
date: "`r Sys.Date()`"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source: embed
social: menu
theme: united
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(leaflet)
library(sf)
library(rgdal)
```
# Dataset iris {data-icon="fa-chart-bar"}
**Se muestran gráficos obtenidos del dataset iris usando ggplot2**
## Base de datos
```{r Tabla de datos}
print(iris)
```
Column {data-width=350}
-----------------------------------------------------------------------
### Petal Length
```{r Longitud de petalos de flores estudiadas}
iris %>% group_by(Species) %>% summarise(prom=mean(Petal.Length)) %>%
ggplot(aes(x=Species,y=prom,color=Species,fill=Species))+
geom_bar(stat = "identity")+
labs(x="Especies",y="Valores medios",fill="Especies",color="Especies",
title = "Promedio de la longitud de pétalos en flores estudiadas",
subtitle = "Datos tomados del dataset iris")+
theme_bw()
```
Column {data-width=350}
-----------------------------------------------------------------------
### Petal Width
```{r Ancho de petalos de flores estudiadas}
iris %>% group_by(Species) %>% summarise(prom=mean(Petal.Width)) %>%
ggplot(aes(x=Species,y=prom,color=Species,fill=Species))+
geom_bar(stat = "identity")+
labs(x="Especies",y="Valores medios",fill="Especies",color="Especies",
title = "Promedio del ancho de pétalos en flores estudiadas",
subtitle = "Datos tomados del dataset iris")+
theme_bw()
```
# Dataset airquality {data-icon="fa-chart-bar"}
**Airquality es un dataset que viene dentro de Rstudio y podemos usarlo para practicar la visualización de datos de tipo ambiental.**
## Base de datos airquality
```{r dataset}
print(airquality)
```
Row {data-width=850}
-----------------------------------------------------------------------
### Graficando serie de tiempo con ggplot2
```{r Serie de tiempo, fig.align='center'}
airquality %>% group_by(Month) %>% select(c(1,4)) %>%
ggplot(aes(x=Temp,y=Ozone, color=factor(Month)))+
geom_line()+facet_grid(Month~.)+
labs(x="Temperatura (°F)",y="Ozono (ppb)",fill="Meses",color="Meses",
title = "Gráfico de Ozono vs Temperatura",
subtitle = "Datos tomados del dataset aiquality")+
theme_bw()
```
# Ubicación de depósitos de material excedente usando leaflet
***Los Depósitos de Material Excedente (DME) son lugares designados para acopiar material sobrante de actividades, generalmente de construcción. A partir de puntos tomados en campo puede generarse polígonos y gracias a leaflet se pueden representar como mapas interactivos.***
## {.tabset}
### Primer Mapa
```{r Basemap de Esri}
# Polígono 1
points <- read.csv("DME 253+500 A.csv", header = F)
names(points) <- c("id","x","y")
point <- st_as_sf(points,coords = c("x","y"),crs=32719)
polig <- st_as_sf(points,coords = c("x","y"),crs=32719) %>%
summarise(geometry=st_combine(geometry)) %>%
st_cast("POLYGON")
# Polígono 2
points2 <- read.csv("DME 253+500 B.csv", header = F)
names(points2) <- c("id","x","y")
point2 <- st_as_sf(points2,coords = c("x","y"),crs=32719)
polig2 <- st_as_sf(points2,coords = c("x","y"),crs=32719) %>%
summarise(geometry=st_combine(geometry)) %>%
st_cast("POLYGON")
leaflet() %>% addTiles() %>% addProviderTiles(providers$Esri.WorldImagery) %>%
addPolygons(data=st_transform(polig,crs = 4326),
label = "235+500 A",
popup = "DME 235 + 500 A") %>%
addPolygons(data=st_transform(polig2,crs = 4326),
label = "235+500 B",
popup = "DME 235 + 500 B") %>%
setView(zoom = 16, lat = -13.674,ln=-70.47)
```
### Segundo mapa
```{r Basemap de OpenStreetMap}
leaflet() %>% addTiles() %>%
addProviderTiles(providers$OpenStreetMap) %>%
addPolygons(data=st_transform(polig,crs = 4326),
label = "235+500 A",
popup = "DME 235 + 500 A") %>%
addPolygons(data=st_transform(polig2,crs = 4326),
label = "235+500 B",
popup = "DME 235 + 500 B")
```