FECHA: 22/11/2025
#Estadística Descriptiva
#LLUMITASIG DANIELA
#15/11/2025
library(gt)
library(dplyr)
#Cargar los datos
datos<-read.csv("C:/Users/JOSELYN/Desktop/kangle/stations (1).csv",header = TRUE,dec = ".",sep = ",")
#Tablas Cualitativas Nominales
#State
State<- datos$State
#Tabla de distribución de frecuencia
TDF_State<- data.frame(table(State))
ni <- TDF_State$Freq
hi <- round((ni / sum(ni)) *100,2)
State <- TDF_State$State
TDF_State <- data.frame(State,ni,hi)
Summary <- data.frame(State = "TOTAL", ni=sum(ni), hi = 100)
TDF_State_suma <- rbind(TDF_State,Summary)
colnames(TDF_State_suma) <- c("Nombre_Oficial", "ni", "hi(%)")
# TABLA
TDF_State_suma %>%
gt() %>%
tab_header(
title = md("Tabla Nro. 2"),
subtitle = md("Tabla de distribución de regiones donde se encuentran las estaciones de monitoreo")
) %>%
tab_source_note(
source_note = md("Fuente: Datos procesados por el autor a partir de archivo city.day.csv ")
) %>%
tab_style(
style = cell_borders(
sides = "left",
color = "black",
weight = px(2),
style = "solid"
),
locations = cells_body()
) %>%
tab_style(
style = cell_borders(
sides = "right",
color = "black",
weight = px(2),
style = "solid"
),
locations = cells_body()
) %>%
tab_style(
style = cell_borders(
sides = "left",
color = "black",
weight = px(2),
style = "solid"
),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_borders(
sides = "right",
color = "black",
weight = px(2),
style = "solid"
),
locations = cells_column_labels()
)%>%
tab_options(
table.border.top.color = "black",
table.border.bottom.color = "black",
table.border.top.style = "solid",
table.border.bottom.style = "solid",
column_labels.border.top.color = "black",
column_labels.border.bottom.color = "black",
column_labels.border.bottom.width = px(2),
row.striping.include_table_body = TRUE,
heading.border.bottom.color = "black",
heading.border.bottom.width = px(2),
table_body.hlines.color = "gray",
table_body.border.bottom.color = "black"
)
| Tabla Nro. 2 |
| Tabla de distribución de regiones donde se encuentran las estaciones de monitoreo |
| Nombre_Oficial |
ni |
hi(%) |
| Andhra Pradesh |
5 |
2.17 |
| Assam |
1 |
0.43 |
| Bihar |
10 |
4.35 |
| Chandigarh |
1 |
0.43 |
| Delhi |
38 |
16.52 |
| Gujarat |
6 |
2.61 |
| Haryana |
29 |
12.61 |
| Jharkhand |
1 |
0.43 |
| Karnataka |
20 |
8.70 |
| Kerala |
8 |
3.48 |
| Madhya Pradesh |
16 |
6.96 |
| Maharashtra |
22 |
9.57 |
| Meghalaya |
1 |
0.43 |
| Mizoram |
1 |
0.43 |
| Odisha |
2 |
0.87 |
| Punjab |
8 |
3.48 |
| Rajasthan |
10 |
4.35 |
| Tamil Nadu |
5 |
2.17 |
| Telangana |
6 |
2.61 |
| Uttar Pradesh |
26 |
11.30 |
| West Bengal |
14 |
6.09 |
| TOTAL |
230 |
100.00 |
| Fuente: Datos procesados por el autor a partir de archivo city.day.csv |