#Tablas Cualitativas Nominales
#PROVINCIA
Provincia <- datos$Province
# Tabla de distribución de frecuencia
TDF_provincia <- data.frame(table(Provincia))
ni <- TDF_provincia$Freq
hi <- round((ni / sum(ni)) * 100, 2)
Provincia <- TDF_provincia$Provincia
TDF_provincia <- data.frame(Provincia, ni, hi)
Sumatoria <- data.frame(Provincia = "TOTAL", ni = sum(ni), hi = 100)
TDF_provincia_suma <- rbind(TDF_provincia, Sumatoria)
colnames(TDF_provincia_suma) <- c("Provincia", "ni", "hi(%)")
colnames(TDF_provincia) <- c("Provincia", "ni", "hi (%)")
# Tabla
kable(TDF_provincia_suma, align = 'c',
caption = "Tabla de Distribucion de Frecuencias de las Provincias de estudio
de contaminación del agua en China") %>%
kable_styling(full_width = FALSE, position = "center",
bootstrap_options = c("striped", "hover", "condensed"))
Tabla de Distribucion de Frecuencias de las Provincias de estudio de
contaminación del agua en China
|
Provincia
|
ni
|
hi(%)
|
|
Beijing
|
299
|
9.97
|
|
Guangdong
|
301
|
10.03
|
|
Henan
|
292
|
9.73
|
|
Hubei
|
292
|
9.73
|
|
Jiangsu
|
293
|
9.77
|
|
Shandong
|
300
|
10.00
|
|
Shanghai
|
312
|
10.40
|
|
Sichuan
|
311
|
10.37
|
|
Yunnan
|
296
|
9.87
|
|
Zhejiang
|
304
|
10.13
|
|
TOTAL
|
3000
|
100.00
|
#CIUDAD
Ciudad <- datos$City
# Tabla de distribución de frecuencia
TDF_ciudad <- data.frame(table(Ciudad))
ni <- TDF_ciudad$Freq
hi <- round((ni / sum(ni)) * 100, 2)
Ciudad <- TDF_ciudad$Ciudad
TDF_ciudad <- data.frame(Ciudad, ni, hi)
Sumatoria <- data.frame(Ciudad = "TOTAL", ni = sum(ni), hi = 100)
TDF_ciudad_suma <- rbind(TDF_ciudad, Sumatoria)
colnames(TDF_ciudad_suma) <- c("Ciudad", "ni", "hi(%)")
colnames(TDF_ciudad) <- c("Ciudad", "ni", "hi (%)")
# Tabla
kable(TDF_ciudad_suma, align = 'c',
caption = "Tabla de Distribucion de Frecuencias de las Ciudades del estudio
de contaminación del agua en China") %>%
kable_styling(full_width = FALSE, position = "center",
bootstrap_options = c("striped", "hover", "condensed"))
Tabla de Distribucion de Frecuencias de las Ciudades del estudio de
contaminación del agua en China
|
Ciudad
|
ni
|
hi(%)
|
|
Beijing
|
299
|
9.97
|
|
Chengdu
|
165
|
5.50
|
|
Dali
|
144
|
4.80
|
|
Guangzhou
|
146
|
4.87
|
|
Hangzhou
|
148
|
4.93
|
|
Jinan
|
160
|
5.33
|
|
Kunming
|
152
|
5.07
|
|
Luoyang
|
138
|
4.60
|
|
Mianyang
|
146
|
4.87
|
|
Nanjing
|
153
|
5.10
|
|
Ningbo
|
156
|
5.20
|
|
Qingdao
|
140
|
4.67
|
|
Shanghai
|
312
|
10.40
|
|
Shenzhen
|
155
|
5.17
|
|
Suzhou
|
140
|
4.67
|
|
Wuhan
|
154
|
5.13
|
|
Yichang
|
138
|
4.60
|
|
Zhengzhou
|
154
|
5.13
|
|
TOTAL
|
3000
|
100.00
|
#ESTACION
Estacion <- datos$Monitoring_Station
# Tabla de distribución de frecuencia
TDF_estacion <- data.frame(table(Estacion))
ni <- TDF_estacion$Freq
hi <- round((ni / sum(ni)) * 100, 2)
Estacion <- TDF_estacion$Estacion
TDF_estacion <- data.frame(Estacion, ni, hi)
Sumatoria <- data.frame(Estacion = "TOTAL", ni = sum(ni), hi = 100)
TDF_estacion_suma <- rbind(TDF_estacion, Sumatoria)
colnames(TDF_estacion_suma) <- c("Estacion", "ni", "hi(%)")
colnames(TDF_estacion) <- c("Estación", "ni", "hi (%)")
# Tabla
kable(TDF_estacion_suma, align = 'c',
caption = "Tabla de Distribucion de Frecuencias de las Estaciones de estudio
de contaminación del agua en China") %>%
kable_styling(full_width = FALSE, position = "center",
bootstrap_options = c("striped", "hover", "condensed"))
Tabla de Distribucion de Frecuencias de las Estaciones de estudio de
contaminación del agua en China
|
Estacion
|
ni
|
hi(%)
|
|
Beijing_Station_1
|
26
|
0.87
|
|
Beijing_Station_10
|
29
|
0.97
|
|
Beijing_Station_2
|
34
|
1.13
|
|
Beijing_Station_3
|
29
|
0.97
|
|
Beijing_Station_4
|
27
|
0.90
|
|
Beijing_Station_5
|
33
|
1.10
|
|
Beijing_Station_6
|
26
|
0.87
|
|
Beijing_Station_7
|
42
|
1.40
|
|
Beijing_Station_8
|
15
|
0.50
|
|
Beijing_Station_9
|
38
|
1.27
|
|
Chengdu_Station_1
|
17
|
0.57
|
|
Chengdu_Station_10
|
17
|
0.57
|
|
Chengdu_Station_2
|
20
|
0.67
|
|
Chengdu_Station_3
|
15
|
0.50
|
|
Chengdu_Station_4
|
16
|
0.53
|
|
Chengdu_Station_5
|
18
|
0.60
|
|
Chengdu_Station_6
|
14
|
0.47
|
|
Chengdu_Station_7
|
11
|
0.37
|
|
Chengdu_Station_8
|
18
|
0.60
|
|
Chengdu_Station_9
|
19
|
0.63
|
|
Dali_Station_1
|
13
|
0.43
|
|
Dali_Station_10
|
12
|
0.40
|
|
Dali_Station_2
|
20
|
0.67
|
|
Dali_Station_3
|
14
|
0.47
|
|
Dali_Station_4
|
12
|
0.40
|
|
Dali_Station_5
|
11
|
0.37
|
|
Dali_Station_6
|
15
|
0.50
|
|
Dali_Station_7
|
20
|
0.67
|
|
Dali_Station_8
|
14
|
0.47
|
|
Dali_Station_9
|
13
|
0.43
|
|
Guangzhou_Station_1
|
20
|
0.67
|
|
Guangzhou_Station_10
|
11
|
0.37
|
|
Guangzhou_Station_2
|
13
|
0.43
|
|
Guangzhou_Station_3
|
13
|
0.43
|
|
Guangzhou_Station_4
|
12
|
0.40
|
|
Guangzhou_Station_5
|
17
|
0.57
|
|
Guangzhou_Station_6
|
14
|
0.47
|
|
Guangzhou_Station_7
|
19
|
0.63
|
|
Guangzhou_Station_8
|
17
|
0.57
|
|
Guangzhou_Station_9
|
10
|
0.33
|
|
Hangzhou_Station_1
|
15
|
0.50
|
|
Hangzhou_Station_10
|
12
|
0.40
|
|
Hangzhou_Station_2
|
16
|
0.53
|
|
Hangzhou_Station_3
|
10
|
0.33
|
|
Hangzhou_Station_4
|
14
|
0.47
|
|
Hangzhou_Station_5
|
20
|
0.67
|
|
Hangzhou_Station_6
|
21
|
0.70
|
|
Hangzhou_Station_7
|
13
|
0.43
|
|
Hangzhou_Station_8
|
9
|
0.30
|
|
Hangzhou_Station_9
|
18
|
0.60
|
|
Jinan_Station_1
|
16
|
0.53
|
|
Jinan_Station_10
|
21
|
0.70
|
|
Jinan_Station_2
|
17
|
0.57
|
|
Jinan_Station_3
|
13
|
0.43
|
|
Jinan_Station_4
|
25
|
0.83
|
|
Jinan_Station_5
|
10
|
0.33
|
|
Jinan_Station_6
|
19
|
0.63
|
|
Jinan_Station_7
|
16
|
0.53
|
|
Jinan_Station_8
|
14
|
0.47
|
|
Jinan_Station_9
|
9
|
0.30
|
|
Kunming_Station_1
|
20
|
0.67
|
|
Kunming_Station_10
|
15
|
0.50
|
|
Kunming_Station_2
|
16
|
0.53
|
|
Kunming_Station_3
|
12
|
0.40
|
|
Kunming_Station_4
|
9
|
0.30
|
|
Kunming_Station_5
|
14
|
0.47
|
|
Kunming_Station_6
|
19
|
0.63
|
|
Kunming_Station_7
|
15
|
0.50
|
|
Kunming_Station_8
|
15
|
0.50
|
|
Kunming_Station_9
|
17
|
0.57
|
|
Luoyang_Station_1
|
18
|
0.60
|
|
Luoyang_Station_10
|
11
|
0.37
|
|
Luoyang_Station_2
|
12
|
0.40
|
|
Luoyang_Station_3
|
12
|
0.40
|
|
Luoyang_Station_4
|
11
|
0.37
|
|
Luoyang_Station_5
|
10
|
0.33
|
|
Luoyang_Station_6
|
19
|
0.63
|
|
Luoyang_Station_7
|
13
|
0.43
|
|
Luoyang_Station_8
|
13
|
0.43
|
|
Luoyang_Station_9
|
19
|
0.63
|
|
Mianyang_Station_1
|
15
|
0.50
|
|
Mianyang_Station_10
|
9
|
0.30
|
|
Mianyang_Station_2
|
10
|
0.33
|
|
Mianyang_Station_3
|
16
|
0.53
|
|
Mianyang_Station_4
|
18
|
0.60
|
|
Mianyang_Station_5
|
21
|
0.70
|
|
Mianyang_Station_6
|
18
|
0.60
|
|
Mianyang_Station_7
|
20
|
0.67
|
|
Mianyang_Station_8
|
10
|
0.33
|
|
Mianyang_Station_9
|
9
|
0.30
|
|
Nanjing_Station_1
|
8
|
0.27
|
|
Nanjing_Station_10
|
25
|
0.83
|
|
Nanjing_Station_2
|
11
|
0.37
|
|
Nanjing_Station_3
|
17
|
0.57
|
|
Nanjing_Station_4
|
20
|
0.67
|
|
Nanjing_Station_5
|
18
|
0.60
|
|
Nanjing_Station_6
|
14
|
0.47
|
|
Nanjing_Station_7
|
16
|
0.53
|
|
Nanjing_Station_8
|
12
|
0.40
|
|
Nanjing_Station_9
|
12
|
0.40
|
|
Ningbo_Station_1
|
16
|
0.53
|
|
Ningbo_Station_10
|
12
|
0.40
|
|
Ningbo_Station_2
|
14
|
0.47
|
|
Ningbo_Station_3
|
13
|
0.43
|
|
Ningbo_Station_4
|
18
|
0.60
|
|
Ningbo_Station_5
|
12
|
0.40
|
|
Ningbo_Station_6
|
19
|
0.63
|
|
Ningbo_Station_7
|
14
|
0.47
|
|
Ningbo_Station_8
|
20
|
0.67
|
|
Ningbo_Station_9
|
18
|
0.60
|
|
Qingdao_Station_1
|
15
|
0.50
|
|
Qingdao_Station_10
|
15
|
0.50
|
|
Qingdao_Station_2
|
13
|
0.43
|
|
Qingdao_Station_3
|
13
|
0.43
|
|
Qingdao_Station_4
|
9
|
0.30
|
|
Qingdao_Station_5
|
18
|
0.60
|
|
Qingdao_Station_6
|
15
|
0.50
|
|
Qingdao_Station_7
|
18
|
0.60
|
|
Qingdao_Station_8
|
13
|
0.43
|
|
Qingdao_Station_9
|
11
|
0.37
|
|
Shanghai_Station_1
|
34
|
1.13
|
|
Shanghai_Station_10
|
28
|
0.93
|
|
Shanghai_Station_2
|
39
|
1.30
|
|
Shanghai_Station_3
|
29
|
0.97
|
|
Shanghai_Station_4
|
39
|
1.30
|
|
Shanghai_Station_5
|
30
|
1.00
|
|
Shanghai_Station_6
|
28
|
0.93
|
|
Shanghai_Station_7
|
33
|
1.10
|
|
Shanghai_Station_8
|
26
|
0.87
|
|
Shanghai_Station_9
|
26
|
0.87
|
|
Shenzhen_Station_1
|
20
|
0.67
|
|
Shenzhen_Station_10
|
10
|
0.33
|
|
Shenzhen_Station_2
|
13
|
0.43
|
|
Shenzhen_Station_3
|
16
|
0.53
|
|
Shenzhen_Station_4
|
21
|
0.70
|
|
Shenzhen_Station_5
|
18
|
0.60
|
|
Shenzhen_Station_6
|
9
|
0.30
|
|
Shenzhen_Station_7
|
15
|
0.50
|
|
Shenzhen_Station_8
|
13
|
0.43
|
|
Shenzhen_Station_9
|
20
|
0.67
|
|
Suzhou_Station_1
|
11
|
0.37
|
|
Suzhou_Station_10
|
12
|
0.40
|
|
Suzhou_Station_2
|
12
|
0.40
|
|
Suzhou_Station_3
|
17
|
0.57
|
|
Suzhou_Station_4
|
11
|
0.37
|
|
Suzhou_Station_5
|
9
|
0.30
|
|
Suzhou_Station_6
|
15
|
0.50
|
|
Suzhou_Station_7
|
21
|
0.70
|
|
Suzhou_Station_8
|
16
|
0.53
|
|
Suzhou_Station_9
|
16
|
0.53
|
|
Wuhan_Station_1
|
16
|
0.53
|
|
Wuhan_Station_10
|
18
|
0.60
|
|
Wuhan_Station_2
|
13
|
0.43
|
|
Wuhan_Station_3
|
16
|
0.53
|
|
Wuhan_Station_4
|
15
|
0.50
|
|
Wuhan_Station_5
|
11
|
0.37
|
|
Wuhan_Station_6
|
19
|
0.63
|
|
Wuhan_Station_7
|
18
|
0.60
|
|
Wuhan_Station_8
|
11
|
0.37
|
|
Wuhan_Station_9
|
17
|
0.57
|
|
Yichang_Station_1
|
14
|
0.47
|
|
Yichang_Station_10
|
8
|
0.27
|
|
Yichang_Station_2
|
16
|
0.53
|
|
Yichang_Station_3
|
11
|
0.37
|
|
Yichang_Station_4
|
14
|
0.47
|
|
Yichang_Station_5
|
17
|
0.57
|
|
Yichang_Station_6
|
19
|
0.63
|
|
Yichang_Station_7
|
19
|
0.63
|
|
Yichang_Station_8
|
5
|
0.17
|
|
Yichang_Station_9
|
15
|
0.50
|
|
Zhengzhou_Station_1
|
13
|
0.43
|
|
Zhengzhou_Station_10
|
17
|
0.57
|
|
Zhengzhou_Station_2
|
19
|
0.63
|
|
Zhengzhou_Station_3
|
17
|
0.57
|
|
Zhengzhou_Station_4
|
20
|
0.67
|
|
Zhengzhou_Station_5
|
12
|
0.40
|
|
Zhengzhou_Station_6
|
12
|
0.40
|
|
Zhengzhou_Station_7
|
17
|
0.57
|
|
Zhengzhou_Station_8
|
15
|
0.50
|
|
Zhengzhou_Station_9
|
12
|
0.40
|
|
TOTAL
|
3000
|
100.00
|
#OBSERVACIONES
Observaciones <- datos$Remarks
# Tabla de distribución de frecuencia
TDF_observaciones <- data.frame(table(Observaciones))
ni <- TDF_observaciones$Freq
hi <- round((ni / sum(ni)) * 100, 2)
Observaciones <- TDF_observaciones$Observaciones
TDF_observaciones <- data.frame(Observaciones, ni, hi)
Sumatoria <- data.frame(Observaciones = "TOTAL", ni = sum(ni), hi = 100)
TDF_observaciones_suma <- rbind(TDF_observaciones, Sumatoria)
colnames(TDF_observaciones_suma) <- c("Observaciones", "ni", "hi(%)")
colnames(TDF_observaciones) <- c("Observaciones", "ni", "hi (%)")
# Tabla
kable(TDF_observaciones_suma, align = 'c',
caption = "Tabla de Distribucion de Frecuencias de las Observaciones de estudio
de contaminación del agua en China") %>%
kable_styling(full_width = FALSE, position = "center",
bootstrap_options = c("striped", "hover", "condensed"))
Tabla de Distribucion de Frecuencias de las Observaciones de estudio de
contaminación del agua en China
|
Observaciones
|
ni
|
hi(%)
|
|
|
752
|
25.07
|
|
High pollution spike detected
|
780
|
26.00
|
|
Monitoring recommended
|
745
|
24.83
|
|
Requires attention
|
723
|
24.10
|
|
TOTAL
|
3000
|
100.00
|