Librerías

Extraer datos y cortar columnas

library(readxl)
CPE <- read_excel("CPE.xlsx")
CPE1 <- CPE
CPE1 <- CPE1[,-c(15,16,17,18,19)]

class(CPE1)
## [1] "tbl_df"     "tbl"        "data.frame"

Mapeo de datos

MO

MO1 <- CPE1$MO
CANTIDAD_MO <- cut(x = MO1, breaks = 5)
unique(CANTIDAD_MO)
## [1] (1.31,2.13]  (2.13,2.95]  (2.95,3.78]  (0.486,1.31] (3.78,4.6]  
## Levels: (0.486,1.31] (1.31,2.13] (2.13,2.95] (2.95,3.78] (3.78,4.6]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_MO)) +
  geom_point()

## Ca

Ca1 <- CPE1$Ca
CANTIDAD_Ca <- cut(x = Ca1, breaks = 6)
unique(CANTIDAD_Ca)
## [1] (7.07,10.2]  (3.91,7.07]  (10.2,13.4]  (13.4,16.6]  (0.722,3.91]
## [6] (16.6,19.7] 
## 6 Levels: (0.722,3.91] (3.91,7.07] (7.07,10.2] (10.2,13.4] ... (16.6,19.7]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Ca)) +
  geom_point()

## Mg

Mg1 <- CPE1$Mg
CANTIDAD_Mg <- cut(x = Mg1, breaks = 6)
unique(CANTIDAD_Mg)
## [1] (1.02,2.01]   (0.0205,1.02] (2.01,3]      (4.98,5.97]   (3,3.99]     
## [6] (3.99,4.98]  
## 6 Levels: (0.0205,1.02] (1.02,2.01] (2.01,3] (3,3.99] ... (4.98,5.97]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Mg)) +
  geom_point()

## K

K1 <- CPE1$K
CANTIDAD_K <- cut(x = K1, breaks = 3)
unique(CANTIDAD_K)
## [1] (0.00442,0.277] (0.277,0.548]   (0.548,0.821]  
## Levels: (0.00442,0.277] (0.277,0.548] (0.548,0.821]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_K)) +
  geom_point()

Na

Na1 <- CPE1$Na
CANTIDAD_Na <- cut(x = Na1, breaks = 4)
unique(CANTIDAD_Na)
## [1] (0.236,0.424]  (0.0474,0.236] (0.424,0.612]  (0.612,0.801] 
## Levels: (0.0474,0.236] (0.236,0.424] (0.424,0.612] (0.612,0.801]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Na)) +
  geom_point()

CICE

CICE1 <- CPE1$CICE
CANTIDAD_CICE <- cut(x = CICE1, breaks = 6)
unique(CANTIDAD_CICE)
## [1] (8.65,12.5]  (4.79,8.65]  (12.5,16.4]  (20.2,24.1]  (16.4,20.2] 
## [6] (0.912,4.79]
## 6 Levels: (0.912,4.79] (4.79,8.65] (8.65,12.5] (12.5,16.4] ... (20.2,24.1]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_CICE)) +
  geom_point()

CE

CE1 <- CPE1$CE
CANTIDAD_CE <- cut(x = CE1, breaks = 4)
unique(CANTIDAD_CE)
## [1] (0.0446,0.257] (0.257,0.468]  (0.679,0.891]  (0.468,0.679] 
## Levels: (0.0446,0.257] (0.257,0.468] (0.468,0.679] (0.679,0.891]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_CE)) +
  geom_point()

nuevos_grupos <- quantile(CE1, probs = c(0.25, 0.5, 0.75))
nuevos_grupos
##       25%       50%       75% 
## 0.2095106 0.2763532 0.3828258
q_1 = nuevos_grupos[1];q_1 # cuantil 1
##       25% 
## 0.2095106
q_2 = nuevos_grupos[2];q_2 # cuantil 2
##       50% 
## 0.2763532
q_3 = nuevos_grupos[3];q_3 # cuantil 3
##       75% 
## 0.3828258
data1 <- subset(CE1, CE1 < q_1);data1 #Calcio menor del cuartil inferior q_1 
##   [1] 0.1299864 0.1257592 0.1410828 0.1632756 0.1727868 0.1997352 0.1690880
##   [8] 0.1949796 0.1606336 0.1817696 0.1400000 0.2044908 0.2092464 0.1722584
##  [15] 0.2034340 0.1812412 0.2000000 0.1500000 0.2050192 0.2007920 0.1896956
##  [22] 0.2087180 0.2018488 0.2066044 0.1986784 0.2066044 0.1949796 0.1167764
##  [29] 0.2060760 0.2034340 0.1859968 0.1426680 0.0475560 0.1949796 0.2018488
##  [36] 0.2034340 0.2060760 0.1321000 0.2013204 0.2060760 0.2055476 0.2044908
##  [43] 0.1944512 0.1812412 0.1585200 0.1960364 0.1194184 0.1976216 0.1347420
##  [50] 0.1659176 0.2000000 0.1700000 0.1791276 0.1685596 0.1405544 0.2039624
##  [57] 0.2081896 0.2039624 0.1928660 0.1675028 0.0454424 0.0480844 0.1675028
##  [64] 0.1511224 0.1738436 0.1949796 0.0454424 0.2066044 0.1891672 0.2000000
##  [71] 0.1616904 0.1976216 0.0496696 0.2071328 0.1331568 0.1384408 0.0496696
##  [78] 0.0480844 0.2013204 0.1759572 0.1685596 0.1616904 0.1733152 0.2066044
##  [85] 0.1659176 0.1278728 0.1247024 0.1268160 0.1416112 0.1548212 0.0887712
##  [92] 0.1579916 0.1706732 0.0491412 0.0459708 0.0480844 0.2055476 0.0464992
##  [99] 0.1210036 0.1928660 0.1717300
data2 <- subset(CE1, CE1 < q_2 & CE1 > q_1);data2
##  [1] 0.2694840 0.2446492 0.2235132 0.2610296 0.2134736 0.2118884 0.2515184
##  [8] 0.2177008 0.2113600 0.2499332 0.2504616 0.2140020 0.2261552 0.2700000
## [15] 0.2758248 0.2610296 0.2100000 0.2509900 0.2700124 0.2182292 0.2100000
## [22] 0.2200000 0.2187576 0.2488764 0.2515184 0.2742396 0.2400000 0.2636716
## [29] 0.2118884 0.2187576 0.2599728 0.2747680 0.2673704 0.2605012 0.2282688
## [36] 0.2525752 0.2731828 0.2430640 0.2642000 0.2483480 0.2700124 0.2097748
## [43] 0.2552172 0.2300000 0.2192860 0.2377800 0.2605012 0.2673704 0.2303824
## [50] 0.2103032 0.2224564 0.2208712 0.2166440 0.2372516 0.2700000 0.2400000
## [57] 0.2097748 0.2361948 0.2409504 0.2103032 0.2546888 0.2256268 0.2488764
## [64] 0.2600000 0.2578592 0.2240416 0.2400000 0.2200000 0.2203428 0.2409504
## [71] 0.2689556 0.2583876 0.2472912 0.2235132 0.2425356 0.2103032 0.2219280
## [78] 0.2150588 0.2615580 0.2557456 0.2610296 0.2393652 0.2721260 0.2256268
## [85] 0.2140020 0.2155872 0.2420072 0.2652568 0.2562740 0.2277404 0.2472912
## [92] 0.2700124 0.2742396 0.2187576 0.2747680 0.2229848 0.2182292 0.2472912
## [99] 0.2182292
data3 <- subset(CE1, CE1 < q_3 & CE1 > q_2);data3
##  [1] 0.2874496 0.2800520 0.2922052 0.3281364 0.3106992 0.2800520 0.3217956
##  [8] 0.3328920 0.3070004 0.3365908 0.2916768 0.3397612 0.3180968 0.3100000
## [15] 0.3318352 0.2943188 0.3200000 0.3001312 0.3815048 0.3138696 0.3500000
## [22] 0.3700000 0.2937904 0.3128128 0.2869212 0.2900000 0.2911484 0.2937904
## [29] 0.3228524 0.3661812 0.3006596 0.2906200 0.2779384 0.3645960 0.3799196
## [36] 0.3233808 0.3265512 0.3133412 0.3143980 0.3276080 0.2800520 0.3133412
## [43] 0.3143980 0.3138696 0.3545564 0.3302500 0.3048868 0.3328920 0.3059436
## [50] 0.3070004 0.3619540 0.3075288 0.3207388 0.3143980 0.3054152 0.3534996
## [57] 0.3344772 0.3519144 0.3200000 0.3455736 0.3048868 0.3593120 0.2943188
## [64] 0.3667096 0.3800000 0.3700000 0.3392328 0.2811088 0.3307784 0.3170400
## [71] 0.2795236 0.2832224 0.3577268 0.2826940 0.3376476 0.2969608 0.3307784
## [78] 0.3223240 0.3799196 0.3297216 0.2800000 0.3064720 0.3200000 0.2900000
## [85] 0.3128128 0.3429316 0.3239092 0.3165116 0.3291932 0.3000000 0.3519144
## [92] 0.3276080 0.3133412 0.3725220 0.3207388 0.2943188 0.3128128 0.3043584
## [99] 0.3070004
data4 <- subset(CE1, CE1 > q_3);data4
##   [1] 0.4147940 0.4095100 0.8400000 0.5717288 0.6034328 0.4216632 0.4961676
##   [8] 0.5014516 0.4760884 0.5754276 0.4195496 0.4300000 0.4105668 0.5300000
##  [15] 0.4100000 0.5146616 0.3984136 0.4073964 0.6663124 0.3862604 0.4184928
##  [22] 0.5215308 0.5669732 0.6752952 0.8200000 0.5616892 0.4311744 0.4581228
##  [29] 0.4174360 0.4089816 0.4739748 0.5828252 0.4734464 0.5236444 0.8877120
##  [36] 0.8153212 0.8073952 0.5030368 0.4396288 0.6737100 0.6383072 0.4311744
##  [43] 0.6155860 0.6145292 0.7200000 0.3841468 0.5690868 0.7165104 0.5136048
##  [50] 0.5881092 0.4607648 0.4047544 0.4900000 0.4800000 0.5231160 0.8400000
##  [57] 0.4068680 0.4692192 0.5907512 0.7133400 0.6800000 0.4570660 0.6050180
##  [64] 0.4697476 0.4449128 0.4126804 0.7265500 0.3968284 0.4375152 0.3931296
##  [71] 0.7725208 0.4221916 0.4105668 0.8454400 0.4787304 0.5960352 0.5606324
##  [78] 0.3936580 0.7587824 0.4977528 0.6568012 0.5900000 0.8800000 0.7556120
##  [85] 0.4110952 0.5500644 0.6589148 0.4058112 0.4945824 0.6034328 0.8900000
##  [92] 0.5241728 0.8700000 0.5389680 0.8200000 0.6361936 0.5717288 0.4163792
##  [99] 0.5595756 0.5220592 0.6663124
colors <- ifelse(CE1 %in% data1, 'yellow3',
                 ifelse(CE1 %in% data2, 'red3',
                        ifelse(CE1 %in% data3, 'green3', 'black')))
ggplot(CPE1, aes(Long, Lat, fill= colors))+
  geom_point(color=colors)+
  scale_fill_discrete(name = 'Conductividad eléctrica en suelo', labels = c('Muy Bajo', 'Bajo', 'Medio', 'Alto'))

Fe

Fe1 <- CPE1$Fe
CANTIDAD_Fe <- cut(x = Fe1, breaks = 4)
unique(CANTIDAD_Fe)
## [1] (11.3,300]     (300,588]      (588,877]      (877,1.17e+03]
## Levels: (11.3,300] (300,588] (588,877] (877,1.17e+03]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Fe)) +
  geom_point()

Cu

Cu1 <- CPE1$Cu
CANTIDAD_Cu <- cut(x = Cu1, breaks = 4)
unique(CANTIDAD_Cu)
## [1] (3.03,5.56] (0.49,3.03] (5.56,8.1]  (8.1,10.6] 
## Levels: (0.49,3.03] (3.03,5.56] (5.56,8.1] (8.1,10.6]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Cu)) +
  geom_point()

Zn

Zn1 <- CPE1$Zn
CANTIDAD_Zn <- cut(x = Zn1, breaks = 5)
unique(CANTIDAD_Zn)
## [1] (0.488,2.8] (2.8,5.1]   (5.1,7.41]  (9.71,12]   (7.41,9.71]
## Levels: (0.488,2.8] (2.8,5.1] (5.1,7.41] (7.41,9.71] (9.71,12]
ggplot(CPE1, aes(x = Long, y = Lat, color = CANTIDAD_Zn)) +
  geom_point()