Análisis Estadístico por Países
Grupo 1
2026-16-03
CARGA DE DATOS Y LIBRERÍAS
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
fig.align = "center"
)
datos <- read.csv("C:/Users/USER/Documents/PROYECTO ESTADISTICA/CMDB_Data.csv",
header = TRUE,
sep = ";",
dec = ".",
fileEncoding = "latin1")
str(datos)## 'data.frame': 1366 obs. of 103 variables:
## $ ï..LAB_ID : chr "C355417" "C360759" "C360762" "C360763" ...
## $ PREVIOUS_LAB_ID1 : chr "" "" "" "" ...
## $ PREVIOUS_LAB_ID2 : chr "" "" "" "" ...
## $ PREVIOUS_LAB_ID3 : chr "" "" "" "" ...
## $ FIELD_ID : chr "RM0001" "RM0027" "RM0030" "RM0031" ...
## $ JOB_ID : chr "MRP11968" "MRP12307" "MRP12307" "MRP12307" ...
## $ PREVIOUS_JOB_ID1 : chr "" "" "" "" ...
## $ PREVIOUS_JOB_ID2 : chr "" "" "" "" ...
## $ PREVIOUS_JOB_ID3 : chr "" "" "" "" ...
## $ SUBMITTER : chr "Rare Metals Task" "Rare Metals Task" "Rare Metals Task" "Rare Metals Task" ...
## $ PROJECT_NAME : chr "Critical and Rare Metals" "Critical and Rare Metals" "Critical and Rare Metals" "Critical and Rare Metals" ...
## $ DATE_SUBMITTED : chr "30/6/2011" "31/8/2011" "31/8/2011" "31/8/2011" ...
## $ COLLECTION : chr "Mackay-Keck Ore Deposits Collection" "Mackay-Stanford Ore Deposits Collection" "Mackay-Stanford Ore Deposits Collection" "Mackay-Stanford Ore Deposits Collection" ...
## $ COLLECTION_ID : chr "PHNC08_39_1183" "OD21441" "OD22811" "OD25716" ...
## $ CONTINENT : chr "North America" "South America" "South America" "Africa" ...
## $ COUNTRY : chr "United States" "Chile" "Chile" "South Africa" ...
## $ STATE_PROVINCE : chr "Nevada" "Antofagasta" "Tarapacá" "Transvaal" ...
## $ COUNTY : chr "Lyon" "El Loa" "El Tamarugal" "" ...
## $ DISTRICT_NAME : chr "Yerington" "Chuquicamata" "Collahuasi/Quebrada Blanca" "" ...
## $ DEPOSIT_NAME : chr "Pumpkin Hollow" "" "" "" ...
## $ MINE_NAME : chr "Pumpkin Hollow" "Chuquicamata mine" "Collahuasi district" "" ...
## $ DISTRICT_NAME_COLLECT: chr "Yerington" "" "" "" ...
## $ DEPOSIT_NAME_COLLECT : chr "" "" "" "" ...
## $ MINE_NAME_COLLECT : chr "Pumpkin Hollow" "Chuquicamata" "Poduosa mine" "Messina Mines Ltd." ...
## $ LOCATE_DESC : chr "" "" "Level 25" "" ...
## $ LATITUDE : chr "38,94021" "-22,2871" "-21,0309" "-24,7" ...
## $ LONGITUDE : chr "-119,05178" "-68,8991" "-68,74951" "29,3" ...
## $ DATUM : chr "WGS84" "WGS84" "WGS84" "" ...
## $ LATITUDE_COLLECT : chr "38,92492" "22,28944" "" "" ...
## $ LONGITUDE_COLLECT : chr "-119,1071" "-68,90111" "" "" ...
## $ DATUM_COLLECT : chr "" "WGS84" "" "" ...
## $ COORDINATES_QUAL : chr "100 m" "Exact" "" "" ...
## $ COORDINATES_SOURCE : chr "1) iTouchMap.com, approx, A. Orkild-Norton; 2) Mineral Resource Deposit Database Deposit ID 10174173, ore body, M. Granitto" "1) Mindat.org, approx, A. Orkild-Norton; 2) Open-File Report 2017-1079 ID 549, mine, M. Granitto" "1) No coordinates; 2) Mineral Resource Deposit Database Deposit ID 10057511, district, M. Granitto" "1) No coordinates; 2) Google Earth Pro, approx ctr of former province of Transvaal, M. Granitto" ...
## $ PRIMARY_CLASS : chr "rock" "rock" "rock" "rock" ...
## $ SYSTEM_TYPE : chr "IOA-IOCG" "Porphyry Cu-Mo-Au" "Porphyry Cu-Mo-Au" "IOA-IOCG" ...
## $ DEPOSIT_TYPE : chr "IOCG" "Supergene Cu" "Porphyry Cu" "IOCG" ...
## $ SAMPLE_DESC : chr "Nearly solid chalcopyrite mixed with small light brown irregular inclusions of unknown mineralogy; clouds of ma"| __truncated__ "Chalcocite-bronchatite-antlerite(?); highly microfractured igneous rock with green copper sulfates coating microfractures" "Bornite-chalcopyrite; mostly massive chalcopyrite with numerous inclusions of micro-chalcopyrite and widely sca"| __truncated__ "Massive chalcopyrite, IOCG in shear zone; mostly massive fine grain cuprite with widely distributed malachite t"| __truncated__ ...
## $ Al_pct_AES_ST : chr "0,33" "6,65" "0,46" "0,7" ...
## $ Ca_pct_AES_ST : chr "1,1" "0,4" "-0,1" "0,3" ...
## $ Fe_pct_AES_ST : chr "42,4" "0,25" "6,98" "27,8" ...
## $ K_pct_AES_ST : chr "-0,1" "6,1" "0,2" "-0,1" ...
## $ Mg_pct_AES_ST : chr "0,57" "0,1" "0,01" "0,33" ...
## $ Mn_pct_AES_ST : chr "0,02" "-0,01" "-0,01" "-0,01" ...
## $ P_pct_AES_ST : chr "-0,01" "0,01" "0,05" "0,01" ...
## $ S_pct_AES_ST : chr "" "" "" "" ...
## $ Si_pct_AES_ST : chr "" "" "" "" ...
## $ Ti_pct_AES_ST : chr "0,01" "0,11" "-0,01" "-0,01" ...
## $ F_pct_ISE_Fuse : chr "" "" "" "" ...
## $ Ag_ppm_MS_ST : chr "58" "6" "468" "16" ...
## $ As_ppm_MS_ST : chr "-30" "-30" "90" "-30" ...
## $ Au_ppm : chr "" "" "" "" ...
## $ Au_AM : chr "" "" "" "" ...
## $ B_ppm_AES_ST : int NA NA NA NA NA NA NA NA NA NA ...
## $ Ba_ppm_AES_ST : chr "-0,5" "924" "121" "174" ...
## $ Be_ppm_AES_ST : int -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 ...
## $ Bi_ppm_MS_ST : chr "1,5" "3,6" "190" "0,4" ...
## $ Cd_ppm_MS_ST : chr "3,6" "-0,2" "0,9" "-0,2" ...
## $ Ce_ppm_MS_ST : chr "0,4" "8,8" "16,3" "3,5" ...
## $ Co_ppm_MS_ST : chr "209" "-0,5" "1,3" "44,8" ...
## $ Cr_ppm_AES_ST : int -10 -10 -10 30 20 20 60 40 20 10 ...
## $ Cs_ppm_MS_ST : chr "0,5" "1,4" "0,2" "-0,1" ...
## $ Cu_ppm_AES_ST : chr "50000,11111" "23300" "50000,11111" "50000,11111" ...
## $ Dy_ppm_MS_ST : chr "-0,05" "0,32" "1,38" "0,37" ...
## $ Er_ppm_MS_ST : chr "-0,05" "0,22" "0,77" "0,23" ...
## $ Eu_ppm_MS_ST : chr "-0,05" "0,14" "0,17" "0,1" ...
## $ Ga_ppm_MS_ST : chr "5" "15" "6" "3" ...
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## $ Ge_ppm_MS_ST : int -1 5 -1 -1 3 8 8 1 2 2 ...
## $ Hf_ppm_MS_ST : int -1 4 -1 -1 5 13 12 2 3 6 ...
## $ Ho_ppm_MS_ST : chr "-0,05" "0,07" "0,25" "0,07" ...
## $ In_ppm_MS_ST : chr "6,4" "-0,2" "3,7" "0,2" ...
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## $ Nd_ppm_MS_ST : chr "0,2" "3,8" "9,1" "1,7" ...
## $ Ni_ppm_AES_ST : chr "144" "6" "-5" "48" ...
## $ Pb_ppm_MS_ST : chr "23" "16" "188" "39" ...
## $ Pd_ppm_FA_MS : chr "" "" "" "" ...
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## [list output truncated]Se cargaron correctamente los datos para el análisis de distribución geográfica.
ANÁLISIS POR PAIS (PARES DE VALORES)
TDFCOUNTRY <- table(datos$COUNTRY)
tabla_agrupada <- as.data.frame(TDFCOUNTRY)
colnames(tabla_agrupada) <- c("COUNTRY", "ni")
tabla_agrupada$hi <- tabla_agrupada$ni / sum(tabla_agrupada$ni)
tabla_agrupada$hi_porc <- round(tabla_agrupada$hi * 100, 2)
Total <- data.frame(COUNTRY = "TOTAL",
ni = sum(tabla_agrupada$ni),
hi = sum(tabla_agrupada$hi),
hi_porc = sum(tabla_agrupada$hi) * 100)
tabla_final <- rbind(tabla_agrupada, Total)
knitr::kable(tabla_final, caption = "Tabla 1: Frecuencias de la variable COUNTRY")| COUNTRY | ni | hi | hi_porc |
|---|---|---|---|
| 1 | 0.0007321 | 0.07 | |
| Argentina | 4 | 0.0029283 | 0.29 |
| Australia | 39 | 0.0285505 | 2.86 |
| Brazil | 17 | 0.0124451 | 1.24 |
| Burma | 1 | 0.0007321 | 0.07 |
| Canada | 59 | 0.0431918 | 4.32 |
| Chile | 53 | 0.0387994 | 3.88 |
| China | 3 | 0.0021962 | 0.22 |
| Cuba | 2 | 0.0014641 | 0.15 |
| Democratic Republic of Congo | 4 | 0.0029283 | 0.29 |
| Finland | 4 | 0.0029283 | 0.29 |
| Germany | 2 | 0.0014641 | 0.15 |
| Honduras | 1 | 0.0007321 | 0.07 |
| India | 1 | 0.0007321 | 0.07 |
| Indonesia | 4 | 0.0029283 | 0.29 |
| Japan | 18 | 0.0131772 | 1.32 |
| Mauritania | 4 | 0.0029283 | 0.29 |
| Mexico | 30 | 0.0219619 | 2.20 |
| Namibia | 3 | 0.0021962 | 0.22 |
| Norway | 2 | 0.0014641 | 0.15 |
| Papua New Guinea | 1 | 0.0007321 | 0.07 |
| Peru | 19 | 0.0139092 | 1.39 |
| Philippines | 3 | 0.0021962 | 0.22 |
| Poland | 4 | 0.0029283 | 0.29 |
| Portugal | 4 | 0.0029283 | 0.29 |
| Russia | 1 | 0.0007321 | 0.07 |
| South Africa | 10 | 0.0073206 | 0.73 |
| Sweden | 38 | 0.0278184 | 2.78 |
| United States | 1021 | 0.7474378 | 74.74 |
| Vietnam | 6 | 0.0043924 | 0.44 |
| Zambia | 7 | 0.0051245 | 0.51 |
| TOTAL | 1366 | 1.0000000 | 100.00 |
CLASIFICACIÓN POR REGIONES
norte_america <- c("United States", "Canada", "Mexico")
latino_america <- c("Brazil", "Chile", "Peru", "Argentina", "Colombia", "Ecuador")
africa <- c("South Africa", "Democratic Republic of the Congo", "Ghana", "Mali")
asia <- c("China", "India", "Indonesia", "Kazakhstan")
europa <- c("Russia", "Sweden", "Poland", "Germany")
oceania <- c("Australia", "Papua New Guinea", "New Zealand", "New Caledonia")
datos$REGION <- "Otra / No Clasificado"
datos$REGION[datos$COUNTRY %in% norte_america] <- "Norte America"
datos$REGION[datos$COUNTRY %in% latino_america] <- "Latino America"
datos$REGION[datos$COUNTRY %in% africa] <- "Africa"
datos$REGION[datos$COUNTRY %in% asia] <- "Asia"
datos$REGION[datos$COUNTRY %in% europa] <- "Europa"
datos$REGION[datos$COUNTRY %in% oceania] <- "Oceania"
TDFREGION <- table(datos$REGION)
tabla_regiones <- as.data.frame(TDFREGION)
colnames(tabla_regiones) <- c("REGION", "ni")
tabla_regiones$hi <- tabla_regiones$ni / sum(tabla_regiones$ni)
tabla_regiones$hi_porc <- round(tabla_regiones$hi * 100, 2)
Total_Region <- data.frame(REGION = "TOTAL",
ni = sum(tabla_regiones$ni),
hi = sum(tabla_regiones$hi),
hi_porc = sum(tabla_regiones$hi) * 100)
tabla_final_regiones <- rbind(tabla_regiones, Total_Region)
knitr::kable(tabla_final_regiones, caption = "Tabla 2: Frecuencias Agrupadas por Región Geográfica")| REGION | ni | hi | hi_porc |
|---|---|---|---|
| Africa | 10 | 0.0073206 | 0.73 |
| Asia | 8 | 0.0058565 | 0.59 |
| Europa | 45 | 0.0329429 | 3.29 |
| Latino America | 93 | 0.0680820 | 6.81 |
| Norte America | 1110 | 0.8125915 | 81.26 |
| Oceania | 40 | 0.0292826 | 2.93 |
| Otra / No Clasificado | 60 | 0.0439239 | 4.39 |
| TOTAL | 1366 | 1.0000000 | 100.00 |
VISUALIZACIÓN DE COMPARATIVAS BÁSICAS
barplot(TDFREGION,
main = "Gráfica 1: Muestras de Elementos Críticos por Región (LOCAL)",
ylab = "Cantidad de depósitos por región",
xlab = "Región",
col = "pink",
las = 1,
cex.names = 0.7,
ylim = c(0, 1400))
barplot(TDFREGION,
main = "Gráfica 2: Muestras de Elementos Críticos por Región (GLOBAL)",
ylab = "Cantidad de muestras por región",
xlab = "Región",
col = "skyblue",
las = 1,
cex.names = 0.7,
ylim = c(0, 1200))
ANÁLISIS DE FRECUENCIA ABSOLUTA
freq_abs <- TDFREGION
bar_centers <- barplot(freq_abs,
main = "Gráfica 3: Distribución de Muestras por Región",
ylab = "Cantidad",
xlab = "Región",
col = "orange",
las = 1,
cex.names = 0.75,
ylim = c(0, max(freq_abs) * 1.2))
text(x = bar_centers,
y = freq_abs,
labels = freq_abs,
pos = 3,
cex = 0.8,
col = "black")
ANÁLISIS DE FRECUENCIA RELATIVA (PORCENTAJES)
freq_rel <- prop.table(TDFREGION)
freq_rel_porc <- round(freq_rel * 100, 2)
bar_centers_rel <- barplot(freq_rel_porc,
main = "Gráfica 4: Distribución Porcentual Total por Región",
ylab = "Porcentaje (%)",
xlab = "Región",
col = "orchid",
las = 1,
cex.names = 0.7,
ylim = c(0, 100),
yaxt = "n",
yaxs = "i")
axis(2, at = seq(0, 100, by = 20), las = 1, cex.axis = 0.8)
text(x = bar_centers_rel,
y = freq_rel_porc,
labels = paste0(freq_rel_porc, "%"),
pos = 3,
cex = 0.8,
col = "black")
COMPOSICIÓN GLOBAL (GRÁFICA CIRCULAR)
Grupo_reg <- TDFREGION
hi_reg_porc <- round(prop.table(Grupo_reg) * 100, 1)
colores <- c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494")
etiquetas_leyenda <- paste0(names(Grupo_reg), " (", hi_reg_porc, "%)")
par(mar = c(2, 2, 3, 12), xpd = TRUE)
pie(hi_reg_porc,
labels = NA,
col = colores,
main = "Gráfica 5: Distribución Porcentual de Muestras por Región",
radius = 0.9)
x0 <- 1.0
x1 <- 2.4
y1 <- 0.9
y0 <- -0.3
rect(xleft = x0, ybottom = y0, xright = x1, ytop = y1, col = "white", border = "black")
text(x = (x0 + x1)/2, y = y1 - 0.15, labels = "Regiones", font = 2, cex = 1.0)
legend(x = (x0 + x1)/2,
y = y1 - 0.25,
legend = etiquetas_leyenda,
fill = colores,
cex = 0.8,
bty = "n",
xjust = 0.5,
y.intersp = 1.4)