#MindanaoStateUniversity
#GeneralSantosCity
#Submitted by: Vienne Joyce H. Duga & Elvie Mae Cadungog
# Submitted to: Prof. Carlito Daarol
#Bsmath
#Mat108
folder <- "/cloud/project/"
name <- "CornPalay.csv"
file <- paste0(folder,"/",name)
data <- read.csv(file)
dim(data)
## [1] 256 18
colnames(data)
## [1] "ARMM.Region" "CORDILLERA.REGION..CAR."
## [3] "MIMAROPA.REGION" "REGION.I..ILOCOS.REGION."
## [5] "REGION.II..CAGAYAN.VALLEY." "REGION.III..CENTRAL.LUZON."
## [7] "REGION.IV.A..CALABARZON." "REGION.IX..ZAMBO.Penin."
## [9] "REGION.V..BICOL.REGION." "REGION.VI..WEST.VISAYAS."
## [11] "REGION.VII..CENTRAL.VISAYAS." "REGION.VIII..EAST.VISAYAS."
## [13] "REGION.X..NORTHERN.MIN." "REGION.XI..DAVAO.REGION."
## [15] "REGION.XII..SOCCSKSARGEN." "REGION.XIII..CARAGA."
## [17] "Crop" "TypeCrop"
head(data)
## ARMM.Region CORDILLERA.REGION..CAR. MIMAROPA.REGION REGION.I..ILOCOS.REGION.
## 1 35615 44156 97221 185215
## 2 28117 58373 109447 68461
## 3 28316 29287 65620 82150
## 4 40024 92503 172723 517037
## 5 37811 42013 93821 196722
## 6 33857 90937 112360 72749
## REGION.II..CAGAYAN.VALLEY. REGION.III..CENTRAL.LUZON.
## 1 374342 324251
## 2 521146 606719
## 3 266236 106113
## 4 456480 675030
## 5 313519 278590
## 6 592656 652675
## REGION.IV.A..CALABARZON. REGION.IX..ZAMBO.Penin. REGION.V..BICOL.REGION.
## 1 97377 85632 107588
## 2 98076 55102 119342
## 3 36353 84677 115910
## 4 94874 76316 116043
## 5 75609 63974 108350
## 6 97448 53113 124783
## REGION.VI..WEST.VISAYAS. REGION.VII..CENTRAL.VISAYAS.
## 1 253507 32724
## 2 120417 22924
## 3 343181 20025
## 4 259667 47851
## 5 263748 37494
## 6 120581 25781
## REGION.VIII..EAST.VISAYAS. REGION.X..NORTHERN.MIN. REGION.XI..DAVAO.REGION.
## 1 98855 113282 105778
## 2 72483 113569 52556
## 3 51050 96241 67328
## 4 92031 138703 127240
## 5 105323 108920 106082
## 6 77094 142893 62949
## REGION.XII..SOCCSKSARGEN. REGION.XIII..CARAGA. Crop TypeCrop
## 1 234088 23962 Palay Irrigated
## 2 168143 81313 Palay Irrigated
## 3 308459 34426 Palay Irrigated
## 4 179248 81753 Palay Irrigated
## 5 298253 25109 Palay Irrigated
## 6 101398 92643 Palay Irrigated
Crops <- unique(data$Crop)
TypeCrop <- unique(data$TypeCrop)
column1 <- table(data$Crop)
column2 <- table(data$TypeCrop)
dataframe1 <- data.frame(column1, column2)
print(dataframe1)
## Var1 Freq Var1.1 Freq.1
## 1 Corn 128 Irrigated 64
## 2 Palay 128 Rainfed 64
## 3 Corn 128 White 64
## 4 Palay 128 Yellow 64
df <- matrix(nrow = 1:256, 17, 18)
colSums(df)
## [1] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17