# Read the CSV files
FOOD.DATA.GROUP1<- read.csv("FOOD-DATA-GROUP1.csv")
FOOD.DATA.GROUP2<- read.csv("FOOD-DATA-GROUP2.csv")
FOOD.DATA.GROUP3<- read.csv("FOOD-DATA-GROUP3.csv")
FOOD.DATA.GROUP4 <- read.csv("FOOD-DATA-GROUP4.csv")
FOOD.DATA.GROUP5<- read.csv("FOOD-DATA-GROUP5.csv")
# Append (combine) all rows
food_data <- rbind(FOOD.DATA.GROUP1, FOOD.DATA.GROUP2, FOOD.DATA.GROUP3, FOOD.DATA.GROUP4, FOOD.DATA.GROUP5)
# View the combined data
head(food_data) X Unnamed..0 food Caloric.Value Fat
1 0 0 cream cheese 51 5.0
2 1 1 neufchatel cheese 215 19.4
3 2 2 requeijao cremoso light catupiry 49 3.6
4 3 3 ricotta cheese 30 2.0
5 4 4 cream cheese low fat 30 2.3
6 5 5 cream cheese fat free 19 0.2
Saturated.Fats Monounsaturated.Fats Polyunsaturated.Fats Carbohydrates Sugars
1 2.9 1.300 0.200 0.8 0.500
2 10.9 4.900 0.800 3.1 2.700
3 2.3 0.900 0.000 0.9 3.400
4 1.3 0.500 0.002 1.5 0.091
5 1.4 0.600 0.042 1.2 0.900
6 0.1 0.091 0.075 1.4 1.000
Protein Dietary.Fiber Cholesterol Sodium Water Vitamin.A Vitamin.B1
1 0.9 0.0 14.6 0.016 7.6 0.200 0.033
2 7.8 0.0 62.9 0.300 53.6 0.200 0.099
3 0.8 0.1 0.0 0.000 0.0 0.000 0.000
4 1.5 0.0 9.8 0.017 14.7 0.075 0.019
5 1.2 0.0 8.1 0.046 10.0 0.016 0.080
6 2.8 0.0 2.2 0.100 12.9 0.063 0.020
Vitamin.B11 Vitamin.B12 Vitamin.B2 Vitamin.B3 Vitamin.B5 Vitamin.B6 Vitamin.C
1 0.064 0.092 0.097 0.084 0.052 0.096 0.004
2 0.079 0.090 0.100 0.200 0.500 0.078 0.000
3 0.000 0.000 0.000 0.000 0.000 0.000 0.000
4 0.079 0.091 0.027 0.041 0.016 0.007 0.006
5 0.062 0.049 0.026 0.080 0.100 0.003 0.000
6 0.089 0.092 0.021 0.025 0.200 0.038 0.000
Vitamin.D Vitamin.E Vitamin.K Calcium Copper Iron Magnesium Manganese
1 0.000 0.000 0.100 0.008 14.100 0.082 0.027 1.300
2 0.000 0.300 0.045 99.500 0.034 0.100 8.500 0.088
3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
4 0.000 0.001 0.011 0.097 41.200 0.097 0.096 4.000
5 0.036 0.009 0.019 22.200 0.072 0.008 1.200 0.098
6 0.000 0.049 0.059 63.200 0.039 0.053 4.000 0.028
Phosphorus Potassium Selenium Zinc Nutrition.Density
1 0.091 15.5 19.100 0.039 7.070
2 117.300 129.2 0.054 0.700 130.100
3 0.000 0.0 0.000 0.000 5.400
4 0.024 30.8 43.800 0.035 5.196
5 22.800 37.1 0.034 0.053 27.007
6 94.100 50.0 0.013 0.300 67.679# Check the dimensions
dim(food_data)[1] 2395 37