HM8_Courtney
NIcholas Courtney
2022-10-10
1.Import the Wine.csv file and name the dataframe “Wine”.
library(readr)
Wine <- read.csv("Wine.csv")2. Describe the structure of the Wine data frame immediately after import:
a. What is the dimension of the data frame?
The dataframe has 178 Rows by 14 Columns
dim(Wine)## [1] 178 14b. What are the numeric/integer variables?
The following variables are listed as numerics/integers:
Alcohol, Malic_Acid, Ash, Ash_Alcalinity, Magnesium,
Total_Phenols,
Flavanoids, Nonflavanoid_Phenols, Proanthocyanins, Color_Intensity,
Hue,
OD280_OD315, Proline
c. What are the character variables?
The variable “Type” is a character.
str(Wine)## 'data.frame': 178 obs. of 14 variables:
## $ Type : chr "A" "A" "A" "A" ...
## $ Alcohol : num 14.2 13.2 13.2 14.4 13.2 ...
## $ Malic_Acid : num 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
## $ Ash : num 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
## $ Ash_Alcalinity : num 15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
## $ Magnesium : int 127 100 101 113 118 112 96 121 97 98 ...
## $ Total_Phenols : num 2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...
## $ Flavanoids : num 3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 NA 3.15 ...
## $ Nonflavanoid_Phenols: num 0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...
## $ Proanthocyanins : num 2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...
## $ Color_Intensity : num 5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...
## $ Hue : num 1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...
## $ OD280_OD315 : num 3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...
## $ Proline : int 1065 1050 1185 1480 735 1450 1290 1295 1045 1045 ...3. Format structure/types of data
a. Type should be a factor
b. All other columns should be numeric or integer
Wine$Type <- as.factor(Wine$Type)
Wine$Alcohol <- as.numeric(Wine$Alcohol)
Wine$Malic_Acid <- as.numeric(Wine$Malic_Acid)
Wine$Ash <- as.numeric(Wine$Ash)
Wine$Ash_Alcalinity <- as.numeric(Wine$Ash_Alcalinity)
Wine$Magnesium <- as.numeric(Wine$Magnesium)
Wine$Total_Phenols <- as.numeric(Wine$Total_Phenols)
Wine$Flavanoids <- as.numeric(Wine$Flavanoids)
Wine$Nonflavanoid_Phenols <- as.numeric(Wine$Nonflavanoid_Phenols)
Wine$Proanthocyanins <- as.numeric(Wine$Proanthocyanins)
Wine$Color_Intensity <- as.numeric(Wine$Color_Intensity)
Wine$Hue <- as.numeric(Wine$Hue)
Wine$OD280_OD315 <- as.numeric(Wine$OD280_OD315)
Wine$Proline <- as.numeric(Wine$Proline)4. Clean the data.
Check factors for consistency
levels(Wine$Type)## [1] "A" "B" "C"The resulting factors identified, “A” “B” “C” are accurate.
a. Duplicates, if any, should be removed
Testing for Duplicates
any(duplicated(Wine))## [1] FALSEThe results of the test indicate there are no duplicates
b. Any observations missing the type value should be removed
Test to see if there are any observations missing the Type value
any(is.na(Wine))## [1] TRUEcolSums(is.na(Wine))## Type Alcohol Malic_Acid
## 0 0 0
## Ash Ash_Alcalinity Magnesium
## 0 0 0
## Total_Phenols Flavanoids Nonflavanoid_Phenols
## 0 1 0
## Proanthocyanins Color_Intensity Hue
## 1 0 0
## OD280_OD315 Proline
## 0 0describe(Wine)## vars n mean sd median trimmed mad min
## Type* 1 178 1.94 0.78 2.00 1.92 1.48 1.00
## Alcohol 2 178 13.00 0.81 13.05 13.01 1.01 11.03
## Malic_Acid 3 178 2.34 1.12 1.87 2.21 0.77 0.74
## Ash 4 178 2.37 0.27 2.36 2.37 0.24 1.36
## Ash_Alcalinity 5 178 19.49 3.34 19.50 19.42 3.04 10.60
## Magnesium 6 178 99.74 14.28 98.00 98.44 14.83 70.00
## Total_Phenols 7 178 2.30 0.63 2.36 2.29 0.75 0.98
## Flavanoids 8 177 2.02 1.00 2.13 2.01 1.23 0.34
## Nonflavanoid_Phenols 9 178 0.36 0.12 0.34 0.36 0.13 0.13
## Proanthocyanins 10 177 1.58 0.57 1.55 1.56 0.55 0.41
## Color_Intensity 11 178 5.06 2.32 4.69 4.83 2.24 1.28
## Hue 12 178 0.96 0.23 0.96 0.96 0.24 0.48
## OD280_OD315 13 178 2.61 0.71 2.78 2.63 0.77 1.27
## Proline 14 178 746.89 314.91 673.50 719.30 300.23 278.00
## max range skew kurtosis se
## Type* 3.00 2.00 0.11 -1.34 0.06
## Alcohol 14.83 3.80 -0.05 -0.89 0.06
## Malic_Acid 5.80 5.06 1.02 0.22 0.08
## Ash 3.23 1.87 -0.17 1.03 0.02
## Ash_Alcalinity 30.00 19.40 0.21 0.40 0.25
## Magnesium 162.00 92.00 1.08 1.96 1.07
## Total_Phenols 3.88 2.90 0.09 -0.87 0.05
## Flavanoids 5.08 4.74 0.04 -0.91 0.08
## Nonflavanoid_Phenols 0.66 0.53 0.44 -0.68 0.01
## Proanthocyanins 3.58 3.17 0.51 0.53 0.04
## Color_Intensity 13.00 11.72 0.85 0.30 0.17
## Hue 1.71 1.23 0.02 -0.40 0.02
## OD280_OD315 4.00 2.73 -0.30 -1.11 0.05
## Proline 1680.00 1402.00 0.75 -0.31 23.60The results of the test indicate there are no observations missing the Type value
c. Any observations missing a numeric or integer value should have the
missing value set to the average of that column
Step 1: Test to see if any data is NA
any(is.na(Wine)) ## [1] TRUEThe result shows that this data set does include NA values
Step 2: Test to see which columns have NA values.
colSums(is.na(Wine))## Type Alcohol Malic_Acid
## 0 0 0
## Ash Ash_Alcalinity Magnesium
## 0 0 0
## Total_Phenols Flavanoids Nonflavanoid_Phenols
## 0 1 0
## Proanthocyanins Color_Intensity Hue
## 1 0 0
## OD280_OD315 Proline
## 0 0The result shows that the Flavanoids and Proanthocyanins columns each have one NA value
Step 3: Test to see which row has an NA value for Flavanoids
Wine$Flavanoids %>% is.na %>% which## [1] 9The result show that row 9 has an NA value
Step 4: Turn NA value into the mean of the column for Flavanoids
Wine$Flavanoids[is.na(Wine$Flavanoids)] <- mean(Wine$Flavanoids,na.rm=TRUE)Test to see which row has an NA value for Proanthocyanins
Wine$Proanthocyanins %>% is.na %>% which## [1] 14Step 5: Turn NA value into the mean of the column for Proanthocyanins
Wine$Proanthocyanins[is.na(Wine$Proanthocyanins)] <- mean(Wine$Proanthocyanins,na.rm=TRUE)5. Summarize the numeric variables. Be sure to examine the minimum, first quartile, median, mean, third quartile, and maximum.
summary(Wine)## Type Alcohol Malic_Acid Ash Ash_Alcalinity
## A:59 Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
## B:71 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
## C:48 Median :13.05 Median :1.865 Median :2.360 Median :19.50
## Mean :13.00 Mean :2.336 Mean :2.367 Mean :19.49
## 3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558 3rd Qu.:21.50
## Max. :14.83 Max. :5.800 Max. :3.230 Max. :30.00
## Magnesium Total_Phenols Flavanoids Nonflavanoid_Phenols
## Min. : 70.00 Min. :0.980 Min. :0.340 Min. :0.1300
## 1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205 1st Qu.:0.2700
## Median : 98.00 Median :2.355 Median :2.120 Median :0.3400
## Mean : 99.74 Mean :2.295 Mean :2.024 Mean :0.3619
## 3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.842 3rd Qu.:0.4375
## Max. :162.00 Max. :3.880 Max. :5.080 Max. :0.6600
## Proanthocyanins Color_Intensity Hue OD280_OD315
## Min. :0.410 Min. : 1.280 Min. :0.4800 Min. :1.270
## 1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825 1st Qu.:1.938
## Median :1.555 Median : 4.690 Median :0.9650 Median :2.780
## Mean :1.584 Mean : 5.058 Mean :0.9574 Mean :2.612
## 3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200 3rd Qu.:3.170
## Max. :3.580 Max. :13.000 Max. :1.7100 Max. :4.000
## Proline
## Min. : 278.0
## 1st Qu.: 500.5
## Median : 673.5
## Mean : 746.9
## 3rd Qu.: 985.0
## Max. :1680.06. Create a new factor variable called high_Magnesium. If the Magnesium level is less than 100, set the value to “low.” Otherwise, the value is “high.”
high_Magnesium <- ifelse(Wine$Magnesium<100, "low", "high")
high_Magnesium <- as.factor(high_Magnesium)7. Create a boxplot of Malic_Acid.
boxplot(Wine$Malic_Acid, main = 'Malic Acid Boxplot', ylab = 'Malic Acid Level',col='Red')
8. Create a scatterplot of Malic_Acid and Ash. Add a trend line. Add labels to the plot.
plot(Wine$Malic_Acid,Wine$Ash, main = 'Malic Acid & Ash Scatterplot', xlab = "Malic Acid", ylab = "Ash")
abline(lm(Ash ~ Malic_Acid,data=Wine),col='red')
9. Create three boxplots (on the same graph) of Proline by Type. Add labels to the plots. Fill the boxes with the color.
plot(Wine$Proline ~ Wine$Type, main = "Proline by Type", xlab = "Type", ylab = "Proline", col='Red' )
10. Create a new data frame called Wine_A, where the only data is Type “A” and Magnesium > 100. Show the first five rows of this new data frame.
Wine_A <- subset(Wine, Wine$Type == "A" & Wine$Magnesium > 100)
Wine_A <- data.frame(Wine_A)
Wine_A[1:5,]## Type Alcohol Malic_Acid Ash Ash_Alcalinity Magnesium Total_Phenols
## 1 A 14.23 1.71 2.43 15.6 127 2.80
## 3 A 13.16 2.36 2.67 18.6 101 2.80
## 4 A 14.37 1.95 2.50 16.8 113 3.85
## 5 A 13.24 2.59 2.87 21.0 118 2.80
## 6 A 14.20 1.76 2.45 15.2 112 3.27
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 3.06 0.28 2.29 5.64 1.04
## 3 3.24 0.30 2.81 5.68 1.03
## 4 3.49 0.24 2.18 7.80 0.86
## 5 2.69 0.39 1.82 4.32 1.04
## 6 3.39 0.34 1.97 6.75 1.05
## OD280_OD315 Proline
## 1 3.92 1065
## 3 3.17 1185
## 4 3.45 1480
## 5 2.93 735
## 6 2.85 1450The following questions use the Wine dataframe.
11. Display the average of alcohol by type
avgAlcByType <- aggregate(Wine$Alcohol ~ Wine$Type, FUN = 'mean')
avgAlcByType## Wine$Type Wine$Alcohol
## 1 A 13.74475
## 2 B 12.27873
## 3 C 13.1537512. Using the package corrplot, report the Pearson’s correlation coefficients for the numeric variables. Remove the top half of the chart and the diagonal.
Are there variables that are correlated?
Correlation plot indicates that the strongest correlation is between Total Phenols and Flavanoids. There is also a farily strong correlation between Flavanoids and OD280_OD315. The strongest negative correclation is between Hue and Malic Acid.
numerics <- Wine[,unlist(lapply(Wine, is.numeric))]
corrplot(cor(numerics), type = 'lower', diag = FALSE)