##introduction to R R is a powerful, open-source programming language and environment designed for statistical computing and data analysis. Widely used by data scientists and statisticians, R offers extensive libraries, data visualization tools, and statistical techniques. Its flexibility and versatility make it a preferred choice for exploring, modeling, and visualizing data.
##basic components Variables: Store and manipulate data. Functions: Perform operations or computations. Data Structures: Organize and store data. Control Structures: Control the flow of program execution (e.g., loops and conditionals). Libraries (Packages): Extend R’s capabilities with specialized functions. Data Import/Export: Read and write data from/to various formats. Documentation: Add comments and documentation for code clarity. Graphics: Create visualizations and plots.
##datatypes in r Numeric: Represents numbers (e.g., 3.14, -42). Character (String): Stores text (e.g., “Hello, World!”). Logical: Represents TRUE or FALSE values. Date and Time: Handles date and time values.
# Define variables with different data types
numeric_var <- 42
character_var <- "Hello, World!"
logical_var <- TRUE
date_time_var <- as.POSIXct("2023-01-15 14:30:00")
# Print variables
cat("Numeric Variable:", numeric_var, "\n")## Numeric Variable: 42cat("Character Variable:", character_var, "\n")## Character Variable: Hello, World!cat("Logical Variable:", logical_var, "\n")## Logical Variable: TRUEcat("Date and Time Variable:", date_time_var, "\n")## Date and Time Variable: 1673773200##data structures in r Vector: A one-dimensional array of elements of the same data type (e.g., c(1, 2, 3)). Matrix: A two-dimensional array of elements of the same data type. List: A versatile data structure that can hold elements of different data types (e.g., list(1, “apple”, TRUE)). Data Frame: A tabular structure where columns can have different data types (e.g., data imported from a CSV file).
# Create data structures
vector_example <- c(1, 2, 3, 4, 5)
matrix_example <- matrix(1:6, nrow = 2, ncol = 3)
list_example <- list(1, "apple", TRUE)
data_frame_example <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 22),
Score = c(90, 85, 92)
)
# Print data structures
cat("Vector Example:", vector_example, "\n")## Vector Example: 1 2 3 4 5cat("Matrix Example:\n")## Matrix Example:print(matrix_example)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6cat("List Example:\n")## List Example:print(list_example)## [[1]]
## [1] 1
##
## [[2]]
## [1] "apple"
##
## [[3]]
## [1] TRUEcat("Data Frame Example:\n")## Data Frame Example:print(data_frame_example)## Name Age Score
## 1 Alice 25 90
## 2 Bob 30 85
## 3 Charlie 22 92# a.Create two vectors of integers
vector1 <- c(1, 2, 3)
vector2 <- c(4, 5, 6)
# Add the two vectors element-wise
result_vector <- vector1 + vector2
# Print the result
print(result_vector)## [1] 5 7 9we have added 2 vectors successfully.
# b.Create a vector
my_vector <- c(2, 4, 6, 8, 10)
# Calculate the sum, mean, and product
sum_result <- sum(my_vector)
mean_result <- mean(my_vector)
product_result <- prod(my_vector)
# Print the results
print(paste("Sum:", sum_result))## [1] "Sum: 30"print(paste("Mean:", mean_result))## [1] "Mean: 6"print(paste("Product:", product_result))## [1] "Product: 3840"we have found sum,mean and product of vector elements.
# c.Create a vector
my_vector <- c(3, 1, 7, 2, 9)
# Find the minimum and maximum
min_value <- min(my_vector)
max_value <- max(my_vector)
# Print the results
print(paste("Minimum:", min_value))## [1] "Minimum: 1"print(paste("Maximum:", max_value))## [1] "Maximum: 9"#d. Create a list
my_list <- list(
string_element = "Hello, World",
numeric_element = 42,
vector_element = c(1, 2, 3),
logical_element = TRUE
)
# Print the list
print(my_list)## $string_element
## [1] "Hello, World"
##
## $numeric_element
## [1] 42
##
## $vector_element
## [1] 1 2 3
##
## $logical_element
## [1] TRUEa heterogeneous list is made.
#e. Create a list with named elements
my_list <- list(
vector_element = c(1, 2, 3),
matrix_element = matrix(1:6, nrow = 2),
nested_list = list(a = "apple", b = "banana")
)
# Access the first and second elements of the list
first_element <- my_list$vector_element
second_element <- my_list$matrix_element
# Print the accessed elements
print(first_element)## [1] 1 2 3print(second_element)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6#f. Create a 3x5 matrix filled with zeros
my_matrix <- matrix(0, nrow = 3, ncol = 5)
# Print the matrix
print(my_matrix)## [,1] [,2] [,3] [,4] [,5]
## [1,] 0 0 0 0 0
## [2,] 0 0 0 0 0
## [3,] 0 0 0 0 0#g. Create a sample matrix
my_matrix <- matrix(1:12, nrow = 3)
# Access specific elements
element_1 <- my_matrix[2, 3] # 3rd column, 2nd row
element_2 <- my_matrix[3, ] # 3rd row
element_3 <- my_matrix[, 4] # 4th column
# Print the accessed elements
print(element_1)## [1] 8print(element_2)## [1] 3 6 9 12print(element_3)## [1] 10 11 12#h. Create vectors
name <- c("Alice", "Bob", "Charlie")
age <- c(25, 30, 35)
# Create a DataFrame
df <- data.frame(Name = name, Age = age)
# Display the DataFrame
print(df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35#i. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
# New data to insert
new_data <- data.frame(Name = c("Charlie", "David"), Age = c(35, 40))
# Insert new rows
df <- rbind(df, new_data)
# Display the updated DataFrame
print(df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35
## 4 David 40#j. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
# Add a new column
df$Salary <- c(50000, 60000)
# Display the updated DataFrame
print(df)## Name Age Salary
## 1 Alice 25 50000
## 2 Bob 30 60000#k. Create a DataFrame
df <- data.frame(Name = c("Alice", "Bob", "Charlie", "David"), Age = c(25, 30, 35, 40))
# Extract the first 2 rows
first_two_rows <- df[1:2, ]
# Display the extracted rows
print(first_two_rows)## Name Age
## 1 Alice 25
## 2 Bob 30#l. Create a DataFrame
df <- data.frame(Name = c("Charlie", "Alice", "Bob"), Age = c(35, 25, 30))
# Sort the DataFrame by the "Age" column
sorted_df <- df[order(df$Age), ]
# Display the sorted DataFrame
print(sorted_df)## Name Age
## 2 Alice 25
## 3 Bob 30
## 1 Charlie 35#m. Create two DataFrames
df1 <- data.frame(ID = 1:3, Name = c("Alice", "Bob", "Charlie"))
df2 <- data.frame(ID = 2:4, Salary = c(50000, 60000, 70000))
# Merge the DataFrames based on the "ID" column
merged_df <- merge(df1, df2, by = "ID", all = TRUE)
# Display the merged DataFrame
print(merged_df)## ID Name Salary
## 1 1 Alice NA
## 2 2 Bob 50000
## 3 3 Charlie 60000
## 4 4 <NA> 70000#n. Create two DataFrames
df1 <- data.frame(Name = c("Alice", "Bob"), Age = c(25, 30))
df2 <- data.frame(Name = c("Charlie", "David"), Age = c(35, 40))
# Append df2 to the end of df1
appended_df <- rbind(df1, df2)
# Display the appended DataFrame
print(appended_df)## Name Age
## 1 Alice 25
## 2 Bob 30
## 3 Charlie 35
## 4 David 40#o. Load the dplyr package
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Create a sample DataFrame
df <- data.frame(Group = c("A", "A", "B", "B", "C"),
Value = c(10, 15, 25, 20, 30))
# Select rows with maximum value in each group
result <- df %>%
group_by(Group) %>%
filter(Value == max(Value))
# Display the result
print(result)## # A tibble: 3 × 2
## # Groups: Group [3]
## Group Value
## <chr> <dbl>
## 1 A 15
## 2 B 25
## 3 C 30#p. Create two dataframes
df1 <- data.frame(ID = 1:4, Name = c("Alice", "Bob", "Charlie", "David"))
df2 <- data.frame(ID = 2:5, Salary = c(50000, 60000, 70000, 55000))
# Merge the dataframes based on the "ID" column
merged_df <- merge(df1, df2, by = "ID", all = TRUE)
# Display the merged dataframe
print(merged_df)## ID Name Salary
## 1 1 Alice NA
## 2 2 Bob 50000
## 3 3 Charlie 60000
## 4 4 David 70000
## 5 5 <NA> 55000#q.a. Read data from the console
data <- as.numeric(readline("Enter a number: "))## Enter a number:print(data)## [1] NA#q.b. reading data from csv file
data=read.csv("C:/Users/java/Desktop/heart.csv")
data## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 2 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 4 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 5 62 0 0 138 294 1 1 106 0 1.9 1 3 2
## 6 58 0 0 100 248 0 0 122 0 1.0 1 0 2
## 7 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 8 55 1 0 160 289 0 0 145 1 0.8 1 1 3
## 9 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 10 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 11 71 0 0 112 149 0 1 125 0 1.6 1 0 2
## 12 43 0 0 132 341 1 0 136 1 3.0 1 0 3
## 13 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 14 51 1 0 140 298 0 1 122 1 4.2 1 3 3
## 15 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 16 34 0 1 118 210 0 1 192 0 0.7 2 0 2
## 17 51 0 2 140 308 0 0 142 0 1.5 2 1 2
## 18 54 1 0 124 266 0 0 109 1 2.2 1 1 3
## 19 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 20 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 21 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 22 67 0 0 106 223 0 1 142 0 0.3 2 2 2
## 23 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 24 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 25 42 0 2 120 209 0 1 173 0 0.0 1 0 2
## 26 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 27 44 1 2 130 233 0 1 179 1 0.4 2 0 2
## 28 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 29 56 1 2 130 256 1 0 142 1 0.6 1 1 1
## 30 55 0 0 180 327 0 2 117 1 3.4 1 0 2
## 31 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 32 50 0 1 120 244 0 1 162 0 1.1 2 0 2
## 33 57 1 0 130 131 0 1 115 1 1.2 1 1 3
## 34 70 1 2 160 269 0 1 112 1 2.9 1 1 3
## 35 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 36 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 37 51 1 3 125 213 0 0 125 1 1.4 2 1 2
## 38 59 1 0 138 271 0 0 182 0 0.0 2 0 2
## 39 64 1 0 128 263 0 1 105 1 0.2 1 1 3
## 40 57 1 2 128 229 0 0 150 0 0.4 1 1 3
## 41 65 0 2 160 360 0 0 151 0 0.8 2 0 2
## 42 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 43 61 0 0 130 330 0 0 169 0 0.0 2 0 2
## 44 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 45 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 46 42 1 0 140 226 0 1 178 0 0.0 2 0 2
## 47 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 48 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 49 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 50 60 1 0 117 230 1 1 160 1 1.4 2 2 3
## 51 58 0 3 150 283 1 0 162 0 1.0 2 0 2
## 52 57 0 0 140 241 0 1 123 1 0.2 1 0 3
## 53 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 54 49 1 2 120 188 0 1 139 0 2.0 1 3 3
## 55 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 56 55 1 0 140 217 0 1 111 1 5.6 0 0 3
## 57 56 1 3 120 193 0 0 162 0 1.9 1 0 3
## 58 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 59 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 60 57 1 1 154 232 0 0 164 0 0.0 2 1 2
## 61 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 62 66 0 2 146 278 0 0 152 0 0.0 1 1 2
## 63 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 64 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 65 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 66 59 1 3 170 288 0 0 159 0 0.2 1 0 3
## 67 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 68 42 1 0 136 315 0 1 125 1 1.8 1 0 1
## 69 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 70 62 0 0 160 164 0 0 145 0 6.2 0 3 3
## 71 59 1 0 170 326 0 0 140 1 3.4 0 0 3
## 72 61 1 0 140 207 0 0 138 1 1.9 2 1 3
## 73 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 74 59 1 0 140 177 0 1 162 1 0.0 2 1 3
## 75 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 76 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 77 48 1 2 124 255 1 1 175 0 0.0 2 2 2
## 78 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 79 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 80 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 81 50 1 2 140 233 0 1 163 0 0.6 1 1 3
## 82 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 83 46 1 2 150 231 0 1 147 0 3.6 1 0 2
## 84 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 85 37 0 2 120 215 0 1 170 0 0.0 2 0 2
## 86 44 1 1 120 220 0 1 170 0 0.0 2 0 2
## 87 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 88 59 0 0 174 249 0 1 143 1 0.0 1 0 2
## 89 62 0 0 140 268 0 0 160 0 3.6 0 2 2
## 90 68 1 0 144 193 1 1 141 0 3.4 1 2 3
## 91 54 0 2 108 267 0 0 167 0 0.0 2 0 2
## 92 62 0 0 124 209 0 1 163 0 0.0 2 0 2
## 93 63 1 0 140 187 0 0 144 1 4.0 2 2 3
## 94 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 95 62 1 1 128 208 1 0 140 0 0.0 2 0 2
## 96 45 0 0 138 236 0 0 152 1 0.2 1 0 2
## 97 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 98 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 99 65 1 0 110 248 0 0 158 0 0.6 2 2 1
## 100 76 0 2 140 197 0 2 116 0 1.1 1 0 2
## 101 43 0 2 122 213 0 1 165 0 0.2 1 0 2
## 102 57 1 2 150 126 1 1 173 0 0.2 2 1 3
## 103 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 104 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 105 52 1 3 118 186 0 0 190 0 0.0 1 0 1
## 106 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 107 51 1 0 140 299 0 1 173 1 1.6 2 0 3
## 108 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 109 40 1 0 152 223 0 1 181 0 0.0 2 0 3
## 110 54 1 0 110 206 0 0 108 1 0.0 1 1 2
## 111 44 1 0 110 197 0 0 177 0 0.0 2 1 2
## 112 53 1 0 142 226 0 0 111 1 0.0 2 0 3
## 113 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 114 57 1 0 110 335 0 1 143 1 3.0 1 1 3
## 115 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 116 61 0 0 145 307 0 0 146 1 1.0 1 0 3
## 117 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 118 43 1 0 120 177 0 0 120 1 2.5 1 0 3
## 119 29 1 1 130 204 0 0 202 0 0.0 2 0 2
## 120 42 1 1 120 295 0 1 162 0 0.0 2 0 2
## 121 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 122 44 1 0 120 169 0 1 144 1 2.8 0 0 1
## 123 60 1 0 145 282 0 0 142 1 2.8 1 2 3
## 124 65 0 2 140 417 1 0 157 0 0.8 2 1 2
## 125 61 1 0 120 260 0 1 140 1 3.6 1 1 3
## 126 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 127 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 128 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 129 52 1 2 138 223 0 1 169 0 0.0 2 4 2
## 130 57 1 0 140 192 0 1 148 0 0.4 1 0 1
## 131 60 0 3 150 240 0 1 171 0 0.9 2 0 2
## 132 51 0 2 130 256 0 0 149 0 0.5 2 0 2
## 133 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 134 50 1 2 129 196 0 1 163 0 0.0 2 0 2
## 135 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 136 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 137 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 138 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 139 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 140 41 1 1 110 235 0 1 153 0 0.0 2 0 2
## 141 57 1 0 152 274 0 1 88 1 1.2 1 1 3
## 142 63 0 0 124 197 0 1 136 1 0.0 1 0 2
## 143 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 144 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 145 47 1 0 112 204 0 1 143 0 0.1 2 0 2
## 146 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 147 51 0 2 120 295 0 0 157 0 0.6 2 0 2
## 148 41 1 0 110 172 0 0 158 0 0.0 2 0 3
## 149 52 1 3 152 298 1 1 178 0 1.2 1 0 3
## 150 39 1 2 140 321 0 0 182 0 0.0 2 0 2
## 151 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 152 54 1 1 192 283 0 0 195 0 0.0 2 1 3
## 153 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 154 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 155 63 1 0 130 330 1 0 132 1 1.8 2 3 3
## 156 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 157 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 158 54 1 2 120 258 0 0 147 0 0.4 1 0 3
## 159 67 0 2 115 564 0 0 160 0 1.6 1 0 3
## 160 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 161 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 162 51 1 2 100 222 0 1 143 1 1.2 1 0 2
## 163 77 1 0 125 304 0 0 162 1 0.0 2 3 2
## 164 48 1 0 124 274 0 0 166 0 0.5 1 0 3
## 165 56 1 0 125 249 1 0 144 1 1.2 1 1 2
## 166 59 1 0 170 326 0 0 140 1 3.4 0 0 3
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## 168 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 169 43 1 2 130 315 0 1 162 0 1.9 2 1 2
## 170 45 0 1 112 160 0 1 138 0 0.0 1 0 2
## 171 43 1 0 150 247 0 1 171 0 1.5 2 0 2
## 172 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 173 56 1 1 120 240 0 1 169 0 0.0 0 0 2
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## 175 54 1 0 110 239 0 1 126 1 2.8 1 1 3
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## 177 56 1 0 130 283 1 0 103 1 1.6 0 0 3
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## 179 44 1 0 110 197 0 0 177 0 0.0 2 1 2
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## 182 64 1 3 110 211 0 0 144 1 1.8 1 0 2
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## 187 40 1 0 110 167 0 0 114 1 2.0 1 0 3
## 188 56 1 0 125 249 1 0 144 1 1.2 1 1 2
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## 191 41 1 2 112 250 0 1 179 0 0.0 2 0 2
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## 195 67 1 0 160 286 0 0 108 1 1.5 1 3 2
## 196 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 197 58 1 0 100 234 0 1 156 0 0.1 2 1 3
## 198 45 1 0 115 260 0 0 185 0 0.0 2 0 2
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## 207 59 1 2 126 218 1 1 134 0 2.2 1 1 1
## 208 41 1 2 112 250 0 1 179 0 0.0 2 0 2
## 209 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 210 62 1 1 120 281 0 0 103 0 1.4 1 1 3
## 211 42 1 2 120 240 1 1 194 0 0.8 0 0 3
## 212 67 1 0 100 299 0 0 125 1 0.9 1 2 2
## 213 50 1 0 150 243 0 0 128 0 2.6 1 0 3
## 214 43 1 2 130 315 0 1 162 0 1.9 2 1 2
## 215 45 1 1 128 308 0 0 170 0 0.0 2 0 2
## 216 49 1 1 130 266 0 1 171 0 0.6 2 0 2
## 217 65 1 0 135 254 0 0 127 0 2.8 1 1 3
## 218 41 1 1 120 157 0 1 182 0 0.0 2 0 2
## 219 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 220 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 221 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## 222 63 1 0 130 254 0 0 147 0 1.4 1 1 3
## 223 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 224 39 0 2 94 199 0 1 179 0 0.0 2 0 2
## 225 51 1 0 140 261 0 0 186 1 0.0 2 0 2
## 226 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 227 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 228 44 0 2 118 242 0 1 149 0 0.3 1 1 2
## 229 52 1 1 128 205 1 1 184 0 0.0 2 0 2
## 230 66 0 0 178 228 1 1 165 1 1.0 1 2 3
## 231 58 1 0 125 300 0 0 171 0 0.0 2 2 3
## 232 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 233 60 1 0 125 258 0 0 141 1 2.8 1 1 3
## 234 41 0 1 126 306 0 1 163 0 0.0 2 0 2
## 235 49 0 0 130 269 0 1 163 0 0.0 2 0 2
## 236 64 1 3 170 227 0 0 155 0 0.6 1 0 3
## 237 49 1 2 118 149 0 0 126 0 0.8 2 3 2
## 238 57 1 1 124 261 0 1 141 0 0.3 2 0 3
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## 241 54 0 1 132 288 1 0 159 1 0.0 2 1 2
## 242 67 1 2 152 212 0 0 150 0 0.8 1 0 3
## 243 38 1 2 138 175 0 1 173 0 0.0 2 4 2
## 244 60 1 2 140 185 0 0 155 0 3.0 1 0 2
## 245 51 1 2 125 245 1 0 166 0 2.4 1 0 2
## 246 44 1 1 130 219 0 0 188 0 0.0 2 0 2
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## 248 46 1 0 140 311 0 1 120 1 1.8 1 2 3
## 249 39 0 2 138 220 0 1 152 0 0.0 1 0 2
## 250 42 1 2 130 180 0 1 150 0 0.0 2 0 2
## 251 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 252 45 0 1 112 160 0 1 138 0 0.0 1 0 2
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## 254 57 1 0 165 289 1 0 124 0 1.0 1 3 3
## 255 35 1 0 120 198 0 1 130 1 1.6 1 0 3
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## 257 35 0 0 138 183 0 1 182 0 1.4 2 0 2
## 258 64 0 0 180 325 0 1 154 1 0.0 2 0 2
## 259 38 1 3 120 231 0 1 182 1 3.8 1 0 3
## 260 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 261 44 1 2 120 226 0 1 169 0 0.0 2 0 2
## 262 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 263 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 264 55 0 1 132 342 0 1 166 0 1.2 2 0 2
## 265 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 266 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 267 53 1 0 123 282 0 1 95 1 2.0 1 2 3
## 268 67 1 0 120 237 0 1 71 0 1.0 1 0 2
## 269 58 1 2 132 224 0 0 173 0 3.2 2 2 3
## 270 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 271 43 1 0 110 211 0 1 161 0 0.0 2 0 3
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