Dis3
Yinda Chen
2023-09-16
I. Classes are types of data in R, types of basic variables in R,
such as numeric, integer, character, logical and complex. And one object
can have more than one classes. For example, “1” is an object, and its
class is integer. “Hello” is also an object, and its class is
character.
Data Structure are some ways to organize and store the
data. Common data structures in R include vectors, matrices, arrays,
lists, data frames, and factors. For example, a vector is a
one-dimensional array that can hold elements of the same data type, like
a sequence of numbers or character strings.
Vec <- c(1, 2, 3)
print(Vec)## [1] 1 2 3
I pick CO2 from base R
data()
mydata <- CO2
class(mydata)## [1] "nfnGroupedData" "nfGroupedData" "groupedData" "data.frame"typeof(mydata)## [1] "list"The function class() shows the dataset’s type is data.frame, and the
function typeof() shows the dataset’s type is list. class() shows the
class of an object while typeof() shows the low level data type.
II.
my_vector <- c(2, 4, 6, 8, 10, 12, 14)
R_StandardDeviation_InBuilt <- sd(my_vector)
print(R_StandardDeviation_InBuilt)## [1] 4.320494mean_vector <- mean(my_vector)
squared_diff <- (my_vector - mean_vector)^2
variance <- sum(squared_diff) / (length(my_vector) - 1)
R_StandardDeviation_Hand <- sqrt(variance)
print(R_StandardDeviation_Hand)## [1] 4.320494test1 <- c(10, 20, 30)
test2 <- c(20, 30, 40)
sdtest1 <- sd(test1)
sdtest2 <- sd(test2)
cat(sdtest1, sdtest2)## 10 10I speculate it should be standard deviation
test1 <- c(3, 5, 6, 7, 8, 12, 14, 23, 15, 32)
test2 <- c(5, 18, 23, 54, 108)
mean1 <- mean(test1)
mean2 <- mean(test2)
cat(mean1, mean2)## 12.5 41.6I speculate it should be mean value
# function that change Celsius or Fahrenheit to the other one
my_function <- function(x, y){
# input number in x, and input C in y representing C to F, input F in y representing F to C
if (y == "C" | y == "c"){
x = 32 + 1.8*x
result <- list(x, "F")
}else if(y == "F"| y == "f"){
x = 5*(x - 32)/9
result <- list(x, "C")
}else{
result <- "ERROR"
}
return(result)
}Test the function
print(my_function(77, "F"))## [[1]]
## [1] 25
##
## [[2]]
## [1] "C"IV.I still using CO2 as the dataset. CO2 is a cross-sectional dataset.
print(mydata)## Plant Type Treatment conc uptake
## 1 Qn1 Quebec nonchilled 95 16.0
## 2 Qn1 Quebec nonchilled 175 30.4
## 3 Qn1 Quebec nonchilled 250 34.8
## 4 Qn1 Quebec nonchilled 350 37.2
## 5 Qn1 Quebec nonchilled 500 35.3
## 6 Qn1 Quebec nonchilled 675 39.2
## 7 Qn1 Quebec nonchilled 1000 39.7
## 8 Qn2 Quebec nonchilled 95 13.6
## 9 Qn2 Quebec nonchilled 175 27.3
## 10 Qn2 Quebec nonchilled 250 37.1
## 11 Qn2 Quebec nonchilled 350 41.8
## 12 Qn2 Quebec nonchilled 500 40.6
## 13 Qn2 Quebec nonchilled 675 41.4
## 14 Qn2 Quebec nonchilled 1000 44.3
## 15 Qn3 Quebec nonchilled 95 16.2
## 16 Qn3 Quebec nonchilled 175 32.4
## 17 Qn3 Quebec nonchilled 250 40.3
## 18 Qn3 Quebec nonchilled 350 42.1
## 19 Qn3 Quebec nonchilled 500 42.9
## 20 Qn3 Quebec nonchilled 675 43.9
## 21 Qn3 Quebec nonchilled 1000 45.5
## 22 Qc1 Quebec chilled 95 14.2
## 23 Qc1 Quebec chilled 175 24.1
## 24 Qc1 Quebec chilled 250 30.3
## 25 Qc1 Quebec chilled 350 34.6
## 26 Qc1 Quebec chilled 500 32.5
## 27 Qc1 Quebec chilled 675 35.4
## 28 Qc1 Quebec chilled 1000 38.7
## 29 Qc2 Quebec chilled 95 9.3
## 30 Qc2 Quebec chilled 175 27.3
## 31 Qc2 Quebec chilled 250 35.0
## 32 Qc2 Quebec chilled 350 38.8
## 33 Qc2 Quebec chilled 500 38.6
## 34 Qc2 Quebec chilled 675 37.5
## 35 Qc2 Quebec chilled 1000 42.4
## 36 Qc3 Quebec chilled 95 15.1
## 37 Qc3 Quebec chilled 175 21.0
## 38 Qc3 Quebec chilled 250 38.1
## 39 Qc3 Quebec chilled 350 34.0
## 40 Qc3 Quebec chilled 500 38.9
## 41 Qc3 Quebec chilled 675 39.6
## 42 Qc3 Quebec chilled 1000 41.4
## 43 Mn1 Mississippi nonchilled 95 10.6
## 44 Mn1 Mississippi nonchilled 175 19.2
## 45 Mn1 Mississippi nonchilled 250 26.2
## 46 Mn1 Mississippi nonchilled 350 30.0
## 47 Mn1 Mississippi nonchilled 500 30.9
## 48 Mn1 Mississippi nonchilled 675 32.4
## 49 Mn1 Mississippi nonchilled 1000 35.5
## 50 Mn2 Mississippi nonchilled 95 12.0
## 51 Mn2 Mississippi nonchilled 175 22.0
## 52 Mn2 Mississippi nonchilled 250 30.6
## 53 Mn2 Mississippi nonchilled 350 31.8
## 54 Mn2 Mississippi nonchilled 500 32.4
## 55 Mn2 Mississippi nonchilled 675 31.1
## 56 Mn2 Mississippi nonchilled 1000 31.5
## 57 Mn3 Mississippi nonchilled 95 11.3
## 58 Mn3 Mississippi nonchilled 175 19.4
## 59 Mn3 Mississippi nonchilled 250 25.8
## 60 Mn3 Mississippi nonchilled 350 27.9
## 61 Mn3 Mississippi nonchilled 500 28.5
## 62 Mn3 Mississippi nonchilled 675 28.1
## 63 Mn3 Mississippi nonchilled 1000 27.8
## 64 Mc1 Mississippi chilled 95 10.5
## 65 Mc1 Mississippi chilled 175 14.9
## 66 Mc1 Mississippi chilled 250 18.1
## 67 Mc1 Mississippi chilled 350 18.9
## 68 Mc1 Mississippi chilled 500 19.5
## 69 Mc1 Mississippi chilled 675 22.2
## 70 Mc1 Mississippi chilled 1000 21.9
## 71 Mc2 Mississippi chilled 95 7.7
## 72 Mc2 Mississippi chilled 175 11.4
## 73 Mc2 Mississippi chilled 250 12.3
## 74 Mc2 Mississippi chilled 350 13.0
## 75 Mc2 Mississippi chilled 500 12.5
## 76 Mc2 Mississippi chilled 675 13.7
## 77 Mc2 Mississippi chilled 1000 14.4
## 78 Mc3 Mississippi chilled 95 10.6
## 79 Mc3 Mississippi chilled 175 18.0
## 80 Mc3 Mississippi chilled 250 17.9
## 81 Mc3 Mississippi chilled 350 17.9
## 82 Mc3 Mississippi chilled 500 17.9
## 83 Mc3 Mississippi chilled 675 18.9
## 84 Mc3 Mississippi chilled 1000 19.9library(ggplot2)
library(e1071)
ggplot(mydata, aes(x = conc)) + geom_density() + geom_density(fill="red") + labs(title = "Density Plot", x = "conc", y = "Density")
skew <- skewness(mydata$conc)
print(skew)## [1] 0.7201458As we can see, this is a right skewed distribution. The skewness value is between 0.5 and 1, so it’s Morderate Skewness.