Homework 10
Sam Brewer
3/30/2022
Homework 10
- Setting counter variable and for loop to calculate the number of zeros in a numeric vector
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.2## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v stringr 1.4.0
## v tidyr 1.2.0 v forcats 0.5.1
## v readr 2.1.2## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'tibble' was built under R version 4.1.2## Warning: package 'tidyr' was built under R version 4.1.2## Warning: package 'readr' was built under R version 4.1.2## Warning: package 'purrr' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## Warning: package 'forcats' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()####################################################
# FUNCTION: count_zeros
# purpose: calculate the number of zeroes in a numeric vector
# input: numeric vector
# output: numeric value
# ----------------------------------
count_zeros <- function(size=100) {
num_vec <- floor(runif(size,
min=0,
max=10))
counter <- 0
for(i in 1:length(num_vec)){
if(num_vec[i]==0){
counter <- (counter + 1)
}
}
return(counter)
}
count_zeros()## [1] 10- Subsetting instead for question 1
num_vec <- floor(runif(100, min=0, max=10))
sum(num_vec==0)## [1] 7Question 3
####################################################
# FUNCTION: matrix_math
# purpose: multiply row and column numbers in matrix
# input: two integers representing the number of rows and columns in a matrix
# output: matrix of these dimensions
# ----------------------------------
two_integers <- floor(runif(2,
min=1,
max=5))
matrix_math <- function(x=NULL,y=NULL) {
new_matrix <- matrix(data=NA,
nrow=x,
ncol=y)
for(i in 1:nrow(new_matrix)){
for(j in 1:ncol(new_matrix)){
new_matrix[i,j] <- i*j
}
}
return(new_matrix)
}
x1 <- two_integers[1]
y1 <- two_integers[2]
matrix_math(x=x1,y=y1)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 2 4 6
## [3,] 3 6 9Question 4
dataset <- c(rep("Group1",3),
rep("Group2",3),
rep("Group3",3))
print(dataset)## [1] "Group1" "Group1" "Group1" "Group2" "Group2" "Group2" "Group3" "Group3"
## [9] "Group3"response <- c(runif(4) +1, runif(5) + 10)
df1 <- data.frame(Group=dataset, Response.Variable=response)
print(df)## function (x, df1, df2, ncp, log = FALSE)
## {
## if (missing(ncp))
## .Call(C_df, x, df1, df2, log)
## else .Call(C_dnf, x, df1, df2, ncp, log)
## }
## <bytecode: 0x0000000023e79db0>
## <environment: namespace:stats>####################################################
# FUNCTION: 4b
# purpose: reshuffles the response variable
# calculates the mean of each group in the reshuffled data
# and store the means
# input: numeric vector
# output: vector of length 3
# ----------------------------------
four_b <- function(df=df1) {
df2 <- data.frame(Group=df1$Group, Response.Variable=sample(x=df1$Response.Variable,size=length(df1$Response.Variable)))
with_means <- group_by(df2, Group)
grouped_data <- summarize(with_means, meanRes = mean(Response.Variable, na.rm=T), N = n())
return(grouped_data)
}
stats <- data.frame(NA)
for(i in 1:100){
stats[i,2:4]<- four_b() %>%
pivot_wider(names_from=Group,
values_from = meanRes) %>%
select(Group1:Group3)
stats[i,1] <- i
}
names(stats)[1] <- "Rep"
print(stats)## Rep Group1 Group2 Group3
## 1 1 7.542136 4.431518 7.475646
## 2 2 1.338706 10.545603 7.564991
## 3 3 4.154976 7.769701 7.524623
## 4 4 1.286965 7.671698 10.490638
## 5 5 7.391637 1.286965 10.770699
## 6 6 7.397532 7.593877 4.457892
## 7 7 4.281774 4.529815 10.637712
## 8 8 4.365783 4.220710 10.862807
## 9 9 4.500771 10.582746 4.365783
## 10 10 7.527849 7.489640 4.431811
## 11 11 7.602444 1.286965 10.559891
## 12 12 7.619957 7.299528 4.529815
## 13 13 7.262075 7.671698 4.515528
## 14 14 4.394065 7.368781 7.686454
## 15 15 4.449031 10.490638 4.509632
## 16 16 4.431518 7.475646 7.542136
## 17 17 4.431518 10.637712 4.380071
## 18 18 4.457892 4.500771 10.490638
## 19 19 7.368781 4.529815 7.550704
## 20 20 7.593877 4.304720 7.550704
## 21 21 4.394065 7.475646 7.579589
## 22 22 4.070966 7.622627 7.755707
## 23 23 7.671698 4.380071 7.397532
## 24 24 7.608633 4.070966 7.769701
## 25 25 4.281774 10.582746 4.584781
## 26 26 4.435036 7.602444 7.411820
## 27 27 4.379777 7.593877 7.475646
## 28 28 7.542136 7.475646 4.431518
## 29 29 7.452791 7.616732 4.379777
## 30 30 4.154976 4.445805 10.848519
## 31 31 4.380071 7.602444 7.466785
## 32 32 7.671698 7.262075 4.515528
## 33 33 4.523626 7.262075 7.663599
## 34 34 4.394065 4.206716 10.848519
## 35 35 7.622627 7.391637 4.435036
## 36 36 4.435036 10.582746 4.431518
## 37 37 1.338706 10.559891 7.550704
## 38 38 4.168970 4.417524 10.862807
## 39 39 7.475646 1.202955 10.770699
## 40 40 7.593877 10.568459 1.286965
## 41 41 4.435036 4.220710 10.793554
## 42 42 4.351027 10.568459 4.529815
## 43 43 7.472883 4.486777 7.489640
## 44 44 4.380071 10.848519 4.220710
## 45 45 7.313816 4.471886 7.663599
## 46 46 7.489640 7.527849 4.431811
## 47 47 4.431518 10.582746 4.435036
## 48 48 7.489640 7.593877 4.365783
## 49 49 4.445805 10.637712 4.365783
## 50 50 10.545603 7.616732 1.286965
## 51 51 7.391637 7.527849 4.529815
## 52 52 7.663599 4.445805 7.339896
## 53 53 4.500771 7.383538 7.564991
## 54 54 1.188961 10.637712 7.622627
## 55 55 10.582746 7.579589 1.286965
## 56 56 4.435036 7.397532 7.616732
## 57 57 4.394065 4.509632 10.545603
## 58 58 1.286965 7.593877 10.568459
## 59 59 4.373882 10.559891 4.515528
## 60 60 4.584781 4.296061 10.568459
## 61 61 7.542136 4.220710 7.686454
## 62 62 7.691880 4.281774 7.475646
## 63 63 4.296061 4.515528 10.637712
## 64 64 7.452791 7.602444 4.394065
## 65 65 7.677593 7.262075 4.509632
## 66 66 4.529815 7.339896 7.579589
## 67 67 7.313816 7.686454 4.449031
## 68 68 10.862807 4.206716 4.379777
## 69 69 4.435036 7.411820 7.602444
## 70 70 7.700742 4.168970 7.579589
## 71 71 7.452791 4.523626 7.472883
## 72 72 7.677593 4.296061 7.475646
## 73 73 4.380071 7.299528 7.769701
## 74 74 10.559891 1.286965 7.602444
## 75 75 10.545603 1.338706 7.564991
## 76 76 7.391637 4.584781 7.472883
## 77 77 4.486777 7.247788 7.714736
## 78 78 7.564991 4.417524 7.466785
## 79 79 7.368781 4.379777 7.700742
## 80 80 7.247788 7.671698 4.529815
## 81 81 7.677593 7.564991 4.206716
## 82 82 7.677886 7.391637 4.379777
## 83 83 7.677886 4.431518 7.339896
## 84 84 7.299528 7.700742 4.449031
## 85 85 4.070966 10.848519 4.529815
## 86 86 4.471886 7.383538 7.593877
## 87 87 4.220710 10.862807 4.365783
## 88 88 1.286965 10.862807 7.299528
## 89 89 7.677593 7.616732 4.154976
## 90 90 7.593877 7.475646 4.379777
## 91 91 7.579589 7.714736 4.154976
## 92 92 7.317041 4.607636 7.524623
## 93 93 7.691880 4.281774 7.475646
## 94 94 4.379777 7.616732 7.452791
## 95 95 7.391637 7.663599 4.394065
## 96 96 4.449031 7.391637 7.608633
## 97 97 7.472883 7.466785 4.509632
## 98 98 4.304720 10.862807 4.281774
## 99 99 7.593877 7.383538 4.471886
## 100 100 7.313816 7.677593 4.457892qplot(stats$Group1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(stats$Group2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(stats$Group3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.