4.18 Problem Set: Statistics
2023-01-25
R Markdown
#1. Create three vectors of numbers. For each, print the vector, the mean, the median, the variance, and the standard deviation.
A.Vector 1 should have the mean and median equal.
B. Vector 2 should have the mean greater than the median.
C. Vector 3 should have the mean less than the median.
# Vector 1
vec1 <- c(1, 2, 3, 4, 5)
mean1 <- mean(vec1)
median1 <- median(vec1)
var1 <- var(vec1)
sd1 <- sd(vec1)
print("Vector 1:")## [1] "Vector 1:"print(vec1)## [1] 1 2 3 4 5print("Mean:")## [1] "Mean:"print(mean1)## [1] 3print("Median:")## [1] "Median:"print(median1)## [1] 3print("Variance:")## [1] "Variance:"print(var1)## [1] 2.5print("Standard Deviation:")## [1] "Standard Deviation:"print(sd1)## [1] 1.581139# Vector 2
vec2 <- c(1, 2, 3, 4, 6)
mean2 <- mean(vec2)
median2 <- median(vec2)
var2 <- var(vec2)
sd2 <- sd(vec2)
print("Vector 2:")## [1] "Vector 2:"print(vec2)## [1] 1 2 3 4 6print("Mean:")## [1] "Mean:"print(mean2)## [1] 3.2print("Median:")## [1] "Median:"print(median2)## [1] 3print("Variance:")## [1] "Variance:"print(var2)## [1] 3.7print("Standard Deviation:")## [1] "Standard Deviation:"print(sd2)## [1] 1.923538# Vector 3
vec3 <- c(1, 2, 4, 4, 6)
mean3 <- mean(vec3)
median3 <- median(vec3)
var3 <- var(vec3)
sd3 <- sd(vec3)
print("Vector 3:")## [1] "Vector 3:"print(vec3)## [1] 1 2 4 4 6print("Mean:")## [1] "Mean:"print(mean3)## [1] 3.4print("Median:")## [1] "Median:"print(median3)## [1] 4print("Variance:")## [1] "Variance:"print(var3)## [1] 3.8print("Standard Deviation:")## [1] "Standard Deviation:"print(sd3)## [1] 1.949359#2.Use R to solve each of the following problems.
A. The head of quality control stops by a factory to inspect the next 20 widgets produced. Over the past six months, 98% of widgets have passed inspection. What is the probability that less than 10 widgets pass inspection?
B.The average test score in a large college statistics course with multiple sections is 85 and the standard deviation is five points. If you choose a student at random, what is the probability that their score is between 80 and 85?
C. Over the past three years at the farm you manage, the watermelons average about nine pounds each with a standard deviation of two pounds. What is the probability that a watermelon randomly selected from your harvest will weigh more than 10 pounds?
D. You manage a help desk. 90% of your tickets are resolved with the first reply. If you randomly select 10 tickets for review, what is the probability that all 10 were resolved with the first reply?
# Problem A
test_size <- 20
test_prob <- 0.98
pbinom(10, size = test_size, prob = test_prob)## [1] 1.574945e-12# Problem B
mean <- 85
sd <- 5
z <- (80-mean)/sd
Probabilty <- pnorm(abs(z))
print(Probabilty)## [1] 0.8413447# Problem C
mean <- 9
sd <- 2
prob3 <- pnorm(10, mean = mean, sd = sd, lower.tail = FALSE)
print(prob3)## [1] 0.3085375# Problem D
test_size <- 10
test_prob <- 0.90
dbinom(10 , size = test_size , prob = test_prob)## [1] 0.34867843. From the MASS package, import the dataset “immer.”
A. Read the documentation and describe the dataset in your own words.
B. Is this data tidy? If not, tidy it and print the tidy dataset. Save your tidy dataset in a variable and work with it through the rest of the problem set.
C. Use any tools that you like in R to answer the following questions.
#What values are present in the location variable? How many rows are there for each value? #What values are present in the variety variable? How many rows are there for each value? #How many years are represented in the dataset? How many rows are there for each year? #What is the mean yield? #What is the first quartile, median, and third quartile for yield? #What are the minimum and maximum values for yield? #What are the variance and standard deviation for yield? #Create two boxplots for yield. #The first boxplot should have bars that extend from the minimum to the maximum. #The second boxplot should have bars that extend from (Q1 - 1.5 x IQR) to (Q3 + 1.5 x IQR)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 1.0.1
## ✔ tibble 3.1.7 ✔ dplyr 1.0.10
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectdata(immer)#A #The “immer” dataset is a data set from a study on the effects of a fertilizer treatment on the yield of a barley variety called “Immer” grown in four different locations. The data consists of a randomized block design with three replicates per location. The treatment is a factor with two levels: control and nitro. The response variable is the yield of barley in bushels per acre. It includes four factors, location, replicate, nitro and block and a response variable yield.
#B #This data is not in tidy format, as the location, replicate, nitro and block variables are spread across multiple columns. To make this data tidy, we can use the “gather” function from the tidyr package to gather these variables into a single column called “variable” and the corresponding values into a column called “value”.
#Tidying up the data
#Renaming collums
names(immer)[3] <- 1931
names(immer)[4] <- 1932
# Load tidyverse package to use pivot
library(tidyverse)
# Pivot immer df from wide to longer and assign tidy df to newImmer
newImmer <- pivot_longer(
data = immer,
cols = colnames(immer[3:4]),
names_to = "Year",
values_to = "Yield"
)
print(newImmer)## # A tibble: 60 × 4
## Loc Var Year Yield
## <fct> <fct> <chr> <dbl>
## 1 UF M 1931 81
## 2 UF M 1932 80.7
## 3 UF S 1931 105.
## 4 UF S 1932 82.3
## 5 UF V 1931 120.
## 6 UF V 1932 80.4
## 7 UF T 1931 110.
## 8 UF T 1932 87.2
## 9 UF P 1931 98.3
## 10 UF P 1932 84.2
## # … with 50 more rows#C
unique(newImmer)## # A tibble: 60 × 4
## Loc Var Year Yield
## <fct> <fct> <chr> <dbl>
## 1 UF M 1931 81
## 2 UF M 1932 80.7
## 3 UF S 1931 105.
## 4 UF S 1932 82.3
## 5 UF V 1931 120.
## 6 UF V 1932 80.4
## 7 UF T 1931 110.
## 8 UF T 1932 87.2
## 9 UF P 1931 98.3
## 10 UF P 1932 84.2
## # … with 50 more rowsLocation valuables: UF, W, M, C, GR, and D. Data set presents
10 rows for these values.
#Variety variables: M, S, V, T, and P. Data set presents 12 rows #for these values.
# Statistical summary of the newImmer df
summary(newImmer)## Loc Var Year Yield
## C :10 M:12 Length:60 Min. : 49.90
## D :10 P:12 Class :character 1st Qu.: 80.62
## GR:10 S:12 Mode :character Median : 97.50
## M :10 T:12 Mean :101.09
## UF:10 V:12 3rd Qu.:119.72
## W :10 Max. :191.50#Mean yield - 101.09 #First quartile for yield - 80.62 #Median for yield - 97.50 #Third quartile for yield - 119.72 #Minimum value for yield - 49.90 #Maximum value for yield - 191.50
var(newImmer$Yield)## [1] 758.1755sd(newImmer$Yield)## [1] 27.53499#Variance for yield - 758.18 #Sd for yield - 27.53
boxplot(newImmer$Yield,
main = "Barley Yield",
horizontal = TRUE,
range = 0) 
#Boxplot #1
boxplot(newImmer$Yield,
main = "Barley Yield (Q1 - 1.5 x IQR) to (Q3 + 1.5 x IQR)",
horizontal = TRUE,
range = 1.5) 
## Boxplot #2
#4 From the MASS package, import the dataset “Pima.tr.” #Read the documentation and describe the data in your own words. #Is the data tidy? If not, tidy it. #Write a loop that calculates and prints the mean of each numeric variable.
?Pima.tr
pimadataset <- Pima.tr
pimadataset <- as_tibble(pimadataset)
(pimadataset)## # A tibble: 200 × 8
## npreg glu bp skin bmi ped age type
## <int> <int> <int> <int> <dbl> <dbl> <int> <fct>
## 1 5 86 68 28 30.2 0.364 24 No
## 2 7 195 70 33 25.1 0.163 55 Yes
## 3 5 77 82 41 35.8 0.156 35 No
## 4 0 165 76 43 47.9 0.259 26 No
## 5 0 107 60 25 26.4 0.133 23 No
## 6 5 97 76 27 35.6 0.378 52 Yes
## 7 3 83 58 31 34.3 0.336 25 No
## 8 1 193 50 16 25.9 0.655 24 No
## 9 3 142 80 15 32.4 0.2 63 No
## 10 2 128 78 37 43.3 1.22 31 Yes
## # … with 190 more rowsdata(Pima.tr)
unique(Pima.tr)## npreg glu bp skin bmi ped age type
## 1 5 86 68 28 30.2 0.364 24 No
## 2 7 195 70 33 25.1 0.163 55 Yes
## 3 5 77 82 41 35.8 0.156 35 No
## 4 0 165 76 43 47.9 0.259 26 No
## 5 0 107 60 25 26.4 0.133 23 No
## 6 5 97 76 27 35.6 0.378 52 Yes
## 7 3 83 58 31 34.3 0.336 25 No
## 8 1 193 50 16 25.9 0.655 24 No
## 9 3 142 80 15 32.4 0.200 63 No
## 10 2 128 78 37 43.3 1.224 31 Yes
## 11 0 137 40 35 43.1 2.288 33 Yes
## 12 9 154 78 30 30.9 0.164 45 No
## 13 1 189 60 23 30.1 0.398 59 Yes
## 14 12 92 62 7 27.6 0.926 44 Yes
## 15 1 86 66 52 41.3 0.917 29 No
## 16 4 99 76 15 23.2 0.223 21 No
## 17 1 109 60 8 25.4 0.947 21 No
## 18 11 143 94 33 36.6 0.254 51 Yes
## 19 1 149 68 29 29.3 0.349 42 Yes
## 20 0 139 62 17 22.1 0.207 21 No
## 21 2 99 70 16 20.4 0.235 27 No
## 22 1 100 66 29 32.0 0.444 42 No
## 23 4 83 86 19 29.3 0.317 34 No
## 24 0 101 64 17 21.0 0.252 21 No
## 25 1 87 68 34 37.6 0.401 24 No
## 26 9 164 84 21 30.8 0.831 32 Yes
## 27 1 99 58 10 25.4 0.551 21 No
## 28 0 140 65 26 42.6 0.431 24 Yes
## 29 5 108 72 43 36.1 0.263 33 No
## 30 2 110 74 29 32.4 0.698 27 No
## 31 1 79 60 42 43.5 0.678 23 No
## 32 3 148 66 25 32.5 0.256 22 No
## 33 0 121 66 30 34.3 0.203 33 Yes
## 34 3 158 64 13 31.2 0.295 24 No
## 35 2 105 80 45 33.7 0.711 29 Yes
## 36 13 145 82 19 22.2 0.245 57 No
## 37 1 79 80 25 25.4 0.583 22 No
## 38 1 71 48 18 20.4 0.323 22 No
## 39 0 102 86 17 29.3 0.695 27 No
## 40 0 119 66 27 38.8 0.259 22 No
## 41 8 176 90 34 33.7 0.467 58 Yes
## 42 1 97 68 21 27.2 1.095 22 No
## 43 4 129 60 12 27.5 0.527 31 No
## 44 1 97 64 19 18.2 0.299 21 No
## 45 0 86 68 32 35.8 0.238 25 No
## 46 2 125 60 20 33.8 0.088 31 No
## 47 5 123 74 40 34.1 0.269 28 No
## 48 2 92 76 20 24.2 1.698 28 No
## 49 3 171 72 33 33.3 0.199 24 Yes
## 50 1 199 76 43 42.9 1.394 22 Yes
## 51 3 116 74 15 26.3 0.107 24 No
## 52 2 83 66 23 32.2 0.497 22 No
## 53 8 154 78 32 32.4 0.443 45 Yes
## 54 1 114 66 36 38.1 0.289 21 No
## 55 1 106 70 28 34.2 0.142 22 No
## 56 4 127 88 11 34.5 0.598 28 No
## 57 1 124 74 36 27.8 0.100 30 No
## 58 1 109 38 18 23.1 0.407 26 No
## 59 2 123 48 32 42.1 0.520 26 No
## 60 8 167 106 46 37.6 0.165 43 Yes
## 61 7 184 84 33 35.5 0.355 41 Yes
## 62 1 96 64 27 33.2 0.289 21 No
## 63 10 129 76 28 35.9 0.280 39 No
## 64 6 92 62 32 32.0 0.085 46 No
## 65 6 109 60 27 25.0 0.206 27 No
## 66 5 139 80 35 31.6 0.361 25 Yes
## 67 6 134 70 23 35.4 0.542 29 Yes
## 68 3 106 54 21 30.9 0.292 24 No
## 69 0 131 66 40 34.3 0.196 22 Yes
## 70 0 135 94 46 40.6 0.284 26 No
## 71 5 158 84 41 39.4 0.395 29 Yes
## 72 3 112 74 30 31.6 0.197 25 Yes
## 73 8 181 68 36 30.1 0.615 60 Yes
## 74 2 121 70 32 39.1 0.886 23 No
## 75 1 168 88 29 35.0 0.905 52 Yes
## 76 1 144 82 46 46.1 0.335 46 Yes
## 77 2 101 58 17 24.2 0.614 23 No
## 78 2 96 68 13 21.1 0.647 26 No
## 79 3 107 62 13 22.9 0.678 23 Yes
## 80 12 121 78 17 26.5 0.259 62 No
## 81 2 100 64 23 29.7 0.368 21 No
## 82 4 154 72 29 31.3 0.338 37 No
## 83 6 125 78 31 27.6 0.565 49 Yes
## 84 10 125 70 26 31.1 0.205 41 Yes
## 85 2 122 76 27 35.9 0.483 26 No
## 86 2 114 68 22 28.7 0.092 25 No
## 87 1 115 70 30 34.6 0.529 32 Yes
## 88 7 114 76 17 23.8 0.466 31 No
## 89 2 115 64 22 30.8 0.421 21 No
## 90 1 130 60 23 28.6 0.692 21 No
## 91 1 79 75 30 32.0 0.396 22 No
## 92 4 112 78 40 39.4 0.236 38 No
## 93 7 150 78 29 35.2 0.692 54 Yes
## 94 1 91 54 25 25.2 0.234 23 No
## 95 1 100 72 12 25.3 0.658 28 No
## 96 12 140 82 43 39.2 0.528 58 Yes
## 97 4 110 76 20 28.4 0.118 27 No
## 98 2 94 76 18 31.6 0.649 23 No
## 99 2 84 50 23 30.4 0.968 21 No
## 100 10 148 84 48 37.6 1.001 51 Yes
## 101 3 61 82 28 34.4 0.243 46 No
## 102 4 117 62 12 29.7 0.380 30 Yes
## 103 3 99 80 11 19.3 0.284 30 No
## 104 3 80 82 31 34.2 1.292 27 Yes
## 105 4 154 62 31 32.8 0.237 23 No
## 106 6 103 72 32 37.7 0.324 55 No
## 107 6 111 64 39 34.2 0.260 24 No
## 108 0 124 70 20 27.4 0.254 36 Yes
## 109 1 143 74 22 26.2 0.256 21 No
## 110 1 81 74 41 46.3 1.096 32 No
## 111 4 189 110 31 28.5 0.680 37 No
## 112 4 116 72 12 22.1 0.463 37 No
## 113 7 103 66 32 39.1 0.344 31 Yes
## 114 8 124 76 24 28.7 0.687 52 Yes
## 115 1 71 78 50 33.2 0.422 21 No
## 116 0 137 84 27 27.3 0.231 59 No
## 117 9 112 82 32 34.2 0.260 36 Yes
## 118 4 148 60 27 30.9 0.150 29 Yes
## 119 1 136 74 50 37.4 0.399 24 No
## 120 9 145 80 46 37.9 0.637 40 Yes
## 121 1 93 56 11 22.5 0.417 22 No
## 122 1 107 72 30 30.8 0.821 24 No
## 123 12 151 70 40 41.8 0.742 38 Yes
## 124 1 97 70 40 38.1 0.218 30 No
## 125 5 144 82 26 32.0 0.452 58 Yes
## 126 2 112 86 42 38.4 0.246 28 No
## 127 2 99 52 15 24.6 0.637 21 No
## 128 1 109 56 21 25.2 0.833 23 No
## 129 1 120 80 48 38.9 1.162 41 No
## 130 7 187 68 39 37.7 0.254 41 Yes
## 131 3 129 92 49 36.4 0.968 32 Yes
## 132 7 179 95 31 34.2 0.164 60 No
## 133 6 80 66 30 26.2 0.313 41 No
## 134 2 105 58 40 34.9 0.225 25 No
## 135 3 191 68 15 30.9 0.299 34 No
## 136 0 95 80 45 36.5 0.330 26 No
## 137 4 99 72 17 25.6 0.294 28 No
## 138 0 137 68 14 24.8 0.143 21 No
## 139 1 97 70 15 18.2 0.147 21 No
## 140 0 100 88 60 46.8 0.962 31 No
## 141 1 167 74 17 23.4 0.447 33 Yes
## 142 0 180 90 26 36.5 0.314 35 Yes
## 143 2 122 70 27 36.8 0.340 27 No
## 144 1 90 62 12 27.2 0.580 24 No
## 145 3 120 70 30 42.9 0.452 30 No
## 146 6 154 78 41 46.1 0.571 27 No
## 147 2 56 56 28 24.2 0.332 22 No
## 148 0 177 60 29 34.6 1.072 21 Yes
## 149 3 124 80 33 33.2 0.305 26 No
## 150 8 85 55 20 24.4 0.136 42 No
## 151 12 88 74 40 35.3 0.378 48 No
## 152 9 152 78 34 34.2 0.893 33 Yes
## 153 0 198 66 32 41.3 0.502 28 Yes
## 154 0 188 82 14 32.0 0.682 22 Yes
## 155 5 139 64 35 28.6 0.411 26 No
## 156 7 168 88 42 38.2 0.787 40 Yes
## 157 2 197 70 99 34.7 0.575 62 Yes
## 158 2 142 82 18 24.7 0.761 21 No
## 159 8 126 74 38 25.9 0.162 39 No
## 160 3 158 76 36 31.6 0.851 28 Yes
## 161 3 130 78 23 28.4 0.323 34 Yes
## 162 2 100 54 28 37.8 0.498 24 No
## 163 1 164 82 43 32.8 0.341 50 No
## 164 4 95 60 32 35.4 0.284 28 No
## 165 2 122 52 43 36.2 0.816 28 No
## 166 4 85 58 22 27.8 0.306 28 No
## 167 0 151 90 46 42.1 0.371 21 Yes
## 168 6 144 72 27 33.9 0.255 40 No
## 169 3 111 90 12 28.4 0.495 29 No
## 170 1 107 68 19 26.5 0.165 24 No
## 171 6 115 60 39 33.7 0.245 40 Yes
## 172 5 105 72 29 36.9 0.159 28 No
## 173 7 194 68 28 35.9 0.745 41 Yes
## 174 4 184 78 39 37.0 0.264 31 Yes
## 175 0 95 85 25 37.4 0.247 24 Yes
## 176 7 124 70 33 25.5 0.161 37 No
## 177 1 111 62 13 24.0 0.138 23 No
## 178 7 137 90 41 32.0 0.391 39 No
## 179 9 57 80 37 32.8 0.096 41 No
## 180 2 157 74 35 39.4 0.134 30 No
## 181 2 95 54 14 26.1 0.748 22 No
## 182 12 140 85 33 37.4 0.244 41 No
## 183 0 117 66 31 30.8 0.493 22 No
## 184 8 100 74 40 39.4 0.661 43 Yes
## 185 9 123 70 44 33.1 0.374 40 No
## 186 0 138 60 35 34.6 0.534 21 Yes
## 187 14 100 78 25 36.6 0.412 46 Yes
## 188 14 175 62 30 33.6 0.212 38 Yes
## 189 0 74 52 10 27.8 0.269 22 No
## 190 1 133 102 28 32.8 0.234 45 Yes
## 191 0 119 64 18 34.9 0.725 23 No
## 192 5 155 84 44 38.7 0.619 34 No
## 193 1 128 48 45 40.5 0.613 24 Yes
## 194 2 112 68 22 34.1 0.315 26 No
## 195 1 140 74 26 24.1 0.828 23 No
## 196 2 141 58 34 25.4 0.699 24 No
## 197 7 129 68 49 38.5 0.439 43 Yes
## 198 0 106 70 37 39.4 0.605 22 No
## 199 1 118 58 36 33.3 0.261 23 No
## 200 8 155 62 26 34.0 0.543 46 Yesnames(Pima.tr)## [1] "npreg" "glu" "bp" "skin" "bmi" "ped" "age" "type"duplicated(Pima.tr)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEglimpse(Pima.tr)## Rows: 200
## Columns: 8
## $ npreg <int> 5, 7, 5, 0, 0, 5, 3, 1, 3, 2, 0, 9, 1, 12, 1, 4, 1, 11, 1, 0, 2,…
## $ glu <int> 86, 195, 77, 165, 107, 97, 83, 193, 142, 128, 137, 154, 189, 92,…
## $ bp <int> 68, 70, 82, 76, 60, 76, 58, 50, 80, 78, 40, 78, 60, 62, 66, 76, …
## $ skin <int> 28, 33, 41, 43, 25, 27, 31, 16, 15, 37, 35, 30, 23, 7, 52, 15, 8…
## $ bmi <dbl> 30.2, 25.1, 35.8, 47.9, 26.4, 35.6, 34.3, 25.9, 32.4, 43.3, 43.1…
## $ ped <dbl> 0.364, 0.163, 0.156, 0.259, 0.133, 0.378, 0.336, 0.655, 0.200, 1…
## $ age <int> 24, 55, 35, 26, 23, 52, 25, 24, 63, 31, 33, 45, 59, 44, 29, 21, …
## $ type <fct> No, Yes, No, No, No, Yes, No, No, No, Yes, Yes, No, Yes, Yes, No…pimadataset[pimadataset$npreg == 1 & pimadataset$age == 22,]## # A tibble: 7 × 8
## npreg glu bp skin bmi ped age type
## <int> <int> <int> <int> <dbl> <dbl> <int> <fct>
## 1 1 79 80 25 25.4 0.583 22 No
## 2 1 71 48 18 20.4 0.323 22 No
## 3 1 97 68 21 27.2 1.10 22 No
## 4 1 199 76 43 42.9 1.39 22 Yes
## 5 1 106 70 28 34.2 0.142 22 No
## 6 1 79 75 30 32 0.396 22 No
## 7 1 93 56 11 22.5 0.417 22 No#A The data consists of several medical predictor variables such as the number of pregnancies the patient has had, their BMI, insulin level, age and so on. The response variable is a binary variable indicating whether the patient has diabetes or not.
#The Pima.tr data set is in a tidy format as the columns are representing variables and the rows are representing observations.
#Write a loop that calculates and prints the mean of each numeric variable
for (i in c(2:5)) {
print(mean(pimadataset[[i]]))
}## [1] 123.97
## [1] 71.26
## [1] 29.215
## [1] 32.31