Q1. Create a function that takes in a name as a string argument, and prints out “Hello name”.
hello_you <- function(name){
print(paste("Greetings", name))
}
hello_you('Jedi')## [1] "Greetings Jedi"Q2. Create a function that will return the division of two integers 10 and 5.
prod <- function(num1, num2){
return(num1 / num2)
}
prod(10, 5)## [1] 2Q1 Create a 2 x 3 matrix with values from 1 to 6, populated horizontally.
mat1 <- matrix(1:6, nrow = 2, byrow = TRUE)
mat1## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6Q3. confirm that mat is a matrix using is.matrix()
is.matrix(mat1)## [1] TRUEQ4. Create a 5 by 5 matrix consisting of the numbers 1-25 and assign it to the name mat2.
The top row should be the numbers 1-5.
mat2 <- matrix(1:25, nrow = 5, byrow = T)
mat2## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 6 7 8 9 10
## [3,] 11 12 13 14 15
## [4,] 16 17 18 19 20
## [5,] 21 22 23 24 25Q5. Using index notation, grab a sub-section of mat2
Hint: mat2[0:1, 2:3] returns 2 3
mat2[2:3, 2:3]## [,1] [,2]
## [1,] 7 8
## [2,] 12 13Q6. Using index notation, grab a sub-section of mat2
mat2[4:5, 4:5]## [,1] [,2]
## [1,] 19 20
## [2,] 24 25Q7. What is the sum of all the elements in mat2?
sum(mat2)## [1] 325Q8. Multiply each element of mat2 by 2
mat2 * 2## [,1] [,2] [,3] [,4] [,5]
## [1,] 2 4 6 8 10
## [2,] 12 14 16 18 20
## [3,] 22 24 26 28 30
## [4,] 32 34 36 38 40
## [5,] 42 44 46 48 50Q1. Load the data set “statex77.csv”
Assign it to df
df <- read.csv("statex77.csv", header = TRUE)
df## X Population Income Illiteracy Life.Exp Murder HS.Grad
## 1 Alabama 3615 3624 2.1 69.05 15.1 41.3
## 2 Alaska 365 6315 1.5 69.31 11.3 66.7
## 3 Arizona 2212 4530 1.8 70.55 7.8 58.1
## 4 Arkansas 2110 3378 1.9 70.66 10.1 39.9
## 5 California 21198 5114 1.1 71.71 10.3 62.6
## 6 Colorado 2541 4884 0.7 72.06 6.8 63.9
## 7 Connecticut 3100 5348 1.1 72.48 3.1 56.0
## 8 Delaware 579 4809 0.9 70.06 6.2 54.6
## 9 Florida 8277 4815 1.3 70.66 10.7 52.6
## 10 Georgia 4931 4091 2.0 68.54 13.9 40.6
## 11 Hawaii 868 4963 1.9 73.60 6.2 61.9
## 12 Idaho 813 4119 0.6 71.87 5.3 59.5
## 13 Illinois 11197 5107 0.9 70.14 10.3 52.6
## 14 Indiana 5313 4458 0.7 70.88 7.1 52.9
## 15 Iowa 2861 4628 0.5 72.56 2.3 59.0
## 16 Kansas 2280 4669 0.6 72.58 4.5 59.9
## 17 Kentucky 3387 3712 1.6 70.10 10.6 38.5
## 18 Louisiana 3806 3545 2.8 68.76 13.2 42.2
## 19 Maine 1058 3694 0.7 70.39 2.7 54.7
## 20 Maryland 4122 5299 0.9 70.22 8.5 52.3
## 21 Massachusetts 5814 4755 1.1 71.83 3.3 58.5
## 22 Michigan 9111 4751 0.9 70.63 11.1 52.8
## 23 Minnesota 3921 4675 0.6 72.96 2.3 57.6
## 24 Mississippi 2341 3098 2.4 68.09 12.5 41.0
## 25 Missouri 4767 4254 0.8 70.69 9.3 48.8
## 26 Montana 746 4347 0.6 70.56 5.0 59.2
## 27 Nebraska 1544 4508 0.6 72.60 2.9 59.3
## 28 Nevada 590 5149 0.5 69.03 11.5 65.2
## 29 New Hampshire 812 4281 0.7 71.23 3.3 57.6
## 30 New Jersey 7333 5237 1.1 70.93 5.2 52.5
## 31 New Mexico 1144 3601 2.2 70.32 9.7 55.2
## 32 New York 18076 4903 1.4 70.55 10.9 52.7
## 33 North Carolina 5441 3875 1.8 69.21 11.1 38.5
## 34 North Dakota 637 5087 0.8 72.78 1.4 50.3
## 35 Ohio 10735 4561 0.8 70.82 7.4 53.2
## 36 Oklahoma 2715 3983 1.1 71.42 6.4 51.6
## 37 Oregon 2284 4660 0.6 72.13 4.2 60.0
## 38 Pennsylvania 11860 4449 1.0 70.43 6.1 50.2
## 39 Rhode Island 931 4558 1.3 71.90 2.4 46.4
## 40 South Carolina 2816 3635 2.3 67.96 11.6 37.8
## 41 South Dakota 681 4167 0.5 72.08 1.7 53.3
## 42 Tennessee 4173 3821 1.7 70.11 11.0 41.8
## 43 Texas 12237 4188 2.2 70.90 12.2 47.4
## 44 Utah 1203 4022 0.6 72.90 4.5 67.3
## 45 Vermont 472 3907 0.6 71.64 5.5 57.1
## 46 Virginia 4981 4701 1.4 70.08 9.5 47.8
## 47 Washington 3559 4864 0.6 71.72 4.3 63.5
## 48 West Virginia 1799 3617 1.4 69.48 6.7 41.6
## 49 Wisconsin 4589 4468 0.7 72.48 3.0 54.5
## 50 Wyoming 376 4566 0.6 70.29 6.9 62.9
## Frost Area
## 1 20 50708
## 2 152 566432
## 3 15 113417
## 4 65 51945
## 5 20 156361
## 6 166 103766
## 7 139 4862
## 8 103 1982
## 9 11 54090
## 10 60 58073
## 11 0 6425
## 12 126 82677
## 13 127 55748
## 14 122 36097
## 15 140 55941
## 16 114 81787
## 17 95 39650
## 18 12 44930
## 19 161 30920
## 20 101 9891
## 21 103 7826
## 22 125 56817
## 23 160 79289
## 24 50 47296
## 25 108 68995
## 26 155 145587
## 27 139 76483
## 28 188 109889
## 29 174 9027
## 30 115 7521
## 31 120 121412
## 32 82 47831
## 33 80 48798
## 34 186 69273
## 35 124 40975
## 36 82 68782
## 37 44 96184
## 38 126 44966
## 39 127 1049
## 40 65 30225
## 41 172 75955
## 42 70 41328
## 43 35 262134
## 44 137 82096
## 45 168 9267
## 46 85 39780
## 47 32 66570
## 48 100 24070
## 49 149 54464
## 50 173 97203Q2. Display the first 6 rows of df
head(df)## X Population Income Illiteracy Life.Exp Murder HS.Grad Frost
## 1 Alabama 3615 3624 2.1 69.05 15.1 41.3 20
## 2 Alaska 365 6315 1.5 69.31 11.3 66.7 152
## 3 Arizona 2212 4530 1.8 70.55 7.8 58.1 15
## 4 Arkansas 2110 3378 1.9 70.66 10.1 39.9 65
## 5 California 21198 5114 1.1 71.71 10.3 62.6 20
## 6 Colorado 2541 4884 0.7 72.06 6.8 63.9 166
## Area
## 1 50708
## 2 566432
## 3 113417
## 4 51945
## 5 156361
## 6 103766names(df)## [1] "X" "Population" "Income" "Illiteracy" "Life.Exp"
## [6] "Murder" "HS.Grad" "Frost" "Area"Q3. What is the average life Expectancy for all the states?
mean(df[, "Life.Exp"])## [1] 70.8786mean(df$Life.Exp)## [1] 70.8786Q4. Select rows(states) that have Income that are higher than 5000
df[df$Income > 5000, ]## X Population Income Illiteracy Life.Exp Murder HS.Grad Frost
## 2 Alaska 365 6315 1.5 69.31 11.3 66.7 152
## 5 California 21198 5114 1.1 71.71 10.3 62.6 20
## 7 Connecticut 3100 5348 1.1 72.48 3.1 56.0 139
## 13 Illinois 11197 5107 0.9 70.14 10.3 52.6 127
## 20 Maryland 4122 5299 0.9 70.22 8.5 52.3 101
## 28 Nevada 590 5149 0.5 69.03 11.5 65.2 188
## 30 New Jersey 7333 5237 1.1 70.93 5.2 52.5 115
## 34 North Dakota 637 5087 0.8 72.78 1.4 50.3 186
## Area
## 2 566432
## 5 156361
## 7 4862
## 13 55748
## 20 9891
## 28 109889
## 30 7521
## 34 69273Q5. Select columns Population, Income, HS Grad, and Area
df[c('Population', 'Income', 'HS.Grad', 'Area')]## Population Income HS.Grad Area
## 1 3615 3624 41.3 50708
## 2 365 6315 66.7 566432
## 3 2212 4530 58.1 113417
## 4 2110 3378 39.9 51945
## 5 21198 5114 62.6 156361
## 6 2541 4884 63.9 103766
## 7 3100 5348 56.0 4862
## 8 579 4809 54.6 1982
## 9 8277 4815 52.6 54090
## 10 4931 4091 40.6 58073
## 11 868 4963 61.9 6425
## 12 813 4119 59.5 82677
## 13 11197 5107 52.6 55748
## 14 5313 4458 52.9 36097
## 15 2861 4628 59.0 55941
## 16 2280 4669 59.9 81787
## 17 3387 3712 38.5 39650
## 18 3806 3545 42.2 44930
## 19 1058 3694 54.7 30920
## 20 4122 5299 52.3 9891
## 21 5814 4755 58.5 7826
## 22 9111 4751 52.8 56817
## 23 3921 4675 57.6 79289
## 24 2341 3098 41.0 47296
## 25 4767 4254 48.8 68995
## 26 746 4347 59.2 145587
## 27 1544 4508 59.3 76483
## 28 590 5149 65.2 109889
## 29 812 4281 57.6 9027
## 30 7333 5237 52.5 7521
## 31 1144 3601 55.2 121412
## 32 18076 4903 52.7 47831
## 33 5441 3875 38.5 48798
## 34 637 5087 50.3 69273
## 35 10735 4561 53.2 40975
## 36 2715 3983 51.6 68782
## 37 2284 4660 60.0 96184
## 38 11860 4449 50.2 44966
## 39 931 4558 46.4 1049
## 40 2816 3635 37.8 30225
## 41 681 4167 53.3 75955
## 42 4173 3821 41.8 41328
## 43 12237 4188 47.4 262134
## 44 1203 4022 67.3 82096
## 45 472 3907 57.1 9267
## 46 4981 4701 47.8 39780
## 47 3559 4864 63.5 66570
## 48 1799 3617 41.6 24070
## 49 4589 4468 54.5 54464
## 50 376 4566 62.9 97203Q6. Create a new column called total Income, which is calculated by Population * Income
df[, 'Total Income'] <- df$Population * df$Income
df## X Population Income Illiteracy Life.Exp Murder HS.Grad
## 1 Alabama 3615 3624 2.1 69.05 15.1 41.3
## 2 Alaska 365 6315 1.5 69.31 11.3 66.7
## 3 Arizona 2212 4530 1.8 70.55 7.8 58.1
## 4 Arkansas 2110 3378 1.9 70.66 10.1 39.9
## 5 California 21198 5114 1.1 71.71 10.3 62.6
## 6 Colorado 2541 4884 0.7 72.06 6.8 63.9
## 7 Connecticut 3100 5348 1.1 72.48 3.1 56.0
## 8 Delaware 579 4809 0.9 70.06 6.2 54.6
## 9 Florida 8277 4815 1.3 70.66 10.7 52.6
## 10 Georgia 4931 4091 2.0 68.54 13.9 40.6
## 11 Hawaii 868 4963 1.9 73.60 6.2 61.9
## 12 Idaho 813 4119 0.6 71.87 5.3 59.5
## 13 Illinois 11197 5107 0.9 70.14 10.3 52.6
## 14 Indiana 5313 4458 0.7 70.88 7.1 52.9
## 15 Iowa 2861 4628 0.5 72.56 2.3 59.0
## 16 Kansas 2280 4669 0.6 72.58 4.5 59.9
## 17 Kentucky 3387 3712 1.6 70.10 10.6 38.5
## 18 Louisiana 3806 3545 2.8 68.76 13.2 42.2
## 19 Maine 1058 3694 0.7 70.39 2.7 54.7
## 20 Maryland 4122 5299 0.9 70.22 8.5 52.3
## 21 Massachusetts 5814 4755 1.1 71.83 3.3 58.5
## 22 Michigan 9111 4751 0.9 70.63 11.1 52.8
## 23 Minnesota 3921 4675 0.6 72.96 2.3 57.6
## 24 Mississippi 2341 3098 2.4 68.09 12.5 41.0
## 25 Missouri 4767 4254 0.8 70.69 9.3 48.8
## 26 Montana 746 4347 0.6 70.56 5.0 59.2
## 27 Nebraska 1544 4508 0.6 72.60 2.9 59.3
## 28 Nevada 590 5149 0.5 69.03 11.5 65.2
## 29 New Hampshire 812 4281 0.7 71.23 3.3 57.6
## 30 New Jersey 7333 5237 1.1 70.93 5.2 52.5
## 31 New Mexico 1144 3601 2.2 70.32 9.7 55.2
## 32 New York 18076 4903 1.4 70.55 10.9 52.7
## 33 North Carolina 5441 3875 1.8 69.21 11.1 38.5
## 34 North Dakota 637 5087 0.8 72.78 1.4 50.3
## 35 Ohio 10735 4561 0.8 70.82 7.4 53.2
## 36 Oklahoma 2715 3983 1.1 71.42 6.4 51.6
## 37 Oregon 2284 4660 0.6 72.13 4.2 60.0
## 38 Pennsylvania 11860 4449 1.0 70.43 6.1 50.2
## 39 Rhode Island 931 4558 1.3 71.90 2.4 46.4
## 40 South Carolina 2816 3635 2.3 67.96 11.6 37.8
## 41 South Dakota 681 4167 0.5 72.08 1.7 53.3
## 42 Tennessee 4173 3821 1.7 70.11 11.0 41.8
## 43 Texas 12237 4188 2.2 70.90 12.2 47.4
## 44 Utah 1203 4022 0.6 72.90 4.5 67.3
## 45 Vermont 472 3907 0.6 71.64 5.5 57.1
## 46 Virginia 4981 4701 1.4 70.08 9.5 47.8
## 47 Washington 3559 4864 0.6 71.72 4.3 63.5
## 48 West Virginia 1799 3617 1.4 69.48 6.7 41.6
## 49 Wisconsin 4589 4468 0.7 72.48 3.0 54.5
## 50 Wyoming 376 4566 0.6 70.29 6.9 62.9
## Frost Area Total Income
## 1 20 50708 13100760
## 2 152 566432 2304975
## 3 15 113417 10020360
## 4 65 51945 7127580
## 5 20 156361 108406572
## 6 166 103766 12410244
## 7 139 4862 16578800
## 8 103 1982 2784411
## 9 11 54090 39853755
## 10 60 58073 20172721
## 11 0 6425 4307884
## 12 126 82677 3348747
## 13 127 55748 57183079
## 14 122 36097 23685354
## 15 140 55941 13240708
## 16 114 81787 10645320
## 17 95 39650 12572544
## 18 12 44930 13492270
## 19 161 30920 3908252
## 20 101 9891 21842478
## 21 103 7826 27645570
## 22 125 56817 43286361
## 23 160 79289 18330675
## 24 50 47296 7252418
## 25 108 68995 20278818
## 26 155 145587 3242862
## 27 139 76483 6960352
## 28 188 109889 3037910
## 29 174 9027 3476172
## 30 115 7521 38402921
## 31 120 121412 4119544
## 32 82 47831 88626628
## 33 80 48798 21083875
## 34 186 69273 3240419
## 35 124 40975 48962335
## 36 82 68782 10813845
## 37 44 96184 10643440
## 38 126 44966 52765140
## 39 127 1049 4243498
## 40 65 30225 10236160
## 41 172 75955 2837727
## 42 70 41328 15945033
## 43 35 262134 51248556
## 44 137 82096 4838466
## 45 168 9267 1844104
## 46 85 39780 23415681
## 47 32 66570 17310976
## 48 100 24070 6506983
## 49 149 54464 20503652
## 50 173 97203 1716816Q7. the Life Exp column has two decimal places. Use round() to reduce this accuracy to only 1 decimal place
hint: use help(round) to see the documentation
df$Life.Exp <- round(df$Life.Exp, 1)
df## X Population Income Illiteracy Life.Exp Murder HS.Grad
## 1 Alabama 3615 3624 2.1 69.0 15.1 41.3
## 2 Alaska 365 6315 1.5 69.3 11.3 66.7
## 3 Arizona 2212 4530 1.8 70.5 7.8 58.1
## 4 Arkansas 2110 3378 1.9 70.7 10.1 39.9
## 5 California 21198 5114 1.1 71.7 10.3 62.6
## 6 Colorado 2541 4884 0.7 72.1 6.8 63.9
## 7 Connecticut 3100 5348 1.1 72.5 3.1 56.0
## 8 Delaware 579 4809 0.9 70.1 6.2 54.6
## 9 Florida 8277 4815 1.3 70.7 10.7 52.6
## 10 Georgia 4931 4091 2.0 68.5 13.9 40.6
## 11 Hawaii 868 4963 1.9 73.6 6.2 61.9
## 12 Idaho 813 4119 0.6 71.9 5.3 59.5
## 13 Illinois 11197 5107 0.9 70.1 10.3 52.6
## 14 Indiana 5313 4458 0.7 70.9 7.1 52.9
## 15 Iowa 2861 4628 0.5 72.6 2.3 59.0
## 16 Kansas 2280 4669 0.6 72.6 4.5 59.9
## 17 Kentucky 3387 3712 1.6 70.1 10.6 38.5
## 18 Louisiana 3806 3545 2.8 68.8 13.2 42.2
## 19 Maine 1058 3694 0.7 70.4 2.7 54.7
## 20 Maryland 4122 5299 0.9 70.2 8.5 52.3
## 21 Massachusetts 5814 4755 1.1 71.8 3.3 58.5
## 22 Michigan 9111 4751 0.9 70.6 11.1 52.8
## 23 Minnesota 3921 4675 0.6 73.0 2.3 57.6
## 24 Mississippi 2341 3098 2.4 68.1 12.5 41.0
## 25 Missouri 4767 4254 0.8 70.7 9.3 48.8
## 26 Montana 746 4347 0.6 70.6 5.0 59.2
## 27 Nebraska 1544 4508 0.6 72.6 2.9 59.3
## 28 Nevada 590 5149 0.5 69.0 11.5 65.2
## 29 New Hampshire 812 4281 0.7 71.2 3.3 57.6
## 30 New Jersey 7333 5237 1.1 70.9 5.2 52.5
## 31 New Mexico 1144 3601 2.2 70.3 9.7 55.2
## 32 New York 18076 4903 1.4 70.5 10.9 52.7
## 33 North Carolina 5441 3875 1.8 69.2 11.1 38.5
## 34 North Dakota 637 5087 0.8 72.8 1.4 50.3
## 35 Ohio 10735 4561 0.8 70.8 7.4 53.2
## 36 Oklahoma 2715 3983 1.1 71.4 6.4 51.6
## 37 Oregon 2284 4660 0.6 72.1 4.2 60.0
## 38 Pennsylvania 11860 4449 1.0 70.4 6.1 50.2
## 39 Rhode Island 931 4558 1.3 71.9 2.4 46.4
## 40 South Carolina 2816 3635 2.3 68.0 11.6 37.8
## 41 South Dakota 681 4167 0.5 72.1 1.7 53.3
## 42 Tennessee 4173 3821 1.7 70.1 11.0 41.8
## 43 Texas 12237 4188 2.2 70.9 12.2 47.4
## 44 Utah 1203 4022 0.6 72.9 4.5 67.3
## 45 Vermont 472 3907 0.6 71.6 5.5 57.1
## 46 Virginia 4981 4701 1.4 70.1 9.5 47.8
## 47 Washington 3559 4864 0.6 71.7 4.3 63.5
## 48 West Virginia 1799 3617 1.4 69.5 6.7 41.6
## 49 Wisconsin 4589 4468 0.7 72.5 3.0 54.5
## 50 Wyoming 376 4566 0.6 70.3 6.9 62.9
## Frost Area Total Income
## 1 20 50708 13100760
## 2 152 566432 2304975
## 3 15 113417 10020360
## 4 65 51945 7127580
## 5 20 156361 108406572
## 6 166 103766 12410244
## 7 139 4862 16578800
## 8 103 1982 2784411
## 9 11 54090 39853755
## 10 60 58073 20172721
## 11 0 6425 4307884
## 12 126 82677 3348747
## 13 127 55748 57183079
## 14 122 36097 23685354
## 15 140 55941 13240708
## 16 114 81787 10645320
## 17 95 39650 12572544
## 18 12 44930 13492270
## 19 161 30920 3908252
## 20 101 9891 21842478
## 21 103 7826 27645570
## 22 125 56817 43286361
## 23 160 79289 18330675
## 24 50 47296 7252418
## 25 108 68995 20278818
## 26 155 145587 3242862
## 27 139 76483 6960352
## 28 188 109889 3037910
## 29 174 9027 3476172
## 30 115 7521 38402921
## 31 120 121412 4119544
## 32 82 47831 88626628
## 33 80 48798 21083875
## 34 186 69273 3240419
## 35 124 40975 48962335
## 36 82 68782 10813845
## 37 44 96184 10643440
## 38 126 44966 52765140
## 39 127 1049 4243498
## 40 65 30225 10236160
## 41 172 75955 2837727
## 42 70 41328 15945033
## 43 35 262134 51248556
## 44 137 82096 4838466
## 45 168 9267 1844104
## 46 85 39780 23415681
## 47 32 66570 17310976
## 48 100 24070 6506983
## 49 149 54464 20503652
## 50 173 97203 1716816Q8. What is the average life expectancy for states that have more than 5000 income AND HS Grad is higher than 50?
mean(df[df$Income > 5000 & df$HS.Grad > 50, ]$Life.Exp)## [1] 70.8125Q9. What is the average life expectancy for states that have less than 4000 AND HS Grad is lower than 45?
mean(df[df$Income < 4000 & df$HS.Grad < 45, ]$Life.Exp)## [1] 69.27778