Installing to read Excel files:
# downloads and installs the R package called rio from CRAN (the main public R package repository) onto your computer
install.packages('rio')## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)# saves the URL of an Excel file hosted on GitHub into a variable called linkGit, so you can easily refer to it later in your code instead of retyping the full link
linkGit="https://github.com/DACSS601HW/HW2/raw/refs/heads/main/FSI-2023-DOWNLOAD.xlsx"
# downloads the Excel file from the GitHub link stored in linkGit, reads it into R, and stores the data in a variable called fragility23
fragility23=rio::import(file = linkGit) #object that will hold the result# gives you a compact summary of the internal structure of the object fragility23
str(fragility23)## 'data.frame': 179 obs. of 16 variables:
## $ Country : chr "Somalia" "Yemen" "South Sudan" "Congo Democratic Republic" ...
## $ Year : num 2023 2023 2023 2023 2023 ...
## $ Rank : chr "1st" "2nd" "3rd" "4th" ...
## $ Total : num 112 109 109 107 107 ...
## $ S1: Demographic Pressures : num 10 9.6 9.7 9.7 7.4 9.2 8.8 9.3 9.5 8.8 ...
## $ S2: Refugees and IDPs : num 9 9.6 10 9.8 9.1 8.6 9.6 9.5 9 7.7 ...
## $ C3: Group Grievance : num 8.7 8.8 8.6 9.4 9.1 8.3 9.3 8.1 8.1 5.5 ...
## $ E3: Human Flight and Brain Drain: num 8.6 6.4 6.5 6.4 8 8.5 7.5 6.2 7.7 8.3 ...
## $ E2: Economic Inequality : num 9.1 7.9 8.6 8.4 6.5 8.2 8.5 9.6 8.7 9.2 ...
## $ E1: Economy : num 9.5 9.9 8.6 8.1 9.6 9.6 9.3 8.2 8.4 8.9 ...
## $ P1: State Legitimacy : num 9.6 9.8 9.8 9.3 10 9.4 9.4 8.9 9.1 9.9 ...
## $ P2: Public Services : num 9.8 9.6 9.7 9.3 9 10 8.6 10 9.6 9.8 ...
## $ P3: Human Rights : num 9 9.6 8.7 9.3 9.1 8.7 9.2 9.1 8.4 8.7 ...
## $ C1: Security Apparatus : num 9.5 8.6 9.9 8.8 9.4 9.7 8.3 8 8.7 6.8 ...
## $ C2: Factionalized Elites : num 10 9.9 9.2 9.6 9.9 8.7 9.6 9.4 9.5 9.7 ...
## $ X1: External Intervention : num 9.1 9.2 9.2 9.1 10 7.7 8.1 9.4 7.9 9.6 ...# sqrt() computes the square root of its input. It is a numeric vectorized function
# What actually happens here - fragility23 is a data frame, not a numeric object, so R will throw an error
sqrt(fragility23)# selects columns 4 and 5 from the data frame fragility23 and then attempts to take the square root of every value in those columns
sqrt(fragility23[,4:5])## Total S1: Demographic Pressures
## 1 10.578280 3.162278
## 2 10.435516 3.098387
## 3 10.416333 3.114482
## 4 10.353743 3.114482
## 5 10.348913 2.720294
## 6 10.324728 3.033150
## 7 10.305338 2.966479
## 8 10.281051 3.049590
## 9 10.227414 3.082207
## 10 10.143964 2.966479
## 11 10.019980 3.130495
## 12 10.009995 2.645751
## 13 9.974969 2.966479
## 14 9.924717 2.966479
## 15 9.899495 3.098387
## 16 9.843780 2.949576
## 17 9.803061 2.449490
## 18 9.792855 2.701851
## 19 9.721111 2.774887
## 20 9.705668 2.949576
## 21 9.695360 2.880972
## 22 9.695360 3.098387
## 23 9.695360 3.000000
## 24 9.664368 2.983287
## 25 9.581232 2.408319
## 26 9.565563 3.033150
## 27 9.560335 2.863564
## 28 9.523655 2.915476
## 29 9.513149 2.549510
## 30 9.502631 2.683282
## 31 9.481561 2.983287
## 32 9.481561 2.880972
## 33 9.428680 2.898275
## 34 9.375500 2.880972
## 35 9.370165 2.846050
## 36 9.332738 2.932576
## 37 9.327379 2.932576
## 38 9.327379 2.701851
## 39 9.322017 3.049590
## 40 9.241212 2.607681
## 41 9.230385 2.774887
## 42 9.186947 3.016621
## 43 9.121403 3.098387
## 44 9.071935 2.683282
## 45 9.066422 2.863564
## 46 9.066422 2.664583
## 47 9.060905 2.720294
## 48 9.044335 3.065942
## 49 9.038805 3.098387
## 50 9.033272 2.588436
## 51 9.022195 2.898275
## 52 9.011104 2.121320
## 53 8.983318 2.190890
## 54 8.961027 2.529822
## 55 8.955445 2.846050
## 56 8.921883 2.664583
## 57 8.921883 2.626785
## 58 8.893818 2.949576
## 59 8.837420 2.792848
## 60 8.837420 2.720294
## 61 8.820431 2.810694
## 62 8.814760 2.236068
## 63 8.803408 2.949576
## 64 8.792042 2.664583
## 65 8.752143 2.932576
## 66 8.734987 2.932576
## 67 8.723531 2.863564
## 68 8.700575 2.509980
## 69 8.694826 2.387467
## 70 8.642916 2.529822
## 71 8.631338 2.949576
## 72 8.613942 2.645751
## 73 8.608136 2.915476
## 74 8.561542 2.828427
## 75 8.549854 2.756810
## 76 8.526429 1.949359
## 77 8.502941 2.469818
## 78 8.485281 2.828427
## 79 8.479387 2.167948
## 80 8.455767 2.720294
## 81 8.408329 2.626785
## 82 8.378544 2.144761
## 83 8.366600 2.236068
## 84 8.360622 2.190890
## 85 8.354639 2.626785
## 86 8.348653 2.626785
## 87 8.330666 2.588436
## 88 8.324662 2.408319
## 89 8.324662 2.607681
## 90 8.258329 2.097618
## 91 8.246211 2.469818
## 92 8.240146 2.024846
## 93 8.215838 1.949359
## 94 8.209750 2.323790
## 95 8.173127 2.073644
## 96 8.148620 2.323790
## 97 8.148620 2.073644
## 98 8.099383 2.645751
## 99 8.093207 2.588436
## 100 8.080842 2.073644
## 101 8.068457 2.167948
## 102 8.068457 2.024846
## 103 8.068457 2.549510
## 104 8.031189 2.366432
## 105 7.981228 2.387467
## 106 7.930952 2.280351
## 107 7.893035 2.720294
## 108 7.867655 2.190890
## 109 7.848567 2.408319
## 110 7.797435 2.529822
## 111 7.784600 2.024846
## 112 7.765307 2.846050
## 113 7.765307 2.049390
## 114 7.752419 2.489980
## 115 7.733046 2.236068
## 116 7.726578 2.345208
## 117 7.713624 2.428992
## 118 7.635444 2.097618
## 119 7.615773 2.024846
## 120 7.549834 1.843909
## 121 7.536577 2.024846
## 122 7.436397 2.792848
## 123 7.422937 2.024846
## 124 7.416198 2.258318
## 125 7.395945 1.673320
## 126 7.334848 1.923538
## 127 7.328028 2.024846
## 128 7.300685 2.049390
## 129 7.280110 2.121320
## 130 7.273239 2.024846
## 131 7.197222 2.323790
## 132 7.162402 2.190890
## 133 7.155418 2.097618
## 134 7.014271 2.509980
## 135 6.985700 1.870829
## 136 6.978539 2.213594
## 137 6.978539 2.049390
## 138 6.978539 2.190890
## 139 6.811755 2.387467
## 140 6.737952 1.923538
## 141 6.730527 2.280351
## 142 6.723095 1.949359
## 143 6.640783 2.190890
## 144 6.595453 1.816590
## 145 6.580274 1.788854
## 146 6.526868 2.144761
## 147 6.496153 2.428992
## 148 6.473021 1.843909
## 149 6.363961 1.732051
## 150 6.356099 1.702939
## 151 6.340347 1.788854
## 152 6.276942 1.897367
## 153 6.212890 1.702939
## 154 6.164414 1.760682
## 155 6.148170 1.612452
## 156 6.082763 1.897367
## 157 5.865151 1.843909
## 158 5.744563 1.923538
## 159 5.612486 1.581139
## 160 5.603570 2.049390
## 161 5.522681 2.428992
## 162 5.366563 1.760682
## 163 5.224940 1.897367
## 164 5.069517 2.000000
## 165 5.049752 1.581139
## 166 4.959839 1.516575
## 167 4.939636 1.897367
## 168 4.690416 1.949359
## 169 4.582576 1.581139
## 170 4.538722 1.732051
## 171 4.415880 1.549193
## 172 4.415880 1.673320
## 173 4.347413 1.095445
## 174 4.230839 1.516575
## 175 4.219005 1.549193
## 176 4.086563 1.048809
## 177 4.000000 1.303840
## 178 3.962323 1.224745
## 179 3.807887 1.183216# computes the square root of every value in the column named Total from the data frame fragility23
sqrt(fragility23$Total)## [1] 10.578280 10.435516 10.416333 10.353743 10.348913 10.324728 10.305338
## [8] 10.281051 10.227414 10.143964 10.019980 10.009995 9.974969 9.924717
## [15] 9.899495 9.843780 9.803061 9.792855 9.721111 9.705668 9.695360
## [22] 9.695360 9.695360 9.664368 9.581232 9.565563 9.560335 9.523655
## [29] 9.513149 9.502631 9.481561 9.481561 9.428680 9.375500 9.370165
## [36] 9.332738 9.327379 9.327379 9.322017 9.241212 9.230385 9.186947
## [43] 9.121403 9.071935 9.066422 9.066422 9.060905 9.044335 9.038805
## [50] 9.033272 9.022195 9.011104 8.983318 8.961027 8.955445 8.921883
## [57] 8.921883 8.893818 8.837420 8.837420 8.820431 8.814760 8.803408
## [64] 8.792042 8.752143 8.734987 8.723531 8.700575 8.694826 8.642916
## [71] 8.631338 8.613942 8.608136 8.561542 8.549854 8.526429 8.502941
## [78] 8.485281 8.479387 8.455767 8.408329 8.378544 8.366600 8.360622
## [85] 8.354639 8.348653 8.330666 8.324662 8.324662 8.258329 8.246211
## [92] 8.240146 8.215838 8.209750 8.173127 8.148620 8.148620 8.099383
## [99] 8.093207 8.080842 8.068457 8.068457 8.068457 8.031189 7.981228
## [106] 7.930952 7.893035 7.867655 7.848567 7.797435 7.784600 7.765307
## [113] 7.765307 7.752419 7.733046 7.726578 7.713624 7.635444 7.615773
## [120] 7.549834 7.536577 7.436397 7.422937 7.416198 7.395945 7.334848
## [127] 7.328028 7.300685 7.280110 7.273239 7.197222 7.162402 7.155418
## [134] 7.014271 6.985700 6.978539 6.978539 6.978539 6.811755 6.737952
## [141] 6.730527 6.723095 6.640783 6.595453 6.580274 6.526868 6.496153
## [148] 6.473021 6.363961 6.356099 6.340347 6.276942 6.212890 6.164414
## [155] 6.148170 6.082763 5.865151 5.744563 5.612486 5.603570 5.522681
## [162] 5.366563 5.224940 5.069517 5.049752 4.959839 4.939636 4.690416
## [169] 4.582576 4.538722 4.415880 4.415880 4.347413 4.230839 4.219005
## [176] 4.086563 4.000000 3.962323 3.807887# calculates the square root of the very first value in the Total column of the fragility23 data frame.
# instead of transforming the entire column, this line operates on one single number
sqrt(fragility23$Total[1])## [1] 10.57828# attempts to add up all the values in columns 4 and 5 of the data frame fragility23
sum(fragility23[,4:5])## [1] 12850.1# calculates the sum of each column in columns 4 and 5 of the data frame fragility23 and then prints the results
print(apply(fragility23[,4:5],2,sum))## Total S1: Demographic Pressures
## 11784.0 1066.1# tells you the basic type of the object returned by the apply() call that sums columns 4 and 5
typeof(apply(fragility23[,4:5],2,sum))## [1] "double"If you do not see list, then it is a vector. ⏫
# calculates the sum of each row across columns 4 and 5 of the fragility23 data frame and prints the results
print(apply(fragility23[,4:5],1,sum))## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 121.9 118.5 118.2 116.9 114.5 115.8 115.0 115.0 114.1 111.7 110.2 107.2 108.3
## 14 15 16 17 18 19 20 21 22 23 24 25 26
## 107.3 107.6 105.6 102.1 103.2 102.2 102.9 102.3 103.6 103.0 102.3 97.6 100.7
## 27 28 29 30 31 32 33 34 35 36 37 38 39
## 99.6 99.2 97.0 97.5 98.8 98.2 97.3 96.2 95.9 95.7 95.6 94.3 96.2
## 40 41 42 43 44 45 46 47 48 49 50 51 52
## 92.2 92.9 93.5 92.8 89.5 90.4 89.3 89.5 91.2 91.3 88.3 89.8 85.7
## 53 54 55 56 57 58 59 60 61 62 63 64 65
## 85.5 86.7 88.3 86.7 86.5 87.8 85.9 85.5 85.7 82.7 86.2 84.4 85.2
## 66 67 68 69 70 71 72 73 74 75 76 77 78
## 84.9 84.3 82.0 81.3 81.1 83.2 81.2 82.6 81.3 80.7 76.5 78.4 80.0
## 79 80 81 82 83 84 85 86 87 88 89 90 91
## 76.6 78.9 77.6 74.8 75.0 74.7 76.7 76.6 76.1 75.1 76.1 72.6 74.1
## 92 93 94 95 96 97 98 99 100 101 102 103 104
## 72.0 71.3 72.8 71.1 71.8 70.7 72.6 72.2 69.6 69.8 69.2 71.6 70.1
## 105 106 107 108 109 110 111 112 113 114 115 116 117
## 69.4 68.1 69.7 66.7 67.4 67.2 64.7 68.4 64.5 66.3 64.8 65.2 65.4
## 118 119 120 121 122 123 124 125 126 127 128 129 130
## 62.7 62.1 60.4 60.9 63.1 59.2 60.1 57.5 57.5 57.8 57.5 57.5 57.0
## 131 132 133 134 135 136 137 138 139 140 141 142 143
## 57.2 56.1 55.6 55.5 52.3 53.6 52.9 53.5 52.1 49.1 50.5 49.0 48.9
## 144 145 146 147 148 149 150 151 152 153 154 155 156
## 46.8 46.5 47.2 48.1 45.3 43.5 43.3 43.4 43.0 41.5 41.1 40.4 40.6
## 157 158 159 160 161 162 163 164 165 166 167 168 169
## 37.8 36.7 34.0 35.6 36.4 31.9 30.9 29.7 28.0 26.9 28.0 25.8 23.5
## 170 171 172 173 174 175 176 177 178 179
## 23.6 21.9 22.3 20.1 20.2 20.2 17.8 17.7 17.2 15.9# calculates the sum of each column in columns 4 and 5 of fragility23 and returns the result as a list, then prints it
print(lapply(fragility23[,4:5],sum))## $Total
## [1] 11784
##
## $`S1: Demographic Pressures`
## [1] 1066.1Notice output of lapply:
# tells you the underlying data type of the object returned by the lapply() call that sums columns 4 and 5
typeof(lapply(fragility23[,4:5],sum))## [1] "list"# tells you the R class of the object returned by the lapply() call
class(lapply(fragility23[,4:5],sum))## [1] "list"Notice output of sapply:
# calculates the sum of each column in columns 4 and 5 of fragility23 and returns the result as a numeric vector instead of a list, then prints it
print(sapply(fragility23[,4:5],sum))## Total S1: Demographic Pressures
## 11784.0 1066.1# tells you the R class of the object returned by the sapply() call that sums columns 4 and 5
class(sapply(fragility23[,4:5],sum))## [1] "numeric"Similarly:
# calculates the square root of each value in columns 4 and 5 of fragility23 and returns the result as a list, then prints it
print(lapply(fragility23[,4:5],sqrt))## $Total
## [1] 10.578280 10.435516 10.416333 10.353743 10.348913 10.324728 10.305338
## [8] 10.281051 10.227414 10.143964 10.019980 10.009995 9.974969 9.924717
## [15] 9.899495 9.843780 9.803061 9.792855 9.721111 9.705668 9.695360
## [22] 9.695360 9.695360 9.664368 9.581232 9.565563 9.560335 9.523655
## [29] 9.513149 9.502631 9.481561 9.481561 9.428680 9.375500 9.370165
## [36] 9.332738 9.327379 9.327379 9.322017 9.241212 9.230385 9.186947
## [43] 9.121403 9.071935 9.066422 9.066422 9.060905 9.044335 9.038805
## [50] 9.033272 9.022195 9.011104 8.983318 8.961027 8.955445 8.921883
## [57] 8.921883 8.893818 8.837420 8.837420 8.820431 8.814760 8.803408
## [64] 8.792042 8.752143 8.734987 8.723531 8.700575 8.694826 8.642916
## [71] 8.631338 8.613942 8.608136 8.561542 8.549854 8.526429 8.502941
## [78] 8.485281 8.479387 8.455767 8.408329 8.378544 8.366600 8.360622
## [85] 8.354639 8.348653 8.330666 8.324662 8.324662 8.258329 8.246211
## [92] 8.240146 8.215838 8.209750 8.173127 8.148620 8.148620 8.099383
## [99] 8.093207 8.080842 8.068457 8.068457 8.068457 8.031189 7.981228
## [106] 7.930952 7.893035 7.867655 7.848567 7.797435 7.784600 7.765307
## [113] 7.765307 7.752419 7.733046 7.726578 7.713624 7.635444 7.615773
## [120] 7.549834 7.536577 7.436397 7.422937 7.416198 7.395945 7.334848
## [127] 7.328028 7.300685 7.280110 7.273239 7.197222 7.162402 7.155418
## [134] 7.014271 6.985700 6.978539 6.978539 6.978539 6.811755 6.737952
## [141] 6.730527 6.723095 6.640783 6.595453 6.580274 6.526868 6.496153
## [148] 6.473021 6.363961 6.356099 6.340347 6.276942 6.212890 6.164414
## [155] 6.148170 6.082763 5.865151 5.744563 5.612486 5.603570 5.522681
## [162] 5.366563 5.224940 5.069517 5.049752 4.959839 4.939636 4.690416
## [169] 4.582576 4.538722 4.415880 4.415880 4.347413 4.230839 4.219005
## [176] 4.086563 4.000000 3.962323 3.807887
##
## $`S1: Demographic Pressures`
## [1] 3.162278 3.098387 3.114482 3.114482 2.720294 3.033150 2.966479 3.049590
## [9] 3.082207 2.966479 3.130495 2.645751 2.966479 2.966479 3.098387 2.949576
## [17] 2.449490 2.701851 2.774887 2.949576 2.880972 3.098387 3.000000 2.983287
## [25] 2.408319 3.033150 2.863564 2.915476 2.549510 2.683282 2.983287 2.880972
## [33] 2.898275 2.880972 2.846050 2.932576 2.932576 2.701851 3.049590 2.607681
## [41] 2.774887 3.016621 3.098387 2.683282 2.863564 2.664583 2.720294 3.065942
## [49] 3.098387 2.588436 2.898275 2.121320 2.190890 2.529822 2.846050 2.664583
## [57] 2.626785 2.949576 2.792848 2.720294 2.810694 2.236068 2.949576 2.664583
## [65] 2.932576 2.932576 2.863564 2.509980 2.387467 2.529822 2.949576 2.645751
## [73] 2.915476 2.828427 2.756810 1.949359 2.469818 2.828427 2.167948 2.720294
## [81] 2.626785 2.144761 2.236068 2.190890 2.626785 2.626785 2.588436 2.408319
## [89] 2.607681 2.097618 2.469818 2.024846 1.949359 2.323790 2.073644 2.323790
## [97] 2.073644 2.645751 2.588436 2.073644 2.167948 2.024846 2.549510 2.366432
## [105] 2.387467 2.280351 2.720294 2.190890 2.408319 2.529822 2.024846 2.846050
## [113] 2.049390 2.489980 2.236068 2.345208 2.428992 2.097618 2.024846 1.843909
## [121] 2.024846 2.792848 2.024846 2.258318 1.673320 1.923538 2.024846 2.049390
## [129] 2.121320 2.024846 2.323790 2.190890 2.097618 2.509980 1.870829 2.213594
## [137] 2.049390 2.190890 2.387467 1.923538 2.280351 1.949359 2.190890 1.816590
## [145] 1.788854 2.144761 2.428992 1.843909 1.732051 1.702939 1.788854 1.897367
## [153] 1.702939 1.760682 1.612452 1.897367 1.843909 1.923538 1.581139 2.049390
## [161] 2.428992 1.760682 1.897367 2.000000 1.581139 1.516575 1.897367 1.949359
## [169] 1.581139 1.732051 1.549193 1.673320 1.095445 1.516575 1.549193 1.048809
## [177] 1.303840 1.224745 1.183216# tells you the R class of the object returned by lapply(fragility23[, 4:5], sqrt)
class(lapply(fragility23[,4:5],sqrt))## [1] "list"# calculates the square root of each value in columns 4 and 5 of fragility23 and returns the result as a numeric matrix or vector, then prints it
print(sapply(fragility23[,4:5],sqrt))## Total S1: Demographic Pressures
## [1,] 10.578280 3.162278
## [2,] 10.435516 3.098387
## [3,] 10.416333 3.114482
## [4,] 10.353743 3.114482
## [5,] 10.348913 2.720294
## [6,] 10.324728 3.033150
## [7,] 10.305338 2.966479
## [8,] 10.281051 3.049590
## [9,] 10.227414 3.082207
## [10,] 10.143964 2.966479
## [11,] 10.019980 3.130495
## [12,] 10.009995 2.645751
## [13,] 9.974969 2.966479
## [14,] 9.924717 2.966479
## [15,] 9.899495 3.098387
## [16,] 9.843780 2.949576
## [17,] 9.803061 2.449490
## [18,] 9.792855 2.701851
## [19,] 9.721111 2.774887
## [20,] 9.705668 2.949576
## [21,] 9.695360 2.880972
## [22,] 9.695360 3.098387
## [23,] 9.695360 3.000000
## [24,] 9.664368 2.983287
## [25,] 9.581232 2.408319
## [26,] 9.565563 3.033150
## [27,] 9.560335 2.863564
## [28,] 9.523655 2.915476
## [29,] 9.513149 2.549510
## [30,] 9.502631 2.683282
## [31,] 9.481561 2.983287
## [32,] 9.481561 2.880972
## [33,] 9.428680 2.898275
## [34,] 9.375500 2.880972
## [35,] 9.370165 2.846050
## [36,] 9.332738 2.932576
## [37,] 9.327379 2.932576
## [38,] 9.327379 2.701851
## [39,] 9.322017 3.049590
## [40,] 9.241212 2.607681
## [41,] 9.230385 2.774887
## [42,] 9.186947 3.016621
## [43,] 9.121403 3.098387
## [44,] 9.071935 2.683282
## [45,] 9.066422 2.863564
## [46,] 9.066422 2.664583
## [47,] 9.060905 2.720294
## [48,] 9.044335 3.065942
## [49,] 9.038805 3.098387
## [50,] 9.033272 2.588436
## [51,] 9.022195 2.898275
## [52,] 9.011104 2.121320
## [53,] 8.983318 2.190890
## [54,] 8.961027 2.529822
## [55,] 8.955445 2.846050
## [56,] 8.921883 2.664583
## [57,] 8.921883 2.626785
## [58,] 8.893818 2.949576
## [59,] 8.837420 2.792848
## [60,] 8.837420 2.720294
## [61,] 8.820431 2.810694
## [62,] 8.814760 2.236068
## [63,] 8.803408 2.949576
## [64,] 8.792042 2.664583
## [65,] 8.752143 2.932576
## [66,] 8.734987 2.932576
## [67,] 8.723531 2.863564
## [68,] 8.700575 2.509980
## [69,] 8.694826 2.387467
## [70,] 8.642916 2.529822
## [71,] 8.631338 2.949576
## [72,] 8.613942 2.645751
## [73,] 8.608136 2.915476
## [74,] 8.561542 2.828427
## [75,] 8.549854 2.756810
## [76,] 8.526429 1.949359
## [77,] 8.502941 2.469818
## [78,] 8.485281 2.828427
## [79,] 8.479387 2.167948
## [80,] 8.455767 2.720294
## [81,] 8.408329 2.626785
## [82,] 8.378544 2.144761
## [83,] 8.366600 2.236068
## [84,] 8.360622 2.190890
## [85,] 8.354639 2.626785
## [86,] 8.348653 2.626785
## [87,] 8.330666 2.588436
## [88,] 8.324662 2.408319
## [89,] 8.324662 2.607681
## [90,] 8.258329 2.097618
## [91,] 8.246211 2.469818
## [92,] 8.240146 2.024846
## [93,] 8.215838 1.949359
## [94,] 8.209750 2.323790
## [95,] 8.173127 2.073644
## [96,] 8.148620 2.323790
## [97,] 8.148620 2.073644
## [98,] 8.099383 2.645751
## [99,] 8.093207 2.588436
## [100,] 8.080842 2.073644
## [101,] 8.068457 2.167948
## [102,] 8.068457 2.024846
## [103,] 8.068457 2.549510
## [104,] 8.031189 2.366432
## [105,] 7.981228 2.387467
## [106,] 7.930952 2.280351
## [107,] 7.893035 2.720294
## [108,] 7.867655 2.190890
## [109,] 7.848567 2.408319
## [110,] 7.797435 2.529822
## [111,] 7.784600 2.024846
## [112,] 7.765307 2.846050
## [113,] 7.765307 2.049390
## [114,] 7.752419 2.489980
## [115,] 7.733046 2.236068
## [116,] 7.726578 2.345208
## [117,] 7.713624 2.428992
## [118,] 7.635444 2.097618
## [119,] 7.615773 2.024846
## [120,] 7.549834 1.843909
## [121,] 7.536577 2.024846
## [122,] 7.436397 2.792848
## [123,] 7.422937 2.024846
## [124,] 7.416198 2.258318
## [125,] 7.395945 1.673320
## [126,] 7.334848 1.923538
## [127,] 7.328028 2.024846
## [128,] 7.300685 2.049390
## [129,] 7.280110 2.121320
## [130,] 7.273239 2.024846
## [131,] 7.197222 2.323790
## [132,] 7.162402 2.190890
## [133,] 7.155418 2.097618
## [134,] 7.014271 2.509980
## [135,] 6.985700 1.870829
## [136,] 6.978539 2.213594
## [137,] 6.978539 2.049390
## [138,] 6.978539 2.190890
## [139,] 6.811755 2.387467
## [140,] 6.737952 1.923538
## [141,] 6.730527 2.280351
## [142,] 6.723095 1.949359
## [143,] 6.640783 2.190890
## [144,] 6.595453 1.816590
## [145,] 6.580274 1.788854
## [146,] 6.526868 2.144761
## [147,] 6.496153 2.428992
## [148,] 6.473021 1.843909
## [149,] 6.363961 1.732051
## [150,] 6.356099 1.702939
## [151,] 6.340347 1.788854
## [152,] 6.276942 1.897367
## [153,] 6.212890 1.702939
## [154,] 6.164414 1.760682
## [155,] 6.148170 1.612452
## [156,] 6.082763 1.897367
## [157,] 5.865151 1.843909
## [158,] 5.744563 1.923538
## [159,] 5.612486 1.581139
## [160,] 5.603570 2.049390
## [161,] 5.522681 2.428992
## [162,] 5.366563 1.760682
## [163,] 5.224940 1.897367
## [164,] 5.069517 2.000000
## [165,] 5.049752 1.581139
## [166,] 4.959839 1.516575
## [167,] 4.939636 1.897367
## [168,] 4.690416 1.949359
## [169,] 4.582576 1.581139
## [170,] 4.538722 1.732051
## [171,] 4.415880 1.549193
## [172,] 4.415880 1.673320
## [173,] 4.347413 1.095445
## [174,] 4.230839 1.516575
## [175,] 4.219005 1.549193
## [176,] 4.086563 1.048809
## [177,] 4.000000 1.303840
## [178,] 3.962323 1.224745
## [179,] 3.807887 1.183216# tells you the R class of the object returned by sapply(fragility23[, 4:5], sqrt)
class(sapply(fragility23[,4:5],sqrt))## [1] "matrix" "array"Now our own function:
# What it does -
#1 Takes a data frame as input (assumed to have at least 2 columns)
#2 Looks at the second column (numeric variable) and computes its average.
#3 Creates a new column Status that labels each row as either:("Above Average" if the value is greater than the mean, "Below/At Average" otherwise)
#4 Returns the original data frame with the new Status column added
#1 Defines a function named theOnesOK.
#2 DF_country_and_variable is the input data frame
theOnesOK = function(DF_country_and_variable) {
#1 Pulls out the second column of the data frame.
#2 Stores it in variable_values for easier processing.
#3 Assumes this column is numeric
variable_values <- DF_country_and_variable[,2]
#1 Calculates the average value of the column.
#2 na.rm = TRUE ignores missing values (NA) when computing the mean
avg_value <- mean(variable_values, na.rm = TRUE)
#1 ifelse() is a vectorized conditional:
#2 For each value in variable_values, it checks if it is greater than avg_value.
#3 Returns "Above Average" if true, otherwise "Below/At Average"
is_above <- ifelse(variable_values > avg_value, "Above Average", "Below/At Average")
#1 Adds the Status vector as a new column in the original data frame
DF_country_and_variable$Status <- is_above
#1 Outputs the data frame with the new Status column
return(DF_country_and_variable)
}# calling your theOnesOK function on a subset of your fragility23 data frame
theOnesOK(fragility23[,c('Country','S1: Demographic Pressures')])## Country S1: Demographic Pressures Status
## 1 Somalia 10.0 Above Average
## 2 Yemen 9.6 Above Average
## 3 South Sudan 9.7 Above Average
## 4 Congo Democratic Republic 9.7 Above Average
## 5 Syria 7.4 Above Average
## 6 Afghanistan 9.2 Above Average
## 7 Sudan 8.8 Above Average
## 8 Central African Republic 9.3 Above Average
## 9 Chad 9.5 Above Average
## 10 Haiti 8.8 Above Average
## 11 Ethiopia 9.8 Above Average
## 12 Myanmar 7.0 Above Average
## 13 Mali 8.8 Above Average
## 14 Guinea 8.8 Above Average
## 15 Nigeria 9.6 Above Average
## 16 Zimbabwe 8.7 Above Average
## 17 Libya 6.0 Above Average
## 18 Ukraine 7.3 Above Average
## 19 Eritrea 7.7 Above Average
## 20 Burundi 8.7 Above Average
## 21 Burkina Faso 8.3 Above Average
## 22 Mozambique 9.6 Above Average
## 23 Cameroon 9.0 Above Average
## 24 Niger 8.9 Above Average
## 25 Lebanon 5.8 Below/At Average
## 26 Uganda 9.2 Above Average
## 27 Iraq 8.2 Above Average
## 28 Congo Republic 8.5 Above Average
## 29 Venezuela 6.5 Above Average
## 30 Sri Lanka 7.2 Above Average
## 31 Guinea Bissau 8.9 Above Average
## 32 Pakistan 8.3 Above Average
## 33 Liberia 8.4 Above Average
## 34 Palestine 8.3 Above Average
## 35 Kenya 8.1 Above Average
## 36 Cote d'Ivoire 8.6 Above Average
## 37 Mauritania 8.6 Above Average
## 38 North Korea 7.3 Above Average
## 39 Angola 9.3 Above Average
## 40 Iran 6.8 Above Average
## 41 Bangladesh 7.7 Above Average
## 42 Equatorial Guinea 9.1 Above Average
## 43 Malawi 9.6 Above Average
## 44 Rwanda 7.2 Above Average
## 45 Comoros 8.2 Above Average
## 46 Djibouti 7.1 Above Average
## 47 Togo 7.4 Above Average
## 48 Zambia 9.4 Above Average
## 49 Madagascar 9.6 Above Average
## 50 Egypt 6.7 Above Average
## 51 Sierra Leone 8.4 Above Average
## 52 Turkey 4.5 Below/At Average
## 53 Russia 4.8 Below/At Average
## 54 Cambodia 6.4 Above Average
## 55 Nepal 8.1 Above Average
## 56 Solomon Islands 7.1 Above Average
## 57 Honduras 6.9 Above Average
## 58 Swaziland 8.7 Above Average
## 59 Colombia 7.8 Above Average
## 60 Papua New Guinea 7.4 Above Average
## 61 Philippines 7.9 Above Average
## 62 Nicaragua 5.0 Below/At Average
## 63 Timor-Leste 8.7 Above Average
## 64 Guatemala 7.1 Above Average
## 65 Tanzania 8.6 Above Average
## 66 Lesotho 8.6 Above Average
## 67 Gambia 8.2 Above Average
## 68 Jordan 6.3 Above Average
## 69 Kyrgyz Republic 5.7 Below/At Average
## 70 Laos 6.4 Above Average
## 71 Brazil 8.7 Above Average
## 72 Tajikistan 7.0 Above Average
## 73 India 8.5 Above Average
## 74 Benin 8.0 Above Average
## 75 Peru 7.6 Above Average
## 76 Azerbaijan 3.8 Below/At Average
## 77 Bosnia and Herzegovina 6.1 Above Average
## 78 South Africa 8.0 Above Average
## 79 Georgia 4.7 Below/At Average
## 80 Senegal 7.4 Above Average
## 81 Bolivia 6.9 Above Average
## 82 Fiji 4.6 Below/At Average
## 83 Algeria 5.0 Below/At Average
## 84 Belarus 4.8 Below/At Average
## 85 Mexico 6.9 Above Average
## 86 Sao Tome and Principe 6.9 Above Average
## 87 Ecuador 6.7 Above Average
## 88 Micronesia 5.8 Below/At Average
## 89 El Salvador 6.8 Above Average
## 90 Morocco 4.4 Below/At Average
## 91 Thailand 6.1 Above Average
## 92 Serbia 4.1 Below/At Average
## 93 Armenia 3.8 Below/At Average
## 94 Moldova 5.4 Below/At Average
## 95 Uzbekistan 4.3 Below/At Average
## 96 Bhutan 5.4 Below/At Average
## 97 Tunisia 4.3 Below/At Average
## 98 Indonesia 7.0 Above Average
## 99 Gabon 6.7 Above Average
## 100 Saudi Arabia 4.3 Below/At Average
## 101 Samoa 4.7 Below/At Average
## 102 Bahrain 4.1 Below/At Average
## 103 China 6.5 Above Average
## 104 Turkmenistan 5.6 Below/At Average
## 105 Paraguay 5.7 Below/At Average
## 106 Maldives 5.2 Below/At Average
## 107 Ghana 7.4 Above Average
## 108 Jamaica 4.8 Below/At Average
## 109 Guyana 5.8 Below/At Average
## 110 Dominican Republic 6.4 Above Average
## 111 Kazakhstan 4.1 Below/At Average
## 112 Namibia 8.1 Above Average
## 113 Macedonia 4.2 Below/At Average
## 114 Cape Verde 6.2 Above Average
## 115 Belize 5.0 Below/At Average
## 116 Suriname 5.5 Below/At Average
## 117 Cuba 5.9 Below/At Average
## 118 Vietnam 4.4 Below/At Average
## 119 Montenegro 4.1 Below/At Average
## 120 Cyprus 3.4 Below/At Average
## 121 Albania 4.1 Below/At Average
## 122 Botswana 7.8 Above Average
## 123 Greece 4.1 Below/At Average
## 124 Malaysia 5.1 Below/At Average
## 125 Brunei Darussalam 2.8 Below/At Average
## 126 Antigua and Barbuda 3.7 Below/At Average
## 127 Grenada 4.1 Below/At Average
## 128 Seychelles 4.2 Below/At Average
## 129 Romania 4.5 Below/At Average
## 130 Trinidad and Tobago 4.1 Below/At Average
## 131 Bulgaria 5.4 Below/At Average
## 132 Mongolia 4.8 Below/At Average
## 133 Kuwait 4.4 Below/At Average
## 134 Bahamas 6.3 Above Average
## 135 Hungary 3.5 Below/At Average
## 136 Panama 4.9 Below/At Average
## 137 Oman 4.2 Below/At Average
## 138 Croatia 4.8 Below/At Average
## 139 Argentina 5.7 Below/At Average
## 140 Barbados 3.7 Below/At Average
## 141 United States 5.2 Below/At Average
## 142 Poland 3.8 Below/At Average
## 143 Israel 4.8 Below/At Average
## 144 Spain 3.3 Below/At Average
## 145 Latvia 3.2 Below/At Average
## 146 Italy 4.6 Below/At Average
## 147 Chile 5.9 Below/At Average
## 148 United Kingdom 3.4 Below/At Average
## 149 Qatar 3.0 Below/At Average
## 150 Costa Rica 2.9 Below/At Average
## 151 Czech Republic 3.2 Below/At Average
## 152 Lithuania 3.6 Below/At Average
## 153 Estonia 2.9 Below/At Average
## 154 Mauritius 3.1 Below/At Average
## 155 Slovak Republic 2.6 Below/At Average
## 156 United Arab Emirates 3.6 Below/At Average
## 157 Uruguay 3.4 Below/At Average
## 158 Malta 3.7 Below/At Average
## 159 South Korea 2.5 Below/At Average
## 160 Belgium 4.2 Below/At Average
## 161 Japan 5.9 Below/At Average
## 162 France 3.1 Below/At Average
## 163 Slovenia 3.6 Below/At Average
## 164 Portugal 4.0 Below/At Average
## 165 Singapore 2.5 Below/At Average
## 166 Germany 2.3 Below/At Average
## 167 Austria 3.6 Below/At Average
## 168 Australia 3.8 Below/At Average
## 169 Netherlands 2.5 Below/At Average
## 170 Sweden 3.0 Below/At Average
## 171 Luxembourg 2.4 Below/At Average
## 172 Ireland 2.8 Below/At Average
## 173 Canada 1.2 Below/At Average
## 174 Denmark 2.3 Below/At Average
## 175 Switzerland 2.4 Below/At Average
## 176 New Zealand 1.1 Below/At Average
## 177 Finland 1.7 Below/At Average
## 178 Iceland 1.5 Below/At Average
## 179 Norway 1.4 Below/At Average# What it does -
#1 Takes a data frame (DF) as input.
#2 Selects one column as a “Country” or identifier column (CountryColumn, default 'Country').
#3 Computes the row-wise mean across a set of columns specified by positionsToUse.
#4 Returns a new data frame containing:1 The CountryColumn, 2 A new column named "average" containing the row-wise mean of the selected columns.)
#1 DF: input data frame
#2 positionsToUse: vector of column indices or names to calculate the mean
#3 CountryColumn: name of the identifier column (default is 'Country')
mystery=function(DF,positionsToUse,CountryColumn='Country'){
#1 Subsets the data frame to just the country column.
#2 drop = FALSE ensures that it stays as a data frame instead of a vector.
newDF=DF[,c(CountryColumn),drop = FALSE]
#1 Defines the name of the new column that will store the row-wise mean.
average='average'
#1 DF[, positionsToUse]: selects the columns to calculate the mean across.
#2 apply(..., 1, mean, na.rm = TRUE):
#A 1 → operate row-wise
#B mean → calculate the mean of each row
#C na.rm = TRUE → ignore NA values
#3 Stores the result as a new column named "average" in newDF.
newDF[,average]=apply(DF[,positionsToUse],1,mean,na.rm = TRUE)
#1 Returns a data frame with:
#A Country (or identifier column)
#B average column
return(newDF[,c(CountryColumn,average)])
}# calls your mystery function on the fragility23 data frame
mystery(fragility23,4:6)## Country average
## 1 Somalia 43.633333
## 2 Yemen 42.700000
## 3 South Sudan 42.733333
## 4 Congo Democratic Republic 42.233333
## 5 Syria 41.200000
## 6 Afghanistan 41.466667
## 7 Sudan 41.533333
## 8 Central African Republic 41.500000
## 9 Chad 41.033333
## 10 Haiti 39.800000
## 11 Ethiopia 39.900000
## 12 Myanmar 38.800000
## 13 Mali 38.933333
## 14 Guinea 37.833333
## 15 Nigeria 38.000000
## 16 Zimbabwe 37.733333
## 17 Libya 36.533333
## 18 Ukraine 37.733333
## 19 Eritrea 36.266667
## 20 Burundi 36.966667
## 21 Burkina Faso 36.833333
## 22 Mozambique 37.166667
## 23 Cameroon 37.133333
## 24 Niger 36.733333
## 25 Lebanon 35.466667
## 26 Uganda 36.533333
## 27 Iraq 35.766667
## 28 Congo Republic 35.233333
## 29 Venezuela 34.500000
## 30 Sri Lanka 34.700000
## 31 Guinea Bissau 34.766667
## 32 Pakistan 35.266667
## 33 Liberia 34.900000
## 34 Palestine 34.133333
## 35 Kenya 34.400000
## 36 Cote d'Ivoire 33.900000
## 37 Mauritania 34.633333
## 38 North Korea 32.500000
## 39 Angola 33.833333
## 40 Iran 32.566667
## 41 Bangladesh 33.466667
## 42 Equatorial Guinea 33.000000
## 43 Malawi 32.933333
## 44 Rwanda 32.266667
## 45 Comoros 31.966667
## 46 Djibouti 32.133333
## 47 Togo 31.733333
## 48 Zambia 32.033333
## 49 Madagascar 32.100000
## 50 Egypt 31.266667
## 51 Sierra Leone 32.400000
## 52 Turkey 31.300000
## 53 Russia 30.100000
## 54 Cambodia 30.200000
## 55 Nepal 31.633333
## 56 Solomon Islands 30.566667
## 57 Honduras 30.766667
## 58 Swaziland 30.500000
## 59 Colombia 31.233333
## 60 Papua New Guinea 30.133333
## 61 Philippines 30.500000
## 62 Nicaragua 29.266667
## 63 Timor-Leste 30.633333
## 64 Guatemala 29.900000
## 65 Tanzania 30.033333
## 66 Lesotho 29.600000
## 67 Gambia 30.033333
## 68 Jordan 30.133333
## 69 Kyrgyz Republic 28.400000
## 70 Laos 28.833333
## 71 Brazil 28.900000
## 72 Tajikistan 28.333333
## 73 India 28.900000
## 74 Benin 29.066667
## 75 Peru 28.400000
## 76 Azerbaijan 27.600000
## 77 Bosnia and Herzegovina 28.533333
## 78 South Africa 28.000000
## 79 Georgia 27.700000
## 80 Senegal 28.233333
## 81 Bolivia 26.733333
## 82 Fiji 26.000000
## 83 Algeria 27.066667
## 84 Belarus 25.966667
## 85 Mexico 27.400000
## 86 Sao Tome and Principe 27.266667
## 87 Ecuador 27.033333
## 88 Micronesia 26.433333
## 89 El Salvador 27.133333
## 90 Morocco 25.900000
## 91 Thailand 26.333333
## 92 Serbia 26.266667
## 93 Armenia 26.033333
## 94 Moldova 26.266667
## 95 Uzbekistan 24.966667
## 96 Bhutan 25.866667
## 97 Tunisia 24.533333
## 98 Indonesia 25.666667
## 99 Gabon 24.933333
## 100 Saudi Arabia 24.266667
## 101 Samoa 24.433333
## 102 Bahrain 23.533333
## 103 China 24.900000
## 104 Turkmenistan 24.133333
## 105 Paraguay 24.100000
## 106 Maldives 23.866667
## 107 Ghana 24.400000
## 108 Jamaica 22.966667
## 109 Guyana 23.466667
## 110 Dominican Republic 23.266667
## 111 Kazakhstan 22.333333
## 112 Namibia 24.000000
## 113 Macedonia 23.600000
## 114 Cape Verde 23.300000
## 115 Belize 22.600000
## 116 Suriname 22.666667
## 117 Cuba 22.866667
## 118 Vietnam 22.200000
## 119 Montenegro 21.900000
## 120 Cyprus 22.100000
## 121 Albania 21.233333
## 122 Botswana 22.066667
## 123 Greece 21.500000
## 124 Malaysia 21.100000
## 125 Brunei Darussalam 19.533333
## 126 Antigua and Barbuda 20.066667
## 127 Grenada 20.000000
## 128 Seychelles 19.933333
## 129 Romania 20.500000
## 130 Trinidad and Tobago 19.833333
## 131 Bulgaria 20.533333
## 132 Mongolia 19.466667
## 133 Kuwait 19.200000
## 134 Bahamas 19.333333
## 135 Hungary 19.233333
## 136 Panama 18.666667
## 137 Oman 18.166667
## 138 Croatia 20.100000
## 139 Argentina 17.900000
## 140 Barbados 17.000000
## 141 United States 17.566667
## 142 Poland 18.300000
## 143 Israel 16.866667
## 144 Spain 16.233333
## 145 Latvia 16.500000
## 146 Italy 17.166667
## 147 Chile 16.533333
## 148 United Kingdom 15.900000
## 149 Qatar 14.933333
## 150 Costa Rica 16.066667
## 151 Czech Republic 15.966667
## 152 Lithuania 15.300000
## 153 Estonia 14.700000
## 154 Mauritius 14.233333
## 155 Slovak Republic 14.566667
## 156 United Arab Emirates 14.000000
## 157 Uruguay 13.200000
## 158 Malta 13.300000
## 159 South Korea 11.700000
## 160 Belgium 12.566667
## 161 Japan 13.000000
## 162 France 11.433333
## 163 Slovenia 11.166667
## 164 Portugal 10.366667
## 165 Singapore 9.500000
## 166 Germany 10.466667
## 167 Austria 10.833333
## 168 Australia 9.166667
## 169 Netherlands 8.633333
## 170 Sweden 9.100000
## 171 Luxembourg 8.233333
## 172 Ireland 7.966667
## 173 Canada 7.366667
## 174 Denmark 7.733333
## 175 Switzerland 7.800000
## 176 New Zealand 6.333333
## 177 Finland 6.533333
## 178 Iceland 6.233333
## 179 Norway 5.866667# What it does -
#1This function takes a data frame and a numeric column, calculates the average (mean) of that column, and then labels each row as either "Above Average" or "Below/At Average" depending on whether the row’s value is greater than the mean. It then returns a smaller data frame containing:
#A A country (or identifier) column.
#B A new column that shows the above/below average status for the specified variable.
#2 In short, it classifies each observation relative to the average of a chosen numeric variable.
# What it does differently -
#1 Accepts the full data frame (DF) and the name of the column to analyze (DFvariable).
#2 Doesn’t require you to subset the data frame manually beforehand.
#3 Uses DF[, DFvariable] → selects the variable by name instead of assuming it’s always the second column.
#4 Safer and more flexible for larger datasets with multiple columns.
#5 Dynamically generates the column name based on the variable.
#6 Example: if DFvariable = "S1", the new column will be Status_on S1.
#7 Can specify which column contains the country/identifier via CountryColumn.
#8 Defaults to "Country", but can be changed.
#9 Returns a data frame with just the Country column and the new status column, not all the other columns.
theOnesOK2 = function(DF, DFvariable, CountryColumn='Country') {
variable_values <- DF[,DFvariable]
avg_value <- mean(variable_values, na.rm = TRUE)
is_above <- ifelse(variable_values > avg_value, "Above Average", "Below/At Average")
newname = paste('Status_on', DFvariable)
DF[,newname] <- is_above
return(DF[,c(CountryColumn, newname)])
}#1 DF → the full data frame.
#2 DFvariable → the name of the column (as a string) to analyze.
#3 CountryColumn → the column containing identifiers (default is "Country").
theOnesOK2 = function(DF, DFvariable, CountryColumn='Country') {
#1 Extracts the column specified by DFvariable from the data frame.
#2 Assumes it contains numeric values.
variable_values <- DF[,DFvariable]
#1 Computes the average (mean) of the selected column.
#2 na.rm = TRUE ensures missing values (NA) are ignored.
avg_value <- mean(variable_values, na.rm = TRUE)
#1 Compares each value in the column to the mean.
#2 Creates a vector of strings:
#A "Above Average" if the value is greater than the mean.
#B "Below/At Average" if the value is less than or equal to the mean.
is_above <- ifelse(variable_values > avg_value, "Above Average", "Below/At Average")
#1 Creates a dynamic column name for the new status column.
#2 Example: if DFvariable = "S1", the new column will be "Status_on S1".
newname = paste('Status_on', DFvariable)
#1 Adds the is_above vector to the data frame as a new column with the name created above.
DF[,newname] <- is_above
#1 Returns a subset of the data frame containing only:
#A The country/identifier column.
#B The new status column showing above/below average classification.
return(DF[,c(CountryColumn, newname)])
}# What it does - same thing as 'mystery'
# What it does differently -
#1 Nothing - cosmetically it is different because of the use of the 'space bar'
mystery2 = function(DF, positionsToUse, CountryColumn='Country'){
newDF = DF[,c(CountryColumn), drop = FALSE]
average = 'average'
newDF[,average] = apply(DF[,positionsToUse], 1, mean, na.rm = TRUE)
return(newDF[,c(CountryColumn, average)])
}# SAME
mystery2 = function(DF, positionsToUse, CountryColumn='Country'){
# SAME
newDF = DF[,c(CountryColumn), drop = FALSE]
# SAME
average = 'average'
# SAME
newDF[,average] = apply(DF[,positionsToUse], 1, mean, na.rm = TRUE)
# SAME
return(newDF[,c(CountryColumn, average)])
}