library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(datasets)
library(dplyr)
library(readr)GE<- read_csv("C:/Users/delia/Documents/GLOBAL.EDUCATION.csv",show_col_types = FALSE);
GE## # A tibble: 202 × 29
## `Countries and areas` Latitude Longitude OOSR_Pre0Primary_Age_Male
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 33.9 67.7 0
## 2 Albania 41.2 20.2 4
## 3 Algeria 28.0 1.66 0
## 4 Andorra 42.5 1.52 0
## 5 Angola 11.2 17.9 31
## 6 Anguilla 18.2 63.1 14
## 7 Antigua and Barbuda 17.1 61.8 14
## 8 Argentina 38.4 63.6 2
## 9 Armenia 40.1 45.0 52
## 10 Australia 25.3 134. 13
## # ℹ 192 more rows
## # ℹ 25 more variables: OOSR_Pre0Primary_Age_Female <dbl>,
## # OOSR_Primary_Age_Male <dbl>, OOSR_Primary_Age_Female <dbl>,
## # OOSR_Lower_Secondary_Age_Male <dbl>, OOSR_Lower_Secondary_Age_Female <dbl>,
## # OOSR_Upper_Secondary_Age_Male <dbl>, OOSR_Upper_Secondary_Age_Female <dbl>,
## # Completion_Rate_Primary_Male <dbl>, Completion_Rate_Primary_Female <dbl>,
## # Completion_Rate_Lower_Secondary_Male <dbl>, …glimpse(GE);## Rows: 202
## Columns: 29
## $ `Countries and areas` <chr> "Afghanistan", "Albania", "Alg…
## $ Latitude <dbl> 33.93911, 41.15333, 28.03389, …
## $ Longitude <dbl> 67.709953, 20.168331, 1.659626…
## $ OOSR_Pre0Primary_Age_Male <dbl> 0, 4, 0, 0, 31, 14, 14, 2, 52,…
## $ OOSR_Pre0Primary_Age_Female <dbl> 0, 2, 0, 0, 39, 0, 4, 2, 50, 1…
## $ OOSR_Primary_Age_Male <dbl> 0, 6, 0, 0, 0, 0, 4, 0, 9, 0, …
## $ OOSR_Primary_Age_Female <dbl> 0, 3, 0, 0, 0, 0, 1, 0, 9, 0, …
## $ OOSR_Lower_Secondary_Age_Male <dbl> 0, 6, 0, 0, 0, 0, 1, 0, 11, 2,…
## $ OOSR_Lower_Secondary_Age_Female <dbl> 0, 1, 0, 0, 0, 0, 2, 0, 9, 3, …
## $ OOSR_Upper_Secondary_Age_Male <dbl> 44, 21, 0, 0, 0, 0, 14, 15, 16…
## $ OOSR_Upper_Secondary_Age_Female <dbl> 69, 15, 0, 0, 0, 0, 12, 7, 4, …
## $ Completion_Rate_Primary_Male <dbl> 67, 94, 93, 0, 63, 0, 0, 91, 9…
## $ Completion_Rate_Primary_Female <dbl> 40, 96, 93, 0, 57, 0, 0, 94, 9…
## $ Completion_Rate_Lower_Secondary_Male <dbl> 49, 98, 49, 0, 42, 0, 0, 70, 9…
## $ Completion_Rate_Lower_Secondary_Female <dbl> 26, 97, 65, 0, 32, 0, 0, 79, 9…
## $ Completion_Rate_Upper_Secondary_Male <dbl> 32, 76, 22, 0, 24, 0, 0, 46, 6…
## $ Completion_Rate_Upper_Secondary_Female <dbl> 14, 80, 37, 0, 15, 0, 0, 53, 7…
## $ Grade_2_3_Proficiency_Reading <dbl> 22, 0, 0, 0, 0, 0, 0, 76, 0, 9…
## $ Grade_2_3_Proficiency_Math <dbl> 25, 0, 0, 0, 0, 0, 0, 71, 0, 7…
## $ Primary_End_Proficiency_Reading <dbl> 13, 0, 0, 0, 0, 0, 0, 46, 0, 0…
## $ Primary_End_Proficiency_Math <dbl> 11, 0, 0, 0, 0, 0, 0, 56, 55, …
## $ Lower_Secondary_End_Proficiency_Reading <dbl> 0, 48, 21, 0, 0, 0, 0, 48, 0, …
## $ Lower_Secondary_End_Proficiency_Math <dbl> 0, 58, 19, 0, 0, 0, 0, 31, 50,…
## $ Youth_15_24_Literacy_Rate_Male <dbl> 74, 99, 98, 0, 0, 0, 0, 99, 0,…
## $ Youth_15_24_Literacy_Rate_Female <dbl> 56, 100, 97, 0, 0, 0, 0, 100, …
## $ Birth_Rate <dbl> 32.49, 11.78, 24.28, 7.20, 40.…
## $ Gross_Primary_Education_Enrollment <dbl> 104.0, 107.0, 109.9, 106.4, 11…
## $ Gross_Tertiary_Education_Enrollment <dbl> 9.7, 55.0, 51.4, 0.0, 9.3, 0.0…
## $ Unemployment_Rate <dbl> 11.12, 12.33, 11.70, 0.00, 6.8…#SELECT
select(.data = GE,'Countries and areas','Birth_Rate','Gross_Primary_Education_Enrollment','Unemployment_Rate') %>% print(n=203)## # A tibble: 202 × 4
## `Countries and areas` Birth_Rate Gross_Primary_Educat…¹ Unemployment_Rate
## <chr> <dbl> <dbl> <dbl>
## 1 "Afghanistan" 32.5 104 11.1
## 2 "Albania" 11.8 107 12.3
## 3 "Algeria" 24.3 110. 11.7
## 4 "Andorra" 7.2 106. 0
## 5 "Angola" 40.7 114. 6.89
## 6 "Anguilla" 0 0 0
## 7 "Antigua and Barbuda" 15.3 105 0
## 8 "Argentina" 17.0 110. 9.79
## 9 "Armenia" 14.0 92.7 17.0
## 10 "Australia" 12.6 100. 5.27
## 11 "Austria" 9.7 103. 4.67
## 12 "Azerbaijan" 14 99.7 5.51
## 13 "The Bahamas" 14.0 81.4 10.4
## 14 "Bahrain" 14.0 99.4 0.71
## 15 "Bangladesh" 18.2 116. 4.19
## 16 "Barbados" 10.6 99.4 10.3
## 17 "Belarus" 9.9 100. 4.59
## 18 "Belgium" 10.3 104. 5.59
## 19 "Belize" 20.8 112. 6.41
## 20 "Benin" 36.2 122 2.23
## 21 "Bhutan" 17.3 100. 2.34
## 22 "Bolivia" 21.8 98.2 3.5
## 23 "Bosnia and Herzegovina" 8.11 0 18.4
## 24 "Botswana" 24.8 103. 18.2
## 25 "Brazil" 13.9 115. 12.1
## 26 "British Virgin Islands" 0 0 0
## 27 "Brunei" 14.9 103. 9.12
## 28 "Bulgaria" 8.9 89.3 4.34
## 29 "Burkina Faso" 37.9 96.1 6.26
## 30 "Burundi" 39.0 121. 1.43
## 31 "Cape Verde" 19.5 104 12.2
## 32 "Cambodia" 22.5 107. 0.68
## 33 "Cameroon" 35.4 103. 3.38
## 34 "Canada" 10.1 101. 5.56
## 35 "Central African Republ… 35.4 102 3.68
## 36 "Chad" 42.2 86.8 1.89
## 37 "Chile" 12.4 101. 7.09
## 38 "China" 10.9 100. 4.32
## 39 "Colombia" 14.9 114. 9.71
## 40 "Comoros" 31.9 99.5 4.34
## 41 "Republic of the Congo" 32.9 107. 9.47
## 42 "Cook Islands" 0 0 0
## 43 "Costa Rica" 14.0 113. 11.8
## 44 "Ivory Coast" 35.7 99.8 3.32
## 45 "Croatia" 9 96.5 6.93
## 46 "Cuba" 10.2 102. 1.64
## 47 "Cyprus" 10.5 99.3 7.27
## 48 "Czech Republic" 10.7 101. 1.93
## 49 "North Korea" 13.9 113. 2.74
## 50 "Democratic Republic of… 41.2 108 4.24
## 51 "Denmark" 10.6 101. 4.91
## 52 "Djibouti" 21.5 75.3 10.3
## 53 "Dominica" 12 115. 0
## 54 "Dominican Republic" 19.5 106. 5.84
## 55 "Ecuador" 19.7 103. 3.97
## 56 "Egypt" 26.4 106. 10.8
## 57 "El Salvador" 18.2 94.8 4.11
## 58 "Equatorial Guinea" 33.2 61.8 6.43
## 59 "Eritrea" 30.3 68.4 5.14
## 60 "Estonia" 10.9 97.2 5.11
## 61 "Eswatini" 0 0 0
## 62 "Ethiopia" 32.3 101 2.08
## 63 "Fiji" 21.3 106. 4.1
## 64 "Finland" 8.6 100. 6.59
## 65 "France" 11.3 102. 8.43
## 66 "Gabon" 31.6 140. 20
## 67 "The Gambia" 38.5 98 9.06
## 68 "Georgia" 13.5 98.6 14.4
## 69 "Germany" 9.5 104 3.04
## 70 "Ghana" 29.4 105. 4.33
## 71 "Greece" 8.1 99.6 17.2
## 72 "Grenada" 16.5 107. 0
## 73 "Guatemala" 24.6 102. 2.46
## 74 "Guinea" 36.4 91.5 4.3
## 75 "Guinea0Bissau" 35.1 119. 2.47
## 76 "Guyana" 20.0 97.8 11.8
## 77 "Haiti" 24.4 114. 13.8
## 78 "Vatican City" 0 0 0
## 79 "Honduras" 21.6 91.5 5.39
## 80 "Hungary" 9.6 101. 3.4
## 81 "Iceland" 12 100. 2.84
## 82 "India" 17.9 113 5.36
## 83 "Indonesia" 18.1 106. 4.69
## 84 "Iran" 18.8 111. 11.4
## 85 "Iraq" 29.1 109. 12.8
## 86 "Republic of Ireland" 12.5 101. 4.93
## 87 "Israel" 20.8 105. 3.86
## 88 "Italy" 7.3 102. 9.89
## 89 "Jamaica" 16.1 91 8
## 90 "Japan" 7.4 98.8 2.29
## 91 "Jordan" 22.0 81.5 14.7
## 92 "Kazakhstan" 21.8 104. 4.59
## 93 "Kenya" 28.8 103. 2.64
## 94 "Kiribati" 27.9 101. 0
## 95 "Kuwait" 13.9 92.4 2.18
## 96 "Kyrgyzstan" 27.1 108. 6.33
## 97 "Laos" 23.6 102. 0.63
## 98 "Latvia" 10 99.4 6.52
## 99 "Lebanon" 17.6 95.1 6.23
## 100 "Lesotho" 26.8 121. 23.4
## 101 "Liberia" 33.0 85.1 2.81
## 102 "Libya" 18.8 109 18.6
## 103 "Liechtenstein" 9.9 105. 0
## 104 "Lithuania" 10 104. 6.35
## 105 "Luxembourg" 10.3 102. 5.36
## 106 "Madagascar" 32.7 142. 1.76
## 107 "Malawi" 34.1 142. 5.65
## 108 "Malaysia" 16.8 105. 3.32
## 109 "Maldives" 14.2 97.1 6.14
## 110 "Mali" 41.5 75.6 7.22
## 111 "Malta" 9.2 105 3.47
## 112 "Marshall Islands" 29.0 84.7 0
## 113 "Mauritania" 33.7 99.9 9.55
## 114 "Mauritius" 10.2 101. 6.67
## 115 "Mexico" 17.6 106. 3.42
## 116 "Federated States of Mi… 22.8 97.2 0
## 117 "Monaco" 5.9 0 0
## 118 "Mongolia" 24.1 104 6.01
## 119 "Montenegro" 11.7 100 14.9
## 120 "Montserrat" 0 0 0
## 121 "Morocco" 18.9 114. 9.02
## 122 "Mozambique" 37.5 113. 3.24
## 123 "Myanmar" 17.6 112. 1.58
## 124 "Namibia" 28.6 124. 20.3
## 125 "Nauru" 0 0 0
## 126 "Nepal" 19.9 142. 1.41
## 127 "Netherlands" 9.7 104. 3.2
## 128 "New Zealand" 12.0 100 4.07
## 129 "Nicaragua" 20.6 121. 6.84
## 130 "Niger" 46.1 74.7 0.47
## 131 "Nigeria" 37.9 84.7 8.1
## 132 "Niue" 0 0 0
## 133 "North Macedonia" 0 0 0
## 134 "Norway" 10.4 100. 3.35
## 135 "Oman" 19.2 103. 2.67
## 136 "Pakistan" 28.2 94.3 4.45
## 137 "Palau" 14 113. 0
## 138 "Panama" 19.0 94.4 3.9
## 139 "Papua New Guinea" 27.1 108. 2.46
## 140 "Paraguay" 20.6 104. 4.81
## 141 "Peru" 18.0 107. 3.31
## 142 "Philippines" 20.6 108. 2.15
## 143 "Poland" 10.2 100 3.47
## 144 "Portugal" 8.5 106. 6.33
## 145 "Qatar" 9.54 104. 0.09
## 146 "South Korea" 6.4 98.1 4.15
## 147 "Moldova" 10.1 90.6 5.47
## 148 "Romania" 9.6 85.2 3.98
## 149 "Russia" 11.5 103. 4.59
## 150 "Rwanda" 31.7 133 1.03
## 151 "Saint Kitts and Nevis" 12.6 109. 0
## 152 "Saint Lucia" 12 103. 20.7
## 153 "Saint Vincent and the … 14.2 113. 18.9
## 154 "Samoa" 24.4 110. 8.36
## 155 "San Marino" 6.8 108. 0
## 156 "S\xef\xbf\xbd\xef\xbf\… 31.5 107. 13.4
## 157 "Saudi Arabia" 17.8 99.8 5.93
## 158 "Senegal" 34.5 81 6.6
## 159 "Serbia" 9.2 100. 12.7
## 160 "Seychelles" 17.1 100. 0
## 161 "Sierra Leone" 33.4 113. 4.43
## 162 "Singapore" 8.8 101. 4.11
## 163 "Slovakia" 10.6 98.7 5.56
## 164 "Slovenia" 9.4 100. 4.2
## 165 "Solomon Islands" 32.4 106. 0.58
## 166 "Somalia" 41.8 23.4 11.4
## 167 "South Africa" 20.5 101. 28.2
## 168 "South Sudan" 35.0 73 12.2
## 169 "Spain" 7.9 103. 14.0
## 170 "Sri Lanka" 15.8 100. 4.2
## 171 "Palestinian National A… 0 0 0
## 172 "Sudan" 32.2 76.8 16.5
## 173 "Suriname" 18.5 109. 7.33
## 174 "Sweden" 11.4 127. 6.48
## 175 "Switzerland" 10 105. 4.58
## 176 "Syria" 23.7 81.7 8.37
## 177 "Tajikistan" 30.8 101. 11.0
## 178 "Thailand" 10.3 99.8 0.75
## 179 "East Timor" 29.4 115. 4.55
## 180 "Togo" 33.1 124. 2.04
## 181 "Tokelau" 0 0 0
## 182 "Tonga" 24.3 116. 1.12
## 183 "Trinidad and Tobago" 12.9 106. 2.69
## 184 "Tunisia" 17.6 115. 16.0
## 185 "Turkey" 16.0 93.2 13.5
## 186 "Turkmenistan" 23.8 88.4 3.91
## 187 "Turks and Caicos Islan… 0 0 0
## 188 "Tuvalu" 0 0 0
## 189 "Uganda" 38.1 103. 1.84
## 190 "Ukraine" 8.7 99 8.88
## 191 "United Arab Emirates" 10.3 108. 2.35
## 192 "United Kingdom" 11 101. 3.85
## 193 "Tanzania" 36.7 94.2 1.98
## 194 "United States" 11.6 102. 14.7
## 195 "Uruguay" 13.9 108. 8.73
## 196 "Uzbekistan" 23.3 104. 5.92
## 197 "Vanuatu" 29.6 109. 4.39
## 198 "Venezuela" 17.9 97.2 8.8
## 199 "Vietnam" 16.8 111. 2.01
## 200 "Yemen" 30.4 93.6 12.9
## 201 "Zambia" 36.2 98.7 11.4
## 202 "Zimbabwe" 30.7 110. 4.95
## # ℹ abbreviated name: ¹Gross_Primary_Education_Enrollment#FILTER
GE %>%
select('Countries and areas',Birth_Rate) %>% filter(Birth_Rate > 18.1) %>% print(n=93)## # A tibble: 93 × 2
## `Countries and areas` Birth_Rate
## <chr> <dbl>
## 1 "Afghanistan" 32.5
## 2 "Algeria" 24.3
## 3 "Angola" 40.7
## 4 "Bangladesh" 18.2
## 5 "Belize" 20.8
## 6 "Benin" 36.2
## 7 "Bolivia" 21.8
## 8 "Botswana" 24.8
## 9 "Burkina Faso" 37.9
## 10 "Burundi" 39.0
## 11 "Cape Verde" 19.5
## 12 "Cambodia" 22.5
## 13 "Cameroon" 35.4
## 14 "Central African Republic" 35.4
## 15 "Chad" 42.2
## 16 "Comoros" 31.9
## 17 "Republic of the Congo" 32.9
## 18 "Ivory Coast" 35.7
## 19 "Democratic Republic of the Congo" 41.2
## 20 "Djibouti" 21.5
## 21 "Dominican Republic" 19.5
## 22 "Ecuador" 19.7
## 23 "Egypt" 26.4
## 24 "El Salvador" 18.2
## 25 "Equatorial Guinea" 33.2
## 26 "Eritrea" 30.3
## 27 "Ethiopia" 32.3
## 28 "Fiji" 21.3
## 29 "Gabon" 31.6
## 30 "The Gambia" 38.5
## 31 "Ghana" 29.4
## 32 "Guatemala" 24.6
## 33 "Guinea" 36.4
## 34 "Guinea0Bissau" 35.1
## 35 "Guyana" 20.0
## 36 "Haiti" 24.4
## 37 "Honduras" 21.6
## 38 "Iran" 18.8
## 39 "Iraq" 29.1
## 40 "Israel" 20.8
## 41 "Jordan" 22.0
## 42 "Kazakhstan" 21.8
## 43 "Kenya" 28.8
## 44 "Kiribati" 27.9
## 45 "Kyrgyzstan" 27.1
## 46 "Laos" 23.6
## 47 "Lesotho" 26.8
## 48 "Liberia" 33.0
## 49 "Libya" 18.8
## 50 "Madagascar" 32.7
## 51 "Malawi" 34.1
## 52 "Mali" 41.5
## 53 "Marshall Islands" 29.0
## 54 "Mauritania" 33.7
## 55 "Federated States of Micronesia" 22.8
## 56 "Mongolia" 24.1
## 57 "Morocco" 18.9
## 58 "Mozambique" 37.5
## 59 "Namibia" 28.6
## 60 "Nepal" 19.9
## 61 "Nicaragua" 20.6
## 62 "Niger" 46.1
## 63 "Nigeria" 37.9
## 64 "Oman" 19.2
## 65 "Pakistan" 28.2
## 66 "Panama" 19.0
## 67 "Papua New Guinea" 27.1
## 68 "Paraguay" 20.6
## 69 "Philippines" 20.6
## 70 "Rwanda" 31.7
## 71 "Samoa" 24.4
## 72 "S\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xe… 31.5
## 73 "Senegal" 34.5
## 74 "Sierra Leone" 33.4
## 75 "Solomon Islands" 32.4
## 76 "Somalia" 41.8
## 77 "South Africa" 20.5
## 78 "South Sudan" 35.0
## 79 "Sudan" 32.2
## 80 "Suriname" 18.5
## 81 "Syria" 23.7
## 82 "Tajikistan" 30.8
## 83 "East Timor" 29.4
## 84 "Togo" 33.1
## 85 "Tonga" 24.3
## 86 "Turkmenistan" 23.8
## 87 "Uganda" 38.1
## 88 "Tanzania" 36.7
## 89 "Uzbekistan" 23.3
## 90 "Vanuatu" 29.6
## 91 "Yemen" 30.4
## 92 "Zambia" 36.2
## 93 "Zimbabwe" 30.7#ARRANGE
GE %>%
select('Countries and areas',Birth_Rate) %>%
filter(Birth_Rate > 18.1 & Birth_Rate <= 32.5) %>%
arrange(Birth_Rate) %>% print(n=63)## # A tibble: 63 × 2
## `Countries and areas` Birth_Rate
## <chr> <dbl>
## 1 "Bangladesh" 18.2
## 2 "El Salvador" 18.2
## 3 "Suriname" 18.5
## 4 "Iran" 18.8
## 5 "Libya" 18.8
## 6 "Morocco" 18.9
## 7 "Panama" 19.0
## 8 "Oman" 19.2
## 9 "Cape Verde" 19.5
## 10 "Dominican Republic" 19.5
## 11 "Ecuador" 19.7
## 12 "Nepal" 19.9
## 13 "Guyana" 20.0
## 14 "South Africa" 20.5
## 15 "Philippines" 20.6
## 16 "Paraguay" 20.6
## 17 "Nicaragua" 20.6
## 18 "Belize" 20.8
## 19 "Israel" 20.8
## 20 "Fiji" 21.3
## 21 "Djibouti" 21.5
## 22 "Honduras" 21.6
## 23 "Bolivia" 21.8
## 24 "Kazakhstan" 21.8
## 25 "Jordan" 22.0
## 26 "Cambodia" 22.5
## 27 "Federated States of Micronesia" 22.8
## 28 "Uzbekistan" 23.3
## 29 "Laos" 23.6
## 30 "Syria" 23.7
## 31 "Turkmenistan" 23.8
## 32 "Mongolia" 24.1
## 33 "Algeria" 24.3
## 34 "Tonga" 24.3
## 35 "Haiti" 24.4
## 36 "Samoa" 24.4
## 37 "Guatemala" 24.6
## 38 "Botswana" 24.8
## 39 "Egypt" 26.4
## 40 "Lesotho" 26.8
## 41 "Papua New Guinea" 27.1
## 42 "Kyrgyzstan" 27.1
## 43 "Kiribati" 27.9
## 44 "Pakistan" 28.2
## 45 "Namibia" 28.6
## 46 "Kenya" 28.8
## 47 "Marshall Islands" 29.0
## 48 "Iraq" 29.1
## 49 "Ghana" 29.4
## 50 "East Timor" 29.4
## 51 "Vanuatu" 29.6
## 52 "Eritrea" 30.3
## 53 "Yemen" 30.4
## 54 "Zimbabwe" 30.7
## 55 "Tajikistan" 30.8
## 56 "S\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xef\xbf\xbd\xe… 31.5
## 57 "Gabon" 31.6
## 58 "Rwanda" 31.7
## 59 "Comoros" 31.9
## 60 "Sudan" 32.2
## 61 "Ethiopia" 32.3
## 62 "Solomon Islands" 32.4
## 63 "Afghanistan" 32.5#MUTATE
GE %>%
mutate(Persentase_Unemployment_Rate = (Unemployment_Rate *100) / Birth_Rate) %>%
select('Countries and areas',Persentase_Unemployment_Rate,Birth_Rate) %>%
mutate('Countries and areas'= NULL) %>% print(n=202)## # A tibble: 202 × 2
## Persentase_Unemployment_Rate Birth_Rate
## <dbl> <dbl>
## 1 34.2 32.5
## 2 105. 11.8
## 3 48.2 24.3
## 4 0 7.2
## 5 16.9 40.7
## 6 NaN 0
## 7 0 15.3
## 8 57.5 17.0
## 9 121. 14.0
## 10 41.8 12.6
## 11 48.1 9.7
## 12 39.4 14
## 13 74.2 14.0
## 14 5.08 14.0
## 15 23.0 18.2
## 16 97.0 10.6
## 17 46.4 9.9
## 18 54.3 10.3
## 19 30.8 20.8
## 20 6.16 36.2
## 21 13.6 17.3
## 22 16.1 21.8
## 23 227. 8.11
## 24 73.3 24.8
## 25 86.8 13.9
## 26 NaN 0
## 27 61.2 14.9
## 28 48.8 8.9
## 29 16.5 37.9
## 30 3.67 39.0
## 31 62.9 19.5
## 32 3.03 22.5
## 33 9.55 35.4
## 34 55.0 10.1
## 35 10.4 35.4
## 36 4.48 42.2
## 37 57.0 12.4
## 38 39.6 10.9
## 39 65.3 14.9
## 40 13.6 31.9
## 41 28.8 32.9
## 42 NaN 0
## 43 84.8 14.0
## 44 9.29 35.7
## 45 77 9
## 46 16.1 10.2
## 47 69.5 10.5
## 48 18.0 10.7
## 49 19.7 13.9
## 50 10.3 41.2
## 51 46.3 10.6
## 52 48.0 21.5
## 53 0 12
## 54 29.9 19.5
## 55 20.1 19.7
## 56 40.8 26.4
## 57 22.5 18.2
## 58 19.3 33.2
## 59 17.0 30.3
## 60 46.9 10.9
## 61 NaN 0
## 62 6.43 32.3
## 63 19.3 21.3
## 64 76.6 8.6
## 65 74.6 11.3
## 66 63.3 31.6
## 67 23.5 38.5
## 68 107. 13.5
## 69 32 9.5
## 70 14.7 29.4
## 71 213. 8.1
## 72 0 16.5
## 73 10.0 24.6
## 74 11.8 36.4
## 75 7.03 35.1
## 76 59.3 20.0
## 77 56.6 24.4
## 78 NaN 0
## 79 25.0 21.6
## 80 35.4 9.6
## 81 23.7 12
## 82 30.0 17.9
## 83 26.0 18.1
## 84 60.6 18.8
## 85 44.1 29.1
## 86 39.4 12.5
## 87 18.6 20.8
## 88 135. 7.3
## 89 49.7 16.1
## 90 30.9 7.4
## 91 67.0 22.0
## 92 21.1 21.8
## 93 9.18 28.8
## 94 0 27.9
## 95 15.6 13.9
## 96 23.4 27.1
## 97 2.68 23.6
## 98 65.2 10
## 99 35.5 17.6
## 100 87.3 26.8
## 101 8.50 33.0
## 102 98.6 18.8
## 103 0 9.9
## 104 63.5 10
## 105 52.0 10.3
## 106 5.39 32.7
## 107 16.6 34.1
## 108 19.8 16.8
## 109 43.2 14.2
## 110 17.4 41.5
## 111 37.7 9.2
## 112 0 29.0
## 113 28.3 33.7
## 114 65.4 10.2
## 115 19.4 17.6
## 116 0 22.8
## 117 0 5.9
## 118 24.9 24.1
## 119 127. 11.7
## 120 NaN 0
## 121 47.6 18.9
## 122 8.64 37.5
## 123 9.00 17.6
## 124 70.8 28.6
## 125 NaN 0
## 126 7.09 19.9
## 127 33.0 9.7
## 128 34.0 12.0
## 129 33.1 20.6
## 130 1.02 46.1
## 131 21.4 37.9
## 132 NaN 0
## 133 NaN 0
## 134 32.2 10.4
## 135 13.9 19.2
## 136 15.8 28.2
## 137 0 14
## 138 20.5 19.0
## 139 9.09 27.1
## 140 23.4 20.6
## 141 18.4 18.0
## 142 10.5 20.6
## 143 34.0 10.2
## 144 74.5 8.5
## 145 0.943 9.54
## 146 64.8 6.4
## 147 54.2 10.1
## 148 41.5 9.6
## 149 39.9 11.5
## 150 3.25 31.7
## 151 0 12.6
## 152 173. 12
## 153 133. 14.2
## 154 34.3 24.4
## 155 0 6.8
## 156 42.4 31.5
## 157 33.3 17.8
## 158 19.1 34.5
## 159 138. 9.2
## 160 0 17.1
## 161 13.3 33.4
## 162 46.7 8.8
## 163 52.5 10.6
## 164 44.7 9.4
## 165 1.79 32.4
## 166 27.2 41.8
## 167 137. 20.5
## 168 35.0 35.0
## 169 177. 7.9
## 170 26.5 15.8
## 171 NaN 0
## 172 51.4 32.2
## 173 39.5 18.5
## 174 56.8 11.4
## 175 45.8 10
## 176 35.3 23.7
## 177 35.8 30.8
## 178 7.25 10.3
## 179 15.5 29.4
## 180 6.16 33.1
## 181 NaN 0
## 182 4.61 24.3
## 183 20.8 12.9
## 184 91.2 17.6
## 185 84.2 16.0
## 186 16.4 23.8
## 187 NaN 0
## 188 NaN 0
## 189 4.82 38.1
## 190 102. 8.7
## 191 22.7 10.3
## 192 35 11
## 193 5.40 36.7
## 194 127. 11.6
## 195 63.0 13.9
## 196 25.4 23.3
## 197 14.8 29.6
## 198 49.2 17.9
## 199 12 16.8
## 200 42.4 30.4
## 201 31.6 36.2
## 202 16.1 30.7#SUMMARIZE
GE %>%
summarize(mean_Unemployment_Rate = mean(Unemployment_Rate, ra.rm = TRUE),
median_Birth_Rate = median(Birth_Rate, na.rm = TRUE),
sd_Unemployment_Rate = sd(Unemployment_Rate,na.rm =TRUE),
q1_Birth_Rate = quantile(Birth_Rate,probs =0.25,na.rm = TRUE)
)## # A tibble: 1 × 4
## mean_Unemployment_Rate median_Birth_Rate sd_Unemployment_Rate q1_Birth_Rate
## <dbl> <dbl> <dbl> <dbl>
## 1 6 17.6 5.27 10.4