library("readxl")
##Import untidy excel file
untidy<-read_excel("Untidy1.xlsx")
untidy
## # A tibble: 3 x 4
## `Political Affiliation` x2015 x2016 x2017
## <chr> <dbl> <dbl> <dbl>
## 1 Repulican 12 10 9
## 2 Democrat 14 12 6
## 3 Independent 6 12 3
## Import tidy excel file
tidy<-read_excel("tidy1.xlsx")
tidy
## # A tibble: 81 x 2
## Republican `2015`
## <chr> <dbl>
## 1 Republican 2015
## 2 Republican 2015
## 3 Republican 2015
## 4 Republican 2015
## 5 Republican 2015
## 6 Republican 2015
## 7 Republican 2015
## 8 Republican 2015
## 9 Republican 2015
## 10 Republican 2015
## # ... with 71 more rows
## Tidy file #1 10 observation, 2 variables
dataset<-read.csv("dataset1.csv")
dataset
## program sex
## 1 auto male
## 2 auto male
## 3 auto male
## 4 auto male
## 5 auto male
## 6 auto male
## 7 auto male
## 8 auto male
## 9 auto male
## 10 auto male
## 11 auto male
## 12 auto male
## 13 auto male
## 14 auto male
## 15 auto male
## 16 auto male
## 17 auto male
## 18 auto male
## 19 auto male
## 20 auto male
## 21 auto male
## 22 auto male
## 23 auto male
## 24 auto male
## 25 auto male
## 26 auto male
## 27 auto male
## 28 auto male
## 29 auto male
## 30 auto male
## 31 auto male
## 32 auto male
## 33 auto male
## 34 auto male
## 35 auto male
## 36 auto male
## 37 auto female
## 38 auto female
## 39 auto female
## 40 auto female
## 41 carpentry male
## 42 carpentry male
## 43 carpentry male
## 44 carpentry male
## 45 carpentry male
## 46 carpentry male
## 47 carpentry male
## 48 carpentry male
## 49 carpentry male
## 50 carpentry male
## 51 carpentry male
## 52 carpentry male
## 53 carpentry male
## 54 carpentry male
## 55 carpentry male
## 56 carpentry male
## 57 carpentry male
## 58 carpentry male
## 59 carpentry male
## 60 carpentry male
## 61 carpentry male
## 62 carpentry male
## 63 carpentry male
## 64 carpentry male
## 65 carpentry male
## 66 carpentry male
## 67 carpentry male
## 68 carpentry male
## 69 carpentry male
## 70 carpentry male
## 71 carpentry male
## 72 carpentry male
## 73 carpentry female
## 74 carpentry female
## 75 welding male
## 76 welding male
## 77 welding male
## 78 welding male
## 79 welding male
## 80 welding male
## 81 welding male
## 82 welding male
## 83 welding male
## 84 welding male
## 85 welding male
## 86 welding male
## 87 welding male
## 88 welding male
## 89 welding male
## 90 welding male
## 91 welding male
## 92 welding male
## 93 welding male
## 94 welding male
## 95 welding male
## 96 welding male
## 97 welding male
## 98 welding male
## 99 welding male
## 100 welding male
## 101 welding male
## 102 welding male
## 103 welding male
## 104 welding male
## 105 welding male
## 106 welding male
## 107 welding male
## 108 welding male
## 109 welding female
## 110 welding female
## 111 welding female
## 112 welding female
## 113 food male
## 114 food male
## 115 food male
## 116 food male
## 117 food male
## 118 food male
## 119 food male
## 120 food male
## 121 food male
## 122 food male
## 123 food male
## 124 food male
## 125 food male
## 126 food female
## 127 food female
## 128 food female
## 129 food female
## 130 food female
## 131 food female
## 132 food female
## 133 food female
## 134 food female
## 135 food female
## 136 food female
## 137 bussiness male
## 138 bussiness male
## 139 bussiness female
## 140 bussiness female
## 141 bussiness female
## 142 bussiness female
## 143 bussiness female
## 144 bussiness female
## 145 bussiness female
## 146 bussiness female
## 147 cosmo female
## 148 cosmo female
## 149 cosmo female
## 150 cosmo female
## 151 cosmo female
## 152 cosmo female
## 153 cosmo female
## 154 cosmo female
## 155 cosmo female
## 156 cosmo female
## 157 cosmo female
## 158 cosmo female
## 159 cosmo female
## 160 cosmo female
## 161 cosmo female
## 162 cosmo female
## 163 cosmo female
## 164 cosmo female
## 165 cosmo female
## 166 cosmo female
## 167 cosmo female
## 168 cosmo female
## 169 cosmo female
## 170 cosmo female
## 171 cosmo female
## 172 cosmo female
## 173 cosmo female
## 174 cosmo female
## 175 cosmo female
## 176 cosmo female
## 177 cosmo female
## 178 cosmo female
## 179 cosmo female
## 180 cosmo female
## 181 cosmo female
## 182 cosmo female
## 183 cosmo female
## 184 security male
## 185 security male
## 186 security male
## 187 security male
## 188 security male
## 189 security male
## 190 security male
## 191 security male
## 192 security male
## 193 security male
## 194 security male
## 195 security male
## 196 security male
## 197 security male
## 198 security male
## 199 security male
## 200 security male
## 201 security male
## 202 security male
## 203 security male
## 204 security male
## 205 security male
## 206 security male
## 207 security male
## 208 security female
## 209 security female
## 210 security female
## 211 electrical male
## 212 electrical male
## 213 electrical male
## 214 electrical male
## 215 electrical male
## 216 electrical male
## 217 electrical male
## 218 electrical male
## 219 electrical male
## 220 electrical male
## 221 electrical male
## 222 electrical male
## 223 electrical male
## 224 electrical male
## 225 electrical male
## 226 electrical male
## 227 electrical male
## 228 electrical male
## 229 electrical male
## 230 electrical male
## 231 electrical male
## 232 electrical male
## 233 electrical male
## 234 electrical male
## 235 electrical male
## 236 power_equip male
## 237 power_equip male
## 238 power_equip male
## 239 power_equip male
## 240 power_equip male
## 241 power_equip male
## 242 power_equip male
## 243 power_equip male
## 244 power_equip male
## 245 power_equip male
## 246 power_equip male
## 247 power_equip male
## 248 power_equip male
## 249 power_equip male
## 250 power_equip male
## 251 power_equip male
## 252 power_equip male
## 253 power_equip male
## 254 power_equip male
## 255 power_equip male
## 256 power_equip male
## 257 power_equip male
## 258 power_equip male
## 259 power_equip male
## 260 power_equip male
## 261 power_equip male
## 262 power_equip male
## 263 power_equip male
## 264 power_equip male
## 265 collision male
## 266 collision male
## 267 collision male
## 268 collision male
## 269 collision male
## 270 collision male
## 271 collision male
## 272 collision male
## 273 collision male
## 274 collision male
## 275 collision male
## 276 collision male
## 277 collision male
## 278 collision male
## 279 collision male
## 280 collision male
## 281 collision female
## 282 collision female
## 283 collision female
# This dataset was collected from my CTC it is the number of
# nontraditional v. traditional students in 10 of our 13 Programs of Study
#The variables represent the number of males and females in each program.
#The level of measurement id "nominal"
## Tidy file #2 10 observations, 2 variables
dataset2<-read.csv("dataset2.csv")
dataset2
## player year hr
## 1 mark trumbro 2015 22
## 2 mark trumbro 2016 47
## 3 nelson cruz 2015 44
## 4 nelson cruz 2016 43
## 5 brian dozoer 2015 28
## 6 brian dozoer 2016 42
## 7 khris davis 2015 27
## 8 khris davis 2016 42
## 9 edwin encarnacion 2015 39
## 10 edwin encarnacion 2016 42
## 11 nolan arenado 2015 42
## 12 nolan arenado 2016 41
## 13 chris davis 2015 24
## 14 chris davis 2016 40
## 15 todd frazier 2015 35
## 16 todd frazier 2016 39
## 17 robinson cano 2015 21
## 18 robinson cano 2016 39
# This data set was collected from MLB.com it is the top ten home run hitters in 2016 compared to
# their homeruns from 2015.
# The variables are the year in which the homeruns were hit and the amount of homeruns each player hit in
# a perticular year.
#The level of measurement is "interval"
## Tidy file # 3 10 observations, 2 variables.
library(readr)
dataset3 <- read_csv("~/R Projects/Practice Assignment 4/Practice Assignment 4/dataset3.csv")
## Parsed with column specification:
## cols(
## scranton = col_character(),
## republican = col_character()
## )
dataset3
## # A tibble: 67 x 2
## scranton republican
## <chr> <chr>
## 1 scranton republican
## 2 scranton republican
## 3 scranton democrat
## 4 scranton democrat
## 5 scranton democrat
## 6 scranton democrat
## 7 philidelphia republican
## 8 philidelphia republican
## 9 philidelphia democrat
## 10 philidelphia democrat
## # ... with 57 more rows
# This data set represents the cities I live within 2 hours of and "fictishous numbers" representing
# the number of deomcrates and rebulicans in the cities.
# The variables represnt the number of rebublicans vs. the number of democrates in each city.
# The level of measurement is the lowest level "nominal"