1a create an example of untidy data in excel from local compute and print

library(readxl)
pap44 <- read_excel("~/wfed540/pap44.xlsx",2)
pap44
## # A tibble: 13 x 4
##       X__1  X__2 `untidy data`      X__3
##      <chr> <chr>         <chr>     <chr>
##  1 country  year           key    values
##  2   ghana  2015      homocide       200
##  3   ghana  2015    population  27000000
##  4   ghana  2016      homocide       100
##  5   ghana  2016    population  25000000
##  6 nigeria  2015      homocide       150
##  7 nigeria  2015    population 165000000
##  8 nigeria  2016      homocide       120
##  9 nigeria  2016    population 160000000
## 10  gambia  2015      homocide        60
## 11  gambia  2015    population   4000000
## 12  gambia  2016      homocide        90
## 13  gambia  2016    population   4000000

1b create an example of a tidy dataset in excel from local compute and print

library(readxl)
pap44 <- read_excel("~/wfed540/pap44.xlsx")
pap44
## # A tibble: 7 x 4
##      X__1  X__2 `tidy data`       X__3
##     <chr> <chr>       <chr>      <chr>
## 1 country  year    homocide population
## 2   ghana  2015         200   27000000
## 3   ghana  2016         100   25000000
## 4 nigeria  2015         150  165000000
## 5 nigeria  2016         120  160000000
## 6  gambia  2015          60    4000000
## 7  gambia  2016          90    4000000

2 collect 10 observations two variables(departments and number of cars used) and print

library(readxl)
depcars<-read_excel("~/wfed540/depcars.xlsx")
depcars
## # A tibble: 10 x 2
##    department  cars
##         <chr> <dbl>
##  1       park     4
##  2     police     6
##  3   hospital     9
##  4     school     7
##  5    stadium     3
##  6    daycare     6
##  7     prison     4
##  8    cariage     2
##  9      water     2
## 10   electric     6

2 collect 10 observations two variables (companys and profits)and print

library(readxl)
pap44 <- read_excel("~/wfed540/pap44.xlsx",3)
pap44
## # A tibble: 10 x 2
##       company  profit
##         <chr>   <dbl>
##  1      apple 2.0e+07
##  2       dell 3.0e+07
##  3     toyota 5.0e+08
##  4   mercedex 5.5e+08
##  5     nissan 6.5e+08
##  6     google 2.5e+08
##  7 cartapilar 6.9e+08
##  8    komatsu 9.9e+08
##  9     walmat 3.5e+08
## 10     target 2.5e+08

2 collect 10 observations two variables(animals and temperature) and print

library(readxl)
pap44 <- read_excel("~/wfed540/pap44.xlsx",4)
pap44
## # A tibble: 10 x 2
##          kind temperature
##         <chr>       <dbl>
##  1        cow          91
##  2        dog          90
##  3    antelop          93
##  4 chempanzee          98
##  5       goat          88
##  6      sheep          82
##  7       fowl          87
##  8       fish          86
##  9   elephant          81
## 10       deer          88
2c narative of how the data were collected

  the fisrt dataset was collected from a government agency to determine the number of cars used by city's departments. this assist the agency to determine maintenance and fuel cost.
  
  the second the dataset was collected from the walstreet journal about the profit declared by the select companies listed on the stock exchange.
  
  the third dataset was collected by vertinary a graduate  reseacher who was testing the temperature of animals at a different regions of the world.
  
  2d definition of the variables
  
  the variables are departments and numbersof cars used.
  the departments are nominal, whiles the number of cars are ratio. its ratio because it gives the exact of car 
  
  the variables used in the second observation are companies and profits. the companies are nominal and the profits are ratios.
  
  the variables used in the third observation are animals and temperature.
  
  the animals are nominal and the temperatures are interval scale measurement.