Install the `tidyverse` package.

if(!require(tidyverse)) install.packages("tidyverse")

## Loading required package: tidyverse

## -- Attaching packages --------------------------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.1.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.8
## v tidyr   0.8.2     v stringr 1.3.1
## v readr   1.3.1     v forcats 0.3.0

## -- Conflicts ------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(tidyverse)

Read in the dataset.

File location: https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Males.csv

It is important to be able to read files of differing formats as well as web-based files.

Read in the `.txt` file from your desktop.

wages <- read_csv("C:/Users/djpri/Desktop/CUNY/Bridge_R/week2/wages_education.txt")

## Parsed with column specification:
## cols(
##   X1 = col_double(),
##   nr = col_double(),
##   year = col_double(),
##   school = col_double(),
##   exper = col_double(),
##   union = col_character(),
##   ethn = col_character(),
##   maried = col_character(),
##   health = col_character(),
##   wage = col_double(),
##   industry = col_character(),
##   occupation = col_character(),
##   residence = col_character()
## )

Read in the web-based `.csv` file from its URL.

wages_url <- "https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Males.csv"

Name the dataset.

wages_web <- read.csv(wages_url)

View the wages data imported from your desktop.

head(wages)

## # A tibble: 6 x 13
##      X1    nr  year school exper union ethn  maried health  wage industry
##   <dbl> <dbl> <dbl>  <dbl> <dbl> <chr> <chr> <chr>  <chr>  <dbl> <chr>   
## 1     1    13  1980     14     1 no    other no     no      1.20 Busines~
## 2     2    13  1981     14     2 yes   other no     no      1.85 Persona~
## 3     3    13  1982     14     3 no    other no     no      1.34 Busines~
## 4     4    13  1983     14     4 no    other no     no      1.43 Busines~
## 5     5    13  1984     14     5 no    other no     no      1.57 Persona~
## 6     6    13  1985     14     6 no    other no     no      1.70 Busines~
## # ... with 2 more variables: occupation <chr>, residence <chr>

tail(wages)

## # A tibble: 6 x 13
##      X1    nr  year school exper union ethn  maried health  wage industry
##   <dbl> <dbl> <dbl>  <dbl> <dbl> <chr> <chr> <chr>  <chr>  <dbl> <chr>   
## 1  4355 12548  1982      9     7 no    other no     no     0.832 Constru~
## 2  4356 12548  1983      9     8 no    other yes    no     1.59  Constru~
## 3  4357 12548  1984      9     9 yes   other yes    no     1.21  Constru~
## 4  4358 12548  1985      9    10 no    other yes    no     1.77  Constru~
## 5  4359 12548  1986      9    11 yes   other yes    no     1.75  Profess~
## 6  4360 12548  1987      9    12 yes   other yes    no     1.47  Public_~
## # ... with 2 more variables: occupation <chr>, residence <chr>

View the wages_web data imported from its URL.

head(wages_web)

##   X nr year school exper union  ethn maried health     wage
## 1 1 13 1980     14     1    no other     no     no 1.197540
## 2 2 13 1981     14     2   yes other     no     no 1.853060
## 3 3 13 1982     14     3    no other     no     no 1.344462
## 4 4 13 1983     14     4    no other     no     no 1.433213
## 5 5 13 1984     14     5    no other     no     no 1.568125
## 6 6 13 1985     14     6    no other     no     no 1.699891
##                      industry                          occupation
## 1 Business_and_Repair_Service                     Service_Workers
## 2            Personal_Service                     Service_Workers
## 3 Business_and_Repair_Service                     Service_Workers
## 4 Business_and_Repair_Service                     Service_Workers
## 5            Personal_Service      Craftsmen, Foremen_and_kindred
## 6 Business_and_Repair_Service Managers, Officials_and_Proprietors
##    residence
## 1 north_east
## 2 north_east
## 3 north_east
## 4 north_east
## 5 north_east
## 6 north_east

tail(wages_web)

##         X    nr year school exper union  ethn maried health      wage
## 4355 4355 12548 1982      9     7    no other     no     no 0.8324815
## 4356 4356 12548 1983      9     8    no other    yes     no 1.5918787
## 4357 4357 12548 1984      9     9   yes other    yes     no 1.2125428
## 4358 4358 12548 1985      9    10    no other    yes     no 1.7659618
## 4359 4359 12548 1986      9    11   yes other    yes     no 1.7458942
## 4360 4360 12548 1987      9    12   yes other    yes     no 1.4665429
##                              industry                     occupation
## 4355                     Construction Craftsmen, Foremen_and_kindred
## 4356                     Construction Craftsmen, Foremen_and_kindred
## 4357                     Construction Craftsmen, Foremen_and_kindred
## 4358                     Construction Craftsmen, Foremen_and_kindred
## 4359 Professional_and_Related Service Craftsmen, Foremen_and_kindred
## 4360            Public_Administration Craftsmen, Foremen_and_kindred
##      residence
## 4355      <NA>
## 4356      <NA>
## 4357      <NA>
## 4358      <NA>
## 4359      <NA>
## 4360      <NA>

Q1A.Use the `summary()` function to gain an overview of the data set.

Then display the `mean()` and `median()` for at least two attributes.

*The mean() is a measure of central tendency along with the median() and mode().

*The mean() is obtained by dividing the the sum of all values by the number of values in the dataset.

*The median() is the value of the middle term in a data set that has been ranked in increasing order.

*The mode() is the value that occurs with the highest frequency in a data set.

Apply the `summary()` function to the wages data.

summary(wages)

##        X1             nr             year          school     
##  Min.   :   1   Min.   :   13   Min.   :1980   Min.   : 3.00  
##  1st Qu.:1091   1st Qu.: 2329   1st Qu.:1982   1st Qu.:11.00  
##  Median :2180   Median : 4569   Median :1984   Median :12.00  
##  Mean   :2180   Mean   : 5262   Mean   :1984   Mean   :11.77  
##  3rd Qu.:3270   3rd Qu.: 8406   3rd Qu.:1985   3rd Qu.:12.00  
##  Max.   :4360   Max.   :12548   Max.   :1987   Max.   :16.00  
##      exper           union               ethn              maried         
##  Min.   : 0.000   Length:4360        Length:4360        Length:4360       
##  1st Qu.: 4.000   Class :character   Class :character   Class :character  
##  Median : 6.000   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 6.515                                                           
##  3rd Qu.: 9.000                                                           
##  Max.   :18.000                                                           
##     health               wage          industry          occupation       
##  Length:4360        Min.   :-3.579   Length:4360        Length:4360       
##  Class :character   1st Qu.: 1.351   Class :character   Class :character  
##  Mode  :character   Median : 1.671   Mode  :character   Mode  :character  
##                     Mean   : 1.649                                        
##                     3rd Qu.: 1.991                                        
##                     Max.   : 4.052                                        
##   residence        
##  Length:4360       
##  Class :character  
##  Mode  :character  
##                    
##                    
##

Q1B. Display the `mean()` and `median()` for three attributes:

#mean() & median() for year
mean(wages$year)

## [1] 1983.5

median(wages$year)

## [1] 1983.5

#mean() & median() years of schooling
mean(wages$school)

## [1] 11.76697

median(wages$school)

## [1] 12

#mean() & median() log of hourly wages
mean(wages$wage)

## [1] 1.649147

median(wages$wage)

## [1] 1.671143

Organize your findings in a `tribble` using the tidyverse’s `dplyr` package.

wages_trib <- tribble(
  ~measure, ~year, ~school, ~wage, 
  #-------/-------/---------/---------
  "mean", mean(wages$year), mean(wages$school), mean(wages$wage),
  "median", median(wages$year), median(wages$school), median(wages$wage)
)

print(wages_trib)

## # A tibble: 2 x 4
##   measure  year school  wage
##   <chr>   <dbl>  <dbl> <dbl>
## 1 mean    1984.   11.8  1.65
## 2 median  1984.   12    1.67

Q2. Create a new `data.frame()` with a subset of the columns and rows. Make sure to rename it.

Use the `subset.data.frame()` function to accomplish this task.

wages_df <- subset.data.frame(wages, 
                              year >= 1983 & 
                              school >= 12 & 
                              union == "yes" & 
                              wage >= 1.6, 
                              c('year', 
                                'school', 
                                'union', 
                                'wage'))

View the first ten rows of your wages_df `data.frame()`.

head(wages_df)

## # A tibble: 6 x 4
##    year school union  wage
##   <dbl>  <dbl> <chr> <dbl>
## 1  1983     12 yes    1.75
## 2  1983     12 yes    2.19
## 3  1983     12 yes    1.65
## 4  1985     12 yes    1.83
## 5  1986     12 yes    1.88
## 6  1987     12 yes    2.02

Q3. Create new column names for the new `data.frame()`.

Use the `rename()` function with the wages_trib tribble to

create the new names within your wages_df `data.frame()`.

wages_df <- rename(wages_trib,  
                 Year=year,     
                 School=school, 
                 Wage=wage
                 )

view the first and last ten rows of your data.frame().

head(wages_df)

## # A tibble: 2 x 4
##   measure  Year School  Wage
##   <chr>   <dbl>  <dbl> <dbl>
## 1 mean    1984.   11.8  1.65
## 2 median  1984.   12    1.67

tail(wages_df)

## # A tibble: 2 x 4
##   measure  Year School  Wage
##   <chr>   <dbl>  <dbl> <dbl>
## 1 mean    1984.   11.8  1.65
## 2 median  1984.   12    1.67

Q4. Use the `summary()` function to create an overview of your new `data.frame()`.

summary(wages_df)

##    measure               Year          School           Wage      
##  Length:2           Min.   :1984   Min.   :11.77   Min.   :1.649  
##  Class :character   1st Qu.:1984   1st Qu.:11.83   1st Qu.:1.655  
##  Mode  :character   Median :1984   Median :11.88   Median :1.660  
##                     Mean   :1984   Mean   :11.88   Mean   :1.660  
##                     3rd Qu.:1984   3rd Qu.:11.94   3rd Qu.:1.666  
##                     Max.   :1984   Max.   :12.00   Max.   :1.671

Print the `mean()` and `median()` for the same two attributes.

Below we will use the orignial wages dataset.

#Year: mean() & median() 
mean(wages$year)

## [1] 1983.5

median(wages$year)

## [1] 1983.5

#Wage: mean() & median()
mean(wages$wage)

## [1] 1.649147

median(wages$wage)

## [1] 1.671143

Organize the results into a `tribble` for ease of view.

mm1 <- tribble(
  ~measure, ~Year, ~Wage,
  #------/------/-------
  "mean", mean(wages$year), mean(wages$wage),
  "median", median(wages$year), median(wages$wage) 
)

Print your `tribble`.

print(mm1)

## # A tibble: 2 x 3
##   measure  Year  Wage
##   <chr>   <dbl> <dbl>
## 1 mean    1984.  1.65
## 2 median  1984.  1.67

Q5. For at least three values in a column, please rename so that every value in that column is renamed.

For example, suppose I have twenty values of the letter “e” in one column.

Rename those values so that all twenty would show as excellent.

Using the original wages data we will convert the `yes` responses

in the union column from `yes` to `Union_Member` and

the `no` responses in the union column from `no` to `Non_Union_Member`.

Use a `For Loop` along with an `if-else` statement to execute the changes.

i <- 1
  for (row in wages$union){
    if (row == "yes"){
      wages[[i,'union']] <- "Union_Member" #[[x]]=retrieve components
    }else if (row == "no"){
      wages[[i,'union']] <- "Non_Union_Member" 
    }
    i <- i + 1
  }

Display enough rows to see examples of all of steps 1-5 above.

Use the `head()` & `tail()` functions to view the first

and last part of our changed wages `data.frame()`.

head(wages)

## # A tibble: 6 x 13
##      X1    nr  year school exper union ethn  maried health  wage industry
##   <dbl> <dbl> <dbl>  <dbl> <dbl> <chr> <chr> <chr>  <chr>  <dbl> <chr>   
## 1     1    13  1980     14     1 Non_~ other no     no      1.20 Busines~
## 2     2    13  1981     14     2 Unio~ other no     no      1.85 Persona~
## 3     3    13  1982     14     3 Non_~ other no     no      1.34 Busines~
## 4     4    13  1983     14     4 Non_~ other no     no      1.43 Busines~
## 5     5    13  1984     14     5 Non_~ other no     no      1.57 Persona~
## 6     6    13  1985     14     6 Non_~ other no     no      1.70 Busines~
## # ... with 2 more variables: occupation <chr>, residence <chr>

tail(wages)

## # A tibble: 6 x 13
##      X1    nr  year school exper union ethn  maried health  wage industry
##   <dbl> <dbl> <dbl>  <dbl> <dbl> <chr> <chr> <chr>  <chr>  <dbl> <chr>   
## 1  4355 12548  1982      9     7 Non_~ other no     no     0.832 Constru~
## 2  4356 12548  1983      9     8 Non_~ other yes    no     1.59  Constru~
## 3  4357 12548  1984      9     9 Unio~ other yes    no     1.21  Constru~
## 4  4358 12548  1985      9    10 Non_~ other yes    no     1.77  Constru~
## 5  4359 12548  1986      9    11 Unio~ other yes    no     1.75  Profess~
## 6  4360 12548  1987      9    12 Unio~ other yes    no     1.47  Public_~
## # ... with 2 more variables: occupation <chr>, residence <chr>

R Bridge Week 2 Homework

Dustin Price

January 1, 2019

Install the `tidyverse` package.

Read in the dataset.

File location: https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Males.csv

It is important to be able to read files of differing formats as well as web-based files.

Read in the `.txt` file from your desktop.

Read in the web-based `.csv` file from its URL.

Name the dataset.

View the wages data imported from your desktop.

View the wages_web data imported from its URL.

Q1A.Use the `summary()` function to gain an overview of the data set.

Then display the `mean()` and `median()` for at least two attributes.

Apply the `summary()` function to the wages data.

Q1B. Display the `mean()` and `median()` for three attributes:

Organize your findings in a `tribble` using the tidyverse’s `dplyr` package.

Q2. Create a new `data.frame()` with a subset of the columns and rows. Make sure to rename it.

Use the `subset.data.frame()` function to accomplish this task.

View the first ten rows of your wages_df `data.frame()`.

Q3. Create new column names for the new `data.frame()`.

Use the `rename()` function with the wages_trib tribble to

create the new names within your wages_df `data.frame()`.

view the first and last ten rows of your data.frame().

Q4. Use the `summary()` function to create an overview of your new `data.frame()`.

Print the `mean()` and `median()` for the same two attributes.

Below we will use the orignial wages dataset.

Organize the results into a `tribble` for ease of view.

Print your `tribble`.

Q5. For at least three values in a column, please rename so that every value in that column is renamed.

For example, suppose I have twenty values of the letter “e” in one column.

Rename those values so that all twenty would show as excellent.

Using the original wages data we will convert the `yes` responses

in the union column from `yes` to `Union_Member` and

the `no` responses in the union column from `no` to `Non_Union_Member`.

Use a `For Loop` along with an `if-else` statement to execute the changes.

Display enough rows to see examples of all of steps 1-5 above.

Use the `head()` & `tail()` functions to view the first

and last part of our changed wages `data.frame()`.

R Bridge Week 2 Homework

Dustin Price

January 1, 2019

Install the tidyverse package.

Read in the dataset.

File location: **https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Males.csv**

It is important to be able to read files of differing formats as well as web-based files.

Read in the .txt file from your desktop.

Read in the web-based .csv file from its URL.

Name the dataset.

View the wages data imported from your desktop.

View the wages_web data imported from its URL.

Q1A.Use the summary() function to gain an overview of the data set.

Then display the mean() and median() for at least two attributes.

Apply the summary() function to the wages data.

Q1B. Display the mean() and median() for three attributes:

Organize your findings in a tribble using the tidyverse’s dplyr package.

Q2. Create a new data.frame() with a subset of the columns and rows. Make sure to rename it.

Use the subset.data.frame() function to accomplish this task.

View the first ten rows of your wages_df data.frame().

Q3. Create new column names for the new data.frame().

Use the rename() function with the wages_trib tribble to

create the new names within your wages_df data.frame().

view the first and last ten rows of your data.frame().

Q4. Use the summary() function to create an overview of your new data.frame().

Print the mean() and median() for the same two attributes.

Below we will use the orignial wages dataset.

Organize the results into a tribble for ease of view.

Print your tribble.

Q5. For at least three values in a column, please rename so that every value in that column is renamed.

For example, suppose I have twenty values of the letter “e” in one column.

Rename those values so that all twenty would show as excellent.

Using the original wages data we will convert the yes responses

in the union column from yes to Union_Member and

the no responses in the union column from no to Non_Union_Member.

Use a For Loop along with an if-else statement to execute the changes.

Display enough rows to see examples of all of steps 1-5 above.

Use the head() & tail() functions to view the first

and last part of our changed wages data.frame().

Install the `tidyverse` package.

File location: https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Males.csv

Read in the `.txt` file from your desktop.

Read in the web-based `.csv` file from its URL.

Q1A.Use the `summary()` function to gain an overview of the data set.

Then display the `mean()` and `median()` for at least two attributes.

Apply the `summary()` function to the wages data.

Q1B. Display the `mean()` and `median()` for three attributes:

Organize your findings in a `tribble` using the tidyverse’s `dplyr` package.

Q2. Create a new `data.frame()` with a subset of the columns and rows. Make sure to rename it.

Use the `subset.data.frame()` function to accomplish this task.

View the first ten rows of your wages_df `data.frame()`.

Q3. Create new column names for the new `data.frame()`.

Use the `rename()` function with the wages_trib tribble to

create the new names within your wages_df `data.frame()`.

Q4. Use the `summary()` function to create an overview of your new `data.frame()`.

Print the `mean()` and `median()` for the same two attributes.

Organize the results into a `tribble` for ease of view.

Print your `tribble`.

Using the original wages data we will convert the `yes` responses

in the union column from `yes` to `Union_Member` and

the `no` responses in the union column from `no` to `Non_Union_Member`.

Use a `For Loop` along with an `if-else` statement to execute the changes.

Use the `head()` & `tail()` functions to view the first

and last part of our changed wages `data.frame()`.