R Workshop Bridge Program Homework 2

R Bridge Course Week 2 Assignment One of the challenges in working with data is wrangling. In this assignment we will use R to perform this task. Here is a list of data sets: http://vincentarelbundock.github.io/Rdatasets/ (click on the csv index for a list) Please select one, download it and perform the following tasks:

—–selected guns.csv—–

#Two ways to read the file, one from local directory, another from github repo.
setwd("/users/carlos/downloads/")
gun_data <- read.csv("guns.csv")
head(gun_data)

library(RCurl)

## Loading required package: bitops

git_guns <- getURL("https://raw.githubusercontent.com/cfalvarez36/MSDS_R_Programming/main/Guns.csv")
guns_2 <- read.csv(text = git_guns)

Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes of your data.

summary(guns_2)

##        X             year         violent           murder      
##  Min.   :   1   Min.   :1977   Min.   :  47.0   Min.   : 0.200  
##  1st Qu.: 294   1st Qu.:1982   1st Qu.: 283.1   1st Qu.: 3.700  
##  Median : 587   Median :1988   Median : 443.0   Median : 6.400  
##  Mean   : 587   Mean   :1988   Mean   : 503.1   Mean   : 7.665  
##  3rd Qu.: 880   3rd Qu.:1994   3rd Qu.: 650.9   3rd Qu.: 9.800  
##  Max.   :1173   Max.   :1999   Max.   :2921.8   Max.   :80.600  
##                                                                 
##     robbery         prisoners           afam              cauc      
##  Min.   :   6.4   Min.   :  19.0   Min.   : 0.2482   Min.   :21.78  
##  1st Qu.:  71.1   1st Qu.: 114.0   1st Qu.: 2.2022   1st Qu.:59.94  
##  Median : 124.1   Median : 187.0   Median : 4.0262   Median :65.06  
##  Mean   : 161.8   Mean   : 226.6   Mean   : 5.3362   Mean   :62.95  
##  3rd Qu.: 192.7   3rd Qu.: 291.0   3rd Qu.: 6.8507   3rd Qu.:69.20  
##  Max.   :1635.1   Max.   :1913.0   Max.   :26.9796   Max.   :76.53  
##                                                                     
##       male         population          income         density         
##  Min.   :12.21   Min.   : 0.4027   Min.   : 8555   Min.   : 0.000707  
##  1st Qu.:14.65   1st Qu.: 1.1877   1st Qu.:11935   1st Qu.: 0.031911  
##  Median :15.90   Median : 3.2713   Median :13402   Median : 0.081569  
##  Mean   :16.08   Mean   : 4.8163   Mean   :13725   Mean   : 0.352038  
##  3rd Qu.:17.53   3rd Qu.: 5.6856   3rd Qu.:15271   3rd Qu.: 0.177718  
##  Max.   :22.35   Max.   :33.1451   Max.   :23647   Max.   :11.102120  
##                                                                       
##         state       law     
##  Alabama   :  23   no :888  
##  Alaska    :  23   yes:285  
##  Arizona   :  23            
##  Arkansas  :  23            
##  California:  23            
##  Colorado  :  23            
##  (Other)   :1035

summary(gun_data)

##        X             year         violent           murder      
##  Min.   :   1   Min.   :1977   Min.   :  47.0   Min.   : 0.200  
##  1st Qu.: 294   1st Qu.:1982   1st Qu.: 283.1   1st Qu.: 3.700  
##  Median : 587   Median :1988   Median : 443.0   Median : 6.400  
##  Mean   : 587   Mean   :1988   Mean   : 503.1   Mean   : 7.665  
##  3rd Qu.: 880   3rd Qu.:1994   3rd Qu.: 650.9   3rd Qu.: 9.800  
##  Max.   :1173   Max.   :1999   Max.   :2921.8   Max.   :80.600  
##                                                                 
##     robbery         prisoners           afam              cauc      
##  Min.   :   6.4   Min.   :  19.0   Min.   : 0.2482   Min.   :21.78  
##  1st Qu.:  71.1   1st Qu.: 114.0   1st Qu.: 2.2022   1st Qu.:59.94  
##  Median : 124.1   Median : 187.0   Median : 4.0262   Median :65.06  
##  Mean   : 161.8   Mean   : 226.6   Mean   : 5.3362   Mean   :62.95  
##  3rd Qu.: 192.7   3rd Qu.: 291.0   3rd Qu.: 6.8507   3rd Qu.:69.20  
##  Max.   :1635.1   Max.   :1913.0   Max.   :26.9796   Max.   :76.53  
##                                                                     
##       male         population          income         density         
##  Min.   :12.21   Min.   : 0.4027   Min.   : 8555   Min.   : 0.000707  
##  1st Qu.:14.65   1st Qu.: 1.1877   1st Qu.:11935   1st Qu.: 0.031911  
##  Median :15.90   Median : 3.2713   Median :13402   Median : 0.081569  
##  Mean   :16.08   Mean   : 4.8163   Mean   :13725   Mean   : 0.352038  
##  3rd Qu.:17.53   3rd Qu.: 5.6856   3rd Qu.:15271   3rd Qu.: 0.177718  
##  Max.   :22.35   Max.   :33.1451   Max.   :23647   Max.   :11.102120  
##                                                                       
##         state       law     
##  Alabama   :  23   no :888  
##  Alaska    :  23   yes:285  
##  Arizona   :  23            
##  Arkansas  :  23            
##  California:  23            
##  Colorado  :  23            
##  (Other)   :1035

Create a new data frame with a subset of the columns AND rows. There are several ways to do this so feel free to try a couple if you want. Make sure to rename the new data set so it simply just doesn’t write it over.

ny_gun <- subset(gun_data, state == "New York")
ny_gun

Create new column names for each column in the new data frame created in step 2.

ny_gun_edit <- setNames(ny_gun, c("Index","Year","Violence", "Murder", "Theft", "Prisoners", "African_American", "Caucasian", "Male", "Population", "Income", "Density", "State", "Carry_Law"))
ny_gun_edit

colnames(ny_gun)

##  [1] "X"          "year"       "violent"    "murder"     "robbery"   
##  [6] "prisoners"  "afam"       "cauc"       "male"       "population"
## [11] "income"     "density"    "state"      "law"

colnames(ny_gun_edit)

##  [1] "Index"            "Year"             "Violence"        
##  [4] "Murder"           "Theft"            "Prisoners"       
##  [7] "African_American" "Caucasian"        "Male"            
## [10] "Population"       "Income"           "Density"         
## [13] "State"            "Carry_Law"

Use the summary function to create an overview of your new data frame created in step 2. Then print the mean and median for the same two attributes. Please compare (i.e. tell me how the values changed and why).

summary(ny_gun_edit)

##      Index            Year         Violence          Murder     
##  Min.   :737.0   Min.   :1977   Min.   : 588.8   Min.   : 5.00  
##  1st Qu.:742.5   1st Qu.:1982   1st Qu.: 841.5   1st Qu.: 9.80  
##  Median :748.0   Median :1988   Median : 965.6   Median :11.10  
##  Mean   :748.0   Mean   :1988   Mean   : 941.3   Mean   :10.67  
##  3rd Qu.:753.5   3rd Qu.:1994   3rd Qu.:1071.5   3rd Qu.:12.50  
##  Max.   :759.0   Max.   :1999   Max.   :1180.9   Max.   :14.50  
##                                                                 
##      Theft         Prisoners     African_American   Caucasian    
##  Min.   :240.8   Min.   : 98.0   Min.   :5.272    Min.   :55.93  
##  1st Qu.:472.4   1st Qu.:151.5   1st Qu.:6.246    1st Qu.:56.90  
##  Median :514.1   Median :229.0   Median :7.330    Median :58.52  
##  Mean   :501.8   Mean   :244.7   Mean   :7.142    Mean   :58.95  
##  3rd Qu.:588.1   3rd Qu.:347.0   3rd Qu.:8.055    3rd Qu.:61.06  
##  Max.   :684.0   Max.   :397.0   Max.   :8.679    Max.   :62.96  
##                                                                  
##       Male         Population        Income         Density      
##  Min.   :13.20   Min.   :17.57   Min.   :13404   Min.   :0.3676  
##  1st Qu.:13.96   1st Qu.:17.72   1st Qu.:13980   1st Qu.:0.3729  
##  Median :15.47   Median :17.94   Median :17349   Median :0.3787  
##  Mean   :15.27   Mean   :17.91   Mean   :16447   Mean   :0.3780  
##  3rd Qu.:16.56   3rd Qu.:18.14   3rd Qu.:17829   3rd Qu.:0.3842  
##  Max.   :17.11   Max.   :18.20   Max.   :20273   Max.   :0.3853  
##                                                                  
##         State    Carry_Law
##  New York  :23   no :23   
##  Alabama   : 0   yes: 0   
##  Alaska    : 0            
##  Arizona   : 0            
##  Arkansas  : 0            
##  California: 0            
##  (Other)   : 0

gun_robbery_mean <- mean(gun_data$robbery)
gun_robbery_median <- median(gun_data$robbery)
gun_theft_mean <- mean(ny_gun_edit$Theft)
gun_theft_median <- median(ny_gun_edit$Theft)

mean_median_df = data.frame(gun_robbery_mean, gun_robbery_median, gun_theft_mean, gun_theft_median)
mean_median_df

The values changed because the scope has changed. What was oringinally focusing on the number of rows below:

nrow(gun_data[!is.na(gun_data$robbery), ])

## [1] 1173

has now changed to focus on this amount of rows:

nrow(ny_gun_edit[!is.na(ny_gun_edit$Theft), ])

## [1] 23

The mean and median will definitely change if we’re comparing the calculations between 1173 values and 23 values.

For at least 3 different/distinct values in a column please rename so that every value in that column is renamed. For example, change the letter “e” to “excellent”, the letter “a” to “average’ and the word “bad” to “terrible”.

ny_gun_edit_1 <- replace(ny_gun_edit$Theft, ny_gun_edit$Theft==514.1, 0)
ny_gun_edit_2 <- replace(ny_gun_edit_1, ny_gun_edit_1==610.7, 1)
ny_gun_edit_3 <- replace(ny_gun_edit_2, ny_gun_edit_2==641.3, 2)
ny_gun_edit_3 #Shows the final product where three values have been changed.

##  [1] 472.6 472.1 529.6   2.0 684.0   1.0 536.5 506.9 504.4   0.0 503.3
## [12] 544.4 579.3 624.7 622.1 596.9 561.2 476.7 399.7 340.0 309.3 270.3
## [23] 240.8

Display enough rows to see examples of all of steps 1-5 above. This means use a function to show me enough row values that I can see the changes.

#This function will show you the final subset dataframe with changed column names and specific rows updated.
example_row <- function(){
  library(RCurl)
  git_guns <- getURL("https://raw.githubusercontent.com/cfalvarez36/MSDS_R_Programming/main/Guns.csv")
  guns_2 <- read.csv(text = git_guns)
  ny_gun <- subset(guns_2, state == "New York")
  ny_gun_edit <- setNames(ny_gun, c("Index","Year","Violence", "Murder", "Theft", "Prisoners", "African_American", "Caucasian", "Male", "Population", "Income", "Density", "State", "Carry_Law"))
  summary(ny_gun_edit)
  ny_gun_edit_1 <- replace(ny_gun_edit$Theft, ny_gun_edit$Theft==514.1, 0)
  ny_gun_edit_2 <- replace(ny_gun_edit_1, ny_gun_edit_1==610.7, 1)
  ny_gun_edit_3 <- replace(ny_gun_edit_2, ny_gun_edit_2==641.3, 2)
  ny_gun_edit <- replace(ny_gun_edit, 5, ny_gun_edit_3)
  return(ny_gun_edit)
}
example_row()

BONUS – place the original .csv in a github file and have R read from the link. This should be your own github – not the file source. This will be a very useful skill as you progress in your data science education and career.

Please submit your .rmd file and the .csv file as well as a link to your RPubs.

R Workshop Bridge Program Homework 2

Carlos_Alvarez

1/16/2022