Week 2 R - Data Science Workshop

Questions

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

HELPrct <- read.csv("https://raw.githubusercontent.com/murphystout/HELPrct/master/HELPrct.csv", stringsAsFactors = FALSE)

summary(HELPrct)

##        X            age         anysubstatus       anysub         
##  Min.   :  1   Min.   :19.00   Min.   :0.0000   Length:453        
##  1st Qu.:114   1st Qu.:30.00   1st Qu.:1.0000   Class :character  
##  Median :227   Median :35.00   Median :1.0000   Mode  :character  
##  Mean   :227   Mean   :35.65   Mean   :0.7724                     
##  3rd Qu.:340   3rd Qu.:40.00   3rd Qu.:1.0000                     
##  Max.   :453   Max.   :60.00   Max.   :1.0000                     
##                                NA's   :207                        
##       cesd             d1           daysanysub        dayslink    
##  Min.   : 1.00   Min.   :  0.00   Min.   :  0.00   Min.   :  2.0  
##  1st Qu.:25.00   1st Qu.:  1.00   1st Qu.:  5.00   1st Qu.: 74.0  
##  Median :34.00   Median :  2.00   Median : 33.00   Median :361.0  
##  Mean   :32.85   Mean   :  3.06   Mean   : 75.31   Mean   :255.6  
##  3rd Qu.:41.00   3rd Qu.:  3.00   3rd Qu.:164.25   3rd Qu.:365.0  
##  Max.   :60.00   Max.   :100.00   Max.   :268.00   Max.   :456.0  
##                                   NA's   :209      NA's   :22     
##     drugrisk           e2b             female           sex           
##  Min.   : 0.000   Min.   : 1.000   Min.   :0.0000   Length:453        
##  1st Qu.: 0.000   1st Qu.: 1.000   1st Qu.:0.0000   Class :character  
##  Median : 0.000   Median : 2.000   Median :0.0000   Mode  :character  
##  Mean   : 1.887   Mean   : 2.505   Mean   :0.2362                     
##  3rd Qu.: 1.000   3rd Qu.: 3.000   3rd Qu.:0.0000                     
##  Max.   :21.000   Max.   :21.000   Max.   :1.0000                     
##  NA's   :1        NA's   :239                                         
##      g1b              homeless               i1               i2        
##  Length:453         Length:453         Min.   :  0.00   Min.   :  0.00  
##  Class :character   Class :character   1st Qu.:  3.00   1st Qu.:  4.00  
##  Mode  :character   Mode  :character   Median : 13.00   Median : 18.00  
##                                        Mean   : 17.91   Mean   : 24.55  
##                                        3rd Qu.: 26.00   3rd Qu.: 33.00  
##                                        Max.   :142.00   Max.   :184.00  
##                                                                         
##        id            indtot        linkstatus         link          
##  Min.   :  1.0   Min.   : 4.00   Min.   :0.0000   Length:453        
##  1st Qu.:119.0   1st Qu.:32.00   1st Qu.:0.0000   Class :character  
##  Median :233.0   Median :38.00   Median :0.0000   Mode  :character  
##  Mean   :233.4   Mean   :35.73   Mean   :0.3782                     
##  3rd Qu.:348.0   3rd Qu.:41.00   3rd Qu.:1.0000                     
##  Max.   :470.0   Max.   :45.00   Max.   :1.0000                     
##                                  NA's   :22                         
##       mcs              pcs            pss_fr         racegrp         
##  Min.   : 6.763   Min.   :14.07   Min.   : 0.000   Length:453        
##  1st Qu.:21.676   1st Qu.:40.38   1st Qu.: 3.000   Class :character  
##  Median :28.602   Median :48.88   Median : 7.000   Mode  :character  
##  Mean   :31.677   Mean   :48.05   Mean   : 6.706                     
##  3rd Qu.:40.941   3rd Qu.:56.95   3rd Qu.:10.000                     
##  Max.   :62.175   Max.   :74.81   Max.   :14.000                     
##                                                                      
##    satreat             sexrisk        substance            treat          
##  Length:453         Min.   : 0.000   Length:453         Length:453        
##  Class :character   1st Qu.: 3.000   Class :character   Class :character  
##  Mode  :character   Median : 4.000   Mode  :character   Mode  :character  
##                     Mean   : 4.642                                        
##                     3rd Qu.: 6.000                                        
##                     Max.   :14.000                                        
##                                                                           
##    avg_drinks       max_drinks    
##  Min.   :  0.00   Min.   :  0.00  
##  1st Qu.:  3.00   1st Qu.:  4.00  
##  Median : 13.00   Median : 18.00  
##  Mean   : 17.91   Mean   : 24.55  
##  3rd Qu.: 26.00   3rd Qu.: 33.00  
##  Max.   :142.00   Max.   :184.00  
##

mean(HELPrct$age)

## [1] 35.65342

median(HELPrct$age)

## [1] 35

mean(HELPrct$daysanysub, na.rm = TRUE)

## [1] 75.30738

median(HELPrct$daysanysub, na.rm = TRUE)

## [1] 33

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

males <- subset(HELPrct, sex == "male")

Create new column names for the data frame.

colnames(males) <- c("id_1", "age", "any_substance_status", "any_substance", "cesd", "d1", "days_using_any_substance", "dayslink", "drugrisk", "e2b", "female","sex","g1b", "homeless", "i1", "i2", "id_2", "indtot", "linkstatus","link","mcs","pcs","pss_fr","race_group","substance_abuse_treatment","sexrisk","substance","treatment","average_drinks","max_drinks")

Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare.

summary(males)

##       id_1            age        any_substance_status any_substance     
##  Min.   :  1.0   Min.   :19.00   Min.   :0.0000       Length:346        
##  1st Qu.:108.8   1st Qu.:30.00   1st Qu.:1.0000       Class :character  
##  Median :236.5   Median :35.00   Median :1.0000       Mode  :character  
##  Mean   :230.1   Mean   :35.47   Mean   :0.7936                         
##  3rd Qu.:346.8   3rd Qu.:40.00   3rd Qu.:1.0000                         
##  Max.   :453.0   Max.   :60.00   Max.   :1.0000                         
##                                  NA's   :157                            
##       cesd            d1          days_using_any_substance    dayslink    
##  Min.   : 1.0   Min.   :  0.000   Min.   :  0.00           Min.   :  2.0  
##  1st Qu.:24.0   1st Qu.:  1.000   1st Qu.:  4.50           1st Qu.: 65.0  
##  Median :32.5   Median :  2.000   Median : 34.00           Median :358.0  
##  Mean   :31.6   Mean   :  2.928   Mean   : 72.73           Mean   :251.2  
##  3rd Qu.:40.0   3rd Qu.:  3.000   3rd Qu.:154.00           3rd Qu.:365.0  
##  Max.   :58.0   Max.   :100.000   Max.   :260.00           Max.   :456.0  
##                                   NA's   :159              NA's   :16     
##     drugrisk          e2b             female      sex           
##  Min.   : 0.00   Min.   : 1.000   Min.   :0   Length:346        
##  1st Qu.: 0.00   1st Qu.: 1.000   1st Qu.:0   Class :character  
##  Median : 0.00   Median : 2.000   Median :0   Mode  :character  
##  Mean   : 1.93   Mean   : 2.614   Mean   :0                     
##  3rd Qu.: 1.00   3rd Qu.: 3.000   3rd Qu.:0                     
##  Max.   :21.00   Max.   :21.000   Max.   :0                     
##  NA's   :1       NA's   :175                                    
##      g1b              homeless               i1               i2        
##  Length:346         Length:346         Min.   :  0.00   Min.   :  0.00  
##  Class :character   Class :character   1st Qu.:  4.00   1st Qu.:  6.00  
##  Mode  :character   Mode  :character   Median : 13.00   Median : 19.00  
##                                        Mean   : 18.83   Mean   : 25.95  
##                                        3rd Qu.: 26.00   3rd Qu.: 36.00  
##                                        Max.   :142.00   Max.   :184.00  
##                                                                         
##       id_2           indtot        linkstatus        link          
##  Min.   :  1.0   Min.   : 4.00   Min.   :0.000   Length:346        
##  1st Qu.:113.2   1st Qu.:34.00   1st Qu.:0.000   Class :character  
##  Median :238.5   Median :38.00   Median :0.000   Mode  :character  
##  Mean   :234.7   Mean   :36.77   Mean   :0.397                     
##  3rd Qu.:354.5   3rd Qu.:41.00   3rd Qu.:1.000                     
##  Max.   :469.0   Max.   :45.00   Max.   :1.000                     
##                                  NA's   :16                        
##       mcs              pcs            pss_fr        race_group       
##  Min.   : 6.763   Min.   :14.07   Min.   : 0.000   Length:346        
##  1st Qu.:22.687   1st Qu.:41.91   1st Qu.: 3.000   Class :character  
##  Median :29.683   Median :50.23   Median : 6.000   Mode  :character  
##  Mean   :32.523   Mean   :48.99   Mean   : 6.558                     
##  3rd Qu.:41.352   3rd Qu.:57.64   3rd Qu.:10.000                     
##  Max.   :62.175   Max.   :74.81   Max.   :14.000                     
##                                                                      
##  substance_abuse_treatment    sexrisk      substance        
##  Length:346                Min.   : 0.0   Length:346        
##  Class :character          1st Qu.: 3.0   Class :character  
##  Mode  :character          Median : 4.0   Mode  :character  
##                            Mean   : 4.5                     
##                            3rd Qu.: 6.0                     
##                            Max.   :13.0                     
##                                                             
##   treatment         average_drinks     max_drinks    
##  Length:346         Min.   :  0.00   Min.   :  0.00  
##  Class :character   1st Qu.:  4.00   1st Qu.:  6.00  
##  Mode  :character   Median : 13.00   Median : 19.00  
##                     Mean   : 18.83   Mean   : 25.95  
##                     3rd Qu.: 26.00   3rd Qu.: 36.00  
##                     Max.   :142.00   Max.   :184.00  
##

mean(males$age)

## [1] 35.46821

median(males$age)

## [1] 35

mean(males$days_using_any_substance, na.rm = TRUE)

## [1] 72.72727

median(males$days_using_any_substance, na.rm = TRUE)

## [1] 34

Note that the median age is the same, but the mean age of the males is slightly lower then the overall population.

Also males have slightly lower mean for “days_using_any_substance” than the general population.

For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter ???e??? in one column. Rename those values so that all 20 would show as ???excellent???.

males$homeless[males$homeless == "housed"] = "No"
males$homeless[males$homeless == "homeless"] = "Yes"

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

head(males)

##    id_1 age any_substance_status any_substance cesd d1
## 1     1  37                    1           yes   49  3
## 2     2  37                    1           yes   30 22
## 3     3  26                    1           yes   39  0
## 5     5  32                    1           yes   39 12
## 8     8  28                    1           yes   32  1
## 10   10  39                    1           yes   46  4
##    days_using_any_substance dayslink drugrisk e2b female  sex g1b homeless
## 1                       177      225        0  NA      0 male yes       No
## 2                         2       NA        0  NA      0 male yes      Yes
## 3                         3      365       20  NA      0 male  no       No
## 5                         2       57        0   1      0 male  no      Yes
## 8                        47      365        7   8      0 male yes      Yes
## 10                      115      382       20   3      0 male  no      Yes
##    i1 i2 id_2 indtot linkstatus link       mcs      pcs pss_fr race_group
## 1  13 26    1     39          1  yes 25.111990 58.41369      0      black
## 2  56 62    2     43         NA <NA> 26.670307 36.03694      1      white
## 3   0  0    3     41          0   no  6.762923 74.80633     13      black
## 5  10 13    5     38          1  yes 21.675755 37.34558     10      black
## 8  12 24    8     44          0   no  9.160530 65.13801      4      white
## 10 20 27   10     44          0   no 36.143761 22.61060      0      white
##    substance_abuse_treatment sexrisk substance treatment average_drinks
## 1                         no       4   cocaine       yes             13
## 2                         no       7   alcohol       yes             56
## 3                         no       2    heroin        no              0
## 5                         no       6   cocaine        no             10
## 8                        yes       6   alcohol       yes             12
## 10                       yes       0    heroin       yes             20
##    max_drinks
## 1          26
## 2          62
## 3           0
## 5          13
## 8          24
## 10         27

BONUS ??? place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

Done is Question 1, i.e.: https://raw.githubusercontent.com/murphystout/HELPrct/master/HELPrct.csv

Week 2 R - Data Science Workshop

Michael Hayes

January 1, 2019

Questions