Pulling in the data from my github
library(RCurl)
my_git_url <- getURL("https://raw.githubusercontent.com/aelsaeyed/BridgeR/main/datasets/hw2/HealthInsurance.csv")
# hinsurance_csv <- read.csv("/Users/aelsaeyed/BridgeR/datasets/hw2/HealthInsurance.csv", quote = "" )
hinsurance_csv <- read.csv(text = my_git_url, quote = "")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.
my_summary = summary(hinsurance_csv)
my_summary## X.. X.health. X.age. X.limit.
## Length:8802 Length:8802 Min. :18.00 Length:8802
## Class :character Class :character 1st Qu.:30.00 Class :character
## Mode :character Mode :character Median :39.00 Mode :character
## Mean :38.94
## 3rd Qu.:48.00
## Max. :62.00
## X.gender. X.insurance. X.married. X.selfemp.
## Length:8802 Length:8802 Length:8802 Length:8802
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## X.family. X.region. X.ethnicity. X.education.
## Min. : 1.000 Length:8802 Length:8802 Length:8802
## 1st Qu.: 2.000 Class :character Class :character Class :character
## Median : 3.000 Mode :character Mode :character Mode :character
## Mean : 3.094
## 3rd Qu.: 4.000
## Max. :14.000mean1 = mean(hinsurance_csv$X.age)
median1 = median(hinsurance_csv$X.age)
mean1## [1] 38.93683median1## [1] 39mean2 = mean(hinsurance_csv$X.family)
median2 = median(hinsurance_csv$X.family)
mean2## [1] 3.093501median2## [1] 3Create 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.
insurance_subset = subset(hinsurance_csv, hinsurance_csv$X.age > 35 & hinsurance_csv$X.family >= 2)
insurance_new_cols = insurance_subset[, c("X.age.", "X.family.", "X.ethnicity.", "X.region.")]
summary(insurance_new_cols)## X.age. X.family. X.ethnicity. X.region.
## Min. :36.00 Min. : 2.000 Length:4586 Length:4586
## 1st Qu.:40.00 1st Qu.: 2.000 Class :character Class :character
## Median :46.00 Median : 3.000 Mode :character Mode :character
## Mean :46.36 Mean : 3.348
## 3rd Qu.:52.00 3rd Qu.: 4.000
## Max. :62.00 Max. :14.000Create new column names for each column in the new data frame created in step 2.
names(insurance_new_cols) <- c('age', 'family', 'ethnicity', 'region')
summary(insurance_new_cols)## age family ethnicity region
## Min. :36.00 Min. : 2.000 Length:4586 Length:4586
## 1st Qu.:40.00 1st Qu.: 2.000 Class :character Class :character
## Median :46.00 Median : 3.000 Mode :character Mode :character
## Mean :46.36 Mean : 3.348
## 3rd Qu.:52.00 3rd Qu.: 4.000
## Max. :62.00 Max. :14.000Use the summary function to create an overview of your new data frame created in step 2. The print the mean and median for the same two attributes. Please compare (i.e. tell me how the values changed and why).
summary(insurance_new_cols)## age family ethnicity region
## Min. :36.00 Min. : 2.000 Length:4586 Length:4586
## 1st Qu.:40.00 1st Qu.: 2.000 Class :character Class :character
## Median :46.00 Median : 3.000 Mode :character Mode :character
## Mean :46.36 Mean : 3.348
## 3rd Qu.:52.00 3rd Qu.: 4.000
## Max. :62.00 Max. :14.000newmean1 = mean(insurance_new_cols$age)
newmedian1 = median(insurance_new_cols$age)
newmean1## [1] 46.35543newmedian1## [1] 46newmean2 = mean(insurance_new_cols$family)
newmedian2 = median(insurance_new_cols$family)
newmean2## [1] 3.348234newmedian2## [1] 3#The mean and median of age rose dramatically because I previously created a subset of the original data by filtering for rows containing ages greater than 35. The mean family size went up a little bit because I also filtered for rows where the size is 2 or greater. It seems the mean family size is still the same. 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”.
#resetting the index
row.names(insurance_new_cols) <- NULL
insurance_new_cols$ethnicity[insurance_new_cols$ethnicity == '"cauc"'] <- "Caucasian"
insurance_new_cols$ethnicity[insurance_new_cols$ethnicity == '"afam"'] <- "African American"
insurance_new_cols$region[insurance_new_cols$region == '"west"'] <- "West Coast"
insurance_new_cols$region[insurance_new_cols$region == '"south"'] <- "Southern Coast"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.
head(insurance_new_cols, 20)## age family ethnicity region
## 1 54 5 Caucasian West Coast
## 2 39 5 Caucasian West Coast
## 3 56 2 Caucasian West Coast
## 4 60 2 Caucasian Southern Coast
## 5 62 2 Caucasian Southern Coast
## 6 52 2 African American "northeast"
## 7 50 2 African American "northeast"
## 8 38 7 Caucasian West Coast
## 9 48 8 Caucasian Southern Coast
## 10 48 8 Caucasian Southern Coast
## 11 53 2 Caucasian "midwest"
## 12 50 2 Caucasian "midwest"
## 13 43 2 Caucasian Southern Coast
## 14 40 4 Caucasian "midwest"
## 15 53 6 African American "northeast"
## 16 40 6 African American "northeast"
## 17 51 3 Caucasian West Coast
## 18 58 3 Caucasian West Coast
## 19 45 4 Caucasian West Coast
## 20 43 4 Caucasian "midwest"