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: 1. 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.
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.
Create new column names for each column in the new data frame created in step 2.
Use 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).
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”.
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.
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.
require(tidyverse) ## Loading required package: tidyverse## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()file <- 'YouthRisk2007.csv'
data <- read.csv(file)
data <- data.frame(data) %>% na.omit() # not counting N/A Values
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## 13179 13179 1 0 9 14 1 0summary(data) #Use the summary function to gain an overview of the data set## X ride.alc.driver female grade
## Min. : 1 Min. :0.0000 Min. :0.000 Min. : 9.00
## 1st Qu.: 3331 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:10.00
## Median : 6536 Median :0.0000 Median :1.000 Median :11.00
## Mean : 6554 Mean :0.3134 Mean :0.526 Mean :10.52
## 3rd Qu.: 9779 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:12.00
## Max. :13179 Max. :1.0000 Max. :1.000 Max. :12.00
## age4 smoke DriverLicense
## Min. :14.00 Min. :0.0000 Min. :0.0000
## 1st Qu.:15.00 1st Qu.:0.0000 1st Qu.:0.0000
## Median :16.00 Median :1.0000 Median :1.0000
## Mean :16.15 Mean :0.5356 Mean :0.6762
## 3rd Qu.:17.00 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :18.00 Max. :1.0000 Max. :1.0000sprintf("The mean value of grade is %.2f", mean(data$grade))## [1] "The mean value of grade is 10.52"sprintf("The median value of grade is %.2f", median(data$grade))## [1] "The median value of grade is 11.00"sprintf("The mean value of age is %.2f", mean(data$age4))## [1] "The mean value of age is 16.15"sprintf("The median value of age is %.2f", median(data$age4))## [1] "The median value of age is 16.00"#Create a new data frame with a subset of the columns AND rows
data_subset <- data[1:20,c("ride.alc.driver", "female", "grade", "age4")]
data_subset## ride.alc.driver female grade age4
## 1 1 1 10 15
## 2 1 1 10 18
## 4 0 0 11 17
## 5 0 0 11 17
## 6 0 0 11 17
## 7 0 0 11 17
## 8 0 0 11 17
## 9 0 0 11 16
## 10 0 1 11 17
## 11 0 1 11 17
## 12 0 0 11 18
## 13 0 1 9 14
## 14 1 1 9 15
## 15 0 0 9 15
## 16 0 0 9 14
## 17 0 0 9 15
## 18 0 0 9 16
## 19 0 0 9 14
## 20 0 0 9 14
## 21 0 0 9 15#Create new column names for each column in the new data frame
colnames(data_subset) <- c("DUI_Driver", "Female", "Grade","Age")# Change names
colnames(data_subset) #show New Names## [1] "DUI_Driver" "Female" "Grade" "Age"summary(data_subset) #Use the summary function to gain an overview of the data set## DUI_Driver Female Grade Age
## Min. :0.00 Min. :0.0 Min. : 9 Min. :14.0
## 1st Qu.:0.00 1st Qu.:0.0 1st Qu.: 9 1st Qu.:15.0
## Median :0.00 Median :0.0 Median :10 Median :16.0
## Mean :0.15 Mean :0.3 Mean :10 Mean :15.9
## 3rd Qu.:0.00 3rd Qu.:1.0 3rd Qu.:11 3rd Qu.:17.0
## Max. :1.00 Max. :1.0 Max. :11 Max. :18.0sprintf("The mean value of grade is %.2f", mean(data_subset$Grade))## [1] "The mean value of grade is 10.00"sprintf("The median value of grade is %.2f", median(data_subset$Grade))## [1] "The median value of grade is 10.00"sprintf("The mean value of age is %.2f", mean(data_subset$Age))## [1] "The mean value of age is 15.90"sprintf("The median value of age is %.2f", median(data_subset$Age))## [1] "The median value of age is 16.00"My subset only have 15 records compared to my original data where i have over 1k records although the median age is the same.
require(stringr)
data_subset$Female <- data_subset ["Female"]
data_subset$Female <- sapply(data_subset$Female, as.character)
unique(data_subset$Female)## Female
## [1,] "1"
## [2,] "0"most of column are either 1, or 0 the ones with real data I cannot change therefore decided to change the Female column to a yes to represent Female and no to represent Males
data_subset$Female <- str_replace(data_subset$Female,"0","No")
data_subset$Female <- str_replace(data_subset$Female,"1","Yes")
unique(data_subset$Female)## [1] "Yes" "No"data_subset## DUI_Driver Female Grade Age
## 1 1 Yes 10 15
## 2 1 Yes 10 18
## 4 0 No 11 17
## 5 0 No 11 17
## 6 0 No 11 17
## 7 0 No 11 17
## 8 0 No 11 17
## 9 0 No 11 16
## 10 0 Yes 11 17
## 11 0 Yes 11 17
## 12 0 No 11 18
## 13 0 Yes 9 14
## 14 1 Yes 9 15
## 15 0 No 9 15
## 16 0 No 9 14
## 17 0 No 9 15
## 18 0 No 9 16
## 19 0 No 9 14
## 20 0 No 9 14
## 21 0 No 9 15