Project2_pt2

Part 2 of my additional practice with data clean-up and transformations using tidyr and dplyr. CRAN documentation available for tidyr is available here and dplr documentation can be found here.

library(tidyr)
library(dplyr)
library(stringr)

This chart presents various details regarding living former Presidents of the United States:

1. Create a CSV file that includes all of the information above. You’re encouraged to use a “wide” structure similar to how the information appears above, so that you can practice tidying and transformations:

# CSV file was created in OpenOffice to quickly match the source chart very closely
# This could also be done using the data.frame() and write.csv() functions.

2. Read the information from your CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data.

presidents <- read.csv("presidents.csv")
presidents

##          President Term_of_Office                      DOB
## 1 George H.W. Bush      1989-1993   June 12, 1924 (age 91)
## 2     Jimmy Carter      1977-1981 October 1, 1924 (age 90)
## 3   George W. Bush      2001-2009    July 6, 1946 (age 69)
## 4     Bill Clinton      1993-2001 August 19, 1946 (age 69)

Restructure the dataset

colnames(presidents)

## [1] "President"      "Term_of_Office" "DOB"

# Split the Term_of_Office column into two separate columns
presidents$Start_Term <- lapply(strsplit(as.character(presidents$Term_of_Office), "\\-"), "[", 2)
presidents$End_Term <- lapply(strsplit(as.character(presidents$Term_of_Office), "\\-"), "[", 1)

# Drop the old column
presidents <- subset(presidents, select = c(President,DOB,Start_Term,End_Term))
presidents

##          President                      DOB Start_Term End_Term
## 1 George H.W. Bush   June 12, 1924 (age 91)       1993     1989
## 2     Jimmy Carter October 1, 1924 (age 90)       1981     1977
## 3   George W. Bush    July 6, 1946 (age 69)       2009     2001
## 4     Bill Clinton August 19, 1946 (age 69)       2001     1993

# Pull the presidents' age out of the DOB column and create a new Age column
presidents$Age <- lapply(strsplit(as.character(presidents$DOB), "\\("), "[", 2)
ages <-  unlist(str_extract_all(presidents$Age, "[[:digit:]]{1,}"))
presidents$Age <- ages
presidents

##          President                      DOB Start_Term End_Term Age
## 1 George H.W. Bush   June 12, 1924 (age 91)       1993     1989  91
## 2     Jimmy Carter October 1, 1924 (age 90)       1981     1977  90
## 3   George W. Bush    July 6, 1946 (age 69)       2009     2001  69
## 4     Bill Clinton August 19, 1946 (age 69)       2001     1993  69

# Drop old DOB column as we will not be using it from here forward
presidents <- subset(presidents, select = c(President,Start_Term,End_Term,Age))
presidents

##          President Start_Term End_Term Age
## 1 George H.W. Bush       1993     1989  91
## 2     Jimmy Carter       1981     1977  90
## 3   George W. Bush       2009     2001  69
## 4     Bill Clinton       2001     1993  69

3. For analysis:

• Give a list of presidents who had one term in office (4 years) and presidents who had two terms in office (8 years)
• Which president is the oldest?
• What is the average age of all presidents?

# Give a list of presidents who had one term in office (4 years) and presidents who had two terms in office (8 years)

# Convert term columns to numeric values
presidents$Start_Term <- as.numeric(presidents$Start_Term)
presidents$End_Term <- as.numeric(presidents$End_Term)

# Add a column to indicate number of years in office (Just to make final result more clear)
presidents$Yrs_Office <- abs(presidents$Start_Term-presidents$End_Term)

# Create a table of presidents with two terms 
terms_2 <- presidents %>% filter(abs(presidents$Start_Term-presidents$End_Term) > 4)
terms_2

##        President Start_Term End_Term Age Yrs_Office
## 1 George W. Bush       2009     2001  69          8
## 2   Bill Clinton       2001     1993  69          8

# Create a table of presidents with one term 
terms_1 <- presidents %>% filter(abs(presidents$Start_Term-presidents$End_Term) < 5)
terms_1

##          President Start_Term End_Term Age Yrs_Office
## 1 George H.W. Bush       1993     1989  91          4
## 2     Jimmy Carter       1981     1977  90          4

# Which president is the oldest?

# Convert age column to numeric
presidents$Age <- as.numeric(presidents$Age)
max_age <- presidents %>% slice(which.max(Age))
max_age

##          President Start_Term End_Term Age Yrs_Office
## 1 George H.W. Bush       1993     1989  91          4

# Find the average age of all presidents
av_age_pres <- mean(presidents$Age)
av_age_pres

## [1] 79.75