This notebook gathers and organzies data from three data sets witht he aim of transofrming the data from a wide dataset to a long dataset.
library(RCurl)
library(tidyr)
library(dplyr)
library(data.table)Link: https://www.usclimatedata.com/
The climate data looks are tempeatures over the course of a year in NY.
climate_data<- getURL('https://raw.githubusercontent.com/KevinJpotter/data_607/master/data/ny_climate%20.csv')
climate_1 <- read.csv( text = climate_data, nrows=4)
climate_2 <- read.csv(text = climate_data, skip = 6)
climate <- merge(climate_1,climate_2, by = 'X')
# transpose
t_climate <- as.data.frame(t(climate)[-1,])
names(t_climate) <- climate[,1]
rownames(t_climate) <- 1:nrow(t_climate)
t_climate$month <- names(climate)[-1]Link: https://github.com/chitrarth2018/607-Project-2/blob/master/mbta.xlsx
The data is from MBTA and relates to the ridership across different modes of transportation in Boston. The data can be used to analyze if there is a time based trend in the ridership numbers across different modes of transport.
commute_data <- getURL('https://raw.githubusercontent.com/KevinJpotter/data_607/master/data/mbta.csv')
commute <- read.csv( text = commute_data, header = TRUE, check.names = F)
t_commute <- as.data.frame(t(commute)[-1,])
names(t_commute) <- commute[,1]
rownames(t_commute) <- 1:nrow(t_commute)
t_commute$date <- names(commute)[-1]
t_commute <- select(t_commute, -c(1))Link: https://www.ibiblio.org/hyperwar/AAF/StatDigest/aafsd-3.html
The below data are for the manufaucting of military planes, going through the type and location of the planes.
military_data <- getURL('https://raw.githubusercontent.com/KevinJpotter/data_607/master/data/military_airplanes.csv')
military <- read.csv( text = military_data, check.names=F)
t_military <- as.data.frame(t(military)[-1,])
names(t_military) <- military[,1]
rownames(t_military) <- 1:nrow(t_military)
t_military$date <- names(military)[-1]
t_military <- t_military[,colSums(is.na(t_military))<nrow(t_military)]