Explore Data Portal

Create a R script or R markdown file. Download a dataset that is in csv

format. Write code to import a dataset and explore the dataset.

$$ Explore the open data portal for a city or state you are interested. Do

you find any datasets interesting?

$$ Download a csv dataset from the portal.

$$ Check the first $5$ lines of the data.

$$ Get a statistical summary of the data.

Read JSON file with R

Create a R script or R markdown file. Follow the instructions to import

and explore json data.

# install the packages

install.packages$("$rjson$")$

install.packages$("$jsonlite$")$

# load the packages

library$(\text{'}$rjson$\text{'})$

library$(\text{'}$jsonlite$\text{'})$

# find the directory where your file is saved

setwd$($filepath$)$

# read the file with stream$\_$in

json$\_$file $<-$ "snowstorm$\_$sample.json"

storm$\_$tweets $<-$ stream$\_$in$($file$($json$\_$file$))$

# check the dimension of storm$\_$tweets

nrow$($storm$\_$tweets$)$

ncol$($storm$\_$tweets$)$

#extract the elements from storm tweets.

date$\_$time $=$ storm$\_$tweets$created$\_$at

username $=$ storm$\_$tweets$user$screen$\_$name

tweet$\_$text $=$ storm$\_$tweets$text

# check the values

str$($date$\_$time$)$

str$($username$)$

str$($tweet$\_$text$)$

# create a new data frame with the extracted information

storm $<-$ data.frame$($date$\_$time, username, tweet$\_$text$)$

# simple summary of the dataset

head$($storm$)$

str$($storm$)$

#Process date$\_$time information, count number of tweets for each

minute in the dataset