R/eq_clean_data.R
In moralmar/earthquakesWithR:
################ ~~~~~~~~~~~~~~~~~ ######## ~~~~~~~~~~~~~~~~~ #################
## ##
## MSDR - Capstone Project NOAA ##
## Loading/Cleaning the Data ##
## ##
## Marco R. Morales ##
## ##
## ##
## created: 09.07.2017 last update: 17.07.2017 ##
################# ~~~~~~~~~~~~~~~~~ ######## ~~~~~~~~~~~~~~~~~ #################
## function: handle_negative_dates ---------------------------------------------
##
##
#' Handling negative dates (BC dates)
#'
#' \code{handle_negative_dates} takes negative dates
#'
#' This function takes ONLY negative dates in form three inputs:
#' year, month and the day. With some estimates and mirroring, it
#' gives back a date with a date-class, which is negative
#' Important to note is, that the return date is not 100% precise.
#' It is within +/- 5 days (accuracy). This is due to the mirroring
#' which was an estimate, and due to the fact that not all month were
#' equal in days.
#'
#' @param y The year as a nummeric input
#' @param m The month as a nummeric input
#' @param d the day as a numeric input
#'
#' @return Date as a date class, and which is negative
#'
#' @examples
#' handle_negative_dates(-100, 1, 1)
#'
#' @export
#'
handle_negative_dates <- function(y, m, d) {
# to do:
# a check if year (y) from input
# is really negative.
# if not: ERROR
origin_date <- base::as.numeric(as.Date("0 1 1", "%Y %m %d", origin = "1970-01-01"))
# > origin_date
# [1] -719528
mirror_date <- base::as.Date(paste(-y-1, 13-m, abs(27-d)+1, sep = '-'), '%Y-%m-%d', origin = '1970-01-01') # -y -1 (?) # abs(27-d)+1
# example with year: y = -2000
# > mirror_date
# [1] "2000-01-01"
mirror_date_num <- base::as.numeric(mirror_date)
# > mirror_date_num
# [1] 10957
delta <- origin_date - mirror_date_num
# > delta
# [1] -730485
# delta: how many days between year 0 and mirror_date
# what's left: add days from: year 0 to 1970-01-01
days_gone_by <- delta + origin_date
# remember: delta is negative // origin_date is negative
date_new <- base::as.Date(days_gone_by, origin = "1970-01-01")
# > date_new
# [1] "-2000-01-01"
# > class(date_new)
# [1] "Date"
base::return(date_new)
} # END function handle_negative_dates
## function: handle_positive_dates ---------------------------------------------
##
##
#' Handling positive dates (BC dates)
#'
#' \code{handle_positive_dates} takes positive dates
#'
#' This function takes a positive date via an input of
#' year, month and day (seperately) and gives back the date
#' as a date class. very simple function.
#'
#' @param y The year as a nummeric input
#' @param m The month as a nummeric input
#' @param d the day as a numeric input
#'
#' @return Date as a date class, and which is positive
#'
#' @examples
#' handle_positive_dates(2010, 10, 10)
#'
#' @export
#'
handle_positive_dates <- function(y, m, d) {
date_paste <- base::paste0(y, "-", m, "-", d)
date_new <- base::as.Date(date_paste, format = "%Y-%m-%d")
base::return(date_new)
} # END function handle_positive_dates
## function: eq_location_clean() ---------------------------------------------
##
##
#' Cleans the variable "LOCATION_NAME"
#'
#' \code{eq_location_clean} strips down from the LOCATION_NAME variable the
#' country. The function input is the whole earth quake data, of which it then
#' mutates the LOCATION_NAME.
#'
#' @param data A data frame of NOAA significant earthquake data
#'
#' @importFrom magrittr "%>%"
#' @importFrom purrr map2_chr
#' @importFrom stringr str_trim
#' @importFrom stringr str_to_title
#' @importFrom dplyr mutate
#'
#' @return it returns the data frame back, but with the mutated LOCATION_NAME
#'
#' @export
#'
eq_location_clean <- function(data){
# how does it look like?
# > head(data$LOCATION_NAME)
# [1] "JORDAN: BAB-A-DARAA,AL-KARAK" "TURKMENISTAN: W"
# [3] "SYRIA: UGARIT" "GREECE: THERA ISLAND (SANTORINI)"
# [5] "ISRAEL: ARIHA (JERICHO)" "ITALY: LACUS CIMINI"
data <- data %>%
dplyr::mutate(
LOCATION_NAME = purrr::map2_chr(COUNTRY, LOCATION_NAME,
# and now: what to do with these two variables?
# let's define a function:
function(COUNTRY, LOCATION_NAME) {
base::gsub(paste0(COUNTRY, ":"),
"",
LOCATION_NAME)
}),
# we still have some white spaces left:
LOCATION_NAME = stringr::str_trim(LOCATION_NAME),
# and as required by the assignment:
LOCATION_NAME = stringr::str_to_title(LOCATION_NAME)
)
} # END eq_location_clean()
## function: eq_clean_data() ---------------------------------------------
##
##
# function eq_clean_data()
# eq_clean_data()
# After downloading and reading in the dataset, the overall task for this module is
# to write a function named eq_clean_data()that takes raw NOAA data frame and returns
# a clean data frame. The clean data frame should have the following:
# The clean data frame should have the following:
# 1. A date column created by uniting the year, month, day and converting it to the Date class
# 2. LATITUDE and LONGITUDE columns converted to numeric class
# 3. In addition, write a function eq_location_clean() that cleans the LOCATION_NAME column by
# stripping out the country name (including the colon) and converts names to title case (as opposed
# to all caps). This will be needed later for annotating visualizations. This function should be applied
# to the raw data to produce a cleaned up version of the LOCATION_NAME column.
#' Loads and Cleans the Earth Quake data
#'
#' \code{eq_clean_data} loads the data, cleans the data (including the LOCATION_NAME variable)
#' and gives back the data frame. There is no input to this function
#'
#' @importFrom magrittr "%>%"
#' @importFrom readr read_tsv
#' @importFrom dplyr mutate
#'
#' @return it returns the data frame, cleaned and with correct dates (with date class)
#'
#' @export
eq_clean_data <- function() {
# (!!) original name of data is "results"
# (!!) rename it to NOAAearthquakes
data("NOAAearthquakesRAW")
data <- NOAAearthquakesRAW %>%
# data <- readr::read_tsv("./inst/extdata/NOAAearthquakes") %>%
# replacing NA's in MONTH
# This is not that good
# I am chaing the characteristic of "when" it happend
# Might need to be changed later (?)
# before:
# > summary(data$MONTH)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 1.000 4.000 7.000 6.503 9.000 12.000 405
dplyr::mutate(MONTH = base::replace(MONTH, base::which(base::is.na(MONTH)), 1)) %>%
# replacing NA's in DAY
# before:
# > summary(data$DAY)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 1.00 8.00 16.00 15.75 23.00 31.00 556
dplyr::mutate(DAY = base::replace(DAY, base::which(base::is.na(DAY)), 1)) %>%
dplyr::mutate(date = base::ifelse(YEAR < 0,
handle_negative_dates(YEAR, MONTH, DAY),
handle_positive_dates(YEAR, MONTH, DAY)
)) %>%
dplyr::mutate(date = base::as.Date(date, origin = "1970-01-01")) %>%
dplyr::mutate(LATITUDE = base::as.numeric(LATITUDE)) %>%
dplyr::mutate(LONGITUDE = base::as.numeric(LONGITUDE))
# clean location via eq_location_clean
data <- eq_location_clean(data)
} # END function eq_clean_data()
moralmar/earthquakesWithR documentation built on May 20, 2019, 7:57 a.m.
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################ ~~~~~~~~~~~~~~~~~ ######## ~~~~~~~~~~~~~~~~~ #################
## ##
## MSDR - Capstone Project NOAA ##
## Loading/Cleaning the Data ##
## ##
## Marco R. Morales ##
## ##
## ##
## created: 09.07.2017 last update: 17.07.2017 ##
################# ~~~~~~~~~~~~~~~~~ ######## ~~~~~~~~~~~~~~~~~ #################
## function: handle_negative_dates ---------------------------------------------
##
##
#' Handling negative dates (BC dates)
#'
#' \code{handle_negative_dates} takes negative dates
#'
#' This function takes ONLY negative dates in form three inputs:
#' year, month and the day. With some estimates and mirroring, it
#' gives back a date with a date-class, which is negative
#' Important to note is, that the return date is not 100% precise.
#' It is within +/- 5 days (accuracy). This is due to the mirroring
#' which was an estimate, and due to the fact that not all month were
#' equal in days.
#'
#' @param y The year as a nummeric input
#' @param m The month as a nummeric input
#' @param d the day as a numeric input
#'
#' @return Date as a date class, and which is negative
#'
#' @examples
#' handle_negative_dates(-100, 1, 1)
#'
#' @export
#'
handle_negative_dates <- function(y, m, d) {
# to do:
# a check if year (y) from input
# is really negative.
# if not: ERROR
origin_date <- base::as.numeric(as.Date("0 1 1", "%Y %m %d", origin = "1970-01-01"))
# > origin_date
# [1] -719528
mirror_date <- base::as.Date(paste(-y-1, 13-m, abs(27-d)+1, sep = '-'), '%Y-%m-%d', origin = '1970-01-01') # -y -1 (?) # abs(27-d)+1
# example with year: y = -2000
# > mirror_date
# [1] "2000-01-01"
mirror_date_num <- base::as.numeric(mirror_date)
# > mirror_date_num
# [1] 10957
delta <- origin_date - mirror_date_num
# > delta
# [1] -730485
# delta: how many days between year 0 and mirror_date
# what's left: add days from: year 0 to 1970-01-01
days_gone_by <- delta + origin_date
# remember: delta is negative // origin_date is negative
date_new <- base::as.Date(days_gone_by, origin = "1970-01-01")
# > date_new
# [1] "-2000-01-01"
# > class(date_new)
# [1] "Date"
base::return(date_new)
} # END function handle_negative_dates
## function: handle_positive_dates ---------------------------------------------
##
##
#' Handling positive dates (BC dates)
#'
#' \code{handle_positive_dates} takes positive dates
#'
#' This function takes a positive date via an input of
#' year, month and day (seperately) and gives back the date
#' as a date class. very simple function.
#'
#' @param y The year as a nummeric input
#' @param m The month as a nummeric input
#' @param d the day as a numeric input
#'
#' @return Date as a date class, and which is positive
#'
#' @examples
#' handle_positive_dates(2010, 10, 10)
#'
#' @export
#'
handle_positive_dates <- function(y, m, d) {
date_paste <- base::paste0(y, "-", m, "-", d)
date_new <- base::as.Date(date_paste, format = "%Y-%m-%d")
base::return(date_new)
} # END function handle_positive_dates
## function: eq_location_clean() ---------------------------------------------
##
##
#' Cleans the variable "LOCATION_NAME"
#'
#' \code{eq_location_clean} strips down from the LOCATION_NAME variable the
#' country. The function input is the whole earth quake data, of which it then
#' mutates the LOCATION_NAME.
#'
#' @param data A data frame of NOAA significant earthquake data
#'
#' @importFrom magrittr "%>%"
#' @importFrom purrr map2_chr
#' @importFrom stringr str_trim
#' @importFrom stringr str_to_title
#' @importFrom dplyr mutate
#'
#' @return it returns the data frame back, but with the mutated LOCATION_NAME
#'
#' @export
#'
eq_location_clean <- function(data){
# how does it look like?
# > head(data$LOCATION_NAME)
# [1] "JORDAN: BAB-A-DARAA,AL-KARAK" "TURKMENISTAN: W"
# [3] "SYRIA: UGARIT" "GREECE: THERA ISLAND (SANTORINI)"
# [5] "ISRAEL: ARIHA (JERICHO)" "ITALY: LACUS CIMINI"
data <- data %>%
dplyr::mutate(
LOCATION_NAME = purrr::map2_chr(COUNTRY, LOCATION_NAME,
# and now: what to do with these two variables?
# let's define a function:
function(COUNTRY, LOCATION_NAME) {
base::gsub(paste0(COUNTRY, ":"),
"",
LOCATION_NAME)
}),
# we still have some white spaces left:
LOCATION_NAME = stringr::str_trim(LOCATION_NAME),
# and as required by the assignment:
LOCATION_NAME = stringr::str_to_title(LOCATION_NAME)
)
} # END eq_location_clean()
## function: eq_clean_data() ---------------------------------------------
##
##
# function eq_clean_data()
# eq_clean_data()
# After downloading and reading in the dataset, the overall task for this module is
# to write a function named eq_clean_data()that takes raw NOAA data frame and returns
# a clean data frame. The clean data frame should have the following:
# The clean data frame should have the following:
# 1. A date column created by uniting the year, month, day and converting it to the Date class
# 2. LATITUDE and LONGITUDE columns converted to numeric class
# 3. In addition, write a function eq_location_clean() that cleans the LOCATION_NAME column by
# stripping out the country name (including the colon) and converts names to title case (as opposed
# to all caps). This will be needed later for annotating visualizations. This function should be applied
# to the raw data to produce a cleaned up version of the LOCATION_NAME column.
#' Loads and Cleans the Earth Quake data
#'
#' \code{eq_clean_data} loads the data, cleans the data (including the LOCATION_NAME variable)
#' and gives back the data frame. There is no input to this function
#'
#' @importFrom magrittr "%>%"
#' @importFrom readr read_tsv
#' @importFrom dplyr mutate
#'
#' @return it returns the data frame, cleaned and with correct dates (with date class)
#'
#' @export
eq_clean_data <- function() {
# (!!) original name of data is "results"
# (!!) rename it to NOAAearthquakes
data("NOAAearthquakesRAW")
data <- NOAAearthquakesRAW %>%
# data <- readr::read_tsv("./inst/extdata/NOAAearthquakes") %>%
# replacing NA's in MONTH
# This is not that good
# I am chaing the characteristic of "when" it happend
# Might need to be changed later (?)
# before:
# > summary(data$MONTH)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 1.000 4.000 7.000 6.503 9.000 12.000 405
dplyr::mutate(MONTH = base::replace(MONTH, base::which(base::is.na(MONTH)), 1)) %>%
# replacing NA's in DAY
# before:
# > summary(data$DAY)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 1.00 8.00 16.00 15.75 23.00 31.00 556
dplyr::mutate(DAY = base::replace(DAY, base::which(base::is.na(DAY)), 1)) %>%
dplyr::mutate(date = base::ifelse(YEAR < 0,
handle_negative_dates(YEAR, MONTH, DAY),
handle_positive_dates(YEAR, MONTH, DAY)
)) %>%
dplyr::mutate(date = base::as.Date(date, origin = "1970-01-01")) %>%
dplyr::mutate(LATITUDE = base::as.numeric(LATITUDE)) %>%
dplyr::mutate(LONGITUDE = base::as.numeric(LONGITUDE))
# clean location via eq_location_clean
data <- eq_location_clean(data)
} # END function eq_clean_data()
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