Damiano Oldoni damianooldoni

## apply_str_squish_to_all_df.R
library(dplyr)
library(stringr)

df <- data.frame(a = c("  aZe  aze s", "wxc  s     aze   "),
                 b = c("  12    12 ", "34e e4  "),
                 stringsAsFactors = FALSE)
df <- df %>%
  rowwise() %>%
  mutate_all(funs(str_squish(.))) %>%
  ungroup()

## equals_sign_cheat_sheet.py
# integers, tuples: "=" makes a copy of the object. Changes of the original object after "=" instruction doesn't affect the copy.
a = 2
b = a
a = 3
print(a) # 3
print(b) # 2

# tuples
a = (2, 3)
b = a

## Maximum Heart Rate Calculation
#' Function to caclulate the theoretical maximum Heart Rate (HRmax)
#'
#' @param sex (character) "M" or "F"
#' @param age (numeric) Age in years
#' @param weight (numeric) Weight in kilograms
#'
#' @return theoretical maximum Heart Rate
#' @examples
#' get_HRmax(sex = "M", age = 35, weight = 61)
#'

## get_df_with_issues
devtools::install_github("ropensci/rgbif")
library(rgbif)
library(tidyverse)
library(rgbif)

# GBIF taxon keys (from Manual of Alien Plants of Belgium)
keys <- c(141264614, 8421432)

# The issues are returned as codes
example_duplicates <- map_df(keys,

## check_correctness_GBIF_rgbif_downloads.Rmd
---
title: "Compare CSV and DwC files from GBIF internetsite, GBIF manual downloads and `rgbif` authomatic downloads"
author:
- Damiano Oldoni
date: "October 16, 2018"
output:
  html_document:
    toc: true
    toc_depth: 3
    toc_float: true

## check_correctness_occurrences_with_hasCooridnate.Rmd
---
title: "Check correctness downloads with query parameter `hasCoordinate`"
author:
  - Damiano Oldoni
date: "October 18, 2018"
output:
  html_document:
  toc: true
toc_depth: 3
toc_float: true

## mutate_via_pmap_purrr.R
#' Change value of a column (\code{remarks}) for rows with some fields equal to some rows in a second df (called here \code{df_ref}).
#' Notice that all \code{pmap()} family of functions demands same number of columns as the input df.
#' It is strongly recommended to use the column names as inputs of the function of \code{pmap()}.
#' In this specific example we require that \code{col3} is of type \code{character} and we use \code{pmap_chr()} to return a character.

library(purrr)
library(dplyr)

df <-
   df %>%

## type_GBIF_occurrence_fields.R
library(readr)
col_types <- cols(
     acceptedNameUsage = col_character(),
     acceptedNameUsageID = col_character(),
     accessRights = col_character(),
     associatedReferences = col_character(),
     associatedSequences = col_character(),
     associatedTaxa = col_character(),
     bed = col_character(),
     behavior = col_character(),

## use_of_filter_at.R
# Example 1: select rows of data with NA in all columns starting with substring bb_:
filtered_data <-
  data %>%
  filter_at(vars(starts_with("bb_")), all_vars(is.na(.)))

# Example 2: filter rows of data with NA in one of the columns starting with substring bb_:

filtered_data <-
  data %>%
  filter_at(vars(starts_with("bb_")), any_vars(is.na(.)))

## mutate_with_char_var_as_col_name.R
# Dummy df
df_1 <- tibble::tibble(col1 = c("a","b"),
                       col2 = c(1,2))

# Name new column
new_col <- "col3"

# Add new column
dplyr::mutate(df_1, !!new_col := paste0(col1, ":", col2, "."))
	library(dplyr)
	library(stringr)

	df <- data.frame(a = c(" aZe aze s", "wxc s aze "),
	b = c(" 12 12 ", "34e e4 "),
	stringsAsFactors = FALSE)
	df <- df %>%
	rowwise() %>%
	mutate_all(funs(str_squish(.))) %>%
	ungroup()
	# integers, tuples: "=" makes a copy of the object. Changes of the original object after "=" instruction doesn't affect the copy.
	a = 2
	b = a
	a = 3
	print(a) # 3
	print(b) # 2

	# tuples
	a = (2, 3)
	b = a
	#' Function to caclulate the theoretical maximum Heart Rate (HRmax)
	#'
	#' @param sex (character) "M" or "F"
	#' @param age (numeric) Age in years
	#' @param weight (numeric) Weight in kilograms
	#'
	#' @return theoretical maximum Heart Rate
	#' @examples
	#' get_HRmax(sex = "M", age = 35, weight = 61)
	#'
	devtools::install_github("ropensci/rgbif")
	library(rgbif)
	library(tidyverse)
	library(rgbif)

	# GBIF taxon keys (from Manual of Alien Plants of Belgium)
	keys <- c(141264614, 8421432)

	# The issues are returned as codes
	example_duplicates <- map_df(keys,
	---
	title: "Compare CSV and DwC files from GBIF internetsite, GBIF manual downloads and `rgbif` authomatic downloads"
	author:
	- Damiano Oldoni
	date: "October 16, 2018"
	output:
	html_document:
	toc: true
	toc_depth: 3
	toc_float: true
	---
	title: "Check correctness downloads with query parameter `hasCoordinate`"
	author:
	- Damiano Oldoni
	date: "October 18, 2018"
	output:
	html_document:
	toc: true
	toc_depth: 3
	toc_float: true
	#' Change value of a column (\code{remarks}) for rows with some fields equal to some rows in a second df (called here \code{df_ref}).
	#' Notice that all \code{pmap()} family of functions demands same number of columns as the input df.
	#' It is strongly recommended to use the column names as inputs of the function of \code{pmap()}.
	#' In this specific example we require that \code{col3} is of type \code{character} and we use \code{pmap_chr()} to return a character.

	library(purrr)
	library(dplyr)

	df <-
	df %>%
	library(readr)
	col_types <- cols(
	acceptedNameUsage = col_character(),
	acceptedNameUsageID = col_character(),
	accessRights = col_character(),
	associatedReferences = col_character(),
	associatedSequences = col_character(),
	associatedTaxa = col_character(),
	bed = col_character(),
	behavior = col_character(),
	# Example 1: select rows of data with NA in all columns starting with substring bb_:
	filtered_data <-
	data %>%
	filter_at(vars(starts_with("bb_")), all_vars(is.na(.)))

	# Example 2: filter rows of data with NA in one of the columns starting with substring bb_:

	filtered_data <-
	data %>%
	filter_at(vars(starts_with("bb_")), any_vars(is.na(.)))
	# Dummy df
	df_1 <- tibble::tibble(col1 = c("a","b"),
	col2 = c(1,2))

	# Name new column
	new_col <- "col3"

	# Add new column
	dplyr::mutate(df_1, !!new_col := paste0(col1, ":", col2, "."))