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set.seed(1)
m = array(runif(12) * 10, c(3, 4))
class(m)
typeof(m)
dput(m)
# > class(m)
# [1] "matrix"
# > typeof(m)
# [1] "double"

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set.seed(1)
m = array(runif(12) * 10, c(3, 4))
dput(m)
# > dput(m)
# structure(c(2.655086631421, 3.7212389963679, 5.72853363351896, 
# 9.08207789994776, 2.01681931037456, 8.98389684967697, 9.44675268605351, 
# 6.60797792486846, 6.2911404389888, 0.617862704675645, 2.05974574899301, 
# 1.76556752528995), .Dim = 3:4)
m2 = as.integer(m)
dput(m2)

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#!/bin/sh

# onedrive-ubu.sh
# A BASH script to set up OneDrive integration with GNOME on Ubuntu
# Copyright (C) 2018 Garrett Mills ([email protected])
# This software is licensed under the MIT License.
# This sofware is provided without warranty or even any implied intent of purpose.
#
# OnedriveD: https://github.com/xybu/onedrived-dev
#

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---
title: "Effect of compression type and file complexity on saveRDS size and speed"
author: "David Robinson"
date: "April 20, 2015"
output: html_document
---

```{r echo = FALSE}
knitr::opts_chunk$set(cache = TRUE, message = FALSE)
```

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# 셀레늄 패키지와 rvest 패키지를 설치합니다.
source("https://install-github.me/ropensci/RSelenium")
install.packages("rvest")
install.packages("dplyr")

# 패키지를 불러서 사용할 준비를 합니다.
library(RSelenium)
library(rvest)
library(dplyr)

mkim0710

ID_207_Group_Project_Version_2_Vertical_July_24_2018_07_34 %>% mutate(
    Q3138rowMeansReverseCoded = as.numeric(Q31 + 6 - Q32 + Q33 + 6 - Q34 + Q35 + Q36 + 6 - Q37 + 6 - Q38)/8  # remove labels
    , Q3138rowSumsReverseCoded = as.numeric(Q31 + 6 - Q32 + Q33 + 6 - Q34 + Q35 + Q36 + 6 - Q37 + 6 - Q38)  # remove labels
) %>% select(Q31, Q32, Q33, Q34, Q35, Q36, Q37, Q38, matches("Q3138"))
# > ID_207_Group_Project_Version_2_Vertical_July_24_2018_07_34 %>% mutate(
# +     Q3138rowMeansReverseCoded = as.numeric(Q31 + 6 - Q32 + Q33 + 6 - Q34 + Q35 + Q36 + 6 - Q37 + 6 - Q38)/8  # remove labels
# +     , Q3138rowSumsReverseCoded = as.numeric(Q31 + 6 - Q32 + Q33 + 6 - Q34 + Q35 + Q36 + 6 - Q37 + 6 - Q38)  # remove labels
# + ) %>% select(Q31, Q32, Q33, Q34, Q35, Q36, Q37, Q38, matches("Q3138"))
# # A tibble: 191 x 10

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ID_207_Group_Project_Version_2_Vertical_July_24_2018_07_34 %>% mutate(
    Q3138rowMeans = rowMeans(.[, c("Q31", "Q32", "Q33", "Q34", "Q35", "Q36", "Q37", "Q38")], na.rm = T)
    , Q3138rowSums = rowSums(.[, c("Q31", "Q32", "Q33", "Q34", "Q35", "Q36", "Q37", "Q38")], na.rm = T)
    , Q4044rowMeans = rowMeans(.[, c("Q40", "Q41", "Q42", "Q43", "Q44")], na.rm = T)
    , Q4044rowSums = rowSums(.[, c("Q40", "Q41", "Q42", "Q43", "Q44")], na.rm = T)
) %>% select(Q31, Q32, Q33, Q34, Q35, Q36, Q37, Q38, matches("Q3138"), Q40, Q41, Q42, Q43, Q44, matches("Q4044")) %>% 
# > ID_207_Group_Project_Version_2_Vertical_July_24_2018_07_34 %>% mutate(
# +     Q3138rowMeans = rowMeans(.[, c("Q31", "Q32", "Q33", "Q34", "Q35", "Q36", "Q37", "Q38")], na.rm = T)
# +     , Q3138rowSums = rowSums(.[, c("Q31", "Q32", "Q33", "Q34", "Q35", "Q36", "Q37", "Q38")], na.rm = T)

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--#@@ [Research] HCCI MDD annual prevalence 180501.pptm -p128.sql ----
select count(z_patid), count(distinct z_patid)
from hcci.MEMBER_SDDV2_1115
where (
	z_patid in (
		select z_patid
		from hcci.MEMBER_SDDV2_1115
		where (
			gdr in ('1', '2')

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#@ function.11matching.by.factors() -----
function.11matching.by.factors = function(data, varname4MatchingUpon = "diabetes", varnames4MatchingBy = c("age", "sex"), seed = 1) {
    library(tidyverse)
    if ("RowNum_original" %in% names(data)) {
        warning('"RowNum_original" %in% names(data)')
    } else {
        data$RowNum_original = 1:nrow(data)
    }
    set.seed(seed)
    data$runif = runif(nrow(data))

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How to Ask for Help using R
========================================================

The key to getting good help with an R problem is to provide a minimally working 
reproducible example (MWRE). Making an MWRE is really easy with R, and it will 
help ensure that those helping you can identify the source of the error, and 
ideally submit to you back the corrected code to fix the error instead of sending
you hunting for code that works. To have an MWRE you need the following items:

- a minimal dataset that produces the error