Sungpil Han
2017년 1학기 강의용으로 만든 자료입니다.
knitr::opts_chunk$set(error = TRUE, warning = FALSE)
Week09 <- c("tidyverse", "shiny", "dplyr", "ggplot2", "rmarkdown", "knitr", "flexdashboard", "ggsci") #install.packages(Week09)
LibraryWeek09 <- lapply(Week09, library, character.only = TRUE)## -- Attaching packages -------------------------------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 3.1.0 √ purrr 0.2.5
## √ tibble 2.0.1 √ dplyr 0.7.8
## √ tidyr 0.8.2 √ stringr 1.3.1
## √ readr 1.3.1 √ forcats 0.3.0
## -- Conflicts ----------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
#assign data to a variable
x <- c(77, 66, 88)
mean(x)## [1] 77
max(x)## [1] 88
#charts
plot(x)
barplot(x)
pie(x)
#' Plot navigation
#' export to the clipboard
#' Publish to http://rpubs.com/
# Demo: RStudio
#' - History
#' - Run from the beginneing
#' - Run to the end
#' - Plot# Create Shiny Web App
shiny::runApp("Shiny")
shiny::runApp("pk_pd") # PMx_2016## Examples: ggplot2
library(ggplot2)
qplot(displ, hwy, data = mpg)
qplot(displ, hwy, data = mpg, color = drv)
qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
qplot(hwy, data = mpg, fill = drv)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(displ, hwy, data = mpg, facets = . ~ drv)
qplot(hwy, data = mpg, facets = drv ~ ., binwidth = 2)
maacs <- read.csv("data-raw/maacs.csv", as.is = TRUE)
str(maacs)## 'data.frame': 750 obs. of 5 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ eno : num 141 124 126 164 99 68 41 50 12 30 ...
## $ duBedMusM: num 2423 2793 3055 775 1634 ...
## $ pm25 : num 15.6 34.4 39 33.2 27.1 ...
## $ mopos : chr "yes" "yes" "yes" "yes" ...
# examples of qplot
qplot(log(eno), data = maacs)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(log(eno), data = maacs, fill = mopos)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(log(eno), data = maacs, geom = "density")
qplot(log(eno), data = maacs, geom = "density", color = mopos)
qplot(log(pm25), log(eno), data = maacs)
qplot(log(pm25), log(eno), data = maacs, shape = mopos)
qplot(log(pm25), log(eno), data = maacs, color = mopos)
qplot(log(pm25), log(eno), data = maacs, color = mopos, geom = c("point", "smooth"), method = "lm")
qplot(log(pm25), log(eno), data = maacs, geom = c("point", "smooth"), methods = "lm", facets = . ~ mopos)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
devtools::session_info()## - Session info ----------------------------------------------------------
## setting value
## version R version 3.5.2 (2018-12-20)
## os Windows 7 x64 SP 1
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate Korean_Korea.949
## ctype Korean_Korea.949
## tz Asia/Seoul
## date 2019-01-14
##
## - Packages --------------------------------------------------------------
## package * version date lib
## assertthat 0.2.0 2017-04-11 [1]
## backports 1.1.3 2018-12-14 [1]
## bindr 0.1.1 2018-03-13 [1]
## bindrcpp 0.2.2 2018-03-29 [1]
## broom 0.5.1 2018-12-05 [1]
## callr 3.1.1 2018-12-21 [1]
## cellranger 1.1.0 2016-07-27 [1]
## cli 1.0.1 2018-09-25 [1]
## colorspace 1.4-0 2019-01-13 [1]
## crayon 1.3.4 2018-10-25 [1]
## desc 1.2.0 2018-05-01 [1]
## devtools 2.0.1 2018-10-26 [1]
## digest 0.6.18 2018-10-10 [1]
## dplyr * 0.7.8 2018-11-10 [1]
## evaluate 0.12 2018-10-09 [1]
## flexdashboard * 0.5.1.1 2018-06-29 [1]
## forcats * 0.3.0 2018-02-19 [1]
## fs 1.2.6 2018-08-23 [1]
## generics 0.0.2 2018-11-29 [1]
## ggplot2 * 3.1.0 2018-10-25 [1]
## ggsci * 2.9 2018-05-14 [1]
## glue 1.3.0 2018-07-17 [1]
## gtable 0.2.0 2016-02-26 [1]
## haven 2.0.0 2018-11-22 [1]
## hms 0.4.2 2018-03-10 [1]
## htmltools 0.3.6 2017-04-28 [1]
## httpuv 1.4.5.1 2018-12-18 [1]
## httr 1.4.0 2018-12-11 [1]
## jsonlite 1.6 2018-12-07 [1]
## knitr * 1.21 2018-12-10 [1]
## labeling 0.3 2014-08-23 [1]
## later 0.7.5 2018-09-18 [1]
## lattice 0.20-38 2018-11-04 [1]
## lazyeval 0.2.1 2017-10-29 [1]
## lubridate 1.7.4 2018-04-11 [1]
## magrittr 1.5 2014-11-22 [1]
## memoise 1.1.0 2017-04-21 [1]
## mime 0.6 2018-10-05 [1]
## modelr 0.1.2 2018-05-11 [1]
## munsell 0.5.0 2018-06-12 [1]
## nlme 3.1-137 2018-04-07 [2]
## pillar 1.3.1 2018-12-15 [1]
## pkgbuild 1.0.2 2018-10-16 [1]
## pkgconfig 2.0.2 2018-08-16 [1]
## pkgload 1.0.2 2018-10-29 [1]
## plyr 1.8.4 2016-06-08 [1]
## prettyunits 1.0.2 2015-07-13 [1]
## processx 3.2.1 2018-12-05 [1]
## promises 1.0.1 2018-04-13 [1]
## ps 1.3.0 2018-12-21 [1]
## purrr * 0.2.5 2018-05-29 [1]
## R6 2.3.0 2018-10-04 [1]
## Rcpp 1.0.0 2018-11-07 [1]
## readr * 1.3.1 2018-12-21 [1]
## readxl 1.2.0 2018-12-19 [1]
## remotes 2.0.2 2018-10-30 [1]
## reshape2 1.4.3 2017-12-11 [1]
## rlang 0.3.1 2019-01-08 [1]
## rmarkdown * 1.11 2018-12-08 [1]
## rprojroot 1.3-2 2018-01-03 [1]
## rstudioapi 0.9.0 2019-01-09 [1]
## rvest 0.3.2 2016-06-17 [1]
## scales 1.0.0 2018-08-09 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## shiny * 1.2.0 2018-11-02 [1]
## stringi 1.2.4 2018-07-20 [1]
## stringr * 1.3.1 2018-05-10 [1]
## testthat 2.0.1 2018-10-13 [1]
## tibble * 2.0.1 2019-01-12 [1]
## tidyr * 0.8.2 2018-10-28 [1]
## tidyselect 0.2.5 2018-10-11 [1]
## tidyverse * 1.2.1 2017-11-14 [1]
## usethis 1.4.0 2018-08-14 [1]
## withr 2.1.2 2018-03-15 [1]
## xfun 0.4 2018-10-23 [1]
## xml2 1.2.0 2018-01-24 [1]
## xtable 1.8-3 2018-08-29 [1]
## yaml 2.2.0 2018-07-25 [1]
## source
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## [1] C:/Users/mdlhs/Rlib
## [2] C:/Program Files/R/R-3.5.2/library