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bd81e37dfcf6b3cdb4c0bd715854b39667cedc7d | d6ff1e6257582f785915e3a0fad3d4896ebd9acb | /R_old/OVERALL_TRANSPIRATION.R | dd4c315e6edbe8f2886bcf7adad85997b5a0dd40 | [] | no_license | RemkoDuursma/Kelly2015NewPhyt | 355084d7d719c30b87200b75887f5521c270b1b5 | 447f263f726e68298ee47746b4de438fbc8fdebf | refs/heads/master | 2021-01-15T13:02:00.392000 | 2015-09-08T04:56:15 | 2015-09-08T04:56:15 | 42,089,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,247 | r | OVERALL_TRANSPIRATION.R | setwd("C:/Documents and Settings/Jeffrey Kelly/Desktop/EUC DATA/EUC OVERALL BIOMASS")
PILBIOMASS<-read.csv("PILTRANSAA.csv",sep=",", header=TRUE)
names(PILBIOMASS)
str(PILBIOMASS)
windows(width=8, height=4) #, pointsize=18)
par(xaxs="i",yaxs="i")
par(las=2)
par(mar=c(4.5,4.5,1,1))
par(xaxs="i",yaxs="i")
par(mfrow=c... |
7e8c94c982763d3b9a74d47bf81ecba200e74f3e | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/2774/plotly/coord.R | c489eb9d4c358419e3fd6f91a129c297999fc8aa | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955000 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | coord.R | #' *** This won't be possible until plotly.js implements aspect ratios... ***
#'
#' #' Force the aspect ratio according to x and y scales
#' #'
#' #' When x and y are numeric variables measured on the same scale,
#' #' or are related in some meaningful way, forcing the aspect ratio of the
#' #' plot to be proportiona... |
87612036fd5fa980712ac1e05cfc398425c50685 | 86c0b4c6c1746ebf0441c62421748190d057067d | /plot/mass.R | 20769a023b830ddef7c35ab7c099b5ac260e9f87 | [
"MIT"
] | permissive | yufree/democode | 372f0684c49505965b0ba5abe0675c2b6f7fb3da | 0a332ac34a95677ce859b49033bdd2be3dfbe3c4 | refs/heads/master | 2022-09-13T11:08:55.152000 | 2022-08-28T23:09:00 | 2022-08-28T23:09:00 | 20,328,810 | 5 | 14 | null | 2017-01-06T16:07:25 | 2014-05-30T12:41:28 | HTML | UTF-8 | R | false | false | 1,185 | r | mass.R | source("http://bioconductor.org/biocLite.R")
biocLite("mzR")
library(mzR)
all <- openMSfile('./FULL200.CDF')
df <- header(all)
bb <- peaks(all)
aaaa <- sapply(bb,as.data.frame)
oddvals <- seq(1, ncol(aaaa), by=2)
aaaaa <- unlist(aaaa[oddvals])
ccc <- unique(c(aaaaa))
ccc <- ccc[order(ccc)]
# bbb <- sapply(b... |
99a524e8baa9751bbd5db7787f3567c66a6e8bee | 4450235f92ae60899df1749dc2fed83101582318 | /ThesisRpackage/R/3Article_old/GSE42861_function.R | 4e60f4de4028e99df28eb3e6e687f0b5409e866e | [
"MIT"
] | permissive | cayek/Thesis | c2f5048e793d33cc40c8576257d2c9016bc84c96 | 14d7c3fd03aac0ee940e883e37114420aa614b41 | refs/heads/master | 2021-03-27T20:35:08.500000 | 2017-11-18T10:50:58 | 2017-11-18T10:50:58 | 84,567,700 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,617 | r | GSE42861_function.R | #' main experiment
#'
#' @export
GSE42861_experiment <- function(s, save = TRUE) {
# glm
glm <- Method(name = "glm",
hypothesis.testing.method = phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE),
... |
ac457a941d93eb56777aeb1bda10707ce8907e13 | c54d1c0a3d81bddb25f3f55078f305ad6c15997b | /R/get_internal_tree.R | ffd29651c660f728d71fcc716d3ca033637fb637 | [] | no_license | cran/genpathmox | dc065d3b5ea1c8632068fe3d9bfa7b063045bb2c | 517be94b39d8742cd3d39aedc152e026d865afd6 | refs/heads/master | 2023-01-12T03:39:55.183000 | 2022-12-22T10:00:12 | 2022-12-22T10:00:12 | 25,984,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,114 | r | get_internal_tree.R | #' ############################################################################################
#' @title Calculating size (numeber of individual of a node)
#' @details
#' Internal function
#' @param x matrix or dataframe with data.
#' @param size value indicating the minimun threshold of number of observations for a... |
84afd0009d68337cd59225335f8ca45ec7753b3d | c2061964216f76ad0f440c76dbfe1119e0279a22 | /R/API-methods.R | 65f3d6cff7f46778421a4f00c57d3ebfa0b38824 | [] | no_license | cran/antaresRead | 046829e05e411adfb55fc652ad49ea84f2610264 | f6a182b21854e12c5c470afcd38c26f44fb2b8d5 | refs/heads/master | 2023-04-16T10:45:23.521000 | 2023-04-06T16:20:02 | 2023-04-06T16:20:02 | 87,090,660 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,663 | r | API-methods.R |
#' API methods
#'
#' @param endpoint API endpoint to interrogate, it will be added after `default_endpoint`.
#' Can be a full URL (by wrapping ìn [I()]), in that case `default_endpoint` is ignored.
#' @param ... Additional arguments passed to API method.
#' @param default_endpoint Default endpoint to use.
#' @... |
d29addc45ad1540ad95c8544e8002562baf29435 | d8affab3b21ca33c2b6397e28171c4ad69b03d98 | /regression.R | 471e4414ef889e20c3e50e5acbebf24faa2d7f99 | [] | no_license | nupurkok/analytics | 3e69e9eb88d9eb6cc4f33ae105b7993c46a69fce | b0b76dd306e443aae010cac55ffcda484c39ad42 | refs/heads/master | 2020-03-28T15:22:27.782000 | 2018-09-16T13:03:53 | 2018-09-16T13:03:53 | 148,586,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | regression.R | women
str(women)
cor(women$height, women$weight)
cov(women$height, women$weight)
plot(women)
#create linear model
fit1 = lm (formula=weight ~ height,data = women)
summary(fit1)
fitted(fit1)
cbind(women, fitted(fit1), residuals(fit1))
ndata1 = data.frame(height = c(62.5, 63.5))
predict(fit1, newdata = ndata1)
#mul... |
46c4e6309d7e779524b8b1a79263f38885577650 | ebb09f52b1ee12d8ae8d4c493e6f1079ee57868c | /ExploratoryDataAnalysis/Project2/plot1.R | 344f1ab64d1fa16fc56bc45754d6205e3ffc4c86 | [] | no_license | r6brian/datasciencecoursera | a1723f812a34eee7094dfaa0bfde6c618b349d6c | 548944d3ba68d302160f05158fb90859bc4c8bae | refs/heads/master | 2021-01-19T10:29:54.605000 | 2015-08-23T20:00:04 | 2015-08-23T20:00:04 | 26,268,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 634 | r | plot1.R | # 1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
# Read data files
NEI <- readRDS("data/exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("data/exdata-data-NEI_data/Source_Classification_Code.rds")
# aggregrate based upon Emissions and Years
totalEmissions <- aggregate(Em... |
d016bf7c1cea2be45570d0826610230b375be3ce | 9bc17a169325375bc993b540d2ad0f0810ca0e76 | /R/twoway.plots.R | a98edb8797477c8f6316b7dfb57853a3015db298 | [] | no_license | alanarnholt/PASWR | 335b960db32232a19d08560938d26f168e43b0d6 | f11b56cff44d32c3683e29e15988b6a37ba8bfd4 | refs/heads/master | 2022-06-16T11:34:24.098000 | 2022-05-14T22:56:11 | 2022-05-14T22:56:11 | 52,523,116 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,375 | r | twoway.plots.R | #' @title Exploratory Graphs for Two Factor Designs
#'
#' @description Function creates side-by-side boxplots for each factor, a design plot (means), and an interaction plot.
#'
#' @param Y response variable
#' @param fac1 factor one
#' @param fac2 factor two
#' @param COL a vector with two colors
#'
#' @author Ala... |
b4e93e3bcccb0eb0d1014bd355bcfff5a5be6187 | 280019f481fe09da00296f45e5fa530051780756 | /ui.R | 10b50c14611abb664f6dbfc7ea4c164e2ac58b15 | [] | no_license | linareja/2017_Buenos_Aires_Elections | 1effb2b1d39bf660e9fa678a6a78ac3000f2122c | d500aaedb233fe541fe00dc63f0d488043467111 | refs/heads/master | 2021-09-12T16:33:33.715000 | 2018-04-18T18:19:56 | 2018-04-18T18:19:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,802 | r | ui.R |
library(shiny)
dashboardPage(
dashboardHeader(title = "2017 Elections in Buenos Aires Province"),
dashboardSidebar( sidebarMenu(
menuItem("Overview", tabName = "overview", icon = icon("globe")),
menuItem("Analysis", tabName = "analysis", icon = icon("bar-chart"))
)),
dashboardBody(
tabItems(
... |
564a95d83be7184c25e4953fc74f13401f3970ba | b6ed5857732c3261abab33a6665e7193d6862aef | /tests/testthat/test-read-oneshot-eav.R | d2b16cc13808d4cf760c57f846c793651568b48e | [
"MIT"
] | permissive | cran/REDCapR | 5ac1ebdb03fbf7dfa1aab23a2c23f711adcd4847 | a1aa09eb27fb627207255018fa41e30fa5d4b0fc | refs/heads/master | 2022-08-27T14:49:33.798000 | 2022-08-10T15:10:18 | 2022-08-10T15:10:18 | 24,255,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,860 | r | test-read-oneshot-eav.R | library(testthat)
credential <- retrieve_credential_testing()
update_expectation <- FALSE
test_that("smoke test", {
testthat::skip_on_cran()
expect_message(
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token)
)
})
test_that("default", {
testtha... |
ae049e4f7dded0c1877205b17e89aab67356d759 | cf4263e82b2c118bc3ecea5dc62d561e7487cbd3 | /tests/testthat/test_flatten_data.R | 327e274c4b13ccbaaa4edf5a2d6be774fcc94394 | [
"MIT"
] | permissive | EDIorg/ecocomDP | 151a2d519ff740d466fafab74df5171a6ef196bf | 0554d64ce81f35ed59985d9d991203d88fe1621f | refs/heads/main | 2023-08-14T02:07:19.274000 | 2023-06-19T22:27:30 | 2023-06-19T22:27:30 | 94,339,321 | 26 | 10 | NOASSERTION | 2023-07-26T22:21:00 | 2017-06-14T14:22:43 | R | UTF-8 | R | false | false | 7,103 | r | test_flatten_data.R | context("flatten_data()")
# Compare L0 flat and L1 flat - The column names and values of the L0 flat and L1 flattened tables should match, with an exception:
# 1.) Primary keys, row identifiers, of the ancillary tables are now present.
# Column presence -------------------------------------------------------------
t... |
8ce7a9d3e16bf2b520b938c008850a5ca1577fb8 | 92456ce1d280dd99f0df1cc2a2567c5021286f03 | /R/prepare_data.R | 5c8fabf25b3ad3505598af1c3c14f7a6948f57d1 | [] | no_license | nzfarhad/AFG_MSNA_19_Analysis | 41643620a065ff3eaba40779624101b55562efe4 | 66b4cfe032b7665475606dcab5eae4fcacba0e9c | refs/heads/master | 2020-07-28T17:27:34.829000 | 2020-01-28T10:01:02 | 2020-01-28T10:01:02 | 209,478,936 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 100,120 | r | prepare_data.R | # Title: Preparation of data for woa survey
# Authors: Sayed Nabizada, Jarod Lapp, Christopher Jarvis,
# Date created: 20/09/2019
# Date last changed: 25/09/2019
# Purpose: This script is for recoding variables in the whole of
# of Afghanistan survey data
# indicators and composite scores are create... |
17d0b4508a89eda9690757bbd1a506dc8eba11fb | de83a2d0fef79a480bde5d607937f0d002aa879e | /P2C2M.SNAPP/R/draw.samples2.R | 4afd6ee40fe1adb1f7db29b2654b926047494a2b | [] | no_license | P2C2M/P2C2M_SNAPP | 0565abc0ea93195c9622dc5d4e693ccde17bebc7 | 94cd62285419a79f5d03666ec2ea3e818803d0db | refs/heads/master | 2020-05-07T18:54:40.440000 | 2020-01-10T15:59:45 | 2020-01-10T15:59:45 | 180,788,408 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | draw.samples2.R | ##### Randomly sample from posterior #####
draw.samples <- function(num_sims, gens_run, sample_unif){ # num.sims = user input # of simulations to perform; gens_run = # of markov steps saved; sample_unif = if true, sample posterior uniformly. Otherwise sample randomly
burnin <- ceiling(gens_run * 0.10)
non_burnin ... |
5e6123c9c6678ffff155f6d6bb0973954d846370 | 925c515b771a8ea7ca31cc530308d594c30fba07 | /code/TableS3.R | 3591bcb04a019b009fd1c4d141478c8c465a6176 | [] | no_license | melofton/freshwater-forecasting-review | 41ba42f0aee6180d7a731fcf838dccc8f7590588 | c06097cbab6d88c1dc30d0f2c3cf8a3baddaeacc | refs/heads/main | 2023-07-06T21:54:48.183000 | 2023-06-27T20:18:46 | 2023-06-27T20:18:46 | 478,673,588 | 0 | 1 | null | 2022-07-08T19:45:20 | 2022-04-06T18:05:25 | R | UTF-8 | R | false | false | 541 | r | TableS3.R | #Matrix analysis
#Author: Mary Lofton
#Date: 06JUL22
#clear environment
rm(list = ls())
#set-up
pacman::p_load(tidyverse, lubridate, cowplot,ggbeeswarm, viridis)
#read in data
dat5 <- read_csv("./data/cleaned_matrix.csv")
##Table 3 ####
dat10 <- dat5 %>%
mutate(ecosystem_type = ifelse(ecosystem == "river" | grep... |
1bc891cc48422875088ad36e2f4ff1053e811f2d | 218aae83a9d0994561991ba8affe528f1e381457 | /R/edgepoints.R | e7cee35ceeef11b83b9b9d0b6abfaa1dc7d09ef5 | [] | no_license | cran/edci | 4efcf830e8cec5d1522397140afd5650655b66b3 | d24ed3f7d6bd543f5b1fa07b8db821d42c8fe795 | refs/heads/master | 2020-12-25T16:56:26.204000 | 2018-05-16T20:49:37 | 2018-05-16T20:49:37 | 17,718,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,698 | r | edgepoints.R | edgepoints = function(data, h1n, h2n, asteps = 4, estimator = "kernel", kernel = "mean",
score = "gauss", sigma = 1, kernelfunc = NULL, margin = FALSE) {
epDelta = function(x) {
if (x < 0)
-1
else
1
}
epAt = function(x, y) {
if (x == 0) {
if (y >= 0)
pi/2
else
... |
4e4ff604aaf7b5ff470c8227b043cf073c00c388 | d3fdbf9442b8e0ffbc208ad50087f0ece05f405e | /Modulo 3- Resampling-Bayesianos-Markov/Ejercicio 3.3/Ejercicio3_3_MarianaSilvera.R | c9857fb242ead0825c194c8628a35108f8e2f36e | [] | no_license | msilvera/R-DataAnalysis2021-OTGA | b176f5f48076ce57ed1c7935fbe37ada31f21bda | 1bc03219b4d36c73d2196534c111878476d4373d | refs/heads/main | 2023-06-14T21:55:42.036000 | 2021-07-04T18:38:51 | 2021-07-04T18:38:51 | 380,082,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 947 | r | Ejercicio3_3_MarianaSilvera.R | library(FSAdata)
library(MASS)
library(dplyr)
#library(help="FSAdata")
#cargo los datos
#data <- WalleyeErie2
summary(WalleyeErie2)
data <-subset(x=WalleyeErie2, subset = !is.na(w)) #elimino los datos incompletos
summary(data)
set.seed(1) # semilla para el random
data <- data %>% mutate_at(vars("age"), ... |
eb3d9c97b02f6f8d4ca16e857d987432473f6d4c | 89d2d6b83bb0fcad3db66b139a617b0cc40bf34a | /R3-Aliona.R | dfc622532aca8311dc0e2430e94dcfdf29a65c9b | [] | no_license | alionahst/R3 | 5e6760cab681ab10149267ed31884ccb16cc6eb5 | d980eddc32efd762b3178bc3933b8ba486929944 | refs/heads/master | 2023-01-03T05:25:17.275000 | 2020-10-20T22:13:42 | 2020-10-20T22:13:42 | 305,684,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,799 | r | R3-Aliona.R | #Chapter 3: Basic graphics and data - Aliona Hoste
demo(graphics)
plot(iris)
#1. Plot a cheat-sheet with values of color and point type (col = , and pch = ) from 1 to 25, and export it as a jpeg of 15 cm wide, 6 cm high and resolution 100 points per cm.
plot(0, 0, xlim = c(0, 26), ylim = c(0.5, 1.5)
, yla... |
7015d5870ad5056141a600ab0b532cfd67a48a59 | e56da52eb0eaccad038b8027c0a753d9eb2ff19e | /man-roxygen/tipsForTreeGeneration.R | b3874469bd8aa861c1cbae942f72fce3a7ff9898 | [] | no_license | ms609/TreeTools | fb1b656968aba57ab975ba1b88a3ddf465155235 | 3a2dfdef2e01d98bf1b58c8ee057350238a02b06 | refs/heads/master | 2023-08-31T10:02:01.031000 | 2023-08-18T12:21:10 | 2023-08-18T12:21:10 | 215,972,277 | 16 | 5 | null | 2023-08-16T16:04:19 | 2019-10-18T08:02:40 | R | UTF-8 | R | false | false | 174 | r | tipsForTreeGeneration.R | #' @param tips An integer specifying the number of tips, or a character vector
#' naming the tips, or any other object from which [`TipLabels()`] can
#' extract leaf labels.
|
15f17c33f851b0ab97d37c7507f338f9cc08551e | d30fa10aa7b3837145a1d1f0bcff6a55372ea4eb | /plot_kmer_dist.R | a39c14daba7632aabe23b7da8c1d0a54f095915a | [] | no_license | mborche2/Matts_Satellite_Size_Code | 541bfdada9a61238ecb6c59594dbfd5e60766e97 | 824fcf6e8f4ab555df774baa9cd8caf6dd8200ae | refs/heads/master | 2023-03-28T07:29:31.677000 | 2021-03-23T18:57:52 | 2021-03-23T18:57:52 | 348,837,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | r | plot_kmer_dist.R | library(ggplot2)
setwd("/n/core/bigDataAI/Genomics/Gerton/jennifer_gerton/jeg10/")
for (i in 1:21){
filenam <- paste("plots/kmer_frequency_asp/kmer_frequency_",toString(i),"_array.tsv",sep = "")
array_specifics <- read.table(filenam,header = FALSE)
freq_table <- table(array_specifics[,2])
freq_df <- as.data.fr... |
393e68c42ae3b36432c1265386c913a44b8e6d7e | c97fa9aadc45c44fad6433ae10c772060bde355c | /MyNotes/03 - Geting and Cleaning Data/01 Class_Data.Table_Package.R | 41cd46ae55c1ae3679c91b186b783fad89090d5a | [] | no_license | vitorefigenio/datasciencecoursera | 9866816242d39fa9fc9520bc4d543efc815afeb5 | 03722d0c7c6d219ec84f48e02065493f6657cc0a | refs/heads/master | 2021-01-17T11:17:58.099000 | 2016-02-28T03:06:37 | 2016-02-28T03:06:37 | 29,034,385 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 943 | r | 01 Class_Data.Table_Package.R | #data.table package
# Create Data.Table
install.packages("data.table")
library(data.table)
DF = data.frame(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DF,3)
DT = data.table(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DT,3)
# comando ara ver tdas as abelas criadas na memória
tables()
... |
12d5a52eb7e5fb10a0b5d87bdc8740c29b7c2a5a | 39315660a0226ae527ec8e0c7e6ae866df675b5f | /exercise1/computeCost.R | 5057e5c02d42dd31073fb2393dff4c7ded690bc3 | [] | no_license | Lemmawool/R-Practice | 28a7ce208f7d012eb4bc886fdb27b72754a171e9 | 0c3bed53e27953e9f19f92fd6e7b595a7e379262 | refs/heads/master | 2021-05-14T13:44:03.051000 | 2018-01-22T02:02:51 | 2018-01-22T02:02:51 | 115,955,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 105 | r | computeCost.R | computeCost <- function(X, y, theta){
m = length(y)
return ((1/(2*m)) * sum((X %*% theta - y) ^ 2))
} |
e662f9c90536aa7a7802ef2046cda55ac460d02e | 63227ea5a4085bb789824448502c95a98d8f375f | /cachematrix.R | 4e87ebeb3d25a82fe63b07127301dae99ce920d6 | [] | no_license | lfdelama/ProgrammingAssignment2 | f81f6ae4cf9246cc21a2fce019bc59a04949303d | 417909969f9fdd8c4d23700e1fcf535237a2c2ec | refs/heads/master | 2020-12-24T14:18:50.589000 | 2014-05-22T21:32:07 | 2014-05-22T21:32:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,314 | r | cachematrix.R | ## These two functions below are used to cache the inverse of a square matrix,
## so every time the same inverse is required, it doesn't need to be recomputed.
## This function creates a special matrix which
## contains a list of the following functions:
## - set, to set the value of the matrix
## - get, to get the ... |
48f4c3afd8bf9957f151bbbad760e9b7f9c317fe | 64e7ac1d0437b1d874b4ed070e6bda152decddee | /plot2.R | e2892d66195304ed5b560890e85b152215d7920e | [] | no_license | mooctus/ExData_Plotting1 | 072db8facebd27a8a8aab985be057b9b2c2b8122 | 005cba7dd9d88e94113a57eb6f8d77b9a3618811 | refs/heads/master | 2021-01-12T20:07:17.994000 | 2014-05-09T15:54:17 | 2014-05-09T15:54:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | plot2.R | Sys.setlocale(category = "LC_ALL", locale = "C")
df <- read.table(file="household_power_consumption.txt", sep=";", na.strings="?", header=TRUE)
df$Time <- strptime(
paste0(df$Date, " ", df$Time),
format=paste0("%d/%m/%Y %H:%M:%S")
)
df$Date <- as.Date(df$Date,format="%d/%m/%Y")
df1 <- df[df$Date %in% as.Date(c('200... |
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