library(knitr)
library(rmdformats)
install.packages("rmdformats")
library(tibble)
library(readr)
dat1 <- read_csv("bigfive.csv")
## NOTICE THE DIFFERENCE HERE:
iris[,1]
as_tibble(iris)[,1]
as_tibble(iris)[,1]
iris[,1]
as.tibble(iris)
as_tibble(iris)
library(tibble)
#library(formatR)
glimpse(data2)
library(knitr)
library(rmdformats)
## Global options
options(max.print="75")
opts_chunk$set(echo=TRUE,
cache=TRUE,
prompt=FALSE,
tidy=TRUE,
comment=NA,
message=FALSE,
warning=FALSE)
opts_knit$set(width=75)
library(tibble)
library(readr)
dat1 <- read_csv("bigfive.csv")
## NOTICE THE DIFFERENCE HERE:
iris[,1]
as_tibble(iris)[,1]
#head(data)
library(readxl)
data2 <- read_excel("bigfive-codingcomplete.xlsx")
## do the same thing with gdata read.xls
#data <- read.xls("bigfive-codingcomplete.xlsx")
library(tibble)
#library(formatR)
glimpse(data2)
print(data2)
curl_download("https://lo.unisa.edu.au/pluginfile.php/1020313/mod_book/chapter/106604/HLTH1025_2016.xlsx",destfile="HLTH1025_2016.xlsx")
library(curl)
## This won't work!
#data3 <- read_excel("https://lo.unisa.edu.au/pluginfile.php/1020313/mod_book/chapter/106604/HLTH1025_2016.xlsx")
curl_download("https://lo.unisa.edu.au/pluginfile.php/1020313/mod_book/chapter/106604/HLTH1025_2016.xlsx",destfile="HLTH1025_2016.xlsx")
data3 <- read_excel("HLTH1025_2016.xlsx",sheet="Data")
codesheet <- read_excel("HLTH1025_2016.xlsx",sheet=1,skip=2)
data3 <- read_excel("HLTH1025_2016.xlsx",sheet="Data")
codesheet <- read_excel("HLTH1025_2016.xlsx",sheet=1,skip=2)
library(haven)
data3.spss <- read_spss("https://lo.unisa.edu.au/pluginfile.php/1020313/mod_book/chapter/106604/HLTH1025_2016.sav")
data3.spss
glimpse(data3.spss)
install.packages("prepdat")
library("prepdat")
##This might be needed on windows:
#install.packages("rtools")
utils::unzip("data.zip",exdir="data")  ##unzip the data file. This may not work on some platforms, and you may need to do it by hand.
library(prepdat)
data <- file_merge(folder_path="data", has_header=T,
raw_file_extension="csv",
raw_file_name="globallocal*")
head(data)
data$within <- as.numeric(data$correctresp)
dat2 <- prep(dataset=data,
dvc="rt",
id="subnum",
within_vars=c("within"),
save_results=F, save_summary=T,
results_path="data")
|
3 || 5
data
library(dplyr)
select(data,subnum:type)
data %>% select(subnum:type)
slice(select(data,subnum:type),10:20)
filter(slice(select(data,subnum:type),10:20),trial<15)
data %>% select(subnum:type) %>% slice(10:20) %>% filter(trial<15)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)
data2
data2 %>% select(Subnum,Gender, Education, Extra:Openness)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree"))
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree")) %>%
group_by(Gender, Education) %>%
summarize (E=mean(Extra), A=mean(Agreeable), C=mean(Consc), N=mean(Neuro),O=mean(Openness))
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree")) %>%
group_by(Gender, Education) %>%
summarize (E=mean(Extra), A=mean(Agreeable), C=mean(Consc), N=mean(Neuro),O=mean(Openness)) %>%
pivot_longer(cols=E:O)
library(tidyr)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree")) %>%
group_by(Gender, Education) %>%
summarize (E=mean(Extra), A=mean(Agreeable), C=mean(Consc), N=mean(Neuro),O=mean(Openness)) %>%
pivot_longer(cols=E:O)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree")) %>%
group_by(Gender, Education) %>%
summarize (E=mean(Extra), A=mean(Agreeable), C=mean(Consc), N=mean(Neuro),O=mean(Openness)) %>%
pivot_longer(cols=E:O) %>%
ggplot(aes(x=Gender,group=Education,color=Education,y=value)) + geom_point() + geom_line() +facet_wrap(.~name,ncol=5) + theme_bw() + ylim(1,5)
library(ggplot2)
data2 %>% select(Subnum,Gender, Education, Extra:Openness)  %>% mutate(Education=recode(Education, `1`="1.High school",`2`="2.College degree",`3`="3.Post-secondary degree")) %>%
group_by(Gender, Education) %>%
summarize (E=mean(Extra), A=mean(Agreeable), C=mean(Consc), N=mean(Neuro),O=mean(Openness)) %>%
pivot_longer(cols=E:O) %>%
ggplot(aes(x=Gender,group=Education,color=Education,y=value)) + geom_point() + geom_line() +facet_wrap(.~name,ncol=5) + theme_bw() + ylim(1,5)
install.packages(c("broom", "callr", "cpp11", "datawizard", "deSolve", "dplyr", "effectsize", "emmeans", "expm", "gam", "generics", "ggrepel", "gtools", "hms", "insight", "jpeg", "maptools", "MatrixModels", "minqa", "network", "parameters", "pbapply", "performance", "pkgload", "plotly", "plyr", "processx", "ps", "purrr", "R.utils", "Rcpp", "RcppEigen", "readxl", "rgl", "sp", "statnet.common", "testthat", "tidyr", "tidyselect", "tigris", "vioplot"))
unlink("data-libraries_cache", recursive = TRUE)
knit_with_parameters("~/Dropbox/courses/5220-s2023/web-5220/psy5220/daily/Day01/data-libraries.Rmd")