install.packages("googlesheets4")
library(googlesheets4)
forestArea <- read_sheet("https://docs.google.com/spreadsheets/d/1xNrO6somZmrpxob9z5ZydSUA1RYCnh2etSYMk8X-sn8/edit#gid=192622591")
forestArea <- read_sheet("https://docs.google.com/spreadsheets/d/1xNrO6somZmrpxob9z5ZydSUA1RYCnh2etSYMk8X-sn8/edit#gid=192622591",sheet = "vfa" )
View(forestArea)
rawdata <- read_sheet("https://docs.google.com/spreadsheets/d/1xNrO6somZmrpxob9z5ZydSUA1RYCnh2etSYMk8X-sn8/edit#gid=192622591",sheet = "vfa" )
View(rawdata)
rawdata <- read_sheet("https://docs.google.com/spreadsheets/d/1xNrO6somZmrpxob9z5ZydSUA1RYCnh2etSYMk8X-sn8/edit#gid=192622591",sheet = "rawData" )
View(rawdata)
data.mod1 = lm(ph ~ trt, data = rawdata)
summary(data.mod1)
View(rawdata)
op <- std(c(11, 21, 19, 46))
std <- function(a) sd(a) / sqrt(length(a))
op <- std(c(11, 21, 19, 46))
print(op)
View(rawdata)
View(rawdata)
c(11, 21, 19, 46) -> k
View(rawdata)
View(rawdata)
View(rawdata)
rawdata1<- rawdata %>% filter(time==6)
install.packages("tidyverse")
library(tidyverse)
rawdata1<- rawdata %>% filter(time==6)
View(rawdata1)
aov1 = aov(ph ~ trt, data = rawdata1)
summary(aov1)
View(rawdata1)
plus<- function(a) a+1
View(plus)
plus(1)
plus<- function(a,b) a+b+1
plus(1, 2)
plus<- function(a) a+1
num <-c(1,2,3,4,5)
for (a in num) {
b <- plus(a)
print(b)
}
View(rawdata1)
rawdata1[, 1]
trt<-rawdata1[, 1]
str(trt)
trt<-rawdata1[, 1] %>% unique()
library(tidyverse)
trt<-rawdata1[, 1] %>% unique()
View(trt)
rownames(rawdata1)
names(rawdata1)
result<- names(rawdata1)
result<- names(rawdata1)[3:25]
View(trt)
as.character(rawdata1)
as.character(trt)
trt<- as.character(trt)
trt<-rawdata1[, 1] %>% unique()
str(trt)
View(trt)
trt<-rawdata1[, 1] %>% unique() %>% dplyr::pull(trt)
View(rawdata1)
rawdata1 %>% filter(trt ==c) %>% select(ph)
install.packages("tidyverse")
library(tidyverse)
rawdata1 %>% filter(trt ==c) %>% select(ph)
rawdata1 %>% filter(trt =="c") %>% select(ph)
c<- rawdata1 %>% filter(trt =="c") %>% select(ph)
View(c)
install.packages("dplyr")
install.packages("dplyr")
library(dplyr)
c<- rawdata1 %>% filter(trt =="c") %>% select(ph)
c<- rawdata1 %>% filter(trt =="C") %>% select(ph)
View(c)
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% mean()
%>% dplyr::pul(trt)%>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% dplyr::pul(trt)%>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>%  dplyr::pull(trt)%>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph)[,1] %>%  dplyr::pull(trt)%>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% as.character() %>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% as.vector() %>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% as.numeric() %>% mean()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph) %>% as.numeric()
c<- rawdata1 %>% filter(trt =="C") %>% select(ph)
View(c)
mean(c)
as.character(c)->C
C <- c[, 1] %>% dplyr::pull(ph)
mean(c)
mean(C)
View(c)
View(c)
View(rawdata1)
View(rawdata)
View(rawdata1)
View(rawdata1)
View(c)
C <- c[, 1]
View(C)
C <- c[, 1]%>% dplyr::pull(ph)
View(c)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C)
mean(C)
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C)
}
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% view()
}
mean(C) %>% print()
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% print()
}
df1=data.frame()
View(df1)
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% rbind(df1,)
}
mean(C) %>% bind_rows(df1)
df1=data.frame(matrix(ncol=4))
View(df1)
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% bind_rows(df1)
}
mean(C) %>% bind_cols(df1)
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% bind_cols(df1)
}
View(df1)
View(df1)
colnames(df1)<-trt
for(i in trt){
c<- rawdata1 %>% filter(trt ==i) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C) %>% bind_cols(df1)
}
View(df1)
mean(C) %>% bind_cols(df1)
mean <-function(x){
c<- rawdata1 %>% filter(trt ==x) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C)
}
mean(i)
mf <-function(x){
c<- rawdata1 %>% filter(trt ==x) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C)
}
for(i in trt){
mf(i) = df1[,i]
}
mf <-function(x){
c<- rawdata1 %>% filter(trt ==x) %>% select(ph)
C <- c[, 1]%>% dplyr::pull(ph)
mean(C)
}
for(i in trt){
mf(i) -> df1[,i]
}
for(i in trt){
mf(i) = df1[,i]
}
mf <-function(x){
a<- rawdata1 %>% filter(trt ==x) %>% select(ph)
b <- a[, 1]%>% dplyr::pull(ph)
c <- mean(b)
return(c)
}
for(i in trt){
mf(i) = df1[,i]
}
for(i in trt){
df1[,i] <- mf(i)
}
View(df1)
a<- rawdata1 %>% filter(trt =="C") %>% select(ph)
View(a)
View(mf)
for(i in trt){
df1[,i] <- mf(i)
}
mf <- function(x){
a <- rawdata1 %>% filter(trt ==x) %>% select(ph)
b <- a[, 1]%>% dplyr::pull(ph)
c <- mean(b)
return(c)
}
for(i in trt){
df1[,i] <- mf(i)
}
mf <- function(x){
a <- rawdata1 %>% filter(trt == x) %>% select(ph)
b <- a[, 1]%>% dplyr::pull(ph)
c <- mean(b)
return(c)
}
for(i in trt){
df1[,i] <- mf(i)
}
mf <- function(x){
a <- rawdata1 %>% filter(trt == x)
b <-  unlist(a) %>% select(ph)
c <- mean(b)
return(c)
}
for(i in trt){
df1[,i] <- mf(i)
}
a <- rawdata1 %>% filter(trt == x)
a <- rawdata1 %>% filter(trt == "C")
b <-  unlist(a) %>% select(ph)
a <- rawdata1 %>% filter(trt == "C")
unlist(a) %>% select(all_of(ph)) ->b
getwd()
getwd()
install.packages(nycflights13)
library(tidyverse)
install.packages("nycflights13")
library(nycflights13)
library(tidyverse)
install.packages("nycflights13")
install.packages("nycflights13")
library(nycflights13)
library(nycflights13)
library(nycflights13)
View(c)
nycflights13
nycflights13::
flights
a <- filter(flights, year=="2013", month == "6", day == "3")
a <- filter(flights, year=="2013", month == "6", day == "3")
a <- filter(flights, year=="2013", month == "6", day == "3")
a <- filter(flights, year==2013, month == 6, day == 3)
a <- filter(flights, year==2013, month== 6, day == 3)
a <- filter(flights, year==2013)
a <- filter(flights, "year" == 2013, "month" == 6. "day" == 3)
a <- filter(flights, year == "2013", month == "6", day == "3")
flights
filter(flights, month == 1, day == 1)
a <- filter(flights, year == 2013, month == 6, day == 3)
a <- filter(flights, month == 6, day == 3)
a <- filter(flights, month == 6)
dat <- flights
View(dat)
a <- filter(dat, month == 6)
library(tidyverse)
a <- filter(dat, month == 6)
a <- filter(dat, month == 6, day == 3)
View(dat)
a <- filter(dat, month == 6, day == 3, tailnum == N1140)
a <- filter(dat, month == 6, day == 3, tailnum == "N1140")
View(a)
a <- filter(dat, month == 6, day == 3, flight == "N1140")
a <- filter(dat, month == 6, day == 3, flight == 1140)
a <- filter(dat, month == 6, day == 3, tailnum == 1140)
a <- filter(dat, month == 6, day == 3, tailnum == "N"1140)
a <- filter(dat, month == 6, day == 3, flights == "N"1140)
a <- filter(dat, month == 6, day == 3, flight == N1140)
a <- filter(dat, month == 6, day == 3, flight == 'N1140')
a <- filter(dat, month == 6, day == 3, flight == "N1140")
a <- filter(dat, month == 6, day == 3, tailnum == "N1140")
a <- filter(dat, month == 6, day == 3, tailnum == 1140)
a <- filter(dat, month == 6, day == 3)
a <- filter(dat, month == 6, day == 3, tailnum == "N11140")
dat <- flights::weather
library(tidyverse)
library(nycflights13)
dat <- flights::weather
dat <- nycflights13::weather
View(a)
View(dat)
a <- filter(dat, month == 6, day == 3, tailnum == "N11140")
View(a)
dat <- nycflights13::weather
a <- filter(dat, month == 6, day == 3, tailnum == "N11140")
a <- select(dat, month, day, temp, humid, wind_speed)
View(a)
b <- filter(a, month == 6, day ==3)
View(b)
dat1<- flights
a <- select(dat2, month, day, temp, humid, wind_speed)%<%filter(dat2, month == 6, day ==3)
library(dplyr)
a <- select(dat2, month, day, temp, humid, wind_speed) %<% filter(dat2, month == 6, day ==3)
a <- select(dat2, month, day, temp, humid, wind_speed) %>% filter(dat2, month == 6, day ==3)
dat2 <- nycflights13::weather
a <- select(dat2, month, day, temp, humid, wind_speed) %>% filter(dat2, month == 6, day ==3)
a<- filter(dat1, month == 6, day == 3, tailnumber = "N11140")
a<- filter(dat1, month == 6, day == 3, tailnumber = "N11140")
a<- filter(dat1, month == 6, day == 3, tailnum = "N11140")
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140")
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140") %<% select( , origin)
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140") %>% select( , origin)
View(a)
b <- select(dat2, month, day, origin, temp, humid, wind_speed)
View(dat)
View(b)
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140") %>% select( , hour)
b <- select(dat2, month, day, hour, temp, humid, wind_speed)
b <- select(dat2, month, day, hour, temp, humid, wind_speed) %>% filter( , month == 6, day == 3, hour == 6)
dat2 <- nycflights13::weather
b <- select(dat2, month, day, hour, temp, humid, wind_speed)
b <- select(dat2, month, day, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6)
View(b)
a<- filter(dat1, month == 6, day == 3, hour == 6)
View(a)
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140")::weather
a::weather
b <- select(dat2, month, day, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR)")
b <- select(dat2, month, day, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
b <- select(dat2, month, day,origin hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
b <- select(dat2, month, day,origin, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
View(b)
View(a)
a<- filter(dat1, month == 6, day == 3, tailnum == "N11140") %>% select( , hour, origin)
dat2 <- nycflights13::weather
b <- select(dat2, month, day,origin, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
View(a)
View(b)
flights
flights
a<- filter(flights, month == 6, day == 3, tailnum == "N11140") %>% select( , hour, origin)
dat <- nycflights13::weather
b <- select(dat, month, day,origin, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
flights
a<- filter(flights, month == 6, day == 3, tailnum == "N11140") %>% select( , hour, origin)
dat <- nycflights13::weather
b <- select(dat, month, day,origin, hour, temp, humid, wind_speed)%>% filter( , month == 6, day == 3, hour == 6, origin == "EWR")
b <- filter(dat , month == 6, day == 3, hour == 6, origin == "EWR")
View(b)
b <- filter(dat , month == 6, day == 3, hour == 6, origin == "EWR") %>% filter( , temp, humid, wind_speed  )
b <- filter(dat , month == 6, day == 3, hour == 6, origin == "EWR") %>% select( , temp, humid, wind_speed)
View(b)
data(words)
force(words)
str_view(c, "app")
str_view(c, "app")
#2
typeof(c)
str_view(c, "app")
list_view(c, "app")
data_view(c, "app")
regexpr("app", c)
library(nycflights13)
library(dplyr)
library(tidyverse)
flights
a<- filter(flights, month == 6, day == 3, tailnum == "N11140") %>% select( , hour, origin)
dat <- nycflights13::weather
b <- filter(dat , month == 6, day == 3, hour == 6, origin == "EWR") %>% select( , temp, humid, wind_speed)
c <- data(words)
regexpr("app", c)
grep("[app]", c)
library(nycflights13)
library(dplyr)
library(tidyverse)
library(stringr)
library(lubridate)
df1 <- flights %>%
filter( , month == 6, day == 3) %>%
select( , month, day, hour, origin, tailnum)
df2 <- nycflights13::weather %>%
filter( , month == 6, day ==3)%>%
select( , month, day, hour, origin, humid, temp, wind_speed)
df3 <- inner_join(df1, df2) %>%
filter( , tailnum == 'N11140')
View(df1)
View(df2)
View(df3)
data("words")
app <- words[str_detect(words,'app')]
df_app <- as.data.frame(app) %>%
filter( , nchar(app) >= 5)
#3
df <- data.frame(
t1 = rnorm(100, mean = 10, sd = 5),
t2 = rnorm(100, mean = 10, sd = 5),
t3 = rnorm(100, mean = 10, sd = 5),
date = ymd(sample(20221001:20221031, 100, replace = TRUE))
)
mean_date <- df %>%
group_by(date)%>%
summarise(t1_mean = mean(t1),
t2_mean = mean(t2),
t3_mean = mean(t3)
)
mean_date <- mean_date%>%
separate(date, into = c("year", "month", "day"), sep = "-")%>%
unite(date, year, month, day, sep = "")
mean_date <- transform(mean_date, date = substr(date, 3, 8))
mean <- df%>%
arrange(date)%>%
summarise(mean = (t1 + t2 + t3)/3, date = date)
mean <- mean%>%
group_by(date)%>%
summarise(t1_t2_t3_mean = mean(mean))
mean <- arrange(mean, t1_t2_t3_mean)
View(mean_date)
View(mean)
View(mean)
View(mean_date)
View(df3)
View(df_app)
View(df)
getwd()
setwd("C:/R")
getwd()
setwd("C:/R")
getwd()
setwd("C:/Rstudy")
setwd("C:/R_Study")
install.packages("tidyverse")
library(tidyverse)
glimpse(penguins)
install.packages("glimpse")
Library(glimpse)
View(penguins)
View(penguins)
tibbles
tibble
tibble
install.packages("tibble")
install.packages("tibble")
install.packages("tibble")
library(tibble)
View(penguins)
tibble(penguins)
tibble("penguins")
tibble("penguins")
install.packages("tibbles")
library(tibbles)
install.packages("tibble")
library(tibble)
install.packages("tibble")
install.packages("tibble")
library(tibble)
tibble("penguins")
penguins
install.packages("penguins")
library(penguins)
install.packages("tidyverse")
library(tidyverse)
install.packages("tibble")
library(tibble)
install.packages("tibble")
install.packages("penguins")
install.packages("palmerpenguins : :penguins")
?penguins
install.packages("tidyverse")
library(tidyverse)
install.packages("tibble")
library(tibble)
install.packages("ggplot2")
library(ggplot2)
install.packages("ggplot2")
install.packages("tibble")
getwd()
setwd("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens")
#Packages loading ----
install.packages("metafor")
install.packages("meta")
install.packages("tidyverse")
#library loading----
library(meta)
library(tidyverse)
library(metafor)
library(openxlsx)
df1<-read.csv("Egg_production.csv", header=T, stringsAsFactors = F )
df1 <- read_csv("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens/Egg_production.csv")
head(df1)
out<-metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=df1, sm="SMD", studlab=paste(author, year))
out
forest(out, calcwidth.hetstat= T, print.pval.Q = F, print.stat = F, comb.fixed=F, leftcols="studlab", hestat=F, common = F, test.overall.random = T, rightcols=c("effect", "ci"))
df2<-read.csv("Egg_weight.csv", header=T, stringsAsFactors = F )
df2 <- read_csv("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens/Egg_weight.csv")
head(df2)
out<-metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=df2, sm="SMD", studlab=paste(author, year))
out
forest(out, calcwidth.hetstat= T, print.pval.Q = F, print.stat = F, comb.fixed=F, leftcols="studlab", hestat=F, common = F, test.overall.random = T, rightcols=c("effect", "ci"))
df4<-read.csv("Feed_intake.csv", header=T, stringsAsFactors = F )
df4 <- read_csv("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens/Feed_intake.csv")
head(df4)
out<-metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=df4, sm="SMD", studlab=paste(author, year))
out
forest(out, calcwidth.hetstat= T, print.pval.Q = F, print.stat = F, comb.fixed=F, leftcols="studlab", hestat=F, common = F, test.overall.random = T, rightcols=c("effect", "ci"))
df5<-read.csv("Feed_conversion_ratio.csv", header=T, stringsAsFactors = F )
df5 <- read_csv("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens/Feed_conversion_ratio.csv")
head(df5)
out<-metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=df5, sm="SMD", studlab=paste(author, year))
out
forest(out, calcwidth.hetstat= T, print.pval.Q = F, print.stat = F, comb.fixed=F, leftcols="studlab", hestat=F, common = F, test.overall.random = T, rightcols=c("effect", "ci"))
df3<-read.csv("Egg_yolk_color.csv", header=T, stringsAsFactors = F )
df3 <- read_csv("C:/Users/myj08/OneDrive/문서/Meta-analysis of Laying hens/Egg_yolk_color.csv")
head(df3)
out<-metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=df3, sm="SMD", studlab=paste(author, year))
out
forest(out, calcwidth.hetstat= T, print.pval.Q = F, print.stat = F, comb.fixed=F, leftcols="studlab", hestat=F, common = F, test.overall.random = T, rightcols=c("effect", "ci"))
