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求大神写一个函数求1000以内的所有质数

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关键词：R语言

primes <- function(n){
  p <- 2:n
  i <- 1
  while (p <= sqrt(n)) {
    p <-  p[p %% p != 0 | p==p]
    i <- i+1
  }
  p
}

# Ex 1
df<-data.frame(
  x=rnorm(120,c(0,2,4)),
  y=rnorm(120,c(1,2,1)),
  z=letters[1:3]
)
ggplot(data=df,mapping=aes(x=x,y=y,color=z))+geom_point()+geom_line()
ggplot(data=df,mapping=aes(x=x,y=y))+geom_point()+facet_wrap(~z,3)

#=============================================================================
# Ex 2
scores<-dplyr::data_frame(
  person=rep(c("A","b","c"),each=2),
  time=rep(c("pre","post"),3),
  class1=round(rnorm(6,mean=80,sd=4),0),
  class2=round(jitter(class1,15),0)
)


#=============================================================================
# Ex 3
flights_airlines<-flights %>%
  left_join(airlines,by="carrier")
  
flights_airlines<-flights_airlines %>%
  filter(str_detect(name,"Inc.$"))
flights_airlines<-flights_airlines %>%
  mutate(
    prop_vowels=str_count(flights_airlines$name, "[aeiouAEIOU]")/str_length(flights_airlines$name)
  )
flights_airlines<-flights_airlines %>%
  mutate(vowels_gr=ifelse(prop_vowels>=0.45,1,NA),
         vowels_gr=ifelse(prop_vowels<0.45,2,vowels_gr)
         )
flights_airlines %>%
  group_by(vowels_gr) %>%
  summarise(vowel_count=n())
flights_airlines %>%
  filter(vowels_gr==1) %>%
  group_by(dest) %>%
  summarise(dest_count1=n()) %>%
  arrange(desc(dest_count1)) %>%
  head(7)
flights_airlines %>%
  filter(vowels_gr==2) %>%
  group_by(dest) %>%
  summarise(dest_count2=n()) %>%
  arrange(desc(dest_count2)) %>%
  head(7)


#=============================================================================
# Ex 4
mydata<-flights %>%
  filter(dep_delay<0,arr_delay>0)
mydata1<-mydata %>%
  group_by(dest) %>%
  summarise(dest_num=n(),mean_dist=mean(distance,na.rm = T),mean_arr_delay=mean(arr_delay,na.rm = T))
ggplot(data=mydata1,mapping=aes(x=mean_dist,y=mean_arr_delay))+geom_point()+geom_smooth(method = lm)
flights %>%
  left_join(weather,by=c("origin"="origin","time_hour"="time_hour"))
#=============================================================================
# Ex 5
sign_fun<-function(x,y,a){
  t.test(x,y)
  judge<-ifelse(p_value<a,print("reject H0"),print("fail to reject H0"))
  output<-ifelse(is.character(x)|is.character(y),print("Invalid",judge))
  return(list(p_value,judge,))
}

好难哪啊啊啊啊啊啊

求问+11111111

primes <- function(n){
  p <- 2:n
  i <- 1
  while (p[i] <= sqrt(n)) {
    p <-  p[p %% p[i] != 0 | p==p[i]]
    i <- i+1
  }
  p
}

三、
```{r}
#1
flights_airlines <- left_join(flights,airlines)
flights_airlines
#2
flights_airlines <-flights_airlines%>%
  filter(str_detect(name,"(Inc.)$"))
#3
fa1 <- flights_airlines%>%
  mutate(prop_vowels=str_count(name,"[aeiouAEIOU]")/str_length(name),vowel_gr=ifelse(prop_vowels>=0.45,1,2))
fa1
  count(fa1,vowel_gr)
#4
fa2 <- fa1%>%
  count(vowel_gr,dest)
filter(fa2,vowel_gr==1)%>%
  arrange(desc(n))%>%
  head(7)
filter(fa2,vowel_gr==2)%>%
  arrange(desc(n))%>%
  head(7)
```
四、
```{r}
#1
flights1<- flights%>%
  filter(dep_delay<=0&arr_delay>0)
flights1
#2
flights2 <- flights1%>%
  filter(!is.na(arr_time))%>%
  group_by(dest)%>%
  summarise(n=n(),mean_dis=mean(distance),mean_time=mean(arr_delay))
flights2
#3
ggplot(data=flights2,aes(x=mean_time,y=mean_dis))+
  geom_point()+
  geom_smooth()
#4
left_join(flights,weather,by=c("origin","time_hour"))
```
五
```{r}
#(1)
f <- function(x1,x2,conf){
  if(is.character(x1,x2)){
    print("Invalid Input Errors.")
  }else{
  n1=length(x1)
  n2=length(x2)
  mean_x1=mean(x1)
  mean_x2=mean(x2)
  d_bar=mean_x1-mean_x2
  se=sqrt(var(x1)/n1+var(x2)/n2)
  t=d_bar/se
  p=pt(t,df=n1+n2-1)*2
  if(p)
  list(
    p_value=p,
   
  )
  )
  }

#======================================================================================

library(tidyverse)
library(nycflights13)

#======================================================================================
#Ex 1
df<-data.frame(
  x=rnorm(120,c(0,2,4)),
  y=rnorm(120,c(1,2,1)),
  z=letters[1:3]
)
#(1)
ggplot(data = df,mapping = aes(x=x,y=y,color=z))+geom_point(size=3)

#(2)
ggplot(data = df,mapping = aes(x=x,y=y))+geom_point(size=3)+facet_wrap(~z,nrow = 3)

#(3)


#======================================================================================
#Ex 2




#======================================================================================
#Ex 3
#(1)
flights_airlines<-left_join(flights,airlines,by="carrier")
head(flights_airlines,n=6)

#(2)
flights_airlines<-filter(flights_airlines,str_detect(flights_airlines$name,"[Inc.]$"))
head(flights_planes,n=6)


#(3)
flights_airlines%>%
  mutate(prop_vowels=str_count(flights_airlines$name,"[aeiouAEIOU]")/nchar(flights_airlines$name))%>%
  group_by(prop_vowels)%>%
  mutate(vowel_gr=ifelse(prop_vowels>=0.45,1,2))%>%
  count(vowel_gr)

#(4)
flights_airlines%>%
  mutate(prop_vowels=str_count(flights_airlines$name,"[aeiouAEIOU]")/nchar(flights_airlines$name))%>%
  group_by(prop_vowels)%>%
  mutate(vowel_gr=ifelse(prop_vowels>=0.45,1,2))%>%
  count(vowel_gr)%>%
  summarise(times=)



#======================================================================================
#Ex 4

#(1)
flights%>%filter(dep_delay<=0&arr_delay>=0)

#(2)
tibble1<-flights%>%
  filter(dep_delay<=0&arr_delay>=0)%>%
  group_by(dest)%>%
  summarise(ndest=n(),avg_dist=mean(distance,na.rm = TRUE),avg_dely=mean(arr_delay,na.rm = TRUE))

#(3)
ggplot(tibble1,mapping = aes(x=avg_dist,y=avg_dely))+geom_point()+geom_smooth()

#(4)
left_join(flights,weather,by=c("origin","time_hour"))

#======================================================================================
#Ex 5

#(1)
myfunc<-function(a,b,c)

三、
```{r}
#1
flights_airlines <- left_join(flights,airlines)
flights_airlines
#2
flights_airlines <-flights_airlines%>%
  filter(str_detect(name,"(Inc.)$"))
#3
fa1 <- flights_airlines%>%
  mutate(prop_vowels=str_count(name,"[aeiouAEIOU]")/str_length(name),vowel_gr=ifelse(prop_vowels>=0.45,1,2))
fa1
  count(fa1,vowel_gr)
#4
fa2 <- fa1%>%
  count(vowel_gr,dest)
filter(fa2,vowel_gr==1)%>%
  arrange(desc(n))%>%
  head(7)
filter(fa2,vowel_gr==2)%>%
  arrange(desc(n))%>%
  head(7)
```
四、
```{r}
#1
flights1<- flights%>%
  filter(dep_delay<=0&arr_delay>0)
flights1
#2
flights2 <- flights1%>%
  filter(!is.na(arr_time))%>%
  group_by(dest)%>%
  summarise(n=n(),mean_dis=mean(distance),mean_time=mean(arr_delay))
flights2
#3
ggplot(data=flights2,aes(x=mean_time,y=mean_dis))+
  geom_point()+
  geom_smooth()
#4
left_join(flights,weather,by=c("origin","time_hour"))
```
五
```{r}
#(1)
f <- function(x1,x2,conf){
  if(is.character(x1,x2)){
    print("Invalid Input Errors.")
  }else{
  n1=length(x1)
  n2=length(x2)
  mean_x1=mean(x1)
  mean_x2=mean(x2)
  d_bar=mean_x1-mean_x2
  se=sqrt(var(x1)/n1+var(x2)/n2)
  t=d_bar/se
  p=pt(t,df=n1+n2-1)*2
  if(p)
  list(
    p_value=p,
   
  )
  )
  }
```

sml什么情况没有我发的？？？？？