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.Rhistory
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getwd()
?getwd
??kruskal
??mean
sessioninfo()
sessionInfo()
3+5
5-5
25*5
25/5+5*5
weight_kg <- 55
(weight_kg <- 55)
#this is a comment
2.2*weight_kg
weight_lb <- 2.2*weight_kg
(weight_lb <- 2.2*weight_kg)
(x <- 5)
mass <- 47.5
age <- 122
mass <- mass * 2.0
age <- age - 20
mass_index <- mass/age
##
## ## Why do you think it happens?
##
## ## You've probably noticed that objects of different types get
## ## converted into a single, shared type within a vector. In R, we call
## ## converting objects from one class into another class
## ## _coercion_. These conversions happen according to a hierarchy,
## ## whereby some types get preferentially coerced into other types. Can
## ## you draw a diagram that represents the hierarchy of how these data
## ## types are coerced?
heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)
sqrt(2)
round(sqrt(2))
round(14.35)
round(14.35,1)
args(round)
args(sqrt)
weight_g <-c(50,60,65,82)
animals <- c("mouse", "dog","Cat")
(animals <- c("mouse", "dog","Cat"))
length(weight_g)
length(animals)
class(animals)
number <- c(1,2,3,"c")
class(number)
str(animals)
1:10
10:1
animals(1)
animals[1]
weight_g <-c(weight_g,90)
str(weight_g)
weight_g[6] <-c(100)
str(weight_g)
weight_g <-c(11,weight_g,90)
str(weight_g)
weight_g[9] <-250
str(weight_g)
c(1,2,"a")
c("a","b",1)
c(1.5,2.5,1)
c(1.5,2.5,1, "n")
a<-c(1.5,2.5,1, "n")
class(a)
c(1,3,5,FALSE)
animals[1]
animals[2]
animals[3]
animals[2:3]
animals[c(2:3)]
weight_g
weight_g[weight_g < 60]
weight_g[weight_g < 60 AND weight_g > 90]
weight_g[weight_g < 60 & weight_g > 90]
weight_g[weight_g < 60 | weight_g == 60]
weight_g[weight_g < 60 & weight_g ==60]
heights <- c(2,3,4,NA,S)
heights <- c(2,3,4,NA,S)
heights <- c(2,3,4,NA,5)
mean(heights)
mean(heights, na.rm=TRUE)
na.omit(heights)
heights[complete.cases(heights)]
mean(heights[complete.cases(heights)])
download.file("https://ndownloader.figshare.com/files/2292169","data/portal_data_joined.csv")
surveys <- read.csv("data/portal_data_joined.csv")
download.file("https://ndownloader.figshare.com/files/2292169","data/portal_data_joined.csv")
surveys <- read.csv("data/portal_data_joined.csv")
View(surveys)
View(surveys)
View(surveys)
dim(sueveys)
dim(surveys)
NCOL(surveys)
head(surveys)
colnames(surveys)
str(surveys)
summary(surveys)
surveys[1,2]
surveys[3,4]
surveys[5,]
surveys[,7]
surveys[1:3,]
surveys[,1:3]
surveys[$species_id]
surveys$species_id
sex<-factor(c("male","female","male","male","female"))
levels(Sex)
levels(sex)
nlevels(sex)
library("tidyverse")
#library("tidyverse")
str(surveys)
select(surveys,species_id,weight)
select(surveys,1,2)
filter(surveys,year==1995)
str(surveys)
surveys %>%
select(species_id,weight) %>%
filter(year==1997)
surveys %>%
filter(year==1997) %>%
select(species_id,weight)
surveys %>%
filter(year==1995) %>%
select(species_id,weight)
surveys %>%
filter(weight < 5) %>%
select(species_id,sex,weight)
surveys %>%
head (1:5) %>%
select(species_id,sex,weight)
surveys %>%
head(n = 5) %>%
select(species_id,sex,weight)
exercise <- surveys %>%
filter(weight < 5) %>%
select(species_id,sex,weight)
(exercise <- surveys %>%
filter(weight < 5) %>%
select(species_id,sex,weight))
surveys %>%
mutate()weight_kg=weight/1000)
surveys %>%
mutate(weight_kg=weight/1000)
data_with_kg <- surveys %>%
mutate(weight_kg=weight/1000)
data_with_kg <- surveys %>%
mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 1000)
surveys %>%
mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 1000)
surveys %>%
mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2)
surveys %>%
mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2) %>%
head()
surveys %>%
mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2) %>%
head(n=10)
surveys %>%
filter(!is.na(weight)) %>% mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2) %>%
head()
new_d < -surveys %>%
filter(!is.na(weight)) %>% mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2) %>%
head()
new_d <- surveys %>%
filter(!is.na(weight)) %>% mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2) %>%
head()
new_d <- surveys %>%
filter(!is.na(weight)) %>% mutate(weight_kg=weight/1000, weight_kg2=weight_kg * 2)
nrows(new_d)
nrow(new_d)
surveys %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
group_by(sex) %>%
summarise(mean_weight = mean(filter(!is.na(weight)))
surveys %>%
filter(!is.na(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
filter(!is.na(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
filter(!is_empty(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
filter(!is.na(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight))
surveys %>%
filter(!is.na(weight)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight))
surveys %>%
filter(!is.na(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight))
surveys %>%
filter(!is.na(sex)) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
%>%
group_by(sex) %>%
filter(!is.na(sex))
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
group_by(sex) %>%
filter(!is.na(sex))
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
group_by(sex) %>%
filter(!is.na(sex)) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
filter(!(sex=="")) %>%
group_by(sex) %>%
summarise(mean_weight = mean(weight, na.rm=TRUE))
surveys %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight))
surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight))
summarise_two <- surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight))
View(summarise_two)
summarise_two <- surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight), min_weight=min(weight))
summarise_three <- surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight), min_weight=min(weight))
View(summarise_three)
summarise_four <- surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight), min_weight=min(weight), mx_weight = max(weight))
View(summarise_four)
summarise_five <- surveys %>%
filter(!sex=="") %>%
filter(!is.na(weight)) %>%
group_by(sex,species_id) %>%
summarise(mean_weight=mean(weight), min_weight=min(weight), mx_weight = max(weight), std_weight=sd(weight))
View(summarise_five)
surveys %>%
count(sex)
surveys %>%
filter (!sex=="") %>%
count(sex)
surveys %>%
filter (!sex=="") %>%
count(sex, species)
surveys %>%
filter (!sex=="") %>%
count(sex, species) %>%
arrange(species,desc(n))
surveys %>%
filter (!sex=="") %>%
count(sex, species) %>%
arrange(species,n)
plot(surveys$sex)
surveys_200 <- surveys %>%
surveys_200 <- surveys %>%
head(n=100)
surveys_200<-surveys %>%
head(n=100)
View(surveys_200)
surveys_200<-surveys %>%
head(n=200)
View(surveys_200)
surveys_200<-surveys %>%
filter(!sex=="")
head(n=200)
surveys_200<-surveys %>%
filter(!sex=="") %>%
head(n=200)
View(surveys_200)