#Mammals sleep dataset ##loading the dataset
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## filtering the dataset by piping it to check the relationship between total sleep and brainweight
msleep %>%
select(c(1, 6, 7, 10, 11)) %>%
filter(sleep_total < 5) %>%
ggplot() + geom_line(aes(x = brainwt, y = sleep_total)) + labs(title = "Efects of brain wight on sleep")
## Warning: Removed 3 row(s) containing missing values (geom_path).
mssleep_avg_genus =msleep %>%
## checking the average sleep_total and looking at the distribution between the names
mutate(average_sleep_over = sleep_total -mean(sleep_total))%>%
select(name, genus, sleep_total, bodywt, average_sleep_over) %>%
arrange(average_sleep_over)
mssleep_avg_genus
## # A tibble: 83 × 5
## name genus sleep_total bodywt average_sleep_over
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Giraffe Giraffa 1.9 900. -8.53
## 2 Pilot whale Globicephalus 2.7 800 -7.73
## 3 Horse Equus 2.9 521 -7.53
## 4 Roe deer Capreolus 3 14.8 -7.43
## 5 Donkey Equus 3.1 187 -7.33
## 6 African elephant Loxodonta 3.3 6654 -7.13
## 7 Caspian seal Phoca 3.5 86 -6.93
## 8 Sheep Ovis 3.8 55.5 -6.63
## 9 Asian elephant Elephas 3.9 2547 -6.53
## 10 Cow Bos 4 600 -6.43
## # … with 73 more rows
ggplot(data = mssleep_avg_genus) + geom_line(aes(x =bodywt , y = sleep_total), na.rm = T) + labs(title = " The effect of body weight on sleep total")
##how about the effect of body weight on sleep total? clearly it has an effect on the total sleep of mammals