Here are the results for Dplyr recap file, “Final Exercises”.
First, call the libraries and load data.
library(tidyverse)
load("travel_weather.RData")
travel_weather %>%
select(-London,-Venice) %>%
filter(NYC>Amsterdam) %>%
group_by(year,month) %>%
summarise(NYCwA_diff=round(mean(NYC)-mean(Amsterdam),1)) %>%
arrange(desc(NYCwA_diff))
## # A tibble: 24 x 3
## # Groups: year [3]
## year month NYCwA_diff
## <dbl> <dbl> <dbl>
## 1 2016 8 8.4
## 2 2016 7 8.1
## 3 2017 9 7.9
## 4 2016 4 7.6
## 5 2017 4 7.4
## 6 2017 7 7.3
## 7 2017 8 6.5
## 8 2016 11 6.4
## 9 2016 3 6.3
## 10 2016 6 6.0
## # ... with 14 more rows
travel_weather %>%
gather(key=City,value=Temperature,-year,-month,-day) %>%
group_by(year, month, day) %>%
summarise(max_Temperature = max(Temperature), City = City[which.max(Temperature)])
## # A tibble: 731 x 5
## # Groups: year, month [?]
## year month day max_Temperature City
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 2015 11 1 16 NYC
## 2 2015 11 2 15 NYC
## 3 2015 11 3 16 NYC
## 4 2015 11 4 17 NYC
## 5 2015 11 5 18 NYC
## 6 2015 11 6 21 NYC
## 7 2015 11 7 17 NYC
## 8 2015 11 8 13 Venice
## 9 2015 11 9 13 Amsterdam
## 10 2015 11 10 14 Amsterdam
## # ... with 721 more rows
I couldn’t find a method to include City, which has the maximum value of that day, using methods that are explained in our recap file. So I googled for a solution, which is City = City[which.max(Temperature)]