l’ve tried to do recap exercise of dplyr package with weather data of four travel destinations.I haven’t done all the task yet. The data consists of temperature (in Celsius) history of 4 popular travel destinations (NYC, Amsterdam, London and Venice) between November 2015 and October 2017. Raw data is gathered from Weather Underground and it is only for educational purposes.
setwd("C:/bengi")load("travel_weather.RData")library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statstravel_weather %>%
tbl_df()## # A tibble: 731 x 7
## year month day Amsterdam London NYC Venice
## * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 8 8 16 13
## 2 2015 11 2 10 11 15 10
## 3 2015 11 3 9 11 16 9
## 4 2015 11 4 12 11 17 10
## 5 2015 11 5 13 13 18 12
## 6 2015 11 6 16 14 21 13
## 7 2015 11 7 16 14 17 14
## 8 2015 11 8 12 12 11 13
## 9 2015 11 9 13 12 11 11
## 10 2015 11 10 14 14 12 11
## # ... with 721 more rowsglimpse(travel_weather)## Observations: 731
## Variables: 7
## $ year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015...
## $ month <dbl> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, ...
## $ day <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1...
## $ Amsterdam <dbl> 8, 10, 9, 12, 13, 16, 16, 12, 13, 14, 13, 13, 11, 11...
## $ London <dbl> 8, 11, 11, 11, 13, 14, 14, 12, 12, 14, 13, 12, 10, 1...
## $ NYC <dbl> 16, 15, 16, 17, 18, 21, 17, 11, 11, 12, 12, 13, 11, ...
## $ Venice <dbl> 13, 10, 9, 10, 12, 13, 14, 13, 11, 11, 9, 11, 8, 11,...travel_weather %>% select(year, month, day, Venice)## # A tibble: 731 x 4
## year month day Venice
## * <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 13
## 2 2015 11 2 10
## 3 2015 11 3 9
## 4 2015 11 4 10
## 5 2015 11 5 12
## 6 2015 11 6 13
## 7 2015 11 7 14
## 8 2015 11 8 13
## 9 2015 11 9 11
## 10 2015 11 10 11
## # ... with 721 more rowstravel_weather %>% select(Amsterdam:Venice)## # A tibble: 731 x 4
## Amsterdam London NYC Venice
## * <dbl> <dbl> <dbl> <dbl>
## 1 8 8 16 13
## 2 10 11 15 10
## 3 9 11 16 9
## 4 12 11 17 10
## 5 13 13 18 12
## 6 16 14 21 13
## 7 16 14 17 14
## 8 12 12 11 13
## 9 13 12 11 11
## 10 14 14 12 11
## # ... with 721 more rowstravel_weather %>% arrange(NYC, desc(Amsterdam)) %>% select(-London, -Venice)## # A tibble: 731 x 5
## year month day Amsterdam NYC
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 2 14 2 -14
## 2 2016 2 13 1 -10
## 3 2016 1 5 6 -7
## 4 2017 1 9 6 -7
## 5 2016 12 16 6 -6
## 6 2017 1 8 4 -6
## 7 2016 2 12 2 -6
## 8 2016 1 19 -2 -6
## 9 2017 3 15 9 -5
## 10 2017 3 11 7 -5
## # ... with 721 more rowstravel_weather%>%rename('New York'= NYC)## # A tibble: 731 x 7
## year month day Amsterdam London `New York` Venice
## * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 8 8 16 13
## 2 2015 11 2 10 11 15 10
## 3 2015 11 3 9 11 16 9
## 4 2015 11 4 12 11 17 10
## 5 2015 11 5 13 13 18 12
## 6 2015 11 6 16 14 21 13
## 7 2015 11 7 16 14 17 14
## 8 2015 11 8 12 12 11 13
## 9 2015 11 9 13 12 11 11
## 10 2015 11 10 14 14 12 11
## # ... with 721 more rowsnames(travel_weather)## [1] "year" "month" "day" "Amsterdam" "London" "NYC"
## [7] "Venice" travel_weather %>% filter(day<=3)## # A tibble: 72 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 8 8 16 13
## 2 2015 11 2 10 11 15 10
## 3 2015 11 3 9 11 16 9
## 4 2015 12 1 9 11 9 6
## 5 2015 12 2 10 12 11 8
## 6 2015 12 3 9 11 10 8
## 7 2016 1 1 4 3 3 2
## 8 2016 1 2 6 10 2 0
## 9 2016 1 3 7 8 4 3
## 10 2016 2 1 10 12 11 6
## # ... with 62 more rowstravel_weather %>% filter(month==11 & Venice > NYC)## # A tibble: 20 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 8 12 12 11 13
## 2 2015 11 14 11 10 8 11
## 3 2015 11 15 12 14 9 11
## 4 2015 11 17 13 13 8 9
## 5 2015 11 23 3 3 4 6
## 6 2015 11 24 5 8 4 6
## 7 2016 11 1 10 9 9 11
## 8 2016 11 6 7 4 11 12
## 9 2016 11 7 4 6 8 11
## 10 2016 11 12 1 8 7 9
## 11 2016 11 19 6 4 10 11
## 12 2016 11 20 7 7 3 11
## 13 2016 11 21 10 10 4 12
## 14 2016 11 22 10 9 4 14
## 15 2016 11 23 8 7 4 14
## 16 2016 11 24 6 9 6 13
## 17 2016 11 25 3 7 10 13
## 18 2016 11 26 3 6 7 12
## 19 2016 11 27 5 7 7 11
## 20 2016 11 28 1 6 7 8travel_weather %>% filter(month == 7 & Amsterdam > (London | Venice))## # A tibble: 62 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 7 1 16 16 23 25
## 2 2016 7 2 16 14 21 25
## 3 2016 7 3 14 15 22 24
## 4 2016 7 4 16 16 24 24
## 5 2016 7 5 16 17 26 23
## 6 2016 7 6 14 16 28 24
## 7 2016 7 7 16 18 28 26
## 8 2016 7 8 18 19 25 25
## 9 2016 7 9 18 19 21 27
## 10 2016 7 10 20 20 23 28
## # ... with 52 more rowstravel_weather%>%filter()## # A tibble: 731 x 7
## year month day Amsterdam London NYC Venice
## * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 8 8 16 13
## 2 2015 11 2 10 11 15 10
## 3 2015 11 3 9 11 16 9
## 4 2015 11 4 12 11 17 10
## 5 2015 11 5 13 13 18 12
## 6 2015 11 6 16 14 21 13
## 7 2015 11 7 16 14 17 14
## 8 2015 11 8 12 12 11 13
## 9 2015 11 9 13 12 11 11
## 10 2015 11 10 14 14 12 11
## # ... with 721 more rowstravel_weather %>% arrange(NYC)## # A tibble: 731 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 2 14 2 3 -14 6
## 2 2016 2 13 1 2 -10 4
## 3 2016 1 5 6 8 -7 2
## 4 2017 1 9 6 7 -7 -2
## 5 2016 1 19 -2 0 -6 1
## 6 2016 2 12 2 1 -6 6
## 7 2016 12 16 6 6 -6 4
## 8 2017 1 8 4 9 -6 -2
## 9 2017 1 7 1 8 -5 -3
## 10 2017 3 11 7 10 -5 9
## # ... with 721 more rowstravel_weather %>% arrange(NYC, desc(Amsterdam))## # A tibble: 731 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 2 14 2 3 -14 6
## 2 2016 2 13 1 2 -10 4
## 3 2016 1 5 6 8 -7 2
## 4 2017 1 9 6 7 -7 -2
## 5 2016 12 16 6 6 -6 4
## 6 2017 1 8 4 9 -6 -2
## 7 2016 2 12 2 1 -6 6
## 8 2016 1 19 -2 0 -6 1
## 9 2017 3 15 9 11 -5 10
## 10 2017 3 11 7 10 -5 9
## # ... with 721 more rowstravel_weather %>% arrange(desc(year),desc(month),desc(day))## # A tibble: 731 x 7
## year month day Amsterdam London NYC Venice
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017 10 31 9 9 11 11
## 2 2017 10 30 8 6 12 13
## 3 2017 10 29 11 11 18 9
## 4 2017 10 28 12 10 17 10
## 5 2017 10 27 12 9 13 13
## 6 2017 10 26 13 10 13 13
## 7 2017 10 25 13 14 17 13
## 8 2017 10 24 13 16 21 13
## 9 2017 10 23 13 13 20 13
## 10 2017 10 22 11 11 19 13
## # ... with 721 more rowstravel_weather %>% mutate(VAdiff = Venice - Amsterdam)## # A tibble: 731 x 8
## year month day Amsterdam London NYC Venice VAdiff
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 11 1 8 8 16 13 5
## 2 2015 11 2 10 11 15 10 0
## 3 2015 11 3 9 11 16 9 0
## 4 2015 11 4 12 11 17 10 -2
## 5 2015 11 5 13 13 18 12 -1
## 6 2015 11 6 16 14 21 13 -3
## 7 2015 11 7 16 14 17 14 -2
## 8 2015 11 8 12 12 11 13 1
## 9 2015 11 9 13 12 11 11 -2
## 10 2015 11 10 14 14 12 11 -3
## # ... with 721 more rowstravel_weather %>% mutate(VwarmerA = Venice>Amsterdam)## # A tibble: 731 x 8
## year month day Amsterdam London NYC Venice VwarmerA
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 2015 11 1 8 8 16 13 TRUE
## 2 2015 11 2 10 11 15 10 FALSE
## 3 2015 11 3 9 11 16 9 FALSE
## 4 2015 11 4 12 11 17 10 FALSE
## 5 2015 11 5 13 13 18 12 FALSE
## 6 2015 11 6 16 14 21 13 FALSE
## 7 2015 11 7 16 14 17 14 FALSE
## 8 2015 11 8 12 12 11 13 TRUE
## 9 2015 11 9 13 12 11 11 FALSE
## 10 2015 11 10 14 14 12 11 FALSE
## # ... with 721 more rowstravel_weather %>% summarise(Venice_avg=mean(Venice), NYC_avg=mean(NYC))## # A tibble: 1 x 2
## Venice_avg NYC_avg
## <dbl> <dbl>
## 1 14.31601 14.41313travel_weather %>% group_by(month) %>% summarise(Amsterdam_mean=mean(Amsterdam))%>% round(digit=2)## # A tibble: 12 x 2
## month Amsterdam_mean
## <dbl> <dbl>
## 1 1 3.00
## 2 2 4.32
## 3 3 6.92
## 4 4 8.43
## 5 5 14.48
## 6 6 17.28
## 7 7 18.02
## 8 8 17.68
## 9 9 15.95
## 10 10 11.68
## 11 11 7.65
## 12 12 6.97