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, uniondata<-tibble::as_tibble(airquality)
print(data)## # A tibble: 153 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 41 190 7.4 67 5 1
## 2 36 118 8 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## # ℹ 143 more rows##SUMMARISE
data_summarised <- data %>%
summarise(avg_Temp = mean(Temp),
max_Temp = max(Temp),
min_Temp = min(Temp)
)
head(data_summarised)## # A tibble: 1 × 3
## avg_Temp max_Temp min_Temp
## <dbl> <int> <int>
## 1 77.9 97 56##ARRANGE
data_arranged <- data %>%
arrange(desc(Temp))
head(data_arranged)## # A tibble: 6 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 76 203 9.7 97 8 28
## 2 84 237 6.3 96 8 30
## 3 118 225 2.3 94 8 29
## 4 85 188 6.3 94 8 31
## 5 NA 259 10.9 93 6 11
## 6 73 183 2.8 93 9 3##FILTER
data_filtered <- data %>%
filter(Day < 2)
head(data_filtered)## # A tibble: 5 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 41 190 7.4 67 5 1
## 2 NA 286 8.6 78 6 1
## 3 135 269 4.1 84 7 1
## 4 39 83 6.9 81 8 1
## 5 96 167 6.9 91 9 1##MUTATE
data_mutated <- data %>%
mutate(solar_per_temp = Solar.R / Temp)
head(data_mutated)## # A tibble: 6 × 7
## Ozone Solar.R Wind Temp Month Day solar_per_temp
## <int> <int> <dbl> <int> <int> <int> <dbl>
## 1 41 190 7.4 67 5 1 2.84
## 2 36 118 8 72 5 2 1.64
## 3 12 149 12.6 74 5 3 2.01
## 4 18 313 11.5 62 5 4 5.05
## 5 NA NA 14.3 56 5 5 NA
## 6 28 NA 14.9 66 5 6 NA##SELECT
data_selected <- data %>%
select(Day, Month, Wind)
head(data_selected)## # A tibble: 6 × 3
## Day Month Wind
## <int> <int> <dbl>
## 1 1 5 7.4
## 2 2 5 8
## 3 3 5 12.6
## 4 4 5 11.5
## 5 5 5 14.3
## 6 6 5 14.9##FILTER+ARRANGE
data_filtered_arranged <- data %>%
filter(Day < 6) %>%
arrange(desc(Temp))
head(data_filtered_arranged)## # A tibble: 6 × 6
## Ozone Solar.R Wind Temp Month Day
## <int> <int> <dbl> <int> <int> <int>
## 1 73 183 2.8 93 9 3
## 2 91 189 4.6 93 9 4
## 3 78 197 5.1 92 9 2
## 4 96 167 6.9 91 9 1
## 5 47 95 7.4 87 9 5
## 6 78 NA 6.9 86 8 4