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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00You can also embed plots, for example:
library(tidyverse)
library(dplyr)
library(lubridate)
library(stringr)
library(readxl)
bike <- read_excel("bike.xlsx")
bike<-bike%>%mutate(model = case_when(
model == "CAAD Disc Ultegra" ~ "CAAD12 Disc Ultegra",
model == "Syapse Carbon Tiagra" ~ "Synapse Carbon Tiagra",
model == "Supersix Evo Hi-Mod Utegra" ~ "Supersix Evo Hi-Mod Ultegra",
TRUE ~ model
))
#1 highest month sale
bike$month<-format(as.Date(bike$order_date,format="%Y %m %d"),"%m")
bikes<-bike%>%select(total_price,month)
bikemonth<-bikes%>%group_by(month)%>%summarise(total_price)%>%group_by(month)%>%summarise_at(vars(total_price),funs(sum(.,na.rm=TRUE)))
install.packages("formattable")
library(formattable)
bikemonth$total_price<-currency(bikemonth$total_price,"$",digits = 0)
monthhigh<-bikemonth%>%slice_max(total_price)
monthhigh## # A tibble: 1 × 2
## month total_price
## <chr> <formttbl>
## 1 04 $8,386,170#2 median by bike attribute
bikex<-bike%>%select(total_price,model)
bikemedian<-bikex%>%group_by(model)%>%summarise(total_price)%>%group_by(model)%>%summarise_at(vars(total_price),funs(sum(.,na.rm=TRUE)))
bikemedian$total_price<-currency(bikemedian$total_price,"$",digits = 0)
bikemed<-bikemedian%>%filter(total_price==median(total_price))
bikemed## # A tibble: 1 × 2
## model total_price
## <chr> <formttbl>
## 1 CAAD12 Disc Ultegra $649,040#3 name
bikez<-bike%>%select(price,model,category_1,category_2)
bikebase<-bikez%>%group_by(model)%>%summarise(price)%>%group_by(model)%>%summarise_at(vars(price),funs(sum(.,na.rm=TRUE)))
bikebase$brand<-sub( " .*","",bikebase$model)
bikebase$count<-bikez%>%group_by(model)%>%count(price)
bikebass<-bikebase$count
bikebass$brand<-sub( " .*","",bikebass$model)
bikebasemax<-bikebass%>%group_by(brand)%>%summarise_at(vars(price),funs(max(.,na.rm=TRUE)))
bikebasemax## # A tibble: 17 × 2
## brand price
## <chr> <dbl>
## 1 Bad 3200
## 2 Beast 2770
## 3 CAAD12 5860
## 4 CAAD8 1410
## 5 Catalyst 705
## 6 F-Si 11190
## 7 Fat 3730
## 8 Habit 12250
## 9 Jekyll 7990
## 10 Scalpel 6390
## 11 Scalpel-Si 12790
## 12 Slice 7000
## 13 Supersix 12790
## 14 SuperX 3500
## 15 Synapse 9590
## 16 Trail 1520
## 17 Trigger 8200bikebasemin<-bikebass%>%group_by(brand)%>%summarise_at(vars(price),funs(min(.,na.rm=TRUE)))
bikebasemin## # A tibble: 17 × 2
## brand price
## <chr> <dbl>
## 1 Bad 2660
## 2 Beast 1620
## 3 CAAD12 1680
## 4 CAAD8 815
## 5 Catalyst 415
## 6 F-Si 1840
## 7 Fat 2130
## 8 Habit 1950
## 9 Jekyll 3200
## 10 Scalpel 3200
## 11 Scalpel-Si 3200
## 12 Slice 1950
## 13 Supersix 1840
## 14 SuperX 1750
## 15 Synapse 870
## 16 Trail 815
## 17 Trigger 3200bikebaseaverage<-bikebass%>%group_by(brand)%>%summarise(Mean=mean(price))
bikebaseaverage%>%arrange(desc(Mean),brand)## # A tibble: 17 × 2
## brand Mean
## <chr> <dbl>
## 1 Scalpel-Si 6927.
## 2 Supersix 5491
## 3 Jekyll 5275
## 4 Trigger 5275
## 5 F-Si 5070
## 6 Habit 5052.
## 7 Scalpel 4795
## 8 Slice 3870
## 9 Synapse 3514.
## 10 CAAD12 3121.
## 11 Bad 2930
## 12 Fat 2930
## 13 SuperX 2415
## 14 Beast 2173.
## 15 Trail 1149
## 16 CAAD8 1126.
## 17 Catalyst 546.Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.