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There are seven fundamental functions of data transformation:

select() selecting variables

filter() provides basic filtering capabilities

group_by() groups data by categorical levels

summarise() summarise data by functions of choice
arrange() ordering data

join() joining separate dataframes

mutate() create new variables
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http://rpubs.com/NateByers/DataManip1

library(devtools)
install_github("NateByers/region5air")
library(region5air)
data(airdata)
head(airdata)

airdata=airdata %>% select(site, datetime, parameter, value) #using select to select 4 columns
                OR   select(airdata, site, datetime:value)

airdata %>% arrange(datetime, parameter,site,value)  #sorting according to these columns
        %>% arrange(airdata, desc(site), datetime)   #sorting with descending order in site


airdata %>% filter(parameter == 44201, site==840170311601) #filtering 
  #replacement of: airdata[airdata$parameter  == 44201 & airdata$site == 840170311601, ]
        %>% filter(airdata, parameter == 44201, poc == 1 | poc == 2)




trainingData %>% 
  filter(Date %in% distinctMissingDates) %>% 
  group_by(Date) %>%
  summarize(total.count = n(),
            count.open = sum(Open == 1),
            count.promo = sum(Promo == 1))
            
imputedData %>% inner_join(missingDateFlags, by = "Date")
             by = c("state_code"= "State")



data frame df:
col1
row1
row2
row3
row4
row5
data frame df1:

col1
row3
row5
Then the desired output df2 is:

col1
row1
row2
row4
anti_join
anti_join(df, df1, by='col1')