filter() asserts all variables are white listed (#566). mutate() makes a rowwise_df when given a rowwise_df (#463). rbind_all() creates tbl_df objects instead of raw data.frames. If select() doesn't match any variables, it returns a 0-column data frame, instead of the original ...
dplyr implements the following verbs useful for data manipulation:select(): focus on a subset of variables filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rowsThey all work as ...
Filtering joins, which filter observations from one table based on whether or not they match an observation in the other table:semi_join(),anti_join(). Set operations, which combine the observations in two data sets as if they were set elements:intersect(),union(),setdiff(). Together, thes...
filter(stints,stints3) summarise(stints,max(stints)) mutate(stints,order_by(yearID,cumsum(stints))) #Joins player_info-select(tbl(lahman_p,Master),playerID,birthYear) hof-select(filter(tbl(lahman_p,HallOfFame),inducted==Y), playerID,votedBy,category) #Matchplayersandtheirhalloffamedata inne...
filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rowsThey all work as similarly as possible across the range of data sources. The main difference is performance:system...
filter() asserts all variables are white listed (#566). mutate() makes a rowwise_df when given a rowwise_df (#463). rbind_all() creates tbl_df objects instead of raw data.frames. If select() doesn't match any variables, it returns a 0-column data frame, instead of the original ...
dplyr implements the following verbs useful for data manipulation:select(): focus on a subset of variables filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rows...
filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rows They all work as similarly as possible across the range of data sources. The main difference is performance: ...
filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rowsThey all work as similarly as possible across the range of data sources. The main difference is performance:system...
filter(): focus on a subset of rows mutate(): add new columns summarise(): reduce each group to a smaller number of summary statistics arrange(): re-order the rowsThey all work as similarly as possible across the range of data sources. The main difference is performance:system...