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## SalariesDplyr has join functions, left_join and right_join are the first ones we are looking at. They take three arguments here, the two dataframes we are joining then the ‘by’ which says how you want them to be joined. There are a lot of datasets for this datacamp exercise and I do not want to wade through them all and find out which are complete because some have been problematic.
left_join and right_join join data frames (or tibbles) together by adding the second one onto the first. So this code below would add artists data onto bands
bands2 <- left_join(bands, artists, by = c("first", "last")) This would do the opposite, but since it is now a right_join, the output would be the same. bands3 <- right_join(artists, bands, by = c("first", "last"))
Join functions also work well with other dpylr stuff, like group_by, filter, select, and so forth. Here we would be making an object, “temp”, by joining artists to bands by the first and last variables, which are the musicians names. temp <- left_join(bands, artists, by = c("first", "last")) Then we are filtering the temp data frame for only people who play the guitar. temp <- filter(temp, instrument == "Guitar") Finally, we select the first, last, and band variables to show the names of guitarists, and which bands they play in. select(temp, first, last, band)
We can do the same thing using pipe operators if we want to. bands %>% left_join(artists, by = c("first", "last")) %>% filter(instrument == "Guitar") %>% select(first, last, band)
There are also full_join, semi_join, inner_join, and anti_join functions. From here on out I am going to use this superhero data where I can. I like superheros more than bands and the data is easier to look at and tell what is going on.
: Return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x. This is a filtering join. This scraps Hellboy since his publisher does not appear in the publishers dataset.
(ijsp <- inner_join(superheroes, publishers))# A tibble: 6 x 5
name alignment gender publisher yr_founded
<chr> <chr> <chr> <chr> <dbl>
1 Magneto bad male Marvel 1939
2 Storm good female Marvel 1939
3 Mystique bad female Marvel 1939
4 Batman good male DC 1934
5 Joker bad male DC 1934
6 Catwoman bad female DC 1934full_join(x, y): Return all rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing. This is a mutating join. This gives us all variables from x = superheroes plus those from y = publishers. Any row is just from one of the dataframes fills up with NAs for the stuff that normally would have come from the other frame.
(fjsp <- full_join(superheroes, publishers))# A tibble: 8 x 5
name alignment gender publisher yr_founded
<chr> <chr> <chr> <chr> <dbl>
1 Magneto bad male Marvel 1939
2 Storm good female Marvel 1939
3 Mystique bad female Marvel 1939
4 Batman good male DC 1934
5 Joker bad male DC 1934
6 Catwoman bad female DC 1934
7 Hellboy good male Dark Horse Comics NA
8 <NA> <NA> <NA> Image 1992semi_join(x, y): Return all rows from x where there are matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x. This is a filtering join.
(sjsp <- semi_join(superheroes, publishers))# A tibble: 6 x 4
name alignment gender publisher
<chr> <chr> <chr> <chr>
1 Magneto bad male Marvel
2 Storm good female Marvel
3 Mystique bad female Marvel
4 Batman good male DC
5 Joker bad male DC
6 Catwoman bad female DC This basically gives us the same thing as the inner_join, but with a different order and without that “year founded” variable that was in the publishers dataset.
Return all rows from x where there are not matching values in y, keeping just columns from x. This is a filtering join. This will only keep Hellboy. Fitting, since he was ditched in the inner join and semi join. We also do not get that founding year thing, since this only keeps the columns from X.
(ajsp <- anti_join(superheroes, publishers))# A tibble: 1 x 4
name alignment gender publisher
<chr> <chr> <chr> <chr>
1 Hellboy good male Dark Horse ComicsThese were all joining by the publisher variable since those are the only columns shared by the two dataframes. R figured this out on its own, so I did not specify by = "publisher" for each.
UNION function in R combines all rows from both the tables and removes duplicate records from the combined dataset Select is an older thing, it takes .data, and what columns to select but it is most commonly used in a pipe.
hero_names <- select(.data=superheroes, name)
hero_names# A tibble: 7 x 1
name
<chr>
1 Magneto
2 Storm
3 Mystique
4 Batman
5 Joker
6 Catwoman
7 Hellboy publisher_names <- select(.data=publishers, publisher)
publisher_names# A tibble: 3 x 1
publisher
<chr>
1 DC
2 Marvel
3 Image df1 = data.frame(CustomerId = c(1:6), Product = c(rep("Oven", 3), rep("Television", 3)))
df2 = data.frame(CustomerId = c(4:7), Product = c(rep("Television", 2), rep("Air conditioner", 2)))
union(df1,df2) CustomerId Product
1 1 Oven
2 2 Oven
3 3 Oven
4 4 Television
5 5 Television
6 6 Television
7 6 Air conditioner
8 7 Air conditionerThis is not a great example of bind_rows but it still shows what it does. Each argument can either be a data frame, a list that could be a data frame, or a list of data frames.
When row-binding, columns are matched by name, and any missing columns will be filled with NA.
When column-binding, rows are matched by position, so all data frames must have the same number of rows. To match by value, not position, see join.
.id is the Data frame identifier.
When .id is supplied, a new column of identifiers is created to link each row to its original data frame. The labels are taken from the named arguments to bind_rows(). When a list of data frames is supplied, the labels are taken from the names of the list. If no names are found a numeric sequence is used instead.
one <- mtcars[1:4, ]
two <- mtcars[11:14, ]
three<-bind_rows(one, two)
three mpg cyl disp hp drat wt qsec vs am gear carb
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3Rename takes a data argument, then new_name = old_name
three%>%
rename(cylinders = cyl) mpg cylinders disp hp drat wt qsec vs am gear carb
1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
7 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
8 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3purrr’s reduce() function is very useful for joining together multiple datasets. Reduce applies a function to a list of objects.
list(superheroes, publishers) %>%
reduce(left_join, by="publisher")# A tibble: 7 x 5
name alignment gender publisher yr_founded
<chr> <chr> <chr> <chr> <dbl>
1 Magneto bad male Marvel 1939
2 Storm good female Marvel 1939
3 Mystique bad female Marvel 1939
4 Batman good male DC 1934
5 Joker bad male DC 1934
6 Catwoman bad female DC 1934
7 Hellboy good male Dark Horse Comics NAHere are some more examples of functions in purrr. These examples should use the lahman datasets and package, but those are some of the datasets that are not working well. Alas. Intersect, arrange, distinct, and the older dplyr functions are all useful here.
lahmanNames%>% reduce(intersect)
lahmanNames %>%
# Bind the data frames in lahmanNames bind_rows(.id = “dataframe”) %>% # Group the result by var group_by(var) %>% # Tally the number of appearances tally() %>% # Filter the data filter(n > 1) %>% # Arrange the results arrange(desc(n))
lahmanNames %>% # Bind the data frames bind_rows(.id = “dataframe”) %>% # Filter the results filter(var==‘playerID’) %>% # Extract the dataframe variable $(dataframe)
players <- Master %>% # Return one row for each distinct player distinct(playerID, nameFirst, nameLast)
players %>% # Find all players who do not appear in Salaries anti_join(Salaries, by = “playerID”) %>% # Count them count()
players %>% anti_join(Salaries, by = “playerID”) %>% # How many unsalaried players appear in Appearances? semi_join(Appearances, by = “playerID”) %>% count()
players %>% # Find all players who do not appear in Salaries anti_join(Salaries, by = “playerID”) %>% # Join them to Appearances left_join(Appearances, by = “playerID”) %>% # Calculate total_games for each player group_by(playerID) %>% summarize(total_games = sum(G_all, na.rm = TRUE)) %>% # Arrange in descending order by total_games arrange(desc(total_games))
players %>% # Find all players who do not appear in Salaries anti_join(Salaries, by = “playerID”) %>% # Join them to Appearances left_join(Batting, by = “playerID”) %>% # Calculate total_games for each player group_by(playerID) %>% summarize(total_at_bat = sum(AB, na.rm = TRUE)) %>% # Arrange in descending order by total_games arrange(desc(total_at_bat))
Finding the distinct players that appear in HallOfFame
nominated <- HallOfFame %>% distinct(playerID)
nominated %>% # Count the number of players in nominated count()
nominated_full <- nominated %>% # Join to Master left_join(Master, by = “playerID”) %>% # Return playerID, nameFirst, nameLast select(playerID, nameFirst, nameLast)
Appearances %>% # Filter Appearances against nominated semi_join(nominated, by = “playerID”) %>% # Find last year played by player group_by(playerID) %>% summarize(last_year = max(yearID)) %>% # Join to full HallOfFame left_join(HallOfFame, by = “playerID”) %>% # Filter for unusual observations filter(last_year >= yearID)