In R, a data frame is a two-dimensional data structure, similar to a
table in a database. The dplyr package provides a powerful
and intuitive grammar for data manipulation.
# Create a data frame
my_dataframe <- data.frame(
name = c("Charlie", "David", "Emily"),
age = c(30, 35, 28),
city = c("New York", "London", "Paris")
)
print(my_dataframe)## name age city
## 1 Charlie 30 New York
## 2 David 35 London
## 3 Emily 28 Parisselect(): select columnsfilter(): filter rowsmutate(): create new columnsarrange(): sort rowssummarise(): summarise datagroup_by(): group dataWe will be using the mtcars dataset for many of these
exercises.
mtcars dataset.# Your code here
data(mtcars)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1mpg, cyl, and hp
columns from the mtcars dataset.# Your code here
select(mtcars, mpg, cyl, hp)## mpg cyl hp
## Mazda RX4 21.0 6 110
## Mazda RX4 Wag 21.0 6 110
## Datsun 710 22.8 4 93
## Hornet 4 Drive 21.4 6 110
## Hornet Sportabout 18.7 8 175
## Valiant 18.1 6 105
## Duster 360 14.3 8 245
## Merc 240D 24.4 4 62
## Merc 230 22.8 4 95
## Merc 280 19.2 6 123
## Merc 280C 17.8 6 123
## Merc 450SE 16.4 8 180
## Merc 450SL 17.3 8 180
## Merc 450SLC 15.2 8 180
## Cadillac Fleetwood 10.4 8 205
## Lincoln Continental 10.4 8 215
## Chrysler Imperial 14.7 8 230
## Fiat 128 32.4 4 66
## Honda Civic 30.4 4 52
## Toyota Corolla 33.9 4 65
## Toyota Corona 21.5 4 97
## Dodge Challenger 15.5 8 150
## AMC Javelin 15.2 8 150
## Camaro Z28 13.3 8 245
## Pontiac Firebird 19.2 8 175
## Fiat X1-9 27.3 4 66
## Porsche 914-2 26.0 4 91
## Lotus Europa 30.4 4 113
## Ford Pantera L 15.8 8 264
## Ferrari Dino 19.7 6 175
## Maserati Bora 15.0 8 335
## Volvo 142E 21.4 4 109mtcars dataset to only include cars with
more than 6 cylinders.# Your code here
filter(mtcars, cyl>6)## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8mtcars dataset called
hp_per_cyl which is the horsepower per cylinder.# Your code here
mutate(mtcars, hp_per_cyl = hp / cyl)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## hp_per_cyl
## Mazda RX4 18.33333
## Mazda RX4 Wag 18.33333
## Datsun 710 23.25000
## Hornet 4 Drive 18.33333
## Hornet Sportabout 21.87500
## Valiant 17.50000
## Duster 360 30.62500
## Merc 240D 15.50000
## Merc 230 23.75000
## Merc 280 20.50000
## Merc 280C 20.50000
## Merc 450SE 22.50000
## Merc 450SL 22.50000
## Merc 450SLC 22.50000
## Cadillac Fleetwood 25.62500
## Lincoln Continental 26.87500
## Chrysler Imperial 28.75000
## Fiat 128 16.50000
## Honda Civic 13.00000
## Toyota Corolla 16.25000
## Toyota Corona 24.25000
## Dodge Challenger 18.75000
## AMC Javelin 18.75000
## Camaro Z28 30.62500
## Pontiac Firebird 21.87500
## Fiat X1-9 16.50000
## Porsche 914-2 22.75000
## Lotus Europa 28.25000
## Ford Pantera L 33.00000
## Ferrari Dino 29.16667
## Maserati Bora 41.87500
## Volvo 142E 27.25000mtcars dataset by the mpg
column in descending order.# Your code here
arrange(mtcars, desc(mpg))## mpg cyl disp hp drat wt qsec vs am gear carb
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4mpg for each cylinder group.# Your code here
summarise(
group_by(mtcars, cyl),
avg_mpg = mean(mpg)
)## # A tibble: 3 × 2
## cyl avg_mpg
## <dbl> <dbl>
## 1 4 26.7
## 2 6 19.7
## 3 8 15.1empty_df.# Your code here
empty_df <- data.frame()
print(empty_df)## data frame with 0 columns and 0 rowsstudents_df from the
following vectors:
id = c(1, 2, 3)name = c("Alice", "Bob", "Charlie")score = c(85, 92, 78)# Your code here
students_df <- data.frame(
id = c(1, 2, 3),
name = c("Alice", "Bob", "Charlie"),
score = c(85, 92, 78)
)
print(students_df)## id name score
## 1 1 Alice 85
## 2 2 Bob 92
## 3 3 Charlie 78students_df data frame.# Your code here
str(students_df)## 'data.frame': 3 obs. of 3 variables:
## $ id : num 1 2 3
## $ name : chr "Alice" "Bob" "Charlie"
## $ score: num 85 92 78summary(students_df)## id name score
## Min. :1.0 Length:3 Min. :78.0
## 1st Qu.:1.5 Class :character 1st Qu.:81.5
## Median :2.0 Mode :character Median :85.0
## Mean :2.0 Mean :85.0
## 3rd Qu.:2.5 3rd Qu.:88.5
## Max. :3.0 Max. :92.0name column from students_df
using the $ operator.# Your code here
students_df$name## [1] "Alice" "Bob" "Charlie"students_df.# Your code here
students_df[1:2, ]## id name score
## 1 1 Alice 85
## 2 2 Bob 92name and
score columns from students_df.# Your code here
students_df[3, c("name", "score")]## name score
## 3 Charlie 78grade to students_df
where scores >= 90 are “A”, scores >= 80 are “B”, and others are
“C”. (Hint: use mutate with case_when).# Your code here
students_df <- mutate(
students_df,
grade = case_when(
score >= 90 ~ "A",
score >= 80 ~ "B",
TRUE ~ "C"
)
)
print(students_df)## id name score grade
## 1 1 Alice 85 B
## 2 2 Bob 92 A
## 3 3 Charlie 78 Cscore column from
students_df.# Your code here
students_df <- select(students_df, -score)
print(students_df)## id name grade
## 1 1 Alice B
## 2 2 Bob A
## 3 3 Charlie Cname column in students_df to
student_name.# Your code here
students_df <- rename(students_df, student_name = name)
print(students_df)## id student_name grade
## 1 1 Alice B
## 2 2 Bob A
## 3 3 Charlie Ccourses_df with
id = c(1, 2, 3) and
course = c("Math", "Science", "History"). Merge
students_df and courses_df by
id.# Your code here
courses_df <- data.frame(
id = c(1, 2, 3),
course = c("Math", "Science", "History")
)
merged_df <- left_join(students_df, courses_df, by = "id")
print(merged_df)## id student_name grade course
## 1 1 Alice B Math
## 2 2 Bob A Science
## 3 3 Charlie C Historydf1 and df2, with
identical columns. Combine them row-wise.# Your code here
df1 <- data.frame(A = 1:2, B = c("X", "Y"))
df2 <- data.frame(A = 3:4, B = c("Z", "W"))
combined_rows_df <- bind_rows(df1, df2)
print(df1)## A B
## 1 1 X
## 2 2 Yprint(df2)## A B
## 1 3 Z
## 2 4 Wprint(combined_rows_df)## A B
## 1 1 X
## 2 2 Y
## 3 3 Z
## 4 4 Wdf3 and df4, with
identical rows. Combine them column-wise.# Your code here
df3 <- data.frame(id = 1:2, C = c(10, 20))
df4 <- data.frame(id = 1:2, D = c("P", "Q"))
combined_cols_df <- bind_cols(df3, df4)## New names:
## • `id` -> `id...1`
## • `id` -> `id...3`print(df3)## id C
## 1 1 10
## 2 2 20print(df4)## id D
## 1 1 P
## 2 2 Qprint(combined_cols_df)## id...1 C id...3 D
## 1 1 10 1 P
## 2 2 20 2 Qmtcars dataset from wide to long format,
keeping cyl and vs as id variables and
gathering mpg, disp, hp into a
metric and value column.# Your code here
mtcars_long <- mtcars %>%
pivot_longer(
cols = c(mpg, disp, hp),
names_to = "metric",
values_to = "value"
)
print(head(mtcars_long))## # A tibble: 6 × 10
## cyl drat wt qsec vs am gear carb metric value
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 6 3.9 2.62 16.5 0 1 4 4 mpg 21
## 2 6 3.9 2.62 16.5 0 1 4 4 disp 160
## 3 6 3.9 2.62 16.5 0 1 4 4 hp 110
## 4 6 3.9 2.88 17.0 0 1 4 4 mpg 21
## 5 6 3.9 2.88 17.0 0 1 4 4 disp 160
## 6 6 3.9 2.88 17.0 0 1 4 4 hp 110mpg column in
mtcars.# Your code here
mean(mtcars$mpg)## [1] 20.09062mtcars.# Your code here
nrow(mtcars)## [1] 32ncol(mtcars)## [1] 11mtcars.# Your code here
colMeans(mtcars)## mpg cyl disp hp drat wt qsec
## 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750
## vs am gear carb
## 0.437500 0.406250 3.687500 2.812500mtcars.# Your code here
rowSums(mtcars)## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 328.980 329.795 259.580 426.135
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 590.310 385.540 656.920 270.980
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 299.570 350.460 349.660 510.740
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 511.500 509.850 728.560 726.644
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 725.695 213.850 195.165 206.955
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 273.775 519.650 506.085 646.280
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 631.175 208.215 272.570 273.683
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 670.690 379.590 694.710 288.890