Sums across columns within a row, while accounting for nonmissingness. Specify the desired columns by passing their explicit column names or by passing a regular expression to matches the column names.
row_sum(
d,
columns_to_average = character(0),
pattern = "",
new_column_name = "row_sum",
threshold_proportion = 0.75,
nonmissing_count_name = NA_character_,
verbose = FALSE
)The data.frame containing the values to sum. Required.
A character vector containing the columns
names to sum.
If empty, pattern is used to select columns. Optional.
A regular expression pattern passed to base::grep()
(with perl = TRUE). Optional
The name of the new column that represents the sum of the specified columns. Required.
Designates the minimum proportion of columns
that have a nonmissing values (within each row) in order to return a sum.
Required; defaults to to 0.75.
In other words, by default, if less than 75% of the specified
cells are missing within a row, the row sum will be NA.
If a non-NA value is passed,
a second column will be added to d that contains the row's count
of nonmissing items among the selected columns.
Must be a valid column name. Optional.
a logical value to designate if extra information is
displayed in the console,
such as which columns are matched by pattern.
The data.frame d,
with the additional column containing the row sum.
If a valid value is passed to nonmissing_count_name,
a second column will be added as well.
If the specified columns are all logicals or integers, the new column will be an integer. Otherwise the new column will be a double.
mtcars |>
OuhscMunge::row_sum(
columns_to_average = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_sum"
)
#> 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
#> engine_sum
#> Mazda RX4 170.0
#> Mazda RX4 Wag 170.0
#> Datsun 710 114.0
#> Hornet 4 Drive 266.0
#> Hornet Sportabout 370.0
#> Valiant 233.0
#> Duster 360 372.0
#> Merc 240D 153.7
#> Merc 230 147.8
#> Merc 280 178.6
#> Merc 280C 178.6
#> Merc 450SE 286.8
#> Merc 450SL 286.8
#> Merc 450SLC 286.8
#> Cadillac Fleetwood 484.0
#> Lincoln Continental 472.0
#> Chrysler Imperial 452.0
#> Fiat 128 84.7
#> Honda Civic 82.7
#> Toyota Corolla 77.1
#> Toyota Corona 126.1
#> Dodge Challenger 328.0
#> AMC Javelin 314.0
#> Camaro Z28 362.0
#> Pontiac Firebird 410.0
#> Fiat X1-9 85.0
#> Porsche 914-2 126.3
#> Lotus Europa 102.1
#> Ford Pantera L 363.0
#> Ferrari Dino 157.0
#> Maserati Bora 317.0
#> Volvo 142E 128.0
mtcars |>
OuhscMunge::row_sum(
columns_to_average = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_sum",
nonmissing_count_name = "engine_nonmissing_count"
)
#> 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
#> engine_sum engine_nonmissing_count
#> Mazda RX4 170.0 4
#> Mazda RX4 Wag 170.0 4
#> Datsun 710 114.0 4
#> Hornet 4 Drive 266.0 4
#> Hornet Sportabout 370.0 4
#> Valiant 233.0 4
#> Duster 360 372.0 4
#> Merc 240D 153.7 4
#> Merc 230 147.8 4
#> Merc 280 178.6 4
#> Merc 280C 178.6 4
#> Merc 450SE 286.8 4
#> Merc 450SL 286.8 4
#> Merc 450SLC 286.8 4
#> Cadillac Fleetwood 484.0 4
#> Lincoln Continental 472.0 4
#> Chrysler Imperial 452.0 4
#> Fiat 128 84.7 4
#> Honda Civic 82.7 4
#> Toyota Corolla 77.1 4
#> Toyota Corona 126.1 4
#> Dodge Challenger 328.0 4
#> AMC Javelin 314.0 4
#> Camaro Z28 362.0 4
#> Pontiac Firebird 410.0 4
#> Fiat X1-9 85.0 4
#> Porsche 914-2 126.3 4
#> Lotus Europa 102.1 4
#> Ford Pantera L 363.0 4
#> Ferrari Dino 157.0 4
#> Maserati Bora 317.0 4
#> Volvo 142E 128.0 4
if (require(tidyr))
tidyr::billboard |>
OuhscMunge::row_sum(
pattern = "^wk\\d{1,2}$",
new_column_name = "week_sum",
threshold_proportion = .1,
verbose = TRUE
) |>
dplyr::select(
artist,
date.entered,
week_sum,
)
#> Loading required package: tidyr
#>
#> Attaching package: ‘tidyr’
#> The following object is masked from ‘package:magrittr’:
#>
#> extract
#> The following columns will be summed:
#> - wk1
#> - wk2
#> - wk3
#> - wk4
#> - wk5
#> - wk6
#> - wk7
#> - wk8
#> - wk9
#> - wk10
#> - wk11
#> - wk12
#> - wk13
#> - wk14
#> - wk15
#> - wk16
#> - wk17
#> - wk18
#> - wk19
#> - wk20
#> - wk21
#> - wk22
#> - wk23
#> - wk24
#> - wk25
#> - wk26
#> - wk27
#> - wk28
#> - wk29
#> - wk30
#> - wk31
#> - wk32
#> - wk33
#> - wk34
#> - wk35
#> - wk36
#> - wk37
#> - wk38
#> - wk39
#> - wk40
#> - wk41
#> - wk42
#> - wk43
#> - wk44
#> - wk45
#> - wk46
#> - wk47
#> - wk48
#> - wk49
#> - wk50
#> - wk51
#> - wk52
#> - wk53
#> - wk54
#> - wk55
#> - wk56
#> - wk57
#> - wk58
#> - wk59
#> - wk60
#> - wk61
#> - wk62
#> - wk63
#> - wk64
#> - wk65
#> - wk66
#> - wk67
#> - wk68
#> - wk69
#> - wk70
#> - wk71
#> - wk72
#> - wk73
#> - wk74
#> - wk75
#> - wk76
#> # A tibble: 317 × 3
#> artist date.entered week_sum
#> <chr> <date> <dbl>
#> 1 2 Pac 2000-02-26 NA
#> 2 2Ge+her 2000-09-02 NA
#> 3 3 Doors Down 2000-04-08 1403
#> 4 3 Doors Down 2000-10-21 1342
#> 5 504 Boyz 2000-04-15 1012
#> 6 98^0 2000-08-19 753
#> 7 A*Teens 2000-07-08 NA
#> 8 Aaliyah 2000-01-29 1041
#> 9 Aaliyah 2000-03-18 533
#> 10 Adams, Yolanda 2000-08-26 1355
#> # ℹ 307 more rows
tidyr::billboard |>
OuhscMunge::row_sum(
pattern = "^wk\\d$",
new_column_name = "week_sum",
verbose = TRUE
) |>
dplyr::select(
artist,
date.entered,
week_sum,
)
#> The following columns will be summed:
#> - wk1
#> - wk2
#> - wk3
#> - wk4
#> - wk5
#> - wk6
#> - wk7
#> - wk8
#> - wk9
#> # A tibble: 317 × 3
#> artist date.entered week_sum
#> <chr> <date> <dbl>
#> 1 2 Pac 2000-02-26 598
#> 2 2Ge+her 2000-09-02 NA
#> 3 3 Doors Down 2000-04-08 567
#> 4 3 Doors Down 2000-10-21 601
#> 5 504 Boyz 2000-04-15 319
#> 6 98^0 2000-08-19 202
#> 7 A*Teens 2000-07-08 NA
#> 8 Aaliyah 2000-01-29 422
#> 9 Aaliyah 2000-03-18 259
#> 10 Adams, Yolanda 2000-08-26 606
#> # ℹ 307 more rows