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Introduction
Over the past several months, I have really started to increase the amount that I have been using scoped dplyr verbs. For those of you who don’t know about these functions, they are handy variants to the normal dplyr verbs, such as filter, mutate, and summarize, that allow you to target multiple columns or all of your columns. These functions allow for you to save yourself time and typing when you want to apply either one or multiple functions to more than one column, a group of columns, or to all of your columns. This post will walk through a few of the ones I use on a regular basis and how I use them!
These scoped verbs typically come in three different flavors:
_if– This allows you to target all columns that mean a specific condition_at– This allows you to target specific columns by name_all– As the name implies, this will apply a function to every column of the data set
Before we get started, let’s go ahead and load the libraries we will be using.
library(dplyr) library(ggplot2) library(tibble) library(stringr) library(gt) # for the sp500 dataset library(janitor)
_if
Let’s first take a look at mutate_if by looking at an example where we want to convert factors to character variables. The data set we will be using for this example is diamonds in the ggplot2 package.
diamonds ## # A tibble: 53,940 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 ## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 ## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 ## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 ## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ## # ... with 53,930 more rows
As we can see, there are three columns of factors in the data set (ord is just an ordered factor) – cut, color, and clarity. If you weren’t using scoped verbs, then you would convert them with something like this.
diamonds %>%
mutate(
cut = as.character(cut),
color = as.character(color),
clarity = as.character(clarity)
)
## # A tibble: 53,940 x 10
## carat cut color clarity depth table price x y z
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39
## # ... with 53,930 more rows
While this certainly works, it is easy to see how this method can get out of hand rather quickly. Now with the scoped variant, it is much cleaner. You have to pass a predicate function that will return TRUE or FALSE for the column (e.g. is.factor) and then it will apply the function (e.g. as.character) to all columns that return TRUE from the predicate.
diamonds %>% mutate_if(is.factor, as.character) ## # A tibble: 53,940 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 ## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 ## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 ## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 ## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ## # ... with 53,930 more rows
The conversion of factors (and other datatypes) is probably the thing I use mutate_if for the most, but you can use it for anything that has to be applied to all columns that meet certain conditions. If we wanted to add 10% of the mean of each numeric column to every value in that column (this is not practical, but just as an illustration) you could do the following.
diamonds %>% mutate_if(is.numeric, list(~.+0.1*mean(., na.rm = TRUE))) ## # A tibble: 53,940 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.310 Ideal E SI2 67.7 60.7 719. 4.52 4.55 2.78 ## 2 0.290 Premium E SI1 66.0 66.7 719. 4.46 4.41 2.66 ## 3 0.310 Good E VS1 63.1 70.7 720. 4.62 4.64 2.66 ## 4 0.370 Premium I VS2 68.6 63.7 727. 4.77 4.80 2.98 ## 5 0.390 Good J SI2 69.5 63.7 728. 4.91 4.92 3.10 ## 6 0.320 Very Good J VVS2 69.0 62.7 729. 4.51 4.53 2.83 ## 7 0.320 Very Good I VVS1 68.5 62.7 729. 4.52 4.55 2.82 ## 8 0.340 Very Good H SI1 68.1 60.7 730. 4.64 4.68 2.88 ## 9 0.300 Fair E VS2 71.3 66.7 730. 4.44 4.35 2.84 ## 10 0.310 Very Good H VS1 65.6 66.7 731. 4.57 4.62 2.74 ## # ... with 53,930 more rows
NOTE: The notations for passing either anonymous functions (such as above) or multiple functions has changed in the release of dplyr 0.8.0. Previously the above code would read mutate_if(is.numeric, funs(.+0.1*mean(., na.rm=TRUE))). The funs function has been soft deprecated in the new release. This means that it can still be used but the newer implementation should be used as it will either no longer be supported or will be removed later. Running the mutate_if call with funs results in the following warning message:
Warning message: funs() is soft deprecated as of dplyr 0.8.0 please use list() instead # Before: funs(name = f(.) # After: list(name = ~f(.)) This warning is displayed once per session.
In this example, the original columns are modified to represent the new value. If you wanted to create new columns for all of the columns that this predicate function applies to, you can give the function a name in our list. The name of the function is appended to the name of every column that it applies to with a _ as a separator.
diamonds %>%
mutate_if(is.numeric, list("new" = ~.+0.1*mean(., na.rm = TRUE)))
## # A tibble: 53,940 x 17
## carat cut color clarity depth table price x y z carat_new
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 0.310
## 2 0.21 Prem~ E SI1 59.8 61 326 3.89 3.84 2.31 0.290
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.310
## 4 0.290 Prem~ I VS2 62.4 58 334 4.2 4.23 2.63 0.370
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 0.390
## 6 0.24 Very~ J VVS2 62.8 57 336 3.94 3.96 2.48 0.320
## 7 0.24 Very~ I VVS1 62.3 57 336 3.95 3.98 2.47 0.320
## 8 0.26 Very~ H SI1 61.9 55 337 4.07 4.11 2.53 0.340
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 0.300
## 10 0.23 Very~ H VS1 59.4 61 338 4 4.05 2.39 0.310
## # ... with 53,930 more rows, and 6 more variables: depth_new <dbl>,
## # table_new <dbl>, price_new <dbl>, x_new <dbl>, y_new <dbl>,
## # z_new <dbl>
Similar to mutate_if, summarize_if/summarise_if works by allowing you to select all the columns that meet a certain condition and summarizing those columns with a given function. It should be noted that in this case, just as with summarize, a function has to be provided that will return only a single value. If you would like to see a way around that requirement, using purrr and scoped verbs, you can see here.
Let’s see how summarize_if works:
diamonds %>%
summarize_if(is.numeric, list("mean" = mean, "median" = median))
## # A tibble: 1 x 14
## carat_mean depth_mean table_mean price_mean x_mean y_mean z_mean
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.798 61.7 57.5 3933. 5.73 5.73 3.54
## # ... with 7 more variables: carat_median <dbl>, depth_median <dbl>,
## # table_median <dbl>, price_median <dbl>, x_median <dbl>,
## # y_median <dbl>, z_median <dbl>
We can also create more custom predicates to be used for our _if functions. Let’s create one that returns whether a column is numeric and has at least one value higher than 50.
higher_fifty <- function(x){
if (is.numeric(x)){
return(any(x > 50))
} else {
return(FALSE)
}
}
diamonds %>%
summarize_if(higher_fifty, list("mean" = mean))
## # A tibble: 1 x 4
## depth_mean table_mean price_mean y_mean
## <dbl> <dbl> <dbl> <dbl>
## 1 61.7 57.5 3933. 5.73
Another useful _if variant is select_if. You may want to select all of the numeric columns in the diamonds data set for further analysis, and select_if is perfect for this case. The implementation is the same as mutate_if and summarize_if, just instead of specifying a function to apply to the selected columns, you only specify a predicate function.
diamonds %>% select_if(is.numeric) ## # A tibble: 53,940 x 7 ## carat depth table price x y z ## <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 56.9 65 327 4.05 4.07 2.31 ## 4 0.290 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 62.8 57 336 3.94 3.96 2.48 ## 7 0.24 62.3 57 336 3.95 3.98 2.47 ## 8 0.26 61.9 55 337 4.07 4.11 2.53 ## 9 0.22 65.1 61 337 3.87 3.78 2.49 ## 10 0.23 59.4 61 338 4 4.05 2.39 ## # ... with 53,930 more rows
The last scoped verb for the _if variants is filter. filter_if is slightly different than the rest of the _if variants, because in addition to operating on columns in the data frame based on a condition, it can also operate on the rows of the data frame based on a condition. This row-wise operation is handled by the .vars_predicate argument in the scoped filter verbs. This argument is used in conjunction with the helper predicate functions all_vars and any_vars.
For this example, we will use the sp500 data set from the gt package. Let’s say that we want to filter all of the rows that did not have a value of greater than $2000 for the entire day. We can do that like this.
gt::sp500 %>% filter_if(is.numeric, all_vars(. > 2000)) ## # A tibble: 249 x 7 ## date open high low close volume adj_close ## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2015-12-31 2061. 2063. 2044. 2044. 2655330000 2044. ## 2 2015-12-30 2077. 2077. 2062. 2063. 2367430000 2063. ## 3 2015-12-29 2061. 2082. 2061. 2078. 2542000000 2078. ## 4 2015-12-28 2058. 2058. 2044. 2056. 2492510000 2056. ## 5 2015-12-24 2064. 2067. 2059. 2061. 1411860000 2061. ## 6 2015-12-23 2042. 2065. 2042. 2064. 3484090000 2064. ## 7 2015-12-22 2023. 2043. 2020. 2039. 3520860000 2039. ## 8 2015-12-21 2010. 2023. 2006. 2021. 3760280000 2021. ## 9 2015-12-18 2041. 2041. 2005. 2006. 6683070000 2006. ## 10 2015-12-17 2074. 2076. 2042. 2042. 4327390000 2042. ## # ... with 239 more rows
all_vars requires that all the columns returning TRUE from the predicate meet the filter requirements. Conversely, any_vars requires that only one of the columns meets the specified requirements.
_at
Now we can take a look at another variant of the dplyr verbs that allows us to target specific columns, _at. These functions are super handy when you want to apply a function to numerous columns by name. For this example, lets use the ever useful mtcars data set with the row names moved to a column named cars. We will assign this modified tibble to cars_tbl.
cars_tbl <- mtcars %>%
rownames_to_column("car") %>%
as_tibble()
Let’s say that we want to normalize the mpg, hp, and drat columns from zero to one. We can do that by writing a simple function and applying it to each column, like this.
norm_dat <- function(x){
(x-min(x))/(max(x)-min(x))
}
cars_tbl %>%
mutate(
mpg = norm_dat(mpg),
hp = norm_dat(hp),
drat = norm_dat(drat)
)
## # A tibble: 32 x 12
## car mpg cyl disp hp drat wt qsec vs am gear carb
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Mazd~ 0.451 6 160 0.205 0.525 2.62 16.5 0 1 4 4
## 2 Mazd~ 0.451 6 160 0.205 0.525 2.88 17.0 0 1 4 4
## 3 Dats~ 0.528 4 108 0.145 0.502 2.32 18.6 1 1 4 1
## 4 Horn~ 0.468 6 258 0.205 0.147 3.22 19.4 1 0 3 1
## 5 Horn~ 0.353 8 360 0.435 0.180 3.44 17.0 0 0 3 2
## 6 Vali~ 0.328 6 225 0.187 0 3.46 20.2 1 0 3 1
## 7 Dust~ 0.166 8 360 0.682 0.207 3.57 15.8 0 0 3 4
## 8 Merc~ 0.596 4 147. 0.0353 0.429 3.19 20 1 0 4 2
## 9 Merc~ 0.528 4 141. 0.152 0.535 3.15 22.9 1 0 4 2
## 10 Merc~ 0.374 6 168. 0.251 0.535 3.44 18.3 1 0 4 4
## # ... with 22 more rows
Just like with the previous example of converting factors to characters, this certainly works but can quickly become cumbersome when applying the same function to multiple columns in a data set. mutate_at allows you to specifically target columns to apply a function to.
cars_tbl%>% mutate_at(vars(mpg, hp, drat), list(~norm_dat)) ## # A tibble: 32 x 12 ## car mpg cyl disp hp drat wt qsec vs am gear carb ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Mazd~ 0.451 6 160 0.205 0.525 2.62 16.5 0 1 4 4 ## 2 Mazd~ 0.451 6 160 0.205 0.525 2.88 17.0 0 1 4 4 ## 3 Dats~ 0.528 4 108 0.145 0.502 2.32 18.6 1 1 4 1 ## 4 Horn~ 0.468 6 258 0.205 0.147 3.22 19.4 1 0 3 1 ## 5 Horn~ 0.353 8 360 0.435 0.180 3.44 17.0 0 0 3 2 ## 6 Vali~ 0.328 6 225 0.187 0 3.46 20.2 1 0 3 1 ## 7 Dust~ 0.166 8 360 0.682 0.207 3.57 15.8 0 0 3 4 ## 8 Merc~ 0.596 4 147. 0.0353 0.429 3.19 20 1 0 4 2 ## 9 Merc~ 0.528 4 141. 0.152 0.535 3.15 22.9 1 0 4 2 ## 10 Merc~ 0.374 6 168. 0.251 0.535 3.44 18.3 1 0 4 4 ## # ... with 22 more rows
We can see how handy this can become and how much time this can save you if you are repeating the same operation on numerous columns within a tibble. An second, but equally advantageous, use of mutate_at is the deselection of columns to which a function should be applied. Say that we want to apply our normalization function to every column except car, vs and am, since they are binary columns. To do this we would use the same methods as your would if you were removing a column with select.
cars_tbl %>% mutate_at(vars(-c(car, vs, am)), list(~norm_dat)) ## # A tibble: 32 x 12 ## car mpg cyl disp hp drat wt qsec vs am gear ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Mazd~ 0.451 0.5 0.222 0.205 0.525 0.283 0.233 0 1 0.5 ## 2 Mazd~ 0.451 0.5 0.222 0.205 0.525 0.348 0.3 0 1 0.5 ## 3 Dats~ 0.528 0 0.0920 0.145 0.502 0.206 0.489 1 1 0.5 ## 4 Horn~ 0.468 0.5 0.466 0.205 0.147 0.435 0.588 1 0 0 ## 5 Horn~ 0.353 1 0.721 0.435 0.180 0.493 0.3 0 0 0 ## 6 Vali~ 0.328 0.5 0.384 0.187 0 0.498 0.681 1 0 0 ## 7 Dust~ 0.166 1 0.721 0.682 0.207 0.526 0.160 0 0 0 ## 8 Merc~ 0.596 0 0.189 0.0353 0.429 0.429 0.655 1 0 0.5 ## 9 Merc~ 0.528 0 0.174 0.152 0.535 0.419 1 1 0 0.5 ## 10 Merc~ 0.374 0.5 0.241 0.251 0.535 0.493 0.452 1 0 0.5 ## # ... with 22 more rows, and 1 more variable: carb <dbl>
Now let’s say that we want to get the mean, sd, median, and count of all values greater than the mean for the mpg, hp, and drat columns. We can do that using the summarize_at function.
cars_summary <- cars_tbl %>%
summarize_at(vars(mpg, hp, drat), list("mean" = mean, "sd" = sd, "median" = median, "n_higher_half" = ~sum(. > mean(.))))
cars_summary
## # A tibble: 1 x 12
## mpg_mean hp_mean drat_mean mpg_sd hp_sd drat_sd mpg_median hp_median
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 20.1 147. 3.60 6.03 68.6 0.535 19.2 123
## # ... with 4 more variables: drat_median <dbl>, mpg_n_higher_half <int>,
## # hp_n_higher_half <int>, drat_n_higher_half <int>
The above example demonstrates how you can simply pass a function name to be applied to the column, and how you can pass slightly more complex functions to the .funs argument using . notation.
_all
The _all variant works similarly to the other two, just now we are by default targeting all of the columns in the data frame. This can be extremely useful if you want to perform transformations on all of the columns in your data set or get summary variables for all of the columns. One place this may be useful is if you are fitting multivariate models and want to normalize all of your variables. Let’s show how these functions work with a quick example of both mutate_all and summarize_all.
First, if we want to normalize all of the columns in a data set, we can do that by applying the norm_dat function defined above with mutate_all. Let’s see what this looks like using the USArrests data set.
USArrests %>% mutate_all(norm_dat) ## Murder Assault UrbanPop Rape ## 1 0.74698795 0.654109589 0.4406780 0.35917313 ## 2 0.55421687 0.746575342 0.2711864 0.96124031 ## 3 0.43975904 0.852739726 0.8135593 0.61240310 ## 4 0.48192771 0.496575342 0.3050847 0.31524548 ## 5 0.49397590 0.791095890 1.0000000 0.86046512 ## 6 0.42771084 0.544520548 0.7796610 0.81136951 ## 7 0.15060241 0.222602740 0.7627119 0.09819121 ## 8 0.30722892 0.660958904 0.6779661 0.21963824 ## 9 0.87951807 0.993150685 0.8135593 0.63565891 ## 10 1.00000000 0.568493151 0.4745763 0.47803618 ## 11 0.27108434 0.003424658 0.8644068 0.33333333 ## 12 0.10843373 0.256849315 0.3728814 0.17829457 ## 13 0.57831325 0.698630137 0.8644068 0.43152455 ## 14 0.38554217 0.232876712 0.5593220 0.35400517 ## 15 0.08433735 0.037671233 0.4237288 0.10335917 ## 16 0.31325301 0.239726027 0.5762712 0.27648579 ## 17 0.53614458 0.219178082 0.3389831 0.23255814 ## 18 0.87951807 0.698630137 0.5762712 0.38501292 ## 19 0.07831325 0.130136986 0.3220339 0.01291990 ## 20 0.63253012 0.873287671 0.5932203 0.52971576 ## 21 0.21686747 0.356164384 0.8983051 0.23255814 ## 22 0.68072289 0.719178082 0.7118644 0.71834625 ## 23 0.11445783 0.092465753 0.5762712 0.19638243 ## 24 0.92168675 0.732876712 0.2033898 0.25322997 ## 25 0.49397590 0.455479452 0.6440678 0.54005168 ## 26 0.31325301 0.219178082 0.3559322 0.23514212 ## 27 0.21084337 0.195205479 0.5084746 0.23772610 ## 28 0.68674699 0.708904110 0.8305085 1.00000000 ## 29 0.07831325 0.041095890 0.4067797 0.05684755 ## 30 0.39759036 0.390410959 0.9661017 0.29715762 ## 31 0.63855422 0.821917808 0.6440678 0.64082687 ## 32 0.62048193 0.715753425 0.9152542 0.48578811 ## 33 0.73493976 1.000000000 0.2203390 0.22739018 ## 34 0.00000000 0.000000000 0.2033898 0.00000000 ## 35 0.39156627 0.256849315 0.7288136 0.36434109 ## 36 0.34939759 0.363013699 0.6101695 0.32816537 ## 37 0.24698795 0.390410959 0.5932203 0.56847545 ## 38 0.33132530 0.208904110 0.6779661 0.19638243 ## 39 0.15662651 0.441780822 0.9322034 0.02583979 ## 40 0.81927711 0.801369863 0.2711864 0.39276486 ## 41 0.18072289 0.140410959 0.2203390 0.14211886 ## 42 0.74698795 0.489726027 0.4576271 0.50645995 ## 43 0.71686747 0.534246575 0.8135593 0.47028424 ## 44 0.14457831 0.256849315 0.8135593 0.40310078 ## 45 0.08433735 0.010273973 0.0000000 0.10077519 ## 46 0.46385542 0.380136986 0.5254237 0.34625323 ## 47 0.19277108 0.342465753 0.6949153 0.48837209 ## 48 0.29518072 0.123287671 0.1186441 0.05167959 ## 49 0.10843373 0.027397260 0.5762712 0.09043928 ## 50 0.36144578 0.397260274 0.4745763 0.21447028
It is that easy! We can also create new names for the mutated columns in the same manner that was shown in the _if section.
USArrests %>%
mutate_all(list("norm" = norm_dat))
## Murder Assault UrbanPop Rape Murder_norm Assault_norm UrbanPop_norm
## 1 13.2 236 58 21.2 0.74698795 0.654109589 0.4406780
## 2 10.0 263 48 44.5 0.55421687 0.746575342 0.2711864
## 3 8.1 294 80 31.0 0.43975904 0.852739726 0.8135593
## 4 8.8 190 50 19.5 0.48192771 0.496575342 0.3050847
## 5 9.0 276 91 40.6 0.49397590 0.791095890 1.0000000
## 6 7.9 204 78 38.7 0.42771084 0.544520548 0.7796610
## 7 3.3 110 77 11.1 0.15060241 0.222602740 0.7627119
## 8 5.9 238 72 15.8 0.30722892 0.660958904 0.6779661
## 9 15.4 335 80 31.9 0.87951807 0.993150685 0.8135593
## 10 17.4 211 60 25.8 1.00000000 0.568493151 0.4745763
## 11 5.3 46 83 20.2 0.27108434 0.003424658 0.8644068
## 12 2.6 120 54 14.2 0.10843373 0.256849315 0.3728814
## 13 10.4 249 83 24.0 0.57831325 0.698630137 0.8644068
## 14 7.2 113 65 21.0 0.38554217 0.232876712 0.5593220
## 15 2.2 56 57 11.3 0.08433735 0.037671233 0.4237288
## 16 6.0 115 66 18.0 0.31325301 0.239726027 0.5762712
## 17 9.7 109 52 16.3 0.53614458 0.219178082 0.3389831
## 18 15.4 249 66 22.2 0.87951807 0.698630137 0.5762712
## 19 2.1 83 51 7.8 0.07831325 0.130136986 0.3220339
## 20 11.3 300 67 27.8 0.63253012 0.873287671 0.5932203
## 21 4.4 149 85 16.3 0.21686747 0.356164384 0.8983051
## 22 12.1 255 74 35.1 0.68072289 0.719178082 0.7118644
## 23 2.7 72 66 14.9 0.11445783 0.092465753 0.5762712
## 24 16.1 259 44 17.1 0.92168675 0.732876712 0.2033898
## 25 9.0 178 70 28.2 0.49397590 0.455479452 0.6440678
## 26 6.0 109 53 16.4 0.31325301 0.219178082 0.3559322
## 27 4.3 102 62 16.5 0.21084337 0.195205479 0.5084746
## 28 12.2 252 81 46.0 0.68674699 0.708904110 0.8305085
## 29 2.1 57 56 9.5 0.07831325 0.041095890 0.4067797
## 30 7.4 159 89 18.8 0.39759036 0.390410959 0.9661017
## 31 11.4 285 70 32.1 0.63855422 0.821917808 0.6440678
## 32 11.1 254 86 26.1 0.62048193 0.715753425 0.9152542
## 33 13.0 337 45 16.1 0.73493976 1.000000000 0.2203390
## 34 0.8 45 44 7.3 0.00000000 0.000000000 0.2033898
## 35 7.3 120 75 21.4 0.39156627 0.256849315 0.7288136
## 36 6.6 151 68 20.0 0.34939759 0.363013699 0.6101695
## 37 4.9 159 67 29.3 0.24698795 0.390410959 0.5932203
## 38 6.3 106 72 14.9 0.33132530 0.208904110 0.6779661
## 39 3.4 174 87 8.3 0.15662651 0.441780822 0.9322034
## 40 14.4 279 48 22.5 0.81927711 0.801369863 0.2711864
## 41 3.8 86 45 12.8 0.18072289 0.140410959 0.2203390
## 42 13.2 188 59 26.9 0.74698795 0.489726027 0.4576271
## 43 12.7 201 80 25.5 0.71686747 0.534246575 0.8135593
## 44 3.2 120 80 22.9 0.14457831 0.256849315 0.8135593
## 45 2.2 48 32 11.2 0.08433735 0.010273973 0.0000000
## 46 8.5 156 63 20.7 0.46385542 0.380136986 0.5254237
## 47 4.0 145 73 26.2 0.19277108 0.342465753 0.6949153
## 48 5.7 81 39 9.3 0.29518072 0.123287671 0.1186441
## 49 2.6 53 66 10.8 0.10843373 0.027397260 0.5762712
## 50 6.8 161 60 15.6 0.36144578 0.397260274 0.4745763
## Rape_norm
## 1 0.35917313
## 2 0.96124031
## 3 0.61240310
## 4 0.31524548
## 5 0.86046512
## 6 0.81136951
## 7 0.09819121
## 8 0.21963824
## 9 0.63565891
## 10 0.47803618
## 11 0.33333333
## 12 0.17829457
## 13 0.43152455
## 14 0.35400517
## 15 0.10335917
## 16 0.27648579
## 17 0.23255814
## 18 0.38501292
## 19 0.01291990
## 20 0.52971576
## 21 0.23255814
## 22 0.71834625
## 23 0.19638243
## 24 0.25322997
## 25 0.54005168
## 26 0.23514212
## 27 0.23772610
## 28 1.00000000
## 29 0.05684755
## 30 0.29715762
## 31 0.64082687
## 32 0.48578811
## 33 0.22739018
## 34 0.00000000
## 35 0.36434109
## 36 0.32816537
## 37 0.56847545
## 38 0.19638243
## 39 0.02583979
## 40 0.39276486
## 41 0.14211886
## 42 0.50645995
## 43 0.47028424
## 44 0.40310078
## 45 0.10077519
## 46 0.34625323
## 47 0.48837209
## 48 0.05167959
## 49 0.09043928
## 50 0.21447028
Now, if we wanted to summarize all of these columns, it would look like this.
USArrests %>% summarize_all(list(mean = mean, median = median)) ## Murder_mean Assault_mean UrbanPop_mean Rape_mean Murder_median ## 1 7.788 170.76 65.54 21.232 7.25 ## Assault_median UrbanPop_median Rape_median ## 1 159 66 20.1
Again, it is that easy!
Now, since the implementation is fairly similar to the other scoped variables, I won’t belabor the point. Instead, I will leave you will one other useful tool from the _all variants that I find helpful. Ordinarily, I prefer to work with my variable names in snake case. Luckily, the janitor package provides a great function, clean_names, to convert column names to all kinds of formats, snake case included. However, I find that when I want to share data with people not working in R, such as my manager, she does not want to see column names in snake case. Unfortunately, as far as I know, there is not a simply function, at this point, to convert R friendly column names back to title case. To accomplish this easily, I use the rename_all function. Let me know you an example with the iris data set after I have converted it to snake case with janitor::clean_names.
iris_tbl <- iris %>% as_tibble() %>% janitor::clean_names() iris_tbl ## # A tibble: 150 x 5 ## sepal_length sepal_width petal_length petal_width species ## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # ... with 140 more rows
As you can see, now the names are super R-friendly. However, we want them to be converted back to title case without the underscores. Let’s see how that can be done.
iris_tbl %>% rename_all(list(~stringr::str_to_title(stringr::str_replace(., "_", " ")))) %>% head() %>% knitr::kable()
| Sepal Length | Sepal Width | Petal Length | Petal Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
This, in my opinion, looks much nicer in a table that you are distributing outside of the R world.
Conclusion
The scoped verbs in the dplyr package are just one more example of why this package is so useful. These functions allow you to apply other functions to your data set across numerous columns without repeating yourself and have the potential to greatly speed up your workflow and reduce the amount of typing that is required if you start to use them in your code.
This post by no means covers all of the scoped verbs that are available, but rather just gives you a taste of how you implement different versions of them. If you have any questions or comments, please share them below!
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