In this module, we are going to learn how to use for loop/while loop
and iteration function introduced in purrr package.
Imagine we have this simple tibble:
df <- tibble(
a = rnorm(10),
b = rnorm(10),
c = rnorm(10),
d = rnorm(10)
)We want to compute the median of each column. You could do with copy-and-paste:
median(df$a)## [1] 0.1170117median(df$b)## [1] -0.2833646median(df$c)## [1] 0.1142113median(df$d)## [1] 0.4670428But that breaks our rule of thumb: never copy and paste more than twice. Instead, we could use a for loop:
output <- vector("double", ncol(df)) # 1. output
for (i in seq_along(df)) { # 2. sequence
output[[i]] <- median(df[[i]]) # 3. body
}
output## [1] 0.1170117 -0.2833646 0.1142113 0.4670428The template of for loops in R is similar to that in Python:
for (<iter_var> in <iterable>) {
# Body
}Here the seq_along() function gives a vector in indices
of the given vector or list
a = letters # The character vector of a-z
seq_along(a)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26seq_along(mpg) # The indices are for each element of a list - in this case each column## [1] 1 2 3 4 5 6 7 8 9 10 11Every for loop has three components:
The output:
output <- vector("double", length(x)). Before you start
the loop, you must always allocate sufficient space for the output. This
is very important for efficiency: if you grow the for loop at each
iteration using c() (for example), your for loop will be
very slow. A general way of creating an empty vector of given length is
the vector() function. It has two arguments: the type of the vector
(“logical”, “integer”, “double”, “character”, etc) and the length of the
vector.
The sequence: i in seq_along(df).
This determines what to loop over: each run of the for loop will assign
i to a different value from seq_along(df). You
might not have seen seq_along() before. It’s a safe version
of the familiar 1:length(l), with an important difference:
if you have a zero-length vector, seq_along() does the
right thing:
y <- vector("double", 0)
seq_along(y)## integer(0)1:length(y)## [1] 1 0output[[i]] <- median(df[[i]]). This is the code that
does the work. It’s run repeatedly, each time with a different value for
i. The first iteration will run
output[[1]] <- median(df[[1]]), the second will run
output[[2]] <- median(df[[2]]), and so on. Here we use
[[]] since we want to work on the elements of a
list/vector.That’s all there is to the for loop! Now is a good time to practice creating some basic (and not so basic) for loops using the exercises below. Then we’ll move on some variations of the for loop that help you solve other problems that will crop up in practice.
nycflights13::flights.iris.out <- ""
for (x in letters) {
out <- stringr::str_c(out, x)
}Once you have the basic for loop under your belt, there are some variations that you should be aware of. These variations are important regardless of how you do iteration, so don’t forget about them once you’ve mastered the FP techniques you’ll learn about in the next section.
There are four variations on the basic theme of the for loop:
Sometimes you want to use a for loop to modify an existing object. For example, remember our challenge from functions. We wanted to rescale every column in a data frame:
df <- tibble(
a = rnorm(10),
b = rnorm(10),
c = rnorm(10),
d = rnorm(10)
)
rescale01 <- function(x) {
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / (rng[2] - rng[1])
}
df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)To solve this with a for loop we again think about the three components:
Output: we already have the output — it’s the same as the input!
Sequence: we can think about a data frame as a
list of columns, so we can iterate over each column with
seq_along(df).
Body: apply rescale01().
This gives us:
for (i in seq_along(df)) {
df[[i]] <- rescale01(df[[i]])
}There are three basic ways to loop over a vector. So far I’ve shown
you the most general: looping over the numeric indices with for
(i in seq_along(xs)), and extracting the value with
x[[i]]. There are two other forms:
Loop over the elements: for (x in xs). This is most
useful if you only care about side-effects, like plotting or saving a
file, because it’s difficult to save the output efficiently since we
don’t have the indices.
Loop over the names: for (nm in names(xs)). This
gives you name, which you can use to access the value with
x[[nm]]. This is useful if you want to use the name in a
plot title or a file name. If you’re creating named output, make sure to
name the results vector like so:
results <- vector("list", length(mpg))
names(results) <- names(mpg)
for (nm in names(mpg)) {
results[[nm]] = is.numeric(mpg[[nm]]) # Output whether each column in "mpg" is a numeric one or not
}
results## $manufacturer
## [1] FALSE
##
## $model
## [1] FALSE
##
## $displ
## [1] TRUE
##
## $year
## [1] TRUE
##
## $cyl
## [1] TRUE
##
## $trans
## [1] FALSE
##
## $drv
## [1] FALSE
##
## $cty
## [1] TRUE
##
## $hwy
## [1] TRUE
##
## $fl
## [1] FALSE
##
## $class
## [1] FALSEIteration over the numeric indices is the most general form, because given the position you can extract both the name and the value:
for (i in seq_along(x)) {
name <- names(x)[[i]]
value <- x[[i]]
}Sometimes you might not know how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:
means <- c(0, 1, 2)
output <- double()
for (i in seq_along(means)) {
n <- sample(100, 1)
output <- c(output, rnorm(n, means[[i]]))
}
str(output)## num [1:248] 0.6235 0.0155 0.3147 -0.7452 0.7381 ...But this is not very efficient in terms of the computational time. A better solution to save the results in a list, and then combine into a single vector after the loop is done:
out <- vector("list", length(means))
for (i in seq_along(means)) {
n <- sample(100, 1)
out[[i]] <- rnorm(n, means[[i]])
}
str(out)## List of 3
## $ : num [1:30] 1.6 -1.72 1.13 1.52 -1.69 ...
## $ : num [1:97] 0.461 1.554 -0.808 -0.962 -0.195 ...
## $ : num [1:78] 1.66 3.02 2.38 1.19 3.23 ...str(unlist(out))## num [1:205] 1.6 -1.72 1.13 1.52 -1.69 ...Here we’ve used unlist() to flatten a list of vectors
into a single vector. A stricter option is to use
purrr::flatten_dbl() — it will throw an error if the input
isn’t a list of doubles.
Given an example, you might be generating a big data frame. Instead
of sequentially binding things in each iteration, save the output in a
list, then use dplyr::bind_rows(output) to combine the
output into a single data frame.
x <- letters
result <- vector("list", length(x))
for (i in seq_along(x)) {
result[[i]] = tibble(x1 = x[[i]], x2 = str_c(x[[i]], x[[i]]), x3 = str_c(x[[i]], x[[i]], x[[i]]))
}
bind_rows(result)## # A tibble: 26 × 3
## x1 x2 x3
## <chr> <chr> <chr>
## 1 a aa aaa
## 2 b bb bbb
## 3 c cc ccc
## 4 d dd ddd
## 5 e ee eee
## 6 f ff fff
## 7 g gg ggg
## 8 h hh hhh
## 9 i ii iii
## 10 j jj jjj
## # … with 16 more rowsSometimes you don’t even know how long the input sequence should run for. This is common when doing simulations. For example, you might want to loop until you get three heads in a row. You can’t do that sort of iteration with the for loop. Instead, you can use a while loop. A while loop is simpler than for loop because it only has two components, a condition and a body:
while (condition) {
# body
}A while loop is also more general than a for loop, because you can rewrite any for loop as a while loop, but you can’t rewrite every while loop as a for loop:
for (i in seq_along(x)) {
# body
}
# Equivalent to
i <- 1
while (i <= length(x)) {
# body
i <- i + 1
}Here’s how we could use a while loop to find how many tries it takes to get three heads in a row:
flip <- function() sample(c("T", "H"), 1)
flips <- 0
nheads <- 0
while (nheads < 3) {
if (flip() == "H") {
nheads <- nheads + 1
} else {
nheads <- 0
}
flips <- flips + 1
}
flips## [1] 19Here the sample function samples from two possible
outcomes “T” and “H” (of equal chance) with the sample size of one. So
each time we run flip(), we simulate tossing a fair
coin.
For loops are not as important in R as they are in other languages because R is a functional programming language. This means that it’s possible to wrap up for loops in a function, and call that function instead of using the for loop directly.
To see why this is important, consider (again) this simple data frame:
df <- tibble(
a = rnorm(10),
b = rnorm(10),
c = rnorm(10),
d = rnorm(10)
)Imagine you want to compute the mean of every column. You could do that with a for loop:
output <- vector("double", length(df))
for (i in seq_along(df)) {
output[[i]] <- mean(df[[i]])
}
output## [1] 0.11153971 0.09223274 -0.13152282 0.08231529You realise that you’re going to want to compute the means of every column pretty frequently, so you extract it out into a function:
col_mean <- function(df) {
output <- vector("double", length(df))
for (i in seq_along(df)) {
output[i] <- mean(df[[i]])
}
output
}Now we can use the function onto any data frames
col_mean(df)## [1] 0.11153971 0.09223274 -0.13152282 0.08231529col_mean(mtcars)## [1] 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250
## [7] 17.848750 0.437500 0.406250 3.687500 2.812500But then you think it’d also be helpful to be able to compute the
median, and the standard deviation, so you copy and paste your
col_mean() function and replace the mean()
with median() and sd():
col_median <- function(df) {
output <- vector("double", length(df))
for (i in seq_along(df)) {
output[i] <- median(df[[i]])
}
output
}
col_sd <- function(df) {
output <- vector("double", length(df))
for (i in seq_along(df)) {
output[i] <- sd(df[[i]])
}
output
}Obviously, it is not convenient to define so many different functions for each type of summary. So we will consider generalising this into a single function:
col_summary <- function(df, fun) {
out <- vector("double", length(df))
for (i in seq_along(df)) {
out[i] <- fun(df[[i]])
}
out
}
col_summary(df, median)## [1] 0.2972156 0.5550991 -0.4963495 -0.1139062col_summary(df, mean)## [1] 0.11153971 0.09223274 -0.13152282 0.08231529In the code above, the function fun itself becomes an
argument of another function col_summary. This is what we
refer to as functional programming.
In the next, we’ll learn about and use the purrr
package, which provides functions that eliminate the need for many
common for loops. The apply family of functions in base R
(apply(), lapply(), tapply(),
etc) solve a similar problem, but purrr is more consistent and thus is
easier to learn.
map functionThe pattern of looping over a vector, doing something to each element and saving the results is so common that the purrr package provides a family of functions to do it for you. There is one function for each type of output:
map() makes a list.map_lgl() makes a logical vector.map_int() makes an integer vector.map_dbl() makes a double vector.map_chr() makes a character vector.Each function takes two key inputs. The first input
is a vector, and the second one is a function name. The map
family applies a function to each element of the vector, and then
returns a new vector (or list) that’s the same length (and has the same
names) as the input.
For example, each data frame is a list with each element being a
column. Therefore map functions would apply the function to
each of the column.
df## # A tibble: 10 × 4
## a b c d
## <dbl> <dbl> <dbl> <dbl>
## 1 -0.367 -1.40 -0.522 1.91
## 2 0.139 -1.55 -2.06 1.01
## 3 -1.74 -1.93 1.23 0.380
## 4 -1.28 1.32 -1.01 -0.506
## 5 1.06 -0.794 -0.764 0.0795
## 6 0.456 1.43 0.526 -0.945
## 7 0.949 1.04 -0.957 -0.410
## 8 1.74 0.378 2.58 -0.307
## 9 0.734 0.732 -0.471 0.290
## 10 -0.575 1.69 0.128 -0.675map(df, mean)## $a
## [1] 0.1115397
##
## $b
## [1] 0.09223274
##
## $c
## [1] -0.1315228
##
## $d
## [1] 0.08231529map(df, median)## $a
## [1] 0.2972156
##
## $b
## [1] 0.5550991
##
## $c
## [1] -0.4963495
##
## $d
## [1] -0.1139062map(df, sd)## $a
## [1] 1.09702
##
## $b
## [1] 1.376884
##
## $c
## [1] 1.314803
##
## $d
## [1] 0.8622901The map function returns a list as the result. In this
example, we can also return a double vector using the
map_dbl() function:
map_dbl(df, mean)## a b c d
## 0.11153971 0.09223274 -0.13152282 0.08231529map_dbl(df, median)## a b c d
## 0.2972156 0.5550991 -0.4963495 -0.1139062map_dbl(df, sd)## a b c d
## 1.0970201 1.3768837 1.3148028 0.8622901The map family has some features to make it very
convenient to use. First, we may simply call additional arguments of the
function inside map functions.
map_dbl(df, mean, trim = 0.1)## a b c d
## 0.13980379 0.14536215 -0.22942871 -0.01756209Here trim is an argument for the mean
function to do the trimmed mean (trimming 10% of data from each end of
the data before computing the mean). But we can simply add them into
map_dbl() or any other map family
functions.
Second, there are a few shortcuts that you can use to replace the
function name. For example, when computing the number of NA
values in each column, we used a formula:
map_dbl(flights, ~sum(is.na(.)))## year month day dep_time sched_dep_time
## 0 0 0 8255 0
## dep_delay arr_time sched_arr_time arr_delay carrier
## 8255 8713 0 9430 0
## flight tailnum origin dest air_time
## 0 2512 0 0 9430
## distance hour minute time_hour
## 0 0 0 0These functions also nicely create names for the vector or list in
the output. Here the . refers to the current list
element.
As another example, we hope to compute the correlation coefficients
between mpg and all other variables in mtcars
data set, we can do the following:
mtcars## 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 2map_dbl(mtcars, ~cor(mtcars$mpg, .))## mpg cyl disp hp drat wt qsec
## 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.6811719 -0.8676594 0.4186840
## vs am gear carb
## 0.6640389 0.5998324 0.4802848 -0.5509251By doing this we see all the correlation coefficients in one shot!
What if there are some non-numeric columns? We can filter them out
and do the same thing. Let’s do the same for mpg data set.
First, we need to keep columns of numeric type only. We can easily do
this by using the map_lgl function that returns a vector of
logical values.
mpg## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # … with 224 more rowscolumn_numeric <- map_lgl(mpg, is.numeric) # See whether each column is numeric or not
mpg_numeric <- mpg[column_numeric] # Only keep numeric columns
mpg_numeric## # A tibble: 234 × 5
## displ year cyl cty hwy
## <dbl> <int> <int> <int> <int>
## 1 1.8 1999 4 18 29
## 2 1.8 1999 4 21 29
## 3 2 2008 4 20 31
## 4 2 2008 4 21 30
## 5 2.8 1999 6 16 26
## 6 2.8 1999 6 18 26
## 7 3.1 2008 6 18 27
## 8 1.8 1999 4 18 26
## 9 1.8 1999 4 16 25
## 10 2 2008 4 20 28
## # … with 224 more rowsNow we can do the same as above to get the correlation coefficients.
Let’s use cty as the measure of fuel efficiency.
map_dbl(mpg_numeric, ~cor(mpg_numeric$cty, .))## displ year cyl cty hwy
## -0.79852397 -0.03723229 -0.80577141 1.00000000 0.95591591It is obvious that hwy is highly correlated with
cty as expected, year has little to do with
fuel efficiency. And larger engines or more cylinders lead to lower fuel
efficiency.
The following codes compute the p-value for t-tests between Attrition
flag and all other numeric variables in the
BankChurners.csv data set.
bank_data <- read_csv("BankChurners.csv")
column_num <- map_lgl(bank_data, is.numeric) # See whether each column is numeric or not
bank_num <- bank_data[column_num] # Only keep numeric columns
bank_num## # A tibble: 10,127 × 14
## Customer_Age Depend…¹ Month…² Total…³ Month…⁴ Conta…⁵ Credi…⁶ Total…⁷ Avg_O…⁸
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 45 3 39 5 1 3 12691 777 11914
## 2 49 5 44 6 1 2 8256 864 7392
## 3 51 3 36 4 1 0 3418 0 3418
## 4 40 4 34 3 4 1 3313 2517 796
## 5 40 3 21 5 1 0 4716 0 4716
## 6 44 2 36 3 1 2 4010 1247 2763
## 7 51 4 46 6 1 3 34516 2264 32252
## 8 32 0 27 2 2 2 29081 1396 27685
## 9 37 3 36 5 2 0 22352 2517 19835
## 10 48 2 36 6 3 3 11656 1677 9979
## # … with 10,117 more rows, 5 more variables: Total_Amt_Chng_Q4_Q1 <dbl>,
## # Total_Trans_Amt <dbl>, Total_Trans_Ct <dbl>, Total_Ct_Chng_Q4_Q1 <dbl>,
## # Avg_Utilization_Ratio <dbl>, and abbreviated variable names
## # ¹Dependent_count, ²Months_on_book, ³Total_Relationship_Count,
## # ⁴Months_Inactive_12_mon, ⁵Contacts_Count_12_mon, ⁶Credit_Limit,
## # ⁷Total_Revolving_Bal, ⁸Avg_Open_To_Buymap_dbl(bank_num, ~t.test(.[bank_data$Attrition_Flag == "Existing Customer"], .[bank_data$Attrition_Flag == "Attrited Customer"])$p.value)## Customer_Age Dependent_count Months_on_book
## 5.771863e-02 5.251960e-02 1.603851e-01
## Total_Relationship_Count Months_Inactive_12_mon Contacts_Count_12_mon
## 3.225023e-48 1.717553e-60 6.687312e-89
## Credit_Limit Total_Revolving_Bal Avg_Open_To_Buy
## 1.642963e-02 7.089719e-113 9.771547e-01
## Total_Amt_Chng_Q4_Q1 Total_Trans_Amt Total_Trans_Ct
## 1.305897e-39 6.349082e-106 0.000000e+00
## Total_Ct_Chng_Q4_Q1 Avg_Utilization_Ratio
## 7.156056e-173 2.782074e-72The following codes compute the p-value for chi-square tests between
Attrition flag and all other categorical variables in the
BankChurners.csv data set.
bank_data <- read_csv("BankChurners.csv")
column_chr <- map_lgl(bank_data, is.character) # See whether each column is numeric or not
bank_chr <- bank_data[column_chr] # Only keep numeric columns
bank_chr## # A tibble: 10,127 × 6
## Attrition_Flag Gender Education_Level Marital_Status Income_Cate…¹ Card_…²
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Existing Customer M High School Married $60K - $80K Blue
## 2 Existing Customer F Graduate Single Less than $4… Blue
## 3 Existing Customer M Graduate Married $80K - $120K Blue
## 4 Existing Customer F High School Unknown Less than $4… Blue
## 5 Existing Customer M Uneducated Married $60K - $80K Blue
## 6 Existing Customer M Graduate Married $40K - $60K Blue
## 7 Existing Customer M Unknown Married $120K + Gold
## 8 Existing Customer M High School Unknown $60K - $80K Silver
## 9 Existing Customer M Uneducated Single $60K - $80K Blue
## 10 Existing Customer M Graduate Single $80K - $120K Blue
## # … with 10,117 more rows, and abbreviated variable names ¹Income_Category,
## # ²Card_Categoryresult <- map_dbl(bank_chr, ~chisq.test(bank_chr$Attrition_Flag, .)$p.value)
result## Attrition_Flag Gender Education_Level Marital_Status Income_Category
## 0.0000000000 0.0001963585 0.0514891315 0.1089126339 0.0250024257
## Card_Category
## 0.5252382798We can further filter columns that has a p-value lower than 0.05
result[result < 0.05]## Attrition_Flag Gender Income_Category
## 0.0000000000 0.0001963585 0.0250024257The way to filter a particula type of data above is not the best one.
A number of functions work with predicate functions
(such as is.factor, is.character etc.) that
return either a single TRUE or FALSE.
keep() and discard() keep elements of the
input where the predicate is TRUE or FALSE respectively:
mpg %>%
keep(is.character) %>%
print()## # A tibble: 234 × 6
## manufacturer model trans drv fl class
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 audi a4 auto(l5) f p compact
## 2 audi a4 manual(m5) f p compact
## 3 audi a4 manual(m6) f p compact
## 4 audi a4 auto(av) f p compact
## 5 audi a4 auto(l5) f p compact
## 6 audi a4 manual(m5) f p compact
## 7 audi a4 auto(av) f p compact
## 8 audi a4 quattro manual(m5) 4 p compact
## 9 audi a4 quattro auto(l5) 4 p compact
## 10 audi a4 quattro manual(m6) 4 p compact
## # … with 224 more rowsmpg %>%
discard(is.character) %>%
print()## # A tibble: 234 × 5
## displ year cyl cty hwy
## <dbl> <int> <int> <int> <int>
## 1 1.8 1999 4 18 29
## 2 1.8 1999 4 21 29
## 3 2 2008 4 20 31
## 4 2 2008 4 21 30
## 5 2.8 1999 6 16 26
## 6 2.8 1999 6 18 26
## 7 3.1 2008 6 18 27
## 8 1.8 1999 4 18 26
## 9 1.8 1999 4 16 25
## 10 2 2008 4 20 28
## # … with 224 more rowssome() and every() determine if the
predicate is true for any or for all of the elements.
some(mpg, is.character)## [1] TRUEevery(mpg, is_character)## [1] FALSEevery(mpg, is_vector)## [1] TRUEFor your midterm project data set, find all columns that have a p-value less than 0.05 when doing t-test or chi-square test with the target variable.