Functions

Writing your own functions

So far we’ve seen many functions, like c(), class(), filter(), dim() …

Why create your own functions?

Cut down on repetitive code (easier to fix things!)
Organize code into manageable chunks
Avoid running code unintentionally
Use names that make sense to you

Writing your own functions

The general syntax for a function is:

function_name <- function(arg1, arg2, ...) {
 <function body>
}

Writing your own functions

Here we will write a function that multiplies some number x by 2:

div_100 <- function(x) x / 100

When you run the line of code above, you make it ready to use (no output yet!). Let’s test it!

div_100(x = 600)

[1] 6

Writing your own functions: `{ }`

Adding the curly brackets - {} - allows you to use functions spanning multiple lines:

div_100 <- function(x) {
  x / 100
}
div_100(x = 10)

[1] 0.1

Writing your own functions: `return`

If we want something specific for the function’s output, we use return():

div_100_plus_4 <- function(x) {
  output_int <- x / 100
  output <- output_int + 4
  return(output)
}
div_100_plus_4(x = 10)

[1] 4.1

Writing your own functions: multiple inputs

Functions can take multiple inputs:

div_100_plus_y <- function(x, y) x / 100 + y
div_100_plus_y(x = 10, y = 3)

[1] 3.1

Writing your own functions: multiple outputs

Functions can return a vector (or other object) with multiple outputs.

x_and_y_plus_2 <- function(x, y) {
  output1 <- x + 2
  output2 <- y + 2

  return(c(output1, output2))
}
result <- x_and_y_plus_2(x = 10, y = 3)
result

[1] 12  5

Writing your own functions: defaults

Functions can have “default” arguments. This lets us use the function without using an argument later:

div_100_plus_y <- function(x = 10, y = 3) x / 100 + y
div_100_plus_y()

[1] 3.1

div_100_plus_y(x = 11, y = 4)

[1] 4.11

Writing another simple function

Let’s write a function, sqdif, that:

takes two numbers x and y with default values of 2 and 3.
takes the difference
squares this difference
then returns the final value

Writing another simple function

sqdif <- function(x = 2, y = 3) (x - y)^2

sqdif()

[1] 1

sqdif(x = 10, y = 5)

[1] 25

sqdif(10, 5)

[1] 25

sqdif(11, 4)

[1] 49

Writing your own functions: characters

Functions can have any kind of input. Here is a function with characters:

loud <- function(word) {
  output <- rep(toupper(word), 5)
  return(output)
}
loud(word = "hooray!")

[1] "HOORAY!" "HOORAY!" "HOORAY!" "HOORAY!" "HOORAY!"

Functions for tibbles - curly braces

# get means and missing for a specific column
get_summary <- function(dataset, col_name) {
    dataset %>%  
    summarise(mean = mean({{col_name}}, na.rm = TRUE),
              na_count = sum(is.na({{col_name}})))
}

Examples:

get_summary(calenviroscreen, CES4.0Score)

# A tibble: 1 × 2
   mean na_count
  <dbl>    <int>
1  28.3      103

get_summary(haa5, perc_pop_exposed_to_exceedances)

# A tibble: 1 × 2
    mean na_count
   <dbl>    <int>
1 0.0591       11

Summary

Simple functions take the form:
- NEW_FUNCTION <- function(x, y){x + y}
- Can specify defaults like function(x = 1, y = 2){x + y} -return will provide a value as output
- print will simply print the value on the screen but not save it

Lab Part 1

🏠 Class Website

💻 Lab

Functions on multiple columns

Using your custom functions: `sapply()`- a base R function

Now that you’ve made a function… You can “apply” functions easily with sapply()!

These functions take the form:

sapply(<a vector, list, data frame>, some_function)

CalEnviroScreen

This dataset was gathered by the California Office of Environmental Health Hazard Assessment. CalEnviroScreen ranks census tracts in California based on potential exposures to pollutants, adverse environmental conditions, socioeconomic factors and the prevalence of certain health conditions. Read more at https://calenviroscreen-oehha.hub.arcgis.com/.

head(calenviroscreen)

# A tibble: 6 × 67
  CensusTract CaliforniaCounty   ZIP Longitude Latitude ApproxLocation
        <dbl> <chr>            <int>     <dbl>    <dbl> <chr>         
1  6001400100 "Alameda "       94704     -122.     37.9 Oakland       
2  6001400200 "Alameda "       94618     -122.     37.8 Oakland       
3  6001400300 "Alameda "       94618     -122.     37.8 Oakland       
4  6001400400 "Alameda "       94609     -122.     37.8 Oakland       
5  6001400500 "Alameda "       94609     -122.     37.8 Oakland       
6  6001400600 "Alameda "       94609     -122.     37.8 Oakland       
# ℹ 61 more variables: CES4.0Score <dbl>, CES4.0Percentile <dbl>,
#   CES4.0PercRange <chr>, Ozone <dbl>, OzonePctl <dbl>, PM2.5 <dbl>,
#   PM2.5.Pctl <dbl>, DieselPM <dbl>, DieselPMPctl <dbl>, DrinkingWater <dbl>,
#   DrinkingWaterPctl <dbl>, Lead <dbl>, LeadPctl <dbl>, Pesticides <dbl>,
#   PesticidesPctl <dbl>, ToxRelease <dbl>, ToxReleasePctl <dbl>,
#   Traffic <dbl>, TrafficPctl <dbl>, CleanupSites <dbl>,
#   CleanupSitesPctl <dbl>, GroundwaterThreats <dbl>, …

Using your custom functions: `sapply()`

🚨 There are no parentheses on the functions! 🚨

You can also pipe into your function.

sapply(calenviroscreen, class) # also: calenviroscreen %>% sapply(class)

              CensusTract          CaliforniaCounty                       ZIP 
                "numeric"               "character"                 "integer" 
                Longitude                  Latitude            ApproxLocation 
                "numeric"                 "numeric"               "character" 
              CES4.0Score          CES4.0Percentile           CES4.0PercRange 
                "numeric"                 "numeric"               "character" 
                    Ozone                 OzonePctl                     PM2.5 
                "numeric"                 "numeric"                 "numeric" 
               PM2.5.Pctl                  DieselPM              DieselPMPctl 
                "numeric"                 "numeric"                 "numeric" 
            DrinkingWater         DrinkingWaterPctl                      Lead 
                "numeric"                 "numeric"                 "numeric" 
                 LeadPctl                Pesticides            PesticidesPctl 
                "numeric"                 "numeric"                 "numeric" 
               ToxRelease            ToxReleasePctl                   Traffic 
                "numeric"                 "numeric"                 "numeric" 
              TrafficPctl              CleanupSites          CleanupSitesPctl 
                "numeric"                 "numeric"                 "numeric" 
       GroundwaterThreats    GroundwaterThreatsPctl                  HazWaste 
                "numeric"                 "numeric"                 "numeric" 
             HazWastePctl            ImpWaterBodies        ImpWaterBodiesPctl 
                "numeric"                 "integer"                 "numeric" 
               SolidWaste            SolidWastePctl           PollutionBurden 
                "numeric"                 "numeric"                 "numeric" 
     PollutionBurdenScore       PollutionBurdenPctl                    Asthma 
                "numeric"                 "numeric"                 "numeric" 
               AsthmaPctl            LowBirthWeight        LowBirthWeightPctl 
                "numeric"                 "numeric"                 "numeric" 
    CardiovascularDisease CardiovascularDiseasePctl                  TotalPop 
                "numeric"                 "numeric"                 "integer" 
       ChildrenPercLess10             PopPerc10to64             ElderlyMore64 
                "numeric"                 "numeric"                 "numeric" 
             HispanicPerc                 WhitePerc            AfAmericanPerc 
                "numeric"                 "numeric"                 "numeric" 
       NativeAmericanPerc         AsianAmericanPerc         OtherMultiplePerc 
                "numeric"                 "numeric"                 "numeric" 
                  PopChar              PopCharScore               PopCharPctl 
                "numeric"                 "numeric"                 "numeric" 
                Education             EducationPctl            LinguisticIsol 
                "numeric"                 "numeric"                 "numeric" 
       LinguisticIsolPctl                   Poverty               PovertyPctl 
                "numeric"                 "numeric"                 "numeric" 
             Unemployment          UnemploymentPctl             HousingBurden 
                "numeric"                 "numeric"                 "numeric" 
        HousingBurdenPctl 
                "numeric"

Using your custom functions: `sapply()`

Use the div_100 function we created earlier to convert 0-100 percentiles to proportions.

calenviroscreen %>%
  select(ends_with("Pctl")) %>%
  sapply(div_100) %>%
  head()

     OzonePctl PM2.5.Pctl DieselPMPctl DrinkingWaterPctl LeadPctl
[1,]    0.0312     0.3627       0.3476            0.0421   0.0774
[2,]    0.0312     0.4197       0.9271            0.0421   0.6820
[3,]    0.0312     0.4390       0.8977            0.0421   0.6418
[4,]    0.0312     0.4281       0.7910            0.0421   0.6708
[5,]    0.0312     0.4281       0.6758            0.0421   0.6795
[6,]    0.0312     0.4281       0.8376            0.0421   0.6970
     PesticidesPctl ToxReleasePctl TrafficPctl CleanupSitesPctl
[1,]              0         0.5603      0.5594           0.5817
[2,]              0         0.5543      0.3749           0.0000
[3,]              0         0.5504      0.4248           0.1183
[4,]              0         0.5590      0.3800           0.0000
[5,]              0         0.5648      0.4868           0.3387
[6,]              0         0.5565      0.6706           0.2262
     GroundwaterThreatsPctl HazWastePctl ImpWaterBodiesPctl SolidWastePctl
[1,]                 0.5242       0.9252             0.2388         0.3572
[2,]                 0.8793       0.2851             0.0000         0.0000
[3,]                 0.8529       0.7407             0.0000         0.0000
[4,]                 0.9256       0.5189             0.0000         0.0000
[5,]                 0.8434       0.5640             0.0000         0.0000
[6,]                 0.7906       0.5827             0.0000         0.0000
     PollutionBurdenPctl AsthmaPctl LowBirthWeightPctl
[1,]              0.2662     0.0444             0.2306
[2,]              0.2418     0.0980             0.2792
[3,]              0.3337     0.2657             0.2162
[4,]              0.2624     0.5598             0.3702
[5,]              0.3140     0.8838             0.1900
[6,]              0.3694     0.9307             0.0503
     CardiovascularDiseasePctl PopCharPctl EducationPctl LinguisticIsolPctl
[1,]                    0.0142      0.0153        0.1255             0.0849
[2,]                    0.1453      0.0165        0.0042             0.0000
[3,]                    0.2011      0.1227        0.2412             0.5336
[4,]                    0.1428      0.1843        0.2029             0.0564
[5,]                    0.3887      0.3016        0.0740             0.1330
[6,]                    0.5278      0.3770        0.0973             0.0627
     PovertyPctl UnemploymentPctl HousingBurdenPctl
[1,]      0.1103               NA            0.1939
[2,]      0.1144           0.1711            0.0067
[3,]      0.1090           0.2941            0.0981
[4,]      0.3642           0.1066            0.3748
[5,]      0.3813           0.2820            0.3748
[6,]      0.2442           0.7167            0.5407

Using your custom functions “on the fly” to iterate

Also called “anonymous function”.

calenviroscreen %>%
  select(ends_with("Pctl")) %>%
  sapply(function(x) x / 100) %>%
  head()

     OzonePctl PM2.5.Pctl DieselPMPctl DrinkingWaterPctl LeadPctl
[1,]    0.0312     0.3627       0.3476            0.0421   0.0774
[2,]    0.0312     0.4197       0.9271            0.0421   0.6820
[3,]    0.0312     0.4390       0.8977            0.0421   0.6418
[4,]    0.0312     0.4281       0.7910            0.0421   0.6708
[5,]    0.0312     0.4281       0.6758            0.0421   0.6795
[6,]    0.0312     0.4281       0.8376            0.0421   0.6970
     PesticidesPctl ToxReleasePctl TrafficPctl CleanupSitesPctl
[1,]              0         0.5603      0.5594           0.5817
[2,]              0         0.5543      0.3749           0.0000
[3,]              0         0.5504      0.4248           0.1183
[4,]              0         0.5590      0.3800           0.0000
[5,]              0         0.5648      0.4868           0.3387
[6,]              0         0.5565      0.6706           0.2262
     GroundwaterThreatsPctl HazWastePctl ImpWaterBodiesPctl SolidWastePctl
[1,]                 0.5242       0.9252             0.2388         0.3572
[2,]                 0.8793       0.2851             0.0000         0.0000
[3,]                 0.8529       0.7407             0.0000         0.0000
[4,]                 0.9256       0.5189             0.0000         0.0000
[5,]                 0.8434       0.5640             0.0000         0.0000
[6,]                 0.7906       0.5827             0.0000         0.0000
     PollutionBurdenPctl AsthmaPctl LowBirthWeightPctl
[1,]              0.2662     0.0444             0.2306
[2,]              0.2418     0.0980             0.2792
[3,]              0.3337     0.2657             0.2162
[4,]              0.2624     0.5598             0.3702
[5,]              0.3140     0.8838             0.1900
[6,]              0.3694     0.9307             0.0503
     CardiovascularDiseasePctl PopCharPctl EducationPctl LinguisticIsolPctl
[1,]                    0.0142      0.0153        0.1255             0.0849
[2,]                    0.1453      0.0165        0.0042             0.0000
[3,]                    0.2011      0.1227        0.2412             0.5336
[4,]                    0.1428      0.1843        0.2029             0.0564
[5,]                    0.3887      0.3016        0.0740             0.1330
[6,]                    0.5278      0.3770        0.0973             0.0627
     PovertyPctl UnemploymentPctl HousingBurdenPctl
[1,]      0.1103               NA            0.1939
[2,]      0.1144           0.1711            0.0067
[3,]      0.1090           0.2941            0.0981
[4,]      0.3642           0.1066            0.3748
[5,]      0.3813           0.2820            0.3748
[6,]      0.2442           0.7167            0.5407

Anonymous functions: alternative syntax

calenviroscreen %>%
  select(ends_with("Pctl")) %>%
  sapply(\(x) x / 100) %>%
  head()

     OzonePctl PM2.5.Pctl DieselPMPctl DrinkingWaterPctl LeadPctl
[1,]    0.0312     0.3627       0.3476            0.0421   0.0774
[2,]    0.0312     0.4197       0.9271            0.0421   0.6820
[3,]    0.0312     0.4390       0.8977            0.0421   0.6418
[4,]    0.0312     0.4281       0.7910            0.0421   0.6708
[5,]    0.0312     0.4281       0.6758            0.0421   0.6795
[6,]    0.0312     0.4281       0.8376            0.0421   0.6970
     PesticidesPctl ToxReleasePctl TrafficPctl CleanupSitesPctl
[1,]              0         0.5603      0.5594           0.5817
[2,]              0         0.5543      0.3749           0.0000
[3,]              0         0.5504      0.4248           0.1183
[4,]              0         0.5590      0.3800           0.0000
[5,]              0         0.5648      0.4868           0.3387
[6,]              0         0.5565      0.6706           0.2262
     GroundwaterThreatsPctl HazWastePctl ImpWaterBodiesPctl SolidWastePctl
[1,]                 0.5242       0.9252             0.2388         0.3572
[2,]                 0.8793       0.2851             0.0000         0.0000
[3,]                 0.8529       0.7407             0.0000         0.0000
[4,]                 0.9256       0.5189             0.0000         0.0000
[5,]                 0.8434       0.5640             0.0000         0.0000
[6,]                 0.7906       0.5827             0.0000         0.0000
     PollutionBurdenPctl AsthmaPctl LowBirthWeightPctl
[1,]              0.2662     0.0444             0.2306
[2,]              0.2418     0.0980             0.2792
[3,]              0.3337     0.2657             0.2162
[4,]              0.2624     0.5598             0.3702
[5,]              0.3140     0.8838             0.1900
[6,]              0.3694     0.9307             0.0503
     CardiovascularDiseasePctl PopCharPctl EducationPctl LinguisticIsolPctl
[1,]                    0.0142      0.0153        0.1255             0.0849
[2,]                    0.1453      0.0165        0.0042             0.0000
[3,]                    0.2011      0.1227        0.2412             0.5336
[4,]                    0.1428      0.1843        0.2029             0.0564
[5,]                    0.3887      0.3016        0.0740             0.1330
[6,]                    0.5278      0.3770        0.0973             0.0627
     PovertyPctl UnemploymentPctl HousingBurdenPctl
[1,]      0.1103               NA            0.1939
[2,]      0.1144           0.1711            0.0067
[3,]      0.1090           0.2941            0.0981
[4,]      0.3642           0.1066            0.3748
[5,]      0.3813           0.2820            0.3748
[6,]      0.2442           0.7167            0.5407

across

Using functions in `mutate()` and `summarize()`

Already know how to use functions to modify columns using mutate() or calculate summary statistics using summarize().

Pesticides - pounds of selected active pesticide / square mile
Poverty - percent of population living below two times the federal poverty level
LowBirthWeight - Percent low birth weight

calenviroscreen %>%
  summarize(max_pest = max(Pesticides, na.rm = T),
            max_pov = max(Poverty, na.rm = T),
            low_bw = max(LowBirthWeight, na.rm = T))

# A tibble: 1 × 3
  max_pest max_pov low_bw
     <dbl>   <dbl>  <dbl>
1   80811.    96.7   13.7

Applying functions with `across` from `dplyr`

across() makes it easy to apply the same transformation to multiple columns. Usually used with summarize() or mutate().

summarize(across(<columns>,function))

mutate(across(<columns>,function))

List columns first : .cols =
List function next: .fns =
If there are arguments to a function (e.g., na.rm = TRUE), use an anonymous function.

Applying functions with `across` from `dplyr`

Combining with summarize()

calenviroscreen %>%
  summarize(across(
    c(Pesticides, Poverty, LowBirthWeight),
    mean # no parentheses
  ))

# A tibble: 1 × 3
  Pesticides Poverty LowBirthWeight
       <dbl>   <dbl>          <dbl>
1       268.      NA             NA

Applying functions with `across` from `dplyr`

Add anonymous function to include additional arguments (e.g., na.rm = T).

calenviroscreen %>%
  summarize(across(
    c(Pesticides, Poverty, LowBirthWeight),
    function(x) mean(x, na.rm = T)
  ))

# A tibble: 1 × 3
  Pesticides Poverty LowBirthWeight
       <dbl>   <dbl>          <dbl>
1       268.    31.3           5.00

Applying functions with `across` from `dplyr`

Can use with other tidyverse functions like group_by!

calenviroscreen %>%
  group_by(CaliforniaCounty) %>% 
  summarize(across(
    c(Pesticides, Poverty, LowBirthWeight),
    function(x) mean(x, na.rm = T)
  ))

# A tibble: 58 × 4
   CaliforniaCounty Pesticides Poverty LowBirthWeight
   <chr>                 <dbl>   <dbl>          <dbl>
 1 "Alameda "            0.948    22.1           5.26
 2 "Alpine "             0        38.9         NaN   
 3 "Amador "             2.01     25.2           4.45
 4 "Butte "            736.       39.9           4.64
 5 "Calaveras "          1.20     28.2           3.55
 6 "Colusa "          1186.       36.5           4.11
 7 "Contra Costa"       10.5      20.6           4.71
 8 "Del Norte"          47.4      48.4         NaN   
 9 "El Dorado"           5.50     20.9           4.28
10 "Fresno "           586.       45.8           5.96
# ℹ 48 more rows

Applying functions with `across` from `dplyr`

Using different tidyselect() options (e.g., starts_with(), ends_with(), contains())

calenviroscreen %>% 
  group_by(CaliforniaCounty) %>%
  summarize(across(contains("PM"), mean))

# A tibble: 58 × 5
   CaliforniaCounty PM2.5 PM2.5.Pctl DieselPM DieselPMPctl
   <chr>            <dbl>      <dbl>    <dbl>        <dbl>
 1 "Alameda "        8.87      31.9    0.350         66.4 
 2 "Alpine "         3.05       0.07   0.003          1.02
 3 "Amador "         8.01      18.9    0.0111         4.18
 4 "Butte "          8.22      23.0    0.106         33.1 
 5 "Calaveras "      8.12      22.6    0.0079         3.08
 6 "Colusa "         7.54      13.3    0.0292        10.6 
 7 "Contra Costa"    8.76      31.0    0.210         48.6 
 8 "Del Norte"       5.71       2.93   0.0301        11.1 
 9 "El Dorado"       6.78       8.91   0.0380        13.6 
10 "Fresno "        13.2       91.4    0.181         44.8 
# ℹ 48 more rows

Applying functions with `across` from `dplyr`

Combining with mutate() - the replace_na function

Here we will use the yearly_co2_emissions data from dasehr

replace_na({data frame}, {list of values}) or replace_na({vector}, {single value})

yearly_co2_emissions %>%
  select(country, starts_with("194")) %>%
  mutate(across(
    c(`1943`, `1944`, `1945`),
    function(x) replace_na(x, replace = 0)
  ))

# A tibble: 192 × 11
   country        `1940` `1941` `1942` `1943` `1944` `1945` `1946` `1947` `1948`
   <chr>           <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1 Afghanistan        NA     NA     NA      0      0      0     NA     NA     NA
 2 Albania           693    627    744    462    154    121    484    928    704
 3 Algeria           238    312    499    469    499    616    763    744    803
 4 Andorra            NA     NA     NA      0      0      0     NA     NA     NA
 5 Angola             NA     NA     NA      0      0      0     NA     NA     NA
 6 Antigua and B…     NA     NA     NA      0      0      0     NA     NA     NA
 7 Argentina       15900  14000  13500  14100  14000  13700  13700  14500  17400
 8 Armenia           848    745    513    655    613    649    730    878    935
 9 Australia       29100  34600  36500  35000  34200  32700  35500  38000  38500
10 Austria          7350   7980   8560   9620   9400   4570  12800  17600  24500
# ℹ 182 more rows
# ℹ 1 more variable: `1949` <dbl>

`purrr` package

Similar to across, purrr is a package that allows you to apply a function to multiple columns in a data frame or multiple data objects in a list.

While we won’t get into purrr too much in this class, its a handy package for you to know about should you get into a situation where you have an irregular list you need to handle!

Multiple Data Frames

Multiple data frames

Lists help us work with multiple data frames

AQ_list <- list(AQ1 = airquality, AQ2 = airquality, AQ3 = airquality)
str(AQ_list)

List of 3
 $ AQ1:'data.frame':    153 obs. of  6 variables:
  ..$ Ozone  : int [1:153] 41 36 12 18 NA 28 23 19 8 NA ...
  ..$ Solar.R: int [1:153] 190 118 149 313 NA NA 299 99 19 194 ...
  ..$ Wind   : num [1:153] 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
  ..$ Temp   : int [1:153] 67 72 74 62 56 66 65 59 61 69 ...
  ..$ Month  : int [1:153] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ Day    : int [1:153] 1 2 3 4 5 6 7 8 9 10 ...
 $ AQ2:'data.frame':    153 obs. of  6 variables:
  ..$ Ozone  : int [1:153] 41 36 12 18 NA 28 23 19 8 NA ...
  ..$ Solar.R: int [1:153] 190 118 149 313 NA NA 299 99 19 194 ...
  ..$ Wind   : num [1:153] 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
  ..$ Temp   : int [1:153] 67 72 74 62 56 66 65 59 61 69 ...
  ..$ Month  : int [1:153] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ Day    : int [1:153] 1 2 3 4 5 6 7 8 9 10 ...
 $ AQ3:'data.frame':    153 obs. of  6 variables:
  ..$ Ozone  : int [1:153] 41 36 12 18 NA 28 23 19 8 NA ...
  ..$ Solar.R: int [1:153] 190 118 149 313 NA NA 299 99 19 194 ...
  ..$ Wind   : num [1:153] 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
  ..$ Temp   : int [1:153] 67 72 74 62 56 66 65 59 61 69 ...
  ..$ Month  : int [1:153] 5 5 5 5 5 5 5 5 5 5 ...
  ..$ Day    : int [1:153] 1 2 3 4 5 6 7 8 9 10 ...

Multiple data frames: `sapply`

AQ_list %>% sapply(class)

         AQ1          AQ2          AQ3 
"data.frame" "data.frame" "data.frame"

AQ_list %>% sapply(nrow)

AQ1 AQ2 AQ3 
153 153 153

AQ_list %>% sapply(colMeans, na.rm = TRUE)

               AQ1        AQ2        AQ3
Ozone    42.129310  42.129310  42.129310
Solar.R 185.931507 185.931507 185.931507
Wind      9.957516   9.957516   9.957516
Temp     77.882353  77.882353  77.882353
Month     6.993464   6.993464   6.993464
Day      15.803922  15.803922  15.803922

Summary

Apply your functions with sapply(<a vector or list>, some_function)
Use across() to apply functions across multiple columns of data
Need to use across within summarize() or mutate()
Can use sapply or purrr to work with multiple data frames within lists simultaneously

Lab Part 2

🏠 Class Website

💻 Lab

The End

Image by Gerd Altmann from Pixabay

Writing your own functions

Writing your own functions

Writing your own functions

Writing your own functions: { }

Writing your own functions: return

Writing your own functions: multiple inputs

Writing your own functions: multiple outputs

Writing your own functions: defaults

Writing another simple function

Writing another simple function

Writing your own functions: characters

Functions for tibbles - curly braces

Summary

Lab Part 1

Functions on multiple columns

Using your custom functions: sapply()- a base R function

CalEnviroScreen

Using your custom functions: sapply()

Using your custom functions: sapply()

Using your custom functions “on the fly” to iterate

Anonymous functions: alternative syntax

across

Using functions in mutate() and summarize()

Applying functions with across from dplyr

Applying functions with across from dplyr

Applying functions with across from dplyr

Applying functions with across from dplyr

Applying functions with across from dplyr

Applying functions with across from dplyr

purrr package

Multiple Data Frames

Multiple data frames

Multiple data frames: sapply

Summary

Lab Part 2

Writing your own functions: `{ }`

Writing your own functions: `return`

Using your custom functions: `sapply()`- a base R function

Using your custom functions: `sapply()`

Using your custom functions: `sapply()`

Using functions in `mutate()` and `summarize()`

Applying functions with `across` from `dplyr`

Applying functions with `across` from `dplyr`

Applying functions with `across` from `dplyr`

Applying functions with `across` from `dplyr`

Applying functions with `across` from `dplyr`

Applying functions with `across` from `dplyr`

`purrr` package

Multiple data frames: `sapply`