Import your data

data("mtcars")
skimr::skim(mtcars)
Data summary
Name mtcars
Number of rows 32
Number of columns 11
_______________________
Column type frequency:
numeric 11
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
mpg 0 1 20.09 6.03 10.40 15.43 19.20 22.80 33.90 ▃▇▅▁▂
cyl 0 1 6.19 1.79 4.00 4.00 6.00 8.00 8.00 ▆▁▃▁▇
disp 0 1 230.72 123.94 71.10 120.83 196.30 326.00 472.00 ▇▃▃▃▂
hp 0 1 146.69 68.56 52.00 96.50 123.00 180.00 335.00 ▇▇▆▃▁
drat 0 1 3.60 0.53 2.76 3.08 3.70 3.92 4.93 ▇▃▇▅▁
wt 0 1 3.22 0.98 1.51 2.58 3.33 3.61 5.42 ▃▃▇▁▂
qsec 0 1 17.85 1.79 14.50 16.89 17.71 18.90 22.90 ▃▇▇▂▁
vs 0 1 0.44 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
am 0 1 0.41 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
gear 0 1 3.69 0.74 3.00 3.00 4.00 4.00 5.00 ▇▁▆▁▂
carb 0 1 2.81 1.62 1.00 2.00 2.00 4.00 8.00 ▇▂▅▁▁ 
mtcars %>% distinct(cyl)
##                   cyl
## Mazda RX4           6
## Datsun 710          4
## Hornet Sportabout   8
data <- read_excel("../00_data/MyData-Charts.xlsx")

data_simpler <- read_excel("../00_data/Datasimpler.xlsx")

Repeat the same operation over different columns of a data frame

Case of numeric variables

mtcars %>%
    
    # mutate(char_var = "A") %>%
    
    map(mean) # map(.x = ., .f = mean)
## $mpg
## [1] 20.09062
## 
## $cyl
## [1] 6.1875
## 
## $disp
## [1] 230.7219
## 
## $hp
## [1] 146.6875
## 
## $drat
## [1] 3.596563
## 
## $wt
## [1] 3.21725
## 
## $qsec
## [1] 17.84875
## 
## $vs
## [1] 0.4375
## 
## $am
## [1] 0.40625
## 
## $gear
## [1] 3.6875
## 
## $carb
## [1] 2.8125

Create your own function

muliply_by_factor <- function(x, factor) {x * factor}
10 %>% muliply_by_factor(factor = 2)
## [1] 20
mtcars %>% map(.x = ., .f = ~muliply_by_factor(x = .x, factor = 2))
## $mpg
##  [1] 42.0 42.0 45.6 42.8 37.4 36.2 28.6 48.8 45.6 38.4 35.6 32.8 34.6 30.4 20.8
## [16] 20.8 29.4 64.8 60.8 67.8 43.0 31.0 30.4 26.6 38.4 54.6 52.0 60.8 31.6 39.4
## [31] 30.0 42.8
## 
## $cyl
##  [1] 12 12  8 12 16 12 16  8  8 12 12 16 16 16 16 16 16  8  8  8  8 16 16 16 16
## [26]  8  8  8 16 12 16  8
## 
## $disp
##  [1] 320.0 320.0 216.0 516.0 720.0 450.0 720.0 293.4 281.6 335.2 335.2 551.6
## [13] 551.6 551.6 944.0 920.0 880.0 157.4 151.4 142.2 240.2 636.0 608.0 700.0
## [25] 800.0 158.0 240.6 190.2 702.0 290.0 602.0 242.0
## 
## $hp
##  [1] 220 220 186 220 350 210 490 124 190 246 246 360 360 360 410 430 460 132 104
## [20] 130 194 300 300 490 350 132 182 226 528 350 670 218
## 
## $drat
##  [1] 7.80 7.80 7.70 6.16 6.30 5.52 6.42 7.38 7.84 7.84 7.84 6.14 6.14 6.14 5.86
## [16] 6.00 6.46 8.16 9.86 8.44 7.40 5.52 6.30 7.46 6.16 8.16 8.86 7.54 8.44 7.24
## [31] 7.08 8.22
## 
## $wt
##  [1]  5.240  5.750  4.640  6.430  6.880  6.920  7.140  6.380  6.300  6.880
## [11]  6.880  8.140  7.460  7.560 10.500 10.848 10.690  4.400  3.230  3.670
## [21]  4.930  7.040  6.870  7.680  7.690  3.870  4.280  3.026  6.340  5.540
## [31]  7.140  5.560
## 
## $qsec
##  [1] 32.92 34.04 37.22 38.88 34.04 40.44 31.68 40.00 45.80 36.60 37.80 34.80
## [13] 35.20 36.00 35.96 35.64 34.84 38.94 37.04 39.80 40.02 33.74 34.60 30.82
## [25] 34.10 37.80 33.40 33.80 29.00 31.00 29.20 37.20
## 
## $vs
##  [1] 0 0 2 2 0 2 0 2 2 2 2 0 0 0 0 0 0 2 2 2 2 0 0 0 0 2 0 2 0 0 0 2
## 
## $am
##  [1] 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 2 2 2 2 2 2 2
## 
## $gear
##  [1]  8  8  8  6  6  6  6  8  8  8  8  6  6  6  6  6  6  8  8  8  6  6  6  6  6
## [26]  8 10 10 10 10 10  8
## 
## $carb
##  [1]  8  8  2  2  4  2  8  4  4  8  8  6  6  6  8  8  8  2  4  2  2  4  4  8  4
## [26]  2  4  4  8 12 16  4
mtcars %>% map_dfc(.x = ., .f = ~muliply_by_factor(x = .x, factor = 2))
## # A tibble: 32 × 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  42      12  320    220  7.8   5.24  32.9     0     2     8     8
##  2  42      12  320    220  7.8   5.75  34.0     0     2     8     8
##  3  45.6     8  216    186  7.7   4.64  37.2     2     2     8     2
##  4  42.8    12  516    220  6.16  6.43  38.9     2     0     6     2
##  5  37.4    16  720    350  6.3   6.88  34.0     0     0     6     4
##  6  36.2    12  450    210  5.52  6.92  40.4     2     0     6     2
##  7  28.6    16  720    490  6.42  7.14  31.7     0     0     6     8
##  8  48.8     8  293.   124  7.38  6.38  40       2     0     8     4
##  9  45.6     8  282.   190  7.84  6.3   45.8     2     0     8     4
## 10  38.4    12  335.   246  7.84  6.88  36.6     2     0     8     8
## # … with 22 more rows
mtcars %>% map(muliply_by_factor, factor = 2)
## $mpg
##  [1] 42.0 42.0 45.6 42.8 37.4 36.2 28.6 48.8 45.6 38.4 35.6 32.8 34.6 30.4 20.8
## [16] 20.8 29.4 64.8 60.8 67.8 43.0 31.0 30.4 26.6 38.4 54.6 52.0 60.8 31.6 39.4
## [31] 30.0 42.8
## 
## $cyl
##  [1] 12 12  8 12 16 12 16  8  8 12 12 16 16 16 16 16 16  8  8  8  8 16 16 16 16
## [26]  8  8  8 16 12 16  8
## 
## $disp
##  [1] 320.0 320.0 216.0 516.0 720.0 450.0 720.0 293.4 281.6 335.2 335.2 551.6
## [13] 551.6 551.6 944.0 920.0 880.0 157.4 151.4 142.2 240.2 636.0 608.0 700.0
## [25] 800.0 158.0 240.6 190.2 702.0 290.0 602.0 242.0
## 
## $hp
##  [1] 220 220 186 220 350 210 490 124 190 246 246 360 360 360 410 430 460 132 104
## [20] 130 194 300 300 490 350 132 182 226 528 350 670 218
## 
## $drat
##  [1] 7.80 7.80 7.70 6.16 6.30 5.52 6.42 7.38 7.84 7.84 7.84 6.14 6.14 6.14 5.86
## [16] 6.00 6.46 8.16 9.86 8.44 7.40 5.52 6.30 7.46 6.16 8.16 8.86 7.54 8.44 7.24
## [31] 7.08 8.22
## 
## $wt
##  [1]  5.240  5.750  4.640  6.430  6.880  6.920  7.140  6.380  6.300  6.880
## [11]  6.880  8.140  7.460  7.560 10.500 10.848 10.690  4.400  3.230  3.670
## [21]  4.930  7.040  6.870  7.680  7.690  3.870  4.280  3.026  6.340  5.540
## [31]  7.140  5.560
## 
## $qsec
##  [1] 32.92 34.04 37.22 38.88 34.04 40.44 31.68 40.00 45.80 36.60 37.80 34.80
## [13] 35.20 36.00 35.96 35.64 34.84 38.94 37.04 39.80 40.02 33.74 34.60 30.82
## [25] 34.10 37.80 33.40 33.80 29.00 31.00 29.20 37.20
## 
## $vs
##  [1] 0 0 2 2 0 2 0 2 2 2 2 0 0 0 0 0 0 2 2 2 2 0 0 0 0 2 0 2 0 0 0 2
## 
## $am
##  [1] 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 0 0 2 2 2 2 2 2 2
## 
## $gear
##  [1]  8  8  8  6  6  6  6  8  8  8  8  6  6  6  6  6  6  8  8  8  6  6  6  6  6
## [26]  8 10 10 10 10 10  8
## 
## $carb
##  [1]  8  8  2  2  4  2  8  4  4  8  8  6  6  6  8  8  8  2  4  2  2  4  4  8  4
## [26]  2  4  4  8 12 16  4

Repeat the same operation over different elements of a list

When you have a grouping variable (factor)

reg_coeff_tbl <- mtcars %>%
    
    # Split the data frame into a list by a factor
    split(.$cyl) %>%
    
    # Repeat the same operation over each element
    map(~lm(mpg ~ wt, data = .)) %>%
    
    # Return regression coefficients in a tidy tibble
    map(broom::tidy, conf.int = TRUE) %>%
    
    # Bind multiple data frames by row
    bind_rows(.id = "cyl") %>%
    
    # Filter for coefficient of interest
    filter(term == "wt")
reg_coeff_tbl %>%
    
    ggplot(aes(estimate, cyl)) +
    geom_point(aes(color = cyl), size = 3) +
    geom_errorbarh(aes(xmin=conf.low, xmax = conf.high)) +
    
    theme(legend.position = "none")

Create your own

Choose either one of the two cases above and apply it to your data

data %>%
    
    # mutate(char_var = "A") %>%
    
    map(max) # map(.x = ., .f = mean)
## $year
## [1] 2021
## 
## $months
## [1] "October-December"
## 
## $state
## [1] "Wyoming"
## 
## $colony_n
## [1] NA
## 
## $colony_max
## [1] "NA"
## 
## $colony_lost
## [1] NA
## 
## $colony_lost_pct
## [1] NA
## 
## $colony_added
## [1] "NA"
## 
## $colony_reno
## [1] "NA"
## 
## $colony_reno_pct
## [1] "NA"
## 
## $`Growth of colonies`
## [1] NA

Create your own function

muliply_by_factor <- function(x, factor) {x * factor}
10 %>% muliply_by_factor(factor = 2)
## [1] 20