Use Dplyr's Summarise_Each to Return One Row Per Function

use dplyr's summarise_each to return one row per function?

You can achieve a similar output combining the dplyr and tidyr packages.
Something along these lines can help

library(dplyr)
library(tidyr)

iris %>%
  select(matches("Petal")) %>%
  summarise_each(funs(min, max)) %>%
  gather(variable, value) %>%
  separate(variable, c("var", "stat"), sep = "\\_") %>%
  spread(var, value)
##   stat Petal.Length Petal.Width
## 1  max          6.9         2.5
## 2  min          1.0         0.1

Reshape results of summarize all efficiently

Here is one option.

library(tidyverse)

mtcars %>%
  gather(Original_Column, Value) %>%
  group_by(Original_Column) %>%
  summarise_all(., .funs = funs(mean, median, sd, max, min, n_distinct)) %>%
  gather(Function, Result, -Original_Column)

# # A tibble: 66 x 3
#    Original_Column Function  Result
#    <chr>           <chr>      <dbl>
#  1 am              mean       0.406
#  2 carb            mean       2.81 
#  3 cyl             mean       6.19 
#  4 disp            mean     231.   
#  5 drat            mean       3.60 
#  6 gear            mean       3.69 
#  7 hp              mean     147.   
#  8 mpg             mean      20.1  
#  9 qsec            mean      17.8  
# 10 vs              mean       0.438
# # ... with 56 more rows

How to use dplyr's coalesce function with group_by() to create one row per person with all values filled in?

We could reorder the values based on the NA elements and then slice the first row

library(dplyr)
test_dataset %>% 
  group_by(name) %>%
  dplyr::mutate(across(starts_with('score'), 
   ~ .x[order(is.na(.x))])) %>% 
  slice_head(n = 1) %>% 
  ungroup

-output

# A tibble: 4 × 3
  name   score1 score2
  <chr>   <dbl>  <dbl>
1 corey       2      5
2 justin      1      7
3 kate       NA     NA
4 sib         2      9

---

Or another option is to use complete.cases after rearranging

test_dataset %>% 
  group_by(name) %>%
  dplyr::mutate(across(starts_with('score'), 
   ~ .x[order(is.na(.x))])) %>% 
  filter(complete.cases(across(starts_with('score')))|row_number() == 1) %>%
   ungroup

-output

# A tibble: 4 × 3
  name   score1 score2
  <chr>   <dbl>  <dbl>
1 justin      1      7
2 corey       2      5
3 sib         2      9
4 kate       NA     NA

Better output with dplyr -- breaking functions and results

Instead of transposing (t) and changing the class types, after the summarise step, do a gather to change it to 'long' format and then spread it back after doing some modifications with separate and unite

library(tidyverse)
ds %>% 
  group_by(group) %>% 
  summarise_at(vars(iq, income, math),funs(mean, sd)) %>% 
  gather(key, val, iq_mean:math_sd) %>% 
  separate(key, into = c('key1', 'key2')) %>% 
  unite(group, group, key2) %>%
  spread(group, val)

dplyr summarise_each with na.rm

Following the links in the doc, it seems you can use funs(mean(., na.rm = TRUE)):

library(dplyr)
by_species <- iris %>% group_by(Species)
by_species %>% summarise_each(funs(mean(., na.rm = TRUE)))

Using Dplyr group_by and Summarise and a Custom Function to Calculate the Mode of Several Groups

We can use

 Mode <- function(x) {
  ux <- unique(x)
  if(!anyDuplicated(x)){
      NA_character_ } else { 
     tbl <-   tabulate(match(x, ux))
     toString(ux[tbl==max(tbl)])
 }
}

DF %>%
   group_by(Category) %>%
   summarise(NumberMode = Mode(Number))
#  Category NumberMode
#    <fctr>      <chr>
#1        A         22
#2        B     12, 14
#3        C       <NA>

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