Dplyr Pipes - How to Change the Original Dataframe

dplyr pipes - How to change the original dataframe

You can definitely do the assignment by using an idiom such as df <- df %>% ... or df %>% ... -> df. But you could also avoid redundancy (i.e., stating df twice) by using the magrittr compound assignment operator %<>% at the beginning of the pipe.

From the magrittr vignette:

The compound assignment pipe operator %<>% can be used as the first pipe in a chain. The effect will be that the result of the pipeline is assigned to the left-hand side object, rather than returning the result as usual.

So with your code, we can do

library(magrittr)  ## came with your dplyr install
df %<>% slice(-(1:3)) %>% select(-c(Col1, Col50, Col51))

This pipes df into the expression and updates df as the result.

Update: In the comments you note an issue setting the column names. Fortunately magrittr has provided functions for setting attributes in a pipe. Try the following.

df %<>% 
    set_colnames(sprintf("Col%d", 1:ncol(.))) %>% 
    slice(-(1:3)) %>%
    select(-c(Col1,Col50,Col51))

Note that since we have a data frame, we can also use setNames() (stats) or set_names() (magrittr) in place of set_colnames().

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Thanks to Steven Beaupre for adding the note from the vignette.

Change the column values withing dplyr pipes

Another option could be:

mtcars %<>%
 mutate_at(vars(1), ~ !!select(., 2) %>% pull() * 4)

   mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1   24   6 160.0 110 3.90 2.620 16.46  0  1    4    4
2   24   6 160.0 110 3.90 2.875 17.02  0  1    4    4
3   16   4 108.0  93 3.85 2.320 18.61  1  1    4    1
4   24   6 258.0 110 3.08 3.215 19.44  1  0    3    1
5   32   8 360.0 175 3.15 3.440 17.02  0  0    3    2
6   24   6 225.0 105 2.76 3.460 20.22  1  0    3    1
7   32   8 360.0 245 3.21 3.570 15.84  0  0    3    4
8   16   4 146.7  62 3.69 3.190 20.00  1  0    4    2
9   16   4 140.8  95 3.92 3.150 22.90  1  0    4    2
10  24   6 167.6 123 3.92 3.440 18.30  1  0    4    4

How to pipe in dplyr

Why not just check if theyre in there and filter them:

mdat %>% filter( sample %in% dge$samples$sample )

It's easier to understand and controll than a join and performance shouldn't be an issue.

How to update values in a dplyr pipe?

This can be taken care of with a nested ifelse statement, i.e,

library(dplyr)

dataset0 %>% 
  mutate(v1 = ifelse(people %in% c('father', 'mother', 'parents'), 'parents', 
             ifelse(people %in% c('girl', 'boy', 'children'), 'children', 'grandparents')))

#        people           v1
#1       father      parents
#2      parents      parents
#3       father      parents
#4     children     children
#5         girl     children
#6          boy     children
#7 grand father grandparents
#8 grand mother grandparents
#9 grandparents grandparents

How to rename all columns in a dataframe after a specified row, using dplyr pipe

We can use row_to_names from janitor

library(janitor)
library(dplyr)
df %>%
    row_to_names(row_number = 1) %>%
    type.convert(as.is = TRUE) %>%
    as_tibble
# A tibble: 3 x 4
#     H1    H2    H3    H4
#  <dbl> <dbl> <dbl> <dbl>
#1   0.9  2.17  2.59  2.24
#2   4.1  4.1   3.8   3.8 
#3   4    4.1   4.1   4.09

Change values of a column conditionally using pipe() function

Two approaches :

Either use as.character in ifelse

library(dplyr)
df %>% mutate(Col3 = ifelse(Col1 == "a", "aa", as.character(Col1)))

Or use stringsAsFactors = FALSE while constructing the dataframe.

df = data.frame("Col1" = letters[1:6], "Col2" = 1:6, stringsAsFactors = FALSE)

Transform data to data.frame with the pipe operator

After the transpose, convert to tibble with as_tibble and change the column names with set_names

library(dplyr)
library(tibble)
x %>% 
  t %>%
  as_tibble(.name_repair = "unique") %>%
  setNames(c("a", "b"))
# A tibble: 1 x 2
#      a     b
#  <int> <int>
#1     1     2

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Or another option if we want to use the OP's syntax would be to wrap the code with {}

x %>%
     {data.frame(a = .[1], b = .[2])} 

dplyr mutate in place

We can use the %<>% compound assignment operator from magrittr to change in place

library(magrittr)
df_test %<>% 
        mutate(a = round(a,0))

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If we are using data.table, the assignment (:=) operator does this in place too without copying

library(data.table)
setDT(df_test)[, a := round(a,0)]    

Extracting outputs from for loops with dplyr pipes into dataframe in R

something like this?

bind_rows(lapply(c("am", "vs"), function(i) {
  mtcars %>% 
    t_test(formula(paste0("mpg ~ ",i)),detailed=T) %>% 
    mutate(var = i)
}))

Output:

# A tibble: 2 × 16
  estimate estimate1 estimate2 .y.   group1 group2    n1    n2 statistic       p    df conf.low conf.high method alternative var  
     <dbl>     <dbl>     <dbl> <chr> <chr>  <chr>  <int> <int>     <dbl>   <dbl> <dbl>    <dbl>     <dbl> <chr>  <chr>       <chr>
1    -7.24      17.1      24.4 mpg   0      1         19    13     -3.77 0.00137  18.3    -11.3     -3.21 T-test two.sided   am   
2    -7.94      16.6      24.6 mpg   0      1         18    14     -4.67 0.00011  22.7    -11.5     -4.42 T-test two.sided   vs   

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