Dplyr Without Hard-Coding the Variable Names

dplyr: how to avoid hard coding variable names when I need them all?

It really seems like everything() (newly fully exported) should do the trick, but it doesn't. Especially if you're going to be doing a lot of operations on all your columns, it may be worth it to make a list column with a vector of each row, on which you can easily call unique, max, etc. Here assembled with purrr, though you could do the same with apply(df, 1, list) %>% lapply(unlist):

library(purrr)

df1 <- df %>% 
    mutate(data = df %>% transpose() %>% map(unlist)) %>% 
    rowwise() %>% 
    filter(length(unique(data)) == 3)

df1
# Source: local data frame [210 x 4]
# Groups: <by row>
#   
#         X1    X2    X3      data
#      <int> <int> <int>    <list>
#   1      3     2     1 <int [3]>
#   2      4     2     1 <int [3]>
#   3      5     2     1 <int [3]>
#   4      6     2     1 <int [3]>
#   5      7     2     1 <int [3]>
#   6      2     3     1 <int [3]>
#   7      4     3     1 <int [3]>
#   8      5     3     1 <int [3]>
#   9      6     3     1 <int [3]>
#   10     7     3     1 <int [3]>
#   ..   ...   ...   ...       ...

df1 %>%
    rowwise() %>%
    filter(max(data) - min(data) == 2) %>%
    ungroup() %>%
    summarise(res = n() / nrow(df1)) %>%
    unlist %>%
    as.fractions()
# res 
# 1/7 

arranging columns using dplyr::select without hardcoding

By the sounds of it, you want to use select_ along with the .dots argument:

> table %>% select_(.dots = col.string)
  empcnt1 wage1 empcnt2 wage2 empcnt3 wage3 cnty
1     200    40     200    40     200    40    1
2     300    50     300    50     300    50    2
3     400    60     400    60     400    60    3

piping dplyr mutate with unknown variable name

You don't need enquo there. That's for turning a value passed as a parameter into a quosure. Instead you need to turn a string into a symbol. For that you can use as.name() or rlang::sym()

ff <- function(tt){

  # find the variable name
  if(any(colnames(tt)=="AD")){
    vv <- quo(AD)
  } else {
    vv <- colnames(tt) %>% .[.!="f1"]
    vv <- as.name(vv)
  }

  # make the mutate
  tt %>% mutate(!!quo_name(vv) := as.factor(!!vv))      
}

Using `dplyr::mutate()` to create several new variables from names specified in a vector

You don't need any quosures here because you are not dealing with quoted expressions. Just create vectors with appropriate names and splice them in. Here I use the fact that vectors of length 1 are recycled, but you could also create vectors of full length:

add_columns <- function(df, columns){
  new <- rep(NA_character_, length(columns))
  names(new) <- columns
  mutate(df, !!!new)
}

Casting unique features in column to variable names and dummy coding original features into variables in R

You can use cSplit_e from my "splitstackshape" package, like this:

library(splitstackshape)
cSplit_e(mydata, "NAMES", sep = ",", type = "character", fill = 0)
#   ID          NAMES NAMES_333 NAMES_4444 NAMES_456 NAMES_765
# 1  1 4444, 333, 456         1          1         1         0
# 2  2            333         1          0         0         0
# 3  3       456, 765         0          0         1         1

If you want to see the underlying function that is called when you use those arguments, you can look at splitstackshape:::charMat, which takes a list generated by strsplit and creates a matrix from it.

Calling the function directly would give you something like this:

splitstackshape:::charMat(
  lapply(strsplit(as.character(mydata$NAMES), ","), 
         function(x) gsub("^\\s+|\\s$", "", x)))
#      333 4444 456 765
# [1,]   1    1   1  NA
# [2,]   1   NA  NA  NA
# [3,]  NA   NA   1   1 

How to apply function row-by-row into a data frame using dplyr without hardcoding the column names

A concise base R option would be

dat$score <- do.call(pmax, dat)/rowSums(dat)

---

In tidyverse we can do

library(tidyverse)
dat %>% 
    mutate(score = do.call(pmax, .)/reduce(., `+`))
#   setosa versicolor virginica     score
#1     50          0         0 1.0000000
#2      0         11        36 0.7659574
#3      0         39        14 0.7358491

Variable name as argument in function - ggplot R

If you call plot_var_aantal with a string like "people" you need to evalute vari as a symbol on the righthand side with !!sym(vari) and on the lefthand side you need to put it into a glue specification "{vari}". The following code should work:

plot_var_aantal<-function(vari){
  eval(substitute(vari), MASTERDATA)
  MASTERDATA%>%
    group_by(COMPANY)%>%
    summarize(AMOUNT_COMP = (sum(AMOUNT, na.rm=TRUE)),
              Type=Type,
              "{vari}" := !!sym(vari)) %>% # changed this line 
    filter(!is.na(Type)) %>%
    ggplot(aes(!! sym(vari),AMOUNT_COMP ), na.rm = TRUE) + # changed this line
    geom_point()+
    facet_wrap(~Type,nrow=4)
  
}

However, without seeing your data it is hard to figure out what COMP_VAR1 = COMP_VAR1 is doing in your dplyr::summarise call. You are not using an aggregating function (like mean or paste(..., collapse = ",")) so the whole summarise is probably not summarising but returning data in the original length. Similarly the line "{vari}" := !!sym(vari) doesn't seem to make sense (although the non-standard evaluation when vari is a string, is correct).

---

Related Topics