Error in fun left invalid argument to unary operator

This error commonly occurs when the is an unexpected use of a unary operator (+, -, !)

This error can occur if you provide an extra + when inputting a multiline ggplot2 command, for example,

ggplot(combined.data, aes(x = region, y = expression, fill = species)) +
   geom_boxplot() + 
   + ggtitle("Title")

You can solve this error by removing the extra + symbols.

The error can also occur if you try to order string columns using the minus sign – with order(). You can solve the error by using the decreasing argument in the order() function call.

This tutorial will go through the error in detail and how to solve it with code examples.

Example #1: Multiline ggplot2 command

Consider the following example, we want to use the gapminder dataset to plot life expectancy across all continents. First, we will install and load gapminder

install.packages("gapminder")
library(gapminder)
head(gapminder)

# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

Now that we have the dataset, we will attempt to create the life expectancy boxplots.

ggplot(gapminder, aes(x=continent, y=lifeExp, fill=continent)) + 
 geom_boxplot() + 
 + ggtitle("Life Expectancy Across Continents")

Let’s run the code to see what happens:

Error in +ggtitle("Life Expectancy Across Continents") : 
  invalid argument to unary operator

The error occurs because we have an extra plus-sign + symbol between geom_boxplot() and ggtitle. This typically happens when we copy and paste multiline code. The + sign indicates a new line.

Solution

We can solve this error by removing the extra + symbol. Let’s look at the revised code:

ggplot(gapminder, aes(x=continent, y=lifeExp, fill=continent)) + 
   geom_boxplot() +
   ggtitle("Life Expectancy Across Continents")

Let’s run the code to see the result:

We successfully plotted the box plots for life expectancy across the five continents.

Example #2: Sorting Data Frame String Column Descending Order

Let’s look at an example of a data frame with three variables, gender, age, and degree. The data frame describes a sample of degree holders.

population = 10
gender=sample(c("male","female"),population,replace=TRUE)
age = sample(25:75, population, replace=TRUE)
degree = sample(c("MA","MSci","BA","PhD"), population, replace=TRUE)
(final.data = data.frame(gender=gender, age=age, degree=degree))

gender age degree
1  female  59   MSci
2  female  41     MA
3  female  72     MA
4  female  32    PhD
5  female  43     BA
6    male  55   MSci
7    male  43     BA
8  female  43     BA
9  female  49    PhD
10   male  31     BA

If we want to sort by decreasing age, we can use the minus sign inside the order() function call as follows:

final.data[order(-final.data$age),]

 gender age degree
3  female  72     MA
1  female  59   MSci
6    male  55   MSci
9  female  49    PhD
5  female  43     BA
7    male  43     BA
8  female  43     BA
2  female  41     MA
4  female  32    PhD
10   male  31     BA

However, we cannot do this with the degree column because it is type character.

final.data[order(-final.data$degree),]

Error in -final.data$degree : invalid argument to unary operator

In R, we cannot provide a character type object as an argument to the unary operator -.

Solution

We can solve this error by using the decreasing argument in the order function call. Let’s look at the revised code:

final.data[order(final.data$degree, decreasing=TRUE),]

Let’s run the code to see the result:

gender age degree
4  female  32    PhD
9  female  49    PhD
1  female  59   MSci
6    male  55   MSci
2  female  41     MA
3  female  72     MA
5  female  43     BA
7    male  43     BA
8  female  43     BA
10   male  31     BA

We successfully sorted the data frame in descending order of the degree column.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on R related errors, go to the articles: 

• How to Solve R Error: mapping should be created with aes() or aes_()
• How to Solve R Error: Could not find function “%”
• How to Solve R Error: Incorrect number of subscripts on matrix

Go to the online courses page on R to learn more about coding in R for data science and machine learning.

Have fun and happy researching!

Я хочу добавить новый столбец, например, как эти таблицы меняются ниже. Где я сохраняю значения «член» и «случайный», но меняю «Подписчик» на «член» и «Клиент» на «случайный» в новом столбце. Я попытался использовать условную мутацию, но это дает мне ошибку «Ошибка в FUN (слева): недопустимый аргумент для унарного оператора».

Если у кого-то есть какие-либо идеи о том, как заставить это работать, я был бы очень признателен за ваш вклад!

Изменения к:

df < - df %>% 
  mutate(membership_type = case_when(member_casual == "member" ~ "member",
                                     member_casual == "casual" ~ "casual",
                                     member_casual == "Subscriber" ~ "member",
                                     member_casual ==  "Customer" ~ "casual"))

>>Error in FUN(left) : invalid argument to unary operator

#Create data frames nested in a list
for (i in 1:6) {
  for (j in 1:4) {
    assign(paste0("v", j), sample.int(100,4))
  }
  assign(paste0("df", i), list(cbind(v1, v2, v3, v4)))
}

inner1 <- list(data1 = df1, data2 = df2)
inner2 <- list(data1 = df3, data2 = df4)
inner3 <- list(data1 = df5, data2 = df6)

outer <- list(group1 = inner1, group2 = inner2, group3 = inner3)

data1.tot <- df1 + df3 + df5
data2.tot <- df2 + df4 + df6

grp <- c("group1", "group2", "group3") #vector of groups to sum across
datas <- lapply(outer, "[[", "data1") #select "data1" from all groups
tot.datas <- lapply(datas[grp], "+") #to sum across selected data
#I know these last two steps can be combined into one but it helps me keep everything straight to separate them

get.df <- function(x, y, z) {
# function to pull out the desired data.frame from the list
# x included as argument to make function applicable to my real data
  output <- x[[y]][[z]]
  output[[1]]
}

output1 <- get.df(x = outer, y = "group1", z = "data1")
output2 <- get.df(x = outer, y = "group2", z = "data1")
data1 <- list(output1, output2)
data1.tot <- Reduce(`+`, data1)

dat <- c("data1", "data2")
grp <- c("group1", "group2", "group3")

for(i in 1:length(dat)) {
  for(j in 1:length(grp)) {
    assign(paste0("out", j), get.df(x = outer, y = grp[j], z = dat[i]))
  }
list(??? #clearly this is where I'm stuck!
}

3 ответа

sum_df1 <- sum(unlist(lapply(outer, "[[", 1)))
sum_df2 <- sum(unlist(lapply(outer, "[[", 2)))

dfs <- seq(1 : 1000)
lapply(dfs, function(x) sum(unlist(lapply(outer, "[[", x))))

sapply(
  X = names(outer[[1]]),
  FUN = function(d) {
    Reduce(x = unlist(lapply(outer, "[[", d), recursive = F), f = "+")
  },
  simplify = F,
  USE.NAMES = T
)

data1.tot <- Reduce(`+`, lapply(outer, "[[", "data1"))

data2.tot <- Reduce(`+`, lapply(outer, "[[", "data2"))

set.seed(9262018)

dfList <- setNames(replicate(6, data.frame(NUM1=runif(50),
                                           NUM2=runif(50),
                                           NUM3=runif(50)), simplify = FALSE),
                   paste0("df", 1:6))

list2env(dfList, .GlobalEnv)

inner1 <- list(data1 = df1, data2 = df2)
inner2 <- list(data1 = df3, data2 = df4)
inner3 <- list(data1 = df5, data2 = df6)

outer <- list(group1 = inner1, group2 = inner2, group3 = inner3)

data1.tot <- Reduce(`+`, lapply(outer, "[[", "data1"))
head(data1.tot, 10)
#         NUM1      NUM2      NUM3
# 1  2.0533870 1.3821609 1.0702992
# 2  2.6046584 1.7260646 1.9699774
# 3  2.2510810 1.6690353 1.4495476
# 4  1.7636879 1.2357098 1.9483906
# 5  1.0189969 2.1191041 1.7466040
# 6  1.3933982 0.7541027 1.0971724
# 7  1.8058803 2.4608417 0.7291335
# 8  1.0763517 1.2494739 1.0480818
# 9  0.7069873 1.5496575 1.2264486
# 10 0.9522526 2.1407523 1.2597422

data2.tot <- Reduce(`+`, lapply(outer, "[[", "data2"))
head(data2.tot, 10)    
#         NUM1      NUM2      NUM3
# 1  1.7568578 0.9322930 1.5579897
# 2  0.9455063 0.9211592 1.7067779
# 3  1.2698614 0.4623059 0.9426310
# 4  1.6791964 1.4304953 1.2435480
# 5  0.8088625 2.6107952 1.2308862
# 6  1.8202400 2.3511104 1.5676112
# 7  0.9765578 0.8870206 0.6725699
# 8  2.6448770 1.8931751 1.8188512
# 9  1.6114870 1.8632245 0.7452924
# 10 0.9710550 1.8367305 2.0994788

all.equal(data1.tot, df1 + df3 + df5)
# [1] TRUE
all.equal(data2.tot, df2 + df4 + df6)
# [1] TRUE

identical(data1.tot, df1 + df3 + df5)
# [1] TRUE
identical(data2.tot, df2 + df4 + df6)
# [1] TRUE

I’m resorting to R to handling averageif() and maxif() like functions because my data set is too large and Excel keeps crashing.

I’m looking for a way to find the average wind based on status, and between columns Start Date and End Date from df. I’d imagine this would involve the between function in R.

status<-c("tropical depression", "tropical depression", "tropical storm")
Begin_Date <- as.Date(c("1974/06/01","1980/06/05","1990/06/07"))
End_Date <- as.Date(c("1975/06/01","1981/07/05","1991/08/07"))
df<-as.data.frame(cbind(status,Begin_Date,End_Date))
df$Begin_Date<-as.Date(Begin_Date)
df$End_Date<-as.Date(End_Date)
df$status<-as.character(status)
storms$date <- as.Date(with(storms, paste(year, month, day,sep="-")), "%Y-%m-%d")

(Note: my actual problem is billing data, so I would need a solution to gather an average based on IDs, between a start billing date and end billing date).

Take the storms dataset from dplyr as an example.

starting with my df below:

               status Begin_Date   End_Date
tropical depression  1974-06-01 1975-06-01
 tropical depression 1980-06-05 1981-07-05
      tropical storm 1990-06-07 1991-08-07 

What I’d like:

               status Begin_Date   End_Date   Avg Wind
tropical depression  1974-06-01 1975-06-01    44.3
 tropical depression 1980-06-05 1981-07-05    66.7
      tropical storm 1990-06-07 1991-08-07    56

I’m trying to get this to be dplyr compliant as well.

My attempt is erroring out:

df$Average_wind < - storms %>% filter (status = df$status, date >= df$Begin_Date,date<df$End_Date) %>% summarise(avg = mean(wind))

Error in FUN(left) : invalid argument to unary operator

The average wind and max wind values in the «I’d like» example are not accurate, simply for formatting purposes.