Error: Problem with `mutate()` input `time_format`. All arguments must be numeric or NA

[ritm]

I have a R script which i am using in R shiny but in R shiny it throws an error when the mutate function is executed as below.I don't understand why its causing an error in r shiny but works fine in R script and how to resolve it.

promax_2020_data_time <- mutate(promax_2020_data,
time_format = hms(promax_2020_data$Time) %>% as.duration())

Error: Problem with mutate() input time_format.
x All arguments must be numeric or NA
i Input time_format is hms(promax_2020_data$Time) %>% as.duration().

promax_2020_data_time is a dataframe.Time column is of char type.I have used the following libraries for the entire code:
library(dplyr)
library(tidyr)
library(hms)
library(lubridate)
library(tidyverse)
library(shiny)

[ritm]

Any update on the same would be highly appreciated.Thanks.

[0sigmas]

Hi ritm,

Could you prepare a reproducible example? With the info you have given is really difficult to narrow down what your problem is.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[ritm]

I have given the code below for both R script as well as R shiny.When the code is executed in R script,it doesn't throw any error but when the same code is executed in R shiny,it throws the error as highlighted earlier.

R script:-

getwd()
setwd("C:/Users/Ritwik/Documents/R")

promax_Jan_Sep_2020_data<-read.csv("Promax_Jan_Sept_2020_Data.csv")

promax_Oct_Dec_2020_data<-read.csv("Promax_Oct_Dec_2020_Data.csv")

promax_2020_data<-rbind(promax_Jan_Sep_2020_data,promax_Oct_Dec_2020_data)

View(promax_2020_data)

class(promax_2020_data)

str(promax_2020_data)

unique(promax_2020_data$Region)


#install.packages("dplyr")
library(dplyr)

#install.packages("tidyr")
library(tidyr)

#install.packages("lubridate")
library(lubridate)

#install.packages("tidyverse")
library(tidyverse)

promax_2020_data <-subset(promax_2020_data,!(Region %in% c("#VALUE!","10.1","10.2","138","145","161")))

unique(promax_2020_data$Region)

promax_2020_data$Region<-gsub("-","",promax_2020_data$Region)

View(promax_2020_data)

promax_2020_data$Time<-format(strptime(promax_2020_data$Time, "%I:%M:%S %p"), "%H:%M:%S")

promax_2020_data$Date<-as.factor(promax_2020_data$Date)

promax_2020_data$Date<-strptime(promax_2020_data$Date,"%m/%d/%Y")

promax_2020_data$Date<-as.Date(promax_2020_data$Date,"%Y-%b-%d")

str(promax_2020_data)

promax_2020_data$month<-format(promax_2020_data$Date,"%b")

unique(promax_2020_data$month)

promax_2020_data$month = factor(promax_2020_data$month, levels = month.abb)


colnames(promax_2020_data$month)<-promax_2020_data$Month

promax_2020_data <- mutate(promax_2020_data, Quarter = case_when(
  month %in% c("Jan", "Feb", "Mar") ~ "Q1",
  month %in% c("Apr", "May", "Jun") ~ "Q2",
  month %in% c("Jul", "Aug", "Sep") ~ "Q3",
  TRUE ~ "Q4"
))


str(promax_2020_data)


unique(promax_2020_data$Quarter)



promax_2020_data_time <- mutate(promax_2020_data,
                                time_format = hms(promax_2020_data$Time) %>% as.duration())

View(promax_2020_data_time)

R shiny:-

#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#

getwd()
setwd("C:/Users/Ritwik/Documents/R")

promax_Jan_Sep_2020_data<-read.csv("Promax_Jan_Sept_2020_Data.csv")

promax_Oct_Dec_2020_data<-read.csv("Promax_Oct_Dec_2020_Data.csv")

promax_2020_data<-rbind(promax_Jan_Sep_2020_data,promax_Oct_Dec_2020_data)

View(promax_2020_data)

class(promax_2020_data)

str(promax_2020_data)

unique(promax_2020_data$Region)

library(dplyr)
library(tidyr)
library(hms)
library(lubridate)
library(tidyverse)
library(shiny)


promax_2020_data <-subset(promax_2020_data,!(Region %in% c("#VALUE!","10.1","10.2","138","145","161")))

unique(promax_2020_data$Region)

promax_2020_data$Region<-gsub("-","",promax_2020_data$Region)

View(promax_2020_data)

promax_2020_data$Time<-format(strptime(promax_2020_data$Time, "%I:%M:%S %p"), "%H:%M:%S")

promax_2020_data$Date<-as.factor(promax_2020_data$Date)

promax_2020_data$Date<-strptime(promax_2020_data$Date,"%m/%d/%Y")

promax_2020_data$Date<-as.Date(promax_2020_data$Date,"%Y-%b-%d")

str(promax_2020_data)

promax_2020_data$month<-format(promax_2020_data$Date,"%b")

unique(promax_2020_data$month)

promax_2020_data$month = factor(promax_2020_data$month, levels = month.abb)


colnames(promax_2020_data$month)<-promax_2020_data$Month

promax_2020_data <- mutate(promax_2020_data, Quarter = case_when(
    month %in% c("Jan", "Feb", "Mar") ~ "Q1",
    month %in% c("Apr", "May", "Jun") ~ "Q2",
    month %in% c("Jul", "Aug", "Sep") ~ "Q3",
    TRUE ~ "Q4"
))


unique(promax_2020_data$Quarter)


promax_2020_data_time <- mutate(promax_2020_data,
                                time_format = hms(promax_2020_data$Time) %>% as.duration())

View(promax_2020_data_time)