Calculation of "average sales share " with dplyr::mutate - r

Question

My data concerns a company and includes Total Sales and the amount of sales in three counties CA , TX and WI.
Data :
> dput(head(WalData))
structure(list(CA = c(11047, 9925, 11322, 12251, 16610, 14696
), TX = c(7381, 5912, 9006, 6226, 9440, 9376), WI = c(6984, 3309,
8883, 9533, 11882, 8664), Total = c(25412, 19146, 29211, 28010,
37932, 32736), date = structure(c(1296518400, 1296604800, 1296691200,
1296777600, 1296864000, 1296950400), tzone = "UTC", class = c("POSIXct",
"POSIXt")), event_type = c("NA", "NA", "NA", "NA", "NA", "Sporting"
), snap_CA = c(1, 1, 1, 1, 1, 1), snap_TX = c(1, 0, 1, 0, 1,
1), snap_WI = c(0, 1, 1, 0, 1, 1)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"))
With the following code i am trying to calculate the average sales share of the three states on the company's total sales.
In addition, i need the same average percentages for each year, month of the year and day of the week.
install.packages("dplyr")
install.packages("lubridate")
library(dplyr)
library(lubridate)
df1 <- df %>%
dplyr::mutate(YEAR = lubridate::year(date),
MONTH = lubridate::month(date),
WEEKDAY = lubridate::wday(date),
P_CA = CA / Total,
P_TX = TX / Total,
P_WI = WI / Total)
# Average per Year
df1 %>%
dplyr::group_by(YEAR) %>%
dplyr::summarise(AV_CA = mean(P_CA, na.rm = TRUE),
AV_TX = mean(P_TX, na.rm = TRUE),
AV_WI = mean(P_WI, na.rm = TRUE))
# Average per Month
df1 %>%
dplyr::group_by(MONTH) %>%
dplyr::summarise(AV_CA = mean(P_CA, na.rm = TRUE),
AV_TX = mean(P_TX, na.rm = TRUE),
AV_WI = mean(P_WI, na.rm = TRUE))
# Average per Weekday
df1 %>%
dplyr::group_by(WEEKDAY) %>%
dplyr::summarise(AV_CA = mean(P_CA, na.rm = TRUE),
AV_TX = mean(P_TX, na.rm = TRUE),
AV_WI = mean(P_WI, na.rm = TRUE))
Output :
> df1 <- df %>%
+ dplyr::mutate(YEAR = lubridate::year(date),
+ MONTH = lubridate::month(date),
+ WEEKDAY = lubridate::wday(date),
+ P_CA = CA / Total,
+ P_TX = TX / Total,
+ P_WI = WI / Total)
Error in UseMethod("mutate_") :
no applicable method for 'mutate_' applied to an object of class "function"
> # Average per Year
> df1 %>%
+ dplyr::group_by(YEAR) %>%
+ dplyr::summarise(AV_CA = mean(P_CA, na.rm = TRUE),
+ AV_TX = mean(P_TX, na.rm = TRUE),
+ AV_WI = mean(P_WI, na.rm = TRUE))
Error in eval(lhs, parent, parent) : object 'df1' not found
It comes with an error : Error in UseMethod("mutate_") :
no applicable method for 'mutate_' applied to an object of class "function"
I cant figure out whats wrong , i double checked the code and the correctness of the data .
Please give a solution .

Answer 1

The issue would be that df is not created as an object in the global env and there is a function with name df if we do ?df
df(x, df1, df2, ncp, log = FALSE)
Basically, the error is based on applying mutate on a function df rather than an object
Checking on a fresh R session with no objects created
df %>%
dplyr::mutate(YEAR = lubridate::year(date),
MONTH = lubridate::month(date),
WEEKDAY = lubridate::wday(date),
P_CA = CA / Total,
P_TX = TX / Total,
P_WI = WI / Total)
Error in UseMethod("mutate_") :
no applicable method for 'mutate_' applied to an object of class "function"
Now, we define 'df' as
df <- WalData
df %>%
dplyr::mutate(YEAR = lubridate::year(date),
MONTH = lubridate::month(date),
WEEKDAY = lubridate::wday(date),
P_CA = CA / Total,
P_TX = TX / Total,
P_WI = WI / Total)
# A tibble: 6 x 15
# CA TX WI Total date event_type snap_CA snap_TX snap_WI YEAR MONTH WEEKDAY P_CA P_TX P_WI
# <dbl> <dbl> <dbl> <dbl> <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 11047 7381 6984 25412 2011-02-01 00:00:00 NA 1 1 0 2011 2 3 0.435 0.290 0.275
#2 9925 5912 3309 19146 2011-02-02 00:00:00 NA 1 0 1 2011 2 4 0.518 0.309 0.173
#3 11322 9006 8883 29211 2011-02-03 00:00:00 NA 1 1 1 2011 2 5 0.388 0.308 0.304
#4 12251 6226 9533 28010 2011-02-04 00:00:00 NA 1 0 0 2011 2 6 0.437 0.222 0.340
#5 16610 9440 11882 37932 2011-02-05 00:00:00 NA 1 1 1 2011 2 7 0.438 0.249 0.313
#6 14696 9376 8664 32736 2011-02-06 00:00:00 Sporting 1 1 1 2011 2 1 0.449 0.286 0.265