Barplot dplyr summarized values - r

Question

I have data from a top 3 ranking. I'm trying to create a plot that would have on the x axis the column name (cost/product), and the y value be the frequency (ideally relative frequency but I'm not sure how to get that in dplyr).
I'm trying to create this in plotly from values summarized in dplyr. I have a dplyr data frame that looks something like this:
likelyReasonFreq<- LikelyRenew_Reason %>%
filter(year==3)%>%
filter(status==1)%>%
summarize(costC = count(cost),
productsC = count(products))
> likelyReasonFreq
costC.x costC.freq productsC.x productsC.freq
1 1 10 1 31
2 2 11 2 40
3 3 17 3 30
4 NA 149 NA 86
I'm trying to create a barplot that shows the total (summed) frequency for cost,and for products. So frequency for cost would be the frequency for # of times ranked 1, 2, or 3 so 38. Essentially I'm summing rows 1:3 (for products it would be 101 (not including NA values).
I'm not sure how to go about this, any ideas??
below is the variable likelyReasonFreq
> dput(head(likelyReasonFreq))
structure(list(costC = structure(list(x = c(1, 2, 3, NA), freq = c(10L,
11L, 17L, 149L)), .Names = c("x", "freq"), row.names = c(NA,
4L), class = "data.frame"), productsC = structure(list(x = c(1,
2, 3, NA), freq = c(31L, 40L, 30L, 86L)), .Names = c("x", "freq"
), row.names = c(NA, 4L), class = "data.frame")), .Names = c("costC",
"productsC"), row.names = c(NA, 4L), class = "data.frame")
I appreciate any advice!

Answer 1

Your data structure is little awkward to work with, you can do a str or glimpse to it to see the problem, however you may fix this as below and then can plot it.
> str(df)
'data.frame': 4 obs. of 2 variables:
$ costC :'data.frame': 4 obs. of 2 variables:
..$ x : num 1 2 3 NA
..$ freq: int 10 11 17 149
$ productsC:'data.frame': 4 obs. of 2 variables:
..$ x : num 1 2 3 NA
..$ freq: int 31 40 30 86
Code to follow for plotting:
library(ggplot2)
library(tidyverse)
df <- df %>% map(unnest) %>% bind_rows(.id="Name") %>% na.omit() #fixing the structure of column taken as a set of two separate columns
df %>%
ggplot(aes(x=Name, y= freq)) +
geom_col()
I hope this is what is expected, although I am not entirely sure of it.
Input data given:
df <- structure(list(costC = structure(list(x = c(1, 2, 3, NA), freq = c(10L,
11L, 17L, 149L)), .Names = c("x", "freq"), row.names = c(NA,
4L), class = "data.frame"), productsC = structure(list(x = c(1,
2, 3, NA), freq = c(31L, 40L, 30L, 86L)), .Names = c("x", "freq"
), row.names = c(NA, 4L), class = "data.frame")), .Names = c("costC",
"productsC"), row.names = c(NA, 4L), class = "data.frame")
Output:
Added after OP request:
Here, I have not removed the NAs instead I have relplaced with a new value '4'. To take a relative sum across groups, I have used cumsum and then divided by the entire sum across both groups to get the relative frequencies.
df <- df %>% map(unnest) %>% bind_rows(.id="Name")
df[is.na(df$x),"x"] <- 4
df %>%
group_by(Name) %>%
mutate(sum_Freq = sum(freq), cum_Freq = cumsum(freq)) %>%
filter(x == 3) %>%
mutate(new_x = cum_Freq*100/sum_Freq) %>%
ggplot(aes(x=Name, y = new_x)) +
geom_col()