[R] Constructing stacked bar plot

[Bert Gunter]

As has already been pointed out to you (several times, I believe) -- **HTML
code is stripped on this *plain text* list**.
Hence, "bolded, red code" is meaningless!

Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Sun, Jun 27, 2021 at 9:10 AM Jeff Reichman <reichmanj using sbcglobal.net>
wrote:

> R-help Forum
>
> I am attempting to create a stacked bar chart but I have to many
> categories.
> The following code works and I end up plotting all 134 countries but really
> only need (say) the top 50 or so.
>
> I am trying to figure out how to further filter out the countries with the
> largest total medal counts to plot. The bolded red code is the point where
> I
> am thinking is the point where I would do this . I've tried several
> different methods but to no avail. Any suggestions?
>
>
> # Load data file matching NOCs with mao regions (countries)
> noc <- read_csv("~/NGA_Files/JuneMakeoverMonday/noc_regions.csv",
>                 col_types = cols(
>                   NOC = col_character(),
>                   region = col_character()
>                 ))
>
> # Add regions to data and remove missing points
> data_regions <- data %>%
>   left_join(noc,by="NOC") %>%
>   filter(!is.na(region))
>
> # Subset to variables of interest
> medals <- data_regions %>%
>   select(region, Medal)
>
> # count number of medals awarded to each Team
> medal_counts_ctry <- medals %>% filter(!is.na(Medal))%>%
>   group_by(region, Medal) %>%
>   summarize(Count=length(Medal))
>
> #head(medal_counts_ctry)
>
> # order Team by total medal count
> levs_medal <- medal_counts_ctry %>%
>   group_by(region) %>%
>   summarize(Total=sum(Count)) %>%
>   arrange(desc(Total))
>
> medal_counts_ctry$region <- factor(medal_counts_ctry$region,
> levels=levs_medal$region)
>
> medal_data <- medal_counts_ctry %>% filter(medal_counts_ctry$.rows > 100)
>
> # plot
> ggplot(medal_data, aes(x=region, y=Count, fill=Medal)) +
>   geom_col() +
>   coord_flip() +
>   scale_fill_manual(values=c("darkorange3","darkgoldenrod1","cornsilk3")) +
>   ggtitle("Historical medal counts from Country Teams") +
>   theme(plot.title = element_text(hjust = 0.5))
>
>
> > str(medal_counts_ctry)
> grouped_df [323 x 3] (S3: grouped_df/tbl_df/tbl/data.frame)
>  $ region: Factor w/ 134 levels "USA","Russia",..: 101 70 70 70 29 29 29 73
> 73 73 ...
>  $ Medal : Factor w/ 3 levels "Bronze","Gold",..: 1 1 2 3 1 2 3 1 2 3 ...
>  $ Count : int [1:323] 2 8 5 4 91 91 92 9 2 5 ...
>  - attr(*, "groups")= tibble [134 x 2] (S3: tbl_df/tbl/data.frame)
>   ..$ region: Factor w/ 134 levels "USA","Russia",..: 1 2 3 4 5 6 7 8 9 10
> ...
>   ..$ .rows : list<int> [1:134]
>   .. ..$ : int [1:3] 307 308 309
>   .. ..$ : int [1:3] 235 236 237
>   .. ..$ : int [1:3] 102 103 104
>   .. ..$ : int [1:3] 296 297 298
>   .. ..$ : int [1:3] 95 96 97
>   .. ..$ : int [1:3] 138 139 140
>   .. ..$ : int [1:3] 263 264 265
>   .. ..$ : int [1:3] 46 47 48
>   .. ..$ : int [1:3] 11 12 13
>   .. ..$ : int [1:3] 117 118 119
>   .. ..$ : int [1:3] 194 195 196
>   .. ..$ : int [1:3] 208 209 210
>   .. ..$ : int [1:3] 52 53 54
>   .. ..$ : int [1:3] 147 148 149
>   .. ..$ : int [1:3] 92 93 94
>   .. ..$ : int [1:3] 266 267 268
>   .. ..$ : int [1:3] 232 233 234
>   .. ..$ : int [1:3] 69 70 71
>   .. ..$ : int [1:3] 253 254 255 ..........
>
> Jeff Reichman
>
>         [[alternative HTML version deleted]]
>
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