[R] Constructing stacked bar plot

[Avi Gross]

Why should that work in a dplyr function?

medal_data <- medal_counts_ctry %>% filter(medal_counts_ctry$.rows > 100)

Generally in dplyr you do not use the dollar sign notation. And is there a
column starting with a period called ".rows" ??

Without seeing what your data looks like, and assuming you have a column at
that point called rows, I might try:

medal_data <- 
  medal_counts_ctry %>% 
  filter(rows > 100)


-----Original Message-----
From: R-help <r-help-bounces using r-project.org> On Behalf Of Jeff Reichman
Sent: Sunday, June 27, 2021 12:36 PM
To: 'Bert Gunter' <bgunter.4567 using gmail.com>
Cc: 'R-help' <R-help using r-project.org>
Subject: Re: [R] Constructing stacked bar plot

This line

 

medal_data <- medal_counts_ctry %>% filter(medal_counts_ctry$.rows > 100)

 

From: Bert Gunter <bgunter.4567 using gmail.com>
Sent: Sunday, June 27, 2021 11:32 AM
To: reichmanj using sbcglobal.net
Cc: R-help <R-help using r-project.org>
Subject: Re: [R] Constructing stacked bar plot

 

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
<mailto: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 <http://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 <http://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

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