Hello,
Something like this?
# count number of medals awarded to each Team
medal_counts_ctry <- medals %>%
na.omit() %>%
count(region, Medal, name = "Count")
#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)) %>%
pull(region)
medal_counts_ctry$region <- factor(medal_counts_ctry$region,
levels = levs_medal)
# keep top 50 medal counts
top_count <- 50
medal_data <- medal_counts_ctry %>%
slice_max(order_by = Count, n = top_count)
Hope this helps,
Rui Barradas
Às 17:10 de 27/06/21, Jeff Reichman escreveu:
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# count number of medals awarded to each Team
medal_counts_ctry <- medals %>%
na.omit() %>%
count(region, Medal, name = "Count")
#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)) %>%
pull(region)
medal_counts_ctry$region <- factor(medal_counts_ctry$region,
levels = levs_medal)
# keep top 50 medal counts
top_count <- 50
medal_data <- medal_counts_ctry %>%
slice_max(order_by = Count, n = top_count)
.. ..$ : int [1:3] 208 209 210
.. ..$ : int [1:3] 52 53 54# count number of medals awarded to each Team
medal_counts_ctry <- medals %>%
na.omit() %>%
count(region, Medal, name = "Count")
#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)) %>%
pull(region)
medal_counts_ctry$region <- factor(medal_counts_ctry$region,
levels = levs_medal)
# keep top 50 medal counts
top_count <- 50
medal_data <- medal_counts_ctry %>%
slice_max(order_by = Count, n = top_count)
.. ..$ : 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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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
