Hello,
I have a data set of about 500 products, and I have done some computations on them to produce a profit margin percentage. I want to focus on the products that fall into a specific range of profit percentage. To be specific, I want to focus on the products with more than 20% profit percentage.
My goal to product a barplot of those products and ignore the rest.
Can I achieve that? I can imagine that the x-axis would be the names of the products, the y-axis would be the percentage and the height of the column would be the profit margin percentage with the percentage on the top of each column.
Or I do not even care for the y axis since it will take a lot of space. All I care is the names of the products on the x-axis and the height of the column represents the percentage.
Also, some columns have kinda long names (not as long as a sentence) so I am sure their names will run over another product's name on the x-axis. I have seen some plots that have the variables name kinda slighted on the x-axis like this:
Can I do the same thing here?
library(tidyverse)
library(scales)
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library(cowplot)
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#> ggsave
library(dplyr)
library(ggplot2)
library(ff)
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#> package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
#> creators: bit bitwhich
#> coercion: as.logical as.integer as.bit as.bitwhich which
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#> - getOption("ffdrop")==TRUE
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#> - getOption("ffcaching")=="mmnoflush" -- consider "ffeachflush" if your system stalls on large writes
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#> clone, clone.default, clone.list
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#> is.factor, is.ordered
library(plotrix)
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library(data.table)
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#> transpose
library(reprex)
sample_long = structure(list(ITEM_CATEGORY_KEY = c(160L, 175L, 176L, 177L,
178L, 179L, 260L, 5996L, 11010L, 11011L, 11012L, 11015L, 11020L,
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13025L, 13030L, 13040L, 13045L, 13099L, 14010L, 14020L, 14030L,
15010L, 15020L, 15030L, 15035L, 15040L, 15060L, 15065L, 15070L,
15080L, 15095L, 15096L, 15099L, 16010L, 16020L, 16030L, 16045L,
16050L, 16051L, 16055L, 16060L, 16099L, 17010L, 17020L, 17030L,
17045L, 17050L, 17060L, 17065L, 17070L, 17080L, 17099L, 18010L,
18020L, 18040L, 18050L, 18060L, 18099L, 19010L, 19020L, 19025L,
19030L, 19035L, 19040L, 19045L, 19060L, 19070L, 19099L, 20020L,
20099L, 21010L, 21015L, 21020L, 21030L, 21099L, 23010L, 23020L,
23030L, 23035L, 23099L, 25010L, 25015L, 25020L, 25040L, 25099L,
26010L, 26015L, 26020L, 26025L, 26026L, 26030L, 26099L, 28010L,
28020L, 28030L, 28099L, 29005L, 29009L, 29010L, 29015L, 29020L,
29023L, 29025L, 29030L, 29035L, 29040L, 29041L, 29042L, 29099L,
30010L, 30020L, 30099L, 31010L, 31015L, 31017L, 31020L, 31030L,
31040L, 31099L, 32010L, 32011L, 32015L, 32020L, 32025L, 32030L,
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58010L, 58012L, 58015L, 58021L, 58022L, 58023L, 58024L, 58025L,
58026L, 58027L, 58028L, 58029L, 58030L, 58033L, 58035L, 58040L,
58045L, 58050L, 58052L, 58060L, 58070L, 58075L, 58083L, 58085L,
58086L, 58090L, 58095L, 58099L, 60010L, 60012L, 60015L, 60016L,
60020L, 60025L, 60030L, 60099L, 61005L, 61010L, 61030L, 61040L,
61099L, 62005L, 62006L, 62009L, 62010L, 62011L, 62012L, 62013L,
62014L, 62015L, 62016L, 62017L, 62020L, 62025L, 62026L, 62027L,
62029L, 62030L, 62032L, 62034L, 62035L, 62040L, 62043L, 62044L,
62050L, 62060L, 62070L, 62071L, 62072L, 62075L, 62080L, 62099L,
63010L, 63015L, 63020L, 63025L, 63030L, 63035L, 63040L, 63099L,
64005L, 64006L, 64007L, 64010L, 64015L, 64017L, 64018L, 64019L,
64020L, 64021L, 64022L, 64025L, 64099L, 66010L, 66015L, 66020L,
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93099L, 98030L, 98080L, 98090L, 98091L, 98097L, 98098L, 98099L,
99999L), Total_Sales = c(100245.73, 9776828, 8822395.37, 9448123.87,
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PL_Margins_Percentage = c(0, 0, 0, 0, 0, 0, 0, 0, 10, 0,
0, 16, 47, 0, 0, 2, 0, 27, 16, 0, 7, 14, 10, 0, 0, 0, 6,
0, 29, 0, 3, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 4, 13,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
1, 5, 19, 6, 0, 0, 0, 0, 7, 3, 0, 0, 0, 3, 4, 0, 0, 0, 9,
0, 0, 18, 0, 5, 3, 4, 0, 0, 7, 6, 21, 7, 15, 0, 0, 0, 0,
0, 0, 6, 21, 8, 10, 13, 26, 14, 17, 23, 19, 13, 0, 0, 0,
2, 0, 0, 0, 0, 0, 0, 0, 0, 9, 7, 0, 0, 0, 2, 0, 0, 23, 0,
0, 0, 14, 18, 11, 3, 9, 0, 0, 0, 0, 0, 0, 0, 17, 0, 1, 0,
8, 5, 6, 2, 8, 10, 5, 3, 0, 0, 0, 0, 4, 9, 21, 19, 2, 0,
0, 1, 0, 0, 0, 0, 21, 0, 0, 11, 0, 0, 19, 13, 0, 2, 2, 7,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23,
15, 0, 10, 0, 11, 0, 5, 39, 36, 41, 35, 5, 29, 25, 36, 27,
0, 6, 0, 0, 0, 0, 0, 0, 0, 15, 1, 0, 9, 5, 28, 19, 1, 1,
5, 0, 0, 14, 48, 0, 4, 1, 0, 7, 20, 6, 0, 8, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 23, 0, 11, 44, 0,
27, 35, 0, 8, 2, 0, 0, 0, 0, 34, 8, 43, 33, 28, 0, 0, 0,
0, 0, 0, 0, 11, 7, 26, 1, 0, 0, 0, 3, 9, 0, 2, 0, 0, 0, 2,
6, 0, 0, 15, 0, 0, 0, 0, 7, 8, 2, 0, 0, 7, 0, 13, 14, 16,
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10, 0, 22, 8, 9, 19, 0, 34, 19, 43, 20, 15, 0, 19, 8, 1,
22, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 30, 0,
10, 40, 4, 28, 3, 0, 5, 0, 0, 4, 3, 0, 0, 21, 6, 0, 0, 0,
0, 0, 0, 4, 3, 2, 29, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 31, 0,
13, 1, 5, 1, 0, 0, 3, 0, 20, 17, 0, 6, 0, 24, 5, 2, 7, 0,
0, 0, 3, 23, 6, 1, 4, 10, 3, 6, 0, 30, 0, 2, 3, 2, 0, 0,
0, 1, 0, 17, 27, 38, 0, 0, 0, 0, 19, 0, 0, 0, 26, 35, 6,
3, 0, 47, 25, 34, 10, 24, 26, 22, 1, 3, 3, 0, 45, 37, 33,
21, 0, 0, 3, 0, 0, 0, 0, 0, 17, 0, 0, 0, 0, 13, 0, 0, 0,
9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -526L))
sample_long_1a = sample_long %>% filter(PL_Margins_Percentage > 0L) %>%
mutate(category = cut(PL_Margins_Percentage,breaks = c(1,11,21,31,41,50),
labels = c('1%-10%','11%-20%',
'21%-30%','31%-40%','41%-50%'),include.lowest = T,right = F))
sample_long_1b <- sample_long_1a %>% group_by(category) %>% summarise(count=n()) %>%
mutate(percent= paste0(round(count/sum(count)*100,1),'%'))
sample_long_2a =
sample_long %>%
filter (PL_Margins_Percentage > 20L, PL_Margins_Percentage < 31L)
ggplot(sample_long_2a) +
geom_bar(aes(x=ITEM_CATEGORY_KEY,y=PL_Margins_Percentage, fill = ITEM_CATEGORY_KEY),stat='identity') +
labs(y='', x='')+
theme(legend.position="none")
Created on 2018-07-06 by the reprex
package (v0.2.0).
