如何创建一个 table 来计算 R 中两个单独 table 中值之间的百分比变化？
How to create a table that calculates percent change between values in two separate tables in R?
我有两个数据tables，每个代表一年的月度价差，下一年也一样。
我想根据我的索引 ('Category Name') 计算值的百分比变化，因此每一行值都会重新计算从第一年到下一年的百分比变化。
参考下面 table 的第一年：
`Category Name` Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Average `Percent Total`
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Salty Snacks 36340 43950 34246 26758 36151 51390 35299 43337 35251 32449 46309 33534 37918. 39.4
2 Candy 23003 23814 19271 14269 21902 30986 22054 27269 22651 20789 30090 21632 23144. 24.0
3 Nutrition Bar 10563 12182 9657 7029 10740 15392 10926 14955 12370 10914 16130 11945 11900. 12.4
4 Cookies 10751 11679 9051 6752 9416 14467 10993 14364 12207 11066 14862 9656 11272 11.7
5 Nuts/Trailmix 4018 4371 3512 2676 3935 5586 3820 5182 4555 3869 5672 3938 4261. 4.43
6 Pastry 3531 4328 3666 2853 3906 5396 3521 4318 3571 3520 4830 4026 3956. 4.11
7 Meat Sticks 2131 2669 1919 1575 2156 3021 2088 2630 2369 2043 2759 2091 2288. 2.38
8 Other 1500 2191 2133 1650 2151 2696 1196 1169 760 751 954 747 1492. 1.55
9 Crackers 1 0 0 0 0 0 0 0 13 39 86 23 13.5 0.01
10 Food 0 0 0 0 0 2 7 3 15 25 30 9 7.58 0.01
这是接下来的几年 table，同样 layout/parameters：
`Category Name` Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Average `Percent Total`
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Salty Snacks 35562 38043 37697 33079 31942 35862 35646 43987 33551 34916 46172 35511 36831. 39.1
2 Candy 22722 21071 20496 17663 19470 21834 21924 26846 21046 22482 29402 22477 22286. 23.6
3 Nutrition Bar 12339 12975 12073 10668 11813 12999 12897 17062 12982 13506 17234 13033 13298. 14.1
4 Cookies 10042 9712 9489 7820 8472 9936 9756 11867 9307 9525 12123 9544 9799. 10.4
5 Pastry 5322 5243 5185 4518 4546 4868 4819 5792 4275 4484 5547 4486 4924. 5.23
6 Nuts/Trailmix 4236 4279 4034 3656 3733 4364 4171 5514 4209 4486 5595 4409 4390. 4.66
7 Meat Sticks 2067 2195 2024 2085 2068 2236 2342 2814 2337 2311 3028 2428 2328. 2.47
8 Crackers 12 4 2 1 0 49 185 459 355 550 839 705 263. 0.28
9 Other 433 177 89 38 28 17 9 4 2 0 1 1 66.6 0.07
10 Food 4 0 5 2 0 0 11 46 48 65 121 77 31.6 0.03
我正在尝试创建一个新的 table 来显示每个类别（咸味零食、糖果等）每个月的百分比变化。我的计划是利用它并从中创建一个格式table。谢谢！！
在base R中，可以根据df1排列df2中的行，然后进行计算。
df2 <- df2[match(df1$Category_Name, df2$Category_Name), ]
cbind(df1[1], round((df2[2:13] - df1[2:13])/df1[2:13] * 100, 2))
或者使用dplyr和tidyr你可以将数据转换为长格式，加入它们执行计算并再次将数据转换为宽格式。
library(dplyr)
library(tidyr)
df1 %>%
select(-Average, -Percent_Total) %>%
pivot_longer(cols = -Category_Name) %>%
left_join(df2 %>%
select(-Average, -Percent_Total) %>%
pivot_longer(cols = -Category_Name), by = c("Category_Name", "name")) %>%
mutate(change = (value.y - value.x)/value.x * 100) %>%
select(-value.x, -value.y) %>%
mutate(change = na_if(change, Inf)) %>%
pivot_wider(names_from = name, values_from = change)
# A tibble: 10 x 13
# Category_Name Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Salty_Snacks -2.14 -13.4 10.1 23.6 -11.6 -30.2 0.983 1.50 -4.82 7.60 -0.296 5.90
# 2 Candy -1.22 -11.5 6.36 23.8 -11.1 -29.5 -0.589 -1.55 -7.09 8.14 -2.29 3.91
# 3 Nutrition_Bar 16.8 6.51 25.0 51.8 9.99 -15.5 18.0 14.1 4.95 23.7 6.84 9.11
# 4 Cookies -6.59 -16.8 4.84 15.8 -10.0 -31.3 -11.3 -17.4 -23.8 -13.9 -18.4 -1.16
# 5 Nuts/Trailmix 5.43 -2.10 14.9 36.6 -5.13 -21.9 9.19 6.41 -7.60 15.9 -1.36 12.0
# 6 Pastry 50.7 21.1 41.4 58.4 16.4 -9.79 36.9 34.1 19.7 27.4 14.8 11.4
# 7 Meat_Sticks -3.00 -17.8 5.47 32.4 -4.08 -26.0 12.2 7.00 -1.35 13.1 9.75 16.1
# 8 Other -71.1 -91.9 -95.8 -97.7 -98.7 -99.4 -99.2 -99.7 -99.7 -100 -99.9 -99.9
# 9 Crackers 1100 NA NA NA NaN NA NA NA 2631. 1310. 876. 2965.
#10 Food NA NaN NA NA NaN -100 57.1 1433. 220. 160 303. 756.
数据
df1 <- structure(list(Category_Name = structure(c(10L, 1L, 6L, 2L, 7L,
9L, 5L, 8L, 3L, 4L), .Label = c("Candy", "Cookies", "Crackers",
"Food", "Meat_Sticks", "Nutrition_Bar", "Nuts/Trailmix", "Other",
"Pastry", "Salty_Snacks"), class = "factor"), Oct = c(36340L,
23003L, 10563L, 10751L, 4018L, 3531L, 2131L, 1500L, 1L, 0L),
Nov = c(43950L, 23814L, 12182L, 11679L, 4371L, 4328L, 2669L,
2191L, 0L, 0L), Dec = c(34246L, 19271L, 9657L, 9051L, 3512L,
3666L, 1919L, 2133L, 0L, 0L), Jan = c(26758L, 14269L, 7029L,
6752L, 2676L, 2853L, 1575L, 1650L, 0L, 0L), Feb = c(36151L,
21902L, 10740L, 9416L, 3935L, 3906L, 2156L, 2151L, 0L, 0L
), Mar = c(51390L, 30986L, 15392L, 14467L, 5586L, 5396L,
3021L, 2696L, 0L, 2L), Apr = c(35299L, 22054L, 10926L, 10993L,
3820L, 3521L, 2088L, 1196L, 0L, 7L), May = c(43337L, 27269L,
14955L, 14364L, 5182L, 4318L, 2630L, 1169L, 0L, 3L), Jun = c(35251L,
22651L, 12370L, 12207L, 4555L, 3571L, 2369L, 760L, 13L, 15L
), Jul = c(32449L, 20789L, 10914L, 11066L, 3869L, 3520L,
2043L, 751L, 39L, 25L), Aug = c(46309L, 30090L, 16130L, 14862L,
5672L, 4830L, 2759L, 954L, 86L, 30L), Sep = c(33534L, 21632L,
11945L, 9656L, 3938L, 4026L, 2091L, 747L, 23L, 9L), Average = c(37918,
23144, 11900, 11272, 4261, 3956, 2288, 1492, 13.5, 7.58),
Percent_Total = c(39.4, 24, 12.4, 11.7, 4.43, 4.11, 2.38,
1.55, 0.01, 0.01)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))
df2 <- structure(list(Category_Name = structure(c(10L, 1L, 6L, 2L, 9L,
7L, 5L, 3L, 8L, 4L), .Label = c("Candy", "Cookies", "Crackers",
"Food", "Meat_Sticks", "Nutrition_Bar", "Nuts/Trailmix", "Other",
"Pastry", "Salty_Snacks"), class = "factor"), Oct = c(35562L,
22722L, 12339L, 10042L, 5322L, 4236L, 2067L, 12L, 433L, 4L),
Nov = c(38043L, 21071L, 12975L, 9712L, 5243L, 4279L, 2195L,
4L, 177L, 0L), Dec = c(37697L, 20496L, 12073L, 9489L, 5185L,
4034L, 2024L, 2L, 89L, 5L), Jan = c(33079L, 17663L, 10668L,
7820L, 4518L, 3656L, 2085L, 1L, 38L, 2L), Feb = c(31942L,
19470L, 11813L, 8472L, 4546L, 3733L, 2068L, 0L, 28L, 0L),
Mar = c(35862L, 21834L, 12999L, 9936L, 4868L, 4364L, 2236L,
49L, 17L, 0L), Apr = c(35646L, 21924L, 12897L, 9756L, 4819L,
4171L, 2342L, 185L, 9L, 11L), May = c(43987L, 26846L, 17062L,
11867L, 5792L, 5514L, 2814L, 459L, 4L, 46L), Jun = c(33551L,
21046L, 12982L, 9307L, 4275L, 4209L, 2337L, 355L, 2L, 48L
), Jul = c(34916L, 22482L, 13506L, 9525L, 4484L, 4486L, 2311L,
550L, 0L, 65L), Aug = c(46172L, 29402L, 17234L, 12123L, 5547L,
5595L, 3028L, 839L, 1L, 121L), Sep = c(35511L, 22477L, 13033L,
9544L, 4486L, 4409L, 2428L, 705L, 1L, 77L), Average = c(36831,
22286, 13298, 9799, 4924, 4390, 2328, 263, 66.6, 31.6), Percent_Total = c(39.1,
23.6, 14.1, 10.4, 5.23, 4.66, 2.47, 0.28, 0.07, 0.03)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))
我有两个数据tables，每个代表一年的月度价差，下一年也一样。
我想根据我的索引 ('Category Name') 计算值的百分比变化，因此每一行值都会重新计算从第一年到下一年的百分比变化。
参考下面 table 的第一年：
`Category Name` Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Average `Percent Total`
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Salty Snacks 36340 43950 34246 26758 36151 51390 35299 43337 35251 32449 46309 33534 37918. 39.4
2 Candy 23003 23814 19271 14269 21902 30986 22054 27269 22651 20789 30090 21632 23144. 24.0
3 Nutrition Bar 10563 12182 9657 7029 10740 15392 10926 14955 12370 10914 16130 11945 11900. 12.4
4 Cookies 10751 11679 9051 6752 9416 14467 10993 14364 12207 11066 14862 9656 11272 11.7
5 Nuts/Trailmix 4018 4371 3512 2676 3935 5586 3820 5182 4555 3869 5672 3938 4261. 4.43
6 Pastry 3531 4328 3666 2853 3906 5396 3521 4318 3571 3520 4830 4026 3956. 4.11
7 Meat Sticks 2131 2669 1919 1575 2156 3021 2088 2630 2369 2043 2759 2091 2288. 2.38
8 Other 1500 2191 2133 1650 2151 2696 1196 1169 760 751 954 747 1492. 1.55
9 Crackers 1 0 0 0 0 0 0 0 13 39 86 23 13.5 0.01
10 Food 0 0 0 0 0 2 7 3 15 25 30 9 7.58 0.01
这是接下来的几年 table，同样 layout/parameters：
`Category Name` Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Average `Percent Total`
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Salty Snacks 35562 38043 37697 33079 31942 35862 35646 43987 33551 34916 46172 35511 36831. 39.1
2 Candy 22722 21071 20496 17663 19470 21834 21924 26846 21046 22482 29402 22477 22286. 23.6
3 Nutrition Bar 12339 12975 12073 10668 11813 12999 12897 17062 12982 13506 17234 13033 13298. 14.1
4 Cookies 10042 9712 9489 7820 8472 9936 9756 11867 9307 9525 12123 9544 9799. 10.4
5 Pastry 5322 5243 5185 4518 4546 4868 4819 5792 4275 4484 5547 4486 4924. 5.23
6 Nuts/Trailmix 4236 4279 4034 3656 3733 4364 4171 5514 4209 4486 5595 4409 4390. 4.66
7 Meat Sticks 2067 2195 2024 2085 2068 2236 2342 2814 2337 2311 3028 2428 2328. 2.47
8 Crackers 12 4 2 1 0 49 185 459 355 550 839 705 263. 0.28
9 Other 433 177 89 38 28 17 9 4 2 0 1 1 66.6 0.07
10 Food 4 0 5 2 0 0 11 46 48 65 121 77 31.6 0.03
我正在尝试创建一个新的 table 来显示每个类别（咸味零食、糖果等）每个月的百分比变化。我的计划是利用它并从中创建一个格式table。谢谢！！
在base R中，可以根据df1排列df2中的行，然后进行计算。
df2 <- df2[match(df1$Category_Name, df2$Category_Name), ]
cbind(df1[1], round((df2[2:13] - df1[2:13])/df1[2:13] * 100, 2))
或者使用dplyr和tidyr你可以将数据转换为长格式，加入它们执行计算并再次将数据转换为宽格式。
library(dplyr)
library(tidyr)
df1 %>%
select(-Average, -Percent_Total) %>%
pivot_longer(cols = -Category_Name) %>%
left_join(df2 %>%
select(-Average, -Percent_Total) %>%
pivot_longer(cols = -Category_Name), by = c("Category_Name", "name")) %>%
mutate(change = (value.y - value.x)/value.x * 100) %>%
select(-value.x, -value.y) %>%
mutate(change = na_if(change, Inf)) %>%
pivot_wider(names_from = name, values_from = change)
# A tibble: 10 x 13
# Category_Name Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Salty_Snacks -2.14 -13.4 10.1 23.6 -11.6 -30.2 0.983 1.50 -4.82 7.60 -0.296 5.90
# 2 Candy -1.22 -11.5 6.36 23.8 -11.1 -29.5 -0.589 -1.55 -7.09 8.14 -2.29 3.91
# 3 Nutrition_Bar 16.8 6.51 25.0 51.8 9.99 -15.5 18.0 14.1 4.95 23.7 6.84 9.11
# 4 Cookies -6.59 -16.8 4.84 15.8 -10.0 -31.3 -11.3 -17.4 -23.8 -13.9 -18.4 -1.16
# 5 Nuts/Trailmix 5.43 -2.10 14.9 36.6 -5.13 -21.9 9.19 6.41 -7.60 15.9 -1.36 12.0
# 6 Pastry 50.7 21.1 41.4 58.4 16.4 -9.79 36.9 34.1 19.7 27.4 14.8 11.4
# 7 Meat_Sticks -3.00 -17.8 5.47 32.4 -4.08 -26.0 12.2 7.00 -1.35 13.1 9.75 16.1
# 8 Other -71.1 -91.9 -95.8 -97.7 -98.7 -99.4 -99.2 -99.7 -99.7 -100 -99.9 -99.9
# 9 Crackers 1100 NA NA NA NaN NA NA NA 2631. 1310. 876. 2965.
#10 Food NA NaN NA NA NaN -100 57.1 1433. 220. 160 303. 756.
数据
df1 <- structure(list(Category_Name = structure(c(10L, 1L, 6L, 2L, 7L,
9L, 5L, 8L, 3L, 4L), .Label = c("Candy", "Cookies", "Crackers",
"Food", "Meat_Sticks", "Nutrition_Bar", "Nuts/Trailmix", "Other",
"Pastry", "Salty_Snacks"), class = "factor"), Oct = c(36340L,
23003L, 10563L, 10751L, 4018L, 3531L, 2131L, 1500L, 1L, 0L),
Nov = c(43950L, 23814L, 12182L, 11679L, 4371L, 4328L, 2669L,
2191L, 0L, 0L), Dec = c(34246L, 19271L, 9657L, 9051L, 3512L,
3666L, 1919L, 2133L, 0L, 0L), Jan = c(26758L, 14269L, 7029L,
6752L, 2676L, 2853L, 1575L, 1650L, 0L, 0L), Feb = c(36151L,
21902L, 10740L, 9416L, 3935L, 3906L, 2156L, 2151L, 0L, 0L
), Mar = c(51390L, 30986L, 15392L, 14467L, 5586L, 5396L,
3021L, 2696L, 0L, 2L), Apr = c(35299L, 22054L, 10926L, 10993L,
3820L, 3521L, 2088L, 1196L, 0L, 7L), May = c(43337L, 27269L,
14955L, 14364L, 5182L, 4318L, 2630L, 1169L, 0L, 3L), Jun = c(35251L,
22651L, 12370L, 12207L, 4555L, 3571L, 2369L, 760L, 13L, 15L
), Jul = c(32449L, 20789L, 10914L, 11066L, 3869L, 3520L,
2043L, 751L, 39L, 25L), Aug = c(46309L, 30090L, 16130L, 14862L,
5672L, 4830L, 2759L, 954L, 86L, 30L), Sep = c(33534L, 21632L,
11945L, 9656L, 3938L, 4026L, 2091L, 747L, 23L, 9L), Average = c(37918,
23144, 11900, 11272, 4261, 3956, 2288, 1492, 13.5, 7.58),
Percent_Total = c(39.4, 24, 12.4, 11.7, 4.43, 4.11, 2.38,
1.55, 0.01, 0.01)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))
df2 <- structure(list(Category_Name = structure(c(10L, 1L, 6L, 2L, 9L,
7L, 5L, 3L, 8L, 4L), .Label = c("Candy", "Cookies", "Crackers",
"Food", "Meat_Sticks", "Nutrition_Bar", "Nuts/Trailmix", "Other",
"Pastry", "Salty_Snacks"), class = "factor"), Oct = c(35562L,
22722L, 12339L, 10042L, 5322L, 4236L, 2067L, 12L, 433L, 4L),
Nov = c(38043L, 21071L, 12975L, 9712L, 5243L, 4279L, 2195L,
4L, 177L, 0L), Dec = c(37697L, 20496L, 12073L, 9489L, 5185L,
4034L, 2024L, 2L, 89L, 5L), Jan = c(33079L, 17663L, 10668L,
7820L, 4518L, 3656L, 2085L, 1L, 38L, 2L), Feb = c(31942L,
19470L, 11813L, 8472L, 4546L, 3733L, 2068L, 0L, 28L, 0L),
Mar = c(35862L, 21834L, 12999L, 9936L, 4868L, 4364L, 2236L,
49L, 17L, 0L), Apr = c(35646L, 21924L, 12897L, 9756L, 4819L,
4171L, 2342L, 185L, 9L, 11L), May = c(43987L, 26846L, 17062L,
11867L, 5792L, 5514L, 2814L, 459L, 4L, 46L), Jun = c(33551L,
21046L, 12982L, 9307L, 4275L, 4209L, 2337L, 355L, 2L, 48L
), Jul = c(34916L, 22482L, 13506L, 9525L, 4484L, 4486L, 2311L,
550L, 0L, 65L), Aug = c(46172L, 29402L, 17234L, 12123L, 5547L,
5595L, 3028L, 839L, 1L, 121L), Sep = c(35511L, 22477L, 13033L,
9544L, 4486L, 4409L, 2428L, 705L, 1L, 77L), Average = c(36831,
22286, 13298, 9799, 4924, 4390, 2328, 263, 66.6, 31.6), Percent_Total = c(39.1,
23.6, 14.1, 10.4, 5.23, 4.66, 2.47, 0.28, 0.07, 0.03)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))