通用解决方案更新以对多列进行排序并过滤 cut.off
Generic solution update to sort multiple columns and filter a cut.off
hy，这里是 提出的解决方案的更新建议。
谁可以为创建更接近 [Expected output] 的输出并使用 dplyr 函数概括解决方案做出贡献。
data.frame(
RC1=c(0.902,0.9,0.899,0.825,0.802,0.745,0.744,0.74,0.382,0.356,0.309,0.295,0.194,0.162,0.162,0.156,0.153,0.147,0.144,0.142,0.123,0.113,0.098,0.062),
RC2=c(0.206,0.282,0.133,0.057,0.091,0.243,-0.068,0.105,0.143,0.173,0.329,0.683,0.253,0.896,-0.155,-0.126,0.06,-0.158,0.952,0.932,-0.077,-0.062,0.322,-0.065),
RC3=c(0.153,-0.029,0.093,0.138,0.289,0.071,0.413,-0.011,-0.069,0.181,0.123,-0.035,0.807,0.104,-0.044,0.504,0.15,-0.004,-0.013,0.106,0.785,-0.053,0.751,0.858),
RC4=c(0.078,0.05,0.219,0.216,0.218,0.114,0.122,0.249,0.726,0.108,0.725,-0.089,0.249,0.146,0.622,-0.189,0.099,0.406,0.05,0.026,-0.018,-0.095,0.007,-0.118),
RC5=c(0.217,0.021,-0.058,0.166,0.352,0.09,0.26,-0.354,0.065,-0.014,0.064,0.359,0.134,-0.114,0.212,0.178,0.878,0.71,-0.019,-0.021,0.015,-0.055,0.165,-0.074),
RC6=c(0.027,-0.007,0.087,0.104,0.045,0.319,0.296,0.205,0.088,0.816,0.229,0.302,0.163,0.059,-0.256,0.604,-0.07,0.394,-0.02,-0.041,0.071,-0.008,0.219,-0.068),
RC7=c(-0.015,-0.15,0.073,0.126,0.06,0.347,0.082,-0.093,-0.155,0.093,-0.045,-0.175,-0.021,0.004,0.052,-0.184,-0.054,-0.008,0.012,-0.004,0.094,0.951,-0.001,-0.118))->df
row.names(df)<- c("X5","X12","X13","X2","X6","X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18")
ord1 <- apply(as.matrix(df),1,function(x) min(which(abs(x)>=0.4),ncol(df)))
ord2 <- df[cbind(1:nrow(df),ord1)]
df[order(ord1,-abs(ord2)),]
df1<-df[ , ]> 0.4
row.names(df1)<- c("X5","X12","X13","X2","X6","X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18")
df1
df[df[,]< 0.4] <- ""
df
Output:
RC1 RC2 RC3 RC4 RC5 RC6 RC7
X5 0.902
X12 0.9
X13 0.899
X2 0.825
X6 0.802
X4 0.745
X3 0.744 0.413
X11 0.74
X15 0.726
X10 0.816
X16 0.725
X8 0.683
X20 0.807
X19 0.896
X17 0.622
X21 0.504 0.604
X9 0.878
X7 0.406 0.71
X22 0.952
X24 0.932
X1 0.785
X14 0.951
X23 0.751
X18 0.858
预期输出：
library(tidyverse)
data <-
data.frame(
id=c("X5","X12","X13","X2","X6", "X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18"),
RC1=c(0.902,0.9,0.899,0.825,0.802,0.745,0.744,0.74,0.382,0.356,0.309,0.295,0.194,0.162,0.162,0.156,0.153,0.147,0.144,0.142,0.123,0.113,0.098,0.062),
RC2=c(0.206,0.282,0.133,0.057,0.091,0.243,-0.068,0.105,0.143,0.173,0.329,0.683,0.253,0.896,-0.155,-0.126,0.06,-0.158,0.952,0.932,-0.077,-0.062,0.322,-0.065),
RC3=c(0.153,-0.029,0.093,0.138,0.289,0.071,0.413,-0.011,-0.069,0.181,0.123,-0.035,0.807,0.104,-0.044,0.504,0.15,-0.004,-0.013,0.106,0.785,-0.053,0.751,0.858),
RC4=c(0.078,0.05,0.219,0.216,0.218,0.114,0.122,0.249,0.726,0.108,0.725,-0.089,0.249,0.146,0.622,-0.189,0.099,0.406,0.05,0.026,-0.018,-0.095,0.007,-0.118),
RC5=c(0.217,0.021,-0.058,0.166,0.352,0.09,0.26,-0.354,0.065,-0.014,0.064,0.359,0.134,-0.114,0.212,0.178,0.878,0.71,-0.019,-0.021,0.015,-0.055,0.165,-0.074),
RC6=c(0.027,-0.007,0.087,0.104,0.045,0.319,0.296,0.205,0.088,0.816,0.229,0.302,0.163,0.059,-0.256,0.604,-0.07,0.394,-0.02,-0.041,0.071,-0.008,0.219,-0.068),
RC7=c(-0.015,-0.15,0.073,0.126,0.06,0.347,0.082,-0.093,-0.155,0.093,-0.045,-0.175,-0.021,0.004,0.052,-0.184,-0.054,-0.008,0.012,-0.004,0.094,0.951,-0.001,-0.118)
)
# Question 1: How to sort the columns, from largest to smallest, in each column, as in the image?
data %>% arrange(-RC1)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X5 0.902 0.206 0.153 0.078 0.217 0.027 -0.015
#> 2 X12 0.900 0.282 -0.029 0.050 0.021 -0.007 -0.150
#> 3 X13 0.899 0.133 0.093 0.219 -0.058 0.087 0.073
#> 4 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 5 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 6 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
#> 7 X3 0.744 -0.068 0.413 0.122 0.260 0.296 0.082
#> 8 X11 0.740 0.105 -0.011 0.249 -0.354 0.205 -0.093
#> 9 X15 0.382 0.143 -0.069 0.726 0.065 0.088 -0.155
#> 10 X10 0.356 0.173 0.181 0.108 -0.014 0.816 0.093
#> 11 X16 0.309 0.329 0.123 0.725 0.064 0.229 -0.045
#> 12 X8 0.295 0.683 -0.035 -0.089 0.359 0.302 -0.175
#> 13 X20 0.194 0.253 0.807 0.249 0.134 0.163 -0.021
#> 14 X19 0.162 0.896 0.104 0.146 -0.114 0.059 0.004
#> 15 X17 0.162 -0.155 -0.044 0.622 0.212 -0.256 0.052
#> 16 X21 0.156 -0.126 0.504 -0.189 0.178 0.604 -0.184
#> 17 X9 0.153 0.060 0.150 0.099 0.878 -0.070 -0.054
#> 18 X7 0.147 -0.158 -0.004 0.406 0.710 0.394 -0.008
#> 19 X22 0.144 0.952 -0.013 0.050 -0.019 -0.020 0.012
#> 20 X24 0.142 0.932 0.106 0.026 -0.021 -0.041 -0.004
#> 21 X1 0.123 -0.077 0.785 -0.018 0.015 0.071 0.094
#> 22 X14 0.113 -0.062 -0.053 -0.095 -0.055 -0.008 0.951
#> 23 X23 0.098 0.322 0.751 0.007 0.165 0.219 -0.001
#> 24 X18 0.062 -0.065 0.858 -0.118 -0.074 -0.068 -0.118
# Question 2: How to hide the values in each column when the value is =< 0.04?
data %>% filter(RC1 > 0.04)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X5 0.902 0.206 0.153 0.078 0.217 0.027 -0.015
#> 2 X12 0.900 0.282 -0.029 0.050 0.021 -0.007 -0.150
#> 3 X13 0.899 0.133 0.093 0.219 -0.058 0.087 0.073
#> 4 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 5 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 6 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
#> 7 X3 0.744 -0.068 0.413 0.122 0.260 0.296 0.082
#> 8 X11 0.740 0.105 -0.011 0.249 -0.354 0.205 -0.093
#> 9 X15 0.382 0.143 -0.069 0.726 0.065 0.088 -0.155
#> 10 X10 0.356 0.173 0.181 0.108 -0.014 0.816 0.093
#> 11 X16 0.309 0.329 0.123 0.725 0.064 0.229 -0.045
#> 12 X8 0.295 0.683 -0.035 -0.089 0.359 0.302 -0.175
#> 13 X20 0.194 0.253 0.807 0.249 0.134 0.163 -0.021
#> 14 X19 0.162 0.896 0.104 0.146 -0.114 0.059 0.004
#> 15 X17 0.162 -0.155 -0.044 0.622 0.212 -0.256 0.052
#> 16 X21 0.156 -0.126 0.504 -0.189 0.178 0.604 -0.184
#> 17 X9 0.153 0.060 0.150 0.099 0.878 -0.070 -0.054
#> 18 X7 0.147 -0.158 -0.004 0.406 0.710 0.394 -0.008
#> 19 X22 0.144 0.952 -0.013 0.050 -0.019 -0.020 0.012
#> 20 X24 0.142 0.932 0.106 0.026 -0.021 -0.041 -0.004
#> 21 X1 0.123 -0.077 0.785 -0.018 0.015 0.071 0.094
#> 22 X14 0.113 -0.062 -0.053 -0.095 -0.055 -0.008 0.951
#> 23 X23 0.098 0.322 0.751 0.007 0.165 0.219 -0.001
#> 24 X18 0.062 -0.065 0.858 -0.118 -0.074 -0.068 -0.118
# Question 3:That the solution is, if possible, generic for n columns
data %>% filter_at(vars(starts_with("RC")), ~ .x > 0.04)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 2 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 3 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
# Question 4: If possible, how visually can the doR output be presented in table format (expected output)?.
# Output is already a table, you can use kable package for HTML table rendering
由 reprex package (v2.0.1)
于 2021-09-09 创建
现在问题解决了，我想这就是你想要的：
library(dplyr)
df %>%
mutate(across(everything(),
~ ifelse(. < 0.4, "", format(., digits = 3)))) %>%
arrange(across(everything(), desc))
# RC1 RC2 RC3 RC4 RC5 RC6 RC7
# 1 0.902
# 2 0.900
# 3 0.899
# 4 0.825
# 5 0.802
# 6 0.745
# 7 0.744 0.413
# 8 0.740
# 9 0.952
# 10 0.932
# 11 0.896
# 12 0.683
# 13 0.858
# 14 0.807
# 15 0.785
# 16 0.751
# 17 0.504 0.604
# 18 0.726
# 19 0.725
# 20 0.622
# 21 0.406 0.710
# 22 0.878
# 23 0.816
# 24 0.951
hy，这里是
data.frame(
RC1=c(0.902,0.9,0.899,0.825,0.802,0.745,0.744,0.74,0.382,0.356,0.309,0.295,0.194,0.162,0.162,0.156,0.153,0.147,0.144,0.142,0.123,0.113,0.098,0.062),
RC2=c(0.206,0.282,0.133,0.057,0.091,0.243,-0.068,0.105,0.143,0.173,0.329,0.683,0.253,0.896,-0.155,-0.126,0.06,-0.158,0.952,0.932,-0.077,-0.062,0.322,-0.065),
RC3=c(0.153,-0.029,0.093,0.138,0.289,0.071,0.413,-0.011,-0.069,0.181,0.123,-0.035,0.807,0.104,-0.044,0.504,0.15,-0.004,-0.013,0.106,0.785,-0.053,0.751,0.858),
RC4=c(0.078,0.05,0.219,0.216,0.218,0.114,0.122,0.249,0.726,0.108,0.725,-0.089,0.249,0.146,0.622,-0.189,0.099,0.406,0.05,0.026,-0.018,-0.095,0.007,-0.118),
RC5=c(0.217,0.021,-0.058,0.166,0.352,0.09,0.26,-0.354,0.065,-0.014,0.064,0.359,0.134,-0.114,0.212,0.178,0.878,0.71,-0.019,-0.021,0.015,-0.055,0.165,-0.074),
RC6=c(0.027,-0.007,0.087,0.104,0.045,0.319,0.296,0.205,0.088,0.816,0.229,0.302,0.163,0.059,-0.256,0.604,-0.07,0.394,-0.02,-0.041,0.071,-0.008,0.219,-0.068),
RC7=c(-0.015,-0.15,0.073,0.126,0.06,0.347,0.082,-0.093,-0.155,0.093,-0.045,-0.175,-0.021,0.004,0.052,-0.184,-0.054,-0.008,0.012,-0.004,0.094,0.951,-0.001,-0.118))->df
row.names(df)<- c("X5","X12","X13","X2","X6","X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18")
ord1 <- apply(as.matrix(df),1,function(x) min(which(abs(x)>=0.4),ncol(df)))
ord2 <- df[cbind(1:nrow(df),ord1)]
df[order(ord1,-abs(ord2)),]
df1<-df[ , ]> 0.4
row.names(df1)<- c("X5","X12","X13","X2","X6","X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18")
df1
df[df[,]< 0.4] <- ""
df
Output:
RC1 RC2 RC3 RC4 RC5 RC6 RC7
X5 0.902
X12 0.9
X13 0.899
X2 0.825
X6 0.802
X4 0.745
X3 0.744 0.413
X11 0.74
X15 0.726
X10 0.816
X16 0.725
X8 0.683
X20 0.807
X19 0.896
X17 0.622
X21 0.504 0.604
X9 0.878
X7 0.406 0.71
X22 0.952
X24 0.932
X1 0.785
X14 0.951
X23 0.751
X18 0.858
预期输出：
library(tidyverse)
data <-
data.frame(
id=c("X5","X12","X13","X2","X6", "X4","X3","X11","X15","X10","X16","X8","X20","X19","X17","X21","X9","X7","X22","X24","X1","X14","X23","X18"),
RC1=c(0.902,0.9,0.899,0.825,0.802,0.745,0.744,0.74,0.382,0.356,0.309,0.295,0.194,0.162,0.162,0.156,0.153,0.147,0.144,0.142,0.123,0.113,0.098,0.062),
RC2=c(0.206,0.282,0.133,0.057,0.091,0.243,-0.068,0.105,0.143,0.173,0.329,0.683,0.253,0.896,-0.155,-0.126,0.06,-0.158,0.952,0.932,-0.077,-0.062,0.322,-0.065),
RC3=c(0.153,-0.029,0.093,0.138,0.289,0.071,0.413,-0.011,-0.069,0.181,0.123,-0.035,0.807,0.104,-0.044,0.504,0.15,-0.004,-0.013,0.106,0.785,-0.053,0.751,0.858),
RC4=c(0.078,0.05,0.219,0.216,0.218,0.114,0.122,0.249,0.726,0.108,0.725,-0.089,0.249,0.146,0.622,-0.189,0.099,0.406,0.05,0.026,-0.018,-0.095,0.007,-0.118),
RC5=c(0.217,0.021,-0.058,0.166,0.352,0.09,0.26,-0.354,0.065,-0.014,0.064,0.359,0.134,-0.114,0.212,0.178,0.878,0.71,-0.019,-0.021,0.015,-0.055,0.165,-0.074),
RC6=c(0.027,-0.007,0.087,0.104,0.045,0.319,0.296,0.205,0.088,0.816,0.229,0.302,0.163,0.059,-0.256,0.604,-0.07,0.394,-0.02,-0.041,0.071,-0.008,0.219,-0.068),
RC7=c(-0.015,-0.15,0.073,0.126,0.06,0.347,0.082,-0.093,-0.155,0.093,-0.045,-0.175,-0.021,0.004,0.052,-0.184,-0.054,-0.008,0.012,-0.004,0.094,0.951,-0.001,-0.118)
)
# Question 1: How to sort the columns, from largest to smallest, in each column, as in the image?
data %>% arrange(-RC1)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X5 0.902 0.206 0.153 0.078 0.217 0.027 -0.015
#> 2 X12 0.900 0.282 -0.029 0.050 0.021 -0.007 -0.150
#> 3 X13 0.899 0.133 0.093 0.219 -0.058 0.087 0.073
#> 4 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 5 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 6 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
#> 7 X3 0.744 -0.068 0.413 0.122 0.260 0.296 0.082
#> 8 X11 0.740 0.105 -0.011 0.249 -0.354 0.205 -0.093
#> 9 X15 0.382 0.143 -0.069 0.726 0.065 0.088 -0.155
#> 10 X10 0.356 0.173 0.181 0.108 -0.014 0.816 0.093
#> 11 X16 0.309 0.329 0.123 0.725 0.064 0.229 -0.045
#> 12 X8 0.295 0.683 -0.035 -0.089 0.359 0.302 -0.175
#> 13 X20 0.194 0.253 0.807 0.249 0.134 0.163 -0.021
#> 14 X19 0.162 0.896 0.104 0.146 -0.114 0.059 0.004
#> 15 X17 0.162 -0.155 -0.044 0.622 0.212 -0.256 0.052
#> 16 X21 0.156 -0.126 0.504 -0.189 0.178 0.604 -0.184
#> 17 X9 0.153 0.060 0.150 0.099 0.878 -0.070 -0.054
#> 18 X7 0.147 -0.158 -0.004 0.406 0.710 0.394 -0.008
#> 19 X22 0.144 0.952 -0.013 0.050 -0.019 -0.020 0.012
#> 20 X24 0.142 0.932 0.106 0.026 -0.021 -0.041 -0.004
#> 21 X1 0.123 -0.077 0.785 -0.018 0.015 0.071 0.094
#> 22 X14 0.113 -0.062 -0.053 -0.095 -0.055 -0.008 0.951
#> 23 X23 0.098 0.322 0.751 0.007 0.165 0.219 -0.001
#> 24 X18 0.062 -0.065 0.858 -0.118 -0.074 -0.068 -0.118
# Question 2: How to hide the values in each column when the value is =< 0.04?
data %>% filter(RC1 > 0.04)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X5 0.902 0.206 0.153 0.078 0.217 0.027 -0.015
#> 2 X12 0.900 0.282 -0.029 0.050 0.021 -0.007 -0.150
#> 3 X13 0.899 0.133 0.093 0.219 -0.058 0.087 0.073
#> 4 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 5 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 6 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
#> 7 X3 0.744 -0.068 0.413 0.122 0.260 0.296 0.082
#> 8 X11 0.740 0.105 -0.011 0.249 -0.354 0.205 -0.093
#> 9 X15 0.382 0.143 -0.069 0.726 0.065 0.088 -0.155
#> 10 X10 0.356 0.173 0.181 0.108 -0.014 0.816 0.093
#> 11 X16 0.309 0.329 0.123 0.725 0.064 0.229 -0.045
#> 12 X8 0.295 0.683 -0.035 -0.089 0.359 0.302 -0.175
#> 13 X20 0.194 0.253 0.807 0.249 0.134 0.163 -0.021
#> 14 X19 0.162 0.896 0.104 0.146 -0.114 0.059 0.004
#> 15 X17 0.162 -0.155 -0.044 0.622 0.212 -0.256 0.052
#> 16 X21 0.156 -0.126 0.504 -0.189 0.178 0.604 -0.184
#> 17 X9 0.153 0.060 0.150 0.099 0.878 -0.070 -0.054
#> 18 X7 0.147 -0.158 -0.004 0.406 0.710 0.394 -0.008
#> 19 X22 0.144 0.952 -0.013 0.050 -0.019 -0.020 0.012
#> 20 X24 0.142 0.932 0.106 0.026 -0.021 -0.041 -0.004
#> 21 X1 0.123 -0.077 0.785 -0.018 0.015 0.071 0.094
#> 22 X14 0.113 -0.062 -0.053 -0.095 -0.055 -0.008 0.951
#> 23 X23 0.098 0.322 0.751 0.007 0.165 0.219 -0.001
#> 24 X18 0.062 -0.065 0.858 -0.118 -0.074 -0.068 -0.118
# Question 3:That the solution is, if possible, generic for n columns
data %>% filter_at(vars(starts_with("RC")), ~ .x > 0.04)
#> id RC1 RC2 RC3 RC4 RC5 RC6 RC7
#> 1 X2 0.825 0.057 0.138 0.216 0.166 0.104 0.126
#> 2 X6 0.802 0.091 0.289 0.218 0.352 0.045 0.060
#> 3 X4 0.745 0.243 0.071 0.114 0.090 0.319 0.347
# Question 4: If possible, how visually can the doR output be presented in table format (expected output)?.
# Output is already a table, you can use kable package for HTML table rendering
由 reprex package (v2.0.1)
于 2021-09-09 创建现在问题解决了，我想这就是你想要的：
library(dplyr)
df %>%
mutate(across(everything(),
~ ifelse(. < 0.4, "", format(., digits = 3)))) %>%
arrange(across(everything(), desc))
# RC1 RC2 RC3 RC4 RC5 RC6 RC7
# 1 0.902
# 2 0.900
# 3 0.899
# 4 0.825
# 5 0.802
# 6 0.745
# 7 0.744 0.413
# 8 0.740
# 9 0.952
# 10 0.932
# 11 0.896
# 12 0.683
# 13 0.858
# 14 0.807
# 15 0.785
# 16 0.751
# 17 0.504 0.604
# 18 0.726
# 19 0.725
# 20 0.622
# 21 0.406 0.710
# 22 0.878
# 23 0.816
# 24 0.951