r中的描述性统计 [英] descriptive statistics in r
问题描述
我试图为我的数据获取描述性统计信息.我经历了很多建议.但是我只想知道是否有任何包可以对下面提供的数据格式执行描述性统计.
I was trying to get descriptive stat for my data. I went through many suggestions. However I just want to know if there is any package(s) to perform descriptive stats on the data format provided below.
head(mydata)
X A1 A2 A3 M1 M2 M3 U1 U2 U3
1 A A A M M M U U U
2 X1 100 200 250 200 230 400 400 100 200
3 X2 600 300 400 300 550 750 800 900 540
4 X3 500 300 200 200 200 100 500 400 600
数据在列上有样本，在行上有变量.第一行是样本名称，第二行是组(A、M、U).我想获得每个组的描述性统计数据.例如，平均值，sd .... 对于 A 组(A1、A2、A3).任何人都可以让我知道我该怎么做.我已经看到大多数关于描述性统计数据的答案，它们都是针对列的.如果问题不清楚，请告诉我.感谢您的帮助.
The data has samples on the column and variables on rows. First row is samples name, second row is groups (A, M, U). I want to get descriptive statistics for each group. For example mean, sd.... for group A (A1, A2, A3). Could anyone please let me know how can I do this. I have seen most of the answers on descriptive stats and they are for columns. Please let me know if the question is not clear. Thank you for your help.
希格斯
推荐答案
@Phil 对他的推荐完全正确.
@Phil is dead right with his recommendation.
您将在 Hadley 的书中学到的关键原则之一是整洁数据原则(基本上:列中的变量，行中的个别观察).如果你想快速了解 tidy 数据，试试这个 小插图.
One of the key principles you'll learn in Hadley's book is the tidy data principle (very basically: variables in columns, individual observations in rows). If you want a quick introduction to tidy data, try this vignette.
有多种方法可以修复和分析您的数据，但这里有一个使用tidyverse"工具的示例.
There are multiple ways to go about fixing and analysing your data, but here is an example using tools from the 'tidyverse'.
# Load useful 'tidy data' packages
library(tidyverse)
# Make 'mydata'
mydata <- data_frame(X = c('', 'X1', 'X2', 'X3'),
A1 = c('A', 100, 600, 500),
A2 = c('A', 200, 300, 300),
A3 = c('A', 250, 400, 200),
M1 = c('M', 200, 300, 200),
M2 = c('M', 230, 550, 200),
M3 = c('M', 400, 750, 100),
U1 = c('U', 400, 800, 500),
U2 = c('U', 100, 900, 400),
U3 = c('U', 200, 540, 600))
# View 'mydata'
mydata
#> # A tibble: 4 x 10
#> X A1 A2 A3 M1 M2 M3 U1 U2 U3
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 "" A A A M M M U U U
#> 2 X1 100 200 250 200 230 400 400 100 200
#> 3 X2 600 300 400 300 550 750 800 900 540
#> 4 X3 500 300 200 200 200 100 500 400 600
转换为整洁的数据框
# Transpose rows and columns and convert resulting matrix back into a dataframe
mydata_new <- as_data_frame(t(mydata))
# View 'mydata_new'
mydata_new
#> # A tibble: 10 x 4
#> V1 V2 V3 V4
#> <chr> <chr> <chr> <chr>
#> 1 "" X1 X2 X3
#> 2 A 100 600 500
#> 3 A 200 300 300
#> 4 A 250 400 200
#> 5 M 200 300 200
#> 6 M 230 550 200
#> 7 M 400 750 100
#> 8 U 400 800 500
#> 9 U 100 900 400
#> 10 U 200 540 600
# Clean 'mydata_new'
## Add column names
colnames(mydata_new) <- c('Group', 'X1', 'X2', 'X3')
## Remove first row
mydata_new <- mydata_new[-1, ]
# View cleaned 'mydata_new'
mydata_new
#> # A tibble: 9 x 4
#> Group X1 X2 X3
#> <chr> <chr> <chr> <chr>
#> 1 A 100 600 500
#> 2 A 200 300 300
#> 3 A 250 400 200
#> 4 M 200 300 200
#> 5 M 230 550 200
#> 6 M 400 750 100
#> 7 U 400 800 500
#> 8 U 100 900 400
#> 9 U 200 540 600
现在汇总数据.
# Summarise numeric data
mydata_new %>%
# Convert all data columns from 'character' to 'numeric'
mutate_at(vars(starts_with('X')),
as.numeric) %>%
# Group data by the grouping variable before summarising
group_by(Group) %>%
# Calculate MEAN and SD for each data column
summarise_at(vars(starts_with('X')),
funs(MEAN = mean, SD = sd))
#> # A tibble: 3 x 7
#> Group X1_MEAN X2_MEAN X3_MEAN X1_SD X2_SD X3_SD
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A 183. 433. 333. 76.4 153. 153.
#> 2 M 277. 533. 167. 108. 225. 57.7
#> 3 U 233. 747. 500 153. 186. 100
<小时>
更新:2018 年 5 月 10 日，关于添加变异系数的查询.
变异系数不是基本的 R 函数，因此创建一个用户定义的函数.
Coefficient of variation is not a base R function, so create a user-defined function.
# Define function: (cv = sd / mean)
coef_var = function(x) {
sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)
}
使用添加的汇总函数重新执行汇总
Re-execute the summary with added summary functions
# Execute summary
mydata_new %>%
# Convert all data columns from 'character' to 'numeric'
mutate_at(vars(starts_with('X')),
as.numeric) %>%
# Group data by the grouping variable before summarising
group_by(Group) %>%
# Calculate summaries each data column
## Call the summary functions with a dummy "." argument so that
## Additional arguments can be added to the called functions
## (e.g., adding na.rm = TRUE to cope with missing data)
## See ?dplyr::funs for details
summarise_at(vars(starts_with('X')),
funs(MEAN = mean(., na.rm = TRUE), # Mean
SD = sd(., na.rm = TRUE), # SD
CV = coef_var, # Coefficient of variation
# Add other summary stats as needed
MEDIAN = median(., na.rm = TRUE), # Median
Q25 = quantile(., prob = 0.25, na.rm = TRUE), # 25th percentile
Q75 = quantile(., prob = 0.75, na.rm = TRUE), # 75th percentile
min = min(., na.rm = TRUE), # Minimum
max = max(., na.rm = TRUE))) # Maximum
#> # A tibble: 3 x 25
#> Group X1_MEAN X2_MEAN X3_MEAN X1_SD X2_SD X3_SD X1_CV X2_CV X3_CV
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A 183. 433. 333. 76.4 153. 153. 0.417 0.353 0.458
#> 2 M 277. 533. 167. 108. 225. 57.7 0.390 0.423 0.346
#> 3 U 233. 747. 500 153. 186. 100 0.655 0.249 0.2
#> # ... with 15 more variables: X1_MEDIAN <dbl>, X2_MEDIAN <dbl>,
#> # X3_MEDIAN <dbl>, X1_Q25 <dbl>, X2_Q25 <dbl>, X3_Q25 <dbl>,
#> # X1_Q75 <dbl>, X2_Q75 <dbl>, X3_Q75 <dbl>, X1_min <dbl>, X2_min <dbl>,
#> # X3_min <dbl>, X1_max <dbl>, X2_max <dbl>, X3_max <dbl>
由 reprex 包 (v0.2.0) 于 2018 年 5 月 10 日创建.
Created on 2018-05-10 by the reprex package (v0.2.0).
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