Analysis in R: Basic commands for manipulating data

Many of you may use Excel to analyze data, but using R may reveal facts that Excel could not. We encourage you to use R to enrich your analysis.

Here are some basic commands, and “KARADA GOOD” introduces a number of packages. Please have a look.

＜おすすめのRに関する書籍です＞

RとRStudioによる教育テストデータの分析 | 堀 一輝, 福原 弘岳, 山田 剛史

Amazonで堀 一輝, 福原 弘岳, 山田 剛史のRとRStudioによる教育テストデータの分析。アマゾンならポイント還元本が多数。

www.amazon.co.jp

How to find the maximum and minimum values

I think the maximum and minimum values are very important indicators.

Commands used in R

Max：max(x, na.rm = FALSE)

Min：min(x, na.rm = FALSE)

# Missing values are excluded by setting na.rm to TRUE
# The default is FALSE
x <- c(NA, 3, 4, 12, 100)
# na.rm is TRUE
max(x, na.rm = TRUE)
[1] 100
# na.rm is FALSE
max(x, na.rm = FALSE)
[1] NA
min(x, na.rm = TRUE)
[1] 3
# na.rm is FALSE
min(x, na.rm = FALSE)

Row and column sums and averages

This can be done in Excel, but there are some tasks, such as specifying a range, that can lead to errors.

Commands used in R

Row Sum：rowSums(x, na.rm = FALSE)

Row Mean：rowMeans(x, na.rm = FALSE)

Col Sum：colSums(x, na.rm = FALSE)

Col mean：colMeans(x, na.rm = FALSE)

# Missing values are excluded by setting na.rm to TRUE
# The default is FALSE
# Create a 3*3 matrix
x <- matrix(c(8, 5, 6, 3, 9, 3, 1, 2, 3), 3, 3)
x 
　　[,1] [,2] [,3]
[1,] 　8　 3　　1 
[2,]　 5　 9　　2 
[3,]　 6　 3　　3

#Row Sum
rowSums(x)
[1] 12 16 12

#Row Mean
rowMeans(x)
[1] 4.000000 5.333333 4.000000

#Col Sum
colSums(x)
[1] 19 15 6
#Col Mean
colMeans(x)
[1] 6.333333 5.000000 2.000000

I hope this makes your analysis a little easier !!

＜おすすめのRに関する書籍です＞

多変量解析の基礎 II　主成分分析（改訂版）: 理論とRによる演習 | 古橋武 | 工学 | Kindleストア

Amazonで古橋武の多変量解析の基礎 II　主成分分析（改訂版）: 理論とRによる演習。一度購入いただいた電子書籍は、KindleおよびFire端末、スマートフォンやタブレットなど、様々な端末でもお楽しみいただけます。

www.amazon.co.jp