data<-c(ID Math History Chem Group 1 S1 98 92 95 J 2 S2 96 90 93 S 3 S3 99 93 96 J 4 S4 100 94 97 J 5 S5 99 93 96 S 6 S6 95 89 92 S 7 S7 93 87 90 J 8 S8 94 88 91 S 9 S9 99 93 96 J 10 S10 97 91 94 J 11 S11 95 89 92 S 12 S12 92 86 89 S 13 S13 91 85 88 J 14 S14 89 83 86 S 15 S15 93 87 90 J 16 S16 99 93 96 S 17 S17 92 86 89 J 18 S18 91 85 88 S 19 S19 99 93 85 J),Check whether there is significant difference in 'Math' between J and S 'Group'

To check if there is a significant difference in 'Math' between 'J' and 'S' groups, we can perform an independent samples t-test. Here is the R code to do that:

# First, subset the data by group
group_J <- data$Math[data$Group == 'J']
group_S <- data$Math[data$Group == 'S']

# Perform independent samples t-test
t.test(group_J, group_S)

The output will include the t-statistic, degrees of freedom, and p-value. If the p-value is less than our chosen level of significance (usually 0.05), it indicates that there is a significant difference in 'Math' between 'J' and 'S' groups.

相关问题

data<-c( ID Math History Chem Group 1 S1 98 92 95 J 2 S2 96 90 93 S 3 S3 99 93 96 J 4 S4 100 94 97 J 5 S5 99 93 96 S 6 S6 95 89 92 S 7 S7 93 87 90 J 8 S8 94 88 91 S 9 S9 99 93 96 J 10 S10 97 91 94 J 11 S11 95 89 92 S 12 S12 92 86 89 S 13 S13 91 85 88 J 14 S14 89 83 86 S 15 S15 93 87 90 J 16 S16 99 93 96 S 17 S17 92 86 89 J 18 S18 91 85 88 S 19 S19 99 93 85 J),plot 'Math', 'History' and 'Chem' scores based on 'Group'

To plot the 'Math', 'History', and 'Chem' scores based on 'Group', we can use the ggplot2 package in R. Here is the R code to do that:

library(ggplot2)

# Convert data to data frame
df <- data.frame(ID = data[seq(1,length(data),5)],
                 Math = data[seq(2,length(data),5)],
                 History = data[seq(3,length(data),5)],
                 Chem = data[seq(4,length(data),5)],
                 Group = data[seq(5,length(data),5)])

# Plot using ggplot2
ggplot(df, aes(x = Group)) +
  geom_boxplot(aes(y = Math, fill = Group), alpha = 0.5) +
  geom_boxplot(aes(y = History, fill = Group), alpha = 0.5) +
  geom_boxplot(aes(y = Chem, fill = Group), alpha = 0.5) +
  labs(x = "Group", y = "Score") +
  scale_fill_manual(values = c("blue", "red")) +
  theme_minimal()

This will create a boxplot for each score ('Math', 'History', and 'Chem') with 'Group' on the X-axis. The blue boxplots represent the 'J' group and the red boxplots represent the 'S' group. The alpha parameter controls the transparency of the boxplots. You can adjust this value to your preference.

data<-c(ID Math History Chem Group 1 S1 98 92 95 J 2 S2 96 90 93 S 3 S3 99 93 96 J 4 S4 100 94 97 J 5 S5 99 93 96 S 6 S6 95 89 92 S 7 S7 93 87 90 J 8 S8 94 88 91 S 9 S9 99 93 96 J 10 S10 97 91 94 J 11 S11 95 89 92 S 12 S12 92 86 89 S 13 S13 91 85 88 J 14 S14 89 83 86 S 15 S15 93 87 90 J 16 S16 99 93 96 S 17 S17 92 86 89 J 18 S18 91 85 88 S 19 S19 99 93 85 J ), 在R语言中，check the mode of each column 代码

可以使用str函数来查看数据框中每列变量的类型，示例代码如下：

data<-data.frame(ID=c(1:19),
                 Math=c(98,96,99,100,99,95,93,94,99,97,95,92,91,89,93,99,92,91,99),
                 History=c(92,90,93,94,93,89,87,88,93,91,89,86,85,83,87,93,86,85,93),
                 Chem=c(95,93,96,97,96,92,90,91,96,94,92,89,88,86,90,96,89,88,85),
                 Group=c("J","S","J","J","S","S","J","S","J","J","S","S","J","S","J",
                         "S","J","S","J"))

str(data)

运行结果如下：

'data.frame':	19 obs. of  5 variables:
 $ ID     : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Math   : num  98 96 99 100 99 95 93 94 99 97 ...
 $ History: num  92 90 93 94 93 89 87 88 93 91 ...
 $ Chem   : num  95 93 96 97 96 92 90 91 96 94 ...
 $ Group  : Factor w/ 2 levels "J","S": 1 2 1 1 2 2 1 2 1 1 ...

从运行结果可以看出，ID、Math、History、Chem列的类型是numeric，Group列的类型是factor。