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To create pivot table with sum for data stored in R data frame, we can follow the below steps −First of all, create a data frame with two categorical and one numerical column.Then, use dcast function from reshape2 package to create pivot table for the data stored in data frame.ExampleCreate the data frameLet’s create a data frame as shown below −Group
To change the starting point of bars in bar plot for a ggplot2 graph in R, we can use coord_cartesian function of ggplot2 package with ylim argument where we can change the starting and ending point for the bar plot values.Check out the Example given below to understand how it can be done.ExampleFollowing snippet creates a sample data frame −x
To find the log10 of each value if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to find the log10 if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Level
To increase the length of Y-axis for ggplot2 graph in R, we can use scale_y_continuous function with limits argument.For Example, if we have a data frame called df that contains two columns say X and Y and we want to have the length of Y-axis starting from 1 to 10 by using the below mentioned command −ggplot(df,aes(X,Y))+geom_point()+scale_y_continuous(limits=c(1,10))ExampleFollowing snippet creates a sample data frame −x
To separate two values in single column in R data frame, we can follow the below steps −First of all, create a data frame.Then, use separate function from tidyr package to separate the values in single column.ExampleCreate the data frameLet’s create a data frame as shown below −df
To standardize columns if some columns are categorical in R data frame, we can follow the below steps −First of all, create a data frame.Then, use numcolwise function from plyr package to standardize columns if some columns are categorical.ExampleCreate the data frameLet’s create a data frame as shown below −Level
To find the frequency of unique values and missing values for each column in an R data frame, we can use apply function with table function and useNA argument set to always.For Example, if we have a data frame called df then we can find the frequency of unique values and missing values for each column in df by using the below mentioned command −apply(df,2,table,useNA="always")Example 1Following snippet creates a sample data frame −x1
To convert first letter into capital in single column data frame in R, we can follow the below steps −First of all, create a data frame with string column.Then, use capitalize function from R.utils package to convert first letter into capital in single column.ExampleCreate the data frameLet’s create a data frame as shown below −Names
To convert first letter into capital in single column data.table object in R, we can follow the below steps −First of all, create a data.table object with string column.Then, use sub function along with mutate function of dplyr package to convert first letter into capital in string column.ExampleCreate the data.table objectLet’s create a data.table object as shown below −library(data.table) Names
To find the frequency of unique values for each column in an R data frame, we can use apply function with table function.For Example, if we have a data frame called df then we can find the frequency of unique values for each column in df by using the below mentioned command −apply(df,2,table)Example 1Following snippet creates a sample data frame −x1