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R Programming Articles
Page 25 of 174
How to deal with Error: stat_count() can only have an x or y aesthetic in R?
To deal with Error: stat_count() can only have an x or y aesthetic, we need to pass the stat="identity" argument inside geom_bar function. Since we do not pass the count for bars and a bar graph can only contain only count variable, hence stat="identity" is needed so that geom_bar considers only one variable in aes for counting. Check out the below example to understand the difference.ExampleConsider the below data frame −factor
Read MoreHow to check if a time series is stationary in R?
To check if a time series is stationary, we can use Dickey-Fuller test using adf.test function of tseries package. For example, if we have a time series object say TimeData then to check whether this time series is stationary or not we can use the command adf.test(TimeData).Example1x1
Read MoreHow to apply multiple AND conditions to a data frame in R?
To apply multiple conditions to a data frame, we can use double and sign that is &&. For example, if we have a data frame called df that contains three columns say x, y, z and we want to add a value to all columns if first element in z equals to 5 then it can be done by using the command −if(df$x && df$y && df$y == 5){ df$x = df$x+10 df$y = df$y+10 df$z = df$z+10 }Example1Consider the below data frame −x1
Read MoreHow to create multiple bar plots for varying categories with same width bars using ggplot2 in R?
To create multiple bar plots for varying categories with same width bars using ggplot2, we would need to play with width argument inside geom_bar function to match the width of the bars in each bar plot. The best way to do this would be setting the larger ones to 0.25 and the shorter ones to 0.50.ExampleConsider the below data frame −x1
Read MoreHow to find the high leverage values for a regression model in R?
To find the high leverage values for a regression model, we first need to find the predicted values or hat values that can be found by using hatvalues function and then define the condition for high leverage and extract them. For example if we have a regression model say M then the hat values can be found by using the command hatvalues(M), now to find the high leverage values that are greater than 0.05 can be found by using the below code −which(hatvalues(M)>0.05)Example1Consider the below data frame −x1
Read MoreHow to add a variable description in R?
To add a variable description in R, we can use comment function and if we want to have a look at the description then structure call of the data frame will be used. For example, if we have a data frame say df that contains a column x then we can describe x by using the command comment(df$x)
Read MoreHow to randomly sample rows from an R data frame using sample_n?
To randomly sample rows from an R data frame using sample_n, we can directly pass the sample size inside sample_n function of dplyr package. For example, if we have data frame called df then to create a random sample of 5 rows in df can be done by using the command −df%>%sample_n(5)Example1Consider the below data frame −x1
Read MoreHow to convert a list to JSON in R?
To convert a list to JSON, we can use toJSON function of jsonlite package. For example, if we have a list called LIST then it can be converted to a JSON by using the command toJSON(LIST,pretty=TRUE,auto_unbox=TRUE). We need to make sure that the package jsonlite is loaded in R environment otherwise the command won’t work.ExampleList
Read MoreHow to find the sum of non-missing values in an R data frame column?
To find the sum of non-missing values in an R data frame column, we can simply use sum function and set the na.rm to TRUE. For example, if we have a data frame called df that contains a column say x which has some missing values then the sum of the non-missing values can be found by using the command sum(df$x,na.rm=TRUE).Example1Consider the below data frame −x1
Read MoreHow to find the column mean by excluding NA's and if all values are NA then output NA in R data frame?
To find the column mean by excluding NA’s can be easily done by using na,rm but if we want to have NA if all the values are NA then it won’t be that straight forward. Therefore, in such situation, we can use ifelse function and return the output as NA if all the values are NA as shown in the below examples.Example1Consider the below data frame −x1
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