[1] "Modified Frequency Table" 1 2 4 6 2 1 0 7 1 0 0 8 0 1 1 9 0 3 0 Example 3: The data frequency and cumulative frequency tables can also be visualized by importing the data set into the working space. The frequency table is plotted to keep col1 of the dataframe in mind....
sum, 'PayDate': 'count', 'interval': np.min }) # make the column names more meaningful salesRFM.rename(columns={ 'PayAmount': 'Monetary', 'PayDate': 'Frequency', 'interval':'Recency' }, inplace=True) salesRFM.head() 代码语言:javascript 代码运行次数:0 运行 AI代码解释 #均值划分 ...
## ## Cell Contents ## |---| ## | Count | ## | Expected Values | ## | Chi-square contribution | ## | Row Percent | ## | Column Percent | ## | Total Percent | ## |---| ## ## Total Observations in Table: 200 ## ## | data1$impro ## data1$treat | marked | ...
The table() function gives you a frequency count of all the unique values in a vector, includingTRUEandFALSE. freq_table <- table(logical_vec) print(freq_table) Output: FALSE TRUE 2 3 3. Using the dplyr Package Suppose you are working with a data frame, then dplyr packages will provide...
## ## Cell Contents ## |---| ## | Count | ## | Expected Values | ## | Chi-square contribution | ## | Row Percent | ## | Column Percent | ## | Total Percent | ## |---| ## ## Total Observations in Table: 200 ## ## | data1$impro ## data1$treat | marked | ...
count(binary) %>% mutate(percent = 100 * n/ sum(n)) 1. 2. 3. # A tibble: 2 × 3 binary n percent <chr> <int> <dbl> 1 FAIL 991 55.2 2 PASS 803 44.8 1. 2. 3. 4. 5. 我们唯一的预测变量是budget_2013,每部电影2013年的的预算(美元)。
## | Count | ## | Expected Values | ## | Chi-square contribution | ## | Row Percent | ## | Column Percent | ## | Total Percent | ## |---| ## ## Total Observations in Table: 200 ## ## | data1$impro ## data1$treat...
## | Count | ## | Expected Values | ## | Chi-square contribution | ## | Row Percent | ## | Column Percent | ## | Total Percent | ## |---| ## ## Total Observations in Table: 200 ## ## | data1$impro ## data1$treat...
expected shared number of species) this conversion will not work. The argument reps specifies the number of randomizations (a minimum of 999 is recommended- default is 9999). set_all_species_equal weights all species equally in the null model instead of weighting species by frequency of ...
Date(data$Date) # Convert character string column to date data_ts <- xts(data[2:6], data$Date) # Convert data frame to time series data_ts # Print time series class(data_ts) # Check class of time series data_zoo<-as.zoo(data_ts) # convert to zoo ### INITILISATION X<-data_...