Error in plotting C5.0 decision tree : how can I make proper model?

Question

This is my Jogging data.

WEATHER   JOGGED_YESTERDAY   CLASSIFICATION
C          N                  +
W          Y                  -
Y          Y                  -
C          Y                  -
Y          N                  -
W          Y                  -
C          N                  -
W          N                  +
C          Y                  -
W          Y                  +
W          N                  +
C          N                  +
Y          N                  -
W          Y                  -

And I want to make simple decision tree using C5.0

jogging <- read.csv("Jogging.csv")
jogging           #training data

library(C50)
set.seed(12345)
jogging_random <- jogging[order(runif(14)), ]
View(jogging_random)

jogging_random$CLASSIFICATION <- ifelse(jogging_random$CLASSIFICATION=="+", 1, 0)
View(jogging_random)
jogging_random$CLASSIFICATION <- as.factor(jogging_random$CLASSIFICATION)
str(jogging_random)

jogging_model <- C5.0(jogging_random[-3], jogging_random$CLASSIFICATION)        
plot(jogging_model)

But this is my result.

Class specified by attribute `outcome'

Read 14 cases (3 attributes) from undefined.data

Decision tree:
 0 (14/5)


Evaluation on training data (14 cases):

        Decision Tree   
      ----------------  
      Size      Errors  

         1    5(35.7%)   <<


       (a)   (b)    <-classified as
      ----  ----
         9          (a): class 0
         5          (b): class 1


Time: 0.0 secs

There is no any branches in the model. How can I make proper decision tree using C5.0? I need to use C5.0 instead of other algorithms because I need to use 'information gain'. I need your help.