This document provides the R code used in the statistical analysis of data for the article titled:

Citizens Assemble: Social discourse through the Irish media ‘before’, ‘during’ and ‘after’ the Citizens’ Assembly on ‘How the State can make Ireland a leader in tackling climate change’.

library(xtable)
library(dplyr)
Labels for newspaper name
PaperLabel <- c('Farmers Journal', 'Irish Times', 'Independent', 'The Journal')
Data$PaperName <- PaperLabel[Data$Paper]
Labels for article tone
Data$Tone <- 'Neg'                                      
Data$Tone[which(Data$Positive == 1)] <- 'Pos'
Data$Tone[which(Data$Neutral == 1)] <- 'Neu'
Labels for article period
PeriodLabel <- c("Before","During","After")            
Data$PeriodName <- PeriodLabel[Data$Period]
Labels for article scale - reliance upon evidence
ScaleLabel <- c("None","Low","Medium","High","Very High")
Data$ScaleName <- ScaleLabel[Data$Scale]
Separating time periods
Before <- Data[which(Data$PeriodName == 'Before'),]     ## March-May 2017
During <- Data[which(Data$PeriodName == 'During'),]     ## Sept-Nov (inclusive) 2017
After  <- Data[which(Data$PeriodName == 'After'),]      ## Dec 2017 - May 2018
Separating media sources
FJ  <- Data[which(Data$PaperName == 'Farmers Journal'),]
IT  <- Data[which(Data$PaperName == 'Irish Times'),]    
Ind <- Data[which(Data$PaperName == 'Independent'),]    
TJ  <- Data[which(Data$PaperName == 'The Journal'),]    

TONE

Summary
Tone of language per paper OVERALL
     
      Farmers Journal Independent Irish Times The Journal
  Neg              54         117         132          45
  Neu              40          33          50          12
  Pos              15          38          39          19

    Pearson's Chi-squared test

data:  table(Data$Tone, Data$PaperName)
X-squared = 18.586, df = 6, p-value = 0.004922

     
      Farmers Journal Independent Irish Times The Journal
  Neg      -2.1215487   1.2283302   0.4351878   0.1183896
  Neu       3.8516290  -2.0476494  -0.0460366  -1.5455038
  Pos      -1.4599184   0.6492109  -0.5004265   1.5119288
Tone of language per paper BEFORE
     
      Farmers Journal Independent Irish Times The Journal
  Neg              11          27          27           8
  Neu               5           2          15           4
  Pos               6           1           9           8

    Pearson's Chi-squared test

data:  table(Before$Tone, Before$PaperName)
X-squared = 20.447, df = 6, p-value = 0.002305

     
      Farmers Journal Independent Irish Times The Journal
  Neg      -0.9852685   3.9306704  -1.2178137  -1.9252160
  Neu       0.2014543  -2.2326424   1.8914551  -0.1362399
  Pos       1.0136233  -2.5715890  -0.4393027   2.5265405
Tone of language per paper DURING
     
      Farmers Journal Independent Irish Times The Journal
  Neg              31          61          86          28
  Neu              20          25          25           3
  Pos               8          26          26           7

    Pearson's Chi-squared test

data:  table(During$Tone, During$PaperName)
X-squared = 12.728, df = 6, p-value = 0.04757

     
      Farmers Journal Independent Irish Times The Journal
  Neg      -1.2019923  -1.3301695   0.9929614   1.8831556
  Neu       2.6458654   0.3857951  -1.0520036  -2.1143548
  Pos      -1.2389391   1.2538640  -0.1471351  -0.1559384
Tone of language per paper AFTER
     
      Farmers Journal Independent Irish Times The Journal
  Neg              12          29          19           9
  Neu              15           6          10           5
  Pos               1          11           4           4

    Pearson's Chi-squared test

data:  table(After$Tone, After$PaperName)
X-squared = 16.62, df = 6, p-value = 0.01079

     
      Farmers Journal Independent Irish Times The Journal
  Neg      -1.4909233   1.3456141   0.3198981  -0.4795061
  Neu       3.2859653  -2.9685508   0.2222540  -0.1035162
  Pos      -2.0364312   1.8414685  -0.7084586   0.7782968

RELIANCE ON EVIDENCE

Summary
Reliance on Evidence OVERALL
           
            Farmers Journal Independent Irish Times The Journal
  High                   31          50          77          24
  Low                    25          55          76          20
  Medium                 46          66          61          17
  Very High               7          17           7          15

    Pearson's Chi-squared test

data:  table(Data$ScaleName, Data$PaperName)
X-squared = 34.502, df = 9, p-value = 7.293e-05

           
            Farmers Journal Independent Irish Times The Journal
  High           -0.5512325  -1.4548590   1.7099469   0.1901968
  Low            -1.6937665  -0.1359512   1.9554162  -0.6774990
  Medium          2.5304969   1.1093330  -1.7635833  -1.9250583
  Very High      -0.5714966   0.8056568  -3.2122061   4.1886165
Reliance on Evidence BEFORE
           
            Farmers Journal Independent Irish Times The Journal
  High                    6           7          15           9
  Low                     4          12          18           3
  Medium                  9          10          17           6
  Very High               3           1           1           2

    Pearson's Chi-squared test

data:  table(Before$ScaleName, Before$PaperName)
X-squared = 11.033, df = 9, p-value = 0.2735

           
            Farmers Journal Independent Irish Times The Journal
  High           -0.3169889  -0.9268292  -0.1362701   1.5897723
  Low            -1.3430319   1.3623273   1.0609597  -1.6070995
  Medium          0.7381837  -0.1079952  -0.1600310  -0.4273188
  Very High       1.7751742  -0.6410497  -1.5029311   0.9089642
Reliance on Evidence DURING
           
            Farmers Journal Independent Irish Times The Journal
  High                   15          28          60          11
  Low                    15          27          44           9
  Medium                 25          45          31           9
  Very High               4          12           2           9

    Pearson's Chi-squared test

data:  table(During$ScaleName, During$PaperName)
X-squared = 39.667, df = 9, p-value = 8.729e-06

           
            Farmers Journal Independent Irish Times The Journal
  High           -1.3500999  -2.1760828   3.4756839  -0.5561094
  Low            -0.3841674  -0.9658221   1.5725399  -0.5522731
  Medium          1.9163397   2.3176728  -2.9637536  -1.1375638
  Very High      -0.3219142   1.3965387  -3.5617286   3.8683358
Reliance on Evidence AFTER
           
            Farmers Journal Independent Irish Times The Journal
  High                   10          15           2           4
  Low                     6          16          14           8
  Medium                 12          11          13           2
  Very High               0           4           4           4

    Pearson's Chi-squared test

data:  table(After$ScaleName, After$PaperName)
X-squared = 20.714, df = 9, p-value = 0.01398

           
            Farmers Journal Independent Irish Times The Journal
  High           1.51812913  1.54263831 -2.90562018 -0.27372224
  Low           -1.73208146 -0.07455984  1.01286494  0.88760830
  Medium         1.62676638 -1.20315049  1.30926214 -1.92293751
  Very High     -1.95749002 -0.26190033  0.57306993  1.96478648

RELIANCE ON EVIDENCE VS. TONE

Summary
Reliance on Evidence vs. tone OVERALL
           
            Neg Neu Pos
  High       88  39  55
  Low       156  17   3
  Medium     77  71  42
  Very High  27   8  11

    Pearson's Chi-squared test

data:  table(Data$ScaleName, Data$Tone)
X-squared = 114.79, df = 6, p-value < 2.2e-16

           
                   Neg        Neu        Pos
  High      -3.3656110 -0.5019989  4.7925942
  Low        9.6481148 -4.9316222 -6.8898522
  Medium    -6.1279521  5.8393929  1.4657264
  Very High  0.0157394 -0.8991007  0.9467105
Reliance on Evidence vs. tone BEFORE
           
            Neg Neu Pos
  High       14   9  14
  Low        34   2   1
  Medium     23  15   4
  Very High   2   0   5

    Pearson's Chi-squared test

data:  table(Before$ScaleName, Before$Tone)
X-squared = 44.508, df = 6, p-value = 5.862e-08

           
                   Neg        Neu        Pos
  High      -3.1859483  0.5676726  3.3639172
  Low        4.8196011 -2.8031281 -3.0856075
  Medium    -0.7459115  2.8510697 -2.0128522
  Very High -1.7071590 -1.4105010  3.5691011
Reliance on Evidence vs. tone DURING
           
            Neg Neu Pos
  High       62  20  32
  Low        85   9   1
  Medium     41  39  30
  Very High  18   5   4

    Pearson's Chi-squared test

data:  table(During$ScaleName, During$Tone)
X-squared = 66.549, df = 6, p-value = 2.081e-12

           
                   Neg        Neu        Pos
  High      -1.3685722 -1.1359178  2.8727803
  Low        6.9797016 -3.2604209 -5.3030317
  Medium    -5.7607170  4.4684165  2.5416322
  Very High  0.7860269 -0.3421646 -0.6230319
Reliance on Evidence vs. tone AFTER
           
            Neg Neu Pos
  High       12  10   9
  Low        37   6   1
  Medium     13  17   8
  Very High   7   3   2

    Pearson's Chi-squared test

data:  table(After$ScaleName, After$Tone)
X-squared = 27.37, df = 6, p-value = 0.0001234

           
                    Neg         Neu         Pos
  High      -2.12908270  0.49030777  2.28240108
  Low        4.78730941 -2.75934470 -3.08548517
  Medium    -3.11874726  2.60047814  1.01836901
  Very High  0.22956436 -0.30574106  0.06625452
END
---
title: "Citizens Assemble"
output: html_notebook
---
  
This document provides the R code used in the statistical analysis of data for the article titled: 
  
#### Citizens Assemble: Social discourse through the Irish media 'before', 'during' and 'after' the *Citizens' Assembly* on 'How the State can make Ireland a leader in tackling climate change'.
```{r, include=FALSE,message=FALSE, warning=FALSE,}
chooseCRANmirror(graphics=FALSE, ind=1)
knitr::opts_chunk$set(echo = TRUE) 
```
```{r, include=FALSE,message=FALSE, warning=FALSE,}
setwd("C:\\Users\\Rhonda\\Desktop\\CA Temp") # Home
Data <- read.csv("NEW_2020.csv")
```

```{r,message=FALSE, warning=FALSE,}
library(xtable)
library(dplyr)
```

##### Labels for newspaper name
```{r, message=FALSE, warning=FALSE, echo=TRUE}
PaperLabel <- c('Farmers Journal', 'Irish Times', 'Independent', 'The Journal')
Data$PaperName <- PaperLabel[Data$Paper]
```
##### Labels for article tone
```{r, message=FALSE, warning=FALSE, echo=TRUE}
Data$Tone <- 'Neg'                                      
Data$Tone[which(Data$Positive == 1)] <- 'Pos'
Data$Tone[which(Data$Neutral == 1)] <- 'Neu'
```
##### Labels for article period
```{r, message=FALSE, warning=FALSE, echo=TRUE}
PeriodLabel <- c("Before","During","After")            
Data$PeriodName <- PeriodLabel[Data$Period]
```
##### Labels for article scale - reliance upon evidence
```{r, message=FALSE, warning=FALSE, echo=TRUE}
ScaleLabel <- c("None","Low","Medium","High","Very High")
Data$ScaleName <- ScaleLabel[Data$Scale]
```
##### Separating time periods
```{r, warning=FALSE, echo=TRUE}
Before <- Data[which(Data$PeriodName == 'Before'),]     ## March-May 2017
During <- Data[which(Data$PeriodName == 'During'),]     ## Sept-Nov (inclusive) 2017
After  <- Data[which(Data$PeriodName == 'After'),]      ## Dec 2017 - May 2018
```
##### Separating media sources
```{r, warning=FALSE, echo=TRUE}
FJ  <- Data[which(Data$PaperName == 'Farmers Journal'),]
IT  <- Data[which(Data$PaperName == 'Irish Times'),]    
Ind <- Data[which(Data$PaperName == 'Independent'),]    
TJ  <- Data[which(Data$PaperName == 'The Journal'),]    
```
### TONE 
##### **Summary**
![](C:/Users/Rhonda/Desktop/CA Temp/Tone.png)  

##### Tone of language per paper OVERALL
```{r, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Data$Tone, Data$PaperName)                         ## Table Overall
chisq.test(table(Data$Tone, Data$PaperName))             ## Chi Square  
chisq.test(table(Data$Tone, Data$PaperName))$stdres      ## Std Res    
```
      
##### Tone of language per paper BEFORE
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Before$Tone, Before$PaperName)                     ## Table Before
chisq.test(table(Before$Tone, Before$PaperName))         ## Chi square test 
chisq.test(table(Before$Tone, Before$PaperName))$stdres  ## Std Res
```
      
##### Tone of language per paper DURING
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(During$Tone, During$PaperName)                     ## Table During
chisq.test(table(During$Tone, During$PaperName))         ## Chi square test 
chisq.test(table(During$Tone, During$PaperName))$stdres  ## Std Res
```
      
##### Tone of language per paper AFTER
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(After$Tone, After$PaperName)                       ## Table After
chisq.test(table(After$Tone, After$PaperName))           ## Chi square test 
chisq.test(table(After$Tone, After$PaperName))$stdres    ## Std Res
```
### RELIANCE ON EVIDENCE 
##### **Summary**
![](C:/Users/Rhonda/Desktop/CA Temp/Reliance on evidence.png)  
 
##### Reliance on Evidence OVERALL 
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Data$ScaleName, Data$PaperName)                      ## Table Overall
chisq.test(table(Data$ScaleName, Data$PaperName))          ## Chi square 
chisq.test(table(Data$ScaleName, Data$PaperName))$stdres   ## Std Res      
```
  
##### Reliance on Evidence BEFORE
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Before$ScaleName, Before$PaperName)                    ## Table Before
chisq.test(table(Before$ScaleName, Before$PaperName))        ## Chi square test          
chisq.test(table(Before$ScaleName, Before$PaperName))$stdres ## Std Res  
```
      
##### Reliance on Evidence DURING
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(During$ScaleName, During$PaperName)                    ## Table During
chisq.test(table(During$ScaleName, During$PaperName))        ## Chi square test
chisq.test(table(During$ScaleName, During$PaperName))$stdres ## Std Res 
```
      
##### Reliance on Evidence AFTER
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(After$ScaleName, After$PaperName)                    ## Table After
chisq.test(table(After$ScaleName, After$PaperName))        ## Chi square test
chisq.test(table(After$ScaleName, After$PaperName))$stdres ## Std Res 
```
### RELIANCE ON EVIDENCE VS. TONE 
##### **Summary**
![](C:/Users/Rhonda/Desktop/CA Temp/Reliance on evidence vs tone.png)  
  
##### Reliance on Evidence vs. tone OVERALL
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Data$ScaleName, Data$Tone)                        ## Table Overall
chisq.test(table(Data$ScaleName, Data$Tone))            ## Chi square                 
chisq.test(table(Data$ScaleName, Data$Tone))$stdres     ## Std Res
```
      
##### Reliance on Evidence vs. tone BEFORE
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(Before$ScaleName, Before$Tone)                    ## Table Before
chisq.test(table(Before$ScaleName, Before$Tone))        ## Chi square
chisq.test(table(Before$ScaleName, Before$Tone))$stdres ## Std Res
```
      
##### Reliance on Evidence vs. tone DURING
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(During$ScaleName, During$Tone)                    ## Table During
chisq.test(table(During$ScaleName, During$Tone))        ## Chi square
chisq.test(table(During$ScaleName, During$Tone))$stdres ## Std Res
```
      
##### Reliance on Evidence vs. tone AFTER
```{r, message=FALSE, warning=FALSE, echo=FALSE, collapse=TRUE}
table(After$ScaleName, After$Tone)                      ## Table After
chisq.test(table(After$ScaleName, After$Tone))          ## Chi square
chisq.test(table(After$ScaleName, After$Tone))$stdres   ## Std Res
```
##### END