Importing, Cleaning, and Formatting
import the data
library(readxl) # use package readxl
fsi_2006 <- read_excel("fsi-2006.xlsx") # read and name file
View(fsi_2006) # view file
fsi_2007 <- read_excel("fsi-2007.xlsx") # read and name file
View(fsi_2007) # view file
fsi_2008 <- read_excel("fsi-2008.xlsx") # read and name file
View(fsi_2008) # view file
fsi_2009 <- read_excel("fsi-2009.xlsx") # read and name file
View(fsi_2009) # view file
fsi_2010 <- read_excel("fsi-2010.xlsx") # read and name file
View(fsi_2010) # view file
fsi_2011 <- read_excel("fsi-2011.xlsx") # read and name file
View(fsi_2011) # view file
fsi_2012 <- read_excel("fsi-2012.xlsx") # read and name file
View(fsi_2012) # view file
fsi_2013 <- read_excel("fsi-2013.xlsx") # read and name file
View(fsi_2013) # view file
fsi_2014 <- read_excel("fsi-2014.xlsx") # read and name file
View(fsi_2014) # view file
fsi_2015 <- read_excel("fsi-2015.xlsx") # read and name file
View(fsi_2015) # view file
fsi_2016 <- read_excel("fsi-2016.xlsx") # read and name file
View(fsi_2016) # view file
fsi_2017 <- read_excel("fsi-2017.xlsx") # read and name file
View(fsi_2017) # view file
fsi_2018 <- read_excel("fsi-2018.xlsx") # read and name file
View(fsi_2018) # view file
fsi_2019 <- read_excel("fsi-2019.xlsx") # read and name file
View(fsi_2019) # view file
fsi_2020 <- read_excel("fsi-2020.xlsx") # read and name file
View(fsi_2020) # view file
fsi_2021 <- read_excel("fsi-2021.xlsx") # read and name file
View(fsi_2021) # view file
fsi_2022_download <- read_excel("fsi-2022-download.xlsx") # read and name file
View(fsi_2022_download) # view file
FSI_2023_DOWNLOAD <- read_excel("FSI-2023-DOWNLOAD.xlsx") # read and name file
View(FSI_2023_DOWNLOAD) # view file
# Changed the year values before appending because when I did it after, there would be an issue where it showed 1970 for one of the data sets. I excluded two data sets because they were already in the correct format.
fsi_2006$Year=format(fsi_2006$Year,"%Y") # change the format of the year column to year only
fsi_2006$Year=as.numeric(fsi_2006$Year) # change the data type to numeric
fsi_2007$Year=format(fsi_2007$Year,"%Y") # change the format of the year column to year only
fsi_2007$Year=as.numeric(fsi_2007$Year) # change the data type to numeric
fsi_2008$Year=format(fsi_2008$Year,"%Y") # change the format of the year column to year only
fsi_2008$Year=as.numeric(fsi_2008$Year) # change the data type to numeric
fsi_2009$Year=format(fsi_2009$Year,"%Y") # change the format of the year column to year only
fsi_2009$Year=as.numeric(fsi_2009$Year) # change the data type to numeric
fsi_2010$Year=format(fsi_2010$Year,"%Y") # change the format of the year column to year only
fsi_2010$Year=as.numeric(fsi_2010$Year) # change the data type to numeric
fsi_2011$Year=format(fsi_2011$Year,"%Y") # change the format of the year column to year only
fsi_2011$Year=as.numeric(fsi_2011$Year) # change the data type to numeric
fsi_2012$Year=format(fsi_2012$Year,"%Y") # change the format of the year column to year only
fsi_2012$Year=as.numeric(fsi_2012$Year) # change the data type to numeric
fsi_2013$Year=format(fsi_2013$Year,"%Y") # change the format of the year column to year only
fsi_2013$Year=as.numeric(fsi_2013$Year) # change the data type to numeric
fsi_2014$Year=format(fsi_2014$Year,"%Y") # change the format of the year column to year only
fsi_2014$Year=as.numeric(fsi_2014$Year) # change the data type to numeric
fsi_2015$Year=format(fsi_2015$Year,"%Y") # change the format of the year column to year only
fsi_2015$Year=as.numeric(fsi_2015$Year) # change the data type to numeric
fsi_2016$Year=format(fsi_2016$Year,"%Y") # change the format of the year column to year only
fsi_2016$Year=as.numeric(fsi_2016$Year) # change the data type to numeric
fsi_2017$Year=format(fsi_2017$Year,"%Y") # change the format of the year column to year only
fsi_2017$Year=as.numeric(fsi_2017$Year) # change the data type to numeric
fsi_2018$Year=format(fsi_2018$Year,"%Y") # change the format of the year column to year only
fsi_2018$Year=as.numeric(fsi_2018$Year) # change the data type to numeric
fsi_2019$Year=format(fsi_2019$Year,"%Y") # change the format of the year column to year only
fsi_2019$Year=as.numeric(fsi_2019$Year) # change the data type to numeric
fsi_2020$Year=format(fsi_2020$Year,"%Y") # change the format of the year column to year only
fsi_2020$Year=as.numeric(fsi_2020$Year) # change the data type to numeric
fsi_2022_download$Year=format(fsi_2022_download$Year,"%Y") # change the format of the year column to year only
fsi_2022_download$Year=as.numeric(fsi_2022_download$Year) # change the data type to numeric
Vertical Concatenating
fsi_2019 <- fsi_2019[,-17] # remove 17th column, it was only in two of the data sets
fsi_2020 <- fsi_2020[,-17] # remove 17th column
fsi_combined=rbind(fsi_2006,fsi_2007,fsi_2008,fsi_2009,fsi_2010,fsi_2011,fsi_2012,fsi_2013,fsi_2014,fsi_2015,fsi_2016,fsi_2017,fsi_2018,fsi_2019,fsi_2020,fsi_2021,fsi_2022_download,FSI_2023_DOWNLOAD) # combine all data sets
fsi_combined # view data set
Check values formats - year = integer, rank = integer
str(fsi_combined) # check data values
## tibble [3,170 × 16] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:3170] "Sudan" "Congo Democratic Republic" "Cote d'Ivoire" "Iraq" ...
## $ Year : num [1:3170] 2006 2006 2006 2006 2006 ...
## $ Rank : chr [1:3170] "1st" "2nd" "3rd" "4th" ...
## $ Total : num [1:3170] 112 110 109 109 109 ...
## $ C1: Security Apparatus : num [1:3170] 9.8 9.8 9.8 9.8 9.4 9.4 10 9.4 9.1 8.2 ...
## $ C2: Factionalized Elites : num [1:3170] 9.1 9.6 9.8 9.7 8.5 9.5 9.8 9.6 9.1 8 ...
## $ C3: Group Grievance : num [1:3170] 9.7 9.1 9.8 9.8 8.5 8.5 8 8.8 8.6 9.1 ...
## $ E1: Economy : num [1:3170] 7.5 8.1 9 8.2 9.8 7.9 8.5 8.4 7 7.5 ...
## $ E2: Economic Inequality : num [1:3170] 9.2 9 8 8.7 9.2 9 7.5 8.3 8.9 8 ...
## $ E3: Human Flight and Brain Drain: num [1:3170] 9.1 8 8.5 9.1 9 8 7 8 8.1 7 ...
## $ P1: State Legitimacy : num [1:3170] 9.5 9 10 8.5 8.9 9.5 10 9.4 8.5 8.3 ...
## $ P2: Public Services : num [1:3170] 9.5 9 8.5 8.3 9.5 9 10 9.3 7.5 8 ...
## $ P3: Human Rights : num [1:3170] 9.8 9.5 9.4 9.7 9.5 9.1 9.5 9.6 8.5 8.2 ...
## $ S1: Demographic Pressures : num [1:3170] 9.6 9.5 8.8 8.9 9.7 9 9 8.8 9.3 7.9 ...
## $ S2: Refugees and IDPs : num [1:3170] 9.7 9.5 7.6 8.3 8.9 9 8.1 5 9.3 9.6 ...
## $ X1: External Intervention : num [1:3170] 9.8 10 10 10 8 8 8.5 10 9.2 10 ...
summary(fsi_combined) # check statistical summary
## Country Year Rank Total
## Length:3170 Min. :2006 Length:3170 Min. : 14.50
## Class :character 1st Qu.:2010 Class :character 1st Qu.: 52.50
## Mode :character Median :2015 Mode :character Median : 73.80
## Mean :2015 Mean : 69.44
## 3rd Qu.:2019 3rd Qu.: 86.30
## Max. :2023 Max. :114.90
## C1: Security Apparatus C2: Factionalized Elites C3: Group Grievance
## Min. : 0.300 Min. : 0.700 Min. : 0.300
## 1st Qu.: 3.925 1st Qu.: 4.425 1st Qu.: 4.325
## Median : 5.900 Median : 7.000 Median : 6.000
## Mean : 5.589 Mean : 6.334 Mean : 5.948
## 3rd Qu.: 7.300 3rd Qu.: 8.200 3rd Qu.: 7.600
## Max. :10.000 Max. :10.000 Max. :10.000
## E1: Economy E2: Economic Inequality E3: Human Flight and Brain Drain
## Min. : 1.000 Min. : 0.500 Min. : 0.400
## 1st Qu.: 4.300 1st Qu.: 4.700 1st Qu.: 4.076
## Median : 5.800 Median : 6.500 Median : 5.900
## Mean : 5.707 Mean : 6.103 Mean : 5.521
## 3rd Qu.: 7.200 3rd Qu.: 7.800 3rd Qu.: 7.100
## Max. :10.000 Max. :10.000 Max. :10.000
## P1: State Legitimacy P2: Public Services P3: Human Rights
## Min. : 0.200 Min. : 0.6267 Min. : 0.300
## 1st Qu.: 4.400 1st Qu.: 3.7000 1st Qu.: 3.800
## Median : 6.700 Median : 5.8000 Median : 6.200
## Mean : 6.151 Mean : 5.6070 Mean : 5.756
## 3rd Qu.: 8.100 3rd Qu.: 7.8000 3rd Qu.: 7.600
## Max. :10.000 Max. :10.0000 Max. :10.000
## S1: Demographic Pressures S2: Refugees and IDPs X1: External Intervention
## Min. : 0.700 Min. : 0.40 Min. : 0.300
## 1st Qu.: 4.200 1st Qu.: 3.00 1st Qu.: 4.000
## Median : 6.200 Median : 4.80 Median : 6.000
## Mean : 6.035 Mean : 5.02 Mean : 5.666
## 3rd Qu.: 8.000 3rd Qu.: 6.90 3rd Qu.: 7.400
## Max. :10.000 Max. :10.00 Max. :10.000
fsi_combined$Rank = gsub("st|nd|rd|th","",fsi_combined$Rank) # format rank to remove any non-numeric part
fsi_combined$Rank = gsub("n/r","NA",fsi_combined$Rank) # change the missing value to NA
fsi_combined$Rank[fsi_combined$Rank=='NA']="0" # View NA as 0 for numeric purposes
fsi_combined$Rank == trimws(fsi_combined$Rank,whitespace = "[\\h\\v]") # remove any white space
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1051] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1065] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1079] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1093] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1107] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1135] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1163] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1191] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1219] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1233] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1247] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1275] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1303] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1317] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1359] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1387] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1401] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1415] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1429] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1443] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1471] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1485] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1499] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1513] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1527] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1555] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1569] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1583] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1597] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1611] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1639] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1653] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1667] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1681] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1695] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1723] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1737] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1751] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1765] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1779] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1807] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1821] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1835] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1849] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1863] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1877] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1891] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1905] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1919] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1933] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1947] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1961] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1975] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1989] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2003] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2017] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2031] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2045] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2059] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2073] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2087] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2101] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2115] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2129] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2143] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2171] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2185] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2199] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2213] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2227] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2255] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2269] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2283] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2297] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2311] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2339] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2353] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2367] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2381] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2395] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2423] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2437] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2465] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2479] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2493] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2507] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2535] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2549] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2563] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2577] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2591] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2605] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2619] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2633] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2647] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2675] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2689] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2703] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2717] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2731] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2745] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2759] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2773] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2787] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2801] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2815] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2829] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2843] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2857] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2871] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2885] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2899] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2927] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2941] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2955] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2969] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2983] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2997] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3011] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3025] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3039] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3053] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3067] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3081] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3095] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3123] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3137] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [3165] TRUE TRUE TRUE TRUE TRUE TRUE
fsi_combined$Rank=as.numeric(fsi_combined$Rank) # change the rank column to numeric
str(fsi_combined) # check data types
## tibble [3,170 × 16] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:3170] "Sudan" "Congo Democratic Republic" "Cote d'Ivoire" "Iraq" ...
## $ Year : num [1:3170] 2006 2006 2006 2006 2006 ...
## $ Rank : num [1:3170] 1 2 3 4 5 6 6 8 9 10 ...
## $ Total : num [1:3170] 112 110 109 109 109 ...
## $ C1: Security Apparatus : num [1:3170] 9.8 9.8 9.8 9.8 9.4 9.4 10 9.4 9.1 8.2 ...
## $ C2: Factionalized Elites : num [1:3170] 9.1 9.6 9.8 9.7 8.5 9.5 9.8 9.6 9.1 8 ...
## $ C3: Group Grievance : num [1:3170] 9.7 9.1 9.8 9.8 8.5 8.5 8 8.8 8.6 9.1 ...
## $ E1: Economy : num [1:3170] 7.5 8.1 9 8.2 9.8 7.9 8.5 8.4 7 7.5 ...
## $ E2: Economic Inequality : num [1:3170] 9.2 9 8 8.7 9.2 9 7.5 8.3 8.9 8 ...
## $ E3: Human Flight and Brain Drain: num [1:3170] 9.1 8 8.5 9.1 9 8 7 8 8.1 7 ...
## $ P1: State Legitimacy : num [1:3170] 9.5 9 10 8.5 8.9 9.5 10 9.4 8.5 8.3 ...
## $ P2: Public Services : num [1:3170] 9.5 9 8.5 8.3 9.5 9 10 9.3 7.5 8 ...
## $ P3: Human Rights : num [1:3170] 9.8 9.5 9.4 9.7 9.5 9.1 9.5 9.6 8.5 8.2 ...
## $ S1: Demographic Pressures : num [1:3170] 9.6 9.5 8.8 8.9 9.7 9 9 8.8 9.3 7.9 ...
## $ S2: Refugees and IDPs : num [1:3170] 9.7 9.5 7.6 8.3 8.9 9 8.1 5 9.3 9.6 ...
## $ X1: External Intervention : num [1:3170] 9.8 10 10 10 8 8 8.5 10 9.2 10 ...
summary(fsi_combined) # check statistical summary
## Country Year Rank Total
## Length:3170 Min. :2006 Min. : 0.00 Min. : 14.50
## Class :character 1st Qu.:2010 1st Qu.: 44.00 1st Qu.: 52.50
## Mode :character Median :2015 Median : 88.00 Median : 73.80
## Mean :2015 Mean : 88.52 Mean : 69.44
## 3rd Qu.:2019 3rd Qu.:132.00 3rd Qu.: 86.30
## Max. :2023 Max. :179.00 Max. :114.90
## C1: Security Apparatus C2: Factionalized Elites C3: Group Grievance
## Min. : 0.300 Min. : 0.700 Min. : 0.300
## 1st Qu.: 3.925 1st Qu.: 4.425 1st Qu.: 4.325
## Median : 5.900 Median : 7.000 Median : 6.000
## Mean : 5.589 Mean : 6.334 Mean : 5.948
## 3rd Qu.: 7.300 3rd Qu.: 8.200 3rd Qu.: 7.600
## Max. :10.000 Max. :10.000 Max. :10.000
## E1: Economy E2: Economic Inequality E3: Human Flight and Brain Drain
## Min. : 1.000 Min. : 0.500 Min. : 0.400
## 1st Qu.: 4.300 1st Qu.: 4.700 1st Qu.: 4.076
## Median : 5.800 Median : 6.500 Median : 5.900
## Mean : 5.707 Mean : 6.103 Mean : 5.521
## 3rd Qu.: 7.200 3rd Qu.: 7.800 3rd Qu.: 7.100
## Max. :10.000 Max. :10.000 Max. :10.000
## P1: State Legitimacy P2: Public Services P3: Human Rights
## Min. : 0.200 Min. : 0.6267 Min. : 0.300
## 1st Qu.: 4.400 1st Qu.: 3.7000 1st Qu.: 3.800
## Median : 6.700 Median : 5.8000 Median : 6.200
## Mean : 6.151 Mean : 5.6070 Mean : 5.756
## 3rd Qu.: 8.100 3rd Qu.: 7.8000 3rd Qu.: 7.600
## Max. :10.000 Max. :10.0000 Max. :10.000
## S1: Demographic Pressures S2: Refugees and IDPs X1: External Intervention
## Min. : 0.700 Min. : 0.40 Min. : 0.300
## 1st Qu.: 4.200 1st Qu.: 3.00 1st Qu.: 4.000
## Median : 6.200 Median : 4.80 Median : 6.000
## Mean : 6.035 Mean : 5.02 Mean : 5.666
## 3rd Qu.: 8.000 3rd Qu.: 6.90 3rd Qu.: 7.400
## Max. :10.000 Max. :10.00 Max. :10.000
names(fsi_combined) # view column names
## [1] "Country" "Year"
## [3] "Rank" "Total"
## [5] "C1: Security Apparatus" "C2: Factionalized Elites"
## [7] "C3: Group Grievance" "E1: Economy"
## [9] "E2: Economic Inequality" "E3: Human Flight and Brain Drain"
## [11] "P1: State Legitimacy" "P2: Public Services"
## [13] "P3: Human Rights" "S1: Demographic Pressures"
## [15] "S2: Refugees and IDPs" "X1: External Intervention"
names(fsi_combined)[5]=c("C1_Security_Apparatus") # change column name
names(fsi_combined)[6]=c("C2_Factionalized_Elites") # change column name
names(fsi_combined)[7]=c("C3_Group_Grievance") # change column name
names(fsi_combined)[8]=c("E1_Economy") # change column name
names(fsi_combined)[9]=c("E2_Economic_Inequity") # change column name
names(fsi_combined)[10]=c("E3_Human_Flight_and_Brain_Drain") # change column name
names(fsi_combined)[11]=c("P1_State_Legitimacy") # change column name
names(fsi_combined)[12]=c("P2_Public_Services") # change column name
names(fsi_combined)[13]=c("P3_Human_Rights") # change column name
names(fsi_combined)[14]=c("S1_Demographic_Pressures") # change column name
names(fsi_combined)[15]=c("S2_Refugees_and_IDPs") # change column name
names(fsi_combined)[16]=c("X1_External_Intervention") # change column name
names(fsi_combined) # view column names
## [1] "Country" "Year"
## [3] "Rank" "Total"
## [5] "C1_Security_Apparatus" "C2_Factionalized_Elites"
## [7] "C3_Group_Grievance" "E1_Economy"
## [9] "E2_Economic_Inequity" "E3_Human_Flight_and_Brain_Drain"
## [11] "P1_State_Legitimacy" "P2_Public_Services"
## [13] "P3_Human_Rights" "S1_Demographic_Pressures"
## [15] "S2_Refugees_and_IDPs" "X1_External_Intervention"
Plotting and Features
Plot total per year using boxplots in ggplot2 with features
library(ggplot2) # use package ggplot2
base=boxplot(data=fsi_combined,Total~Year) # create a boxplot for total by year
theTitle='Fragility States Index Over Time'
theSubtitle='146 Countries, from 2006 to 2023'
theCaption=paste("Source:Fragile States Index\n") # create descriptions for the features
box=base + labs(x="Year",
y="Total",
title = theTitle,
subtitle = theSubtitle,
caption = theCaption) # add the features to the plot.. I know this part isn't working but I can't figure out how to get it right.
Plot variables C1, C2, C3 using histograms
library(dplyr) # use package dplyr
fsi_2013_2023 <- subset(fsi_combined, Year==2013|Year==2023)[c(1,2,5,6,7)] #subset data for only the years 2013 and 2023
fsi_2013_long <- tidyr::pivot_longer(data = fsi_2013_2023[1:178,c(1,3,4,5)],
cols=!Country,
names_to = "Variables",
values_to = "Values") # create long data set with only 2013 and the columns needed
fsi_2023_long <- tidyr::pivot_longer(data = fsi_2013_2023[179:357,c(1,3,4,5)],
cols=!Country,
names_to = "Variables",
values_to = "Values") # create long data set with only 2023 and the columns needed
base = ggplot(data=fsi_2013_long) # create the base for a histogram
base + geom_histogram(aes(x=Values),bins = 10) +
facet_wrap(Variables~., scales = "free", ncol = 1) # create a histogram for 2013 using facets
base = ggplot(data=fsi_2023_long) # create the base for a histogram
base + geom_histogram(aes(x=Values),bins = 10) +
facet_wrap(Variables~., scales = "free", ncol = 1) # create a histogram for 2023 using facets
Save and Export
Save Appended Data
saveRDS(fsi_combined,"FSI_Cleaned_Formatted.RDS") # code is saving the file as an RDS file
FSI_Cleaned_FormattedRDS=readRDS("FSI_Cleaned_Formatted.RDS") # code is reading the RDS file
str(FSI_Cleaned_FormattedRDS) # code is checking the data types of the RDS file
## tibble [3,170 × 16] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:3170] "Sudan" "Congo Democratic Republic" "Cote d'Ivoire" "Iraq" ...
## $ Year : num [1:3170] 2006 2006 2006 2006 2006 ...
## $ Rank : num [1:3170] 1 2 3 4 5 6 6 8 9 10 ...
## $ Total : num [1:3170] 112 110 109 109 109 ...
## $ C1_Security_Apparatus : num [1:3170] 9.8 9.8 9.8 9.8 9.4 9.4 10 9.4 9.1 8.2 ...
## $ C2_Factionalized_Elites : num [1:3170] 9.1 9.6 9.8 9.7 8.5 9.5 9.8 9.6 9.1 8 ...
## $ C3_Group_Grievance : num [1:3170] 9.7 9.1 9.8 9.8 8.5 8.5 8 8.8 8.6 9.1 ...
## $ E1_Economy : num [1:3170] 7.5 8.1 9 8.2 9.8 7.9 8.5 8.4 7 7.5 ...
## $ E2_Economic_Inequity : num [1:3170] 9.2 9 8 8.7 9.2 9 7.5 8.3 8.9 8 ...
## $ E3_Human_Flight_and_Brain_Drain: num [1:3170] 9.1 8 8.5 9.1 9 8 7 8 8.1 7 ...
## $ P1_State_Legitimacy : num [1:3170] 9.5 9 10 8.5 8.9 9.5 10 9.4 8.5 8.3 ...
## $ P2_Public_Services : num [1:3170] 9.5 9 8.5 8.3 9.5 9 10 9.3 7.5 8 ...
## $ P3_Human_Rights : num [1:3170] 9.8 9.5 9.4 9.7 9.5 9.1 9.5 9.6 8.5 8.2 ...
## $ S1_Demographic_Pressures : num [1:3170] 9.6 9.5 8.8 8.9 9.7 9 9 8.8 9.3 7.9 ...
## $ S2_Refugees_and_IDPs : num [1:3170] 9.7 9.5 7.6 8.3 8.9 9 8.1 5 9.3 9.6 ...
## $ X1_External_Intervention : num [1:3170] 9.8 10 10 10 8 8 8.5 10 9.2 10 ...
write.csv(fsi_combined,"FSI_Cleaned_Formatted.csv", row.names = FALSE) # code is saving the data frame as a csv file
FSI_Cleaned_FormattedCSV=read.csv("FSI_Cleaned_Formatted.csv") # code is reading the csv file
str(FSI_Cleaned_FormattedCSV) # code is checking the data types of the csv file
## 'data.frame': 3170 obs. of 16 variables:
## $ Country : chr "Sudan" "Congo Democratic Republic" "Cote d'Ivoire" "Iraq" ...
## $ Year : int 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 ...
## $ Rank : int 1 2 3 4 5 6 6 8 9 10 ...
## $ Total : num 112 110 109 109 109 ...
## $ C1_Security_Apparatus : num 9.8 9.8 9.8 9.8 9.4 9.4 10 9.4 9.1 8.2 ...
## $ C2_Factionalized_Elites : num 9.1 9.6 9.8 9.7 8.5 9.5 9.8 9.6 9.1 8 ...
## $ C3_Group_Grievance : num 9.7 9.1 9.8 9.8 8.5 8.5 8 8.8 8.6 9.1 ...
## $ E1_Economy : num 7.5 8.1 9 8.2 9.8 7.9 8.5 8.4 7 7.5 ...
## $ E2_Economic_Inequity : num 9.2 9 8 8.7 9.2 9 7.5 8.3 8.9 8 ...
## $ E3_Human_Flight_and_Brain_Drain: num 9.1 8 8.5 9.1 9 8 7 8 8.1 7 ...
## $ P1_State_Legitimacy : num 9.5 9 10 8.5 8.9 9.5 10 9.4 8.5 8.3 ...
## $ P2_Public_Services : num 9.5 9 8.5 8.3 9.5 9 10 9.3 7.5 8 ...
## $ P3_Human_Rights : num 9.8 9.5 9.4 9.7 9.5 9.1 9.5 9.6 8.5 8.2 ...
## $ S1_Demographic_Pressures : num 9.6 9.5 8.8 8.9 9.7 9 9 8.8 9.3 7.9 ...
## $ S2_Refugees_and_IDPs : num 9.7 9.5 7.6 8.3 8.9 9 8.1 5 9.3 9.6 ...
## $ X1_External_Intervention : num 9.8 10 10 10 8 8 8.5 10 9.2 10 ...
Save 2013 2023 data
saveRDS(fsi_2013_2023,"FSI_2013_2023_Cleaned_Formatted.RDS") # code is saving the file as an RDS file
FSI_2013_2023_Cleaned_FormattedRDS=readRDS("FSI_2013_2023_Cleaned_Formatted.RDS") # code is reading the RDS file
str(FSI_2013_2023_Cleaned_FormattedRDS) # code is checking the data types of the RDS file
## tibble [357 × 5] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:357] "Somalia" "Congo Democratic Republic" "Sudan" "South Sudan" ...
## $ Year : num [1:357] 2013 2013 2013 2013 2013 ...
## $ C1_Security_Apparatus : num [1:357] 9.7 10 9.8 9.6 9.4 9.8 9.9 7.9 9.7 8.4 ...
## $ C2_Factionalized_Elites: num [1:357] 10 9.5 10 9.8 9.5 9.5 9.4 9 9.1 9.7 ...
## $ C3_Group_Grievance : num [1:357] 9.3 9.4 10 10 8.8 9 9.2 7 8.5 8.4 ...
write.csv(fsi_2013_2023,"FSI_2013_2023_Cleaned_Formatted.csv", row.names = FALSE) # code is saving the data frame as a csv file
FSI_2013_2023_Cleaned_FormattedCSV=read.csv("FSI_2013_2023_Cleaned_Formatted.csv") # code is reading the csv file
str(FSI_2013_2023_Cleaned_FormattedCSV) # code is checking the data types of the csv file
## 'data.frame': 357 obs. of 5 variables:
## $ Country : chr "Somalia" "Congo Democratic Republic" "Sudan" "South Sudan" ...
## $ Year : int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ C1_Security_Apparatus : num 9.7 10 9.8 9.6 9.4 9.8 9.9 7.9 9.7 8.4 ...
## $ C2_Factionalized_Elites: num 10 9.5 10 9.8 9.5 9.5 9.4 9 9.1 9.7 ...
## $ C3_Group_Grievance : num 9.3 9.4 10 10 8.8 9 9.2 7 8.5 8.4 ...
Save Long Data
saveRDS(fsi_2013_long,"FSI_2013_Long_Cleaned_Formatted.RDS") # code is saving the file as an RDS file
FSI_2013_Long_Cleaned_FormattedRDS=readRDS("FSI_2013_Long_Cleaned_Formatted.RDS") # code is reading the RDS file
str(FSI_2013_Long_Cleaned_FormattedRDS) # code is checking the data types of the RDS file
## tibble [534 × 3] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:534] "Somalia" "Somalia" "Somalia" "Congo Democratic Republic" ...
## $ Variables: chr [1:534] "C1_Security_Apparatus" "C2_Factionalized_Elites" "C3_Group_Grievance" "C1_Security_Apparatus" ...
## $ Values : num [1:534] 9.7 10 9.3 10 9.5 9.4 9.8 10 10 9.6 ...
write.csv(fsi_2013_long,"FSI_2013_Long_Cleaned_Formatted.csv", row.names = FALSE) # code is saving the data frame as a csv file
FSI_2013_Long_Cleaned_FormattedCSV=read.csv("FSI_2013_Long_Cleaned_Formatted.csv") # code is reading the csv file
str(FSI_2013_Long_Cleaned_FormattedCSV) # code is checking the data types of the csv file
## 'data.frame': 534 obs. of 3 variables:
## $ Country : chr "Somalia" "Somalia" "Somalia" "Congo Democratic Republic" ...
## $ Variables: chr "C1_Security_Apparatus" "C2_Factionalized_Elites" "C3_Group_Grievance" "C1_Security_Apparatus" ...
## $ Values : num 9.7 10 9.3 10 9.5 9.4 9.8 10 10 9.6 ...
saveRDS(fsi_2023_long,"FSI_2023_Long_Cleaned_Formatted.RDS") # code is saving the file as an RDS file
FSI_2023_Long_Cleaned_FormattedRDS=readRDS("FSI_2023_Long_Cleaned_Formatted.RDS") # code is reading the RDS file
str(FSI_2023_Long_Cleaned_FormattedRDS) # code is checking the data types of the RDS file
## tibble [537 × 3] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:537] "Somalia" "Somalia" "Somalia" "Yemen" ...
## $ Variables: chr [1:537] "C1_Security_Apparatus" "C2_Factionalized_Elites" "C3_Group_Grievance" "C1_Security_Apparatus" ...
## $ Values : num [1:537] 9.5 10 8.7 8.6 9.9 8.8 9.9 9.2 8.6 8.8 ...
write.csv(fsi_2023_long,"FSI_2023_Long_Cleaned_Formatted.csv", row.names = FALSE) # code is saving the data frame as a csv file
FSI_2023_Long_Cleaned_FormattedCSV=read.csv("FSI_2023_Long_Cleaned_Formatted.csv") # code is reading the csv file
str(FSI_2023_Long_Cleaned_FormattedCSV) # code is checking the data types of the csv file
## 'data.frame': 537 obs. of 3 variables:
## $ Country : chr "Somalia" "Somalia" "Somalia" "Yemen" ...
## $ Variables: chr "C1_Security_Apparatus" "C2_Factionalized_Elites" "C3_Group_Grievance" "C1_Security_Apparatus" ...
## $ Values : num 9.5 10 8.7 8.6 9.9 8.8 9.9 9.2 8.6 8.8 ...