HW-5

Analysis of Indian Education System

Niharika Pola
2022-05-12

INTRODUCTION

In this project I have worked on 7 data sets related to the Indian Education System from 2013-2016. First two data sets talk about the Gross Enrollment Ratio and Dropout Ratio, remaining 5 talk about the availability of basic facilities (Water, Electricity, Boys & Girls Toilets and Computers) in Schools. Every data set has State, Year and Percentage data across various levels of the Education - Primary, Upper Primary, Secondary and Higher Secondary.

Lower Primary/ Primary - Nursery to class 1st Upper Primary - Class 1st to 5th Secondary - Class 6th to 8th Higher Secondary/Higher Secondary - Class 9th and 10th

The aim of this project is to perform Exploratory Data Analysis(EDA) of the 7 data sets is to:

  1. Analyze the Gross Enrollment Ratio and Dropout Ratio in the above mentioned classes All over India & across states and understand the,

and provide few recommendations to the Indian Government based on the Analysis.

  1. Compare the states with lowest dropout ratio with the available facilities data sets.

  2. To find out the impact of non-availability of these facilities on the dropout ratio.

  3. To analyze the trends of available facilities data sets across India.

Loading the packages 
library(tidyr)
library(dplyr)
library(readr)
library(stringr)
library(ggplot2)
library(ggmap)

Dataset-1 | Gross Enrollment Ratio from 2013-2016 across all Indian States

Gross Enrollment Ratio (GER) or Gross Enrollment Index (GEI) is a statistical measure used in the education sector, to determine the number of students enrolled in school at several different grade levels (like elementary, middle school and high school), and use it to show the ratio of the number of students who live in that country to those who qualify for the particular grade level.

The GER can be over 100% as it includes students who may be older or younger than the official age group.

For instance, in India it improved from 25.8 to 26.3, the GER includes students who are repeating a grade, those who enrolled late and are older than their classmates, or those who have advanced quickly and are younger than their classmates. This allows the total enrollment to exceed the population that corresponds to that level of education.

Calculation Method

a = number of students enrolled in a given level b = population of the age group corresponds to given level of education India

GER=a/b×100

Reading Dataset-1 
gross_enrollment_ratio <- read_csv("601 Major Project/gross-enrollment-ratio.csv")
dim(gross_enrollment_ratio)

[1] 110  14
head(gross_enrollment_ratio)

# A tibble: 6 x 14
  State_UT              Year  Primary_Boys Primary_Girls Primary_Total
  <chr>                 <chr>        <dbl>         <dbl>         <dbl>
1 Andaman & Nicobar Is~ 2013~         95.9          92.0          93.9
2 Andhra Pradesh        2013~         96.6          96.9          96.7
3 Arunachal Pradesh     2013~        129.          128.          128. 
4 Assam                 2013~        112.          115.          113. 
5 Bihar                 2013~         95.0         101.           98.0
6 Chandigarh            2013~         88.4          96.1          91.8
# ... with 9 more variables: Upper_Primary_Boys <dbl>,
#   Upper_Primary_Girls <dbl>, Upper_Primary_Total <dbl>,
#   Secondary_Boys <dbl>, Secondary_Girls <dbl>,
#   Secondary_Total <dbl>, Higher_Secondary_Boys <chr>,
#   Higher_Secondary_Girls <chr>, Higher_Secondary_Total <chr>
colnames(gross_enrollment_ratio)

 [1] "State_UT"               "Year"                  
 [3] "Primary_Boys"           "Primary_Girls"         
 [5] "Primary_Total"          "Upper_Primary_Boys"    
 [7] "Upper_Primary_Girls"    "Upper_Primary_Total"   
 [9] "Secondary_Boys"         "Secondary_Girls"       
[11] "Secondary_Total"        "Higher_Secondary_Boys" 
[13] "Higher_Secondary_Girls" "Higher_Secondary_Total"

Datatypes of each column

str(gross_enrollment_ratio)

spec_tbl_df [110 x 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT              : chr [1:110] "Andaman & Nicobar Islands" "Andhra Pradesh" "Arunachal Pradesh" "Assam" ...
 $ Year                  : chr [1:110] "2013-14" "2013-14" "2013-14" "2013-14" ...
 $ Primary_Boys          : num [1:110] 95.9 96.6 129.1 111.8 95 ...
 $ Primary_Girls         : num [1:110] 92 96.9 127.8 115.2 101.2 ...
 $ Primary_Total         : num [1:110] 93.9 96.7 128.5 113.4 98 ...
 $ Upper_Primary_Boys    : num [1:110] 94.7 82.8 112.6 87.8 80.6 ...
 $ Upper_Primary_Girls   : num [1:110] 89 84.4 115.3 98.7 94.9 ...
 $ Upper_Primary_Total   : num [1:110] 91.8 83.6 113.9 93.1 87.2 ...
 $ Secondary_Boys        : num [1:110] 102.9 73.8 88.4 65.6 57.7 ...
 $ Secondary_Girls       : num [1:110] 97.4 76.8 84.9 77.2 63 ...
 $ Secondary_Total       : num [1:110] 100.2 75.2 86.7 71.2 60.1 ...
 $ Higher_Secondary_Boys : chr [1:110] "105.4" "59.83" "65.16" "31.78" ...
 $ Higher_Secondary_Girls: chr [1:110] "96.61" "60.83" "65.38" "34.27" ...
 $ Higher_Secondary_Total: chr [1:110] "101.28" "60.3" "65.27" "32.94" ...
 - attr(*, "spec")=
  .. cols(
  ..   State_UT = col_character(),
  ..   Year = col_character(),
  ..   Primary_Boys = col_double(),
  ..   Primary_Girls = col_double(),
  ..   Primary_Total = col_double(),
  ..   Upper_Primary_Boys = col_double(),
  ..   Upper_Primary_Girls = col_double(),
  ..   Upper_Primary_Total = col_double(),
  ..   Secondary_Boys = col_double(),
  ..   Secondary_Girls = col_double(),
  ..   Secondary_Total = col_double(),
  ..   Higher_Secondary_Boys = col_character(),
  ..   Higher_Secondary_Girls = col_character(),
  ..   Higher_Secondary_Total = col_character()
  .. )
 - attr(*, "problems")=<externalptr>

As you can see, 3 columns (Higher_Secondary_Boys, Higher_Secondary_Girls, Higher_Secondary_Total) are character instead of double. They have NR, @ in the observations. The data needs to be cleaned.

Tidying the data

gross_enrollment_ratio[ gross_enrollment_ratio == "NR" ] <- NA
gross_enrollment_ratio[ gross_enrollment_ratio == "@" ] <- NA
ger1 <- data.frame(gross_enrollment_ratio)
ger <- na.exclude(ger1)

ger$Higher_Secondary_Boys = as.numeric(ger$Higher_Secondary_Boys)
ger$Higher_Secondary_Girls = as.numeric(ger$Higher_Secondary_Girls)
ger$Higher_Secondary_Total = as.numeric(ger$Higher_Secondary_Total)

str(ger)

'data.frame':   108 obs. of  14 variables:
 $ State_UT              : chr  "Andaman & Nicobar Islands" "Andhra Pradesh" "Arunachal Pradesh" "Assam" ...
 $ Year                  : chr  "2013-14" "2013-14" "2013-14" "2013-14" ...
 $ Primary_Boys          : num  95.9 96.6 129.1 111.8 95 ...
 $ Primary_Girls         : num  92 96.9 127.8 115.2 101.2 ...
 $ Primary_Total         : num  93.9 96.7 128.5 113.4 98 ...
 $ Upper_Primary_Boys    : num  94.7 82.8 112.6 87.8 80.6 ...
 $ Upper_Primary_Girls   : num  89 84.4 115.3 98.7 94.9 ...
 $ Upper_Primary_Total   : num  91.8 83.6 113.9 93.1 87.2 ...
 $ Secondary_Boys        : num  102.9 73.8 88.4 65.6 57.7 ...
 $ Secondary_Girls       : num  97.4 76.8 84.9 77.2 63 ...
 $ Secondary_Total       : num  100.2 75.2 86.7 71.2 60.1 ...
 $ Higher_Secondary_Boys : num  105.4 59.8 65.2 31.8 23.3 ...
 $ Higher_Secondary_Girls: num  96.6 60.8 65.4 34.3 24.2 ...
 $ Higher_Secondary_Total: num  101.3 60.3 65.3 32.9 23.7 ...
 - attr(*, "na.action")= 'exclude' Named int [1:2] 26 99
  ..- attr(*, "names")= chr [1:2] "26" "99"
all_india_ger <- filter(ger,  State_UT=="All India") %>% 
  arrange(Year)

plotting All India girls enrollment ratio

all_india_ger_girls <- select(all_india_ger,Year, ends_with("girls")) 
head(all_india_ger_girls)

     Year Primary_Girls Upper_Primary_Girls Secondary_Girls
1 2013-14        102.65               92.75           76.47
2 2014-15        101.43               95.29           78.94
3 2015-16        100.69               97.57           80.97
  Higher_Secondary_Girls
1                  51.58
2                  53.81
3                  56.41
  fig1 <- pivot_longer(all_india_ger_girls, c(Primary_Girls, Upper_Primary_Girls, Secondary_Girls, Higher_Secondary_Girls), names_to = "Education_Level", values_to = "GER") 
  ggplot(fig1, aes(x=Year, y=GER, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig1: Gross Enrollment Ratio of Girls in India") +  geom_text(aes(label=GER), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic()

Findings from Fig-1:

Plotting All India boys enrollment ratio

all_india_ger_boys <- select(all_india_ger, Year, ends_with("boys"))
head(all_india_ger_boys)

     Year Primary_Boys Upper_Primary_Boys Secondary_Boys
1 2013-14       100.20              86.31          76.80
2 2014-15        98.85              87.71          78.13
3 2015-16        97.87              88.72          79.16
  Higher_Secondary_Boys
1                 52.77
2                 54.57
3                 55.95
  fig2 <- pivot_longer(all_india_ger_boys, c(Primary_Boys, Upper_Primary_Boys, Secondary_Boys, Higher_Secondary_Boys), names_to = "Education_Level", values_to = "GER") 
  ggplot(fig2, aes(x=Year, y=GER, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig2: Gross Enrollment Ratio of boys in India") + geom_text(aes(label=GER), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic()

Findings from Fig-2:

Plotting All India total enrollment ratio 
fig3 <- pivot_longer(all_india_ger, c(Primary_Boys, Primary_Girls, Primary_Total, Upper_Primary_Boys, Upper_Primary_Girls, Upper_Primary_Total, Secondary_Boys, Secondary_Girls, Secondary_Total, Higher_Secondary_Girls, Higher_Secondary_Boys, Higher_Secondary_Total), names_to = "Education_Level")
ggplot(fig3, aes(x=value, y=Education_Level)) + geom_boxplot(color="red") + geom_text(aes(label=value), size=4, vjust=0) + labs(title = "Fig-3: All India Enrollment Ratio - Education Wise ", x="Enrollment Percentage", y="Education Level") + facet_wrap(~Year)

Findings from Fig-3:

State-Wise Gross Enrollment Analysis 
states_ger <- filter(ger,  State_UT != "All India") 
head(states_ger)

                   State_UT    Year Primary_Boys Primary_Girls
1 Andaman & Nicobar Islands 2013-14        95.88         91.97
2            Andhra Pradesh 2013-14        96.62         96.87
3         Arunachal Pradesh 2013-14       129.12        127.77
4                     Assam 2013-14       111.77        115.16
5                     Bihar 2013-14        95.03        101.15
6                Chandigarh 2013-14        88.42         96.09
  Primary_Total Upper_Primary_Boys Upper_Primary_Girls
1         93.93              94.70               88.98
2         96.74              82.81               84.38
3        128.46             112.64              115.27
4        113.43              87.85               98.69
5         97.96              80.60               94.92
6         91.85              99.93              103.02
  Upper_Primary_Total Secondary_Boys Secondary_Girls Secondary_Total
1               91.83         102.89           97.36          100.16
2               83.57          73.76           76.77           75.20
3              113.94          88.37           84.89           86.65
4               93.13          65.60           77.20           71.21
5               87.24          57.66           62.96           60.08
6              101.27          92.08           92.16           92.11
  Higher_Secondary_Boys Higher_Secondary_Girls Higher_Secondary_Total
1                105.40                  96.61                 101.28
2                 59.83                  60.83                  60.30
3                 65.16                  65.38                  65.27
4                 31.78                  34.27                  32.94
5                 23.33                  24.17                  23.70
6                 90.50                  92.88                  91.49
I used google maps access key to get the Indian map and to get latitude and longitude coordinates for the states. I merged the coordinates data with my existing dataset. 
library(ggmap)
register_google(key = "AIzaSyDc2lDTQRLgvlGtdiZM6hkShq0fW_wv4-0")
coordinates <- geocode(states_ger$State_UT)
plot <- merge(states_ger,coordinates)
head(plot)

                   State_UT    Year Primary_Boys Primary_Girls
1 Andaman & Nicobar Islands 2013-14        95.88         91.97
2            Andhra Pradesh 2013-14        96.62         96.87
3         Arunachal Pradesh 2013-14       129.12        127.77
4                     Assam 2013-14       111.77        115.16
5                     Bihar 2013-14        95.03        101.15
6                Chandigarh 2013-14        88.42         96.09
  Primary_Total Upper_Primary_Boys Upper_Primary_Girls
1         93.93              94.70               88.98
2         96.74              82.81               84.38
3        128.46             112.64              115.27
4        113.43              87.85               98.69
5         97.96              80.60               94.92
6         91.85              99.93              103.02
  Upper_Primary_Total Secondary_Boys Secondary_Girls Secondary_Total
1               91.83         102.89           97.36          100.16
2               83.57          73.76           76.77           75.20
3              113.94          88.37           84.89           86.65
4               93.13          65.60           77.20           71.21
5               87.24          57.66           62.96           60.08
6              101.27          92.08           92.16           92.11
  Higher_Secondary_Boys Higher_Secondary_Girls Higher_Secondary_Total
1                105.40                  96.61                 101.28
2                 59.83                  60.83                  60.30
3                 65.16                  65.38                  65.27
4                 31.78                  34.27                  32.94
5                 23.33                  24.17                  23.70
6                 90.50                  92.88                  91.49
       lon      lat
1 92.65864 11.74009
2 92.65864 11.74009
3 92.65864 11.74009
4 92.65864 11.74009
5 92.65864 11.74009
6 92.65864 11.74009

The below map is a terrain style map of India. I wanted to integrate my data with a choropleth map, however i understood that R-Studio has pre-existing choropleth map for world and USA but not for other countries and ggmap supports very few map types - “terrain”, “satellite”, “hybrid” and “roadmap” but not choropleth. I feel this is a drawback for R-Studio as well as ggmaps.

map <- get_map(location = 'India', zoom = 5, maptype= 'terrain', scale = "auto")

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size= Primary_Boys, colour=Primary_Boys, alpha=0.5))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Upper_Primary_Boys, colour=Upper_Primary_Boys, alpha=0.5))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Secondary_Boys, color=Secondary_Boys, alpha=0.5 ))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Higher_Secondary_Boys, color=Higher_Secondary_Boys, alpha=0.5))


ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Primary_Girls, color=Primary_Girls, alpha=0.5))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Upper_Primary_Girls,color=Upper_Primary_Girls, alpha=0.5))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Secondary_Girls,color=Secondary_Girls, alpha=0.5))

ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Higher_Secondary_Girls,color=Higher_Secondary_Girls, alpha=0.5))

Findings from the maps:

The analysis would have been much more clear if the map is choropleth, I would take this as a scope for improvement in my next projects.

states_ger %>% 
  select(Year, State_UT, Primary_Boys,Upper_Primary_Boys, Secondary_Boys, Higher_Secondary_Boys) %>% 
  group_by(Year) %>% 
  summarise(avg_pb=mean(Primary_Boys), avg_upb=mean(Upper_Primary_Boys), avg_sb=mean(Secondary_Boys), avg_hsb=mean(Higher_Secondary_Boys)) 

# A tibble: 3 x 5
  Year    avg_pb avg_upb avg_sb avg_hsb
  <chr>    <dbl>   <dbl>  <dbl>   <dbl>
1 2013-14   105.    96.9   87.2    60.0
2 2014-15   102.    97.0   88.0    60.4
3 2015-16   100.    98.1   86.9    58.2
states_ger %>% 
  select(Year, Primary_Girls,Upper_Primary_Girls, Secondary_Girls, Higher_Secondary_Girls) %>% 
  group_by(Year) %>% 
  summarise(avg_pb=mean(Primary_Girls), avg_upb=mean(Upper_Primary_Girls), avg_sb=mean(Secondary_Girls), avg_hsb=mean(Higher_Secondary_Girls))

# A tibble: 3 x 5
  Year    avg_pb avg_upb avg_sb avg_hsb
  <chr>    <dbl>   <dbl>  <dbl>   <dbl>
1 2013-14   106.    99.8   88.0    60.5
2 2014-15   103.   102.    89.6    62.2
3 2015-16   101.   104.    89.4    61.8

Findings: * The overall mean enrollment percentage in Primary and Upper Primary levels is greater than or equal to 100% which is very good sign. * But, the overall mean percentage in Secondary and Higher Secondary is almost same in all the three years, this is where the government has to pitch in and take adequate measures.

My further analysis will focus on analyzing the dropout percentage and finding if we can get any correlation between the Gross Enrollment and Dropout.

Data Set-2 | Dropout Ratio/Percentage across all Indian States from 2013-2016

There are varying definitions on the web for Dropout Ratio. I will keep it simple here. Dropout Ratio simply means any student who leaves school for any reason before graduation or completion of a program of studies without transferring to another school.

Reading Dataset-2

dropout_ratio <- read_csv("601 Major Project/dropout-ratio.csv")
head(dropout_ratio)

# A tibble: 6 x 14
  State_UT       year    Primary_Boys Primary_Girls Primary_Total
  <chr>          <chr>   <chr>        <chr>         <chr>        
1 A & N Islands  2012-13 0.83         0.51          0.68         
2 A & N Islands  2013-14 1.35         1.06          1.21         
3 A & N Islands  2014-15 0.47         0.55          0.51         
4 Andhra Pradesh 2012-13 3.3          3.05          3.18         
5 Andhra Pradesh 2013-14 4.31         4.39          4.35         
6 Andhra Pradesh 2014-15 6.57         6.89          6.72         
# ... with 9 more variables: `Upper Primary_Boys` <chr>,
#   `Upper Primary_Girls` <chr>, `Upper Primary_Total` <chr>,
#   `Secondary _Boys` <chr>, `Secondary _Girls` <chr>,
#   `Secondary _Total` <chr>, HrSecondary_Boys <chr>,
#   HrSecondary_Girls <chr>, HrSecondary_Total <chr>
colnames(dropout_ratio)

 [1] "State_UT"            "year"                "Primary_Boys"       
 [4] "Primary_Girls"       "Primary_Total"       "Upper Primary_Boys" 
 [7] "Upper Primary_Girls" "Upper Primary_Total" "Secondary _Boys"    
[10] "Secondary _Girls"    "Secondary _Total"    "HrSecondary_Boys"   
[13] "HrSecondary_Girls"   "HrSecondary_Total"

Datatype of each column

str(dropout_ratio)

spec_tbl_df [110 x 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT           : chr [1:110] "A & N Islands" "A & N Islands" "A & N Islands" "Andhra Pradesh" ...
 $ year               : chr [1:110] "2012-13" "2013-14" "2014-15" "2012-13" ...
 $ Primary_Boys       : chr [1:110] "0.83" "1.35" "0.47" "3.3" ...
 $ Primary_Girls      : chr [1:110] "0.51" "1.06" "0.55" "3.05" ...
 $ Primary_Total      : chr [1:110] "0.68" "1.21" "0.51" "3.18" ...
 $ Upper Primary_Boys : chr [1:110] "Uppe_r_Primary" "NR" "1.44" "3.21" ...
 $ Upper Primary_Girls: chr [1:110] "1.09" "1.54" "1.95" "3.51" ...
 $ Upper Primary_Total: chr [1:110] "1.23" "0.51" "1.69" "3.36" ...
 $ Secondary _Boys    : chr [1:110] "5.57" "8.36" "11.47" "12.21" ...
 $ Secondary _Girls   : chr [1:110] "5.55" "5.98" "8.16" "13.25" ...
 $ Secondary _Total   : chr [1:110] "5.56" "7.2" "9.87" "12.72" ...
 $ HrSecondary_Boys   : chr [1:110] "17.66" "18.94" "21.05" "2.66" ...
 $ HrSecondary_Girls  : chr [1:110] "10.15" "12.2" "12.21" "NR" ...
 $ HrSecondary_Total  : chr [1:110] "14.14" "15.87" "16.93" "0.35" ...
 - attr(*, "spec")=
  .. cols(
  ..   State_UT = col_character(),
  ..   year = col_character(),
  ..   Primary_Boys = col_character(),
  ..   Primary_Girls = col_character(),
  ..   Primary_Total = col_character(),
  ..   `Upper Primary_Boys` = col_character(),
  ..   `Upper Primary_Girls` = col_character(),
  ..   `Upper Primary_Total` = col_character(),
  ..   `Secondary _Boys` = col_character(),
  ..   `Secondary _Girls` = col_character(),
  ..   `Secondary _Total` = col_character(),
  ..   HrSecondary_Boys = col_character(),
  ..   HrSecondary_Girls = col_character(),
  ..   HrSecondary_Total = col_character()
  .. )
 - attr(*, "problems")=<externalptr>

Tidying the data

library(janitor)
dropout_ratio <- clean_names(dropout_ratio)
dim(dropout_ratio)

[1] 110  14
dropout_ratio[ dropout_ratio == "NR" ] <- NA
#dropout_ratio[ dropout_ratio == "upper_primary_boys" ] <- NA
dropout_ratio[ dropout_ratio == "Uppe_r_Primary" ] <- NA
dropout_ratio <- data.frame(dropout_ratio)
dropout_ratio <- na.exclude(dropout_ratio)
dim(dropout_ratio)

[1] 55 14
dropout_ratio$primary_boys = as.numeric(dropout_ratio$primary_boys)
dropout_ratio$primary_girls = as.numeric(dropout_ratio$primary_girls)
dropout_ratio$primary_total = as.numeric(dropout_ratio$primary_total)
dropout_ratio$upper_primary_boys = as.numeric(dropout_ratio$upper_primary_boys)
dropout_ratio$upper_primary_girls = as.numeric(dropout_ratio$upper_primary_girls)
dropout_ratio$upper_primary_total = as.numeric(dropout_ratio$upper_primary_total)
dropout_ratio$secondary_boys = as.numeric(dropout_ratio$secondary_boys)
dropout_ratio$secondary_girls = as.numeric(dropout_ratio$secondary_girls)
dropout_ratio$secondary_total = as.numeric(dropout_ratio$secondary_total)
dropout_ratio$hr_secondary_boys = as.numeric(dropout_ratio$hr_secondary_boys)
dropout_ratio$hr_secondary_girls = as.numeric(dropout_ratio$hr_secondary_girls)
dropout_ratio$hr_secondary_total = as.numeric(dropout_ratio$hr_secondary_total)

str(dropout_ratio)

'data.frame':   55 obs. of  14 variables:
 $ state_ut           : chr  "A & N Islands" "Andhra Pradesh" "Arunachal  Pradesh" "Arunachal Pradesh" ...
 $ year               : chr  "2014-15" "2013-14" "2013-14" "2012-13" ...
 $ primary_boys       : num  0.47 4.31 11.54 15.84 11.51 ...
 $ primary_girls      : num  0.55 4.39 10.22 14.44 10.09 ...
 $ primary_total      : num  0.51 4.35 10.89 15.16 10.82 ...
 $ upper_primary_boys : num  1.44 3.46 4.44 5.86 5.31 7.89 7.6 6.47 3.31 3.7 ...
 $ upper_primary_girls: num  1.95 4.12 6.74 9.06 8.08 6.55 6.54 5.22 5.09 4.4 ...
 $ upper_primary_total: num  1.69 3.78 5.59 7.47 6.71 7.2 7.05 5.85 4.13 4.02 ...
 $ secondary_boys     : num  11.5 11.9 16.1 14 18.3 ...
 $ secondary_girls    : num  8.16 13.37 12.75 11.77 15.81 ...
 $ secondary_total    : num  9.87 12.65 14.49 12.93 17.11 ...
 $ hr_secondary_boys  : num  21.05 12.65 18.57 7.85 19.37 ...
 $ hr_secondary_girls : num  12.21 10.85 15.49 2.14 17.44 ...
 $ hr_secondary_total : num  16.93 11.79 17.07 5.11 18.42 ...
 - attr(*, "na.action")= 'exclude' Named int [1:55] 1 2 4 6 12 13 14 15 16 17 ...
  ..- attr(*, "names")= chr [1:55] "1" "2" "4" "6" ...
all_india_drop <- filter(dropout_ratio, state_ut=="All India") 
dim(all_india_drop)

[1]  1 14
fig4 <- pivot_longer(all_india_drop, c(primary_boys, primary_girls, primary_total, upper_primary_boys, upper_primary_girls, upper_primary_total, secondary_boys, secondary_girls, secondary_total, hr_secondary_girls, hr_secondary_boys, hr_secondary_total), names_to = "EducationLevel")
ggplot(fig4, aes(x=value, y=EducationLevel)) + geom_boxplot(color="red") + geom_text(aes(label=value), size=4) + labs(title = "Fig-4: All India Dropout Ratio - Education Wise ", x="Dropout Percentage", y="Education Level") + facet_wrap(~year)

Findings:

Correlation between Gross Enrollment Ratio and Dropout Ratios: * Comparing Fig-3 and Fig-4, Higher Secondary level has the lowest Enrollment and Secondary Level has the highest dropout. * We can draw a conclusion from this that, the highest dropouts in Secondary is leading to the lowest enrollment in the Higher secondary schools. Indian Government needs to take measures and implement schemes or improve facilities in these two levels.

states_drop <- filter(dropout_ratio,  state_ut != "All India") 
head(states_drop)

            state_ut    year primary_boys primary_girls primary_total
1      A & N Islands 2014-15         0.47          0.55          0.51
2     Andhra Pradesh 2013-14         4.31          4.39          4.35
3 Arunachal  Pradesh 2013-14        11.54         10.22         10.89
4  Arunachal Pradesh 2012-13        15.84         14.44         15.16
5  Arunachal Pradesh 2014-15        11.51         10.09         10.82
6              Assam 2012-13         7.02          5.46          6.24
  upper_primary_boys upper_primary_girls upper_primary_total
1               1.44                1.95                1.69
2               3.46                4.12                3.78
3               4.44                6.74                5.59
4               5.86                9.06                7.47
5               5.31                8.08                6.71
6               7.89                6.55                7.20
  secondary_boys secondary_girls secondary_total hr_secondary_boys
1          11.47            8.16            9.87             21.05
2          11.95           13.37           12.65             12.65
3          16.08           12.75           14.49             18.57
4          13.99           11.77           12.93              7.85
5          18.33           15.81           17.11             19.37
6          25.65           27.79           26.77              4.87
  hr_secondary_girls hr_secondary_total
1              12.21              16.93
2              10.85              11.79
3              15.49              17.07
4               2.14               5.11
5              17.44              18.42
6               4.50               4.69
Analysis of the dropout ratio of Primary Boys 
primary_boys_drop <- states_drop[c("state_ut", "year", "primary_boys")] 
slice_min(primary_boys_drop, primary_boys)

  state_ut    year primary_boys
1  Gujarat 2012-13         0.21
top10 <- arrange(primary_boys_drop, desc(primary_boys))
top10 <- slice_head(top10, n=10)
top10

             state_ut    year primary_boys
1            Nagaland 2013-14        19.09
2             Manipur 2013-14        17.27
3   Arunachal Pradesh 2012-13        15.84
4  Arunachal  Pradesh 2013-14        11.54
5   Arunachal Pradesh 2014-15        11.51
6             Manipur 2012-13        10.24
7             Mizoram 2014-15        10.17
8      Madhya Pradesh 2013-14         9.91
9       Uttar Pradesh 2014-15         9.08
10              Assam 2013-14         8.19
ggplot(top10, aes(x=year, y=primary_boys, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-5: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=primary_boys), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of the dropout ratio of Primary Girls 
primary_girls_drop <- states_drop[c("state_ut", "year", "primary_girls")] 
slice_min(primary_girls_drop, primary_girls)

     state_ut    year primary_girls
1 Daman & Diu 2014-15          0.29
top10 <- arrange(primary_girls_drop, desc(primary_girls))
top10 <- slice_head(top10, n=10)
top10

             state_ut    year primary_girls
1            Nagaland 2013-14         19.74
2             Manipur 2013-14         18.74
3   Arunachal Pradesh 2012-13         14.44
4      Madhya Pradesh 2013-14         10.40
5  Arunachal  Pradesh 2013-14         10.22
6   Arunachal Pradesh 2014-15         10.09
7             Mizoram 2014-15         10.03
8             Manipur 2012-13          9.48
9       Uttar Pradesh 2014-15          8.04
10           Nagaland 2012-13          7.03
ggplot(top10, aes(x=year, y=primary_girls, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-6: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=primary_girls), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of Upper Primary Boys

upper_primary_boys_drop <- states_drop[c("state_ut", "year", "upper_primary_boys")] 
slice_min(upper_primary_boys_drop, upper_primary_boys)

    state_ut    year upper_primary_boys
1 Puducherry 2012-13               0.33
top10 <- arrange(upper_primary_boys_drop, desc(upper_primary_boys))
top10 <- slice_head(top10, n=10)
top10

         state_ut    year upper_primary_boys
1        Nagaland 2013-14              18.08
2        Nagaland 2012-13              10.15
3  Madhya Pradesh 2013-14               9.88
4       Jharkhand 2014-15               9.01
5           Assam 2012-13               7.89
6        Nagaland 2014-15               7.87
7           Assam 2013-14               7.60
8         Manipur 2013-14               7.48
9    Chhattisgarh 2014-15               6.47
10         Sikkim 2013-14               6.35
ggplot(top10, aes(x=year, y=upper_primary_boys, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-7: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Upper Primary ",  y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=upper_primary_boys), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of upper primary girls

upper_primary_girls_drop <- states_drop[c("state_ut", "year", "upper_primary_girls")] 
slice_min(upper_primary_girls_drop, upper_primary_girls)

          state_ut    year upper_primary_girls
1 Himachal Pradesh 2012-13                0.49
top10 <- arrange(upper_primary_girls_drop, desc(upper_primary_girls))
top10 <- slice_head(top10, n=10)
top10

            state_ut    year upper_primary_girls
1           Nagaland 2013-14               17.63
2     Madhya Pradesh 2013-14               13.57
3           Nagaland 2012-13                9.51
4  Arunachal Pradesh 2012-13                9.06
5          Jharkhand 2014-15                8.96
6            Gujarat 2014-15                8.54
7            Gujarat 2012-13                8.19
8  Arunachal Pradesh 2014-15                8.08
9            Gujarat 2013-14                8.04
10          Nagaland 2014-15                7.97
ggplot(top10, aes(x=year, y=upper_primary_girls, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-8: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Upper Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=upper_primary_girls), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of Secondary boys 
secondary_boys_drop <- states_drop[c("state_ut", "year", "secondary_boys")] 
slice_min(secondary_boys_drop, secondary_boys)

          state_ut    year secondary_boys
1 Himachal Pradesh 2014-15           6.31
top10 <- arrange(secondary_boys_drop, desc(secondary_boys))
top10 <- slice_head(top10, n=10)
top10

               state_ut    year secondary_boys
1             Karnataka 2012-13          40.70
2           Daman & Diu 2014-15          34.45
3              Nagaland 2013-14          34.14
4  Dadra & Nagar Haveli 2013-14          30.02
5                 Assam 2013-14          28.59
6               Tripura 2014-15          28.03
7              Nagaland 2012-13          26.70
8               Gujarat 2014-15          26.29
9                 Assam 2012-13          25.65
10       Madhya Pradesh 2013-14          25.21
ggplot(top10, aes(x=year, y=secondary_boys, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-10: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - secondary ",  y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=secondary_boys), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of Secondary Girls 
secondary_girls_drop <- states_drop[c("state_ut", "year", "secondary_girls")] 
slice_min(secondary_girls_drop, secondary_girls)

          state_ut    year secondary_girls
1 Himachal Pradesh 2014-15             5.8
top10 <- arrange(secondary_girls_drop, desc(secondary_girls))
top10 <- slice_head(top10, n=10)
top10

               state_ut    year secondary_girls
1             Karnataka 2012-13           39.07
2              Nagaland 2013-14           36.08
3                 Assam 2013-14           32.10
4           Daman & Diu 2014-15           29.73
5               Tripura 2014-15           28.83
6        Madhya Pradesh 2013-14           27.91
7                 Assam 2012-13           27.79
8               Tripura 2012-13           26.99
9  Dadra & Nagar Haveli 2013-14           26.83
10             Nagaland 2012-13           26.33
ggplot(top10, aes(x=year, y=secondary_girls, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-11: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - secondary ",  y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=secondary_girls), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of Higher-Secondary boys 
hr_secondary_boys_drop <- states_drop[c("state_ut", "year", "hr_secondary_boys")] 
slice_min(hr_secondary_boys_drop, hr_secondary_boys)

        state_ut    year hr_secondary_boys
1 Madhya Pradesh 2013-14              0.52
top10 <- arrange(hr_secondary_boys_drop, desc(hr_secondary_boys))
top10 <- slice_head(top10, n=10)
top10

             state_ut    year hr_secondary_boys
1         Daman & Diu 2014-15             44.38
2       A & N Islands 2014-15             21.05
3           Karnataka 2012-13             19.47
4   Arunachal Pradesh 2014-15             19.37
5            Nagaland 2012-13             18.67
6  Arunachal  Pradesh 2013-14             18.57
7            Nagaland 2013-14             15.36
8         Daman & Diu 2013-14             14.48
9              Sikkim 2013-14             14.11
10    Jammu & Kashmir 2014-15             13.85
ggplot(top10, aes(x=year, y=hr_secondary_boys, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-12: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Higher secondary ",  y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=hr_secondary_boys), size = 4, position = position_dodge(width = .9), vjust = 0, color = "white") + theme_dark() + facet_wrap(~state_ut)

Analysis of dropout ratio of Higher Secondary Girls 
hr_secondary_girls_drop <- states_drop[c("state_ut", "year", "hr_secondary_girls")] 
slice_min(hr_secondary_girls_drop, hr_secondary_girls)

  state_ut    year hr_secondary_girls
1  Gujarat 2012-13                0.3
top10 <- arrange(hr_secondary_girls_drop, desc(hr_secondary_girls))
top10 <- slice_head(top10, n=10)
top10

             state_ut    year hr_secondary_girls
1         Daman & Diu 2014-15              36.05
2            Nagaland 2012-13              17.87
3   Arunachal Pradesh 2014-15              17.44
4  Arunachal  Pradesh 2013-14              15.49
5           Telangana 2013-14              13.20
6            Nagaland 2013-14              12.96
7       A & N Islands 2014-15              12.21
8              Sikkim 2013-14              11.92
9           Karnataka 2012-13              11.26
10    Jammu & Kashmir 2014-15              11.20
ggplot(top10, aes(x=year, y=hr_secondary_girls, fill=year)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-13: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Higher secondary ",  y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=hr_secondary_girls), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut, scales = "free_y")

Reading Dataframe-3 | Percentage of Schools with access to computers

schools_with_comps <- read_csv("601 Major Project/percentage-of-schools-with-comps.csv")
colnames(schools_with_comps)

 [1] "State_UT"                        
 [2] "year"                            
 [3] "Primary_Only"                    
 [4] "Primary_with_U_Primary"          
 [5] "Primary_with_U_Primary_Sec_HrSec"
 [6] "U_Primary_Only"                  
 [7] "U_Primary_With_Sec_HrSec"        
 [8] "Primary_with_U_Primary_Sec"      
 [9] "U_Primary_With_Sec"              
[10] "Sec_Only"                        
[11] "Sec_with_HrSec."                 
[12] "HrSec_Only"                      
[13] "All Schools"                     
schools_with_comps <- rename(schools_with_comps, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")

All_India <- filter(schools_with_comps, State_UT=="All India") %>% 
  select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") 
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Percentage of Schools with access to Computer facility all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic() + facet_wrap(~Education_Level)


library(tidytext)
primary_wise<- select(schools_with_comps, State_UT, year, primary) %>% 
  filter(State_UT != "All India")

primary_wise <- arrange(primary_wise, primary)
primary_wise <- slice_head(primary_wise, n=50)
ggplot(primary_wise, aes(x=primary, y=State_UT, fill=State_UT))+geom_bar(stat="identity")+facet_wrap(~year) + labs(title = "States with lowest percentage of Computer Facility", subtitle = "Education Level - Primary",  x="percentage", y="State name") + geom_text(aes(label=primary), size = 3, position = position_dodge(width = .9), vjust = 0, color = "black")


library(kableExtra)
upper_primary_wise<- select(schools_with_comps, State_UT, year, upper_primary) %>% 
  filter(State_UT != "All India") %>% 
  arrange(upper_primary) %>% 
  slice(1:20)

kable(upper_primary_wise, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table1 : State-wise Percentage of Upper Primary Schools having lowest access to computers") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 1: Table1 : State-wise Percentage of Upper Primary Schools having lowest access to computers
State/Union Territory Year Percentage
Andaman & Nicobar Islands 2013-14 0.00
Andaman & Nicobar Islands 2015-16 0.00
Chandigarh 2013-14 0.00
Chandigarh 2014-15 0.00
Chandigarh 2015-16 0.00
Puducherry 2013-14 0.00
Sikkim 2015-16 0.00
Telangana 2015-16 0.00
West Bengal 2013-14 7.93
Odisha 2013-14 8.24
Jammu And Kashmir 2013-14 8.82
Odisha 2014-15 9.19
Odisha 2015-16 9.28
West Bengal 2014-15 9.32
West Bengal 2015-16 9.97
Bihar 2013-14 10.79
Jammu And Kashmir 2014-15 11.19
Jammu And Kashmir 2015-16 11.28
Bihar 2015-16 11.64
Bihar 2014-15 11.65 
nagaland <- filter(schools_with_comps, State_UT=="Nagaland")
nagaland <- select(nagaland, State_UT, year, primary, upper_primary)
nagaland

# A tibble: 3 x 4
  State_UT year    primary upper_primary
  <chr>    <chr>     <dbl>         <dbl>
1 Nagaland 2013-14    4.98          59.1
2 Nagaland 2014-15    4.97          68.8
3 Nagaland 2015-16    5.53          76.9
ggplot() + geom_line(data=nagaland, mapping=aes(x=year, y=primary, group=State_UT), size=1, color="red") + geom_point(data=nagaland, mapping=aes(x=year, y=primary, group=State_UT), color="black") +
  geom_line(data=nagaland, mapping=aes(x=year, y=upper_primary, group=State_UT), color="blue", size=1) + geom_point(data=nagaland, mapping=aes(x=year, y=upper_primary, group=State_UT), color="black") + labs(title = "Percentage of Schools with access to Computers in Nagaland State", subtitle = "Education Level - Primary and Upper Primary",  x="year", y="dropout percentage") 


secondary_wise<- select(schools_with_comps, State_UT, year, secondary) %>% 
  filter(State_UT != "All India")

secondary_wise <- arrange(secondary_wise, desc(secondary))
secondary_wise

# A tibble: 107 x 3
   State_UT         year    secondary
   <chr>            <chr>       <dbl>
 1 Daman & Diu      2013-14     100  
 2 Himachal Pradesh 2013-14     100  
 3 Kerala           2014-15     100  
 4 Kerala           2015-16     100  
 5 Nagaland         2015-16     100  
 6 Punjab           2013-14     100  
 7 Daman & Diu      2014-15      92.3
 8 Daman & Diu      2015-16      92.3
 9 Maharashtra      2015-16      90.5
10 Maharashtra      2014-15      88.4
# ... with 97 more rows
secondary_wise <- slice_head(secondary_wise, n=40)
ggplot(secondary_wise, aes(x=secondary, y=State_UT, fill=State_UT))+geom_bar(stat="identity")+facet_wrap(~year) + labs(title = "States with highest percentage of Computer Facility", subtitle = "Education Level - secondary",  x="percentage", y="State name") + geom_text(aes(label=secondary), size = 3, position = position_dodge(width = .1), vjust = 0, color = "black")


karnataka <-  filter(schools_with_comps, State_UT=="Karnataka")
karnataka <- select(karnataka, State_UT, year, secondary)
karnataka

# A tibble: 3 x 3
  State_UT  year    secondary
  <chr>     <chr>       <dbl>
1 Karnataka 2013-14      67.0
2 Karnataka 2014-15      69.9
3 Karnataka 2015-16      69.3
ggplot(karnataka, aes(x=year, y=secondary, group=State_UT)) + geom_line(size=1, color="purple") + geom_point() + geom_text(aes(label=secondary), size = 5) + labs(title = "Percentage of Schools with access to Computers in the state of Karnataka", subtitle = "Education Level - Secondary",  x="year", y="Percentage")


hr_secondary_wise<- select(schools_with_comps, State_UT, year, hr_secondary) %>% 
  filter(State_UT != "All India") %>% 
  arrange(hr_secondary) %>% 
  slice(1:20)

kable(hr_secondary_wise, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table2 : State-wise Percentage of Higher Secondary Schools having zero access to computers") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 2: Table2 : State-wise Percentage of Higher Secondary Schools having zero access to computers
State/Union Territory Year Percentage
Andaman & Nicobar Islands 2013-14 0
Andaman & Nicobar Islands 2014-15 0
Andaman & Nicobar Islands 2015-16 0
Arunachal Pradesh 2014-15 0
Arunachal Pradesh 2015-16 0
Chandigarh 2013-14 0
Chandigarh 2014-15 0
Chandigarh 2015-16 0
Chhattisgarh 2013-14 0
Dadra & Nagar Haveli 2013-14 0
Dadra & Nagar Haveli 2014-15 0
Dadra & Nagar Haveli 2015-16 0
Delhi 2013-14 0
Delhi 2014-15 0
Haryana 2013-14 0
Lakshadweep 2013-14 0
Lakshadweep 2014-15 0
Lakshadweep 2015-16 0
Odisha 2013-14 0
Odisha 2014-15 0

Reading Dataframe-4 | Percentage of Schools with Electricity

schools_with_electricity <- read_csv("601 Major Project/percentage-of-schools-with-electricity.csv")
head(schools_with_electricity)

# A tibble: 6 x 13
  State_UT        year  Primary_Only Primary_with_U_~ Primary_with_U_~
  <chr>           <chr>        <dbl>            <dbl>            <dbl>
1 Andaman & Nico~ 2013~         82.4             96.0            100  
2 Andaman & Nico~ 2014~         80.7             96.3            100  
3 Andaman & Nico~ 2015~         82.1             97.6            100  
4 Andhra Pradesh  2013~         87.7             93.6             99.3
5 Andhra Pradesh  2014~         91.1             94.7            100  
6 Andhra Pradesh  2015~         91.6             95.6            100  
# ... with 8 more variables: U_Primary_Only <dbl>,
#   U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
#   U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
#   HrSec_Only <dbl>, `All Schools` <dbl>
colnames(schools_with_electricity)

 [1] "State_UT"                        
 [2] "year"                            
 [3] "Primary_Only"                    
 [4] "Primary_with_U_Primary"          
 [5] "Primary_with_U_Primary_Sec_HrSec"
 [6] "U_Primary_Only"                  
 [7] "U_Primary_With_Sec_HrSec"        
 [8] "Primary_with_U_Primary_Sec"      
 [9] "U_Primary_With_Sec"              
[10] "Sec_Only"                        
[11] "Sec_with_HrSec."                 
[12] "HrSec_Only"                      
[13] "All Schools"                     
schools_with_electricity <- rename(schools_with_electricity, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")

Datatype of each column

str(schools_with_electricity)

spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT                        : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
 $ year                            : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
 $ primary                         : num [1:110] 82.4 80.7 82.1 87.7 91.1 ...
 $ Primary_with_U_Primary          : num [1:110] 96 96.3 97.6 93.6 94.7 ...
 $ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 99.3 100 ...
 $ upper_primary                   : num [1:110] 0 100 0 100 100 ...
 $ U_Primary_With_Sec_HrSec        : num [1:110] 100 100 100 67.5 86.1 ...
 $ Primary_with_U_Primary_Sec      : num [1:110] 100 100 100 96.2 97.6 ...
 $ U_Primary_With_Sec              : num [1:110] 0 0 0 96.2 97.1 ...
 $ secondary                       : num [1:110] 0 0 0 97.5 93.5 ...
 $ Sec_with_HrSec.                 : num [1:110] 100 100 100 100 83.3 ...
 $ hr_secondary                    : num [1:110] 0 0 0 91.3 93.2 ...
 $ All Schools                     : num [1:110] 88.9 88.9 90.1 90.3 92.8 ...
 - attr(*, "spec")=
  .. cols(
  ..   State_UT = col_character(),
  ..   year = col_character(),
  ..   Primary_Only = col_double(),
  ..   Primary_with_U_Primary = col_double(),
  ..   Primary_with_U_Primary_Sec_HrSec = col_double(),
  ..   U_Primary_Only = col_double(),
  ..   U_Primary_With_Sec_HrSec = col_double(),
  ..   Primary_with_U_Primary_Sec = col_double(),
  ..   U_Primary_With_Sec = col_double(),
  ..   Sec_Only = col_double(),
  ..   Sec_with_HrSec. = col_double(),
  ..   HrSec_Only = col_double(),
  ..   `All Schools` = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 
All_India <- filter(schools_with_electricity, State_UT=="All India") %>% 
  select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") 
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Percentage of Schools with access to Electricity all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic() + facet_wrap(~Education_Level)

Nagaland 
states_electricity <- filter(schools_with_electricity, State_UT != "All India")

nagaland <- filter(states_electricity, State_UT == "Nagaland") %>% 
  select(year, primary, upper_primary )

nagaland <- pivot_longer(nagaland, c(primary,upper_primary), names_to = "Education_Level", values_to = "Percentage")
ggplot(nagaland, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position="dodge", stat="identity") + coord_polar() + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + labs(title = "Percentage of Schools with access to Electricity in the state of Nagaland", subtitle = "Education Level - Primary and Upper Primary" )


karnataka <- filter(states_electricity, State_UT == "Karnataka") %>% 
  select(year, secondary, State_UT )

ggplot(karnataka, aes(x=year, y=secondary, group=State_UT)) + geom_line(color="red", size=1) + geom_point() + geom_text(aes(label=secondary), size = 4, position = position_dodge(width = .6), vjust = 0, color = "black") + labs(title = "Percentage of Schools with access to Electricity in the state of Karnataka", subtitle = "Education Level - Secondary" )


states_electricity

# A tibble: 107 x 13
   State_UT            year  primary Primary_with_U_~ Primary_with_U_~
   <chr>               <chr>   <dbl>            <dbl>            <dbl>
 1 Andaman & Nicobar ~ 2013~   82.4              96.0            100  
 2 Andaman & Nicobar ~ 2014~   80.7              96.3            100  
 3 Andaman & Nicobar ~ 2015~   82.1              97.6            100  
 4 Andhra Pradesh      2013~   87.7              93.6             99.3
 5 Andhra Pradesh      2014~   91.1              94.7            100  
 6 Andhra Pradesh      2015~   91.6              95.6            100  
 7 Arunachal Pradesh   2013~   19.7              53.6             92.2
 8 Arunachal Pradesh   2014~   21.5              55.0             96.8
 9 Arunachal Pradesh   2015~   22.6              53.9             95.5
10 Assam               2013~    9.51             51.1             81.2
# ... with 97 more rows, and 8 more variables: upper_primary <dbl>,
#   U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
#   U_Primary_With_Sec <dbl>, secondary <dbl>, Sec_with_HrSec. <dbl>,
#   hr_secondary <dbl>, `All Schools` <dbl>
Daman_Diu <- filter(states_electricity, State_UT == "Daman & Diu") %>% 
  select(State_UT, year, hr_secondary )

kable(Daman_Diu, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table4 : Daman & Diu Percentage of Schools with Electricity") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 3: Table4 : Daman & Diu Percentage of Schools with Electricity
State/Union Territory Year Percentage
Daman & Diu 2013-14 100
Daman & Diu 2014-15 100
Daman & Diu 2015-16 100

Reading Dataframe-5 | Percentage of Schools with water faciltity

schools_with_water <- read_csv("601 Major Project/percentage-of-schools-with-water-facility.csv")
head(schools_with_water)

# A tibble: 6 x 13
  `State/UT`      Year  Primary_Only Primary_with_U_~ Primary_with_U_~
  <chr>           <chr>        <dbl>            <dbl>            <dbl>
1 Andaman & Nico~ 2013~         98.2             98.7            100  
2 Andaman & Nico~ 2014~         99.6             98.8            100  
3 Andaman & Nico~ 2015~        100              100              100  
4 Andhra Pradesh  2013~         86.9             94.5             99.7
5 Andhra Pradesh  2014~         91.8             96.1            100  
6 Andhra Pradesh  2015~         93.9             97.0            100  
# ... with 8 more variables: U_Primary_Only <dbl>,
#   U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
#   U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
#   HrSec_Only <dbl>, `All Schools` <dbl>
colnames(schools_with_water)

 [1] "State/UT"                        
 [2] "Year"                            
 [3] "Primary_Only"                    
 [4] "Primary_with_U_Primary"          
 [5] "Primary_with_U_Primary_Sec_HrSec"
 [6] "U_Primary_Only"                  
 [7] "U_Primary_With_Sec_HrSec"        
 [8] "Primary_with_U_Primary_Sec"      
 [9] "U_Primary_With_Sec"              
[10] "Sec_Only"                        
[11] "Sec_with_HrSec."                 
[12] "HrSec_Only"                      
[13] "All Schools"                     
schools_with_water <- rename(schools_with_water, State_UT="State/UT", primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only" )

Datatype of each column

str(schools_with_water)

spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT                        : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
 $ Year                            : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
 $ primary                         : num [1:110] 98.2 99.5 100 86.9 91.8 ...
 $ Primary_with_U_Primary          : num [1:110] 98.7 98.8 100 94.5 96.1 ...
 $ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 99.7 100 ...
 $ upper_primary                   : num [1:110] 0 100 0 90.9 100 ...
 $ U_Primary_With_Sec_HrSec        : num [1:110] 100 100 100 87.3 90 ...
 $ Primary_with_U_Primary_Sec      : num [1:110] 100 100 100 98.8 99.6 ...
 $ U_Primary_With_Sec              : num [1:110] 0 0 0 96 97.5 ...
 $ secondary                       : num [1:110] 0 0 0 97.5 100 100 0 0 0 88.3 ...
 $ Sec_with_HrSec.                 : num [1:110] 100 100 100 100 100 ...
 $ hr_secondary                    : num [1:110] 0 0 0 97.5 98.4 ...
 $ All Schools                     : num [1:110] 98.7 99.5 100 90.3 93.7 ...
 - attr(*, "spec")=
  .. cols(
  ..   `State/UT` = col_character(),
  ..   Year = col_character(),
  ..   Primary_Only = col_double(),
  ..   Primary_with_U_Primary = col_double(),
  ..   Primary_with_U_Primary_Sec_HrSec = col_double(),
  ..   U_Primary_Only = col_double(),
  ..   U_Primary_With_Sec_HrSec = col_double(),
  ..   Primary_with_U_Primary_Sec = col_double(),
  ..   U_Primary_With_Sec = col_double(),
  ..   Sec_Only = col_double(),
  ..   Sec_with_HrSec. = col_double(),
  ..   HrSec_Only = col_double(),
  ..   `All Schools` = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 
All_India <- filter(schools_with_water, State_UT=="All India") %>% 
  select(Year, primary, upper_primary, secondary, hr_secondary)

All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") 
ggplot(All_India, aes(x=Year, y=Percentage, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Percentage of Schools Water Facility all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 0, color = "black") + theme_classic() + facet_wrap(~Education_Level)

Nagaland 
states_water <- filter(schools_with_water, State_UT != "All India")

nagaland <- filter(states_water, State_UT == "Nagaland") %>% 
  select(Year, primary, upper_primary )

nagaland <- pivot_longer(nagaland, c(primary,upper_primary), names_to = "Education_Level", values_to = "Percentage")
ggplot(nagaland, aes(x=Year, y=Percentage, fill=Education_Level)) + geom_bar(position="dodge", stat="identity") + coord_polar() + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + labs(title = "Percentage of Schools with access to Drinking Water in the state of Nagaland", subtitle = "Education Level - Primary and Upper Primary" )

Karnataka 
karnataka <- filter(schools_with_water, State_UT == "Karnataka") %>% 
  select(Year, secondary, State_UT )

ggplot(karnataka, aes(x=Year, y=secondary, group=State_UT)) + geom_line(color="red", size=1) + geom_point() + geom_text(aes(label=secondary), size = 4, position = position_dodge(width = .6), vjust = 0, color = "black") + labs(title = "Percentage of Schools with access to Drinking Water in the state of Karnataka", subtitle = "Education Level - Secondary" )


Daman_Diu <- filter(schools_with_water, State_UT == "Daman & Diu") %>% 
  select(State_UT, Year, hr_secondary )

kable(Daman_Diu, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table4 : Daman & Diu Percentage of Schools with Drinking Water Facility") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 4: Table4 : Daman & Diu Percentage of Schools with Drinking Water Facility
State/Union Territory Year Percentage
Daman & Diu 2013-14 100
Daman & Diu 2014-15 100
Daman & Diu 2015-16 100

Reading Dataframe-6 | Percentage of Schools with boys toilet

schools_with_boys_toilet <- read_csv("601 Major Project/schools-with-boys-toilet.csv")
head(schools_with_boys_toilet)

# A tibble: 6 x 13
  State_UT        year  Primary_Only Primary_with_U_~ Primary_with_U_~
  <chr>           <chr>        <dbl>            <dbl>            <dbl>
1 Andaman & Nico~ 2013~         91.6             97.4            100  
2 Andaman & Nico~ 2014~        100              100              100  
3 Andaman & Nico~ 2015~        100              100              100  
4 Andhra Pradesh  2013~         53.0             62.6             82.0
5 Andhra Pradesh  2014~         57.9             76.5             96  
6 Andhra Pradesh  2015~         99.6             99.9             99.0
# ... with 8 more variables: U_Primary_Only <dbl>,
#   U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
#   U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
#   HrSec_Only <dbl>, `All Schools` <dbl>
colnames(schools_with_boys_toilet)

 [1] "State_UT"                        
 [2] "year"                            
 [3] "Primary_Only"                    
 [4] "Primary_with_U_Primary"          
 [5] "Primary_with_U_Primary_Sec_HrSec"
 [6] "U_Primary_Only"                  
 [7] "U_Primary_With_Sec_HrSec"        
 [8] "Primary_with_U_Primary_Sec"      
 [9] "U_Primary_With_Sec"              
[10] "Sec_Only"                        
[11] "Sec_with_HrSec."                 
[12] "HrSec_Only"                      
[13] "All Schools"                     
schools_with_boys_toilet <- rename(schools_with_boys_toilet, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")

Datatype of each column

str(schools_with_boys_toilet)

spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT                        : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
 $ year                            : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
 $ primary                         : num [1:110] 91.6 100 100 53 57.9 ...
 $ Primary_with_U_Primary          : num [1:110] 97.4 100 100 62.6 76.5 ...
 $ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 82 96 ...
 $ upper_primary                   : num [1:110] 0 100 0 45.5 75 ...
 $ U_Primary_With_Sec_HrSec        : num [1:110] 100 100 100 64.1 93.3 ...
 $ Primary_with_U_Primary_Sec      : num [1:110] 100 100 100 76.2 91.4 ...
 $ U_Primary_With_Sec              : num [1:110] 0 0 0 60.6 78 ...
 $ secondary                       : num [1:110] 0 0 0 59.3 80.7 ...
 $ Sec_with_HrSec.                 : num [1:110] 100 100 100 85.7 60 ...
 $ hr_secondary                    : num [1:110] 0 0 0 73.4 86.5 ...
 $ All Schools                     : num [1:110] 94.5 100 100 56.9 65.3 ...
 - attr(*, "spec")=
  .. cols(
  ..   State_UT = col_character(),
  ..   year = col_character(),
  ..   Primary_Only = col_double(),
  ..   Primary_with_U_Primary = col_double(),
  ..   Primary_with_U_Primary_Sec_HrSec = col_double(),
  ..   U_Primary_Only = col_double(),
  ..   U_Primary_With_Sec_HrSec = col_double(),
  ..   Primary_with_U_Primary_Sec = col_double(),
  ..   U_Primary_With_Sec = col_double(),
  ..   Sec_Only = col_double(),
  ..   Sec_with_HrSec. = col_double(),
  ..   HrSec_Only = col_double(),
  ..   `All Schools` = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 
All_India <- filter(schools_with_boys_toilet, State_UT=="All India") %>% 
  select(year, primary, upper_primary, secondary, hr_secondary)

All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Percentage of Schools with Boys toilet all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)


states <- c("Nagaland", "Karnataka", "Daman & Diu")
states_with_boys_toilet <- filter(schools_with_boys_toilet, State_UT == states) %>% 
  pivot_longer(c(primary,upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>% 
  select(State_UT, year, Education_Level, Percentage)
  
ggplot(states_with_boys_toilet, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position = "dodge", stat = "identity") + facet_wrap(~State_UT) + theme_dark() + labs(title = "Percentage of Schools with Boys toilet", subtitle = "Daman & Diu, Karnataka, Nagaland")


states_with_no_boys_toilet <- filter(schools_with_boys_toilet, State_UT != "All India", upper_primary==0, secondary==0, hr_secondary==0) %>%
  pivot_longer(c(upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>% 
  select(State_UT, year, Education_Level, Percentage)

kable(states_with_no_boys_toilet, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Education Level", "Percentage"), caption = "Table4 : States with no boys toilet") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 5: Table4 : States with no boys toilet
State/Union Territory Year Education Level Percentage
Andaman & Nicobar Islands 2013-14 upper_primary 0
Andaman & Nicobar Islands 2013-14 secondary 0
Andaman & Nicobar Islands 2013-14 hr_secondary 0
Andaman & Nicobar Islands 2015-16 upper_primary 0
Andaman & Nicobar Islands 2015-16 secondary 0
Andaman & Nicobar Islands 2015-16 hr_secondary 0
Arunachal Pradesh 2013-14 upper_primary 0
Arunachal Pradesh 2013-14 secondary 0
Arunachal Pradesh 2013-14 hr_secondary 0
Chandigarh 2013-14 upper_primary 0
Chandigarh 2013-14 secondary 0
Chandigarh 2013-14 hr_secondary 0
Chandigarh 2014-15 upper_primary 0
Chandigarh 2014-15 secondary 0
Chandigarh 2014-15 hr_secondary 0
Chandigarh 2015-16 upper_primary 0
Chandigarh 2015-16 secondary 0
Chandigarh 2015-16 hr_secondary 0
Dadra & Nagar Haveli 2013-14 upper_primary 0
Dadra & Nagar Haveli 2013-14 secondary 0
Dadra & Nagar Haveli 2013-14 hr_secondary 0

Reading Dataframe-7 | Percentage of Schools with girls toilet

schools_with_girls_toilet <- read_csv("601 Major Project/schools-with-girls-toilet.csv")
schools_with_girls_toilet <- rename(schools_with_girls_toilet, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")
head(schools_with_girls_toilet)

# A tibble: 6 x 13
  State_UT             year  primary Primary_with_U_~ Primary_with_U_~
  <chr>                <chr>   <dbl>            <dbl>            <dbl>
1 All India            2013~    88.7             96.0             98.8
2 All India            2014~    91.2             96.9             99.5
3 All India            2015~    97.0             99.0             99.7
4 Andaman & Nicobar I~ 2013~    89.7             97.4            100  
5 Andaman & Nicobar I~ 2014~   100              100              100  
6 Andaman & Nicobar I~ 2015~   100              100              100  
# ... with 8 more variables: upper_primary <dbl>,
#   U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
#   U_Primary_With_Sec <dbl>, secondary <dbl>, Sec_with_HrSec. <dbl>,
#   hr_secondary <dbl>, `All Schools` <dbl>

Datatype of each column

str(schools_with_girls_toilet)

spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ State_UT                        : chr [1:110] "All India" "All India" "All India" "Andaman & Nicobar Islands" ...
 $ year                            : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
 $ primary                         : num [1:110] 88.7 91.2 97 89.7 100 ...
 $ Primary_with_U_Primary          : num [1:110] 96 96.9 99 97.4 100 ...
 $ Primary_with_U_Primary_Sec_HrSec: num [1:110] 98.8 99.5 99.7 100 100 ...
 $ upper_primary                   : num [1:110] 91.4 91.4 96.3 0 100 ...
 $ U_Primary_With_Sec_HrSec        : num [1:110] 98.2 99.2 99.6 100 100 ...
 $ Primary_with_U_Primary_Sec      : num [1:110] 97.3 98.2 99.3 100 100 ...
 $ U_Primary_With_Sec              : num [1:110] 94.4 96.6 98.8 0 0 ...
 $ secondary                       : num [1:110] 99.1 90.3 95.2 0 0 ...
 $ Sec_with_HrSec.                 : num [1:110] 98.4 94 98.3 100 100 ...
 $ hr_secondary                    : num [1:110] 76.1 90.9 96.2 0 0 ...
 $ All Schools                     : num [1:110] 91.2 93.1 97.5 93.4 100 ...
 - attr(*, "spec")=
  .. cols(
  ..   State_UT = col_character(),
  ..   year = col_character(),
  ..   Primary_Only = col_double(),
  ..   Primary_with_U_Primary = col_double(),
  ..   Primary_with_U_Primary_Sec_HrSec = col_double(),
  ..   U_Primary_Only = col_double(),
  ..   U_Primary_With_Sec_HrSec = col_double(),
  ..   Primary_with_U_Primary_Sec = col_double(),
  ..   U_Primary_With_Sec = col_double(),
  ..   Sec_Only = col_double(),
  ..   Sec_with_HrSec. = col_double(),
  ..   HrSec_Only = col_double(),
  ..   `All Schools` = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 
All_India <- filter(schools_with_girls_toilet, State_UT=="All India") %>% 
  select(year, primary, upper_primary, secondary, hr_secondary)

All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
  geom_bar(position = "dodge", stat = "identity") + labs(title = "Percentage of Schools with Girls toilet all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)


states <- c("Nagaland", "Karnataka", "Daman & Diu")
states_with_girls_toilet <- filter(schools_with_girls_toilet, State_UT == states) %>% 
  pivot_longer(c(primary,upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>% 
  select(State_UT, year, Education_Level, Percentage)
  
ggplot(states_with_girls_toilet, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position = "dodge", stat = "identity") + facet_wrap(~State_UT) + theme_dark() + labs(title = "Percentage of Schools with Girls toilet", subtitle = "Daman & Diu, Karnataka, Nagaland")


states_with_no_girls_toilet <- filter(schools_with_girls_toilet, State_UT != "All India", upper_primary==0, secondary==0, hr_secondary==0) %>%
  pivot_longer(c(upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>% 
  select(State_UT, year, Education_Level, Percentage)

kable(states_with_no_girls_toilet, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Education Level", "Percentage"), caption = "Table4 : States with no girls toilet") %>%
  kable_styling(font_size = 15) %>%
  row_spec(c(1,1,1))
Table 6: Table4 : States with no girls toilet
State/Union Territory Year Education Level Percentage
Andaman & Nicobar Islands 2013-14 upper_primary 0
Andaman & Nicobar Islands 2013-14 secondary 0
Andaman & Nicobar Islands 2013-14 hr_secondary 0
Andaman & Nicobar Islands 2015-16 upper_primary 0
Andaman & Nicobar Islands 2015-16 secondary 0
Andaman & Nicobar Islands 2015-16 hr_secondary 0
Chandigarh 2013-14 upper_primary 0
Chandigarh 2013-14 secondary 0
Chandigarh 2013-14 hr_secondary 0
Chandigarh 2014-15 upper_primary 0
Chandigarh 2014-15 secondary 0
Chandigarh 2014-15 hr_secondary 0
Chandigarh 2015-16 upper_primary 0
Chandigarh 2015-16 secondary 0
Chandigarh 2015-16 hr_secondary 0

Reuse

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Citation

For attribution, please cite this work as

Pola (2022, May 19). Data Analytics and Computational Social Science: HW-5. Retrieved from https://github.com/DACSS/dacss_course_website/posts/httprpubscomniharika901537/

BibTeX citation

@misc{pola2022hw-5,
  author = {Pola, Niharika},
  title = {Data Analytics and Computational Social Science: HW-5},
  url = {https://github.com/DACSS/dacss_course_website/posts/httprpubscomniharika901537/},
  year = {2022}
}
UMass Amherst DACSS